esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Waste glass (WG) is unsustainable due to its nonbiodegradable property. However, its main ingredient is silicon dioxide, which can be utilised as a supplementary cementitious material. Before revising WG, the flexural strength (FS) and alkali-silica reaction (ASR) expansion of WG concrete are two essential properties that must be investigated. This study produced mortar containing activated glass powder using mechanical, chemical, and mechanical-chemical (combined) approaches. The results showed that mortar containing 30% WG powder using the combined method was optimal for improving the FS and mitigating the ASR expansion. The microstructure analysis was implemented to explore the activation effect on the glass powder and mortar. Moreover, a random forest (RF) model was proposed with hyperparameters tuned by beetle antennae search (BAS), aiming at predicting FS and ASR expansion precisely. A large database was established from the experimental results based on 549 samples prepared for the FS test and 183 samples produced for the expansion test. The BAS-RF model presented high correlation coefficients for both FS (0.9545) and ASR (0.9416) data sets, showing much higher accuracy than multiple linear regression and logistic regression. Finally, a sensitivity analysis was conducted to rank the variables based on importance. Apart from the curing time, the particle granularity and content of WG were demonstrated to be the most sensitive variable for FS and expansion, respectively.  
random forest; beetle antennae search; activation methodology; machine learning; flexural strength +
Footnote †: journal: Applied Sciences
## 5 Numerical Results with Adaptive Time-Step Sizes
### Experiment 2 with Adaptive Solvers  
In this experiment, the parameters listed in Equation (20) are used. Figure 12 shows the errors as a function of the running time in a log-log diagram. This experiment shows that the adaptive LNe controllers and the adaptive CNe controllers are again faster than controllers designed based on the Runge-Kutta method. As we mentioned previously, the CFL limit is changing with respect to time, and it can be calculated for the explicit Euler method as \(h_{\text{CFL}}^{\text{EE}}=|2/\lambda_{\text{MAX}}|\). That limit was calculated in this experiment at six selected points in time as follows:  
\[\text{time }\text{point}\in\Big{\{}t^{0}+i\Big{(}t^{fin}-t^{0}\Big{)} \Big{\}},\;\;i\in\{0,\;0.2,\;0.4,\;0.6,\;0.8,\;1\}\]  
We plotted this limit as a function of time with a dashed blue line in Figure 13. At approximately the same level of accuracy, when the produced error was of order \(10^{-4}\), the history of the time-step size was also registered for each adaptive controller in order to check if they can follow the trend of the \(h_{\text{CFL}}^{\text{EE}}\). Figure 13 shows that the LNe3 controllers and CLL controllers could roughly follow the trend of the CFL limit. It means that they could detect the changes in the CFL limit and modify the step size. The Runge-Kutta controllers could follow the general trend, but they suffer from a fluctuating step size. The zoomed area of Figure 13 shows the behavior of the time-step size of the RKCK during a very short time (0.06% of the total time). On other hand, the time-step size in the case of the adaptive LNe3-L2L3 remained roughly constant. The reason behind the fluctuation in the case of the RK solvers is the conditional stability: when the time-step size \(h\) is below the CFL limit (which is slightly larger for RK4 than for the first-order explicit Euler), the error is very small, and the time-step size is increased. When the time-step size exceeds the stability limit, errors are starting to be amplified exponentially. This exponential increase can be very slow at the beginning if \(h\) is still close to the limit, which may yield a further time-step size elevation. Once the increasing error is detected, \(h\) is suddenly decreased to let the errors diffuse away. Then, the errors will be very small again; thus, the cycle starts again. This fluctuation is time-consuming and therefore undesirable. It is among the reasons why adaptive Runge-Kutta controllers are slower than the other solvers in our experiments.  
Figure 11: The \(L_{\infty}\) errors as a function of the running times in Experiment 1.  
Figure 12: The \(L_{\infty}\) errors as a function of the running times in Experiment 2.
