esa-sceva/satcom-chunk-collection huggingface dataset:
# Fourier Ptychographic Reconstruction Method of Self-Training Physical Model
## 4 Experimental Results and Analysis
### Construction of Data Sets
#### 4.1.2 Construction of Real Data Set  
For the field of image super-resolution, the most common method of establishing a data set is to collect a large number of images with better accuracy, and downsample these images to obtain the corresponding true values and inputs. In Fourier ptychographic microscopy, the number of low-resolution images is closely related to the size of the LED array under the FPM system, but the original data are at least hundreds of images. If these images correspond to the neural network, there will be a multi-dimensional tensor with a certain number of channels, and the convolutional layer in the neural network needs to extract the information of the image from the input. These channels will greatly increase the number of network parameters, resulting in a too large network size and reconstruction failure.  
At present, there are two ways to build FPM data sets. The first one is to directly collect the low-resolution image of FPM to generate the input of the whole network model, and then use the high-resolution complex amplitude obtained by the traditional FPM reconstruction algorithm as the output. This method of directly collecting low-resolution images and using them as input is simple and intuitive, and can cover any error encountered in actual imaging. However, this method has a series of shortcomings: (1) The way to obtain the true value is obtained by the traditional FPM reconstruction algorithm. However, the traditional reconstruction algorithm cannot escape the idea of the gradient descent algorithm in iterative updates, which has great limitations. (2) The number of data sets to be collected is too large to obtain such a large data set. The second method is to directly use the simulation image and generate the simulation results through the Fourier stack microscopic imaging model. In order to avoid the first method of collecting data sets, this paper uses the second method to construct FPM data sets.  
We directly select the image as the truth value, and then generate the input corresponding to the true value through the Fourier ptychographic microscopy imaging process. Firstly, 400 high-resolution images are collected, and then 1600 sets of complex amplitudes are obtained by random combinations of phase and amplitude as the truth value of the whole network. Using these complex amplitudes, the input of the network is obtained by Fourier ptychographic microscopy imaging and the synthesis of dual-channel images. The pre-processed image is randomly cropped to obtain 25,600 sets of input data and true values. The process is shown in Figure 9.  
Figure 8: Synthetic input diagram.
# Automatic Identification of Landslides Based on Deep Learning
## 3 Materials and Methods
### U-Net  
U-Net is a semantic segmentation network proposed by [PERSON] in the ISBI Cell Segmentation Competition in 2015. It utilizes a U-shaped network structure to capture contextual information and location information. It was initially used to solve medical image segmentation problems, especially cell-level segmentation tasks, and was gradually used to solve problems in other fields [33].  
The network structure of U-Net, which is an encoder-decoder structure, is shown in Figure 1.The encoder utilizes the idea of stacking convolutional layers, downsampling the feature map through convolution and pooling, and performing four total pooling operations. After each stacking convolution layer operation, the size of the feature map is halved and, at the same time, the pooling result of each step is passed to the decoder; in the decoder, the feature map is first upsampled or deconvolved and then concatted on the channel with the previous feature map of the same size. Convolution and upsampling is then performed, and after upsampling four times, an output result of the same size as the original image is obtained.
###### Abstract  
Road maintenance systems (RMS) are crucial for maintaining safe and efficient road networks. The impact of climate change on road maintenance systems is a concern as it makes them more susceptible to weather events and subsequent damages. To tackle this issue, we propose an RMSD (Road Maintenance Systems Using Deep Learning and Climate Adaptation) technique to improve road maintenance systems based on Deep learning and Climate Adaptation. RMSDC aims to use the multivariate classification technique and divides the dataset into training and test datasets. The RMSDC combines Convolutional Long Short-Term Memory (ConvLSTM) techniques with road weather information and sensor data. However, in emerging nations, the effects of climate change are already apparent, which makes road networks particularly susceptible to extreme weather, floods, and landslides. Therefore, climate adaptation of road networks is essential, especially in developing nations with limited financial resources. To address this issue, we propose an intelligent and effective RMSDC that utilizes deep learning algorithms based on climate change predictions. The ConvLSTM block effectively captures the relationship between input features over time to calculate the root-mean deviation (RMSD). We evaluate RMSDC performance against frameworks for downscaling climate variables using two metrics: root-mean-square error (RMSE) and mean absolute difference. Through real evaluations, RMSDC consistently outperforms approaches with a reduced RMSE of 0.26. These quantitative results highlight how effective RMSDC is in addressing maintenance systems on road networks leading to proactive road maintenance strategies that enhance traffic safety, reduce costs, and improve environmental sustainability.  
