esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Classification
In Figure 8 are reported the pixels corresponding to the ACM present in the analyzed sample set (Figure 8a), the ACM pixels predicted by the developed PLS-DA hierarchical model (Figure 8b) and the pixels not assigned to any of the different defined classes (Figure 8c). The comparison of Figure 8a,b shows how the ACM samples are all identifiable. The presence of false positives in general is low and in any case there is a clear difference in terms of the ACM predicted pixels and the pixels incorrectly assigned to the particles representative of not contaminated samples. The analysis of Figure 8c clearly shows as the unclassified pixels are due to the \"border effect\" present in all the analyzed samples and to the class NC (i.e., not classified individuals), that a class of material not containing asbestos (i.e., drywall) but, at the same time, not belonging, for their detected spectral attributes, to any of the previously defined classes, that is: AGGREGATES MORTAR AND BRICK, FIBER GLASS and ORGANIC. Although the obtained result does not negatively affect ACM identification, they also highlights how low spectral resolution generates more noise in prediction than that produced according to different set up adopted in our previous studies [20; 36; 39]. Increasing the number of the acquired samples, the noise effect will be reduced, thanks to the increase of spectral data set representative of each class. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**rule 4** & **ACM** &
\begin{tabular}{c} **AGGREGATES AND** \\ **MORTAR + BRICK** \\ \end{tabular} & **ORGANIC** \\ \hline Sensitivity (Cal): & 94.0 & 89 & 99.9 \\ Specificity (Cal): & 95.4 & 90.2 & 97.7 \\ Sensitivity (CV): & 94.0 & 89 & 99.9 \\ Specificity (CV): & 95.4 & 90.2 & 97.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Sensitivity and Specificity in calibration (Cal) and cross validation (CV) of the Partial Least Squares Discriminant Analysis (PLS-DA) rule 4 based classifiers. Figure 7: Prediction maps obtained applying the developed hierarchical PLS-DA classifier C&DW “polluted” with asbestos. A good classification was reached for each of the five classes of investigated C&DW mixed with ACM. The unclassified class identifies few samples of drywall and some mortars in which asbestos is present. The identification of the main fibers in the ACM samples was correctly achieved, only few mortar pixels have been classified as ACM, this misclassification not significantly affects ACM recognition. The mortars containing asbestos show, in fact, a spectrum whose characteristic embeds both mortar and smaller asbestos fiber spectral attributes. The sample with ACM is correctly identified but, at this resolution, it is not possible to separate the asbestos fibers less than 300 \(\upmu\)m from the mortar.
###### Abstract
The communication latency introduced by the interactive scheme can be largely alleviated, since edge devices are usually located within one hop to IoT devices. Despite the advantages, one outstanding constraint of this solution, however, is the ever-increasing storage overhead incurred by the offline pre-computation. Specifically, to eliminate the added noises in the returned scalar products, IoT devices shall pre-compute and store the scalar products of the random noises and the network weights. Both computational and storage complexities are linear to the neural network size and the number of inferences performed. While the offline computational cost might be somewhat mitigated by employing powerful computers, the storage overheads are on the IoT devices, which are resource constrained. In practical systems, these overheads can result in the reduced performance of IoT devices, e.g., flight time of drones, or IoT devices needing to be frequently \"replenished\" in one way or another, which introduces other constraints to the operation of IoT devices. Concerning the practical operation of IoT devices, a better trade-off between offline computation/storage and online computation shall be achieved.
# Nuclear Family Type Identification Based on Deep Forest Algorithm in Residential Power Consumption
## 4 Example Analysis  
### Different Time Periods' Experiment  
In order to investigate the impact of load data from different time periods on classification performance, various classification algorithms including SVM, Adaboost, Decision Tree, KNN, Random Forest, and Deep Forest were tested using accuracy rates. The results of each classification model for identifying the number of children in different nuclear families are presented in Figure 5.  
Based on the experimental data shown in Figure 5, it is evident that the model trained on the selected workday data performs significantly better in identifying the number of children in different nuclear families than the model trained on the selected weekend data. This could be attributed to the fact that nuclear families exhibit more stable electricity consumption patterns on workdays compared to weekends. However, a few classifiers displayed better recognition results on weekends than on workdays, and this was observed only during summer when children's holiday uncertainties lead to irregular loads.  
