esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of the Control System  
The entire system includes a robot system, an LSTM predictor, and a variable admittance controller, as shown in Figure 1. The robot system includes a six-DOF serial manipulator. A six-DOF force/torque sensor is installed between the robot and the end tool. After the robot is moved by operator, the trajectory of the robot end tool between \(t\)-\(n\) and \(t\) is collected by the control system, which is used as the input of the LSTM network predictor. LSTM outputs real-time trajectory of the robot's end tool from \(t\) to \(t+m\) in the future. The control system calculates the unit vector \(P_{t+m}\) of the movement direction according to the predicted trajectory. The variable admittance controller sets the proportional coefficient of the damping matrix in the projection direction of \(P_{t+m}\) to \(k\), and adjusts \(k\) to improve the compliance of pHRI.
## 6 Discussion
### Limitations and Future Work  
The experimental results show that the proposed model has broad application prospects. In scenarios involving the identification of cooperative satellites, since the configurations of the satellites are usually known, using these known satellites to train the model can achieve better recognition results in practical applications. Additionally, the model is also applicable to identifying non-cooperative target satellites. In such cases, since the satellite configurations are unknown, the model must rely on features learned from other satellites to identify these unknown satellites. According to the experiments presented in this paper, the model also performs excellently in these scenarios. However, the current model can detect only a limited variety of satellite components and is unable to identify specific satellite names. Future research can focus on further exploring how to determine the specific names of satellites based on their features and identify more satellite components.  
In this paper, although the LTSCD-YOLO model demonstrates significant advantages in detecting typical satellite components, it still has some limitations. Firstly, the TSC-Dataset used in this paper contains a limited number of real satellite images. To improve the training effectiveness and adaptability of the model, future research should include more real satellite images. Secondly, although the proposed model performs well in terms of parameter count, detection speed, and accuracy, there is still room for improvement. With the continuous updates and optimizations of the YOLO model, future research can reduce the number of model parameters and improve detection speed and accuracy based on the updated YOLO models to better meet the demands of the space environment. Finally, considering the complexity and harshness of the space environment, future research can incorporate more transformations in data augmentation to obtain features that are closer to the space environment, thereby further optimizing model training and achieving better results. It is hoped that through further optimization, better performance can be achieved in the detection of typical satellite components in the future.
# Optimized Unconventional Geometric Gates in Superconducting Circuits
## 2 General Approach  
### Geometric Phase  
Two states are said to be physically equivalent (\(|\psi\rangle\sim|\phi\rangle\)) if \(|\psi\rangle=e^{i\kappa}\ |\phi\rangle\) with a real function \(\alpha\). Therefore, a proper space to describe the quantum dynamic evolution is the projective Hilbert space \(\mathcal{P}(\mathcal{H})\)[31]. Here, all states which are equivalent to \(|\psi\rangle\) represent an equivalence class in \(\mathcal{P}(\mathcal{H})\). Consider an L-dimensional quantum system defined by Hamiltonian \(H(t)\). \(\{|\psi_{i}(t)\rangle\}\) represent L orthonormal solutions of the Schrodinger equation \(id_{t}\,|\psi_{i}(t)\rangle=H(t)\,|\psi_{i}(t)\rangle\). If the trajectory of \(|\psi_{i}(t)\rangle\) is projected to a closed curve \(C(t)\) in \(\mathcal{P}(\mathcal{H})\), this evolution is called a cycle. After a cyclic evolution, the state \(|\psi_{i}(T)\rangle\) and \(|\psi_{i}(0)\rangle\) project to the same point in the projective space. Hence, we have \(|\psi_{i}(T)\rangle=e^{i\phi}\,|\psi_{i}(0)\rangle\)[10].  
