esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultrasonic Nondestructive Testing Image Enhancement Model Based on Super-Resolution Imaging
## 2 Theory and Method
### Charbonnier Loss  
In image super-resolution tasks, the loss function can help the model optimize parameters. The most common loss functions are pixel-level loss L1 loss and L2 loss. L2 penalizes larger errors more, but is less sensitive to smaller errors. In practical applications, L1 loss is better than L2 loss in terms of performance and convergence. However, pixel-level loss usually causes the generated super-resolution image to ignore some high-frequency detail information, making the image too smooth and poor in human perception. We use Charbonnier loss to build our model. The advantage of Charbonnier loss is that it can better handle outliers and reduce artifacts, thereby improving the imaging quality of super-resolution. The formula of Charbonnier loss is as follows:  
\[Charbonnier\_Loss(y,\overline{y})=\frac{1}{N}\sum_{i=1}^{N}\rho(y-\overline{y}) \tag{12}\]  
\[\rho(x)=\sqrt{(x^{2}+\epsilon^{2})} \tag{13}\]  
where \(N\) is the number of training samples in each batch, \(y\) is the actual HR image, \(\overline{y}\) is the HR image generated by super-resolution, and \(\epsilon\) is a constant; we set it as \(1\times 10^{-3}\).
# Operation Risk Assessment of Urban Dense Cable Passageway Based on Fuzzy-Analytic Hierarchy Process
## 4 Calculation Results and Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Standardized Layer** & **Indicator Layer** & **Weight Value** \\ \hline \multirow{6}{*}{Cable body risk parameters} & Overheating of cables & 0.1456 \\  & Ageing of cables and accessories & 0.0928 \\  & Overloaded operation & 0.2224 \\  & Mechanical damage & 0.0585 \\  & Grounding issues & 0.0373 \\  & Chemical corrosion factors & 0.0249 \\ \hline \multirow{6}{*}{Cable channel risk parameters} & Blocked or poorly ventilated passageways & 0.0462 \\  & Cable channel overcapacity & 0.0994 \\  & High and low voltage with channel laying & 0.0166 \\  & Irregular cable laying & 0.0279 \\  & Defective access facilities & 0.0090 \\  & Fire hazard & 0.1098 \\ \hline \multirow{6}{*}{Risk parameters for the environment outside the cableway} & Proximity to oil and gas pipelines & 0.0496 \\  & Groundwater level and humidity & 0.0096 \\ \cline{1-1}  & Risk of external damage & 0.0230 \\ \cline{1-1}  & Improper maintenance & 0.0185 \\ \cline{1-1}  & Transportation and building construction & 0.0050 \\ \cline{1-1}  & Urban climatic conditions & 0.0039 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Assessment of the weights corresponding to the 18 elements of the methodological indicator layer. Five typical cable channels were selected in a region of the China Southern Power Grid for risk assessment. The risk monitoring results of each cable channel indicator layer are shown in Table 6.  
Therefore, the five cable channel operation risk assessment results score \(F\) are 0.4000, 0.1975, 0.3841, 0.1024, and 0.2460, respectively. Based on Equation (22), it can be seen that channel 2 and channel 4 have a low risk, channel 5 has a general risk, and channel 1 and channel 3 have a medium risk. For low-risk channels, in accordance with the requirements of differentiated operation and maintenance, the cable channel of the line carries out regular inspections, and hidden dangers and defects are promptly entered into the system for reporting and processing. For general-risk and medium-risk channels, we regularly carry out cable channel group combustion group explosion risk emergency disposal drills to eliminate the occurrence of cable line fire due to cable line failure resulting in large-scale power outage accidents. For high-risk channels, power outages should be immediately overhauled.
# Evaluation of Artificial Intelligence-Based Models for Classifying Defective Photovoltaic Cells
## 2 Materials and Methods
### Materials
In the training phase, the cells used were labeled according to their potency (measured through the I-V curve) and with the following criteria:  
* Group 1: Power \(\geq\) 95%;
* Group 2: 80% \(\leq\) Power \(<\) 95%;
* Group 3: Power \(<\) 80%. According to the measured I-V curves, the classification of the cells in the three proposed groups was as follows:  
* Group 1: 0, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 28, 30, 31, 33, and 53;
* Group 2: 14, 17, 26, 27, 29, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 51, 52, 54, 55, 56, 57, and 58;
* Group 3: 1, 4, 6, 45, 49, 50, and 59. For the test phase, the classifiers only used the \(EL\) images, which were perfectly applicable in the field in common \(PV\) modules. I-V curves were only used in the model training in order to classify the cells based on their production efficiency, and this was possible thanks to the customized \(PV\) solar module, which clearly differentiates this research from previous work.