The models trained using the entire week's data exhibited better recognition results than those trained using only workday or weekend data. This suggests that the electricity usage patterns of nuclear families vary between workdays and weekends, which facilitates the model's learning and training.  
The recognition results of the model trained with autumn data were better than those of the model trained with summer data, indicating that the model is more effective in identifying children from nuclear families while they are in school than during the holiday period. The data from the school period is therefore more useful for actual identification of children. The Deep Forest classifier has significantly improved recognition accuracy over traditional machine learning classifiers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Actual**} & \multicolumn{2}{c}{**Predicted**} \\ \cline{2-3}  & **Positive** & **Negative** \\ \hline Positive & TP & FN \\ Negative & FP & TN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definition for TP, FP, TN and FN.
# Exploring the Evolution of Drought Characteristics in Balochistan, Pakistan
## 2 Materials and Methods
### Study Area  
Balochistan (Figure 1) is the largest province of Pakistan, covering about 44% of its total landmass [34]. It is located between longitudes 71\({}^{\circ}\) and 61\({}^{\circ}\) E and latitudes 32\({}^{\circ}\) and 25\({}^{\circ}\) N. Its southern border includes about two-thirds of the coastline of Pakistan [35]. As it is in the arid zone, slight disturbances in climatic parameters lead to devastating consequences in the shape of flash floods and droughts [36]. The annual rainfall across Balochistan, on average, is less than 250 mm, characterizing it as an arid region. Balochistan is usually categorized into four geographical zones: northern uplands, lower highlands, plains, and deserts [37]. There is a considerable variation in temperature across the province. The northern uplands are characterized by warm summers and cold winters, whereas the winters in the northern districts of the lower highlands are usually extremely cold. However, summers are dry and hot. The plains and deserts get extremely hot in the summers, with temperatures reaching as high as 50 \({}^{\circ}\)C.  
Figure 1: Elevation map of Balochistan.
## 4 Results
### Experimental Results and Analysis
#### 4.2.3 Comparison with Other Mainstream Models  
To validate and evaluate YOLO-Ship model performance, multiple current mainstream target detection network models were trained on the ship component dataset under the same experimental environment and parameter configuration [21], providing comprehensive comparison across different architectural approaches. The baseline selection encompasses classical convolutional architectures and YOLO variants to represent both traditional feature extraction approaches and modern single-stage detection frameworks, ensuring fair comparison across different architectural paradigms. Performance comparison was conducted [22], as shown in Table 2.  
The comparative analysis demonstrates that YOLO-Ship achieves superior performance across all evaluation metrics. Notably, YOLO-Ship outperforms recent YOLO variants, achieving 88.7% mAP, which represents significant improvements over the compared models.
###### Abstract
Moreover, these existing models do not allow for user control over the mathematical approximation level, limiting their flexibility and precision in cases involving strong thermal interactions between boreholes. The newly developed Type 1410 addresses this gap by implementing the exponential integral function through a series expansion with a user-defined number of terms. This approach offers greater modeling flexibility, higher physical consistency, and improved accuracy for long-term thermal simulations in shallow geothermal systems. To simulate complete geothermal heating and cooling systems, these modules are customizable and can be integrated with other TRNSYS components such as heat pump models, storage tanks, and building loads. The modular nature of TRNSYS enables engineers to produce tailored simulations of various system configurations and performance scenarios. Despite its extensive capabilities, TRNSYS lacks a predefined module for modeling thermal interference between boreholes using the integral exponential function, which is a fundamental component in subsurface heat transfer models. The integral exponential function is commonly used to describe temperature evolution in response to heat injection into the ground. However, due to its complexity, it is often approximated by a series expansion. This study presents the development and implementation of a new TRNSYS module (Type 1410) that incorporates the integral exponential function using its series expansion. The module allows for the representation of multiple terms of the series, improving the accuracy of subsurface heat transfer modeling. To validate its performance, the module's results are compared with experimental data from two shallow geothermal boreholes located in Asturias, Spain: Q-Thermie-Uniovi 1 and Q-Thermie-Uniovi 2. These boreholes, which are 7.3 m apart and have a depth of 48 m, were used in a thermal response test where Q-Thermie-Uniovi 1 underwent thermal injection while temperatures were monitored in Q-Thermie-Uniovi 2 over a three-month period.