To calculate the phase \(\phi\), we consider a set of auxiliary orthonormal states \(|\tilde{\psi}_{i}(t)\rangle\), \(i=1,...,L\), which satisfy two conditions, (1) the trajectory of the auxiliary state projects to the same closed curve \(C(t)\) in \(\mathcal{P}(\mathcal{H})\), i.e., \(|\psi_{i}(t)\rangle\) is equivalent to \(|\tilde{\psi}_{i}(t)\rangle\); (2) the auxiliary state satisfies \(|\tilde{\psi}_{i}(T)\rangle=|\tilde{\psi}_{i}(0)\rangle\). Then, we can obtain  
\[|\psi_{i}(t)\rangle=e^{i\tilde{\psi}_{i}(t)}\,|\tilde{\psi}_{i}(t )\rangle\,,\] \[f_{i}(T)-f_{i}(0)=\phi. \tag{1}\]  
Substituting Equation (1) into the Schrodinger equation, we obtain  
\[f_{-}=i\int_{0}^{t}\,\langle\tilde{\psi}_{i}(t^{\prime})|d_{t}|\tilde{\psi}_{ i}(t^{\prime})\rangle\,dt^{\prime}-\int_{0}^{t}\,\langle\psi_{i}(t^{\prime})|H(t)| \psi_{i}(t^{\prime})\rangle\,dt^{\prime}, \tag{2}\]  
where \(f_{-}=f_{i}(t)-f_{i}(0)\). The first term above represents the geometric phase \(\gamma_{gi}(|\tilde{\psi}(t)\rangle)\), and the other is the dynamic phase \(\gamma_{di}(|\tilde{\psi}(t)\rangle)\).  
The choice of auxiliary bases \(\{|\tilde{\psi}_{i}(t)\rangle\}\) is not unique. We consider other states \(\{|\psi^{\prime}_{i}(t)\rangle\}\) which satisfy the above conditions (1) and (2). We can easily get \(|\psi^{\prime}_{i}(t)\rangle=e^{i\xi(t)}\,|\tilde{\psi}_{i}(t)\rangle=e^{i[\xi( t)-f_{i}(t)]}\,|\psi_{i}(t)\rangle\), and \(\xi(T)=\xi(0)\). Substituting \(|\psi_{i}(t)\rangle\) in the Schrodinger equation again, we can obtain the geometric phase \(\gamma_{gi}(|\psi^{\prime}_{i}(T)\rangle)=\gamma_{gi}(|\tilde{\psi}_{i}(T) \rangle)-[\xi(T)-\xi(0)]=\gamma_{gi}(|\tilde{\psi}_{i}(T)\rangle)\). Therefore, the geometric phase \(\gamma_{gi}(|\tilde{\psi}(t)\rangle)\) is \(U(1)\)-gauge-invariant. Notethat the above derivation can be applied to arbitrary \(\ket{\psi_{i}(t)}\) under cyclic evolution and any \(\ket{\tilde{\psi}_{i}(t)}\) which satisfies conditions (1) and (2).
# Botnet Detection Based On Machine Learning Techniques Using DNS Query Data
## 3 Machine Learning Based Botnet Detection
### Introduction to Machine Learning and Its Techniques
#### Common Supervised Machine Learning Techniques  
##### 3.1.2.1. kNN  
kNN (k-Nearest Neighbor) is one of the simplest supervised machine learning algorithms. The idea of kNN is that it will classify the new object based on its \(k\) nearest neighbors, where \(k\) is a predefined positive integer [16]. kNN algorithm is comprehensively described in the following steps:  
- Step 1: Determine the parameter value \(k\).  
- Step 2: Calculate the distance between the new object that needs to be classified with all objects in the training dataset.  
- Step 3: Arrange computed distances in the ascending order and identify \(k\) nearest neighbors with the new object.  
- Step 4: Take all the labels of the \(k\) neighbors selected above.  
- Step 5: Based on the \(k\) labels that have been taken, the label that holds the majority will be assigned to the new object.
