esa-sceva/satcom-chunk-collection huggingface dataset:
# Remaining Useful Life Estimation of Bearings Using Data-Driven Ridge Regression
## 6 Evaluation Setup
### Bayesian Approach  
The Bayesian method estimates the parameters of the degradation model based on its prior probability distribution and current measurements [8]. Two degradation models are considered, namely exponential and linear degradation models, to predict RUL of bearings using experimental data. The discrete-time exponential degradation model is defined as  
\[\hat{h}_{e}(t_{i})=\phi_{e}+\alpha_{e}\exp\left(\theta_{e}t_{i}+\epsilon- \frac{\sigma^{2}}{2}\right) \tag{6}\]  
where the predicted health indicator \(\hat{h}_{e}(t_{i})\) is a function of time, \(\phi_{e}\) is the constant intercept, \(\alpha_{e}\) and \(\theta_{e}\) are stochastic parameters deciding the slope of the model, and \(\epsilon\) is a normally distributed random error with \(N(0,\sigma^{2})\). In addition, \(\alpha_{e}\) is lognormal-distributed and \(\theta_{e}\) is Gaussian-distributed [8].  
We also define the discrete-time linear degradation model as  
\[\hat{h}_{l}(t_{i})=\phi_{l}+\beta_{l}t_{i}+\epsilon \tag{7}\]  
where \(\phi_{l}\) is the model intercept, \(\beta_{l}\) is a Gaussian-distributed random parameter determining the slope, and \(\epsilon\) is a normally distributed random noise with \(N(0,\sigma^{2})\).  
At each time step \(t_{i}\), Bayesian estimator updates the distribution of model parameters of Equations (6) and (7) based on the previous knowledge of the model and the latest observation of \(h(t_{i})\) as the basis for the RUL prediction. We initially set the slope of the exponential degradation model based on the historical training data. If historical data are not available, the prior of the slope parameters are randomly selected with large variances to rely more on the observed data during the initial setup.  
Since the performance of Bayesian methods considerably depends on the models and the data, as discussed below, we also propose an adaptive Bayesian method to choose one of the models between the exponential degradation model and the linear degradation model based on the residual error between the proposed model and measured health indicator.
# Hierarchical LSTM-Based Network Intrusion Detection System Using Hybrid Classification
## 3 Proposed Algorithm
### Proposed NIDS
#### 3.2.3 Hybrid Classifier Combining Packet and Session Classifiers  
Figure 8 shows the overall classification structure of the proposed NIDS. The cell output of each packet classifier is directly input to the corresponding cell of the session classifier without waiting for the next packet, and the output value of the hidden layer for that cell is stored such that the session LSTM continues to classify when the next packet is received. Therefore, the storage space required for each session is only 512 \(\times\) 32-bit floats, or 2048 Bytes. Therefore, unlike the conventional session-based method, which requires all packets to be stored, the proposed method significantly reduces memory usage. In particular, the fixed-size memory used for each session by the proposed method is a significant advantage.  
The computational complexity \(T_{session}\) for classifying one session by the proposed NIDS is given as follows:  
\[T_{session}=\sum_{p=1}^{N}L_{p}/100=\Theta\Big{(}\frac{N\overline{L}}{100} \Big{)}=\Theta\big{(}N\overline{L}\big{)},\]  
where \(L_{p}\) and \(\overline{L}\) denote the size of the \(p\)-th packet in the session and the average packet size, respectively. Therefore, the computational complexity is proportional to the total size of the session traffic, so the computational complexity is higher than that of other competitive NIDSs that require only partial traffic. However, since it has linear complexity, the computational complexity can be reduced by a parallel processing or by increasing the size of the packet partition.  
Figure 8: Proposed hybrid classifier for NIDS.
## 5 Experiments and Results
### Fault Detection and Classification
#### 5.2.1 Dataset and Data Augmentation Strategy  
The created dataset consists of 2100 images taken from all seven fans, with one fan having visible damages allowing for binary classification of healthy vs. faulty. These images are a result of the path-planning experiment outlined in Section 5.1.3, where the front and back of the PFBs were examined autonomously. Of the 2100 total images, 70% were used for training, 10% for validation, and 20% for testing, illustrated in Table 2. Additionally, the raw images were downsized to \(300\times 300\) pixels to allow them to be easily fed to the models for training. Figure 20 provides a visualization sample of the dataset with each of their perspective class labels.  
