esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Complexity Analysis of the VVC Decoder
### Complexity Analysis of the Deblocking Filter  
DBF consists of calculate position and length of the boundaries (CPLB), filtering decision (FD), luma filter (LUF), chroma filter (CHF) and other (OT) functions. Timestamps were set before and after these functions for detailed profiling of the DBF using the six sequences mentioned with respect to the test bench (Section 3B). In Table 6, the average time distribution (in %) for different parts of the DBF over the HGPP and EGPP is illustrated. It can be seen that the LUF function alone consumed from approximately 64% to 74% of the DBF time for the AI and RA configurations over the HGPP and EGPP. The other functions individually consumed from roughly 5% to 16% of the total DBF time. The details of the profiling of the DBF over the HGPP and EGPP are displayed in Figures 9 and 10, respectively. Here, the average time distribution for different parts of the deblocking filter (in %) with QP 22, QP 27, QP 32 and QP 37 is presented.  
Some general conclusions about DBF profiling can be outlined by comparing all the configurations of the sequences. The time spent by the LUF function for the AI configuration increased by 22% when the QP changed from 22 to 37 over both the platforms. For the RA configuration, the increment of the spent time was 7% and 9% over that of the HGPP and EGPP, respectively. Furthermore, the average computational load of the CPLB, FD and CHF functions remained similar for RA configurations over the EGPP. However, the average time consumed by OT decreased approximately 20% and 8% for the AI and RA configurations with the change in QP from 22 to 37 over the HGPP and EGPP.  
The comparison study of DBF profiling over GPPs shows that the computational importance of LUF increased by approximately average 6% and that the computational impact of the CHF, FD, CPLB and OT varied by approximately 1% to 5% over the EGPP, than compared to the HGPP. In conclusion, and considering the detailed results presented in Figures 9 and 10, LUF can be considered a potential candidate for optimization, as it is also the most time-consuming function of the DBF. Other potential candidates are CHF and CPLB.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Conf.** & **CPLB** & **FD** & **LUF** & **CHF** & **OT** \\ \hline \multirow{2}{*}{HGPP} & AI & 5.5 & 6.3 & 63.6 & 8.9 & 15.7 \\  & RA & 10.7 & 7.4 & 68.5 & 5.6 & 7.8 \\ \hline \multirow{2}{*}{EGPP} & AI & 4.9 & 6.8 & 69.3 & 6.8 & 12.2 \\  & RA & 7.3 & 7.9 & 74.2 & 5.0 & 5.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average processing times (in %) for each deblocking filter block profiled over the HGPP and EGPP.  
Figure 8: Average time distribution for different parts of the inter prediction (in %) over the EGPP.  
Figure 10: Average time distribution for different parts of the deblocking filter (in %) over the EGPP.  
Figure 9: Average time distribution for different parts of the deblocking filter (in %) over the HGPP.
## 4 Data Analysis in Maritime IoT Applications
### Benefits and Limitations  
#### 4.3.1 Increased Efficiency  
Data analysis allows maritime organizations to use their resources more effectively. By understanding the patterns in data, organizations can streamline their operations, from vessel navigation to port logistics. As a result, shipping routes can be optimized, vessel speeds can be accurately determined to conserve fuel, and cargo can be loaded and unloaded more efficiently. These enhancements lead to substantial time savings and cost reductions, making maritime operations more efficient [84; 85; 86] (Table 3).
# Dual-IoTID: A Session-Based Dual IoT Device Identification Model
## 5 Performance Evaluation
### Comparison with Previous Works  
As shown in Table 7, we compared Dual-IoTID with recent advanced IoT classification and identification techniques. The IoT Sentinel model selects 23 features from the first 12 packets of each device as device fingerprints, including source IP address, destination IP address, packet attributes, etc., and then uses machine learning algorithms for detection. Its global identification accuracy for 27 IoT devices was 81.50%.  
IoTDevID identifies IoT devices based on packet payloads and systematically studies packet-level features, proposing various methods to filter feature sets for identification, contributing to the automation of feature analysis. IoTDevID achieved an IoT device identification accuracy of only 94.30% in the UNSW dataset.  
IoTTFID [25] is an incremental learning model based on traffic fingerprints, capable of dynamically adapting to environments and identifying new types of IoT devices without requiring retraining from scratch. The model achieves this by extracting traffic fingerprints from new devices, preprocessing them into input vectors, and then updating select parameters of the existing model's networks, thereby extending its identification scope.  