## 2 Materials and Methods
### Geometry of the Studied Parts  
All formed components were pyramidal frusta measuring 105 mm at each base (Figure 2a). Compared to a conical frustum, the pyramidal shape reduces the blank regions prone to bending without the need for a backing plate (Figure 2b). Additionally, pyramids decrease the computational time to a fraction of a comparable conical frustum. The corners of the pyramidal parts were rounded to minimize the force spikes caused by the biaxial deformation state. It was determined that a radius (\"r\" in Figure 2a) of 10 mm approximated the force magnitudes to those of a conical part of similar parameters (Figure 2c). The maximum depth was set at 15 mm because, at this level, the force curve had already reached a steady state or shown its definitive trend.  
The AA1050-H24 parts were formed with blank thickness (\(t_{0}\)) of 0.8 mm and draw angle (\(\alpha\)) set to 45\({}^{\circ}\), 60\({}^{\circ}\), and 70\({}^{\circ}\). The 70\({}^{\circ}\) case was selected to study one part approaching failure. In addition to the aluminum components, two truncated pyramids were manufactured with AISI-SAE 304 steel. They had blank thickness, \(t_{0}\) = 0.5 mm, and draw angles (\(\alpha\)) set to 45\({}^{\circ}\) and 60\({}^{\circ}\).
# An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning
## 4 The Proposed Model
### Supervised Learning
#### 4.2.3 Learning and Parameter Updating  
Supervised learning is one type of learning method, in which a model learns from the training data and then predicts new instances of an event. It results in unlabeled data to be labeled through previous experience and then applies the labeled data to the learning algorithm. After training, the parameters are generated to update the prediction model. [PERSON] et al. presented a solution for supervised learning [15] and our algorithm has been inspired by this solution.  
Algorithm 2 is shown in Figure 6. The algorithm inputs the datasets of sensor data and the FDR. The learning algorithm will deal with all of the data and calculate \(\theta\) for each sensor node, respectively.  
Figure 5: Sensor node determines whether or not to send fake messages.  
We use the logistic function as the hypothesis function, as expressed in Formula (3), where \(\theta=\left[\theta_{0},\theta_{1},\theta_{2}\cdots,\theta_{w}\right]^{T}\). In the formula, \(h_{\theta}(x)\) is a \(w+1\) dimensional parameter vector that we are learning from the training set, \(w\) is the number of features for the training, and \(\theta_{0}\) is the initial value for logistics regression. The learning algorithm and the hypothesis function can realize the goal of logistics regression.  
\[h_{\theta}(x)=\frac{1}{1+\exp\left(-\theta^{T}x\right)} \tag{3}\]  
The main purpose of the learning algorithm is to find \(\theta\), to accurately classify the training data into two categories. When the cost function in Formula (4) reaches the global minimum, \(\theta\) is the optimal solution of the learning algorithm. In the formula, \(m\) is the amount of training data. In the proposed SDASL method, every sensor node has its own training dataset, and thus, each sensor node has its own parameters.  
\[J(\theta)=-\frac{1}{m}\sum_{i=1}^{m}[y^{(i)}\cdot logh_{\theta}(x^{(i)})+(1-y^ {(i)})\log\left(1-h_{\theta}(x^{(i)})\right)] \tag{4}\]  
The gradient descent algorithm is used here to obtain an appropriate \(\theta\) for the hypothesis function which is described in Formula (5), in which \(\alpha\) indicates the length of the gradient descent. Because \(J(\theta)\) is a convex function, we can be sure of finding the local optimal value, which is also the global optimum.  
\[\theta_{i}:=\theta_{i}-\alpha\frac{\partial}{\partial\theta_{i}}J(\theta) \tag{5}\]  
Figure 6: Supervised learning running in the sink node.
