esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Transfer Learning Approach for Identifying Slope Surface Cracks
## 2 Methods  
### Overview  
In this paper, we propose a deep transfer learning approach to effectively and efficiently identify slope surface cracks for early monitoring and warning of geohazards, such as landslides.  
First, the deep learning models are pretrained for the identification of slope surface cracks, and the models are compared and analyzed to screen for an optimized model. Then, a transfer learning strategy is utilized. A small sample dataset of surface cracks in soil and rock masses is established and combined with a large sample dataset of concrete cracks. The pretrained models are applied to the identification of soil and rock masses' slope surface cracks, thereby constructing deep learning models for the identification of slope surface cracks.  
The workflow of the proposed deep transfer learning approach is illustrated in Figure 3.  
**Step 1**: Collecting and processing the concrete crack dataset and pretraining deep learning models for the identification of slope surface cracks.  
1. Collecting and compiling a large sample dataset of concrete cracks.
2. Using convolutional neural network models (e.g., LeNet5, AlexNet, LeNet5 model using InceptionA and InceptionE modules, ResNet18, MobileNet) and Vision Transformer to identify concrete cracks. Establishing a series of pretrained deep learning models for identifying cracks in soil and rock masses slopes.
3. Enhancing the data of each model. Comparing and analyzing the accuracy and calculation efficiency of each model. Analyzing the application range of each model and screening for an optimized model to prepare for the identification of soil and rock masses' slope surface cracks.  
**Step 2**: Sorting out and labeling the dataset of surface cracks in soil and rock masses. Using a transfer learning strategy to obtain refined deep learning models for the identification of slope surface cracks.  
Figure 3: Workflow of the proposed deep learning approach for the identification of slope surface cracks.  
1. For the difficult-to-obtain datasets of surface cracks in soil and rock masses, collecting, sorting, and labeling relevant data. Designing and producing corresponding surface cracks in soil and rock masses small sample datasets and applying them to the identification of soil and rock masses' slope surface cracks.
2. Employing a transfer learning strategy, the training set of large sample dataset of concrete cracks is combined with the training set of the small sample dataset of surface cracks in the soil and rock masses slopes. A series of pretraining models established are employed for deep learning training to obtain a high-precision deep learning model for UAV aerial survey that can be applied to the identification of surface cracks in slopes to provide fast monitoring and early warning of geological disasters.  
Step 1: Construction of Pretrained Deep Learning Models for the Identification of Slope Surface Cracks
## 5 Evaluation
### Evaluation Methodology  
**Experimental testbed:** We establish an experimental testbed with three physical servers, each of which is equipped with two 88-core Intel Xeon E5-2699 v4 processors, 64 G RAM, and one Intel C62x PCIe QAT card. Each server runs on CentOS 7 (Linux kernel 4.8.12) and is equipped with a QAT 1.7 hardware driver for Linux. Finally, we connect these servers via an Intel 40 GbE NIC.  
**Experimental dataset:** The Calgary dataset [31] was created at the University of Calgary. Since then, the Calgary dataset has gradually become most widely used in the field of compression, especially text compression. To evaluate the compression system practically, it consists of nine different types of text data, including typical English writing (e.g., bibliography and news), computer programs, transcripts of terminal sessions, and so on.  
**Performance metrics:** We mainly compared four configurations: no-swap, ZRAM (LZO), ZRAM-QAT (QAT direct offloading mode), and QZRAM. No-swap disables the swap partition, which is the foundation of this evaluation. ZRAM means that the OS uses ZRAM as the swap device, which configures the LZO algorithm to compress data. ZRAM-QAT means that the operating system selects ZRAM as the switch device and changes the compression algorithm to the QAT hardware-assisted GZIP algorithm. QZRAM represents the modification of the single-threaded write process to a multi-threaded write process in the asynchronous offloading mode.
