esa-sceva/satcom-chunk-collection huggingface dataset:
# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 4 Proposed Method
### Supervised Learning  
In CDAE-MN, we first utilize labeled data from the source domain to train a local convolutional denoising auto-encoder (CDAE). The CDAE consists of three sets of parameters: the encoder parameter \(\theta_{E}\), the decoder parameter \(\theta_{D}\), and the classifier parameter \(\theta_{\text{C}}\). The input sample \(x_{d}^{S}\in R^{L}\) corresponds to a one-dimensional time series vibration data of length \(L\). To introduce noise, we randomly deactivate elements of the input sample through dropout. The deactivation rate, denoted as \(\alpha\), is set to 0.5.  
Figure 4: Overall architecture of CDAE-MN.  
After encoding by the encoder, the resulting feature \(b_{a}^{S}\in R^{c*L^{f}}\) is passed to the decoder, generating the decoded feature \(d_{a}^{S}\). Simultaneously, this encoded feature is fed into the classifier, which applies the SoftMax activation function to produce the probability distribution vector \(p_{a}\in R^{C^{S}}\). This vector captures the probabilities associated with each fault type. The overall optimization function of supervised learning in CDAE-MN includes cross-entropy loss and reconstruction loss:  
\[\begin{split} L_{B}\Big{(}X^{S};\theta_{E},\theta_{D},\theta_{C} \Big{)}=&-\frac{1}{N^{S}}\sum_{a=1}^{N^{S}}\sum_{l=1}^{C^{S}} \mathbb{I}\Big{[}y_{a}^{S}=i\Big{]}\log\frac{e^{p_{a}(i)}}{\sum_{j}e^{p_{a}(i)} }\\ &+\lambda\cdot\frac{1}{N^{S}}\sum_{a=1}^{N^{S}}\Big{(}d_{a}^{S}-x_ {a}^{S}\Big{)}^{2},\end{split} \tag{1}\]  
where \(\mathbb{I}\) is an indicator function, \(P_{a}(i)\) representing the \(i\)-th element in the probability vector, \(y_{a}^{S}\) denotes the fault type label of the input sample from the source domain, \(C^{S}\) and \(N^{S}\) denote the total number of fault types and samples in the source domain, respectively, and \(\lambda\) is a hyperparameter that controls the relative importance of the reconstruction loss compared to the cross-entropy loss. It allows you to balance the contributions of both losses during the training process.  
In the local training process, we employ gradient descent with the Adam optimizer to update the parameters:  
\[\begin{split}\theta_{E}&=\theta_{E}-\alpha_{B} \
abla_{\theta}L_{B}(D^{S}),\\ \theta_{D}&=\theta_{D}-\alpha_{B}\
abla_{\theta}L_{ B}(D^{S}),\\ \theta_{C}&=\theta_{C}-\alpha_{B}\
abla_{\theta}L_{ B}(D^{S}).\end{split} \tag{2}\]  
where \(\alpha_{B}\) is the learning rate, and \(D^{S}\) is all data samples in the source domain.  
Then, the parameters of all participates will be transmitted to the centralized cloud server for aggregating using the following manner:  
\[\begin{split}\theta_{E}&=\frac{1}{M}\sum_{i=1}^{M} \theta_{E}^{s,i},\\ \theta_{C}&=\frac{1}{M}\sum_{i=1}^{M}\theta_{C}^{s, i},\\ \theta_{D}&=\frac{1}{M}\sum_{i=1}^{M}\theta_{D}^{s, i}.\end{split} \tag{3}\]  
Finally, the trained encoder of CDAE will be utilized for subsequent feature extraction.
# Deep Learning Architectures for Diagnosis of Diabetic Retinopathy
## 2 Materials and Methods
### Models
#### 2.5.1 CNN's Models: U-Net  
The first model we trained is the U-Net [16], which is a fully convolutional model. It is one of the most famous segmentation architectures and when it was released it obtained SOTA results in different segmentation tasks. It is still highly used in the medical field due to its good performance in situations where little data are available. U-Net benefits greatly from data augmentation techniques that allow to artificially increase the number of images available for the training process. Here we used the original architecture developed for medical image segmentation which has approximately 31M trainable parameters [16].  
We trained this architecture on our training set for 1000 epochs using an initial learning rate of 0.002, Adam optimizer, and a batch size of 2 images. We reduce the learning rate by a factor of two after not improving the validation DICE for 80 epochs. We found that the best hyperparameters for the loss function are \(\theta=1\) and \(\alpha=0\), which implies that for this model the best result is obtained when the DICE is not included in the loss function.
