esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Feature Selection Results  
This study employs Pearson correlation coefficient to assess the linear correlation between each feature and the target variable. Specifically, by calculating the Pearson correlation coefficient between each feature and the target variable, the absolute value of the coefficient is used as a measure of feature importance. A value close to 1 indicates a strong correlation, while a value close to 0 suggests almost no correlation. This approach is utilized to select features that are most correlated with the target variable. The study ultimately presents the correlation between different population information and various auxiliary feature variables, including Pearson correlation coefficients and significance levels, in the form of a heatmap, as shown in Figure 2(a).  
Through the comprehensive analysis of the correlation between population information and feature variables, population density exhibits higher correlation compared to total population. Therefore, population density is selected as the target variable for modeling. Additionally, Points of Interest (POI) data, terrain characteristics, nighttime lights data, building height data, and transportation and water system data show strong correlations with population density, with \(p\)-values less than 0.001, indicating statistical significance and suitability for model construction.  
According to the variance threshold selection method, Figure 2(b) is obtained by calculating the variance of each feature in the dataset. From the figure, it is evident that wetland has low variability in the dataset, providing limited information to the model. Therefore, it may be considered for removal during the modeling process.  
Based on the Relief algorithm, Figure 2(c) is obtained by assessing the degree of correlation between features and the importance of each feature to the target variable, population density. The figure reveals that shrubland, waterbodies, bareland, grassland, permanent snow and ice, and dem have the lowest weights and importance levels with respect to the target variable. These features can be considered for removal.  
Utilizing the recursive feature elimination method with a random forest regression model as the feature selection estimator, Figure 2(d) shows the ranking of feature importance obtained through iterative calculations. Among them, shrubland, wetland, bareland, and permanent snow and ice exhibit the lowest feature importance and may be considered for removal during modeling.  
Considering the results from various feature selection methods, the study decides to remove the features shrubland, wetland, bareland, and permanent snow and ice from the land cover data. The remaining 27 features are selected as auxiliary data for spatializing population modeling.  
Figure 3: _Cont._
# Hybrid Multi-Antenna Techniques for V2X Communications--Prototyping and Experimentation
## 5 Conclusions
\begin{tabular}{l l} AGC & Automatic gain controller \\ BER & Bit error rate \\ EGC & Equal gain combining \\ ESPAR & Electronically switched parasitic array radiator \\ EVM & Error vector magnitude \\ FFT & Fast Fourier transform \\ ITS & Intelligent transportation systems \\ IFFT & Inverse fast Fourier transform \\ KPI & Key performance indicators \\ LLR & Log likelihood ratio \\ MIMO & Multiple-input multiple-output \\ MMSE & Minimum mean square error \\ MRC & Maximal ratio combiner \\ NR & New radio \\ OFDM & Orthogonal frequency division multiplexing \\ PDF & Probability density function \\ RF & Radio frequency \\ QPSK & Quadrature phase-shift keying \\ RAN & Radio access network \\ SFBC & Space-frequency block coding \\ SISO & Single-input single-output \\ \end{tabular}
## 5 Simulation Results
### Results of FIS with Feedback
The minimum amount of energy used in this scenario by using Mamdani FIS without a feedback loop is 0.015 kWh, whereas, when feedback is added to the Mamdani FIS, it can reduce the energy consumption to 0 kWh. The maximum amount of energy consumed in both cases observed is the same, i.e., 0.08 kWh. Similar behavior can be observed by looking at Figure 16. Energy consumed using Sugeno FIS without a feedback loop is 16.43 kWh, as compared to the total energy consumed in a day using Sugeno  
Figure 16: Energy consumption in a day using Sugeno FIS (Scenario II). Figure 14: Temperature variation and heater state (Scenario II). Figure 15: Energy consumption in a day using Mamdani FIS (Scenario II). FIS with a feedback loop of 3.31 kWh, which increases the efficiency of the proposed FIS to achieve the objective of minimization of energy consumption. Similarly, if the energy consumption in a month is observed in Figure 17, the proposed FIS outperforms as compared to the previous technique. Energy consumed in a month using Mamdani FIS and Sugeno FIS without adding a feedback loop is 501 kWh and 493 kWh, respectively, whereas the total amount of energy consumed during a month using Mamdani FIS and Sugeno FIS with the addition of feedback loop is 101 kWh and 99 kWh, respectively. Consequently, the effect of conserving energy consumption can be seen in the total cost reduction. Figure 18 shows the total cost incurred by a residential building consumer using Mamdani FIS and Sugeno FIS with and without adding the feedback loop on the basis of the indoor room temperature variations. In the case of Mamdani FIS with and without the feedback loop, total cost of the energy used in a month is $8.68 and $46.19, respectively, whereas the cost while using Sugeno FIS with and without adding a feedback loop is Figure 9:045.55, respectively. Similar to previous results, Sugeno FIS outperforms in terms of efficiency when energy and cost is considered. Figure 9:1 Monthly energy consumption (Scenario II). Figure 9:2 Monthly electricity cost incurred (Scenario II).
