esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 4 Proposed Scheme
### Multicast Key Generation  
In the downlink communication network, according to the communication protocol, one or more bytes of the address domain can be replaced by \(AAH\), so as to achieve the effect that the current bytes are multicast addresses and realize the function of broadcasting a message to all eligible smart meters. In order to better adapt to the AMI message transmission mode, the multicast key agreement process is also designed after authentication and unicast key agreement. There are multiple smart meters within the jurisdiction of \(DC_{j}\), and the multicast key agreement process will be described by using the interaction between \(DC_{j}\), and \(SM_{i}\) as an example.  
1 After realizing the unicast key agreement, \(DC_{j}\) selects a current timestamp \(t_{3}\) and performs the following calculations:  
\[Y_{i}=T_{v}(M_{1}) \tag{26}\]  
\[GK=H(Y_{1}Y_{2}\ldots Y_{i}\ldots Y_{n}) \tag{27}\]  
\[GK_{i}=GK/Y_{i} \tag{28}\]  
\[M_{9}=H(ID_{i}||PID_{i}||Y_{i}||GK) \tag{29}\]  
\[EM_{i}=Enc_{SK}(GK_{i}||M_{9}||t_{3}) \tag{30}\]  
where \(EM_{i}\) is the encrypted ciphertext using the key \(SK\) between \(DC_{j}\) and \(SM_{i}\). \(DC_{j}\) sends \(EM_{i}\) to \(SM_{i}\).  
Figure 3: Command frame format.  
Figure 2: Information frame format.  
2 After receiving the ciphertext, \(SM_{i}\) decrypts \(M_{i}=Dec_{SK}(EM_{i})\) with the session key to obtain \(\{GK_{i},M_{9},t_{3}\}\), and then verifies the timeliness of the message. If \(|t_{current}\)-\(t_{3}|\leq\Delta T\), \(SM_{i}\) performs the following calculation; otherwise, it terminates the session.  
\[Y_{i}=T_{u}(M_{5}) \tag{31}\]  
\[GK=H(GK_{i}Y_{i}) \tag{32}\]  
3 \(SM_{i}\) verifies whether the equation \(M_{9}=H(ID_{i}||PID_{i}||Y_{i}||GK)\) is established. If successful, \(DC_{j}\) encrypts the instruction information using the group key \(GK\) and sends it to multiple smart meters. \(SM_{i}\) decrypts and performs the corresponding operation using the group key \(GK\); otherwise, \(SM_{i}\) will terminate the session. The MDMS and DC implement multicast key agreement through the same steps as above.
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 4 Proposed Uni-Regression Deep Approximate Forecasting Model (UDAF) in Demand Forecasting
#### NARX Model
The weight parameters \(\alpha\) and \(\beta\) make it possible to adjust the contribution of each model with regard to their relative performance on different aspects of demand dynamics. The architecture of the proposed uni-regression deep approximate forecasting model in demand forecasting is depicted in Figure 2, and its layer details are presented in Figure 3. In addition, the pseudo-code of the developed approach is presented in Algorithm 1. ```
1. Input \(K\)
2. Procedure Preprocessing (input data)
3. return preprocessed data
4. Procedure Feature extraction (preprocessed data)
5. return concatenated retail_features, Statistical_features, Time series_features, Demand indicator_features
6. Procedure Train_test_split (Retail_features, Statistical_features, Time series_features, Demand indicator_features, label)
7. return Train data, Train label, Test data, Test label
8. Procedure Uni-regression deep approximate forecasting model (Train data, Train label)
9. Procedure Testing (model, test data)
10. return output
```  
**Algorithm 1** Pseudo code for UDAF  
Figure 2: Architecture of the proposed uni-regression deep approximate forecasting model in demand forecasting. Figure 3: Layer details for uni-regression deep approximate forecasting model.
