esa-sceva/satcom-chunk-collection huggingface dataset:
# UKF-Based State Estimation for Electrolytic Oxygen Generation System of Space Station
## 6 Conclusions  
Based on the principle analysis and system dynamics of the EOGS, the mathematical model to describe the dynamic characteristics of EOGS is established. On this basis, the UKF based state observer is designed. The simulation experiment is carried out under the condition of setting the same actual working condition as China's tangong-2, with one more astronaut compared with scheduled 2 astronauts. The main conclusions are as follows:  
1. The established dynamic model of the system is relatively simple and accurate. It can be used for the simulation experiment of EOGS, avoiding the complex coupling relationship between the system and other systems caused by energy exchange.
2. Using UKF algorithm to design the observer the state of the system can achieve a high accuracy. The estimation error of the system state is within 2% by comparing the estimation value with the real value.
3. The model and UKF algorithm built in this paper can monitor the state of the system, and diagnose the fault of the core components based on actual observation values simultaneously, which will lay a solid foundation for the subsequent comprehensive fault diagnosis of the EOGS.  
Writing--original draft, M.L.; conceptualization, M.L. and Y.L.; methodology and software, M.L.; funding acquisition, X.L.; supervision and project administration, X.L.; writing--review and editing, Y.L. and W.Z.; testing of existing code components, R.G.; data curation, R.G.; All authors have read and agreed to the published version of the manuscript.  
Figure 8: Comparison of true value and estimated value of oxygen partial pressure in cabin.  
Figure 9: Estimation error of the partial pressure of oxygen in cabin.  
**Funding:** This research was funded by Pre-research project in the manned space filed of China grant number 050403.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Propagation Characteristics and Phenomena of LMS Communication
### Doppler Shift  
The motion of the mobile terminal causes a change in the properties of the environment, giving the received signal a theoretically non-stationary statistic; however, in practice, the channel can be described as almost stationary, as its characteristics can be considered slowly variable (elevation-angle types of obstacles, surface irregularities). The carrier undergoes a Doppler shift due to the movement of the terminal; this shift can be calculated based on the formula:  
\[f_{d}=\frac{s}{c}\cdot f_{c}\cdot cos\theta, \tag{9}\]  
where \(f_{c}\) is the carrier frequency, \(s\) is the mobile terminal speed, \(c\) is the speed of the light, and \(\theta\) is the elevation angle of the direct component [24].  
As a result of this shift, the phase of the generic contribution of the indirect component in reception will have a phase shift of \(2\pi f_{d}t\). The bandwidth of this component for mobile terminal speeds around 100 km/h is about 200 Hz.
## 3 Demonstration of Tunable Shielding Effectiveness in GHz Bands
### Dynamic Control of Shielding Effectiveness
#### 3.2.2 Response Time of Tunability  
The modulation speed and performance of SE tunable materials mainly depend on the carrier concentrations in the dielectric layer. For the established GIG sandwich structure, its tuning speed was greatly limited by the ions in the ion gel [10; 15; 22]. As learned from the above results, the GIG structure could realize the effective tuning of the electromagnetic wave at second level.  
Figure 9: Transmission curves of (**a**) the MIM and (**b**) the BIB structure when applying rectangular and triangle waves with the frequency of 250 mHz and 1 Hz.  
Commonly, the time constant \(\tau\) is often used to express the time process of a transition reaction for a certain quantity decaying exponentially. Thus, the time constant is introduced to quantitatively describe the ability of the sample to dynamically control the electromagnetic wave.  
In this work, the transmission coefficient changing process of the GIG structure in the time domain can be divided into two stages: falling and rising, with the voltage switched between high and low levels, corresponding to the time constants \(\tau_{fall}\) and \(\tau_{rise}\), as shown in Figure 10, where \(\tau_{fall}\) refers to the time required in the process of the transmission coefficient decaying from maximum to minimum for (1-1/e) of the regulation range, and \(\tau_{rise}\) represents the process of the transmission coefficient rising from minimum to the (1-1/e) of the regulation range [10].  
Combined with the test results from Figure 9, the time constants \(\tau_{all}\) and \(\tau_{rise}\) of the MIM and BIB samples were obtained, respectively, as shown in Table 1, where the MIM structure exhibited a faster speed in the falling process. However, when the voltage was switched to 0, the BIB structure spent less time to restore to the initial level. The main reason is that in the falling process, the charging time of the structure mainly depended on ions in ion gel, thus making the BIB structure need more time to dope the Fermi level of the bilayer graphene. When the voltage was switched to 0, the carriers in the graphene dominated, resulting in a smaller \(\tau_{rise}\) for the BIB structure, which had a richer content of carriers.