## 3 Key Parameter Design
### Power Loss of Transformer  
Allowable loss can be calculated as:  
\[\begin{array}{ll}P_{fe}=P_{\Sigma}-P_{cu}=&\left(\frac{P_{a}}{\eta}-P_{o} \right)-\left(P_{pcu}+P_{scu}\right)=\left(\frac{P_{a}}{\eta}-P_{o}\right)- \left(I_{p}^{2}R_{p}+I_{o}^{2}R_{s}\right)\\ &=\left(\frac{P_{a}}{\eta}-P_{o}\right)-\left[\left(\frac{P_{a}}{V_{in}\eta} \right)^{2}(MLT)\left(N_{p}\right)\frac{\text{uH}\Omega}{\text{cm}}10^{-6}+ \left(\frac{P_{a}}{U_{a}}\right)^{2}(MLT)\left(N_{s}\right)\frac{\text{uH} \Omega}{\text{cm}}10^{-6}\right]=3.02\text{ W}\end{array} \tag{20}\]  
where \(P_{\Sigma}\) is the total loss (W), \(P_{cu}\) is the total copper loss (W), \(P_{pcu}\) is the copper loss of the primary side (W), \(P_{scu}\) is the copper loss of the secondary side (W), and \(MLT\) is the average length of a single turn (cm).  
The actual loss can be calculated as:  
\[P^{\prime}_{fe}=\frac{W}{kg}\times W_{ffe}=2.63\;\mathrm{W} \tag{21}\]  
where \(W_{ffe}\) is the weight of allowable iron loss (kg).  
Obviously,  
\[P^{\prime}_{fe}<P_{fe} \tag{22}\]  
The loss in the unit area can be calculated as:  
\[\Phi=\frac{P^{\prime}_{fe}+P_{cu}}{A_{s}}=0.025\;\mathrm{W/cm^{2}} \tag{23}\]  
The temperature increase in the transformer is less than 20 \({}^{\circ}\)C in this case. The coiling method of the transformer additionally obtains a smaller distributed capacitance in layer coiling mode than in slot coiling mode, which has just a couple of nF.
## 5 Deployment and Integrability
### Local Web Dashboard  
A lightweight web front-end can enable clinicians to drag-and-drop single images (mainly JPG/PNG, but DICOM could also be accepted) on an intranet page. Inference would run on the local computer, entirely on the CPU, using Tensorflow-Lite [88] or similar web-deployment machine-learning frameworks.  
Although much slower than inference on GPU or cloud services, this approach provides an alternative workflow in resource-constrained scenarios where PII/PHI cannot be transmitted to external services.
## 3 Results and Discussion
### Measurement of Properties
#### 3.2.4 Determination of Particle Size and Distribution in Test Medium  
The absolute value of the surface charge of ZnO dispersed in DI water was 52.35 mV with NOM and 36.47 mV without NOM, indicating that the absolute value above 30 mV was high enough to be completely dispersed [44] (Figure 3).  
Figure 4: Fourier-transform infrared spectra of ZnO NPs with/without NOM.  
Figure 3: Zeta potential of ZnO NPs with/without NOM. Values on the graph represent mean \(\pm\) standard deviation.  
The average particle size analyzed by DLS was 36.5 nm, suggesting a stable dispersion. Aggregation was not detected even after 96 h of exposure in the state of being dispersed in DI water; the particle size was maintained at 46.4 nm. Further, the size of ZnO with NOM was maintained at 44.4 nm without aggregation after 96 h of exposure (Figure 5a).  
The surface charge of the ZnO dispersed in OECD algae growth test medium was 0.02 mV, indicating a value of approximately 0; this was a condition that is prone to aggregation. ZnO with NOM showed a high absolute value of 26.34 mV, suggesting a good dispersion (Figure 3). The particle sizes of ZnO, with and without NOM, significantly decreased to 36.7 nm from 1178.7 nm after 72 h of exposure; this is attributable to excellent dispersion (Figure 5b).  