## 3 Results and Discussion
### NO Formation Characteristics of NH\({}_{3}\)/CO Combustion
#### 3.3.2 Effect of Excess Air Coefficient \(\alpha\) on NO Production  
We investigated the influence of the excess air coefficient on NO generation characteristics during NH\({}_{3}\)/CO combustion at different combustion temperatures. The excess air coefficients were set as 0.9, 1.0, and 1.1, and the furnace temperature varied from 900 \({}^{\circ}\)C to 1500 \({}^{\circ}\)C. As shown in Figure 8, at the combustion temperature set in the experiment, the NO content in the blue gas generated by combustion gradually increased with the increase in excess air coefficient \(\alpha\).
## 7 Conclusions
In addition, this generalization allows an independent definition of the optimization problem, which does not require a new formulation if different components (such as generation units or loads) are added or even removed from the VPP. For the sake of completeness, a suitable MILP formulation for modelling the OSU was also proposed. The optimal solution of the model corresponds to an operating point of the system that simultaneously minimizes the cost, maximizes the comfort levels, and deals with DR requests. Finally, the ICT architecture of the VPP has been described and discussed by focusing on the communication infrastructure among different VPPs and the DSO (or IO) DMS. The characterization of the communication infrastructure of a pilot plant adopting such an approach has been presented and discussed in a previous study by demonstrating that the application of the proposed VPP concept can be technically feasible and economically sustainable by making proper use of existing communication solutions. This research activity has been partially funded by Regione Lombardia under the Smart Cities and Communities grant no. 40545387 (\"Smart Campus as Urban Open Labs--SCUOLA\"), by the University of Brescia under the grant Health and Wealth 2015 \"Brescia 20-20-20\", and by the University of Brescia as part of the research activities of the eLUX laboratory--\"energy Laboratory as University eXpo\". [PERSON] and [PERSON] conceived the conceptual framework and defined the architecture of the proposed VPP system. [PERSON] provided the theoretical background of the study and the description of the VPP architecture. [PERSON] provided the analysis and description of the ICT technologies to support the integration of the proposed VPP approach in Smart Grids. [PERSON] developed and discussed the optimization model proposed for the OSU. All the authors contributed to the writing and revising of the manuscript. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} AC & Alternating Current \\ BESS & Battery Energy Storage System \\ BMS & Battery Management System \\ DER & Distributed Energy Resource \\ DER-AC & DER Automated Controller \\ DES & Distributed Electrical Storage System \\ DG & Distributed Generation \\ DMS & Distribution Management System \\ DR & Demand Response \\ DSM & Demand-Side Management \\ DSO & Distribution System Operator \\ \end{tabular}
# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
## 4 Empirical Evaluation
### Metrics  
We employ Intersection over Union (IoU) and Hausdorff Distance (HD) to assess the novel lung segmentation and tumor detection models as our primary evaluation metrics. IoU is particularly apt for segmentation tasks as it quantitatively measures the overlap between the predicted segmentation and the ground truth. This metric highly indicates the accuracy of the model in delineating the boundaries of lung regions and tumors, providing a clear picture of how well the model can identify and segregate the target areas from the surrounding tissues. HD, on the other hand, is utilized to gauge the spatial discrepancy between the predicted and actual boundaries of the tumors. This metric is especially suitable for evaluating the precision of tumor detection models, as it reflects the maximumdistance of any point on the predicted boundary from the nearest point on the actual boundary. The smaller the HD value, the closer the prediction of the model is to the true shape of the tumor, indicating a higher level of accuracy in capturing the tumor's geometric nuances [52].  
For the proposed 3D reconstruction model, we continue to use HD along with Euclidean Distance (ED). HD remains a crucial metric due to its effectiveness in measuring the geometric fidelity of the reconstructed tumor shapes compared to real tumors. It ensures that the reconstructed models closely mirror the tumor shapes, maintaining the integrity of critical geometric features. ED complements HD by offering a scalar representation of the straight-line distance between corresponding points on the predicted and true surfaces. This metric is particularly relevant for 3D reconstructions as it provides insight into the average positional accuracy across the entire reconstructed tumor volume. By assessing both the maximum and average deviations (through HD and ED, respectively), we can understand the performance of the model in replicating the complex 3D structures of tumors, ensuring the reconstructions are not only precise in shape but also accurate in spatial positioning.  
