esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Description of Discrete Time Systems  
Let us consider the following time-varying discrete-time system  
\[\mathbf{x}_{k+1}=\mathbf{A}_{k}\mathbf{x}_{k}+\mathbf{B}_{k}\mathbf{u}_{k}+\mathbf{E}_{k}\mathbf{d}_{k}+\mathbf{W} \mathbf{w}_{k}, \tag{1}\]  
where \(\mathbf{x}_{k}\in\mathbb{X}\subset\mathbb{R}^{n}\) is the state vector, \(\mathbf{u}_{k}\in\mathbb{U}\subset\mathbb{R}^{r}\) stands for the input, \(\mathbf{d}_{k}\in\mathbb{D}\subset\mathbb{R}^{n_{d}}\) is an unknown input, and \(\mathbf{w}_{k}\in\mathcal{E}\subset\mathbb{R}^{n_{w}}\) is an exogenous disturbance vector with  
\[\mathbf{w}_{k}\in\mathcal{E},\ \mathcal{E}=\{\mathbf{w}\ :\ \mathbf{w}^{T}\mathbf{Q}_{\mathbf{w}} \mathbf{w}\leq 1\},\ \mathbf{Q}_{\mathbf{w}}\succ 0. \tag{2}\]  
Let \(V_{k}=\mathbf{x}_{k}^{T}\mathbf{P}\mathbf{x}_{k}\) denote a Lyaponov candidate function. For an unforced \(\mathbf{u}_{k}=\mathbf{0}\) and unknown input-free \(\mathbf{d}_{k}=\mathbf{0}\) system (1), the following definitions are recalled [15; 23]:  
Definition 1: The system (1) is strictly quadratically bounded for all allowable \(\mathbf{w}_{k}\in\mathcal{E}\), \(k\geq 0\), if  
\[V_{k}>1\Rightarrow V_{k+1}-V_{k}<0, \tag{3}\]  
for any \(\mathbf{w}_{k}\in\mathcal{E}\).  
Definition 2: A set \(\mathcal{E}_{\mathbf{x}}\) is a robust invariant set for the system (1) for all allowable \(\mathbf{w}_{k}\in\mathcal{E}\) if  
\[\mathbf{x}_{k}\in\mathcal{E}_{\mathbf{x}}\Rightarrow\mathbf{x}_{k+1}\in\mathcal{E}_{\mathbf{ x}}, \tag{4}\]  
for any \(\mathbf{w}_{k}\in\mathcal{E}\).  
It should be pointed out that the strict quadratic boundedness enables decreasing the value of Lyapunov function \(V_{k}\), i.e., it means that \(V_{k+1}<V_{k}\) for any \(\mathbf{w}_{k}\in\mathcal{E}\) when \(V_{k}>1\). If Equation (1) is quadratically bounded and there exists at least one vector \(\mathbf{W}\mathbf{w}_{k}\
eq 0\), then such quadratic boundedness is always strict [15]. Moreover, the notation of the quadratic boundedness can be expressed using the theory of invariant sets [15]. The set \(\mathcal{E}_{\mathbf{x}}=\{\mathbf{x}:\mathbf{x}^{T}\mathbf{P}\mathbf{x}\leq 1\}\) is an invariant set for any \(\mathbf{w}_{k}\in\mathcal{E}\) if \(\mathbf{x}_{k}\in\mathcal{E}_{\mathbf{x}}\) implies \(\mathbf{x}_{k+1}\in\mathcal{E}_{\mathbf{x}}\). Thus, if \(V_{k}>1\), then \(\mathbf{x}_{k}\) is outside an invariant set, and, hence, by Definition 1\(V_{k+1}<V_{k}\). This means that \(V_{k}\) decreases until \(\mathbf{x}_{k}\) is outside \(\mathcal{E}_{\mathbf{x}}\).
