esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Attacks on the Method  
SVM classifier showed excellent results in determining the author of a natural-language text. However, keep in mind that the above experiments were not complicated by deliberate modifications aimed at text anonymization. Anonymization may have a negative impact on the accuracy of authorship identification. This hypothesis was confirmed by an early study [35]. A text anonymization technique was proposed based on a fast correlation filter, dictionary synonymizing, and a universal transformer model with a self-attention mechanism. The results of the study showed that decision-making accuracy can be reduced by almost 50% due to the proposed method of anonymization, keeping the text in readable and understandable form for humans.  
As part of the work, it was decided to evaluate the described anonymization technique on the developed approaches. The results are presented in Table 6. The results of the experiments confirm that deep models are much more resistant to the anonymization technique than the SVM classifier. This is due to their ability to extract unobvious features that are not controlled by the author on a conscious level, while SVM operates on the basis of pre-defined features manually found by experts, and therefore text may be exposed to deliberate confusion by anonymization techniques. It should be noted that in such cases, SVM with aspect analysis shows a bit higher accuracy than SVM without it.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Length of Text, Symbols** & **2 Authors** & **5 Authors** & **10 Authors** & **50 Authors** \\ \hline
1000 & 0.84 & 0.61 & 0.59 & 0.36 \\
5000 & 0.89 & 0.69 & 0.68 & 0.4 \\
10,000 & 0.92 & 0.75 & 0.76 & 0.5 \\
20,000 & 0.95 & 0.78 & 0.79 & 0.56 \\
40,000 & 0.96 & 0.83 & 0.85 & 0.62 \\
60,000 & 0.96 & 0.88 & 0.86 & 0.68 \\
80,000 & 0.99 & 0.93 & 0.91 & 0.73 \\
100,000 & 0.99 & 0.95 & 0.95 & 0.78 \\ Average accuracy & 0.94 & 0.80 & 0.79 & 0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average accuracy of author identification using CNN with attention  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Length of Text, Symbols** & **2 Authors** & **5 Authors** & **10 Authors** & **50 Authors** \\ \hline
1000 & 0.86 & 0.64 & 0.6 & 0.34 \\
5000 & 0.88 & 0.68 & 0.69 & 0.4 \\
10,000 & 0.91 & 0.74 & 0.77 & 0.52 \\
20,000 & 0.94 & 0.8 & 0.81 & 0.59 \\
40,000 & 0.95 & 0.86 & 0.83 & 0.66 \\
60,000 & 0.95 & 0.88 & 0.86 & 0.72 \\
80,000 & 0.98 & 0.94 & 0.92 & 0.76 \\
100,000 & 0.98 & 0.95 & 0.96 & 0.8 \\ Average accuracy & 0.93 & 0.81 & 0.80 & 0.60 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average accuracy of author identification using Transformer
# Energy-Efficient Spatial Query-Centric Geographic Routing Protocol in Wireless Sensor Networks
## 3 QTGrid Routing Protocol
### Cluster Setup
#### 3.1.2 Communication Radius  
Assuming that the transmitted power of a sensor node is adjustable, the most extreme case in intra-cluster communication is that a CH and its CM are located at both ends of the diagonal line of the grid, as shown by the dotted line pointing from A to O in Figure 3. To ensure normal communication between CH A and CM O, the intra-cluster nodes' communication radius R and the side length \(l\) of the grid should satisfy \(R\geq\sqrt{2}l\). In the communication between two adjacent clusters, the most extreme case is represented by the solid line pointing from CH A to CH I in Figure 3. The CH communication radius should meet \(R\geq 2\sqrt{2}l\) to ensure normal communication between CH A and CH I, which is also confirmed in the literature [11]. Therefore, in our simulation setup (Section 4.1), we set the communication range of CHs is equal to \(2\sqrt{2}\) times the side length.  
Figure 3: Communication radius of the node.  
Figure 2: The M code of the expanding monitoring area.
## 4 Experiments and Results
### Baselines
#### 4.2.2 Parameter Settings  
The parameters for all baseline methods are initialized as in the corresponding papers, and they are then carefully tuned to achieve optimal performances.  