deep learning; road maintenance systems; climate change; sensors data; RMSDC technique; multivariate classification; ConvLSTM +
Footnote †: journal:  
minimized while maintaining safe driving conditions and an uninterrupted traffic flow is essential. Deep learning, an artificial intelligence technique that uses reinforcement learning to teach artificial neural networks to perform complex tasks, has achieved superhuman intelligence in various challenging applications [5]. This makes it a suitable tool for complex systems like weather and traffic. Even if human-induced greenhouse gas emissions stopped today, the signs of climate change caused by human activity are already evident and expected to worsen [6]. In February 2018, the concentration of carbon dioxide in the Earth's atmosphere reached over 407 parts per million, the highest recorded in the past 650,000 years. Furthermore, the average global temperature has risen by 1.8 degrees Celsius since 1880 [7]. The long-term effects of human-induced climate change are still uncertain. However, they are predicted to include sea-level rise, heatwaves, more frequent and severe storms, altered precipitation patterns, and increased floods and droughts in some areas [8]. As the world continues to warm, these consequences will likely intensify and place more stress on social and ecological systems [9].
## 3 Comparison of mAP According to Data Pre-Processing Proficiency
### Learning Results Analysis  
Figure 4 shows the results for the learning construction waste image labeled in accordance with a professional level of knowledge, as indicated in mAP. For graph visualization, mAP was showed every two epochs, which means the number of times the entire training data was used. The graph shows the calculated mAP per 10,000 iterations and its estimated tendency as an average value. Contrary to the first hypothesis that stated that the performance of the model learned with a dataset labeled by the professional would be better, the non-professional's learning model was superior. As shown in Table 1, the mAP was 21.75 higher for the box and 26.47 for the mask on average. There are two hypotheses for this result. Firstly, it was surmised that the non-professional's labeling method was similar to that of the COCO dataset, which is the YOLACT model's learning dataset. Secondly, non-professionals tended to annotate a much larger area in images, as shown in Figure 3, and due to this, the parts that overlapped with the predicted mask obviously increased, which led to an overall surge in the mAP. Section 4 further explores this. Moreover, the non-professional's mAP, which had been consistently increasing, reached a peak in 20-30 epoch/2 before falling again, and this tendency was only observed in the non-professional's learning model. This may explain a general problem with the gradient descent observed in the learning process, with the mAP increasing along with the trend and not influencing the model performance. Furthermore, the mAP labeled by the non-professional exhibited much larger variation and was unstable after 40 epoch/2, as compared with that of the professional. The unstable mAP tendency may have been due to an annotation error resulting from the lack of knowledge of the non-professional, but we could not confirm a cause.  
Figure 4: mAP by users: (**a**) mAP of box; (**b**) mAP of mask.
## 5 V2X Technology in the Context of SAE Levels of Autonomy
### A Method for Assessing the Applicability of V2X at Various Levels of Automation  
Assessing the applicability of V2X technology in different use cases of automated driving may prove challenging due to the complex interaction of the automated system and the environment. In order to employ a systematic approach in the planning phase of system design, this paper proposes a mapping of applicability of V2X technology in 6 major areas of interest, as shown in Table 1. The main investigated areas are:* Smart infrastructure--the contribution to the dynamic driving task based on information from the static and dynamic environment;
* Driving strategy--long- and short term trajectory and path planning and navigation;
* Decision making--high-level driving logic behind the automated driving function;
* Collaborative driving--actuation commands and warning signals based on information sharing between the vehicles;
* Driver support--driver assistance in actuation and decision making, provided that control remains in the hands of the driver;
* Driver warning--information sharing about the environment and the status of the vehicle with the purpose of early warning.  
