esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 7 Conclusions  
QKD is a secure communication method that is unconditionally so due to quantum mechanical properties. A multi-wavelength QKD system with a PUF increases the amount of polarization states that can be used to encode data. The additional states can eliminate the need for [PERSON] and [PERSON] to share a subset of their final key to check for eavesdropper presence. We can also add a PUF for enhanced security, since any hackers must possess the physical PUF device to complete the protocol. To implement the protocol, extracting ternary and quaternary messages from a PUF was necessary. We proposed several methods to accomplish and support this, including the concept of bitwise transform and addition modulo 3 and modulo 4. To test the protocols, we worked with Thorlabs to build a custom, two-wavelength version of their QKD emulation educational kit, and presented our work on automating the kit and integrating the PUF and full protocol. We tested the emulation system errors for the ternary protocol (without changing unused states) with only [PERSON] and [PERSON] implemented. Finally, we outlined the next steps of this research effort.  
Conceptualization, B.C. and J.H.; methodology, B.R. and M.P.; emulation hardware, B.R. and I.B.; emulation software, B.R., M.P. and D.G.; PUF hardware, I.B. and M.A.R.; PUF software, M.P., I.B. and M.A.R.; validation, B.R. and M.P.; investigation, B.C., J.H. and B.R.; resources, B.C., J.H., B.R., I.B., M.A.R.; writing--original draft preparation, B.R. and M.P.; writing--review and editing, B.C.; supervision, B.C. and J.H.; project administration, B.C.; funding acquisition, B.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Information Directorate under AFRL award number FA8750-19-2-0503.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Teleportation-Based Error Correction Protocol of Time-Frequency Qubit States
## 2 Time-Frequency Qubit States
### Time-Frequency GKP State
The complex parameters \(\alpha\) and \(\beta\) where the quantum information is encoded, the frequency width \(\sigma,\kappa\), and the periodicity of the state \(\overline{\omega}\). If the state is not too noisy, meaning that \({}_{a}\langle 0_{\omega}|1_{\omega}\rangle_{a}\sim 0\), then the normalization condition of Equation (12) is given by \(|\alpha|^{2}+|\beta|^{2}=1\). Finally, in [1; 4], we define the time-of-arrival and frequency operators, which do not commute and verify the Heisenberg algebra. This is mathematically equivalent to the non-commutativity of time-frequency displacement operators. This property leads to consider the temporal and frequency bandwidth as quantum noise at the single photon level. The GKP states which are defined as the sum of squeezed states in a given mode [9; 40] are designed to be robust against small shifts in position and momentum, which can be caused by a Gaussian quantum channel, but they are also robust against photon losses [40]. On the other hand, time-frequency GKP states are designed to be robust against small shifts in time and frequency. The major difference between GKP states and time-frequency GKP states is that photon losses do not result in errors for the latter. In general, temporal errors are the dominant source of errors, while frequency is considered a robust degree of freedom, as it is barely affected by linear physical processes. Additionally, GKP states can be used for fault-tolerant universal quantum computation [20]. Due to their mathematical similarity with time-frequency GKP states, it is expected that they will lead to the same mathematical result. However, the generation of non-Gaussian states using the degree of freedom of a single photon is relatively simple to implement experimentally. As a result, the experimental implementation of a time-frequency GKP state is considered straightforward. In contrast, creating entanglement gates between two single photons is a more challenging task. For particle-number sensitive encoding, non-Gaussian operations involving the quadrature degree of freedom can be difficult to implement, while two-mode Gaussian operations, such as with a beam splitter, are easier to perform.
# An Advanced Soil Classification Method Employing the Random Forest Technique in Machine Learning
## 3 Unified Soil Classification System (USCS)
### Assignment of Group Symbols and Names  
A two-letter symbol is assigned to the soil based on its classification. For instance, GP signifies poorly graded gravel, SW signifies well graded sand, CL denotes low plasticity clay, and CH indicates high plasticity clay. The USCS offers a systematic and standardized methodology for soil classification, crucial for applications in geotechnical engineering, construction, and other fields requiring an understanding of soil properties and behavior.  
For coarse grained soils, the symbols are as follows: GW for well graded gravel, GP for poorly graded gravel, SW for well graded sand, SP for poorly graded sand, GM for silty gravel, GC for clayey gravel, SM for silty sand, and SC for clayey sand. For fine grained soils, the symbols are: ML for low plasticity silt, MH for high plasticity silt, CL for low plasticity clay, CH for high plasticity clay, OL for organic silt/clay with low plasticity, OH for organic silt/clay with high plasticity, and PT for highly organic soils such as Peat.