## 5 EBCA Testbed
The total number of test points existing in matching areas of all considered data sets is equal to 172,400. The testbed contains also ground truth. Ground truth was prepared manually by identifying in images locations of points corresponding to the same parts of plants. Such an analysis is very time-consuming, however it allows acquiring correct values of disparities in points considered in experiments. The main problem with determining ground truth manually is the occurrence of monochrome areas such as walls for which it is impossible to find points corresponding in different images to the same parts of real objects. However, test data do not contain such surfaces as plants visible in images have non-uniform textures. Ground truth data contain disparities referring to points of raw images \(I_{R}0\). However, stereo matching algorithms process pairs of images \(I_{P}\) which were obtained by applying calibration transformations on images \(I_{R}\). Thus, points in a disparity map obtained as a result of using matching algorithms refer to points of a transformed central image as this image is regarded as a reference one in every considered pair of images. The central image is differently transformed in every considered image pair. In order to compare ground truth with disparity maps generated from different image pairs, it is necessary to perform transformations reversing in disparity maps the calibration process. Such transformations were also prepared by the author of this paper. Files ebmcscalibration.xml available in the testbed contain data necessary to reverse transformations including flipping and rotating images. Maps obtained after reverse transformations can be either compared with ground truth or further processed. Further processing can result in merging maps obtained from individual image pairs in order to acquire a map which has a higher quality than constituent maps. Performing such a data merge is a subject of Exceptions Excluding Merging Method.
## 4 Simulation Results and Comparisons
### Comparisons of Large Problem
A ranking rate analysis was performed to represent the ranking of a solution in the representative subset \(\Theta\). The ranking rate is defined as \(rk/|\Theta|\times 100\%\), where \(rk\) is the ranking of a solution, which can easily be determined from its objective value. Table 4 lists statistics of the objective value and average ranking rate over 30 independent runs that were obtained by four approaches. The mean, standard deviation and standard error of mean of the objective value obtained by the OOTSA for \(N\) = 20 over 30 independent runs were 61.55, 0.52 and 0.0949, respectively. The tiny standard error of mean reveals that most of the objective value obtained by the OOTSA is around the mean or practically near the best solution over 30 independent runs. Thus, the OOTSA approach can usually determine an excellent solution, even if it is not a global optimal solution. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **ABOV \({}^{\ddagger}\)** & \(\frac{\text{ABOV}-\ast}{\ast^{\ddagger}}\)**.100\%** \\ \hline OOTSA, case \(N\) = 20 & 61.55 & 0 \\ \hline PSO with precise evaluation & 68.16 & 10.74\% \\ \hline GA with precise evaluation & 72.92 & 18.47\% \\ \hline ES with precise evaluation & 71.53 & 16.21\% \\ \hline \hline \end{tabular} \({}^{\dagger}\) ABOV, average best-so-far objective value \(F(\mathbf{x})\) at termination. \({}^{\lx@sectionsign}\) \({}^{\ast}\), average best-so-far objective value obtained by OOTSA.  
\end{table} 
Table 3: Statistics of the average best-so-far objective values at termination over 30 independent runs. Figure 7: Comparison of the computing efficiency and solution quality of four methods over 30 independent runs.
## 2 Experimental Setup and Proposed Method
### Technical Indicators  
The self-consumption rate (SCR) and energy self-sufficiency rate (SSR) are widely used technical indicators that form the basis of an energy community. The SSR shows the proportion of electricity demand met by self-generated electricity. The SSR indicates the proportion of self-generated electricity used to meet electricity demands. SCR and SSR are calculated using Equations (7) and (8) [58].  
\[SCR=\frac{E_{PVtoLoad}+E_{btLoLoad}}{E_{PV}}, \tag{7}\]  
\[SSR=\frac{E_{PVtoLoad}+E_{BattoLoad}}{E_{load}}, \tag{8}\]  
where \(E_{PVtoLoad}\) is the amount of energy produced in PV transferred to the loads (kWh), \(E_{PVtBoat}\) is the energy which is injected into the battery from PV (kWh), \(E_{PV}\) is the total PV generation (kWh), \(E_{BattoLoad}\) is the load covered by battery discharge (kWh), and \(E_{Load}\) is the total load demand (kWh).