Table 1 summarizes the main contribution of V2X technology to the dynamic driving task at various levels of automation. According to the applicability matrix, the nature of information transmitted by the communication network largely depends on the engagement of the human driver in the specific ODDs. At lower LoAs, V2X technology supports the driver with warning signals and increases traffic safety in ADAS functionalities, while at LoA 3-5 the emphasis of shared information is pivoting around collaborative and efficient traffic flow in line with the infrastructure and other traffic participants.  
As it is shown in Section 6, the analysis of the dynamic driving tasks and mapping of components to the applicability matrix above, the design of the V2X system is possible at the function level, with special attention on the 6 main focus areas. This paper provides and example of this mapping to LoA 4 valet parking applications based on the method proposed, applying the SAE levels of autonomy context for V2X technology, provided in Section 5.1.
## 3 Review of Portrait Drawing Learning Assistant System
### Drawing Exactness Evaluation Method
#### 3.6.1 Normalized Cross-Correlation  
This approach utilizes the _normalized cross-correlation_ (_NCC_) algorithm [22] to assess the similarity between a user's drawing and the provided facial photograph. _NCC_ is a reliable method for assessing the resemblance between two signals and is especially well-suited for the processing of images [23; 24; 25; 26]. As a widely used similarity measure in matching tasks, _NCC_ stands out for its simplicity and effectiveness [27]. From a technical perspective, _NCC_ identifies matching points between the template and the image by locating the maximum value within the image matrices [28; 29]. This algorithm offers a robust framework on the pixel-by-pixel evaluation of likeness, primarily focusing on accurately reproducing the shapes and positions of facial features. The _NCC_ calculation formula is given as follows [30]:  
\[NCC=\frac{\sum_{x,y}[T(x,y)\cdot I(x+x^{\prime},y+y^{\prime})]}{\sqrt{\sum_{x,y}T(x,y)^{2}\cdot\sum_{x,y}I(x+x^{\prime},y+y^{\prime})^{2}}}\]
## 2 Technical Background
### Millimeter-Wave Wireless Communication Technology  
mmWave operates within the frequency range of 30 to 300 GHz [47]. These waves are a high-frequency band offering wide bandwidth, facilitating high-speed and high-capacity communications. Additionally, since a wide bandwidth is allocated, it can accommodate more terminals or higher traffic volumes [10]. However, a notable drawback is their sensitivity to interference from rain, fog, and other obstacles, resulting in significant distance attenuation [11]. While 5G currently operates Frequency Range 2 (FR2), such as 26.5-29.5 GHz (band n 257), which is categorized under submillimeter waves, this paper focuses on harnessing the 60 GHz band--a license-free spectrum that is ready for implementation [48; 49].  
The 60 GHz band was originally used for motion sensors and radar with high-precision positioning functions, and there are also data communication standards that utilize the 60 GHz band, such as IEEE802.11 ad/ay [50; 51; 52]. Research is also being conducted on IEEE802.11 ad communications utilizing the 60 GHz band for the introduction of 5G in urban areas [53]. In this paper, we utilize IEEE802.11 ad communication standard devices for our experiments, which will be discussed in Section 3. The basic design of IEEE802.11 ad is shown in Appendix A. IEEE802.11 ad uses a directional communication method with beamforming to suppress attenuation and other drawbacks caused by high frequency bands [54]. IEEE802.11 ad includes a function to adjust beamforming according to the procedure shown in Figure 2[47]. This determines the beam direction and improves directivity as following steps.  