## 3 Results and Discussion
### Phase 3: Calibrate the Prototype  
As detailed in the previous section, the calibration of the prototype was performed through iterations with regression lines. In successive calibrations, better and better fits were obtained. The last calibration determined that the error between the measurements obtained by the calibrated equipment (KIMO) and the prototype sensor device was less than the principal milestone of the project: less than 10%. Absolute indicators as RMSE as well as relative ones as MAPE could be used to check this. Therefore, after this process, a reliable prototype was obtained whose performance is comparable to other commercial or even clonic devices on the market such as those studied by [PERSON] et al. [77].  
Figure 13: Location of three the points selected for the CO2 concentration measurement.
## 3 Vibration Source Tracking Based on Various Types of Image Features  
In this research, the tracking target is the change in the image of vibration signals. To track the target, three types of features were extracted from the image: Texture features, intrinsic features and shape features. The shape features were obtained by the histogram of gradient directions, while the intrinsic features were acquired by GoogLeNet (Google, San Francisco, CA, USA). Then, the particle filter was adopted to track the target on the image based on the shape and intrinsic features. Subsequently, the greyscales of the original image pixels were adjusted by the SURF matching algorithm. The salient pixels were given relatively high greyscales. The adjustment helps improve the feature extraction results. The flow of the vibration source tracking is shown in Figure 3.  
As shown in Figure 3, the vibration sources were tracked in the following steps:  
* Step 1: The first frame (\(f_{1}\)) of the image was sampled and the target and background templates were obtained manually; the target template was obtained by shape feature extraction, intrinsic feature extraction, and SURF feature extraction.
* Step 2: Let \(f_{i}\) be the \(i\)-th frame of the image (\(i=2,3,\ldots,\) n). 1. The candidate sample sets were obtained through random generation of sampled particles. 2. A set of SURF features (\(S_{i}\)) was established to reflect the target positions from \(f_{i}\) to \(f_{i-1}\). After matching, the SURF feature point mapping matrix (\(W_{i}\)s) was obtained. The grayscale of the original image was multiplied by 0.7 and then increased by 0.3 at the feature point position, forming the updated samples. 3. The shape and intrinsic similarities (\(\rho_{i}\)) between each candidate sample and the target template were calculated, respectively. 4. The confidence was obtained for each particle, and the particle with the highest confidence was determined as the target position of \(f_{i}\).
* Step 3: If the update condition was satisfied, the target and background templates were resampled. If not, let \(i=i+1\) and return to Step 2.
# Inductive Imaging of the Concealed Defects with Radio-Frequency Atomic Magnetometers
## 4 Optimum Coupling between rf Primary Field and the Object  
The coupling between the rf field and the electrically conductive object is characterised through the skin depth (also referred to as standard penetration depth), i.e., depth the rf field penetrates the object at which its amplitude drops to \(e^{-1}\) of its surface value. The skin depth is a function of the rf primary field frequency, electrical conductivity and magnetic permeability of the object. For skin depths larger than the thickness of the object, the coupling between the rf primary field and the object is weak; and the rf primary field is not significantly affected by propagation through the object. In the context of MIT, the object signature is imprinted in the phase of the field, while the amplitude remains relatively unchanged. An example of this case is when carrying out the inductive imaging of biological objects due to their extremely low electrical conductivity [6]. The opposite case, i.e., skin depth smaller than the object thickness, marks the regime of strong coupling. Detection of the structural defects within the objects requires significant strength of the object response (secondary field) created by the defect. For conductive objects, this translates into the optimum density of the eddy currents at a specific depth, which is a function of the operating frequency. The balance between the rf field penetration depth within the object and the minimum secondary field amplitude required raises the issue of an optimum coupling between the rf field and the object. In our studies, we tune the coupling strength by changing the operating frequency of the magnetometer, i.e., the magnetic resonance and the rf primary field frequencies, over the range of 0.45 kHz to 10 kHz.  
In order to study the optimum coupling between an rf primary field and the object, we performed a series of measurements that approximate real-life defects with both simplified and more complex structures. In order to show the basic properties of the defect signature, we perform simple tests with the edge of the plate. We also discuss the influence of the overlapping signatures from opposing edges of a circular recess in a plate. Subsequently, we present the signals generated by the gradual change in the thickness of a plate, i.e., slope, rather than a sharp edge used in the previous two cases. Finally, we demonstrate measurement of the signatures of the cavities (drilled holes) at different depths that run parallel to the surface of the plate (Figure 1).
# Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method
## 4 Experimental Verification and Analysis
### Experiment II
#### 4.2.2 Experimental Settings  
The sample length used in this example is 1024. There are 3900 samples in total in the dataset, 300 samples for each fault type (a total of 13 fault categories). Of the samples, 70% are used as the training set and 30% as the testing set. \(P_{a-b}\) is used to represent the transfer task from working condition \(a\) to working condition \(b\) in this experiment. In order to better reflect the generalization ability of the model, the diagnostic model parameters used in this experiment are consistent with Experiment I. Baseline, MMD, MKMMD, CORAL, DANN, and ADDA are selected as comparison models to demonstrate the MPGCTN's performance. The feature mining network is also used as a Baseline for direct transfer.