# Wavefront Shaping and Its Application to Enhance Photoacoustic Imaging
## 3 Photoacoustically-Guided Wavefront Shaping (PAWS)
Since the camera can only detect optical intensity, they employed an interferometric approach to simultaneously retrieve both phase and amplitude information of each output mode, with some unmodulated portions as the reference while modulated input modes varying from 0 to 2\(\pi\) independently on the SLM. If all possible input modes on the SLM are visited, the transmission matrix bridging the input and output matrixes can be extracted. With the TM being acquired, arbitrary optical focusing or delivery to any location(s) within the camera's sensing area can be obtained simply by inversing the transmission matrix. Similar to the use of PA signal as feedback in iterative wavefront shaping method, a method called photoacoustic transmission matrix wavefront shaping was developed, using linear PA signal amplitudes as the measured output corresponding to each individual input mode (Figure 5a) [PERSON] et al. (2016). Since photoacoustic signals do not interfere with each other like photons, in order to extract the phase and amplitude information, the authors introduced a new method to get equivalent phase of the output modes to obtain the transmission matrix: the phase of all input modes was set to be identical and scanned from 0 to 2\(\pi\) in 16 steps; each individual photoacoustic signal amplitude from a ultrasound transducer varied and followed a cosine function-like pattern (Figure 5b), from which the photoacoustic transmission matrix can be retrieved. As a result, multiple optical foci can be formed simultaneously by displaying the inversion of the transmission matrix on the SLM screen. Figure 4: Left: schematic of the experiment. Right: (**a**) the initial optical speckle pattern; (**b**) focusing light onto one focus by measuring the TM with 256 controlled units on the SLM; (**c**) the norm of the focusing operator: each line represents the desired output focusing signal on CCD camera; and (**d**) an example of focusing light onto multiple foci (Figure reproduced from ref. [PERSON] et al. (2016)).
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### AI-Enabled Interference Mitigation
#### 4.4.1 AI-Enabled Radio Access Network Intelligent Controllers  
As part of the open RAN alliance, which is closely aligned with 3 GPP's architecture, the integration of near-RT and non-RT RICs is proposed [193]. These controllers use AI/ML models to optimize interference-related functions such as beam management, resource block allocation, and LB [194]. AI agents in RICs continuously monitor interference levels and make autonomous decisions to mitigate it without manual reconfiguration, thus enabling intelligent and adaptive interference coordination [19].
The robustness of the healthy model to the random noise is improved because the distribution probability, instead of a single value, is taken into consideration. 4. The TPA-LSTM-based PDI prediction model is constructed to realize the accurate degradation trend prediction as the basis for predictive maintenance of the FTU. The rest of this article is organized as follows. The framework and procedures of the proposed approach are explained in detail in Section 2. The proposed method is applied on a large practical FTU, and the results are presented in Section 3. The performance of the proposed data imputation model and the trend prediction model is emphatically compared and discussed in Section 4. Finally, the conclusion is given in Section 5.
# On-Orbit Experimental Result of a Non-Deployable 430-MHz-Band Antenna Using a 1U CubeSat Structure
## 5 Testing on the Ground
### Reflection Coefficient  
It is known that a Loop Hentenna with nothing inside has an input impedance of approximately 50 \(\Omega\), but when mounted on a CubeSat, it does not match 50 \(\Omega\) because of the satellite components inside. Therefore, it is matched to 50 \(\Omega\) using a matching circuit placed in the -X panel. This adjustment was carried out in the following way:  
1. Make a through connection in the matching circuit; (L = 0 \(\Omega\), C = no implement in Figure 7)
2. Measure the input impedance by VNA (Rohde & Schwarz, ZNB 20);
3. Calculate the inductance L and capacitance C for the matching circuit;
4. Put L and C on the matching circuit;
5. Measure the input impedance by VNA;
6. Fine tune the values of L and C by looking at the Smith chart;
7. Repeat step 6 until the reflection coefficient is less than \(-10\) dB.  
The results of the adjustments made to the FM of the Japanese satellite Tsuru are shown in Figures 7 and 8. The downlink frequency of BIRDS-4 is 437.375 MHz.  