After the tuning process, the batch size is set to 20 in ML-DTR, 50 in others. The latent dimension of all the model dimensions is set to 256 in all models. The learning rate is set to 0.001. To prevent overfitting, the dropout ratio is set to 0.5 in LightGCN and 0.1 in others.  
Our experiments are conducted with PyTorch running on GPU machines (NVIDIA Tesla V100).  
We selected typical models related to graph convolution for comparison on DBLP, AMiner, and Metallurgical datasets, and the results are presented in Tables 2-4, as shown in Figures 3-5. The recommended lengths \(K=10,20,50\) were chosen for comparison in the experiments to verify the output effect of different recommendation lengths, resulting in the following observations:Figure 4: Performance of different models on DBLP datasets  
Figure 3: Performance of different models on Aminer datasets.  
First and foremost, our proposed PRHN achieves the best performance on the three datasets, especially on the metallurgical datasets, which significantly outperformed all the state-of-the-art baseline methods with HR values and NDCG values higher than the others.  
The main reason is our model, which incorporates domain knowledge into the computational model, especially in the field of metallurgy, where the influence of domain characteristics is more evident. On the other hand, the hotspot information embedded in the literature also has a guiding role in literature recommendation. Integrating hotspot network information into the two-tower recommendation model by aggregation further improves the model's effectiveness.  
Second, the model proposed in this paper works better than the other models. It also illustrates again that literature recommendation should focus on the information inside and outside the literature, i.e., the correlation relationship between the literature. The correlation between the literature has a more significant effect on the literature recommendation.  
Third, the model proposed in this paper is evaluated on various datasets. The best results are obtained on the metallurgical dataset, which again shows that the model is more appropriate for processing metallurgical data. The correctness and feasibility of the model selection method are also shown.
## 3 Experiments
### Multi-Band Signal Reception and Phase Shifting for Multi-Band Phased Array Receiver
The phase changes in the four sets of multi-tone signals are shown in Figure 4f, which are consistent with the relationship \(\pi(2k-1)/\hbar td_{2}/2\). This indicates that the proposed method is capable of achieving multi-band beamforming for reception. The four-band RMSE is 6.33\({}^{\circ}\), primarily attributed to the phase non-flatness of the phase shifter across the broadband range. When the aperture fill time, \(t_{d2}\), exceeds 1/\(f_{52}\), VODL1 is adjusted based on \(\Delta t_{2}=f_{51}t_{d2}/(2f_{52})-[f_{51}t_{d2}/2]/f_{52}\). \(t_{d2}\) is set as 200 ps and the delay \(\Delta t_{2}\) of VODL1 is adjusted to 20 ps. The result of the phase change is shown in Figure 4g. The difference between the experimental results and the ideal results is an integer number of 2\(\pi\), which shows that multi-band beamforming can be achieved. These results demonstrate the effectiveness of the multi-band phase control approach in achieving precise and uniform beam steering across multiple frequency bands.
# Global Power Dynamics in the Contemporary Space System
## 5 System Dynamics--Global Power Dynamics
### The Rise of Regionalization Processes  
A novel phenomenon, known as the regionalization of space, is emerging in the space system. This trend involves space actors opting to collaborate within their own geographical area rather than on a global scale. The majority of these regional processes have institutionalized cooperation and created new regional actors within the space system.  
The European Space Agency (ESA), established in 1975 by its founding members, was the inaugural intergovernmental regional organization dedicated to space activities. This initiative has been met with notable success, expanding to encompass 22 members, predominantly EU countries, and forging meaningful collaborations with prominent global space agencies.  
In the past three decades, numerous regional space processes have emerged, modeled after the ESA paradigm. These include the Asia-Pacific Regional Space Agency Forum(1993), the Asia-Pacific Space Cooperation Organization (2005), the Arab Space Cooperation Group (2019), the Latin American and Caribbean Space Agency (2021), and the African Space Agency under the African Union (2023). All these regional processes are characterized by the pursuit of common objectives, including the establishment of space regulations, the adoption of a unified position in regional and international forums, and the initiation of collaborative initiatives.