Our data-augmentation strategy comprised several transformations, each designed to introduce specific variabilities into the training dataset, thereby encouraging the model to learn invariant and discriminative features for accurate defect detection. This included _RandomHorizontalFlip_, which mirrors images horizontally with a 50% probability, aiming to increase the model's invariance to the orientation of the defect objects; _RandomRotation_, where images are rotated by a random angle within \(\pm 15\) degrees to simulate variations in object orientation relative to the camera, a common occurrence in practical scenarios; _RandomAffine_, applying translation, scaling, and shearing to the images (specifically, it translates images up to 10%, scales by a factor between 0.9 and 1.1, and shears by up to 10 degrees, crucial for simulating real-world positional, size, and perspective variations); and _RandomApply with GaussianBlur_, where a Gaussian blur is applied to a subset of the images (with a probability of 50%), using a kernel size of 3 and a \(\sigma\) range between 0.1 and 2.0. This was designed to mimic the effect of out-of-focus captures and varying image quality,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label** & **Training Set** & **Validation Set** & **Test Set** & **Total** \\ \hline Faulty & 490 & 70 & 140 & 700 \\ \hline Healthy & 980 & 140 & 280 & 1400 \\ \hline Total & 1470 & 210 & 420 & 2100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Path-planning dataset distribution  
Figure 20: Examples of images showing both faulty and healthy PFBs in our created dataset.  
thereby directing the model's attention towards more significant shape and texture patterns rather than overfitting to fine, potentially inconsistent details.
# Application of the Gait Kinematics Index in Patients with Cerebral Palsy
## 2 Materials and Methods
### Experimental Testing  
The patients underwent a medical assessment (neuromuscular tests) and a biomechanical one (gait test). The tests were performed at the Motion Analysis Laboratory using a system enabling the BTS Smart (BTS S.p.A., Milan, Italy) triplanar motion analysis. The BTS system consisted of six IR cameras, allowing for the recording of the location of passive markers in space (sampling rate of 250 Hz), a camera capturing video images, two dynamometric platforms (Kistler Eastern Europe s.r.o., Prague, Czech Republic, sampling rate of 1000 Hz), and a control unit provided with a software program to coordinate the operation of the entirety of the measurement equipment. Using a device for system calibration, the measurement path was connected to the system of coordinates defining the directions of motion. The beginning of the system of coordinates was connected to one of the dynamometric platforms placed in the gait path. The orientation of the system of coordinates defined the motion along the \(X\)-axis as the motion of the center of gravity of the body on the transverse axis (motion in the right and left direction), the motion on the \(Z\)-axis as the motion in the direction of the sagittal axis, and the motion along the vertical \(Y\)-axis as the up-and-down motion. The BTS Smart system, which is based on optical analysis, requires cameras to be calibrated. To accurately calibrate the cameras and markers in 3D space, a wand calibration was used. During calibration, the wand was moved in different directions (X, Y, Z axes) and angles, performing slow and rotational movements to ensure full coverage of the cameras' field of view. A satisfactory calibration result is attained when the calibration error is less than 1 mm for spatial measurements, ensuring the highly accurate tracking of markers in space.  
The location of the markers (15 spherical motion capture markers covered with a reflective material with a diameter of 1.5 cm) complied with the [PERSON] protocol [29] (Figure 1). A model built in the BTS System was used for the calculations. The markers were placed by the same person with 15 years of experience working with motion capture technology, ensuring high-quality and reproducible results.  
Before the commencement of the gait test, the necessary anthropometric measurements were made in accordance with the [PERSON] model, and then each child covered the measurement path (7 m) ten times barefoot. Two complete gait cycles were analyzed from each gait trial, taken from the middle part of the measurement path. This meant that cycles from the acceleration and deceleration phases were not included in the analysis. On average, 20 gait cycles were analyzed per study participant. In further analysis, the average of all strides for each child was used. During the tests, the children moved at their natural velocity. For each subject, 2-3 static (standing) trials were recorded.
