esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 5 Results
### Ablation Studies  
To evaluate the contribution of each module in the proposed architecture, ablation studies were conducted by systematically removing individual components. The results highlight the importance of these modules in achieving optimal performance:  
* NMC Module: This module calculates market regime transition probabilities, playing a critical role in identifying regime shifts. Removing it reduced the accuracy from 97.20% to 92.50% and F1-score from 88.59% to 79.95%, emphasizing its impact on predictive accuracy and overall robustness.
* BERT Module: Responsible for analyzing sentiment from financial news, BERT is essential for capturing textual information relevant to market dynamics. Its exclusion resulted in a significant drop in F1-score, from 88.59% to 70.14%, and accuracy decreased by 8.6%, indicating the loss of sentiment-driven insights.
* VIX and GARCH Features: These exogenous variables model market volatility and trends. Without them, recall decreased from 88.53% to 81.34%, and accuracy dropped by 5.8%, reflecting the diminished ability to handle downturn scenarios effectively.  
The results are summarized in Table 4, demonstrating the critical role of each component in enhancing the overall performance of the model.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Component Removed** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline None (Full Model) & 97.20 & 89.27 & 88.53 & 88.59 \\ NMC Module & 92.50 & 83.14 & 80.32 & 79.95 \\ BERT Module & 88.60 & 70.33 & 66.21 & 70.14 \\ VIX and GARCH Features & 91.40 & 78.12 & 81.34 & 76.92 \\ \hline \end{tabular}
\end{table}
Table 4: Impact of component removal on model performance.  
\begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Tuning Time (s)** & **Training Time (s)** \\ \hline LSTM & 994.36 & 33.04 \\ RNN & 3248.88 & 3.96 \\ Random Forest & 82.83 & 0.43 \\ SVC & 73.95 & 0.02 \\ XGBoost & 123.50 & 0.95 \\ KNN & 10.18 & 0.0025 \\ Naive Bayes & — & 0.01 \\ Adaboost & 2.07 & 0.92 \\ Decision Tree & 3.61 & 0.01 \\ ANN & 57/7.73 & 8.22 \\ Logistic Regression & 8.22 & 0.23 \\ \hline \end{tabular}
\end{table}
Table 3: Computational cost comparison.
# Aveiro Canyon Head (Portugal) Submarine Slope Instability Assessment
## 4 Data Acquisition and Processing
### Inventory of Mass Movements  
The successful output of the IV and LR methods is directly linked to the inventory of the SMM. This inventory in the Aveiro upper canyon was based on the visual interpretation of digital bathymetric model (DBM). Slope instabilities were identified from (a) direct observation of the bottom surface, (b) comparing it with the mean slope angle map and (c) obtaining slide profiles.  
Altogether, 31 SMM's were identified in the bathymetric surface and its source area delimited, as shown in Figure 3.  
Since the DBM has a grid resolution of 50 m, smaller events may have gone unnoticed and the represented features should be considered expeditious.  
The observed instabilities led mainly to rotational slumps and gravitational flows. These slides correspond to a movement of material that moves downslope along a curved or concave rupture plane [12]. Most of these events cause a scoop shaped scar on the surface due to its rotational movement. Figure 4 shows a rotational slide along the slope that exemplifies well the scoop-shaped concave rupture surface.  
The analysis of the P1 profile that crosses the slide seems to corroborate that it is in fact a rotational-type slide (Figure 4). It is possible to observe a first movement at about 1200 m depth, and a largest one located approximately at the 1400 m isobaths (highlighted in Figure 4). This produced a steep escarment, with a clear change in its inclination, with a gap of almost 400 m (from the depth of 1400 to 1800 m).  
As mentioned previously, in the canyon upper course, 31 features related to slope instabilities were identified, corresponding to 44 km\({}^{2}\) of a total unstable area (almost 4.5% of the total study area), with a concentration of movements in the Northern sector (Figure 3).