Figure 6: BIRDS-4 -X panel: (**a**) front; (**b**) back; (**c**) matching circuit and RF connector.  
Figure 7: Schematic of the matching circuit. Balun: TC-1T+ made by Minicircuits. Inductor: L-15C15 NJV4T made by Johanson Technology. Capacitor: 251R14S4R7 BV4T made by Johanson Technology.  
The inductor and capacitor were L603 DS and S603 DS, respectively, from Johanson Technology. The result of \(Z=138.2+\mathrm{j169.7}\)\(\Omega\) at 437.375 MHz gives 44.31 nH and 3.85 pF as the inductor and capacitor values. We first chose 47 nH and 3.9 pF, but the reflection coefficient was not less than \(-10\) dB at 437.375 MHz. Then we tuned the values checking the Smith Chart of VNA. Finally, we came to the values 15 nH and 4.7 pF. The difference from the initial estimate (44.31 nH and 3.85 pF) is probably due to parasitic inductance and capacitance of the wiring. The reflection coefficient was \(-2.4\) dB before putting the matching circuit, but it was improved to \(-36.5\) dB by adjusting the matching circuit.
## 5 Conclusions  
In this paper, we have proposed: (1) a priority-based layered schedule, enabling LLRs to be frequently updated when pipeline conflicts occur, and (2) double update queues that separately update LLRs of overlapping and non-overlapping submatrices, for reducing pipeline conflicts. The increase in percentage of updated LLRs per iteration is up to 31% compared with the state-of-the-art work. Therefore, the performance loss decreases from 0.6 dB to 0.2 dB. The throughput rises to 2.85 Gbps when the SNR is equal to 5.9 dB.  
Considering that the consumed LUTs, FFs, and 36k BRAMs only account for 24%, 10%, and 7% of the FPGA device xc7 vx690t, respectively, for one QC-LDPC decoder core, it is expected that a higher throughput can be obtained easily through a multi-core architecture. Certainly, a higher-end FPGA device for UltraScale+ series has more resources and can embed more LDPC decoder cores. A 13-core LDPC decoder with four iterations can achieve a throughput beyond 100 Gbps at 6.9 dB. The multi-core decoder will be implemented and verified on the UltraScale+ FPGA board in the real-time communication systems in the future work.  
Conceptualization, Y.L. ([PERSON]) and J.Z.; methodology, [PERSON] ([PERSON]) and Y.L. ([PERSON]); software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]), N.Y. and T.C.; formal analysis, Z.W.; investigation, [PERSON] ([PERSON]); resources, J.Z.; data curation, Z.W.; writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]), [PERSON] ([PERSON]), N.Y. and J.Z.; supervision, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Key Research and Development Program of China (2021 YFB2900800), the Science and Technology Commission of Shanghai Municipality (Project No. 20511102400, 20 ZR1420900) and 111 project (D20031).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Numerical Model Validation
In the initial stage of the cylinder entering water, the fluid splashes around the cylinder, forming a thin water curtain, as shown in Figure 7b. As the cylinder continues to sink, an open cavity forms and gradually grows above the cylinder. Simultaneously, the thin water curtain also gradually expands to stand vertically. Owing to the large initial splash velocity of the fluid, the tail of the thin water curtain eventually separates from the thin water curtain, forming a number of small splashed droplets, as shown in Figure 7c. As the cavity continues to expand, the free surface of the fluid forms four obvious cross lines along four corners of the cylinder when observed from the top of the computational domain. Two pairs of cross lines at the left and the right ends of the cylinder shrink inward faster than the other parts of the free surface. Two cross lines at each end approach each other and eventually merge to form a new cross line at the corresponding end, as shown in Figure 7c-f. The two newly formed cross lines continue to approach each other along the axial direction at a faster speed, as shown in Figure 7f-h. The phenomenon observed above is typically referred to as cavity inversion [46]. The remarkable consistency of the results achieved from this work and the reported experimental work proves that the mathematical model and the SPH numerical method developed in this work can accurately and efficiently capture all the physical phenomena of the water entry problems of 3D entities. Figure 6: Time evolutions of the penetration depth \(y-y_{0}\) (**a**) and vertical velocity \(u_{y}\) (**b**) of the cylinder achieved from this numerical work and the reported experimental studies [44; 45].