# Weather-Aware Long-Range Traffic Forecast Using Multi-Module Deep Neural Network
## 4 Experimental Results and Discussion
### Experimental Results
#### 4.2.2 Case II (Holiday)  
Case II corresponds to the holiday season, during which heavy traffic is present on the roads. Figure 10 shows the MAPE, where significant errors appear around 12:00. TW-DNN outperforms others, showing smaller errors by up to 6%, 3%, and 5%, compared to CNN-only, \(k\)-nn and ARIMA, respectively.  
The heatmap of Case II is shown in Figure 11. In real traffic, congestion occurs between 12:00 and 20:00 in some links. TW-DNN detects congestion successfully except in some links. It also forecasts the regular traffic condition similar to the real data. Although the other models do not detect the congestion, they somehow only show small error in this case.  
Figure 11: Speed figure of (**a**) real data, (**b**) TW-DNN, (**c**) CNN-only, (**d**) \(k\)-nn, and (**e**) ARIMA for Case II.  
Figure 10: MAPE error of prediction models for Case II.
## 2 Laser-Generated Ultrasound
### Design and Structures
#### 2.2.2 Focused Transmitters  
High-frequency focused piezoelectric transducers are widely used for high-resolution photoacoustic and ultrasound imaging. The focused acoustic wave is achieved through the spherical surface of a focused transducer or an acoustic lens attached to a flat transducer. Borrowing from this concept, laser-generated focused acoustic wave can be realized using a focused LGUS transmitter consisting of a light-absorbing film coated on a concave spherical surface. There are several studies. (i) To develop scanning ultrasound imaging based on LGUS transmitters, an axicon transmitter is investigated to furnish high lateral resolution [67]. The achieved lateral beam size is \(\sim\)380 \(\upmu\)m with non-diffracting ultrasound, also called X-waves. (ii) A concave LGUS transmitter is tested using a chromium (Cr) film with a thickness of 100 nm as the light-absorbing layer [68]. Due to high-frequency ultrasound generation by the thin film and the diffracting ultrasound by the concave lens, a high lateral beam size of \(\sim\)44 \(\upmu\)m at the focus is measured. The pressure level of 20-30 kPa is achieved. (iii) When CNTs-PDMS is coated on the concave lens, an unprecedented acoustic pressure of \(>\)50 MPa is achieved by the CNTs-PDMS focused LGUS transmitter [30; 69]. Figure 5 shows the experimental setup for the LGUS characterization and the fabricated lens-based LGUS transmitter. A lateral acoustic focal spot of 75 \(\upmu\)m is obtained. Taking advantage of the remarkably high pressure in the high-frequency regime (\(>\)15 MHz), micro-scale ultrasonic fragmentation and a single-cell surgery are demonstrated to show the potential for precision therapy. Moreover, the highly localized pressure and the high negative pressure facilitate the application of well-controlled bubble nucleation [70].  
Figure 4: SEM pictures of the fiber-optic laser-generated ultrasound (LGUS) transmitters using: Au nanopores (**A**); and CNTs-polydimethylsiloxane (CNTs–PDMS) (**B**). Reproduced with permission from: [59], SPIE, 2013 for (**A**); and [54], AIP Publishing, 2014 for (**B**).  
(iv) A new analytical model is developed to provide guidelines for the design of high-intensity and high-frequency LGUS focused transmitters [71]. (v) Focused ultrasound with complex temporal and spatial waveforms can be generated by utilizing arbitrarily shaped surfaces of polymers made by three-dimensional (3D) printing [72].
## Materials and Methods
### Principle of Wavelet Threshold Denoising
#### 2.2.2 Wtd-Ifa  
Combining IFA and wavelet threshold denoising algorithm (WTD), an adaptable wavelet threshold infrared image denoising algorithm (WTD-IFA) is proposed. For swarm intelligence algorithms, it is necessary to establish a fitness function to evaluate the quality of the denoising effect. In this paper, the purpose of denoising the infrared image is to decompose the infrared image containing noise into a noise signal and a relatively pure image signal. It is generally believed that the smaller the correlation between the noise signal and the image signal, the better, so the reconstructed correlation coefficient between the image and the noise signal separated by denoising is used as a fitness function. The purpose of decomposing the image to remove noise is achieved by reducing the value of the fitness function. The fitness function is as follows:  
\[f_{m}=corr(R,N), \tag{15}\]  
where the \(f_{m}\) stands for the correlation coefficient between the reconstructed image and separated noise signal; \(R\) is the reconstructed image and \(N\) is the separated noise signal.  