## 2 Literature Review
In [15], for example, 2D images are used with the goal of learning grasping actions. The authors tried to map images directly with low-level control actions and, in addition, the developed system is able to generalise and pick objects that the model has not been trained with. Moreover, other applications (e.g., [21]) use 3D depth images as state representation, but instead of learning directly from pixels, they encode the images to a lower dimensional space before training the model. This encoding enables to accelerate the training process. Although there are multiple works about learning to control either robotic arms and mobile bases, to the best of our knowledge, references about DRL applied to mobile manipulation are few. The goal of this paper is to show that it is possible to use the arm path planner's feedback to learn a low-level controller for the base. The learned controller is able to guide the robot to a feasible pose in the environment to do the picking trial. To do that, a low-dimensional state representation is used. In [22], the authors proposed to learn, through interaction with the environment, which place is the best to locate the base of a mobile manipulator, in such a way that the arm is able to pick an object from a table. In that way, the robot takes into account its physical conditions and also the environment's conditions to learn which is the optimal place to perform the grasping action. In this case, they acquire experience off-line, and, after applying some classifiers, such as support vector machines, they are able to learn a model that maps the state of the robot with some feasible places in the environment to do the picking trial. Then, the trained model is used on-line. In comparison with the previous approach, the goal of the RL-based algorithms is to learn on-line, while the agent interacts with the environment, improving its policy until it reaches to the optimal behaviour. In addition, the goal of our work is to learn a controller, which gives a low-level control signal in each state to drive the mobile robot, while, in [22], instead of learning a controller to drive the robot up to the optimal pose, they tried to find directly which is the optimal pose.
# A Depression Recognition Method Based on the Alteration of Video Temporal Angle Features
## 5 Conclusions  
This paper introduces facial angle features as a novel replacement for the original visual modality data. The theoretical proof is provided to demonstrate that these angle features exhibit translation invariance in the front-back, left-right, and up-down directions, as well as rotation invariance. This illustrates the effectiveness of the proposed method  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Window\_Size** & **P** & **R** & **F1** \\ \hline
180 s & 0.53 & 0.75 & 0.62 \\
120 s & 0.77 & 0.83 & 0.80 \\
60 s & 0.56 & 0.75 & 0.64 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation experiment for optimal sliding window.  
Figure 9: ROC plot diagram for different model.  
in countering translation and rotation attacks on the head. To address head-up and head-down flips, as well as left and right deflection attacks, this paper presents a corresponding countermeasure scheme that effectively mitigates these attacks. This further demonstrates the robustness of the proposed features.  
For classifier selection, this paper presents a GhostNet deep neural network classifier. The use of hard labels (0, 1) for loss training calculations on the network is abandoned in favor of soft-labels. Three sets of comparative experiments are conducted to evaluate the proposed method's detection performance against other single modes. The experimental results demonstrate the superiority of the proposed method. Furthermore, compared to other multimodal methods, the proposed method outperforms them in certain detection indicators, highlighting its superior feature extraction performance. Additionally, three sets of ablation experiments are performed to investigate the impact of sliding window size. The results verify that a window size of 120 s yields the best effect, achieving an F1-score of 0.80, which surpasses most other single-mode models or mixed modes.  