Compared to IoTDevID, IoT Sentinel, and IoTTFID, Dual-IoTID achieved a higher accuracy rate of 99.48% in the UNSW dataset. It outperforms existing works in terms of classification performance and allows for finer-grained comparisons. Our method can correctly distinguish devices of different types from the same manufacturer, whereas IoT Sentinel and IoTDevID lack effectiveness in identifying such similar IoT devices.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Dataset** & **Accuracy** & **Feature Type** \\ \hline IoT Sentinel & UNSW & 81.50\% & Packet header \\ IoTDevID & UNSW & 94.30\% & Packet header \& Payload \\ IoTTFID & UNSW & 98.09\% & Traffic fingerprints \\ Dual-IoTID & UNSW & 99.48\% & Payload \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparing the result of Dual-IoTID with former studies.  
Figure 4: Confusion Matrix of the experiment with non-IoT device.  
IoT Sentinel and IoTDevID utilize network layer and link layer features for IoT device identification; IoTTFID also utilizes information from the link layer, network layer, and transport layer to generate fingerprints. However, the network environment is not static, and changes in the environment can affect these features. For example, in networks using NAT, the efficiency of device identification can be greatly affected. Unlike these methods, we attempted to identify IoT devices by analyzing the payload of IoT device sessions. Since the payload is unaffected by NAT and changes in the network environment, our method can be applied to more scenarios, achieving accurate IoT device identification.
## 3 Data Analysis and Forecasting Process  
For deep learning method applications, sufficient data to be fed in the model structure is a crucial issue and the key to the successful utilization. In this paper, the big data platform of a company which has a large proportion of EV charging stations in Shenzhen China, one of the largest on-road EV population cities, has been accessed and the data for EV charging loads were obtained and used. This section proposes the preliminaries of data processing methods and the model data formation. Moreover, load forecasting models are addressed.
# Food Waste Detection in Canteen Plates Using YOLOv11
## 2 Related Work
### Implementations in the Food Detection Context
#### 2.2.1 Food Detection  
Food detection has been an important field of research, especially for applications in dietary monitoring and quality control systems. Studies in this category focus on accurately recognizing foods in images, which can be challenging due to the wide variation in food appearance, even within the same category.  
[PERSON] et al. (2016) [20], proposed an innovative architecture, called WISeR (Wide-Slice Residual Networks for Food Recognition), which uses residual networks and slice convolution layers to address food structure, such as the layered appearance of some dishes. This method has significantly improved the accuracy of food recognition, outperforming other approaches.  
[PERSON] et al. (2017) [21] applied Deep Neural Networks (DNN) to classify food dishes. The study showed that DNN outperformed traditional machine learning methods, offering better performance in identifying food dishes, taking into account the unique visual characteristics of each dish, such as shape, color and texture.  
[PERSON] et al. (2020) [22] focused on detecting the state of food (such as raw, processed, cut, or mixed food). The use of features extracted by DNN, combined with Support Vector Machines (SVM), showed that the detection of food states, in addition to food categories, is a feasible and efficient task, outperforming manually made features.
# Design of W-Band GaN-on-Silicon Power Amplifier Using Low Impedance Lines
## 2 Design of W-Band Power Amplifier in GaN on Si HEMTs  
### GaN on Silicon HEMTs  
GaN-on-Si HEMTs are known to have the inferior performance to GaN-on-SiC ones when they are applied for the design of millimeter-wave power amplifier ICs. Nonetheless, they have distinct merits in the low-cost commercial applications for massive productions. They can also be potentially integrated with the conventional Si CMOS ICs, further reducing the fabrication cost and packaging complexity. In this work, W-band power amplifier IC is designed using the commercially-available GaN-on-Si HEMT foundry service of which the 60 nm GaN HEMTs provide typical cut-off and maximum oscillation frequency (\(f_{t}\) and \(f_{max}\)) of 150 and 190 GHz, respectively [12]. A breakdown voltage is 25 V, so that drain bias voltage (\(V_{DD}\)) is set to 10 V for the power amplifier design. Similar \(f_{max}\) can be achieved from the longer gate width HEMT for GaN-on-SiC technology. For example, a 100 nm GaN-on-SiC HEMTs provides the \(f_{max}\) higher than 200 GHz with a breakdown voltage of 50 V, where higher drain bias voltage of \(\sim\)15 V was used for the power amplifier [5].  