# Modeling and Design of a Novel 5-DOF AC-DC Hybrid Magnetic Bearing
## 4 Magnetic Analysis
### Displacement-Force-Current Analysis  
Figure 9 shows the relationship between the axial suspension force and the axial control current with the rotor offset in the X direction. The rotor was in an equilibrium position in the Y and Z directions, and the control current was zero in the X and Y directions. The axial current changed from \(-5\) to \(5\) A. The rotor moved from \(-0.25\) to \(0.25\) mm in the X direction. When the axial current was a fixed value, the change in axial suspension force \(F_{z}\) was very small. This indicated that the change in the rotor displacement in the X direction had little effect on the axial suspension force \(F_{z}\). Due to the magnetic saturation, the slope of the force-current curve decreased when the current was large. However, the relationship was basically linear in the effective working range of the axial control current.  
Figure 10 shows the relationship between the radial suspension force in the X direction and the axial current with the rotor offset in the Z direction. The rotor was in an equilibrium position in the X and Y directions, and the control current was zero in the Y and Z directions. The radial control current in the X direction changed from \(-5\) to \(5\) A. The rotor was displaced from \(-0.25\) to \(0.25\) mm in the Z direction. When the radial control current in the X direction was a fixed value, we can see that the displacement in the Z direction had little effect on the force in the X direction. Although with the increase in the axial displacement, the  
Figure 8: Variations in radial and axial force with current.  
Figure 9: The influence of radial displacement on the axial suspension force–current characteristics.  
relationship between the force in the X direction and the radial current still maintained good linearity.  
Finally, from Figures 9 and 10, it can be concluded that the axial control flux and the radial control flux were decoupled.
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 2 Micromegas Fabrication Techniques
### Other Technologies  
Several other Micromegas detector technologies have been developed over recent years. One example is the Piggyback Micromegas [31; 32; 37]. For this type, the segmented  
Figure 6: Example of an application of the Micromegas with optical for neutron imaging. The scintillation light emitted during the electron avalanche process can pass thought the glass substrate with the ITO layer and can be collected by a camera (**a**). A mirror can be used in order to avoid exposing the camera to the neutron flux (**b**).  
readout is decoupled from the amplification region and the mesh by a ceramic layer. The signal is induced from the resistive anode through a dielectric on a (segmented) readout electrode. Robustness and higher spark protection without significant signal loss can be achieved. This concept also improves the outgassing properties of the detector and makes it more suitable for a sealed detector construction without continuous gas exchange.  
R&D was conducted by CEA-Saclay and Brookhaven National Laboratory to improve the spatial resolution with a readout pitch of a few millimeters in Micromegas as well as GEM and \(\upmu\)m-RWELL detectors through the optimization of \"zigzag\" patterns shown in Figure 7. Due to the particular geometry, the sharing of the charges from the avalanche is increased and allows a better reconstruction of the cluster due to more pads seeing the avalanche. A test beam was conducted in 2019 with a different configuration, demonstrating the possibility to obtain a 150 \(\upmu\)m spatial resolution with a 2 mm pitch [38]. Further studies to export the idea to 2D pads are ongoing, as well as producing larger areas for future readout detectors.
## 3 Using ML for Water Activities
Rigorous data checks and advanced methods like long short-term memory and backpropagation neural network ensure accurate predictions of water consumption levels and timings, even without prior knowledge [238]. The rapid proliferation of ML and data management has led to the expansion of ML applications across various engineering disciplines. This expansion is driven by the recognition of the world's water supply's growing significance in this century. As a result, extensive research has focused on applying ML strategies to integrated water resources management (WRM) [239]. A substantial amount of clean water is lost worldwide due to leaks, but smart water networks can decrease this wastage by reducing water production and purchase, as well as the energy needed for distribution and treatment, as exemplified by a leak management project in the UK that employs advanced metering infrastructure and innovative instruments for precise monitoring and data analysis using the AURA-Alert anomaly detection system [240]. In the field of water supply network optimization and management, accurate forecasting of water demand in urban areas is of great importance, especially in the context of Milan, Italy. The study evaluates several forecasting models for short- and long-term water demand forecasting in urban areas and investigates the potential enhancement achieved by incorporating a wavelet data-driven forecasting framework (WDDFF). The findings reveal that, overall, the incorporation of WDDFF improves the predictive ability of the models. The LSTM wavelet decomposition technique combined with the LSTM technique shows high accuracy, with an R2 value exceeding 0.9 for short- and long-term urban water demand forecasts. In addition, the LightGBM model effectively reduces predictors and demonstrates the ability to predict and identify critical features in the field of hydrology and water resources [241]. In a multitude of studies, ML has proven to be invaluable in addressing diverse challenges in water resource management:
# Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems
## 3 Receivers
### Mmse  
To eliminate the effect of noise amplification due to the ZF receiver, a more robust equalizer, MMSE receiver, has been introduced [7; 40; 41]. The receive signal from the MMSE receiver is given by:  
\[\tilde{y}_{mmse}=(H^{T}H+\frac{N_{l}}{\rho}I)^{-1}H^{T}x \tag{6}\]  
The output SNR of the \(n\)th branch for the MMSE receiver can be represented as  
\[\rho_{mmse,n}=\frac{\rho}{[(H^{H}H+\frac{1}{\rho}I)^{-1}]_{nn}}-1,1\leq n\leq N _{t} \tag{7}\]  
The MMSE receiver takes care of both noise and interference. Furthermore, the MMSE receiver provides an improved solution for the ill-conditioned \(H\) matrices, hence provides improved performance in comparison to the ZF receiver. In terms of complexity, the MF receiver is much less complex than both ZF and MMSE detectors.