## 2 Materials and Methods
### Data Analysis
#### 2.3.2 Image Quality
The random choice of reference frames prevented bias and ensured the inclusion of reference frames from different time points in the gait cycle. For every reference frame, the ten highest CW-SSIM indices were considered, since every acquisition contained minimally ten gait cycles, and averaged. Finally, for every kilometer, the maximum of the ten averaged CW-SSIM indices was taken, to obtain the representative CW-SSIM index. Frames showing probe-skin contact loss were excluded from the field-of-view stability analysis. Figure 3: Probe-skin contact. (**A**) An example of an ultrasound image obtained during probe-skin contact loss. The right part of the frame shows a total reflection artifact, caused by a big difference in acoustic impedance between the ultrasound probe and the air. At the left side, some probe-skin contact is still preserved. (**B**) A representative example of an acquisition during which probe-skin contact loss occurred. Probe-skin contact was defined as an ultrasound frame consisting of less than 80% black pixels. This example was acquired during the first kilometer of volunteer 9. As shown in the subfigure, contact loss occurs cyclically during the same phase of the gait cycle, which was the case in all volunteers with probe-skin contact loss. (**C**) The percentage of frames within the acquisition that was obtained with preserved probe-skin contact. In four subject, probe-skin contact was preserved during the entire measurement. In the other five volunteers, the probe was in contact with the skin during 43–100% of the gait cycle.
# Marine Diesel Engine Fault Detection Based on Xilinx ZYNQ SoC
## 2 Methods
### Universal Communication Protocol  
This section introduces both generic and custom communication protocols. The data obtained are solely used to verify the connection between the sensors and the ZYNQ device, not for input into the algorithm model. The focus is on facilitating mutual communication from the sensor to ZYNQ to the supervisory computer, with the algorithm dataset employing more authoritative public datasets.  
There are various sensors inside a ship's engine room, each supporting different communication protocols and data formats, such as I2C, SPI, and CAN bus protocols. Therefore, it is necessary for the SoC to adapt and handle different communication protocols and ensure stable and reliable data exchange with the sensors. If the SoC merely receives data and directly uploads them to the supervisory computer, it could lead to data confusion and loss. The supervisory computer would also be unable to properly command specific devices, resulting in one-sided communication. Economically, it is also impractical to dedicate one SoC to a single sensor. Consequently, the integrated communication protocol designed in this paper can integrate most protocols. Besides the protocols supported using the chip, it can use an IP core based on the AXI (Advanced eXtensible Interface) bus standard provided by Xilinx or the AXI_GPIO IP core to simulate communication protocols. For instance, in this design, the I2C is simulated to communicate with the MPU6050 module using the I2C protocol.  
A universal communication protocol is used for interaction between the ZYNQ and various shipboard devices. Given the wide range of equipment on board, the communication protocols applied to the ship according to the categories in this paper's dataset include I2C, SPI, and serial communication protocols. These standard protocols are used to connect the devices with the ZYNQ. Once the ZYNQ is connected to the devices, the collected data are stored within the ZYNQ. At this point, a custom communication protocol comes into play. The collected data become a message body, which, after processing using the internal programs of the ZYNQ, results in a header. This header and message body are then transmitted to the supervisory computer via LoRa, as illustrated in the flowchart in Figure 3.  
Figure 3: Communication protocol flowchart.  
Upon receiving a custom protocol command from the PC, the ZYNQ processes this command to determine the destination of the message before forwarding it. Conversely, upon receiving messages uploaded from the device, it processes these messages and uploads them to the PC using the custom protocol. The PC side also has custom protocol parsing code to analyze and identify the source of the messages, thus facilitating mutual communication between the devices, ZYNQ, and PC.
## 3 Noise Measurement and Reduction in Mode-Locked Lasers
### Comb-Line Noise
#### 3.3.1 Comb-Line Noise Measurement Methods
This phenomenon indicates that \(\mathbf{n}\times f_{\textit{rep}}\) and \(f_{\textit{ceo}}\) noise to some extent cancel out, resulting in a lower level of comb-line noise spectrum. The complex coherence was measured to be \(-2\) in a wide range of Fourier frequency in commercial OFC from Menlosystems [123], 25-GHz Er:Yb glass lasers [103], NALM fiber mode-locked lasers [45], and mid-infrared quantum cascade lasers [104] soliton molecules [124]. The technological development in phase measuring algorithm also promotes the radio signals' phase noise analysis. In 2016, [PERSON] et al. proposed a digital phase-measuring algorithm [125]. Based on this algorithm, a phase noise analyzer with high precision could be built using a two-channel FPGA. \(f_{\textit{rep}},f_{\textit{ceo}}\), and \(\mathbf{v}_{\mathrm{n}}\) phase noise PSD of an OFC could be characterized with high precision using this home-built analyzer. Compared with commercial bulky phase noise analyzers, this phase noise analyzer is much simpler and low-cost.