In our future work, we plan to accomplish the following three tasks: (1) construct a larger dataset for depression; (2) investigate more robust feature angles to enhance the performance of our handcrafted features; and (3) explore the fusion of angle features from multiple modalities, including textual speech, with our visual modal data.  
Software, Z.D.; Validation, Y.H.; Resources, J.M.; Writing--original draft, Z.D.; Writing--review & editing, R.J. and W.S.; Project administration, Z.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The \"Pioneer\" and \"Leading Goose\" R&D Program of Zhejiang Province grant number No. 2023C01022 and was funded by National Natural Science Foundation of China grant number No. 62176237.  
Not applicable.  
The data used in this project is available at [[https://dcapswoz.ict.usc.edu/](https://dcapswoz.ict.usc.edu/)]([https://dcapswoz.ict.usc.edu/](https://dcapswoz.ict.usc.edu/)) (accessed on 10 August 2023).  
The authors declare no conflict of interest.
## 3 System Performance Analysis  
In this section, we investigate the QoS of the proposed architecture by deriving SNR expression. Since MAI is negligible in the SAC scheme, system performance is almost decided by the noise terms induced in the photo-detecting process. Thermal noise and PIIN are considered as the main noise sources. For simplicity, a two-class network with the downlink transmission only is taken in the following analysis. The photocurrent obtained at the output of balanced detector is expressed as follows [13]:  
\[I(t)=i_{k}(t)+i_{P}(t)+\overline{i_{P}}(t)+i_{TH}(t), \tag{4}\]  
where \(i_{k}(t)\) is the current of BS \(\#k\), \(i_{P}(t)\) and \(\overline{i_{P}}(t)\) are PINN terms of
###### Abstract  
The problem of integral saturation greatly restricts the engineering application of PID controller in AC speed regulator systems, which also affects the control performance of sensorless drive system with the phase-locked loop (PLL) structure. To address this, this paper proposes a novel variable-proportion desaturation PI (VPDPI) control method for permanent magnet synchronous motor (PMSM) sensorless drive system. Firstly, an overall scheme of sensorless control system with sliding mode observer (SMO)-PLL observer is presented, which includes the equivalent mathematical model and its parameter identification model of PMSM. Secondly, the control principle of the VPDPI algorithm is innovatively designed. Note that the novel regulator scheme consists of two interconnected components in terms of the concepts of threshold segmentation, i.e., the variable-proportion link and desaturation PI part. Meanwhile, the stability analysis of the regulator is further discussed by the root locus and the Bode diagram analysis. Finally, the numerical simulations of speed regulation are carried out under the various operation conditions, and the effectiveness of the proposed method is further verified by experimental platform. Several groups of comparative experiments reveal that the proposed method has a more higher control performance for the sensorless drive system, especially in the ability of overshoot suppression.  
premanent magnet synchronous motor (PMSM); sensorless; variable-proportion desaturation PI (VPDPI); integral saturation; observation performance
# Design and Analysis of Systematic Batched Network Codes
## 2 Ordinary Batched Network Coding
### Decoding Algorithms
#### 2.3.2 Joint Decoding  
The above two-step decoding algorithm can be improved by combining the two steps when the precoding includes LDPC. For LDPC precoding, each parity check constraint can be regarded as a batch with batch size 1 and only one all-zero received packet. Then, the BP decoding of the batches in the first step of the two-step approach can also include the parity checks.  
In practice, the decoding of the LDPC precode and the decoding of the batches in the two-step decoding algorithm can be combined together to improve the performance. The joint decoding algorithm can improve the decoding success rate and reduce the coding overhead of the two-step decoding algorithm, but does not increase the computation cost of the two-step decoding.