# Multi-formalism Models for Performance Engineering
## 3 Queueing Networks Techniques
### Timeout with Quorum based Join
#### 3.1.3 Model Description  
The queuing network depicted in Figure 2 models the application of Figure 1. It is composed of a fork/join that splits a request into two tasks: one models the real request execution (upper branch) and the other represents the occurrence of timeout due to user abandonment (lower branch). The execution of the service demand is modeled by \(K\) servers queuing node, while the timeout is modeled by a delay node. The join node waits until the first task is executed (in this case, the quorum is equal to one). The probability distribution taken into account to describe the service demand is hyper-exponential with an average duration of 1 s and a coefficient variation of 5. The timeout is considered to be deterministic, and arrivals are assumed to be generated according a Poisson process: both the duration of the former and the rate of the latter are varied during the study of the model. The two loggers \(L1\) and \(L2\) in Figure 2 track the instant of time the request in execution flows through them and allow to compute the probability \(P(Timeout)\) that a request ended due to timeout. In particular, since the join has zero duration, a request that ends due to timeout will have the same timestamps recorded by both the loggers.  
Figure 1: An application scenario including user-generated timeouts.
# A Large Area Pixelated Silicon Array Detector for Independent Transit In Vivo Dosimetry
## 5 Conclusions  
The novel, large active area MP987, a custom-built pixelated silicon array detector, with readout system and software has been successfully developed and shown to be well suited to performing dosimetry in EBRT. Characterisation measurements of PDD and OF had a percentage difference of less than 2.5% to the reference detector. A negligible dose per pulse dependence was observed, unlike in earlier implementations of the drop-in technology, showing the MP987 has improved dosimetry characteristics as well as better spatial resolution and active area. The first pre-clinical test on the MP987 show that it can perform transit dosimetry, without requiring the extensive use of correction factors, as with modern EPID dosimetry systems. An absolute dose gamma analysis between the MP987 and the predicted transit dose from the TPS had up to 97.4% pass rate for a lung phantom when a 5%/5 mm criteria were used (suitable for detecting potentially harmful dose errors). The MP987 is an easy to implement in vivo dosimetry tool for EBRT that has the potential to perform real-time, independent dose verification.  
**Author Contributions:** The authors contributed in the following roles: Conceptualization, A.B.R. and M.P.; investigation, O.J.B., I.F., J.A.D., T.C., D.W. and M.P.; methodology, O.J.B., T.C., D.W. and M.P.; software, O.J.B., I.F. and S.A.; formal analysis, O.J.B. and A.T.L.; visualization, O.J.B.; writing--original draft preparation, O.J.B.; writing--review and editing, O.J.B., S.A., A.B.R. and M.P.; supervision, D.W., A.B.R. and M.P.; project administration, A.B.R. and M.P.; funding acquisition, A.B.R. and M.P.; A.P. contribution to M.P. fabrication under CMRP design. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research has been conducted with the support of the Australian Government Research Training Program Scholarship.  
**Institutional Review Board Statement:** Not applicable.  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to acknowledge Illawarra Cancer Care Centre for facilitating this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Finite Element Modelling of an EG Rotor-Bearing System
### Finite EG Shaft Element without Crack  
The equation of motion of an EG shaft element, expressed in Equation (32), can be derived by the application of [PERSON]'s extended principle along with the equations of energy and the work functions [11,12].  
\[([M^{e}]+[N^{e}])\big{\{}\ddot{q}\big{\}}-\Omega[G^{e}]\big{\{}\dot{q}\big{\}}+ [K^{e}]\{q\}=\{Q^{e}\} \tag{32}\]  
where \([M^{e}]\), \([N^{e}]\), \([G^{e}]\) and \([K^{e}]\) are elemental translational mass matrix, rotational mass matrix, gyroscopic matrix, and stiffness matrix, respectively. \(\{Q^{e}\}\) is the excitation vector on the element and \(\{q\}\) is nodal displacement vector. \(\Omega\) is the rotor speed of the shaft in rad/s. The spatial constraint matrices, which are used to derive the elemental mass, stiffness and gyroscopic matrices are analogous to the spatial constraint matrices used in [11].  