# Interior Sound Quality Prediction of Pure Electric Vehicles Based on Transfer Path Synthesis
## 6 Conclusions  
This study has developed an equivalent SQ model of the interior sound in a PEV. The model synthesis was based on the transfer function path technique, and the model itself considered the airborne noise sources (electric drive system and tire) and structure-borne noise sources (suspension and mount systems). In the established sound equivalent model of the PEV interior, the interior noise was synthesized and simulated under each working condition. The synthesized results well agreed with the measured results and exhibited the same overall trends as the measured spectra. Especially, the peaks of the measured and simulated spectra coincided at the key frequency points. The loudness, sharpness, and roughness values of the synthesized and measured in-vehicle noises differed within 7% under each working condition, further confirming higher prediction accuracy of the synthetic equivalent model of the interior PEV SQ. The model meets the expected requirements, and the objective psychoacoustic evaluation parameters predicted by the model equaled the measured  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Contribution** & **Suspension** & **Mount** & **Electric Drive System** & **Tire** \\ \hline Sound pressure level/dB(A) & 57.8 & 50.3 & 52.7 & 49.1 \\ \hline Loudness/sone & 11.4 & 5.1 & 6.5 & 4.5 \\ \hline Sharpness/acum & 0.712 & 0.691 & 0.783 & 0.753 \\ \hline Roughness/asper & 0.439 & 0.629 & 0.677 & 0.513 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sound quality contribution to the paths of interior noise of PEV under 50 km/h.  
values (within small error margins). TPA and TPS that take the SQ of interior noise as the target can predict the interior SQ transmitted by each transfer path. The model truly reflected the psychoacoustic physical characteristics of the in-vehicle noise. The SQ objective parameter value of each transfer paths was quantified. The structure-borne road noise transmitted by the suspension system and the airborne noise transmitted by the electric drive system have great influence on the interior SQ of PEV. The SQ separation method based on the modified TPA and TPS is an effective technique to realize delight sound design for PEVs. Therefore, it provides a technical basis for the improvement of existing products, new product development, acoustic designs, and SQ evaluations of PEV structures.  
Conceptualization, methodology, data curation, funding acquisition, K.Q.; project administration, writing--review and editing, Z.H.; resources, supervision, validation, visualization, J.L.; formal analysis, investigation, R.L.; writing--original draft preparation, D.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China Postdoctoral Science Foundation, grant number 2019M650657.  
Not applicable.  
We would like to thank our mentors and all our colleagues for their help and support.  
The authors declare no conflict of interest.
Motivated by this, we further propose a method employing 'class centers' ([PERSON] et al., 2016) to dynamically acquire class predictions during training as soft targets for computing self-distillation loss, called center self-distillation (CSD), as shown in Figure 1. In this algorithm, one class corresponds to only one class prediction, which drives features under a same class move towards their 'class centers', thereby reducing intra-class variance. In particular, to prevent over-fitting, we generate a soft target for a given sample by moving its corresponding 'center feature' a certain distance towards that sample to retain some intra-class variation. The 'class centers' are computed in a similar way to the center loss ([PERSON] et al., 2016), introducing almost negligible additional parameters and calculations--meaning that it can be easily applied to existing models. In contrast to earlier inter-sample self-distillation techniques, our proposed contrastive self-distillation (CSD) approach obviates the requirement for sampling an extra batch of identically labeled samples for the self-distillation process throughout training. This significant refinement leads to a marked reduction in time consumption. Furthermore, in the proposed framework, label smoothing can be interpreted as a special case of manually setting a fixed set of class centers, which provides a different explanation for why label smoothing works. To verify the generalizability of CSD, we systematically design experiments comparing it with different self-distillation models and output regularization methods. It is shown that the method has greater gains in fine-grained classes, as they are more sensitive to intra-class relations. To summarize, the main contributions of this work include the following:  
* A simple visualization method is designed to observe the change in features and their gradients during training. It can provide intuition regarding how feature learning is influenced by different training methods. * Using this visualization method, it is identified that reducing the intra-class variation in features is the key to inter-sample self-distillation. Base on the above finding, a simple but effective self-distillation technique, in conjunction with class centers, is proposed. It further demonstrates that reducing intra-class variance with self-distillation helps model generalization. * The inter-sample self-distillation and our CSD are experimentally demonstrated to contribute more significantly to fine-grained classification.
# VHF Omnidirectional Range (VOR) Experimental Positioning for Stratospheric Vehicles
## 2 Materials and Methods
### VOR-Based Ground Track Determination  
Two-dimensional positioning (determination of ground track) can be achieved when intersecting two or more radial measurements acquired simultaneously. Latitude and longitude of the balloon position are retrieved as follows.  
Given \(\varphi_{1}\), \(\lambda_{1}\), \(\theta_{1}\), namely, the latitude, longitude, and measured radial from the first station, and \(\varphi_{2}\), \(\lambda_{2}\), \(\theta_{2}\) from the second station, the positioning formula is obtained from the following formulas.  