## 3 Hybrid Network Implementation Using LoRaWAN and LoRaMESH
### LoRaMESH  
Among the main network topologies for IoT is the MESH, where devices can connect directly with the central devices, in this case the coordinators (from the Coordinator) or passing through several routers until reaching the coordinator, this topology can be seen in the Figure 10.  
In this topology, the devices are able to reach great distances because of the jumps between the end-nodes until they reach the coordinator, however they lose in distance between one end-node and another. In this way, the point to point of this topology does not have great alnance, but make the mesh yes. In [122], the authors study this topology using LoRa technology through the LoRaMESH protocol. In this work the authors provide a review of LoRaMESH multihop proposals, additionally perform a comparative analysis and classification as well as discuss open questions and future directions for the use of this technology in various scenarios.
## 5 Conclusions  
In summary, ageing produces fundamental modifications in the colloidal structure of bitumen. Ageing also causes oxidation of bitumens and, consequently, increases the content of large molecules and the bitumen's molecular weight; these changes increase with ageing time. The two conventional ageing processes (RTFOT and PAV) modified the colloidal system of the bitumen, and both showed a certain level of asphaltene content. The RTFOT ageing, for different exposure times, changed the asphaltene structure in a consistent way, while the PAV ageing created a new colloid structural network. From the tangent loss and elastic modulus results, the rheological properties of the aged binders were found to be dependent on bitumen oxidation and changes in microstructures. The impact of these varies with the amount of ageing.  
From the optical microscopy results, we concluded that the microstructural assembly of asphaltenes in unaged and aged bitumen is different, which is consistent with our FT-NMR-SDC observations. The core of this investigation was focused on using FT-NMR-SDC to track changes in the chemical functionalities of an unmodified binder and four types of aged binders manufactured with the same bitumen subjected to RTFOT/PAV ageing conditions. Most interestingly, this technique showed strong ability to monitor the ageing processes and to highlight the structural differences induced during ageing. The AFM results indirectly confirmed what was obtained via the FT-NMR-SDC technique by showing that the size of asphaltenes in aged bitumens is increased when compared with the size of asphaltenes in unaged bitumen.  
Figure 7: AFM phase images of PB, BRTFOT-75, BRTFOT-150, BRTFOT-225, and BPAV.  
The latter results provide new information in addition to that already present in the scientific literature regarding the subject, providing a more complete picture of the ageing process, in particular, on how the ageing techniques used in the laboratory affect the chemical structure of bitumen. This new information can be used in future research for the creation of new rejuvenating additives that are even higher performance than those currently on the market.  
Conceptualization, M.P. and C.O.R.; Writing-Original draft preparation P.C. and D.S.; Writing-review and Editing M.P.; Formal analysis V.L. and M.P.D.S.; Investigation P.C. and M.P.D.S.; Supervision C.O.R. All authors have read and agreed to the published version of the manuscript. This research received no external founding.  
The authors declare no conflicts of interest.
# Spinel Magnetic Iron Oxide Nanoparticles: Properties, Synthesis and Washing Methods
## 2 Crystal Structure and Magnetic Properties of Spinel Iron Oxide  
In natural condition, iron oxide crystallizes according to four distinct structures:  
* Hematite \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) which crystallizes in a trigonal structure and in a space group R\(\overline{3}\)ch. In this compound, iron is at the oxidation state +III.
* Wustite Fe\({}_{1-x}\)O which presents a cubic structure according to the space group Fm\(\overline{3}\)m. This compound is most uncommon and is found almost exclusively in reducing environments. In this one, iron is mainly at the oxidation state +III.
* Magnetic Fe\({}_{3}\)O\({}_{4}\) which crystallizes in a cubic structure according to the space group Fd\(\overline{3}\)m. In this crystallographic structure, called spinel, iron is presented at the oxidation state +II and +III.
* Maghemite \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) which presents a cubic structure (Fd\(\overline{3}\)m or P4\({}_{1}\)32) or a tetragonal structure (P4\({}_{1}\)2\({}_{1}\)2). The structure of this compound is related to the spinel structure, iron is only presented at the oxidation state +III. Maghemite is obtained by sweet oxidation of magnetite.  
In this review, we will focus on magnetite and maghemite for their magnetic properties.