## 4 Applications of CDs in Cancer Theranosis
### Cancer Therapy via Drug Delivery, PTT, PDT, PTT+PDT, siRNA Delivery, SDT, and Gas Therapy
#### 4.2.3 PDT
These R-CDs were utilized as photosensizers in the PDT of human lung (A549) cancer-bearing nude mice under 532 nm laser irradiation for 4 min. The PDT with R-CDs significantly inhibited cancer growth, whereas no significant differences in cancer volume were found among the three control groups (PBS, 532 nm laser irradiation, and R-CDs), confirming the efficacy of R-CDs as photosensizers for PDT of cancer (Figure 12e,f). Recently, the use of afterglow imaging with CDs in imaging-guided PDT has represented a significant advancement for cancer therapy. [PERSON] et al. investigated the anticancer efficacy of CDs against a subcutaneous xenograft cancer model of mouse colon carcinoma (CT26) cells implanted in the armpit of Balb/c mice using CDs synthesized in a solvothermal method by heating the mixture of Ce6 and PEI in N,N-dimethylformamide solution at 160 \({}^{\circ}\)C for 2 h [59]. These CDs demonstrated deep tissue penetration, reaching depths of up to 10 mm in chicken breast tissue. After 7 days of cancer growth, the mice were divided into four treatment groups: PBS, PBS + L, CDs, and CDs + L, where \"L\" refers to 685 nm laser treatment for 10 min. The results indicated that the cancer sizes in the PBS, PBS + L, and CDs groups exhibited significant cancer growth, suggesting that neither CDs nor laser irradiation alone was effective in inhibiting cancer growth, as shown in Figure 12g,h, whereas the CDs + L group exhibited a drastic reduction in cancer growth. After 15 days of the treatment, the mice were sacrificed, and analysis revealed that the cancer weight in the CDs + L group was the smallest among all groups (Figure 12i), with three cancers completely ablated among five cancers (Figure 12g). Furthermore, the study demonstrated the negligible side effects of CDs, as evidenced by the negligible mouse weight changes for all groups (Figure 12j).
# Microcrack Detection Using Spectral Response Data Alone
## 2 Theoretical Approach  
This study aims to improve the existing FRF-based method for microcrack inspection. The FRF (\(H(\omega)\)) is given by Equation (1), where the impact from an impact hammer (\(F(\omega)\)) and the response (\(R(\omega)\)) are measured using acceleration sensors. Under the assumption of a system with linear characteristics, for normal products, the FRF given by Equation (1) can be calculated as shown in Figure 1.  
\[H(\omega)=\frac{R(\omega)}{F(\omega)} \tag{1}\]  
However, if a crack exists, the dynamic characteristics of the system as well as its FRF (\(\widetilde{H}(\omega)\)), input (\(\widetilde{F}(\omega)\)), and output (\(\widetilde{R}(\omega)\)) change, as shown in Figure 2.  
If the input signal applied by the impact hammer is almost identical to the Charpy impact test results shown in Figure 1, that is, \(\widetilde{F}(\omega)\approx F(\omega)\), the FRF shown in Figure 2 can be expressed as  
\[\widetilde{H}(\omega)=\frac{\widetilde{R}(\omega)}{F(\omega)} \tag{2}\]  
Figure 1: Relationship between input impulse and output response in normal system.  
Figure 2: Relationship between input impulse and output response in abnormal system.  