As first step, it is necessary to calculate the angular distance between the two VOR station points, defined as \(\delta_{12}\).  
\[\delta_{12}=2\cdot\sin^{-1}(\sqrt{sin^{2}\bigg{(}\frac{\varphi_{1}-\varphi_{2}}{2 }\bigg{)}+\cos\varphi_{1}\cdot\cos\varphi_{2}\cdot\sin^{2}\bigg{(}\frac{\lambda_ {1}-\lambda_{2}}{2}\bigg{)}}.\]  
It is then necessary to compute the bearings between the two VOR stations positions.  
\[\theta_{a}=2\cdot\text{acos}\bigg{(}\frac{\sin\varphi_{2}-\sin\varphi_{1}\cdot \cos\delta_{12}}{\sin\delta_{12}\cdot\cos\varphi_{1}}\bigg{)},\theta_{b}=2 \cdot\text{acos}\bigg{(}\frac{\sin\varphi_{1}-\sin\varphi_{2}\cdot\cos\delta_{ 12}}{\sin\delta_{12}\cdot\cos\varphi_{2}}\bigg{)}.\]  
The angles \(\theta_{1}\) and \(\theta_{2}\) need to be reformatted as follows to compute the angular distances to the intersection point within the trigonometric function domains.  
\[\text{If}\sin(\lambda_{1}-\lambda_{2})>0:\;\theta_{12}=\theta_{a};\;\theta_{2 1}=2\pi-\theta_{b},Else:\;\theta_{12}=2\pi-\theta_{a};\;\theta_{21}=\theta_{b}.\]  
All the angles among the three points (first and second VOR station, intersection points), are then computed as follows. The subscript indicates the angle vertex, where 1 and 2 are the VOR stations and 3 is the intersection point.  
\[\alpha_{1}=\theta_{1}-\theta_{12},\alpha_{2}=\theta_{21}-\theta_{2}\alpha_{3} =\cos^{-1}(-\cos\alpha_{1}\cdot\cos\alpha_{2}+\sin\alpha_{1}\cdot\sin\alpha_{ 2}\cdot\cos\delta_{12}).\]  
The angular distance between the first VOR station and the intersection point is then computed.  
\[\delta_{13}=\tan^{-1}(\sin\delta_{12}\cdot\sin\alpha_{1}\cdot\sin\alpha_{2} \cdot\cos\alpha_{2}+\cos\alpha_{1}\cdot\cos\alpha_{3}).\]  
It is then possible to compute the latitude and longitude of the intersection point as follows.  
\[\varphi_{3}=\sin^{-1}(\sin\varphi_{1}\cdot\cos\delta_{13}+\cos\varphi_{1}\cdot \cos\theta_{1}\cdot\sin\delta_{13}),\]  
\[\lambda_{3}=\lambda_{1}+\tan^{-1}\bigg{(}\frac{\sin\theta_{1}\cdot\sin\delta_{ 13}\cdot\cos\varphi_{1}}{\cos\delta_{13}-\sin\varphi_{1}\cdot\sin\varphi_{3}} \bigg{)}.\]  
Different optimizations have been performed for position computation on the COTS receiver and SDR.  
The following paragraphs present the results of the positioning test for the two receivers. A final summary is provided in the final sub-section of this paragraph.
# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## Methods
### Comparisons: 1.0 km LAI-RM v. MODIS LAI and 12.0 km MODIS LAI v. EPIC LAI
#### 2.3.2 Comparison 2: MODIS LAI (Mc5; Mc6) and EPIC LAI at 12.0 km  
To compare EPIC-derived LAI with MODIS LAI a 3 \(\times\) 3 grid of nine 12.0 km resolution grid cells were created in ArcMap (i.e., 'Fishnet') centered about the four LAI research sites. The 12.0 km resolution was chosen to correspond with current air quality grid resolution for CMAQ. Spatial aggregation yielded LAI zonal statistics (mean, maximum, minimum, range, standard deviation) across the nine 12.0 km grid resolution cells for the smoothed MODIS LAI (Mc5 [144 pixels] and Mc6 [576 pixels]). Data quality flags were also extracted for both MODIS collections for ASG filter smoothing. EPIC-derived LAI was estimated at this same 12.0 km resolution for comparison. Notched box whisker plots were created to compare the medians of each data type at each date sampled. This informal graphical test displays a 'notch' where notch overlap signifies strong evidence (95% confidence) that the medians do not differ [39].