# Design and Performance Evaluation of eLoran Monitoring System
## 3 Near-Field Monitoring Station Performance Analysis  
The near-field monitoring station is located in the BPL time service station, which mainly monitors the precision of transmission control. The near-field monitoring station transmission control accuracy test uses the BPL timing monitoring receiver to collect the current transformer sampling signal at the root of the transmit antenna, and conducts the timing output comparison test to eliminate the error introduced by the receiver, which is the timing transmission control accuracy. However, since it is not easy to separate the receiver error, the time difference between the 1 PPS signal output from the monitoring receiver and the standard 1 PPS signal is measured directly and analyzed and calculated to obtain the timing and transmission control accuracy [19; 20; 21; 22; 23; 24; 25].  
The 1 PPS signal output from the time-frequency reference unit is the reference, and the 1 PPS signal output from the BPL timing monitoring receiver is the measured signal; the time difference measurement data output from the time interval counter measurement is used for the calculation of the transmission control accuracy.
###### Abstract  
In practical scenarios, rolling bearing vibration signals suffer from detail loss, and information loss occurs during feature dimensionality reduction and fusion, leading to inaccurate life prediction results. To address these issues, this paper first proposes a method for predicting the remaining useful life (RUL) of bearings, which combines an improved U-Net for enhancing vibration signals and a multi-dimensional hybrid gated attention mechanism (MHGAM) for dynamic feature fusion. The enhanced U-Net effectively suppresses the loss of signal details, while the MHGAM adaptively constructs health indices through multi-dimensional weighting, significantly improving prediction accuracy. Initially, the improved U-Net is utilized for signal preprocessing. By comprehensively considering both channel and spatial dimensions, the MHGAM dynamically assigns fusion weights across different dimensions to construct a health index. Subsequently, the health index is used as input for the Bi-GRU network model to obtain the remaining life prediction results. Finally, comparative analyses between the proposed method and other RUL prediction methods are conducted using the IEEE PHM 2012 bearing dataset (Condition 1: rotational speed 1800 r/min with radial load 4000 N; Condition 2: rotational speed 1650 r/min with radial load 4200 N) and engineering test data (rotational speed 1800 r/min with radial load 4000 N). Experimental results from the IEEE PHM 2012 bearing dataset indicate that this method achieves a low mean root mean square error (RMSE = 0.0504) and mean absolute error (MAE = 0.0239). The engineering test verification results demonstrate that the mean values of RMSE and MAE for this method are 7.8% lower than those of the CNN-BiGRU benchmark model and 14.6% lower than those of the TCN-BiGRU model, respectively. In terms of comprehensive prediction performance scores, the average scores improve by 7.8% and 9.3 percentage points compared with the two benchmark models, respectively. Under various test conditions, the prediction results of this method exhibit commendable comprehensive performance, significantly enhancing the prediction accuracy of bearing remaining useful life.  
rolling bearings; improved U-Net; multi-dimensional hybrid gated attention mechanism; remaining useful life prediction
## 2 Project Background and Type A Train  
The project background of super-large diameter (\(D=15.2\) m) twin tunnels is the cross-river twin tunnels of San-yang Road in Wuhan Metro Line 7, as shown in Figure 1[26]. The San-yang Road cross-river twin tunnels are used for urban road, and rail transit line 7 is the largest shield tunnel in China. The primary parameters of super-large diameter twin tunnels are 0.5 \(D\) buried depths, 1.0 \(D\) water depths, and 0.25 \(D\) spacing between the twin tunnels. The tunnel surroundings are silty sand, whose physical and mechanical parameters are shown in Table 1.  
A supposed small diameter (\(d\) = 6.2 m) twin tunnels model was established compared with the \(D\) twin tunnels. The basic parameters are 0.5 \(d\) buried depths, 1.0 \(d\) water depths, and 0.25 \(d\) spacing between twin tunnels.  