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 3 System Design
### Machine Learning  
MATLAB, Python, and Julia have extensive collections of machine learning algorithm libraries. WISCANet-enabled applications can natively access these language libraries at runtime, thereby enabling rapid development and testing of machine learning algorithms using over-the-air data. Run-time and training-time challenges can be addressed using the already available GPU acceleration support, making WISCANet a suitable testbed for directly and efficiently comparing algorithm performance.
# Multimodal Classification of Anxiety Based on Physiological Signals
## 4 Results
### Feature Selection  
Unsupervised feature selection was performed by missing value and variance analysis and based on the pair-wise correlation method. No feature showed zero variance or missing values, and in the case of two features with a correlation coefficient higher than 0.90, the feature with the higher variance was selected. A total of 60 features were selected out of 109 from the three physiological signals (as shown in Table 1).  
Thereafter, supervised feature selection with RFECV was applied to this feature set, implemented inside a pipeline with nested cross-validation, considering the imbalanced data and data balancing techniques, as well as classifier hyperparameter tuning. Thus, we retrieved for each model the best hyperparameters and the best set of features found through the grid-search method applied in the inner loop to the selected features.  
For the imbalanced data, the frequencies of the features chosen at least three times by RFECV are presented in Figure 4. As RFECV was performed inside the pipeline, different sets of features were selected for the various classifiers, with a total of 55 features selected. Thus, Figure 4 displays the number of times that 34 features, out of 55 different features, were selected (for different classifiers). We can observe that Range ECG and Med EMG are present in the feature sets selected for all the classifiers, while Max HR and Range HR are chosen for six of the seven classifiers. Max ECG, Var ECG, MeanNN, TINN, pNN50, Min HR, and Perc 10 EMG are present in five of the seven chosen feature sets. This analysis allows the visualization of which features could be of greater importance for classifying anxiety levels.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Signals** & **Features** \\ \hline \multirow{4}{*}{ECG} & AUCC, Mean ECG, Med ECG, Max ECG, Min ECG, Var ECG, Range \\  & ECG, MeanNN, MadNN, TINN, pNN50, CVNN, LnHF, LF, VHF, LFN, \\  & SampEn, ApEn, SDTT, Max HR, Min HR, Var HR, Range HR, \\ \hline \multirow{4}{*}{EDA} & Min EDA, SD EDA, Range EDA, Mean SCR, Med SCR, Max SCR, Min \\  & SCR, SD SCR, Range SCR, Corr SCR, Nr SCR Peaks/sec, Mean and Med \\ \cline{1-1}  & SCR Peaks Amp, Med SCR \\ \cline{1-1}  & Onsets Amp, Med and Mean SCR Recovery Amp, Mean and Med SCR \\ \cline{1-1}  & Recovery Time and Rise Time, Max SCL, Med SCL, Range SCL, Corr SCL \\ \hline \multirow{4}{*}{EMG} & Min EMG, Mean EMG, Med EMG, Range EMG, Perc 10 EMG, Min Env, Range Env, Perc 10 Env, Nr Pulse Onsets/sec, Nr Activ Peaks/sec, Mean \\ \cline{1-1}  & and Med Amp Activ Peaks, Med Pulse Onsets Amp \\ \hline \hline \end{tabular}
\end{table}
Table 1: Features selected after the unsupervised feature selection.  
Figure 4: Features selected more than three times with RFECV.  
The features selected for the highest scores found for each classifier and the data balancing techniques are presented in Table A2.
## 3 In-Situ Monitoring
### Monitoring Results
#### 3.2.2 Horizontal Displacement of the TOP of the Enclosure Wall  
During foundation pit construction, as the soil pressure in the pit is relieved, the soil pressure behind the enclosure wall will cause lateral displacement. The value of the  
Figure 4: Horizontal displacement curves of piles under different working conditions: (**a**) W1, (**b**) W2, (**c**) W3, (**d**) W4, and (**e**) W5.  
horizontal displacement of the top of the enclosure wall reflects the magnitude of the soil pressure behind the wall, and the change in this value reflects the deformation of the pit, which is related to the stability of the pit. Therefore, the horizontal displacement of the top of the enclosure wall is an important indicator for measuring the safety of the pit support structure. The variation curves of the horizontal displacement of the top of the enclosure wall at the monitoring points W1, W2, W3, W4, and W5, with the same time interval of observations, were recorded as AW1, AW2, AW3, AW4, and AW5 curves, respectively. The observation time was consistent with Figure 4. Monitoring point W2 was also monitored for the ninth time at an interval of 10 days after the eighth monitoring, as shown in Figure 5.  