1. The AP transmits a beacon signal, varying the beam direction. The UE receives this beacon signal using semi-omni-directivity.
2. The operations of Step 1 are repeated, but with the transmitter and receiver exchange their roles.  
In Steps 3 and 4, the communication between the AP and UE is established using the directivities determined, respectively, in Steps 1 and 2, which maximizes the expected received signal strength.
# Detection of Arc Faults in Transformer Windings via Transient Signal Analysis
## 4 Findings
### Simulation Results
Figure 4:0 Time-dependent variation of arc current and voltage. After recording the current and voltage signals obtained from the transformer terminals for 40 ms across five different fault point cases, the FFT was applied to the signals. The spectrum of the transient currents and voltages for these points are presented in Figures 10 and 11, respectively. The frequency spectrum reveals several transient frequencies generated in addition to the fundamental 50 Hz. The frequencies with the largest amplitudes, apart from the fundamental frequency, in the current and voltage spectra are 3550 Hz for Case I, 2750 Hz for Case II, 2500 Hz for Case III, 2350 Hz for Case IV, and 2200 Hz for Case V. Thus, by processing only the current-voltage information at the transformer terminals, it is possible to detect arc faults in the transformer winding and predict fault locations. Figure 4:1 Frequency spectrum of transformer HV side phase-B voltage: (**a**) Case I, (**b**) Case II, (**c**) Case III, (**d**) Case IV, (**e**) Case V.  
Figure 4:2 Transformer HV side winding phase-B terminal current and voltage. The FFT spectrum of the currents and voltages of arc faults at five different locations is analyzed for fault detection, and the frequencies with the highest amplitude, excluding the fundamental frequency, are presented in Figure 4:3 These data indicate that higher amplitude and higher frequency components are generated when the fault point is closer to the transformer terminals. For example, the frequency for the Case I arc fault near the transformer input terminals is 3550 Hz, while the frequency for the Case V arc fault is 2200 Hz. Additionally, it is observed that the arc-generated frequencies in the FFT spectrum change less as the arc fault occurs farther from the transformer terminals. Figure 4:4 Frequency spectrum of the transformer HV side phase-B current: (**a**) Case I, (**b**) Case II, (**c**) Case III, (**d**) Case IV, (**e**) Case V.  
The electric arc is a nonlinear electric circuit element, making it normal for the voltage and current waveforms to be non-sinusoidal. The waveforms of the voltages and currents also change and generate harmonics when the loads on the transformer secondary are non-linear. Nonlinear loads typically generate the 3 rd, 5 th, and 7 th harmonics, corresponding to frequencies of 150 Hz, 250 Hz, and 350 Hz, respectively [33; 34]. While harmonics at higher frequencies may occur due to nonlinear loads, they are not exclusive to them; these higher harmonics often accompany the 3 rd, 5 th, and 7 th harmonics [35; 36]. Additionally, the frequencies generated by the arc range from 2 kHz to 4.5 kHz, indicating that the transformer's internal arc and distortion caused by nonlinear loads can be identified by examining the frequency spectrum.
## 4 Security and Performance Analyses
### Security Analyses
#### 4.2.9. Compliance with HIPAA Privacy/Security Regulations  
1. Patients' understanding: The patient \(Pat\) has signed privacy contract \(w\) during the registration phase, which clearly states how \(MCS\) will use, store and access \(PHI\).
2. Confidentiality (Threat model 2): This subsection concerns three phases--uploading of the patient's \(PHI\), access of the patient's \(PHI\) and emergency-exception handling. In the uploading of the patient's \(PHI\), the doctor \(Doc\) checks the patient's authorization through \(MCS\). \(Doc\) and \(MCS\) generate the key \(Cer_{D}\) by performing the extended chaotic Diffie-Hellman key exchange to ensure the security of \(Pat\)'s \(PHI\). In the patient's \(PHI\) access phase, the doctor \(Doc\) checks \(Pat\)'s authorization \(P_{p}\) through \(MCS\). \(Doc\) and \(MCS\) generate the key \(SK_{D}\) by performing the extended chaotic Diffie-Hellman key exchange to protect \(Pat\)'s \(PHI\). In the emergency-exception-handling phase, the doctor \(Doc\) checks \(Pat\)'s identity through \(MCS\). \(Doc\) and \(MCS\) generate the key \(SK_{D}\) by performing the extended chaotic Diffie-Hellman key exchange to ensure the security of \(Pat\)'s \(PHI\).