The elemental translational mass matrix is expressed as  
\[\begin{array}{c}[M^{e}]=\int\limits_{0}^{L_{e}}m[\psi]^{T}[\psi]ds\\ m=\int\limits_{R_{i}}^{R_{o}}2\pi\rho(r)r^{3}dr\end{array} \tag{33}\]  
The elemental vitf stiffness matrix is expressed as  
\[\begin{array}{c}[K^{e}]=\int\limits_{0}^{L_{e}}\overline{E}[\psi^{r}]^{T}[ \psi^{r}]dyds+\int\limits_{0}^{L_{e}}\overline{\kappa GA}[\psi^{r}]^{T}[\psi^{ r}]dyds\\ \overline{EI}=\int\limits_{R_{i}}^{R_{o}}2\pi E^{e}(r,T)r^{3}dr\\ \overline{\kappa GA}=\int\limits_{R_{i}}^{R_{o}}2\pi r\kappa^{e}(r,T)G^{e}(r, \ T)dr\end{array} \tag{35}\]  
The elemental gyroscopic matrix is expressed as  
\[\begin{array}{c}[G^{e}]=[H^{e}]-[H^{e}]^{T}\\ [H^{e}]=\int\limits_{0}^{L_{e}}I_{p}[\varphi_{w}]^{T}[\varphi_{v}]ds\\ I_{p}=\int\limits_{R_{i}}^{R_{o}}2\pi\rho(r)r^{3}dr\end{array} \tag{36}\]where \(E^{\epsilon}(r,\ T)\) is [PERSON]'s modulus, \(G^{\epsilon}(r,\ T)\) is the modulus of rigidity, \(v^{\epsilon}(r,\ T)\) is [PERSON]'s ratio, \(\kappa^{\epsilon}(r,\ T)\) is the shear form factor, \(\rho(r)\) is density, \(m\) is mass per unit length, \(R_{i}\) is the inner radius of the EG shaft. \(R_{o}\) is the outer radius of the EG shaft, \(L_{e}\) is the length of the element, \(I_{D}\) diametric moment of inertia per unit length, \(I_{P}\) is the polar moment of inertia per unit length, \(A\) is the area of cross-section, and \(I\) is the area moment of inertia. \([\psi]\) and \([\varphi]\) are the spatial constraint matrices associated with the translational and rotational shape functions, respectively. The equation of motion of a uniform disc and isotropic bearings are taken from the literature [17]. The uniform steel disc is located at the mid-span of the EG shaft. The ends of the EG shaft are supported by the linear isotropic bearings. The stiffness of bearings is considered as \(10^{10}\) N/m. Bearings are rigid with high stiffness. The rotor-bearing data is presented in Section 7.
## 3 Results
### Marshall  
The temporal dynamics of GPP in the marshland ecosystem for the period between October 2020 and December 2022 at the daily scale are shown in Figure 5a, both for the EC data and for those obtained by both LUE models. In this ecosystem, a more pronounced cycle is observed than in the case of xeric shrublands, more like a grassland ecosystem, with maximum values at the end of spring and almost zero at the beginning of winter. In the period analyzed, a significant decrease in the maximum values of carbon assimilation was observed, from about 8 g/m\({}^{2}\) in 2021 to 5 g/m\({}^{2}\) in 2022. In this case, both LUE models slightly underestimate, which is more noticeable at the end of summer and beginning of fall.  
Figure 5: (**a**) Temporal dynamics of the GPP for marshland from October 2020 to December 2022 at daily scale. Black line: GPP from the eddy covariance (EC) system, red line: GPP estimated with the LUE model ME, blue line: GPP estimated with the LUE model WA. Validation presented in 1:1 graphs of the data EC vs. LUE models in xeric shrublands in the study period: (**b**) ME; (**c**) WA.  