# Cross-Pol Transponder with Frequency Shifter for
## 4 Conclusions  
In this paper, the authors designed and tested a high gain (91 dB) transponder for operating a GBSAR in bistatic modality. This high gain was obtained by decoupling the transmit and receiving channels by cross-polarization and frequency shift. As the radar needs phase stability both for providing the synthetic aperture and for operating as an interferometer, it was necessary to share the reference signal using a cable and to provide a calibration path between the transponder and the radar head. This solution can possibly be problematic for long-range deployment of the equipment. In order to avoid the cable, a lock-in module in the transponder could use the calibration signal as reference signal; however, this obviously adds further hardware complexity. An alternative solution that can be investigated in future works is to synchronize radar and transponder using the same clock provided by a GPS constellation.  
Compared to the transponder reported in Reference [6], this transponder is fairly more complex and expensive; nevertheless, it is able to obtain a gain (91 dB) that is not achievable otherwise. Therefore, we can state that this enhanced transponder is specially designed for long-range applications where very high gain is required.
# Human Cognitive and Motor Abilities in the Aging Workforce: An Information-Based Model
## 4 An Information-Based Model for Age- and Sex-Dependent Performance Time of Workers
### Information-Based Model of Human Motor-Cognitive Performance  
The model considers the motor and cognitive abilities of a subject with given age and sex in order to evaluate his/her performance in term of completion time in accomplishing a given task with a known cognitive and motor information content:  
\[Performance\ Time(I_{c},\ I_{m},\ s,A)=RT(I_{c},s,A)+MT(I_{m},s,A) \tag{15}\]  
where:  
* \(I_{c}\) [bit] = information content of the cognitive part of the task;
* RT(I\({}_{c}\), s, A) = reaction time (refer to Equation (9));
* \(I_{m}\) [bit] = information content of the motor part of the task (I\({}_{m,ID}+I_{m,MTM}\)); and
* MT(I\({}_{m},\ s,\ A) = movement Time (refer to Equation (12))  
This model is based on a preliminary decomposition of the task in its components (high cognitive-low motor, and high motor-low cognitive). The information content of the two components (I\({}_{c}\), I\({}_{m}\)) can be obtained by means of Equations (1) and (8a), respectively. The time required by the subject to complete the cognitive (RT) and the motor (MT) part of the task are calculated starting from the processing times \(T_{p,c}\) (s, A) and \(T_{p,m}\) (s, A), respectively. \(T_{p,c}\) (s, A) can be obtained from Equation (9) by adopting the parameters corresponding to the subject characteristics (Table 3); \(T_{p,m}\) (s, A) can be obtained from Equation (12) by adopting the parameters corresponding to the subject's characteristics  
Figure 4: \(T_{p,m}\) values influenced by age and sex in case of motor tasks.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sex** & \(\alpha_{m}\) **(ms)** & \(\beta_{m}\) **(ms/bit)** \\ \hline Male & 155.33 & 0.70 \\ Female & 144.55 & 0.67 \\ \hline \end{tabular}
\end{table}
Table 5: Parameters \(\alpha_{m}\) and \(\beta_{m}\) values for male and female individuals.  
(Table 5). The proposed model allows to predict the human performance in the form of completion time for subjects of different age and sex involved in tasks characterized by both a cognitive and a motor component. When ID (Equation (6)) is adopted for the calculation of \(\text{I}_{\text{m,ID}}\) as in the present formulation, the model directly applies to reaching motor tasks; nevertheless, the model is quite general and by adapting the information content of the motor component could be applied to different tasks and contexts, from everyday life activities, to rehabilitation medical treatments, to operations in production systems.
## 4 Experiments
### Ablation Experiment  
**(1) Parameter settings:** The LiDAR sensing range of the mobile robot in the simulation environment is 6 m, the frequency is 10 Hz, the depth camera sensing range is 3 m, the viewing angle is 87\({}^{\circ}\), and the maximum speed of the robot is 1.5 m/s. In Figure 4a-c, the starting point of the navigation movement is set at 1 m above the lower right corner of the wall, while the end point is set at 1 m above the upper left corner of the wall.  