IoU, HD, and ED metrics are defined as follows:  
\[IoU=\frac{|A\cap B|}{|A\cup B|} \tag{7}\]  
where \(A\) denotes the set of pixels in the ground truth mask, and \(B\) represents the pixels in the predicted mask.  
\[HD(A,B)=\max(h(A,B),h(B,A)) \tag{8}\]  
where  
\[h(A,B)=\max_{a\in A}\min_{b\in B}\|a-b\| \tag{9}\]  
In this context, \(A\) and \(B\) are the sets of non-zero pixels in the ground truth and predicted segmentation, respectively, and \(\|a-b\|\) denotes the Euclidean distance between points \(a\) and \(b\).  
\[ED=\sqrt{(x_{2}-x_{1})^{2}+(y_{2}-y_{1})^{2}+(z_{2}-z_{1})^{2}} \tag{10}\]  
Here, \((x_{1},y_{1},z_{1})\) and \((x_{2},y_{2},z_{2})\) represent the coordinates of two points in the 3D space.
# An Orthogonal Wheel Odometer for Positioning in a Relative Coordinate System on a Floating Ground
## 2 Materials and Methods  
The positioning system is the basic part of an intelligent mobile robot, and it is also one of the key research directions in the field of mobile robots. The orthogonal positioning sensor system introduced in this paper is designed based on the positioning principle of the orthogonal wheel and magnetic encoder. Due to the sudden acceleration of the driving wheel, such as acceleration and emergency stop, the phenomenon of the wheel slipping increases the error of the sensor data. In order to solve the influence of this error, the positioning system designed in this paper adopts the driven way to obtain the position coordinates and rotation angle of the robot relative to the initial pose. The positioning system consists of orthogonal wheels, magnetic encoders and a main frame. Figure 3 shows a three-dimensional diagram of the system.  
Figure 1: Physical picture of the positioning system.  
Figure 3: A 3D diagram of positioning sensing system.  
Figure 2: (**a**) Physical picture of encoder and slave wheel installation. (**b**) Physical drawing of sliding mechanism. The range of up and down movement is 6 cm. Every sliding mechanism supports the amount of ground unevenness at \(\pm 3\) cm.  
An orthogonal wheel adopts two identical omnidirectional wheels fixed on the same mechanism. A single orthogonal omnidirectional wheel is shown in Figure 4. Each wheel stand has two degrees of freedom: one is the movement of the axis of the wheel vertically, and the other is the rotation of the axis around the wheel.  
The orthogonal wheels assembly includes a bracket and four omnidirectional wheels. The four orthogonal wheels are fixed on the bracket, and the axes of the four orthogonal wheels are perpendicular to each other. Four orthogonal wheels fixed on the bracket can rotate at the same time to achieve any direction of movement. Each driven wheel group includes a sliding mechanism in addition to a bracket and an orthogonal wheel. By designing the sliding mechanism on the bracket of each orthogonal wheel, it is connected with the driven wheel bracket and the whole frame so that the driven wheel group can keep rolling in contact with the ground when sliding in the vertical direction perpendicular to the orthogonal driven wheel shaft. The sliding mechanism consists of a slider, a slide track and a spring. On the floating plane, four orthogonal wheels interact with each other through vertical sliders and slide up and down according to the conditions of the road so that the wheels can always contact the ground, reducing the possibility of wheel slipping and improving the positioning accuracy of the positioning device. Figure 5 shows the tolerance of the positioning system for floating ground.