Because a coherence function is used to evaluate the similarity of two FRFs, the corresponding indicator (\(I(\omega)\)) is formulated in Equation (3). Here, \(G_{i,j}\) denotes the coherence function between \(i\) and \(j\) function in a frequency domain.  
\[I(\omega)=\frac{\left|G_{H(\omega)\bar{H}(\omega)}\right|^{2}}{G_{H(\omega)H( \omega)}G_{\bar{H}(\omega)\bar{H}(\omega)}} \tag{3}\]  
Upon substituting Equations (1) and (2) into Equation (3), the indicator (3) can be given as  
\[I(\omega)=\frac{\left|G_{[\mathcal{R}(\omega)/f_{\mathcal{U}(\omega)}][\bar{ \mathcal{R}}(\omega)/f_{\mathcal{U}(\omega)}]}\right|^{2}}{G_{[\mathcal{R}( \omega)/f_{\mathcal{U}(\omega)}][\bar{\mathcal{R}}(\omega)/f_{\mathcal{U}( \omega)}][\bar{\mathcal{R}}(\omega)/f_{\mathcal{U}(\omega)}][\bar{\mathcal{R}} (\omega)/f_{\mathcal{U}(\omega)}]}}\approx\frac{\left|G_{R(\omega)\bar{R}( \omega)}\right|}{G_{R(\omega)R(\omega)}G_{\bar{R}(\omega)\bar{R}(\omega)}} \tag{4}\]  
Therefore, the previous indicator is a function of the response data if the input force signal can be controlled as soon as possible. This enables the inspection of microcracks in a mechanical system using the response data alone instead of the FRF.
## 4 Experiments
### Effectiveness of SSL
#### 4.2.3 Comparison with ImageNet Pretraining  
ImageNet is a very large dataset that requires not only a large storage space but also a very large number of GPU hours to initialize a model on it, and in general, it is difficult to train models on this dataset. As shown in Table 2, compared to ImageNet pretraining, our framework needs a much smaller amount of storage space and much fewer GPU hours for training to obtain comparable results on the NAP dataset. With SSL on such limited remote sensing images as pretraining, WRN-50 obtains even better results than those achieved with ImageNet pretraining.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Param (M)**} & \multicolumn{4}{c}{**Pretraining Details**} & \multicolumn{2}{c}{**NWPU-RESISC45**} \\ \cline{3-8}  & & **Total Images** & **Storage (GB)** & **Epochs** & **Pretraining GPU** & **T.R. = 10\%** & **T.R. = 20\%** \\ \hline \multicolumn{8}{l}{_Random Initialization_:} \\ \hline ResNet-50 & 23.60 & — & — & — & — & \(65.89\pm 1.25\) & \(81.44\pm 0.24\) \\ ResNeXt-50 & 23.07 & — & — & — & \(72.10\pm 0.64\) & \(82.26\pm 0.28\) \\ WRN-50 & 66.93 & — & — & — & — & \(65.78\pm 0.41\) & \(81.82\pm 0.58\) \\ \hline \multicolumn{8}{l}{_ImageNet Pretraining_:} \\ \hline ResNet-50 & 23.60 & 1,431,167 & 155.38 & 120 & 227.52 & \(89.99\pm 0.11\) & \(92.95\pm 0.19\) \\ ResNeXt-50 & 23.07 & 1,431,167 & 155.38 & 120 & 270.76 & \(91.04\pm 0.19\) & \(93.59\pm 0.13\) \\ WRN-50 & 66.93 & 1,431,167 & 155.38 & 120 & 338.86 & \(90.52\pm 0.24\) & \(93.53\pm 0.18\) \\ \hline \multicolumn{8}{l}{_SSL on NAP_:} \\ \hline ResNet-50 & 23.60 & 71,900 & 4.59 & 300 & 4.15 & \(89.58\pm 0.25\) & \(92.21\pm 0.12\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of random initialization, ImageNet pretraining, and SSL, where T.R. is an abbreviation for training ratio.  
Figure 6: Using ResNet-50 as the backbone network, we compare the performance of self-supervised models fine-tuned on the NWPU-RESISC45 dataset with a training ratio of 20% for five different image sizes: 96, 128, 160, 192, and 224.