## 4 Design and Results of the Experiments
### Datasets and Environment of the Experiments  
The datasets are preprocessed before the experiment, including image graying, face detection and extraction, and resizing into 48 \(\times\) 48 images. The datasets involved include FER2013 [30], FERPlus [31], CK+ [32], RAF-DB [33], KDEF [34], and JAFFE [35]. FERPlus [31] is a multi-label dataset formed by relabeling based on FER2013 [30]. In order to ensure the effectiveness of the noise detection effect test, in this paper, a total of 7942 samples which are in the original seven emoticon categories and with 10 voters voting for are selected from FERPlus as the correct label sample dataset. The correct sample labels of 10%, 20%, and 30% are randomly modified as noises and recorded as datasets FERpIUS-10, FERPLUS-20, and FERPLUS-30, respectively.  
The operating system for the experiment is win10, the CPU is Intel i7, and the running memory is 16G. The GPU is NVIDIA GeForce RTX 2070 with 8G video memory.
# A Novel Ring Spring Vibration Isolator for Metro Superstructure
## 4 Analysis of Vibration Isolation Structure
### Vibration Response
#### 4.2.2 Comparison between Acceleration and Frequency  
Under the action of the measured ground vertical acceleration caused by the operation of the metro, the same position of the ordinary structure and the vibration isolation structure is selected as the observation point. The vertical acceleration response value of the column above the vibration isolator is extracted for analysis. For brevity, we just select the vertical acceleration and spectra curve of the first, third and sixth floor (Figure 15). The conclusions are drawn as follows:  
(1) Under the excitation of metro vibration, the vertical acceleration amplitude is about 30 mm/s\({}^{2}\). The propagation of vibration on the floor is wavy, which increases first and then decreases and then increases. The acceleration of the top layer is the maximum. The main frequency of vibration is 30 Hz to 50 Hz, which is the same as the input vibration acceleration.  
(2) Under the excitation of metro vibration, the vertical acceleration amplitude of the vibration isolation structure is about 15 mm/s\({}^{2}\). The propagation of vibration on the floor is a translational type, and the amplitude is basically unchanged. The main frequency of vibration is 5 Hz to 15 Hz.  
(3) After the ring spring vibration isolator is installed, the vertical acceleration of the structure is significantly reduced by about 50%. The main frequency of the structure vibration is reduced, and the vibration of the 30 Hz to 50 Hz component is filtered. At the same time, the vibration of the resonance frequency part is amplified (5 Hz to 15 Hz). The ring spring vibration isolator can play a role in vertical vibration isolation, effectively improving the comfort of the structure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency** & **Conventional** & **Vibration** \\
**Direction** & **Structure** & **Isolation Structure** \\ \hline First vertical & 13.86 & 9.76 \\ Second vertical & 15.39 & 14.42 \\ First horizontal & 1.30 & 1.23 \\ Second horizontal & 1.39 & 1.25 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Structure natural frequency (unit: Hz).
# Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis
## 1 Introduction
### Capturing Intelligent Transport System Data to Develop Machine Learning Applications
These limitations in the development and updating of the traffic simulation model, as well as in the collection of data for the integration of ML applications for the effective operation of VTS within the ITS, necessitate the creation of a single adaptive SMTF. This model should provide data for analysis and development of ML models and adapt to the dynamics of real traffic flow. This paper proposes a solution to develop SMTFs that closely align with real-world PTS. The methodology involves a hybrid regression calibration of a macroscopic model using SUMO data, leveraging real-time video stream analysis with the ML algorithm to capture vehicle characteristics within the modeled area. The paper is structured as follows: Section 2 outlines how to represent PTS dynamics within a VTS and details the process for creating and calibrating the proposed SMTF. Section 3 introduces a method for analyzing video streams and extracting relevant data. Section 4 presents simulation results demonstrating the algorithm's performance in calibrating a macroscopic traffic model with new data from the PTS. Additionally, it showcases an experiment assessing the calibrated SUMO model's alignment with real PTS based on video stream data and connected car sensors.