3. Patient's control of \(PHI\): Patient \(Pat\) generates an authorization \(P_{P}\) and sends it to \(MCS\). \(MCS\) checks the authorization \(P_{P}\) that \(Pat\) gives \(Doc\). Then, \(Doc\) negotiates the encryption key \(SK_{D}\) with \(MCS\) and securely accesses \(Pat\)'s \(PHI\), which is encrypted with \(SK_{D}\). Therefore, \(Pat\) must authorize access control to patient information \(PHI\).
4. \(PHI\) integrity (Threat model 4): The proposed scheme ensures the integrity of the medical-record information during the transmission of \(PHI\) by checking the confirmation message \(V_{4}=h(ID_{D}\|SK_{D}\|R_{D}\|IND_{PHI}\|PHI)\).  
5. Consent exception: When the patient has signed a privacy contract \(w\) during registration and an emergency or special situation arises, _Doc_ is authorized to access the patient's medical records or health information _PHI_ from _MCS_. First, _Doc_ and _MCS_ realize mutual authentication by verifying \(\mathit{Cer}_{D}\), where \(\mathit{Cer}_{D}=h(T_{\mathit{T}_{D}\cdot\mathit{MCS}}(x)\mathit{mod}\ p\|T_{S 1})\), \(r_{D}\) is the _Doc_'s private key and \(r_{\mathit{MCS}}\) is the private key of _MCS_. Then, _Doc_ and _MCS_ generate the session key \(SK_{D}\) by using the chaotic-map-based Diffie-Hellman key exchange to ensure the security of _PHI_, where \(SK_{D}=h(T_{a\cdot b}(x)\mathit{mod}\ p)\). Therefore, the proposed scheme provides emergency-exception handling to protect the patients' life and rights.
## 2 System Architecture and Requirements
### Self-Interference Canceler Architecture  
Prior works have proposed different ways of generating the SI cancellation signal. In [18], the SI canceller consists of a variable phase shifter followed by a programmable gain amplifier. Cascading the two functions degrades noise and linearity performances and raises the power consumption. An alternative solution to generate the cancellation signal requires the use of a passive RC-CR quadrature splitter followed by two programmable gain amplifiers [27]. A single-stage RC-CR can be used since a high precision quadrature is not required for this application. Nonetheless, the quadrature splitter loads the driving stage and increases the noise. In this work, the generation of the cancellation signal was achieved by merging the quadrature splitter with the variable gain functions. The canceller acts as two transconductors that generate and in-phase current signal (I) and a quadrature current signal (Q), as shown in Figure 3. Both I and Q are generated by independently programmable transconductors. Hence, the vector sum of these currents can be set to be equal in magnitude and opposite in phase to the SI current at the receiver input.  
An important observation from [27] is that the quadrature precision has relaxed requirements. In fact, assuming that the vector signals (I and Q) generated by the transconductors are not in perfect quadrature but have a phase error \(\Delta\Phi\), as illustrated in Figure 4a. The result is that the sum vector will be I + Q\({}^{\prime}\) instead of I + Q. Since both I and Q are independently programmable, it will be possible to adjust the magnitude of I and Q\({}^{\prime}\) such that the desired vector is generated. However, the phase error will reduce the maximum magnitude that can be generated from A to A\({}^{\prime}=\text{Acos}(\Delta\Phi)\). So, if a 5% range reduction is acceptable, the maximum quadrature error will be \(\text{arccos}(0.95)=18.2^{\circ}\).