###### Abstract  
In the field of passive Brain-computer Interfaces (BCI), the need to develop systems that require rapid setup, suitable for use outside of laboratories is a fundamental challenge, especially now, that the market is flooded with novel EEG headsets with a good quality. However, the lack of control in operational conditions can compromise the performance of the machine learning model behind the BCI system. First, this study focuses on evaluating the performance loss of the BCI system, induced by a different positioning of the EEG headset (and of course sensors), so generating a variation in the control features used to calibrate the machine learning algorithm. This phenomenon is called covariate shift. Detecting covariate shift occurrences in advance allows for preventive measures, such as informing the user to adjust the position of the headset or applying specific corrections in new coming data. We used in this study an unsupervised Machine Learning model, the Isolation Forest, to detect covariate shift occurrence in new coming data. We tested the method on two different datasets, one in a controlled setting (9 participants), and the other in a more realistic setting (10 participants). In the controlled dataset, we simulated the movement of the EEG cap using different channel and reference configurations. For each test configuration, we selected a set of electrodes near the control electrodes. Regarding the realistic dataset, we aimed to simulate the use of the cap outside the laboratory, mimicking the removal and repositioning of the cap by a non-expert user. In both datasets, we recorded multiple test sessions for each configuration while executing a set of Workload tasks. The results obtained using the Isolation Forest model allowed the identification of covariate shift in the data, even with a 15-s recording sample. Moreover, the results showed a strong and significant negative correlation between the percentage of covariate shift detected by the method, and the accuracy of the passive BCI system (\(p\)-value < 0.01). This novel approach opens new perspectives for developing more robust and flexible BCI systems, with the potential to move these technologies towards out-of-the-lab use, without the need for supervision for use by a non-expert user.  
passive brain-computer interface; electroencephalography; machine learning; covariate shift +
Footnote †: journal: Applied Sciences
## 3 WSAN System Models
### Modified AODV over IEEE 802.15.4 for Building Temperature Control
#### 4.2.2 Modified Operations  
We modify the original AODV algorithm in the route request process. Whenever a sensor node receives the RREQ message from its neighbor, the delay-time among them is calculated and compared to the predefined threshold value. The delay-time is the time difference between the current time at the receiving node and the request time (or timestamp) at the source node. It is normalized by the hop-count value which is carried in the RREQ message. Hence, the delay-time in this case is the average value. Additionally, the predefined threshold is the average time that the RREQ message propagates from any sending node at the network layer to any receiving node at the network layer in the case of no background traffic interruption. If the delay-time is greater than the threshold, a node will discard the RREQ message. By using this approach, a relay node can avoid broadcasting the RREQ message to a heavy-load path. Thus, both routing and data messages are frequently transmitted through the path with a low congestion level. Accordingly, this approach can decrease the possibility of the packet collisions occurred in the network as well as the number of route establishment trials. As a result, sensor nodes can save their energy resource larger than using an original AODV approach. How the RREQ message is being processed by a node in the RREQ received function is presented in Figure 9. The threshold can be calculated according to Equations (4-8) and all parameters used for their calculations are defined in Table 5.  
Figure 9: The pseudo code for the RREQ received function. This operation does not require any change in the RREQ message format; therefore, it is able to collaborate with AODV.  