Both LUE models slightly underestimate, with this more pronounced in late summer and early fall (Figure 5). Despite this underestimation, both LUE models produce better results for marshlands than for xeric shrulands (shown in the 1:1 plots and their errors in Figure 5b,c). R\({}^{2}\) is high for both approaches, although better in WA (0.82 and 0.67 for WA and ME, respectively). RMSE and NRMSE showed a similar pattern, but with lower values for WA, 0.43 gC/m\({}^{2}\) and 0.06 compared to 0.64 gC/m2 and 0.1 for ME. Finally, the MBE showed similar values, consistent with the slight underestimation (\(-0.35\) gC/m\({}^{2}\) for ME and \(-0.39\) gC/m\({}^{2}\) for WA).  
Table 4 shows the results using the monthly aggregates. The results improve in both LUE models when using monthly averages, much more clearly than in xeric shrulands, lowering the RMSE values and reaching very high R\({}^{2}\) values, reaching up to 0.93 in the case of WA. The MBE and NRMSE remain constant with similar values compared to the daily scale.  
Figure 6 shows the annual accumulated GPP using the LUE-WA model. In this case, a decrease in carbon assimilation from the hydrological year 2020/2021 to 2021/2022 can be seen very clearly.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**LUE Model** & **Metric** & **Value** \\ \hline ME & R\({}^{2}\) & 0.86 \\  & RMSE & 0.40 \\  & NRMSE & 0.09 \\  & MBE & \(-0.35\) \\ \hline WA & R\({}^{2}\) & 0.93 \\  & RMSE & 0.40 \\  & NRMSE & 0.06 \\  & MBE & \(-0.41\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the agreement and error metrics in marshland using monthly aggregates for ME and WA RMSE and MBE given in gC/m\({}^{2}\).  
Figure 6: Hydrological year maps of accumulated GPP in marshland using the LUE-WA model.
## 5 System Model  
The system model comprises a dual-hop THz-VLC decode-and-forward relaying system. In this model, the first hop utilizes a THz channel to transmit information from the UAV (source node) to the relay node. The relay node decodes the received signal and retransmits it through VLC inside the building, as shown in Figure 7.  
Figure 6: This diagram illustrates the use of THz and VLC in a 4K video streaming scenario. THz communication is utilized for outdoor communication from the UAV, while indoor communication utilizes VLC.  
Figure 7: System model overview.  
The system-level equations are described below:  
\[Y_{sr}=\sqrt{P_{s}}H_{THz}x+n_{SR} \tag{1}\]  
\[Y_{rd}=\sqrt{P_{r}}H_{VLC}\tilde{x}+n_{RD} \tag{2}\]  
where \(P_{s}\) represents the source power, \(P_{r}\) denotes the relay power, \(H_{THz}\) signifies the THz channel from source to relay, \(H_{VLC}\) denotes the VLC channel from relay to destination, \(x\) symbolizes the information signal, \(\tilde{x}\) stands for the signal encoded by the relay, and \(n_{SR}\) and \(n_{RD}\) represent the noise from source to relay and relay to destination, respectively [7].
# Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration
## 4 Experiment and Result Analysis
### Event Detection and Clustering  
Several appliances were selected from House 2, including refrigerator, microwave and dishwasher. The refrigerator and microwave were used frequently and have high power consumption in this house. A dishwasher is a typical multi-state load with adjustable potential. The identification process depends on some statistical features, which requires abundant data samples to extract. Thus, the aggregated power data in one week were selected for the experiment. Figure 6 shows the results of aggregated data in event detection and event clustering in one week.  
Figure 6 illustrates that the significant changes from aggregated power data can be detected accurately using the steady-state segment based on event detection method. After processing all events with the proposed clustering method, eight different clusters were formed, including three \"positive\" clusters and five \"negative\" clusters. It can be seen that the elements in each cluster had similar power value.  