## 4 Experiment Results  
The architecture of the experiment system using ultrasonic sensors and the real experiment platform are shown in Figure 8. As shown in the left figure of Figure 8, there are one transmitter (red) and five receivers (blue) mounted on the edge of a \(60\times 38\) cm\({}^{2}\) platform with a U-shaped sensor rack. The ultrasonic sensors used in this system are Pro-wave 400 PT160, which is a transceiver with a center frequency of 40 KHz such that they can operate as the transmitter or the receiver [35]. The NI data acquisition (DAQ USB-6366) is connected between the computer and all sensors. The DAQ is used to drive the transmitter by a computer with a rectangular burst consisting of eight cycles, acquire the raw data of analog echo signal from sensors, and convert these analog data into digital data and send them to the computer. In the following subsections, there are three cases to be tested such that:  
1. The localization target is a stationary marker pen that stands vertically on the platform at measured coordinates. This testing object is stationary and has a smooth surface.
2. The localization target is one human index finger stationary pointing vertically to the platform at measured coordinates. This testing object is stationary and has an uneven surface.
3. The system will track one moving human finger with different trajectories, and the index finger is pointing vertically to the platform. This testing object is moving and has an uneven surface.  
There are 700 testing frames in Case 1 and Case 2 and 300 testing frames in Case 3.  
Figure 7: Simulation results of the TOF estimation with different SNRs.
# An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments
## 3 Distributed Computational Framework
### Framework Specification
This second advantage comes into play especially with complex applications and where there are many users to be monitored, for example, the number of players of a football team is eleven, in a running race there could be hundreds of athletes. As a result, the proposed framework creates an intelligent BAN to enhance the processing of advanced healthcare applications and could improve the quality of medical monitoring and health analysis. This approach transforms the application into a distributed application represented by the directed graph A, and which can be processed along the set of devices \(\mathcal{D}\). For example, Figure 3b-d illustrate some configurations of distributed computation. Rather than just send the data to the medical staff computers, these scenarios allow a distributed processing of the application. In the case Figure 3b, the first three tasks can be performed by the devices within the BAN and show some data which can be obtained directly from the acquired data to the user, for example, heartbeat count and its evolution along the time in a graph. In the case Figure 3c, further processing is performed to obtain elaborated and precise information before sending them for anomaly detection. The configuration Figure 3d depicts a scheme where two external devices to the BAN come into play: the computation begins inside the BAN, a middle device performs a single task and the rest of the work is processing in other device. Other scenarios can occur depending on the characteristics of the deployed infrastructure and the application decomposition. Figure 3: Diagram of a SD detection application in our proposed IoT-based framework. (**a**) List of tasks and general flow; (**b–d**) Distributed Configurations. 3. Deployment and calibration of the system  
In the distributed system, each task can be run on a different device with different capabilities. To establish the most suitable computer where to run each task, a set of factors must be taken into account: computing resources, battery consumption, network bandwidth availability and latency, etc. In addition, most of these factors could vary over time; for example, the free network bandwidth. For each computer or computer profile, the resources defined in the previous section need to be configured. After that, the performance of the key devices under each profile and context application should be carefully measured. It is necessary to check that the device has always enough capabilities for meeting the requirements. In addition, the distributed application must offer results as expected, switching data flows between devices according to defined data-flow and distributed configurations. The resource planning step should be reviewed and adjusted until the overall system works as expected; proper system modelling and simulation can help in the successful completion of this step.