The primary parameter and the train vibration load of type A train are shown in Figure 2. The vibration load of carriages 2-5 is the same, and the load on the train head mirrors a load of train tail. The details of the train vibration load are shown in Formula (1) [3].  
\[\begin{split} F_{1}=F_{0}[\cos(2\pi f_{1}t)-1]\\ F_{2}=-\frac{F_{0}}{5}[\cos(2\pi f_{2}t)+9]\\ F_{3}=-F_{0}[\cos(2\pi f_{3}t)+1]\\ F_{4}=-\frac{2F_{0}}{5}[\cos(2\pi f_{4}t)+4]\end{split} \tag{1}\]  
where \(F_{1}\) is the function of the front spindle box of the first wheel; \(F_{2}\) is a function of the distance between adjacent wheels; \(F_{3}\) is a function of the middle part of the train; \(F_{4}\) is the buoyancy box function of adjacent cars;\(f_{1},f_{2},f_{3}\), and \(f_{4}\) are the frequencies of these functions, and \(t\) is the running time of the train (considering the dynamic characteristics of train operation, \(F_{0}\) at 120 km/h is 31,221 N) [27].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Silty Sand** & _w/\%_ & \(E_{s}\)/MPa & \(c\)/kPa & \(\varphi^{C}\) & \(\
u\) & \(\gamma\)/kN/m\({}^{3}\) \\ \hline Physical and & & & & & & \\ mechanical parameters & 20.4 & 21.9 & 7.0 & 31.9 & 0.49 & 20 \\ \hline \end{tabular}
**Note:**\(w\) is the water content; \(E_{s}\) is the compression modulus; \(c\) is the cohesion; \(\varphi\) is the friction; \(v\) is the Poisson’s ratio; \(\gamma\) is the bulk density.  
\end{table}
Table 1: Physical and mechanical parameters of silty sand.  
Figure 1: San-yang Road Yangtze River tunnel in Wuhan Metro Line 7.
# Prediction of Harvest Time of Apple Trees: An RNN-Based Approach
## 3 Materials and Methods
### Prediction Strategy
This step can be repeated as many times as the user deems necessary. The input data can be updated at each repetition, and consequently, this change will reflect the prediction value. In this work, we use a conventional pipeline of the variant of RNN called the LSTM, which was first introduced by [PERSON] and [PERSON] [31]. LSTM networks were designed to avoid long-term dependency, work very well on a wide variety of problems, and are widely used today. The central idea behind the LSTM architecture is a continuously updated memory. It consists of an elementary unit called a memory cell and gates. Through the gates, the network acquires the ability to remove or add information to the state of the cell. These gates are the forget gate, input gate, and output gate. The forget gate decides which information will be kept and which will be discarded from previous states. The input gate decides what information is to be updated, and the output port is based on the current state of the cell and decides what information is to be produced. The neural network structure uses a Keras sequential model, providing a series structure where the output of one layer serves as the input for the next. In the implementation of the LSTM network, we used an LSTM layer with 200 neurons. The input_shape parameter was defined according to the dimension of the input layer: that is, the number of columns of features selected in the dataset and the number of lags. The activation function used in this layer was Rectified Linear Unit (ReLU). Then, another layer of the Dense type was included, with 1024 neurons, with activation function Rectified Linear Unit (ReLU). Finally, a Dense layer with one neuron and Linear Activation function is used to return a single continuous value. In the stage of compiling the model, the Adaptive Moment Estimation (Adam) function was used in the optimizer parameter. This function defines how the weights of the neural network are updated. The loss parameter was defined as Mean Absolute Error (MAE). The training of the network in this stage was carried out with 300 epochs. Figure 4: Processing flow. (**a**) Training step. (**b**) Prediction step.