## 4 Results
### Accuracy Evaluation of Different Models
#### 4.2.1 Accuracy Evaluation at Rain Gauges
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Period** & **Data** & _BIAS_ & _RSME_ & _CC_ & _RATIO_ & _POD_ & _FAR_ & _TS_ & _MAR_ \\ \hline \multirow{4}{*}{Training stage} & GPM & \(1.029\) & \(3.874\) & \(0.295\) & \(2.029\) & \(0.524\) & \(0.496\) & \(0.346\) & \(0.476\) \\  & IDW & \(-0.036\) & \(1.998\) & \(0.489\) & \(0.964\) & \(0.552\) & \(0.326\) & \(0.436\) & \(0.448\) \\  & F-SVD & \(-0.027\) & \(1.481\) & \(0.753\) & \(0.973\) & \(0.634\) & \(0.186\) & \(0.554\) & \(0.366\) \\  & LSTM & \(-0.049\) & \(2.070\) & \(0.393\) & \(0.951\) & \(0.507\) & \(0.455\) & \(0.356\) & \(0.493\) \\  & FS-ConvLSTM & \(0.208\) & \(1.410\) & \(0.782\) & \(1.208\) & \(0.644\) & \(0.183\) & \(0.563\) & \(0.356\) \\ \hline \multirow{4}{*}{Testing stage} & GPM & \(1.088\) & \(3.656\) & \(0.290\) & \(2.088\) & \(0.519\) & \(0.533\) & \(0.326\) & \(0.481\) \\  & IDW & \(-0.022\) & \(1.957\) & \(0.437\) & \(0.978\) & \(0.508\) & \(0.370\) & \(0.391\) & \(0.492\) \\ \cline{1-1}  & F-SVD & \(-0.024\) & \(1.487\) & \(0.714\) & \(0.976\) & \(0.602\) & \(0.221\) & \(0.514\) & \(0.398\) \\ \cline{1-1}  & LSTM & \(0.020\) & \(2.007\) & \(0.330\) & \(1.020\) & \(0.482\) & \(0.499\) & \(0.326\) & \(0.518\) \\ \cline{1-1}  & FS-ConvLSTM & \(0.256\) & \(1.404\) & \(0.754\) & \(1.256\) & \(0.612\) & \(0.219\) & \(0.522\) & \(0.388\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the FS-ConvLSTM fusion model in the training and testing stages. As the rainfall magnitude becomes larger, LSTM and FS-ConvLSTM are closer to the measured value in total rainfall; _BIAS_ keeps decreasing, with _RATIO_ becoming closer to 1. Since there is a direct relationship between _RSME_ and rainfall magnitude, the _RSME_ of the fused data also keeps increasing. The _CC_ of FS-ConvLSTM fluctuates around 0.75, with the highest value of 0.778 for torrential rainfall events, while the _CC_ of LSTM fluctuates between 0.2 and 0.4, becoming larger as the rainfall magnitude increases. In terms of classification indicators, as the rainfall event changes from light to heavy, the _POD_ and _TS_ of the fused data show an overall increasing trend, and the _MAR_ and _FAR_ also show an overall decreasing trend, suggesting that the rainfall capture capability keeps improving, and the FS-ConvLSTM performs better than the LSTM. In general, FS-ConvLSTM exhibits advantages over other methods in rainfall events of different magnitudes, and its accuracy improves with the increase in rainfall magnitude.
# A Methodology for Controlling Bias and Fairness in Synthetic Data Generation
## 4 Experiments
### Example of Dataset with Discrimination  
The last aspect presented in this paper focuses on generating fair or unfair datasets. In what follows, an unbiased dataset and a biased dataset are generated starting from the loan approval probabilistic network shown in Figure 3. Such datasets can be used for classification tasks; in fact, as discussed in Section 3.1, the \"Loan Status\" attribute is binary and reflects the granting or not of a loan, given the other attributes. Since this network contains sensitive attributes such as \"Gender\" or \"Ethnicity\", it is reasonable to believe that a fair dataset, with respect to these variables, would be preferable over a discriminatory one. In the case of the attribute \"Gender\", for example, that would mean that the amount of positive outcomes given the gender \"Female\" should be equal to the amount of positive outcomes given the gender \"Male\". Figure 8 summarizes the principal differences between the two datasets. The top half of Figure 8 reports the histogram of attribute \"Gender\" and the correlation heat map of a fair dataset generated with the proposed methodology. In this case, in fact, the outcome \"Loan Status\" is almost not affected by the value of the attribute \"Gender\". These results are obtained by setting a small edges weight to all nodes connected to the attribute \"Discriminatory Factor\" (see Figure 3). Since the edges weight is small, the correlation between variables decreases as well, leading to a more diversified dataset.  
On the contrary, the bottom half of Figure 8 shows the opposite scenario: in this case, the starting network is the same but the generated dataset is built by strengthening the bond between sensitive attributes.  
Increasing these edge weights increases the discrimination towards those records including a \"Female\" value, with the consequent potential risk of bias when training an algorithm on this dataset. Indeed, as can be noticed from the figure, there are very different conditional frequencies for the loan approval status depending on the attribute gender, even if the corresponding correlation matrices do not highlight very large correlations between sensitive attributes and loan approval.  