## 2 The Framework
### The Base Class  
In summary, this re-definition of the framework base class makes it a class template that needs to be instantiated by user code. To create a new opcode, we derive our own class by declaring the required number of output and inputs needs as template arguments (CPOF code uses the csnd namespace and is declared in the plugin.h header file):  
**#include** <plugin.h>  
struct MyPlug : csnd::Plugin<1,l> { };  
The above lines will create a plugin opcode with one output (first template argument) and one input (second template argument). This class defines a complete opcode, but since it is only using the base class stubs, it is also fully non-op. It will inherit the following members from Plugin:  
* outargs: a Params object holding output arguments.
* inargs: input arguments (Params).
* csound: a pointer to the Csound engine object.
* offset: the starting position of an audio vector (for audio opcodes only).
* nsmps: the size of an audio vector (also for audio opcodes only).
* init(),kperf() and aperf() non-op methods, to be reimplemented as needed.
* out_count() and \(in_{c}ount()\): these functions return the number of arguments for output and input, respectively. They are useful for opcodes with variable number of arguments.
* sa_offset(): this method calculates the correct values for offset and nsmps. User called does not need to invoke it, as it is called implicitly by the aperf() template function before it delegates to the plugin code.  
As we have outlined in Section 1.1.1, Csound has two basic passes for opcodes: init and perf-time. The former runs a processing routine once per instrument instantiation (and/or once again if a re-init is called for). Code for this is placed in the Plugin class init() function. Perf-time code runs in a loop and is called once every control (k-)cycle (also known as k-period). The other class methods kperf() and aperf() are called in this loop, for control (scalar) and audio (vectorial) processing. The following examples demonstrate the derivation of plugin classes for each one of these opcode types (i, k or a). Note that k and a opcodes can also use i-time functions if they require some sort of initialisation.  
Figure 1: The Plugin template base class, derived from the opaque C structure OPDS.
# Reducing Motion Blur in Ghost Imaging Via the Hessian Matrix
## 4 Discussion  
In this paper, we did several experiments to simulate motion blur caused by the improper setting between the exposure time of the detectors and the coherence time. From the experiments, we saw motion blur in the images reconstructed by the SC and SC filtered by Lucy-Richardson, which reduced the quality of reconstructed image. We therefore introduced the Hessian matrix into ghost imaging to reduce the influence caused by motion blur. The results showed that when data were filtered by the Hessian matrix, the reconstructed images had smoother edges, thus improving the quality of the reconstructed images and reducing the influence from motion blur. However, as the cost of the method, when we used the Hessian matrix to deal with motion blur, the resolution was reduced, which may cause some problems if the object has a complex structure. In the further experiments, we hope to find another algorithm to solve the problem that the resolution is reduced when applying the Hessian matrix to the ghost imaging, making the method a promising approach to deal with motion blur in ghost imaging.  
[PERSON] carried out the experiments, validation and did the writing and original draft preparation. [PERSON] was responsible for the supervision, [PERSON] was responsible for the methodology and formal analysis, [PERSON] was responsible for the writing, review and editing, [PERSON] was responsible for the supervision and writing, review and editing. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (61801042, 61631014, 61401036, 61471051 and 61531003), the Youth Research and Innovation Program of BUPT (2015 RC12) and the BUPT Excellent Ph.D. Students Foundation (CX2019224).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available because the need of large storage space made it not easy to transfer.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Research Method
### Performance Metrics  
The results were compiled in terms of recognition accuracy, precision, recall, and F1-measure. These terms were determined with the help of True Positive (TP), True Negative (TN), False Negative (FN), and False positive (FP) predictions, made by the classifiers. The effectiveness of the proposed feature-extraction techniques was visualized with the help of the Kernel Density Estimation (KDE) algorithm.  
The mathematical formulations used for the estimation of the mentioned metrics are given in Equations (24)-(27):  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{24}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{25}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{26}\]  
\[\text{F1}-\text{score}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{ \text{Precision}+\text{Recall}} \tag{27}\]