# Features of Structure and Absorption in the Jet-Launching Region of M87
## 3 Results
### Transverse Jet Structure
The panel (b) of Figure 5 shows \(\delta_{app}\) as a function of \(d\), and the jet axis is presented as a black solid line for reference. As the Figure shows, the jet bends southward since \(d=0.75\) mas, the offset reaches its maximum at \(d=1.45\) mas (where it is most clearly seen on the 43 GHz image), where the northern limb veers south, and gradually returns to zero around \(d=3.00\) mas. By comparing the behaviors of each limbs with \(\psi_{app}\) evolving with \(d\), we find that, structural features including the expansion and recollimation in segment 2, 3, 4 and 6, result from behaviors of the northern limb only, while the features in segment 1 and 5 result from behaviors of both limbs. By further comparing with earlier works, we notice that the gradual recollimations in segment 1 between \(d=0.90\) and 1.30 mas and in segment 5 between \(d=3.65\) and 4.25 mas, spatially associate with recollimations in the first and second collimation regime (at \(d=1.1\) and 3.7 mas respectively) suggested by [8]. So they could possibly be the representations of these persistent recollimation features at our observed epoch. Figure 5: **(a)** The apparent opening angle \(\psi_{app}\) as functions of core distance \(d\). Parabolic collimation profile determined by [PERSON] & Nakamura is shown for reference. (**b**) The jet center offset \(\delta_{app}\) as functions of core distance \(d\). The jet axis is are shown for reference. The colors represent the measured frequencies: magenta for 86 GHz, blue for 43 GHz and green for 22 GHz. The uncertainties of \(\psi_{app}\) and \(\delta_{app}\) are estimated as \(\sqrt{\Delta\text{PA}_{\text{North}}^{2}+\Delta\text{PA}_{\text{South}}^{2}}\). Only peaks with intensity above 5\(\sigma\) are presented to make sure the result is reliable.
# A Systematic Review of Insole Sensor Technology: Recent Studies and Future Directions
## 4 Insole Sensor Technology  
### Pressure Sensors  
Pressure sensors are electronic devices specifically engineered to quantify and transform pressure into electrical impulses. They are frequently utilized to measure pressure in the hands and feet, facilitating the examination of user movement. Pressure sensors integrated into intelligent insoles offer significant data by assessing the pressure applied during walking activities [3; 10]. In addition, these sensors can measure the pressure dis  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
tribution across the foot. This is crucial for determining the center of pressure (CoP) as well as other gait characteristics, including step count, gait cycle duration, swing duration, and foot-ground interaction events [33]. Recent developments suggest that innovative materials can precisely and sensitively collect data about the detection of standing posture, identification of sports shoes, and activity modes [3]. However, the most precise technique for measuring CoP is utilizing a force plate integrated into the ground. This makes it possible to capture the immediate ground reaction force while maintaining a stationary stance [23].  
[PERSON] et al. [5], in their work, compare various pressure-sensing mechanisms and state the advantages and disadvantages of each one. The authors demonstrated that the piezoresistive and capacitive sensors are low-cost and have a simple structure and fabrication process, but both have difficulties regarding temperature. They also stated that piezoresistive, capacitive, and piezoelectric sensors are more appropriate for monitoring and preventing neurological and orthopedic diseases. Whereas piezoresistive sensors present a problem concerning power consumption, capacitive sensors and piezoelectric have lower power consumption. On the other hand, piezoresistive and piezoelectric sensors support multi-axis pressure sensing. Piezoelectric sensors are more affordable and have higher force sensitivity but cannot detect static pressure.
## 5 Experiment Verification and Analysis
### Experiment on Datasets of Rolling Bearing in Case Western Reserve University
#### 5.1.4 Analyze the Process and Results
When SNR <0 dB, the accuracy of the model is low, when SNR >4 dB, its accuracy is higher than 95%, and the resolution of different SNR noise based on the method proposed in this paper is higher than that of the model that classifies the original signal directly. It can be seen that based on the model used in this method, the model can maintain high accuracy under noise interference, and it has strong anti-noise stability and generalization ability. In order to further verify the effectiveness of the bearing diagnosis method based on sparse representation and 1 DCNN, the method model used in this paper is compared with the following bearing fault diagnosis methods based on traditional machine learning classification, including the one-dimensional convolution neural network (1 DCNN) model, wavelet packet decomposition + convolution neural network (WPD-CNN) model in reference [45], convolution neural network + support vector machine (CNN-SVM) model based on adaptive features in reference [46], a new data-driven fault diagnosis method-based Wen-CNN model proposed by [PERSON] in reference [21], and a rotating machinery fault diagnosis method (ResCNN) model based on deep residual learning in reference [47]. The comparison between the method used in this paper and other fault classification methods is shown in Figure 12. It can be seen from the figure that the diagnosis model based on sparse representation and the 1 DCNN method can effectively extract fault features and accurately classify bearing faults, it has a higher accuracy, and the diagnosis effect is more significant than other methods. Figure 11: Accuracy of test set under different SNR (test set of CWRU). Figure 12: Comparison of different methods (test set of CWRU).