The filter realization process based on WTD-IFA is as follows:  
1. Wavelet decomposition. Wavelet transform is used to decompose the infrared image with noise.
2. Threshold initialization. Initializing the IFA threshold and initializing the firefly position (wavelet threshold).
3. The wavelet coefficients contract. Equation (6) is used to calculate the wavelet contraction coefficient, and the image is reconstructed based on the wavelet contraction coefficient.
4. Firefly algorithm iteration. The fitness of the reconstructed image is calculated according to Equation (12). If the iteration number of the firefly algorithm is greater than 1, it is judged whether the fitness of the firefly is better than the fitness of the original position. If yes, the firefly position is updated, otherwise, the firefly position is kept unchanged. If the image fitness result meets the termination condition, execute step (6), otherwise execute step (5).
5. Firefly location update. Based on the firefly's current fitness value and the IFA's location update rules, the firefly's new location is calculated. Then go to step (3).
6. Image reconstruction. The image is reconstructed using the global optimal wavelet threshold searched by WTD-IFA.  
The flowchart of the WTD-IFA algorithm is shown in Figure 3.  
In this paper, the wavelet basis function is sym4, and the noisy image is decomposed by 5-level 2D static discrete wavelet transform.
# Multimodal Deep Learning for Group Activity Recognition in Smart Office Environments
## 4 Empirical Results  
In this section we describe the experimental setup for our analysis and report results based on our deep learning approach for the task of activity recognition in smart office environments, which were equipped with some form of audio-visual monitoring system. As previously emphasized, for this study we employed the AMI dataset, which we briefly describe in the following section. For all experiments we considered a 80/20 ratio between training and validation data and used the ResNet-50 architecture as the base model, when not specified otherwise. The specifics of the audio-visual data are described in Sections 3.1 and 3.2 as inputs for the respective networks. The AMI Corpus NITE XML Toolkit ([[http://groups.inf.ed.ac.uk/nxt/](http://groups.inf.ed.ac.uk/nxt/)]([http://groups.inf.ed.ac.uk/nxt/](http://groups.inf.ed.ac.uk/nxt/))) was used to extract the labels for each video. In order to evaluate the efficiency of our approach, besides the loss function introduced in Equation (3), for an easier interpretability we also report the following metrics:  
* _Classification accuracy_, which is the ratio of correct predictions to the total number of input samples: \[Acc=\frac{\sum_{x_{i}\in X_{test}}1\{f(x_{i})=y_{i}\}}{|X_{test}|}\] (4)
* _F1-score_, which is a measure of both the precision of the classifier and its robustness, defined as the harmonic mean between precision and recall: \[F1=2*(Recall*Precision)/(Recall+Precision)\] (5)  
In the following, we describe a comprehensive empirical study, whereby we first investigated the classification task in the context of each separate modality, exploring our approach against several DL base models and different model complexities. Second, we analyzed the results on multimodal data and evaluated the advantages of transfer learning in this context.
## 2 Materials and Methods
### Lamb Waves  
The Lamb waves are one of the types of Ultrasonic Guided Waves (UGW), and they have been known for over a hundred years since [PERSON] discovered them [47]. The Lamb waves propagate in a solid plate with traction-free boundaries, which refers to the elastic displacements [48]. There are two distinct dispersive types of guided waves in a plate with traction-free boundaries: longitudinal waves having symmetric displacements to the centre of the plate and transverse waves having antisymmetric displacements [48]. Compared to traditional ultrasonic technologies, the following useful properties of Lamb waves propagated in solid media can be distinguished, such as:  
Figure 2: The common damage modes in wind turbine blades.  
* The high sensitivity to the properties of the material. Due to this, such waves are used for the inspection of objects of various types, materials, and geometry to determine the changes in the structure, such as defects, delaminations, fatigue cracks, disbonding, corrosion, and others [49, 50].
* Sensitive to cracks at different depths. Depending on the selected mode wavelength, which should be less than the spatial dimensions of the internal defect, the defects are then detected [49]. Using such waves in the lower frequency range, the symmetric S\({}_{0}\) mode possesses sensitivity to deep subsurface defects and the A\({}_{0}\) mode to surface cracks.