Generating unfair datasets can be very helpful to evaluate the effectiveness of bias mitigation approaches when developing machine-learning-based models and classifiers. Indeed, depending on the specific purpose, the proposed methodology can be exploited to generate fair or unfair datasets starting from the same network, i.e., a set of attributes, providing a large scale of options to those in need of a synthetic dataset that reflects specific correlations and patterns between observed variables.  
Figure 7: Mutual information between node \(Z\) and each other node, varying conditional variances. (**a**) M.I. as function of variances \(\sigma_{A}\), \(\sigma_{B}\), \(\sigma_{D}\); edge weight equal to 0.1. (**b**) M.I. as function of variances \(\sigma_{C}\), \(\sigma_{E}\), \(\sigma_{Z}\); edge weight equal to 10.
## 3 Results and Discussion  
The resulting NPs have been first characterized in solution by means of DLS and LDE. Gold and silver NPs have a surface plasmon in the visible region, so they were also characterized using optical absorbance spectroscopy. Subsequently, they were deposited on solid silicon and electron microscopy grid for the XPS and TEM investigations, respectively. The data are summarized in Table 2. The nanoparticle solutions appear to be stable with respect to changes in size (probed by DLS) and charge (probed by LDE) and, in the case of gold and silver, with respect to the absorbance spectrum up to three months.  
The typical surface compositions of NPs, as determined by XPS, are reported in Table 3. C, O, Si, K, Cl, and the specific metal of NPs are found. Limited amounts of salt were detected. Carbon in the order of 20-30%, as well as organic oxygen, can be ascribed to contamination in the XPS analysis chamber. Oxygen is also attributed to a native oxide layer on silicon (substrate for NPs deposition).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Size (Diameter)/nm**} & \\ \hline  & **SPR** & **DLS** & \multicolumn{2}{c}{**TEM**} & \(\zeta\)**-Potential** & **XPS Oxidation** \\ \hline  & & & \(\mathbf{X_{c}}\) **(nm)** & **Average** & **(mV)** & \\ \hline AuNPs & 9 \(\pm\) 2 & 10.2 \(\pm\) 0.6 & 10.2 \(\pm\) 0.2 & 11 \(\pm\) 5 & \(-\)62 \(\pm\) 7 & Au(0) 97.1\% \\ AgNPs & 8 \(\pm\) 2 & 13 \(\pm\) 2 & 10.6 \(\pm\) 0.4 & 11 \(\pm\) 5 & \(-\)44 \(\pm\) 2 & Ag(0) 100\% \\  & & & & & Pt(0) 77.6\% \\ PtNPs & - & 14 \(\pm\) 2 & 11.3 \(\pm\) 0.3 & 11 \(\pm\) 5 & \(-\)58 \(\pm\) 3 & Pt(II) 10.8\% \\  & & & & & Pt(IV) 11.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sizes (diameter), \(\zeta\)-potential and chemical composition of the metal nanoparticles.  
According to the experimental protocol adopted for sample preparation and XPS analysis, oxidation due to air can be excluded.
## 3 Results and Discussion
### Automated Column Sorption System
#### 3.2.5 Breakthrough Curve, Capacity and Usage Rate Analysis
It has a direct impact on the financial viability of performing either replacement (disposal) or regeneration (on- or off-site), and is affected by factors that include HMI influent concentration, zeolite bed depth, and flow rate. Research into other sorbent materials demonstrates that the order of usage rate is consistent with the sorption capacity [51]. Due to the unique automated variable influent feeding rate and sampling technique proposed in this study, the usage rate and performance efficiency are very complex [28]. However, the major removal trend of Pb2+ >> Fe3+ > Cu2+ > Zn2+ >> Ni2+ is well-established and supports previous results [22; 27; 32; 33; 38; 42; 43], providing significant validation of this design. The use of natural zeolites as sorbents in industrial wastewater treatment and environmental management is motivated by the non-toxicity of these minerals, their abundant global availability, and economic feasibility. The removal and recovery processes of HMIs from aqueous solutions by natural zeolites take into consideration the regeneration potential of the zeolite bed to be reused in multiple cycles, as well as the use of the recovered metals [52] in applicable industrial applications. Metal processing effluents contain high concentrations of recoverable metals, triggering a movement towards technologies to recover these metals from AMD waste [53]. The removal-regeneration-recovery process has the potential to generate additional revenue streams with the use of metals of value [54]; such as the HMIs investigated in this innovative study.
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 3 Flexible Packaging and Testing of Sensors
### MEMS Silicon Piezoresistive Pressure Sensors  
The MEMS silicon piezoresistive pressure sensor utilizes the piezoresistive effect of silicon to convert pressure signals into electrical signals through a Wheatstone measurement bridge. The physical photo of the MEMS silicon piezoresistive pressure sensor used in this study is shown in Figure 3, which is highly sensitive, small (2 mm \(\times\) 2.5 mm \(\times\) 0.9 mm), low cost, and highly precise, and its power consumption is low. Based on these features, this type of pressure sensor was applied to pulse testing in this study.  