**(2) Implementation:** The environment shown in Figure 4 contains a limited number of dynamic and static obstacles. The dynamic obstacles move back and forth within the environment at velocities ranging from 0.4 m/s to 0.6 m/s.  
**(3) Algorithm Configurations**  
* Algorithm 1: This algorithm is derived from the TARE algorithm proposed in [13].
* Algorithm 2: This configuration excludes the prediction algorithm module from the system. The path optimization algorithm is incorporated on top of Algorithm 1 to improve performance.
* Ours: This refers to the algorithm proposed in this paper.  
As shown in Table 1, all methods perform well in low-complexity dynamic scenes. However, with a substantial increase in obstacle density, both the obstacle avoidance success rate and target accessibility of Algorithm 1 and Algorithm 2 decline noticeably, especially in Scene C. This limitation primarily stems from their inability to effectively coordinate avoidance among dynamic obstacles moving at varying speeds, resulting in a higher collision risk. Additionally, in densely cluttered environments, factors such as occlusions in the robot's perception system and limited sensing range further increase the likelihood of collisions in complex scenarios like Scene C. In contrast, the method proposed herein significantly enhances motion robustness by integrating a dynamic trajectory prediction model and optimizing the node selection strategy. Although this improvement may cause a slight decrease in overall system efficiency, it substantially increases navigation reliability under challenging conditions.  
Figure 4: Scene **(a)** includes six dynamic obstacles; scene **(b)** includes twelve dynamic obstacles; and scene **(c)** contains twelve dynamic obstacles along with ten static obstacles.
# An Experimental Validated Control Strategy of Maglev Vehicle-Bridge Self-Excited Vibration
## 3 Principle of Self-Excited Vibration
### Stability of Levitation System  
The stability of the levitation subsystem itself is a precondition for the avoidance of the maglev vehicle-bridge self-excited vibration. When studying the stability of the levitation system itself, the vertical displacement of bridge \(y_{\text{B}}\) is set as zero. In this case, the transfer function from the disturbance \(F_{\text{d}}\) to the displacement \(y_{\text{E}}\) of electromagnet is  
\[T_{1}(s)=\frac{y_{\text{E}}(s)}{F_{\text{d}}(s)}=\frac{G_{\text{E}}(s)}{1-G_{ \text{E}}(s)H_{\text{E}}(s)} \tag{20}\]  
where  
\[\left\{\begin{array}{l}G_{\text{E}}(s)=-\frac{1}{m_{\text{E}}s^{2}}\\ H_{\text{E}}(s)=\frac{k_{\text{E}}s^{2}}{2 NAs+k_{\text{B}}}\big{(}k_{\text{a}}s ^{2}+k_{\text{d}}s+k_{\text{p}}\big{)}\end{array}\right. \tag{21}\]  
Figure 2: The equivalent block diagram of maglev vehicle-bridge system.  
The characteristic equation of the transfer function \(T_{1}(s)\) is  
\[\Delta_{1}=2 NAm_{\rm E}s^{3}+(m_{\rm E}k_{\rm B}+k_{\rm B}k_{\rm F}k_{\rm a})s^{2 }+k_{\rm B}k_{\rm F}k_{\rm d}s+k_{\rm B}k_{\rm F}k_{\rm p} \tag{22}\]  
For the levitation subsystem, the parameters \(k_{\rm p}\), \(k_{\rm d}\), \(k_{\rm a}\), \(k_{\rm B}\) are positive and adjustable, and \(k_{\rm F}=4 AB_{0}/\mu_{0}\). According to the Routh-Hurwitz stability criterion, the levitation subsystem is stable when Equation (23) is satisfied.  
\[(m_{\rm E}k_{\rm B}+k_{\rm B}k_{\rm F}k_{\rm a})k_{\rm d}>2 NAm_{\rm E}k_{\rm p} \tag{23}\]