Table 3 shows the effective detection rate of monitoring events of interest to us. From the Table 3, it can be seen that the proposed algorithm can detect the events effectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment Parameters** & **Value** & **Experiment Parameters** & **Value** \\ \hline \(\delta_{p}\) & 100 & \(\delta_{n}\) & 20 \\ \(Thr_{c}\) & 100 & \(\varepsilon_{p}\) & 230 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Threshold parameters.  
Figure 6: Results of event detection and clustering for House 2.
# An Efficient Library for Reliability Block Diagram Evaluation
## 2 Reliability Block Diagrams
### Quantitative Evaluation Using RBDs
#### 2.2.2 Quantitative Evaluation: Identical Components' Formulas  
Under the assumption of \(N\) identical components having probability of _success p_, the following simplified formulas can be used to evaluate the probability of _success_ of an RBD block:  
* **Series**. By substituting \(p_{i}\) with \(p\) in Equation (2), we can compute the simplified probability of _success_ of the series block \(p_{series}\) as: \[p_{series}=p^{N}\] (9)
* **Parallel**. By substituting \(p_{i}\) with \(p\) in Equation (4), we can compute the simplified probability of _success_ of the parallel block \(p_{parallel}\) as: \[p_{parallel}=1-(1-p)^{N}\] (10)
* **\(K\)-out-of-\(N\) (KooN)**. By substituting \(p_{i}\) with \(p\) in Equations (5) and (6), we can compute the simplified probability of _success_ of the \(K\)-out-of-\(N\) block \(R_{KooN}\) as: \[p_{KooN}=\sum_{J=K}^{N}\binom{N}{J}\cdot p^{J}\cdot(1-R)^{N-J}\] (11)
* **Bridge**. By substituting \(p_{A}\) to \(p_{E}\) with \(p\) and \(q_{A}\) to \(q_{E}\) with \(q\) in (8), we obtain simplified probability of _success_ of the bridge block \(p_{bridge}\) as: \[p_{bridge}=p\cdot(1-q^{2})^{2}+q\cdot(1-(1-p^{2})^{2})\] (12)
## 2 Theory and Calculation
### Boosted Regression Tree (BRT)  
Gradient boosting (GB) is one of the machine learning methods used to solve classification and regression problems, and was developed by [PERSON] (1998). The GB algorithm is an optimized algorithm based on the error function. Decision trees form strong prediction models, which are created by merging base prediction models. GB is a method in which a decision tree is repeatedly added so that the next decision tree corrects the error of the previous one. The gradient optimization method is based on adjusting the last result by adding a negative value of the gradient of the function which is to be minimized. Hence, by constantly adjusting the weight of a base learner, it makes it a stronger learner. A schematic diagram of BRT is shown in Figure 5 for demonstration.  
The least-squares boosting (LS Boost) algorithm is an approximation of the function \(F_{m-1}\), which minimizes the expected value \(x_{i}\), of some specified loss function. The calculation of loss function is based on the mean square error, represented in Equation (10):  
\[L(y_{i},\ F(x_{i}))=\frac{1}{n}\sum_{i=1}^{N}(y_{i}-F_{m-1}(x_{i}))^{2} \tag{10}\]  
The BRT model and boosting regression tree \(h_{m}(x;w)\) are formed as follows ([PERSON], 1998; [PERSON], 1998):  
Step 1: Model initialization, represented in Equation (11):  
\[F_{0}(x)=\operatorname*{argmin}_{\gamma}\sum_{i=1}^{N}L(y_{i},\gamma) \tag{11}\]  
Step 2: Iterative procedure of obtaining M regression trees:  
1. The negative value of the gradient of the loss function \(L(y_{i},\ F(x_{i}))\) is calculated, and then it is used as the estimate of the residual, as given in Equation (12): \[\widetilde{y}_{i}=-\bigg{[}\frac{\partial L(y_{i},F(x_{i}))}{\partial F(x_{i} )}\bigg{]}_{F(x)=F_{m-1}(x)}\] (12)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SVM Parameters** & **Values** \\ \hline \(\epsilon\) precision parameter & 0.9 \\ C tolerance parameter & 1600 \\ \(\gamma\) kernel scale factor & 72 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SVM algorithm parameters.  