Therefore, the GaN-on-Si power amplifier in this work is supposed to have a lower gain and operate at lower drain bias voltages compared to the GaN-on-SiC HEMT with the same gate length. The low drain bias voltages limit the power performance of power amplifier, because the resulting low load impedance entails the high degradation in output power and efficiency by the loss components in the matching networks such as conductor and dielectric losses of the microstrip lines [13]. Therefore, it is highly required to design low-loss impedance matching networks in GaN-on-Si HEMT power amplifiers.
## 3 Results
### Microplastic Particles Classification by SWIR-HSI
#### 3.2.3 Hierarchical PLS-DA Classification  
A four-class hierarchical PLS-DA model was built and applied to provide a full classification of the investigated microplastic particles. In Figures 7 and 8, some predicted images showing the identification of the polymer type for each microplastic item, are reported as examples of the obtained classification results. In more detail, the predicted images represent the outcomes obtained from the application of the hierarchical model. All the other results in terms of predicted images are reported in Appendix A.  
The correctness of classification is confirmed by the sensitivity and specificity values of the model in calibration and cross-validation for both the rules of the hierarchical model (Table 3). Their values, which range from 0.991 to 1.000 for both parameters, are good.  
From the prediction maps, it is possible to evaluate the abundance of the different polymers in the investigated marine microplastic samples collected from the sea surface. PE was the most abundant polymer (60.23%), followed by PP (21.41%), EPS (15.64%), PS (0.02%) and NI (2.70%). Such results agree with those previously published for marine microplastic samples collected from the sea surface and can be explained considering that all the identified polymers are characterized by a density lower than that of seawater [64; 65; 66; 67].  
The polymer abundance in each investigated sample is displayed in Figure 9. PE was the most common polymer in the different ocean areas, followed by PP, with only two exceptions: in sample E4 SARMIENTO from Western Atlantic Ocean, PP and EPS were present in equal percentages whereas in sample A06 NS from the Mediterranean Sea, EPS was quantitatively the second one. Moreover, PS microplastics were only found in the NST41 sample coming from the Bay of Biscay.  
Figure 6: Average pre-processed reflectance spectra of the different polymers (**a**) and PCA score plot (PC1–PC2–PC5) (**b**) related to Rule 2.  
Figure 7: Source digital images and corresponding false color prediction maps obtained from the HSI-hierarchical model of some of the examined microplastic samples from the Mediterranean Sea and the Strait of Gibraltar obtained by the application of the hierarchical PLS-DA model.  
Figure 8: Source digital images and corresponding false color prediction maps obtained from the HSI-hierarchical model of some of the examined microplastic samples from the Western Atlantic Ocean (E4 Sermiento) and the Bay of Biscay (ETO 16 and NST 41) were obtained following the application of the hierarchical PLS-DA model.  
Figure 9: Percentages of different identified polymers in the investigated marine microplastic samples collected in different areas. NI: not identified.
# Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network
## 3 Thermal Bridge Detection Method Using Neural Network
### Thermal Bridge Modeling  
The purpose of this study was to detect thermal bridges from thermal images, which have morphologically distinct features that are slightly different from case to case, making them difficult to detect by only using thresholds. An artificial neural network was applied to achieve this purpose. Because of their ability to solve non-linear problems, artificial neural networks (ANNs) are used in applications for predicting the relationship between complex inputs and outputs including subjective judgment and experience [27].  
The ANN is a method developed based on the behavior and structure of the human brain [28]. The ANN is a mathematical model depicting biological neurons. A typical ANN model consists of an input layer, an output layer, and a hidden layer [29]. Neurons are arranged in each layer and fully connected with weights that affect results [30]. Each  
Figure 5: Complexity of data boundaries based on convex hull: (**a**) a cluster that is a thermal bridge and (**b**) a cluster that is not a thermal bridge.  
weight is defined in an optimization way to increase the accuracy in predicting target values. For the non-linear feature of the transfer function between neurons, the ANN is also good at solving the non-linear problems.  
In the case of thermal images including thermal bridges, images of actual thermal bridges must be obtained because there is no large-scale public database. Therefore, this study constructed a structure for learning small datasets because of the lack of training datasets for thermal-bridge modeling. The training process was performed by extracting features from images of previously collected thermal bridges and other thermal-anomaly areas. This study applied a backpropagation neural network, which is appropriate for processing small amounts of data to train the ANN [31]. Among the training parameters, the number of hidden layers has a significant influence on prediction results [32]. This study set the number of hidden layers to one by considering the number of input and output layers. The sigmoid function was applied as the activate function of the hidden layer. Figure 6 shows the structure of the ANN in this study. The datasets for training were randomly divided into training at 70%, validation at 15%, and testing at 15%, which is a good ratio for small datasets [33].