# A Two-Stage Local Path Planning Algorithm Based on Sampling and Optimization Methods
## 4 Simulations and Experiments
### Real Robot Experiments  
For the final validation experiment, tests were conducted using a Scout Mini robot platform. The same processor used in the simulation experiment was employed. A 5 m global path was designated, and a 30 cm \(\times\) 20 cm \(\times\) 50 cm obstacle was placed along this path. The experimental environment is depicted in Figure 9.  
The experimental results, as illustrated in Figure 10, show that the experiments on the real robot are consistent with the simulation results. Additionally, the test environment better reflects the real scene of robot operation. The results demonstrate the effectiveness of the PSF algorithm in complex and confined environments.  
Furthermore, verification experiments on typical tasks were conducted using the PSF algorithm. These tasks resemble the operational tasks of vacuum cleaning robots and disinfection robots, requiring traversal of the environment via a zigzag path. The experimental environment and global path are depicted in Figure 11.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Error (m)** \\ \hline PSF & 0.348 \\ DWA 10 Hz & 0.614 \\ DWA 0 Hz & 0.506 \\ TEB 10 Hz & 0.736 \\ TEB 0 Hz & 0.691 \\ TEB “Goal” & 0.449 \\ TEB “Path” & 0.411 \\ Number of iterations & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Errors.  
Figure 9: The experimental environment and robot platform.  
The robot departs from the origin and first reaches the starting point of the global path. It then traverses five segments of the path to reach the endpoint. Due to cumulative errors in odometry, there may be some influence on local planning. Therefore, for typical task experiments, we utilized high-precision laser odometry. This resulted in minor oscillations in the robot's motion trajectory. The experimental results are shown in Figure 12.  
It is evident that the robot, while achieving obstacle avoidance during typical tasks, also swiftly returns to the global path. Throughout this process, the robot ensures that its local path adheres to kinematic constraints. Thus, the PSF algorithm can meet the task requirements of various mobile operation robots, including reconnaissance robots, guiding robots, floor-cleaning robots, and disinfection robots. It is worth noting that this paper focuses exclusively on relatively simple global paths. Currently, the proposed PSF  
Figure 11: Experimental environment for typical tasks.  
Figure 12: Experimental results under the global path in typical tasks.  
Figure 10: Experimental results under a straight global path.  
algorithm does not support navigation tasks involving curved global paths. However, in future work, the PSF algorithm can be extended to accommodate curved global paths by using a transformation based on the Frenet coordinate system.
## 5 Conclusions  
In this study, we developed a method to improve the serial detection. All three models proposed use noise information to create the soft output for the inner detector in serial detection. However, the second and third models estimate noise information more precisely than the first model. The results showed that the second and third models significantly improved the BER performance. The proposed method also shows that it is less complex than the IRCSDF-GA algorithm. Instead of using the MAP algorithm to create the soft output, our model used the ML algorithm and added the noise information to create a soft output. In addition, the proposed model not only removes the 2D interference but also resists the effect of TMR and position fluctuation, because the neural network can learn and adapt to TMR effects and media noise, which have nonlinear characteristics.  
Finally, in the ideal case, our proposed model can achieve an approximately 1 dB gain at \(10^{-6}\) dB, which is evidently superior to the previously reported serial detection method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Mul/div** & **Add/sub** & **Exp/log** & **Sqrt** \\ \hline
**Our model** & 4291 & 4839 & 100 & 1 \\ \hline
**1 DNN [31]** & 12953 & 11087 & 18 & 16 \\ \hline
**8 DNN [31]** & 101013 & 86528 & 18 & 16 \\ \hline
**LAIP-BCJR [32]** & 291560 & 189279 & 257 & 0 \\ \hline
**CNN [32]** & 113761 & 102062 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The complexity for our proposed and other methods.  
Figure 23: BER of the channel with 15% TMR and 8% position fluctuation.  