# A Gesture-Based Teleoperation System for Compliant Robot Motion
## 4 Experiments  
In order to verify the effectiveness of the gesture-based teleoperation system, the system was implemented on a platform shown in Figure 9a. The platform is based on an industrial robot manipulator: ABB IRB1200, which can carry a payload of up to 7 kg with a reach of 700 mm. The robot is mounted on the workbench. It is equipped with an ATI six DOF force/torque sensor and corresponding force control functionality. A pneumatic gripper was attached to the force sensor to pick up peg and do the peg-in-hole assembly work. The hole was installed at an arbitrary location within the workspace of the manipulator. The diameter of the peg was 12.00 mm, and the clearance between the peg and hole was 0.08 mm. The length of the peg was 100.00 mm and the depth of the hole was 60.00 mm.  
An external computer was used as the teleoperation controller. The leap motion sensor was connected to this computer. Its tasks included recognizing hand gestures, tracking hand motions, controlling the interaction logic and recording the demonstrations.  
The computer and the robot IRC5 controller communicate with each other through Ethernet. The computer sends out motion commands and receives feedback robot states. The robot is preprogrammed with the capability of responding to the motion commands and feeding back its states.  
Figure 9: Experimental platform. (**a**) Photo of the experimental platform. The gesture-based teleoperation interface was used to demonstrate a complete pick-and-place and peg-in-hole assembly task. (**b**) The compliant assembly skills in the peg-in-hole process.
## 3 Experiments  
### Dataset  
Originating from various traffic detectors [45] or powered by online map services such as Google Maps [46], real-time traffic condition maps of transportation networks are regularly archived in the form of snapshots by transportation administration departments 24 h a day, 7 days a week, and are provided online [47; 48]. In order to build a dataset of traffic congestion data, we first create an initial data source in this work by complying with procedures used by these transportation administration departments.  
Figure 3: Architecture of the prediction model.  
Figure 1a is an example of a raw snapshot of a traffic congestion map for an urban area in Guiyang, Guizhou province, China, which was one of top 10 most congested major cities in China in 2019 [49]. Each raw snapshot for this area is 256 pixels wide and 256 pixels high and mainly covers urban arterial roads. Such snapshots are retrieved at a scale of 1:50,000 every 10 min during morning rush hours between 07:00 and 10:00 from 1 January 2019 to 30 September 2019 through a free API provided by an online map service provider AutoNavi [50]. Requests to that API sometimes fail and cause missing data, which are left blank as is in this paper. These snapshots form an initial data source of traffic congestion data.  
After their background and other nonroad elements have been removed as described in Section 2, raw snapshots from our initial data source are transformed into network-only images. Then, we use these network-only images containing road segments marked with congestion levels to build a dataset for traffic congestion research. The congestion level given by the color of each pixel inside images like Figure 1b is linearly converted to a normalized value based on its color (transparent, green, yellow, red, or dark red) to form an original traffic congestion matrix \(P_{t}\)[30]. Specifically, transparent pixels are converted to 0.0, green ones to 0.25, yellow ones to 0.5, red ones to 0.75, and dark red one to 1.0, because traffic congestion levels are categorized by the online service provider based on the calculated linear travel time index. Gray pixels in a network-only image such as Figure 1b indicate road segments inaccessible or with missing data and are treated as transparent ones. After conversion, the size of original traffic congestion matrices is \(256\times 256\).