The results presented in Figure 5 show a significant increase in the horizontal displacement of the top of the enclosure wall during the excavation process. However, after the completion of excavation, the horizontal cross bracing at each observation point effectively suppressed the horizontal deformation of the foundation pit, leading to a smooth and stable change in the lateral displacement of the top of the enclosure wall. The maximum horizontal displacements of the top of the enclosure wall at the monitoring points W1, W2, W3, W4, and W5 were 24 mm, 28 mm, 28 mm, 27 mm, and 24 mm, respectively, with the displacement of the monitoring points W2, W3, and W4 being the largest. The results suggest that the horizontal displacement of soil around the foundation pit increased, but the distribution was not uniform, with the highest displacement occurring in the middle of the foundation pit.
# Research Progress in Methods for the Analysis of the Internal Stability of Landslide Dam Soils
## 5 Conclusions and Prospects  
### Conclusions  
Based on the comprehensive analysis above, the conclusions are as follows:  
1. The qualitative identification methods used to assess internal stability based on the physical properties of soils have two types. One type of method is based on characteristic particle sizes such as \(d_{10}\), \(d_{60}\), \(d_{15}\) and \(d_{85}\). The other method is based on the degree of filling by fine particles, and includes theoretical formulas, empirical formulas, and semi-theoretical semi-empirical formulas.
2. The parameters of the hydraulic criteria used to assess internal stability mainly include the critical hydraulic gradient and critical seepage velocity. These criteria focus on the balance and movement of fine particles, and corresponding formulas are derived based on force balance. The difference lies in their starting points and expressions.
3. The criteria used to assess internal stability considering the stress state include three categories: the lateral confined state, isotropic compression state, and triaxial compression state. Under the lateral confined and isotropic compression states, the greater the vertical or confining pressure, the less potential there is for internal erosion. Under a high confining pressure, particle loss can lead to the collapse of the soil skeleton. The triaxial compression state can simulate the internal erosion of soils under complex stress states in practical engineering.
## 5 Imperfections and Side Channels in Practical CVQKD
### Detection
At this stage, they share a bipartite state \(\rho_{A^{N}B^{N}}\) (possibly correlated by [PERSON] or [PERSON]'s state), which they measure with heterodyne detection. This is similar to the CVQKD protocol with entanglement in the middle of [93]. As in Equation (2), the optimality of Gaussian states [29] guarantees that it is sufficient to know the covariance matrix of the state \(\rho_{A^{N}B^{N}}\) in order to obtain an upper bound on the Holevo information between [PERSON] and the raw key, and in turn, a lower bound on \(K^{\rm asympt}_{\rm coll}\). Composable security against collective attacks, _i.e._, for bipartite states of the form \(\rho^{\otimes N}_{AB}\), can be established by adapting the proof of [24], and composable security against arbitrary attacks can finally be obtained thanks, for instance, to de Finetti reductions [36, 37]. In terms of practical implementations, MDI-QKD is very promising for discrete-variable protocols over long distances [94]; however, the obtained secret key generation rates remain currently relatively low. On the other hand, CV MDI-QKD implementations are limited in range, as [PERSON] needs to be located close to [PERSON]'s (or [PERSON]'s) lab and the channel between [PERSON] (or [PERSON]) and [PERSON] needs to feature small losses and, so, cannot exceed a few kilometers. However, the achievable rates in this case can be very high, within an order of magnitude from the known secret key capacity bounds [45,95]. This configuration is therefore particularly interesting in a network setting with untrusted nodes for achieving high-speed secure communication over relatively short distances [89].