* The ability to propagate over the entire thickness of the object in a relatively long distance (up to 100 m) with low attenuation and fast propagation speed [51].
* The early degradation of materials, the evaluation, and detection of early damage inside the structure are obtained faster, more economically, and more sensitively by using such waves [52]. Due to these properties, the usefulness of the Lamb waves in NDT and SHM is actively discussed, and their applicability is constantly being explored.  
Despite the valuable properties, the Lamb waves have unusual limitations: dispersion phenomenon (the velocity as a function of frequency), infinite number of modes, the convergence of modes, modes interlaced, mode splitting due to edges, scattering, and others that make their application difficult.  
In order to more accurately explain the properties of the Lamb waves, the dispersion curves obtained by the semianalytical finite element (SAFE) method and the experimentally obtained Lamb wave signals are used.
# Coupled Least Squares Identification Algorithms for Multivariate Output-Error Systems
## The Multivariate Auxiliary Model Coupled Identification Algorithm
### The Coupled Auxiliary Model Based Recursive Least Squares Algorithm
For \(i=2,3,\cdots,m\), compute \(\mathbf{L}_{i}(t)\) and \(\mathbf{P}_{i}(t)\) by (30) and (31), and update the parameter estimation vector \(\hat{\mathbf{\vartheta}}_{i}(t)\) by (29). 5. Obtain the parameter estimation vector \(\hat{\mathbf{\vartheta}}(t)=\hat{\mathbf{\vartheta}}_{m}(t)\) and compute \(\mathbf{x}_{\text{a}}(t)\) by (35). 6. If \(t=L\), stop the recursive computation and obtain the parameter estimates; otherwise, increase \(t\) by \(1\) and go to Step 2. **Remark 4**. : _The C-AM-RLS algorithm in (26)-(35) uses the estimate \(\hat{\mathbf{\vartheta}}_{i-1}(t)\) on the right-hand side of (29) instead of \(\hat{\mathbf{\vartheta}}_{i}(t-1)\) on the right-hand side of (14) for \(i=2,3,\cdots,m\). When computing \(\hat{\mathbf{\vartheta}}_{1}(t)\), the C-AM-RLS algorithm uses the estimate \(\hat{\mathbf{\vartheta}}_{m}(t-1)\) on the right-hand side of (26) instead of \(\hat{\mathbf{\vartheta}}_{i}(t-1)\) on the right-hand side of (14) with \(i=1\). Thus, the C-AM-RLS algorithm is different from the S-RLS algorithm._
## 3 Proposed Approach
### Segmentation--Colour Based Approach Using K-Means Clustering  
In order to address the problem of spatial variation in indoor scenes, we have proposed a segmentation approach before the CNN extracts the features. Here we aim at segmenting the colour image [50] in the RGB colour model using K-means clustering. The steps involved are described as follows:  
* Read the input image;
* Extract the red, green, and blue feature vectors;
* The image space is divided into four group centroids (k = 4);
* Classify the colour pixels using K-means clustering;
* Using the index from K-means clustering, every pixel in the image is labelled;
* Separation of objects in the image by using pixel labels;
* Separate the image by segmenting the cluster centroid.  
Thus, all the pixels in each class are identified by the K-means clustering method, and different colours are assigned to each class. The accuracy of the segmentation method is quantitatively evaluated by using the most common matrices, the dice co-efficient (F1-score), and the Jaccard co-efficient (intersection of unity). Both of these matrices are widely used similarity matrices, and they deal with class imbalance.  
\[\mathrm{Dice}=\frac{2*\ TP}{2*TP+FP+FN} \tag{2}\]  
\[\mathrm{Jaccard}=\frac{TP}{TP+FP+FN} \tag{3}\]  
_TP_--True positive, _FP_--False Positive, _FN_--False Negative.