**Author Contributions:** Conceptualization, T.A.N. and J.L.; methodology, T.A.N. and J.L.; software, T.A.N.; validation, T.A.N. and J.L.; formal analysis, T.A.N.; investigation, T.A.N. and J.L.; writing--original draft preparation, T.A.N.; writing--review and editing, T.A.N. and J.L.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2020-2020-0-01602) supervised by the Institute for Information & Communications Technology Planning & Evaluation (IITP).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Discussion  
This study presents the development of a compact four-element MIMO antenna array. A simple and easily replicable two-branch monopole structure was proposed. The two branches generate two distinct resonant frequencies, enabling dual-band operation. The resulting bandwidths are determined by the lengths of each monopole branch. To enhance the frequency response, a reactive load implemented with an open-circuited stub was fed back to one of the ground planes of the coplanar feed. With this integration, the imaginary part of the monopole impedance is compensated, approaching zero and stabilizing the real part over a wider frequency range. As a result, an ultra-wideband antenna is obtained. By arranging these elements in an axial symmetric configuration, a 4-element MIMO antenna is formed, maintaining the same bandwidth as the individual radiator and exhibiting high electromagnetic isolation. The overall dimensions are significantly smaller compared to conventional antennas. In summary, the combination of the T-stub and the two-branch monopole provides a compact array with high performance comparable to previously published works, which often involve larger dimensions or inferior MIMO array metrics.
# On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space
###### Abstract  
An effective characterization of gas flaring is hampered by the lack of systematic, complete and reliable data on its magnitude and spatial distribution. In the last years, a few satellite methods have been developed to provide independent information on gas flaring activity at global, national and local scale. Among these, a MODIS-based method, aimed at the computation of gas flared volumes by an Italian plant, was proposed. In this work, a more general version of this approach, named RST-FLARE, has been developed to provide reliable information on flaring sites localization and gas emitted volumes over a long time period for the Niger Delta region, one of the top five gas flaring areas in the world. Achieved results showed a good level of accuracy, in terms of flaring sites localization (95% of spatial match) and volume estimates (mean bias between in 16% and 20%, at annual scale and 2-9% in the long period) when compared to independent data, provided both by other satellite techniques and national/international organizations. Outcomes of this work seem to indicate that RST-FLARE can be used to provide, at different geographic scales, quite accurate data on gas flaring, suitable for monitoring purposes for governments and local authorities.  
gas flaring; remote sensing; MODIS; RST-FLARE; BTE; flaring sites detection; gas flared volumes computation +
Footnote †: journal: Sensors
# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 3 Driving Behavior Classification and Sharing System
### LSTM Cell Structure  
LSTM is composed of an input gate, an output gate, and a forget gate that combines short-term memory and long-term memory, solving the vanishing gradient problem of general RNNs [38]. Figure 2 shows the general structure of LSTM. LSTM has four interactive modules, and learning is performed through the following equations:  
\[f_{t}=\sigma(W_{f}\cdot\ x_{t}+U_{f}\cdot\ h_{t-1}+b_{f}) \tag{1}\]  
\[i_{t}=\sigma(W_{i}\cdot\ x_{t}+U_{i}\cdot\ h_{t-1}+b_{i}) \tag{2}\]  
\[\bar{C}_{t}=\tanh(W_{c}\cdot\ x_{t}+U_{c}\cdot\ h_{t-1}+b_{c}) \tag{3}\]  
\[C_{t}=f_{t}\otimes\ C_{t-1}+i_{t}\otimes\ \bar{C}_{t} \tag{4}\]  
\[o_{t}=\sigma(W_{o}\cdot\ x_{t}+U_{o}\cdot\ h_{t-1}+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}\otimes\ \tanh\left(C_{t}\right) \tag{6}\]  
where \(\otimes\) means Hadamard product; \(W_{f}\), \(W_{i}\), \(W_{o}\), \(W_{c}\), \(U_{f}\), \(U_{i}\), \(U_{o}\), \(U_{c}\) are the weight parameters; and \(b_{f},b_{i},b_{o},b_{c}\) are the bias vectors of corresponding operations. Here, the values of  
Figure 1: The proposed 4-layer CNN-2 stacked LSTM-V2X system for driving behavior classification and transmission.  
the weight and bias vectors are determined through the network training process. \(C_{t}\) is called cell state or long-term memory. \(h_{t}\) is called hidden state or short-term memory. The LSTM input \(x_{t}\) is concatenated with \(h_{t-1}\), the hidden state of the previous time step. The forget gate \(f_{t}\) of (1) is a gate for forgetting past information. The value sent by the forget gate is the value that takes \(h_{t-1}\) and \(x_{t}\) and takes the sigmoid \(\sigma\). Here, the output range of the sigmoid function is between 0 and 1, and as it gets closer to 0, the value of the previous state is not used. On the other hand, if the output of the sigmoid function is close to 1, it maintains the value of the previous state, affecting the prediction of the current state. The input gate \(i_{t}\otimes\ \tilde{C}_{t}\) is a gate for memorizing the current information. \(i_{t}\) and \(\tilde{C}_{t}\) are expressed in (2) and (3), respectively. \(i_{t}\) and \(\tilde{C}_{t}\) affect the update of cell state and hidden state through the Hadamard product. If the output value of the Forget gate \(f_{t}\) is 0, the Hadamard product result with the previous cell state \(C_{t-1}\) is also 0. In this case, only the result of the input gate is used to determine the current cell state \(C_{t}\) in (4). \(o_{t}\) in (5) is the value of the input \(x_{t}\) at the current time and the hidden state at the previous time \(h_{t-1}\) passed the sigmoid function. In the output gate, \(C_{t}\) passes through the hyperbolic tangent and is determined as a value between \(-1\) and \(1\), and the Hadamard product with \(o_{t}\) is performed. Finally, \(h_{t}\) in (6) is determined the hidden state at the current time and is used the output of the LSTM cell at the current time.