Figure 2: Schematic diagram of pulse wave characteristics.
# Plum Tree Algorithm and Weighted Aggregated Ensembles for Energy Efficiency Estimation
###### Abstract  
This article introduces a novel nature-inspired algorithm called the Plum Tree Algorithm (PTA), which has the biology of the plum trees as its main source of inspiration. The PTA was tested and validated using 24 benchmark objective functions, and it was further applied and compared to the following selection of representative state-of-the-art, nature-inspired algorithms: the Chicken Swarm Optimization (CSO) algorithm, the Particle Swarm Optimization (PSO) algorithm, the Grey Wolf Optimizer (GWO), the Cuckoo Search (CS) algorithm, the Crow Search Algorithm (CSA), and the Horse Optimization Algorithm (HOA). The results obtained with the PTA are comparable to the results obtained by using the other nature-inspired optimization algorithms. The PTA returned the best overall results for the 24 objective functions tested. This article presents the application of the PTA for weight optimization for an ensemble of four machine learning regressors, namely, the Random Forest Regressor (RFR), the Gradient Boosting Regressor (GBR), the AdaBoost Regressor (AdaBoost), and the Extra Trees Regressor (ETR), which are used for the prediction of the heating load and cooling load requirements of buildings, using the Energy Efficiency Dataset from UCI Machine Learning as experimental support. The PTA optimized ensemble-returned results such as those returned by the ensembles optimized with the GWO, the CS, and the CSA.  
plum tree algorithm; nature-inspired optimization; prediction; weighted aggregated ensembles; energy efficiency estimation
## 3 Sensors Configuration Analysis
### Grater Shape Sensor  
The second sensor model was the grater shape (Figure 6). Its main dimensions were:  
* Sensor diameter \(d\) = 1 m;
* The height of the sensor \(H\) = 0.1 m;
* Depth of the ground hole \(h\) = 0.3 m.  
Figure 5: The electric field distribution near the flat plate sensor is placed equal to ground level (**a**) and at height A = 1 m above ground level (**b**).  
Excitation assumed for simulations is described above as a static electric field with a strength equal to \(E=100\) V/m.  
The sensor was located equal to ground level and at \(A=1\) m above them. Figure 7 shows the exemplary distribution of the electric field for given boundary conditions in three dimensions \(E\)(\(x\),\(y\),\(z\)) for the grater sensor placed equal to ground level and next at the height \(A=1\) m above them.  
Figure 6: The grater shape sensor model was taken into consideration.  
Figure 7: _Cont_.  
The electric charge that accumulated on the sensor equal to ground level had a value of Q2_0 = \(-5.51\times 10^{-10}\) [C], and in case the sensor was placed at height \(A\) = 1 m above ground level, it had a value of Q2_1 = \(-2.87\times 10^{-9}\) [C].
## 4 Discussion
### Recommendation
#### 4.2.1 Performance  
For insider threat detection to be effective, improving the performance of the recall rate without sacrificing precision is extremely important [93]. Feature selection of security event data is a potential approach to improve performance. Therefore, added efforts are required for the development of feature selection technologies and tool defenses against insider threats [111]. One such example of the recommendation for selecting features is the use of complex models, such as the LSTM recurrent neural network [96] and gated recurrent unit (GRU), such that a rich representation of user behavior can be learned.
# Enhancing Robustness of Per-Packet Load-Balancing for Fat-Tree
## 3 Load Balancing Based on Symmetric Path Groups (LBSP)
### Basic Idea
Then, as indicated by the green dashed lines, the paths for the flow S2 \(\rightarrow\) D2 are as follows: S2 \(\rightarrow\) T11 \(\rightarrow\) {A8,A10} \(\rightarrow\) {C0,C2,C8,C10} \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Path Group**} & **Link Group** & **Link Group** \\  & **(To Switch–Aggregation Switch)** & **(Aggregation Switch–Core Switch)** \\ \hline
2 & 1 & 0 \\
3 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Path groups and link groups in 8-ary fat-tree. Figure 2: Illustration of partitioning 16 equal-cost paths into four symmetric path groups. \(\rightarrow\) {A4,A6} \(\rightarrow\) T5 \(\rightarrow\) D2. It is important to note that the two flows S1 \(\rightarrow\) D1 and S2 \(\rightarrow\) D2 using the same path group will observe almost the same queue lengths.