## 2 Materials and Methods
### Assessment of Segmentation Accuracy
#### 2.5.4 Global Definition of Accuracy  
The accuracy of the four segmented parcel sets, resulting from the use of four different scale thresholds, was determined globally by averaging the 100 local overall accuracy rates,  
Figure 10: An example of the boundaries of the reference parcel, outlined in aqua green, and the boundaries of the segmented parcel, outlined in purple. The area highlighted in blue represents the overlapping area rate, and the area highlighted in yellow represents the overflowing area rate.  
weighted by the respective segment areas. It resulted in a global accuracy metric named the mean weighted accuracy rate (MWAR), as follows:  
\[MWAR=\frac{\sum_{i=1}N=1\ LOAi\times A(Si)}{\sum_{i=1}N=1\ A(Si)} \tag{5}\]  
The same process was applied to the overlapping and overflowing area rates, which were averaged and weighted for each segment's area. The results are two further global metrics: mean weighted overlapping rate and mean weighted overflowing rate.  
\[MWOIpAR=\frac{\sum_{i=1}N=1\ OlpARi\times A(Si)}{\sum_{i=1}N=1\ A(Si)} \tag{6}\]  
\[MWOfIAR=\frac{\sum_{i=1}N=1\ OlfARi\times A(Si)}{\sum_{i=1}N=1\ A(Si)} \tag{7}\]  
Finally, by counting on the basis of their sign each local area difference related to parcels extracted with a local overall accuracy lower than 80%, it was possible to compute the undersegmentation error rate (number of local-area differences with positive signs) and its complement, the oversegmentation error rate (number of local-area differences with negative signs), for each segmented parcel sets.
## 5 Hybrid Network
### TSMP Modification  
The stationary network dropped off by the MAV implements a low power, low duty cycle TSMP mesh. As nodes are dropped off, they join that mesh, with the routing configurations updating accordingly. The frame length must initially be set short enough to incorporate new nodes into the network on a rate comparable to the deployment of the nodes by the MAV. After the MAV mission, this can be slowed down as necessary.  
Because the MAV is highly mobile, it will not maintain a constant set of neighbors, and so it is not used as a routing node in the mesh. It still listens for advertisement (ADV) packets to maintain synchronization with the network, but never sends out its own ADVs. Instead, it can send an acknowledgment packet (ACK) to an ADV to initiate communication into the network. After sending an ADV, stationary nodes listen to receive the ACK from a MAV. The advertisements are scheduled inversely proportional to neighbor count, so that on average the MAV would be able to hear one ADV every frame within a neighborhood without risking packet collisions. The frame length must be short enough so that the MAV remains within range of at least one node for the duration of a frame.  
An extra slot is added to the frame after the ADV slot for communication from the network to the MAVs. If a node received an ACK to its ADV in the previous slot, it can then confirm with the MAV during the following MAV slot that it will switch to MAV mode for the duration of that frame. The proposed method is very similar to a slotted ALOHA scheme [26]. If two MAVs respond to an ADV at the same time, a packet collision may occur. In this case, a randomized back-off timer is used to retry communication at a later time. Once a MAV receives a clear-to-send signal during the second slot of each frame (ACK slot) it enters one of two throughput modes.
## 3 Methodology
### The Space Sampler Module
#### 3.1.3 Triplet Data Sampling  
Features obtained from the HOG feature extractor are used for sampling triplet data, which were used as triplet pairs for the fine-tuning task. Triplet loss is used for the fine-tuning process when the triplet pairs are inputted, the triplet loss is an improvement on contrastive loss. The goal of triplet loss is to make the samples with similar features as close as possible in spatial position, while the samples with different features are as far away as possible in spatial position. And at the same time, in order to prevent the learned features of the samples from being aggregated into a very small space, it is required for two other samples to be inputted with anchor images, a positive example and a negative example. The negative example should be more distant than the positive example. We randomly sample three images from the training data each time, randomly selected one as the anchor image, and used Euclidean distance to calculate which of the remaining two images is closer to the anchor image and which one is farther. The definition of the sampler is shown in Equation (1):  
\[\left\{\begin{aligned} & x_{i}=x_{anchor}&&\text{ selected as }x_{i},\\ & x_{j}=x_{positive}&& if\ D\big{(}x_{i},x_{j} \big{)}<D(x_{i},x_{k})\\ & x_{k}=x_{negative}&& if\ D(x_{i},x_{k})>D\big{(}x_{i},x_{j} \big{)}\end{aligned}\right. \tag{1}\]  
where \((x_{i},\ \ x_{j},\ \ x_{k})\) is a triplet randomly choice from the training data. \(D\) stands for the distance function, which is set as a regular cosine similarity function. Other similarity metrics such as Euclidean distance can also be used for distance calculations. The labels 'anchor,' 'positive', and 'negative' are calculated by the distance between \(x_{i},\ x_{j},\ \text{and}\ x_{k}\). And the variables \(x_{anchor}\), \(x_{positive}\), and \(x_{negative}\) are used as one of the training pairs of the triplet training task on the CLIP model.