Figure 5: Schematic diagram of the gradient boosted regression tree.  
2. A regression tree \(h_{m}(x;w)\) is optimized for the residual obtained in the previous iteration. The step size of the gradient drop is calculated, as represented in Equation (13): \[(\gamma_{m},w_{m})=\operatorname*{argmin}_{\gamma,w}\sum_{i=1}^{N}[\vec{y}_{i}- \gamma h(x_{i},w_{m})]^{2}\] (13) Step 3: An approximation update is performed, represented in Equation (14): \[F_{m}(x)=F_{m-1}(x)+\
u\gamma_{m}h_{m}(x,w_{m})\] (14) where \(\
u\) represents the learning rate, which prevents the over-fitting of the model.  
The LS Boost algorithm was tested with different parameters for the learning rate, number of boosting stages and leaf size. The best results for BRT modeling were obtained with the algorithm parameters shown in Table 2.
## 4 Results and Discussions
### Accuracy Analysis
Regarding the selection of different models for various application scenarios, RF is commonly applied to structured data processing and feature importance analysis, while BPNN excels in handling tasks involving complex nonlinear relationships. In this study, the superior performance of the II-BPNN model is more suitable for tasks that are highly sensitive to landslide susceptibility, while the II-RF model is more suitable for the structural analysis of specific environmental factors. This emphasizes the importance of selecting appropriate models based on task requirements. In practical applications, considering the characteristics and applicable scenarios of models can effectively enhance model performance for complex and diverse geological tasks.
# A 3D Estimation Method Using an Omnidirectional Camera and a Spherical Mirror
## 6 Simulation  
### Simulation with CG Model of a Room  
The simulation used a CG model of a room to test the proposed method. A spherical mirror with a 0.5 [m] radius was placed 1 [m] from the camera in the CG environment. The omnidirectional image captured in the experiment and its corresponding ground truth image are shown in Figure 9a,b, respectively. Both images have a resolution of 4096 [pixels] \(\times\) 2048 [pixels].  
The first step of the simulation was to estimate the position of the spherical mirror. The mirror image region in Figure 9a is shown in Figure 10a, and the segmented mirror region is displayed in Figure 10b. An elliptical shape was estimated based on the segmented mirror region, as shown in Figure 10c. The result of the spherical mirror position estimation was 1.03358 [m].  
Next, the 3D information estimation results are presented in Figure 11a. The estimation map in Figure 11a visualizes the results by linearly changing the hue, where blue represents the smallest values and red represents the largest values. The difference between the ground truth values and the estimated results at each pixel is shown in Figure 11b. The error map in Figure 11b visualizes the differences by linearly varying the brightness.  
The evaluation metric used for assessment is the Mean Absolute Error (MAE), calculated by summing all the pixels' errors and dividing by the total number of pixels. The proposed method was evaluated for all points in the mirror image. In this evaluation, the estimated depth represents the distance between the mirror surface and the camera center for the directly imaged parts of the image that correspond to the mirror surface. At the same time, the ground truth values are the corresponding distances. The error is calculated as the sum of the absolute differences (Sum of Absolute Differences--SAD) between the estimated and the ground truth depths. The MAE value obtained from the proposed method on this evaluation index was 1.15853 [m].  
Figure 8: Effect of 3D Position Estimation Errors of the Spherical Mirror on 3D Estimation: The effect of the error in the 3D position estimation of the spherical mirror on the 3D estimation is shown by expressing the distance \(\|P\|\) from the camera center to the 3D point with the imaging position of the 3D point (the direct image \(\theta_{i}\), the mirror image \(\phi\)) and the radius \(R\) of the spherical mirror.