The absolute surface charge of the ZnO dispersed in ISO water flea test medium was 23.00 mV, similar to the absolute surface charge of ZnO in the presence of NOM, which was 26.12 mV (Figure 3). However, the particle size of 392.63 nm after 48 h of exposure decreased to 245.03 nm with a reduced aggregation due to the addition of NOM (Figure 5c). Despite a similar surface charge, the addition of NOM improved the dispersion because the NOM coated on the surface of nanomaterials resulted in steric hindrance that made it difficult to access the spaces between the nanomaterials owing to the size of the NOM molecules [29; 30; 42].  
The absolute value of surface charge of the ZnO in the fish test medium was 24.84 mV that slightly increased to 35.11 mV after the addition of NOM, suggesting that the presence of NOM would further facilitate dispersion (Figure 3). The ZnO particle size measured by ELS was 374.37 nm but decreased to 40.87 nm with the addition of NOM, indicating excellent dispersion (Figure 5d).  
The ionic strength increased in the ecotoxicity test medium compared to the DI water and, therefore, the diffused double-layer thickness of ZnO NPs was reduced. This led to a decreased zeta  
Figure 5: Hydrodynamic diameter of ZnO NPs with/without NOM in DI water (**a**), OECD medium (**b**), ISO medium (**c**), and dechlorinated tap water (**d**).  
potential (Figure 3), thereby reducing the inter-particle repulsive force that reduces the aggregation rate of ZnO NPs. It is reported that the dispersion and aggregation of ZnO NPs are strongly affected by pH changes; however, in this study, the pH changes were slight in the range of 7.53 to 7.99.
## 4 Control Efficiency  
The results in the above section have shown that both the steady blowing and unsteady jet methods are promising methods for reducing aerodynamic drag on the square-back Ahmed model. The maximum drag reduction of 26.51% is achieved with a steady blowing jet at an angle of 45\({}^{\circ}\), while the unsteady jet at an angle of 45\({}^{\circ}\) and a control frequency of 272 Hz results in a drag reduction of 17.27%. However, both of these methods require an additional energy to produce the control jets. Therefore, a simple formula is proposed to evaluate the energy efficiency of flow control methods. The powers required to overcome the aerodynamic drag of the model in unactuated and actuated cases are given by Equations (5) and (6), respectively.  
\[\mathrm{P_{D_{0}}}=\frac{1}{2}\mathrm{C_{D_{0}}}\rho\mathrm{AU_{0}^{3}}, \tag{5}\]  
\[\mathrm{P_{D}}=\frac{1}{2}\mathrm{C_{D}}\rho\mathrm{AU_{0}^{3}}, \tag{6}\]  
The power required for the control devices is described as:  
\[\mathrm{P_{j}}=\frac{1}{2}\rho\mathrm{A_{j}}\overline{\mathrm{U}}^{3}\sin \beta, \tag{7}\]  
where \(\overline{\mathbf{U}}=\mathbf{U_{j}}\) with the steady blowing jet and \(\overline{\mathbf{U}}=\frac{2}{7}\int_{0}^{\overline{\mathbf{T}}}\mathbf{U_{ j}dt}\) with the unsteady jet.  
The control energy efficiency is defined as follows:  
\[\eta=\frac{\left(\mathrm{P_{D_{0}}}-\mathrm{P_{D}}\right)-\mathrm{P_{j}}}{ \mathrm{P_{D_{0}}}}=\frac{\Delta\mathrm{C_{D}}}{\mathrm{C_{D_{0}}}}-\frac{ \mathrm{A_{j}}\overline{\mathrm{U}}^{3}\sin\beta}{\mathrm{C_{D_{0}}AU_{0}^{3 }}}, \tag{8}\]  
Figure 27: The normalized streamwise contour at the iso-surface Q = 300,000 s\({}^{-2}\) with the unsteady jet at different control frequencies. (**a**) fc = 68 Hz; (**b**) fc = 272 Hz.  
From Equation (8), the energy efficiency of the steady jet method at a jet angle of 45\({}^{\circ}\) and an unsteady jet at control frequency of 272 Hz are 12.94% and 13.77%, respectively. This demonstrates that the unsteady jet method is more energy efficient, as it requires less external energy.
# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
## 3 Research Methodology
### Landslide Susceptibility Assessment  
The natural breaks method was used to classify landslide susceptibility levels using the GOS model prediction findings. The model's generalisation ability was assessed using a receiver operating characteristic (ROC) curve [32]. The ROC curve plotted specificity on the \(x\)-axis and sensitivity on the \(y\)-axis, with the area under the curve (AUC) quantifying the accuracy or discriminative effectiveness of the prediction. A higher AUC value indicates better predictive performance. Here, specificity (S\({}_{p}\)) and sensitivity (S\({}_{e}\)) represent the proportions of correctly identified landslide and non-landslide points, respectively. Higher values indicate a stronger recognition ability of the model.  
\[S_{p}=\frac{TP}{TP+FN} \tag{10}\]  
\[S_{e}=\frac{TN}{TN+FP}, \tag{11}\]  
where TP refers to the number of samples correctly classified as landslides, FN represents the number of samples misclassified as non-landslide point, TN is the number of samples correctly classified as non-landslide point, and FP represents the number of samples misclassified as landslide point.  
The study selected machine learning methods such as RF, LR, RBFC, and BCS as comparisons to lessen the impact of sample data differences and highlight the merits of the GOS model. These models were chosen based on their distinct strengths in handling different aspects of landslide susceptibility assessment, ensuring a more comprehensive evaluation of the GOS model. The RF method displays a strong nonlinear fitting ability, effectively handling high-dimensional data and lowering overfitting risks. The LR method displays high computational efficiency and good interpretability, making it suitable for situations where the relationships between variables are relatively simple. The RBFC method can map to high-dimensional space using kernel functions, capturing complex nonlinear relationships, and is suitable for handling complex pattern recognition problems. The BCS model calculates similarity and has strong interpretability, making it easy to understand and apply. Compared to GOS, it uses the entire sample data without selective optimisation. This characteristic allows BCS to serve as a reference model for evaluating the efficiency and predictive stability of GOS, particularly in addressing sample imbalance issues.
## 4 Conclusions  
We have demonstrated 100 Gbaud OOK and PAM4 transmitter performance in C-band for short-reach optical interconnect applications as a potential solution for four/eight optical lanes 800 GbE links. We achieve 149 Gbit/s, 150 Gbit/s, and 166 Gbit/s post-FEC bitrates for 80 Gbaud, 90 Gbaud, and 100 Gbaud PAM4 signals and 93 Gbit/s for 100 Gbaud OOK signal. Furthermore, we perform numerical simulations to study the impact of transmitter parameters and chromatic dispersion impact of power penalty for 100 Gbaud OOK, PAM4, and PAM8 signals. We observe the significant chromatic dispersion impact in both experiments and simulations.  
Figure 6: Extinction ratio versus the effective number of bits in terms of received optical power and penalty for 100 Gbaud (**a**,**d**) OOK, (**b**,**e**) 8 PAM4, and (**c**,**f**) PAM8 signals for 6-FFT and 3 FBT equalizer configuration at optical b2b.  
Figure 7: Received power penalty (at BER = 5E-3) as a function of transmitted distance for 100 Gbaud OOK, PAM4, and PAM8 signals with 6-FFT and 3 FBT equalizer configuration (see inset).  
**Author Contributions:** Conceptualization, O.O., X.P., A.U.; methodology, O.O., X.P., A.U., R.S., V.B., S.P.; software, O.O., X.P.; validation, O.O., X.P., A.U., S.S.; formal analysis, O.O., X.P., A.U., R.S., S.S.; investigation, O.O., X.P., A.U., R.S.; resources, O.O.; data curation, O.O., X.P., A.U., R.S., S.S.; writing--original draft preparation, O.O.; writing--review and editing, X.P., A.U., R.S., S.S., V.B., G.J., S.P.; visualization, O.O., X.P., A.U.; supervision, V.B., G.J., S.P.; project administration, O.O., R.S.; funding acquisition, O.O., R.S., V.B., S.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the ERDF-funded CARAT project No. 1.1.1.2/VIAA/4/20/660 and the EU RIA TWILIGHT project no. 871741.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We acknowledge Tektronix for lending us the 200 GSa/s 70 GHz oscilloscope (DPO/77002 SX).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Lightweight NFC Protocol for Privacy Protection in Mobile IoT
## 5 Conclusions  
Security and being lightweight are the two major requirements for the IoT. NFC is a developing communication technology used in mobile IoT. In this paper, we focus on how to use the lightweight NFC authentication protocol to solve privacy protection and some typical attacks under the mobile Internet of Things. We have proposed a lightweight NFC authentication protocol based on the clustering key-management model. A security analysis shows that our protocol can achieve device anonymity that prevents privacy leakage. The protocol can also resist replay attacks and the de-synchronization problem. The cloud server in the system also resists DoS attacks. Besides, our protocol only uses XOR operations or random numbers to encrypt and decrypt the message. A performance analysis and simulation show that the protocol reached the lightweight requirements. Our protocol can be used in many typical mobile IoT networks, such as smart-homes and school attendances. Although the protocol is designed for NFC mobile IoTs, it can also work in RFID authentication systems and provide a lightweight privacy protection solution for traditional IoT networks.  