## 3 Results  
### High Expression of GPRC5A Predicts Poor Survival and Is Associated with Mesenchymal Subtypes  
We first confirmed the expression of GPRC5A in LGG and GBM versus normal tissues using the Gepia database (Figure 1A). The results show a significant upregulation expression of GPRC5A in GBM but not in LGG, as previously published [29]. Next, we assessed the prognostic significance of GPRC5A in GBM patients using the TCGA glioblastoma cohort available on Xena. We divided the samples into two groups based on the GPRC5A median expression for each dataset. Our results demonstrate that patients with higher GPRC5A levels had shorter overall survival (OS) (log-rank \(p=0.009\)) and progression-free interval (PFI) (log-rank \(p<0.0001\)) (Figure 1B) compared to those with lower GPRC5A levels.  
Furthermore, we investigated the association between GPRC5A expression and key genetic mutations in GBM, including _IDH1_, _TP53_, _CDKN2A_, _NF1_, _EGFR_, and _PTEN_. Our results indicate that GPRC5A expression was significantly associated with mutated _NF1_ and _PTEN_, as well as wild-type _EGFR_, but showed no significant differences for other genetic mutations (Figure 1C).  
We also explored GPRC5A expression across GBM subtypes: classical, neural, proneural, and mesenchymal. Our analysis revealed that GPRC5A was significantly highly expressed in the mesenchymal subtype compared to other subtypes (Figure 1D). Patients with mesenchymal subtypes who had higher GPRC5A levels exhibited shorter OS (log-rank \(p=0.047\)) and PFI (log-rank \(p=0.041\)) (Figure 1D) compared to those with lower GPRC5A levels. In contrast, no significant differences in OS and PFI were observed for the other subtypes (Appendix A Figure A1). Our results suggest that GPRC5A expression represents a significant factor in GBM, with implications for prognosis, particularly in the mesenchymal subtype.
## 3 Mathematical Model
### Model Formulation
#### 3.2.1 Objective Function  
In train service planning, passenger travel demand should be satisfied as much as possible, and the efficiency of train operation should also be considered. Minimizing the weighted sum of the remaining passenger demand (\(RPD\)) and train travel time (\(TTT\)) is taken as the objective function, as shown in Equation (1). \[\text{min}\ \ \theta_{1}\cdot RPD+\theta_{2}\cdot TTT \tag{1}\]  
\[RPD=\sum_{m=1}^{|S|-1}\sum_{n=m+1}^{|S|}\sum_{t=1}^{|T|}qre_{t}^{m,n} \tag{2}\]  
\[TTT=\sum_{i=1}^{|K|}\left(a_{i}^{|S|}-d_{i}^{1}\right) \tag{3}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Remaining** & & **Number of Train** \\  & **Passenger Demand** & & **Stops** \\ \hline Service plan I & 0 & 21 & 3 \\ Service plan II & 2 & 20 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Relevant indexes of timetable. The total \(RRD\) is formulated as Equation (2). Fewer remaining passengers means that more travel demand is satisfied. When a passenger submits a reservation request, there is a possibility that it will be rejected due to the limited capacity of train seats. The passenger cannot get the corresponding train service and must remain. Therefore, minimizing the number of remaining passengers is considered one of the objectives in this paper. Equation (3) describes the total \(TTT\). Shorter train travel time indicates that the train could complete its journey within a shorter period, which has a significant impact on the efficiency of train operations. More efficient train operations will not only reduce travel time for passengers but also reduce costs for operators. Therefore, minimizing the train travel time is considered another objective in this paper. \(\theta_{1}\) and \(\theta_{2}\) represent the weights of two parts of the objective, respectively. They are expected to be set with different values, since the weights in the objective often have an obvious influence on the final decision. The value of \(\theta_{1}\) needs to be increased if a demand-oriented service plan is required, and the value of \(\theta_{2}\) needs to be increased if an efficiency -oriented service plan is required.