## 6 Extension to Scenarios with Fusion: CEO Problem
### Numerical Results  
In Figure 14, the limiting probability of decision error is shown, as a function of the correlation coefficient. Different values for the number of sensors \(n\) are considered. One can note that the higher is the correlation coefficient, the lower is the floor, since the correlation is better exploited and it is easier to recover the original information bit. Moreover, note that the majority decision rule does not improve when \(n\) increases from an odd value (e.g., 3) to the next even value (4). Therefore, no performance improvement is observed.  
In Figure 15, the probability of decision error (37) is shown, as a function of the average SNR at the sources, for the CEO problem in a scenario with \(n=3\) sensors and considering (a) regular LDPC-coded schemes, (b) DD LDPC-coded schemes, and (c) SCCCed schemes. The performance in the presence  
Figure 14: Limiting (for large SNR) probability of decision error as a function of the correlation coefficient, for the CEO scenario. Different values for the number of sensors \(n\) are considered.  
of balanced SNR and unbalanced SNR power control strategies is compared with that in the absence of power control. As anticipated from the previous subsection, all curves reach, for large values of \(\overline{E}_{\rm c}/N_{0}\), the BER floor given by (37). Moreover, there is a difference, with respect to the results presented for non-CEO scenarios (i.e., the previously considered multiple access scheme), in the behavior of balanced SNR and unbalanced SNR power control strategies. In fact, in non-CEO scenarios the unbalanced SNR power control strategy guarantees a better performance, in terms of average BER or the outage probability, than the balanced SNR power control strategy only when an SCCC is usedm but not is an LDPC code is under use. In a CEO scenario, instead, the unbalanced SNR power control strategy allows to achieve the limiting floor faster (i.e., for lower SNRs) than the balanced SNR power control strategy in all scenarios (both SCCCed or LDPC coded).  
Figure 15: Error probability, as a function of the average SNR at the sources, for the CEO problem in a scenario with \(n=3\) sensors and considering: (a) regular LDPC-coded schemes, (b) DD LDPC-coded schemes, and (c) SCCCed schemes. The performance in the presence of balanced SNR and unbalanced SNR power control strategies is compared with that associated to the absence of power control.
## 3 Proposed Method
### First Level Modules Definition  
Once we have set the base methods, we are able to define the poses, that is, the modules of the first level in the decomposition framework.  
The success of an upper level of the framework resides in the knowledge of the previous level. That is, in the upper level we want to complete the activity of _walking_; thus, in this level we need to find poses that help to achieve it.  
Just like we have seen in the state of the art, many of the works on biped walking use ZMP trajectories. Basically, what they do is to set the ZMP on one foot of the robot, and with that coordinate, compute the position of the angles of the joints using inverse kinematics. Hence, the modules of this level will be defined as configurations of the joints that lead the robot to a specific position of the ZMP.  
First, we have to discuss how we compute the ZMP. According to Reference [6], the ZMP coordinates can be calculated as follows:  
\[x=\frac{W\cdot((f_{2}+f_{4})-(f_{1}+f_{3}))}{2\cdot(f_{1}+f_{2}+f_{3}+f_{4})} \tag{1}\]  
\[y=\frac{L\cdot((f_{1}+f_{2})-(f_{3}+f_{4}))}{2\cdot(f_{1}+f_{2}+f_{3}+f_{4})} \tag{2}\]  
where \(W\) is the width and \(L\) the length of the foot sole and \(f_{1}+f_{2}+f_{3}\) and \(f_{4}\) are the four sensors located in the sole of a robot's foot; the NAO robot already comes with these sensors built-in.
# New Advances and Challenges of Fall Detection Systems: A Survey
## 4 Challenges and Potentials
### How to Move on from Detection to Prediction  
Fall detection is the initial step to protect the elderly but not the final one. It can help reduce the interval from falling to rescuing, but it cannot prevent the human from being injured. Regarding the study of fall, the Holy Grail is fall prevention and protection. To achieve this goal, fall prediction must also be investigated, which underlies the study of fall protection. We have attempted and achieved good performance of predicting a fall (91.7% in 333 ms) before an elderly person is injured (hitting the floor) using one existing health care dataset [32].