## 2 Related Work
### Speaker Embedding  
Speaker embedding techniques were used to extract the characteristics of speakers from speech signals, which have proven valuable in various tasks such as speaker verification [14], speaker diarization [15], speech synthesis [16], and speaker separation [17]. Similar to Word2 Vec [18] and Wav2 Vec [19; 20], speaker embedding techniques generate fixed-dimensional numeric vectors from speech signals whose lengths vary over time. One of the more well-known speaker embedding vectors is the intermediate vector (\(i\)-vector) [10]. The \(i\)-vector is obtained by projecting a universal background model (UBM) of a speaker to characteristically coordinate vectors, which are trained to maximize the likelihood of the projection by exploiting unsupervised learning techniques. This projection maps high-dimensional representation from the UBM to a desired dimensional representation: the \(i\)-vector. These \(i\)-vectors are applied to speaker verification, facilitating decisions on whether speakers are the same or different [21] using a probabilistic linear discriminant analysis (PLDA) [22] classifier. The \(x\)-vector [12] utilizes deep neural networks (DNN) to extract the speaker characteristics from speeches. Another embedding method uses three-layer long short term memory (LSTM) with a projection exploiting a generalized end-to-end loss function [11]. The resultant embedding vectors are called \(d\)-vectors, and they have been shown to demonstrate a particularly better performance in text-dependent speaker recognition tasks on datasets with short utterances.
## 3 Results and Discussion  
### Box-Behnken Experiment Design and Significance Test  
#### 3.1.1 Box-Behnken Experiment Design  
The Box-Behnken test factors and levels are shown in Table 5, using the uniaxial compressive strength values as the response values and the SP content (X\({}_{1}\)), PFA content (X\({}_{2}\)), and SF content (X\({}_{3}\)) as the investigating factors. The test results and analysis are shown in Table 6.  
The least squares method was used to fit the experimental data, and a regression model was developed as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **X\({}_{1}\)** & **X\({}_{2}\)** & **X\({}_{3}\)** & **Y/Compressive Strength (MPa)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 6: Results and analysis of Box-Behnken experiments.  
Figure 4: The test of compressive strength.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factors** & \(-\)**1** & **0** & **1** \\ \hline X\({}_{1}\) (SP content) & 5\% & 10\% & 15\% \\ X\({}_{2}\) (PFA content) & 10\% & 15\% & 20\% \\ X\({}_{3}\) (SF content) & 2.5\% & 5\% & 7.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Factors and levels of Box-Behnken experiments.  
\[\text{Y}=47.6-0.87\text{X}_{1}\text{-}0.62\text{X}_{2}+0.25\text{X}_{3}- \text{X}_{1}\text{X}_{2}+0.75\text{X}_{1}\text{X}_{3}+0.75\text{X}_{2}\text{X}_{3 }+0.45\text{X}_{1}\text{X}_{1}-3.55\text{X}_{2}\text{X}_{2}-3.8\text{X}_{3} \text{X}_{3} \tag{1}\] \[\text{R}^{2}=0.85 \tag{2}\]
# A Survey of Machine Learning Methods for Time Series Prediction
## 3 Tree-Based Machine Learning Architectures
### Gradient-Boosted Decision Trees
#### 3.2.1 XGBoost  
XGBoost, eXtreme Gradient Boosting [82], introduced by [PERSON] in 2014, is designed to address key limitations of traditional GBDT, particularly computational efficiency and scalability. XGBoost gained immediate popularity due to its significant speed improvements, achieved through innovative approaches in decision tree construction. Unlike the greedy splitting methods used in standard GBDT, XGBoost employs a similarity score to evaluate potential splits. This score measures the homogeneity of observations within a node relative to the target variable to asses the gain provided by a split. To further reduce overfitting, XGBoost incorporates several techniques, including pruning, where branches with a gain below a threshold (hyperparameter gamma, \(\gamma\)) are removed. Similarly, XGBoost has several regularization techniques that prevent overfitting by penalizing complex decision trees.