## 3 Results and Discussion
### Success Probability and Decimal Test for Cycle Slip Repair
The relative positions are estimated by GPS + BDS before and after cycle slips detection and repair, as shown in Figure 14. Before cycle slips detection and repair, the RMSs of X, Y and Z relative position components are 0.07 m, 0.13 m and 0.06 m respectively. After that, the RMSs of X, Y and Z relative position components are 0.002 m, 0.004 m and 0.002 m respectively. The results prove that the cycle slip detection and repair is effective and important. Figure 14: The relative position between successive epochs before (**a**) and after (**b**) cycle slips detection and repair. Figure 13: The detection rates under different decimals.
## 2 Analysis of the Current Situation of Oxygen Supply in Refuge Chamber  
The oxygen supply in the refuge chamber mainly includes pressurized air, compressed oxygen, and self-rescuers. When people are trapped in a mine disaster, they could stay in the refuge chamber and open the pressurized air first. When pressurized air is polluted or the pressurized air equipment is destroyed, the compressed oxygen should be opened immediately. When the compressed oxygen is exhausted, the self-rescuer can be used.  
The pressurized air is supplied by an air compressor and stored in the gasholder. Firstly, the pressurized air is transported by pipe transport and is filtered by an air filter to remove the water, dust, and oil in the air. Secondly, clean, pressurized air enters the refuge chamber through a pressure-reducing valve. The pressurized air can guarantee a stable oxygen concentration and dilute and remove toxic or harmful gases. But the pressurized air pipeline is easily destroyed by explosion accidents and cannot supply adequate air. The schematic diagram is shown in Figure 1.  
If the pressurized air fails, the compressed oxygen should be opened so that it can supply continuous and effective oxygen for at least 96 h. The high-pressure oxygen bottle releases oxygen, and the oxygen gas passes through the pressure-reducing valve and globe valve, finally slowing emissions to the refuge chamber evenly. The schematic diagram is shown in Figure 2.  
When all the pressurized air and compressed oxygen do not work, the self-rescuer can be used. The self-rescuer can afford oxygen for two to three hours if the people stay quietly in the refuge chamber. So, this is the final method to buy as much time as possible until rescue arrives.
## 3 Results and Discussions
### Environmental Assessment Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Impact Category** & **Units** & **Grinding/Sieving (\%)** & \
\begin{tabular}{c} **Chemical** \\ **Activation (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Drying the** \\ **Washed AC (\%)** \\ \end{tabular} & **Total** \\ \hline Abiotic depletion & kg Sb eq. & 10.20 & 25.15 & 25.32 & 0.063 \\ Acidification potential & kg SO\({}_{2}\) eq. & 3.02 & 55.31 & 12.10 & 0.100 \\ Eutrophication & kg PO\({}_{4}\) eq. & 1.01 & 92.20 & 1.18 & 0.019 \\ Global warming & kg CO\({}_{2}\) eq. & 5.32 & 24.85 & 22.03 & 5.210 \\ Ozone layer depletion & kg CFC\({}^{-11}\) eq. & 4.21 & 42.27 & 17.12 & \(4.1284\times 10^{-7}\) \\ Human toxicity & kg 1,4-DB eq. & 2.21 & 57.00 & 12.30 & 3.262 \\ Fresh water aquatic ecotoxicity & kg 1,4-DB eq. & 0.80 & 85.01 & 2.95 & 2.621 \\ Terrestrial ecotoxicity & kg 1,4-DB eq. & 1.00 & 86.17 & 2.25 & 0.011 \\ Photochemical oxidation potential & kg C\({}_{2}\)H\({}_{4}\) & 2.31 & 32.10 & 8.80 & 0.004 \\ Cumulative energy demand & MJ & 3.52 & 43.2 & 21.6 & 70.521 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Environmental impact of AC production process from olive tree.