\[T_{Threshold}{\overset{=}{T}}_{Routing\_layer\_to\_MAC\_layer}+T_{MAC\_layerto\_MAC\_layer}+T_{MAC\_layerto\_Routing\_layer} \tag{4}\]  
\[T_{Routing\_layer\_to\_MAC\_layer}{=}0.0\rhd\{\text{randomvariable\ uniform[0, 1]}\} \tag{5}\]  
\[T_{MAC\_layerto\_MAC\_layer}{=}T_{BO}+T_{CCA}+T_{frame}+T_{IFS} \tag{6}\]  
\[T_{BO}=BO_{slots}\times T_{BOSlot} \tag{7}\]  
\[T_{frame}=(RREQ_{PacketSize}\times 8)/\mathit{Data\_Rate} \tag{8}\]  
Note that \(T_{MAC\_layerto\_Routing\_layer}\) in Equation (4) can be ignored since it is very small in comparison with other terms of the equation. \(T_{MAC\_layerto\_MAC\_layer}\) in Equation (6) does not include the propagation delay, and all parameters used in this equation are calculated according to the IEEE 802.15.4 standard at the 2.4 GHz ISM band. More details can be found in [41, 42]. Note that the threshold set to 0.008840 s as shown in Table 5 may be not suitable for very congested scenarios. In the case that all routes are congested, how to specify an optimal threshold value should be studied further. This work only focuses on performance evaluations and problem demonstrations of routing algorithms in WSANs. The optimal threshold selection for highly congested scenarios is beyond the scope of our current study.
## 3 Experiments and Analysis  
### Experiment Design  
The training platform consisted of a laptop equipped with a 13 th Gen Intel(R) Core (TM) i9-13900 HX (2.20 GHz) CPU, 16 GB of RAM, and an NVIDIA RTX 4060 Laptop GPU. Software tools included CUDA 12.0, cuDNN, Pycharm2022, Python 3.8, and OpenCV 4.4.0.  
During the citrus target detection experiment, the initial learning rate of the optimal hyperparameters in the network is set to 0.001 according to the grid search algorithm, the weight decay coefficient is 0.0005, and the training strategy adopts the momentum gradient descent algorithm with a momentum term of 0.9, and the number of iterations is set to 50.000. Subsequently, we use the network model obtained from the training to detect the citrus image and citrus video, respectively, and to compare the accuracy of the citrus detection and the accuracy of the citrus video tracking.  
For the experimental validation of the citrus counting strategy, we manually counted the 10 citrus videos obtained, and the actual number of citrus fruits in the videos was obtained by averaging the counting results obtained by the researchers. When counting, we played the video frame by frame, first recorded the number of fruits in the first frame, and then recorded the number of newly appeared fruits in the subsequent frames until the end of the video, to get the total number of fruits in the video and completed the manual counting of fruits. Subsequently, based on the algorithm of this paper and the proposed counting strategy, the number of citrus fruits in 10 videos were obtained separately, and the algorithm counting results were compared with the manual counting results to test the citrus video counting accuracy.
# Neural Network-Based Cost-Effective Estimation of Useful Variables to Improve Wind Turbine Control
## 2 Wind Turbine and Wind Speed Modelling and Wind Turbine Control
### Drive-Train Dynamics  
The drive-train dynamics are described by the following equation [7]:  
\[\begin{bmatrix}\Omega\\ w_{g}\end{bmatrix}=\begin{bmatrix}A(s)&B(s)\\ C(s)&D(s)\end{bmatrix}\begin{bmatrix}T_{a}\\ T_{g,r}\end{bmatrix} \tag{3}\]  
Figure 2: Effective wind speeds for turbines 1 to 5.  
where \(\Omega\) is rotor speed, \(w_{g}\) is generator speed, \(T_{a}\) is aerodynamic torque and \(T_{g}\) is generator torque. Neglecting the intermediate to high frequency components, \(A(s)\), \(-B(s)/N\), \(C(s)/N\), and \(-D(s)/N^{2}\) all reduce to  
\[\frac{1}{((I_{1}+N^{2}I_{2})s+(\gamma_{1}+N^{2}\gamma_{2}))}, \tag{4}\]  
where \(\gamma_{2}\) is the high speed shaft external damping coefficient in (Nm/rad/s), \(\gamma_{1}\) is the low-speed shaft external damping coefficient (in Nm/rad/s), \(I_{1}\) is the rotor inertia (in kg m\({}^{2}\)), \(I_{2}\) is the generator inertia (in m\({}^{2}\)) and \(N\) is the gearbox ratio.