## 3 Results
### Sea-ice Concentration Evaluation  
#### 3.2.1 Sentinel-2 Sea-Ice Concentration  
Seventy-nine Sentinel-2 scenes are used for constructing the reference Sentinel-2 SIC dataset. Figure 6 shows one example scene from the Kara Sea on 12 March 2019. The RGB image (Figure 6a) shows that there are regions of open water (dark), regions which have been recently overfrozen (greyish) and regions with thicker ice (bright). Treating the thin-ice class as water results in a number of open-water areas within the ice pack for the Sentinel-2 SIC (see Figure 6c). These areas are shown as 100% SIC if the thin ice is treated as ice (Figure 6d). A large open-water lead and an area of smaller ice floes in the north-western corner of the scene are still represented as reduced SIC. The mean of the Sentinel-2 SIC is reduced from 99.6% to 88.1% if the thin ice is included compared to if it is excluded and the standard deviation increased from 3.3% to 23.6%, respectively. Later, we will compare both cases to the merged, MODIS and AMSR2 SIC.  
Next, we present the time-series of all Sentinel-2 scenes in Figure 7. The mean thin-ice SIC is 94.4%, while the mean of the thick-ice SIC is 87.3%. The mean SIC standard deviations within one scene are 9.2% (thin-ice SIC) and 13.2% (thick-ice SIC). In scenes with 100% SIC, the thin- and thick-ice SIC is equal, i.e., there is no thin ice. In all other cases, the thin-ice SIC is higher, i.e., there is thin ice.
## 3 Results
### Analysis of Characteristics of Cutting Volume by Cutting Conditions
#### 3.2.3 Comparison of Measured Cutting Volume and Calculated Cutting Volume  
The simple calculation method, one of the methods of measuring the cutting volume in the linear cutting test, is calculated from the cutting conditions and number of cuts, as shown in Equation (2).  
\[V_{cal}=s\ \times\ p\ \times\ l\ \times\ (n\ -\ 1) \tag{2}\]  
where \(V_{cal}\) is the calculated cutting volume, \(s\) is the spacing, \(p\) is the penetration depth, \(l\) is the cutting length, and \(n\) is the number of cuts.  
Because the simple calculation method is calculated under the assumption that all rocks between the cutters are removed, there is a problem that the cutting volume is overestimated if the cutter spacing increases more than the optimum spacing [10; 32; 33; 38].  
Figure 12 shows the variation in the ratio of the measured cutting volume to the calculated cutting volume according to \(s/p\). This ratio was between 1.2 and 0.2, regardless of rock strength, and had a significant correlation with \(s/p\) (R\({}^{2}\) = 0.641). When cutting with a disc cutter, this ratio reaches its maximum value, where the \(s/p\) ratio is greater than 5 [10; 32; 33; 38]. However, according to the regression curve in this study, this ratio reached its maximum value at an \(s/p\) of approximately 1.5. This shows that when the spacing between the cuts is close to 1.5 times the penetration depth, the rock between the cutting lines is removed at or deeper than the penetration depth. However, when the spacing between cuts is greater than 1.5 times the penetration depth, ridges are expected  
Figure 10: Relationship between cutting volume and penetration depth.  
to be generated between the cutting lines. The details of the measured cutting volume, calculated cutting volume, and ratio of the measured to calculated cutting volumes are listed in Table 7.  
Figure 11: Relationships between cutting volume and \(s/p\) ratio; (**a**) \(p\) = 3 mm, (**b**) \(p\) = 6 mm, and (c) \(p\) = 9 mm.  
Figure 12: Relationship between \(s/p\) ratio and measured to calculated cutting volume ratio.