# Phased Array Antenna Analysis Workflow Applied to Gateways for LEO Satellite Communications
## 5 Phased Array Modelling
### Transmission Phased Array Modelling with SystemVue
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Constellation** & **SpaceX** & **Telesat** & **OneWeb** & **Amazon** \\ \hline Operation & & & & \\ Frequency & 28.5 & 28.5 & 28.5 & 28.5 \\ (GHz) & & & & \\ \hline Required EIRP & & & & \\ (dBW) & 68.4 & 75.9 & 63.2 & 62.3 \\ \hline Theoretical & & & & \\ Radiant & 3008 & 8318 & 1928 & 1738 \\ Elements (10) & & & & \\ \hline SystemVue & & & & \\ Radiant & 3969 & 9689 & 2128 & 1976 \\ Elements (12) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of radiating elements for transmission using circular configuration. An RF power input of 20 dBm has been assumed for each patch antenna. Figure 14: EIRP vs. number of radiating elements for transmission obtained with Systemvue. As already mentioned, one of the advantages of phased arrays over parabolic antennas is the surface area used. Tables 5 and 6 compares the area of the two solutions, calculated using Equation (15), where \(D\) is the diameter of the antenna, represented in Figure 16. It should be taken into account that phased array thickness is about a few centimetres, while parabolic antennas are higher than 1 m.  
\[A_{circle}=\left(\frac{D}{2}\right)^{2}\pi \tag{15}\]  
Figure 16: Reference parameters used for calculating the area used in transmission. Figure 15: Radiation pattern of the transmission phased array for circular shape configuration and 625 elements. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**LEO Constellation**} & **Diameter** & **Area** & **Above Ground Level** \\  & **(m)** & **(m’)** & **(m)** \\ \hline SpaceX & 1.5 & 1.8 & 1.95 \\ \hline Telesat & 1.5 & 1.8 & 3.2 \\ \hline OneWeb & 3.5 & 9.6 & 4.55 \\ \hline Amazon & 2.4 & 4.5 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Area used by parabolic antennas in transmission.
## 3 Results and Discussion
### Oscillatory Shear Experiments
Three systems out of four, namely, neat bitumen and bitumens modified with P2 KA and LCS, share a similar \(z\) variability in the narrow range 1.12-1.22, confirming the presence of interacting asphaltenes that form lightly entangled networks. On the contrary, PPA shows a slightly higher flow coordination number, especially in the range 5-10 \({}^{\circ}\)C/min, evidencing its network-promoting effect, according to previous investigations [39; 40]. In any case, the calculated \(z\) values indicate that the coordination numbers are slightly affected by the thermal history as evidenced by the upper plot in Figure 3. A more useful parameter is represented by the constant prefactor of Equation (1) characterising the \"interaction strength\" of the three-dimensional structure of a gel, which is very sensitive to the cooling rates. Interestingly, an initial decrement of \(A\) is observed for all the tested specimens in correspondence of the increment 1 \({}^{\circ}\)C/min \(\to\) 5 \({}^{\circ}\)C/min in the cooling rate. Upon further increase to 10 \({}^{\circ}\)C/min, both neat and PPA-modified bitumen show an upturn in \(A\) whereas P2 KA and LCS give rise to a minor change of \(A\) (see the lower plot in Figure 3). What is worthy to remark here is that addition of LCS to bitumen provides the smallest \(A\)-variation within the explored range of cooling rates and this indicates that overall LCS is able to mitigate the effects provoked by a wide range of thermal gradients. The addition of LCS, whose adhesion efficiency has been recently ascertained [41], precludes the growth of nuclei formed during the nucleation stage, thus making the bulk bitumen structure almost unperturbed by drastic temperature variations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cooling Rate (\({}^{\circ}\)C/min)** & **1** & **5** & **10** \\ \hline _Sample_ & \(z\) & \(z\) & \(z\) \\ Bitumen & 1.22 \(\pm\) 0.01 & 1.15 \(\pm\) 0.01 & 1.13 \(\pm\) 0.03 \\ Bitumen + P2 KA 2\% & 1.19 \(\pm\) 0.01 & 1.13 \(\pm\) 0.01 & 1.12 \(\pm\) 0.01 \\ Bitumen + LCS 2\% & 1.13 \(\pm\) 0.01 & 1.18 \(\pm\) 0.01 & 1.15 \(\pm\) 0.01 \\ Bitumen + PPA 2\% & 1.25 \(\pm\) 0.02 & 1.36 \(\pm\) 0.01 & 1.46 \(\pm\) 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dependence of the coordination number \(z\) on the cooling rate, calculated as a fitting parameter in Equation (1) adapted to experimental oscillatory rheological data, \(\mid\)G\({}^{*}\)\(\mid\) vs. frequency, for neat bitumen and bitumens modified with additives.