The future work is to design an authentication protocol for a more complex situation, in which mobile NFC devices have multi-roles and belong to different authentication groups at the same time. This is essential to keep the balance between the resource costs and security requirements.  
Conceptualization, K.F. and C.Z.; Data curation, H.L. and Y.Y.; Formal analysis, K.Y., H.L. and Y.Y.; Funding acquisition, K.F. and H.L.; Investigation, K.F. and C.Z.; Methodology, K.F. and C.Z.; Project administration, K.F.; Writing-original draft, K.F. and C.Z.; Writing-review & editing, K.Y.  
This work was funded by the National Key R&D Program of China (No. 2017 YFB0802300), the National Natural Science Foundation of China (No. 61772403 and No. U1401251), Natural Science Basic Research Plan in Shaanxi Province of China (No. 2017 JM6004), the Fundamental Research Funds for the Central Universities, and National 111 Program of China B16037 and B08038.  
The authors declare no conflict of interest.
## 4 Experiment and Result
### Results and Discussion  
#### 4.3.1 Numerical Results  
We test our ILDACP method on the chosen data and calculate the three metrics from four sub-periods to analyze ILDACP method, the results of three metrics are shown in Tables 2-4. Figure 4 shows the metrics from the first test period when compared with the factorial Hidden Markov Model (FHMM) algorithm that was developed in literature [16]. An open source toolkit using FHMM for NILM is provided to allow for empirical comparisons to be made between energy disaggregation algorithms across multiple data sets. The charts in Figure 5 represent a detailed comparison of model estimation versus measured values of the four considered appliances for the first chosen period.  
See Tables 2-4, F-score and PCE show good performance for values close to 1 and 0 respectively on the date in four sub-periods. The first three periods are in different seasons, the three metrics are not much different, which proved the robustness of data. In the period 3, heat pump is a normally opened appliance. There are two normally-open appliances, which makes the clustering algorithm is unable to cluster correctly, so the heat pump shows poor performance in period 3.  
As shown in Figure 4, when compared with FHMM algorithm, the ILDACP method achieves good performance in detecting the operating states, estimating contribution power consumption, and consumption pattern trace of each appliance The F-score values show that the algorithm can correctly find turned-on states of each appliance with a high rate. F-score values of all the four appliances are higher than FHMM. We can find out why larger contribution appliance generates higher F-score except normally-open appliances, the reason is that the operating states of largest contribution appliance is first detected and estimated, thus the estimation error affects the result behind (the top panel in Figure 4). On the other two metrics, ILDACP obtains PCE values that were lower and R2 higher than FHMM.  
As described above, ILDACP can detect the operating states of the appliances, generate the contribution to the total power consumption by each appliance, and provide the consumption pattern of most appliances in spite of multiple appliances operating simultaneously. Besides, there is another truth needing our attention in the R2 metric shown in Figure 4: though highest accuracy of power contribution estimation for security/network equipment (lowest PCE value in the middle panel of Figure 4), the performance on the R2 is relatively poor. Since the powers of security/network equipment vary in a narrow range compared to the other appliance, thus making accurate power contribution estimation with average value. However, the impact of noise on the trace of it leads to difficult predict (the ILDACP doesn't filter out the noise for such appliances).  
Figure 5: Consumption patterns estimation on four considered appliances by ILDACP Four panels are listed as: (**a**) Clothes dryer; (**b**) Heat pump; (**c**) Fridge; and, (**d**) Security/Network equipment.