## 4 Case Study
### Performance Discussion
In spite of the overall shape being very similar, the PPO algorithm in the eight-rack instance presents a delay of about 50,000 timesteps, making the slope of learning progress much less steep. Also, the decrease in mean reward after reaching the plateau (signifying an exploratory phase of the training) is much deeper in the eight-rack instance for the same reasons provided for the uneven curves in the DQN and A2C algorithms. However, even if the learning curves are similar (or almost equivalent in the DQN and A2C implementations), this does not mean that the quality of the learning is the same as in the validation use case, as can be seen in Table 3.  
Figure 7: Comparison of the normalized mean reward evolution during training for the three RL algorithms in both the validation environment (four racks) and the performance environment (eight racks). As in the validation use case (four racks), the RL agents managed to correctly allocate the incoming items, keeping the warehouse throughput at its optimal value, which is the same as the greedy heuristic (see Algorithm 3). Nonetheless, the final travelled distance is greater than that of the greedy heuristic in all cases (and, as expected, always smaller than the random heuristic), with the DQN implementation being the one that provided the best results in terms of distance. For the four-rack validation instance, the A2C implementation managed to find a policy that, while keeping the optimal throughput, also reduced the travelled distance with respect to the greedy heuristic (see Section 4.5). In the extended instance (eight racks), due to the increased observation and action space, finding such a favorable policy was much more complicated. In any case, this study demonstrates that the learning capacity is maintained for the proposed RL algorithm, even under significant modifications to the environment (i.e., doubling the number of racks). The impact in performance due to a change in the environment is not straightforward to quantify due to the number of factors involved, and the difference between different RL implementations. Furthermore, it is common practice in the RL community to treat each problem instance as a problem on its own, with the necessary parameter tuning and reward calibration to achieve a satisfactory learning. In our case, a careful comparison has been carried out by keeping all factors equal except the number of racks available (which was doubled), finding that the performance is maintained in terms of throughput, but that the travelled distance performance, measured as the difference between the algorithm result and the greedy algorithm result), dropped for all RL implementations. The greatest loss in performance was for the A2C algorithm, with a 31% reduction, followed by the PPO algorithm, with a 23% reduction. Finally, the DQN algorithm, with the use of deep neural networks and a longer exploratory phase, maintained performance, with only a 3% drop in performance.
# Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior
## 4 Analysis of Examples
The maximum and average MAPE of the forecasting within one day and the corresponding cluster numbers are shown in Table 4; apparently the forecasting error is at its minimum when \(K=4\), and it can be seen that \(K=4\) is the best number of clusters and the maximum of load forecasting accuracy can be obtained. Through comparison of experiments A and B, we can see that we can increase the load forecasting accuracy if we cluster the user with consideration of the date type, and then perform load forecasting to obtain the partial system load forecasts and aggregate them to obtain the system load forecast. We can conclude that when the similarity between user electricity behaviors is mined deeply, clustering is conducted, and load forecasting is made on the basis of clustering, we can obtain a more precise accuracy of load forecasting. Through comparison of experiments B and C, in the traditional ELM forecasting model, the historical data will be repeatedly trained together with new data when new data are obtained, while OS-ELM effectively avoids the repeated training data. In this way, the new data will correct the parameters, therefore OS-ELM greatly improves the training speed and the forecasting accuracy by using the partitioned matrix method. According to the above study, for the proposed sample construction method and forecasting model for the chosen data set, the forecasting accuracy is at its maximum when the users are classified into four clusters. The clustering result of the normalized user loads is shown in Figure 9. \begin{table}
\begin{tabular}{c c c} \hline \hline \(K\) & **MAX-MAPE (\%)** & **MID-MAPE (\%)** \\ \hline \(K=1\) & 7.15 & 4.56 \\ \(K=2\) & 6.63 & 3.99 \\ \(K=3\) & 6.23 & 3.48 \\ \(K=4\) & 5.52 & 3.07 \\ \(K=5\) & 6.67 & 3.79 \\ \(K=6\) & 7.19 & 4.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The maximum and average MAPE of load forecasting under different clustering conditions in experiment C.  
Figure 9: The effect pictures as the number of clusters is four. (**a**) The first cluster; (**b**) The second cluster; (**c**) The third cluster; (**d**) The fourth cluster. The good clustering effect is shown in Figure 9; the red line is the center line of clustering, and the other colored lines are the normalized load curves of different users. It can be seen that the load curves of users who are from the same cluster are very similar and the load curves of users who are from various clusters are vastly different.