# Characterization of the Key Aroma Compounds of Shandong Matcha Using HS-SPME-GC/MS and SAFE-GC/MS
## 3 Results
### Differential Volatile Components in Three Grades of Shandong Matcha
#### 3.4.2 Unique Volatile Components in High-Grade Matcha (M-GS)  
In addition to the common components with differences among the three grades of matcha, we also screened out the unique volatile components only present in M-GS by calculating the odor activity value (OAV). The OAV can be used to reasonably evaluate the overall flavor of a food with a single odor component, and key aroma compounds with OAV > 1 are usually considered to be the main contributors to the overall aroma [22]. In this study, we screened 10 key aroma components with OAV > 1 in M-GS (Table 3). For the odor quality of these volatile components, 3-methyl-2-butene-1-thiol (sulfurons), 3-ethyl-phenol (musty), 2-thiophenemethanethiol (fishy), (E,E)-2,4-undecadien (green) and (E,Z)-2,6-nonadienal (green) all presented pleasant aroma. The threshold value of 3-methyl-2-butene-1-thiol was the lowest, while the OAV value was the highest, indicating that 3-methyl-2-butene-1-thiol contributed the most to the aroma of M-GS.  
In this study, there were common volatile components that differed among the three grades of matcha and unique components that existed alone in a certain grade. This strongly suggests the possibility that the aroma of matcha is mostly composed of the odorants common to every grade and that a small number of odorants are distinctive for each grade.
# A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs
## 3 Assessment Indices for Deterministic and Probabilistic Forecasts
### Reliability: Prediction Interval Coverage Probability (PICP)  
To avoid under- or over-confidence, the value PICP is determined to represent the percentage of observations or actual wind power values that fall within a range between lower bounds and upper bounds, where the subscript \(t\) refers to the number of a sample  
Figure 2: Schematic diagram of probabilistic approaches based on ensemble NWPs.  
or a time step. Greater PICP implies that there are more targets in the corresponding PIs. PICP is calculated as follows:  
\[\text{PICP}=\frac{1}{N}\sum_{t=1}^{N}c_{t} \tag{2}\]  
where \(N\) is the total number of samples and \(c_{t}\) is a Boolean value, which is defined as:  
\[c_{t}=\left\{\begin{array}{ll}1,\ y_{t}\in[L_{t},U_{t}]\\ 0,\ y_{t}\
otin[L_{t},U_{t}]\end{array}\right. \tag{3}\]  
where \(y_{t}\), \(L_{t}\), and \(U_{t}\) are the observation value, the lower bound, and the upper bound, respectively, with the same number of timestep \(t\). A confidence level refers to the percentage of all possible samples that can be expected to include the real population parameter. For example, a 50% confidence level implies that 50% of the confidence intervals would include the real population parameter. Generally, PICP must be close to the confidence level that is predetermined in the probabilistic model.
## 3 Results  
### Weather Parameters  
While trivial differences were found in the amounts of average daily solar radiation, air temperature, and dew point temperature among the study seasons, differences of average daily wind speed were significant (Figure 2). The average daily wind speed was nearly 20% greater in the 2019 season than the season 2021 (2.3 vs. 1.9 m s\({}^{-1}\)), mostly occurred during the first six months of the year. A higher wind speed increases crop water use and consequently impacts irrigation management in terms of irrigation amounts and frequency. Average daily solar radiation (241 W m\({}^{-2}\)) and air temperature (22 \({}^{\circ}\)C) were slightly higher, and average dew point temperature (6.6 \({}^{\circ}\)C) was slightly lower in the 2020 season than the other two crop seasons. During the 2020 season, the month of June had the highest average daily solar radiation of 346 W m\({}^{-2}\), followed by May (341 W m\({}^{-2}\)) and July (336 W m\({}^{-2}\)). Greater solar radiation and air temperature increase potential evapotranspiration and as a result irrigation water needs in alfalfa fields. There was higher than normal rainfall in the fall of 2019 season and the winter of 2020 season (Figure 2). Average annual rainfall for this region is 75 mm while the total precipitation was 110 mm in a six-month period of October 2019 through March 2020.
# Analysis and Experimental Test of Electrical Characteristics on Bonding Wire
## 3 Frequency Domain Simulation
### Influence Of Three Loops On S Parameter Of Bonding Wire  
Q-loop, S-loop and M-loop bond modes are shown in Figure 10. The Q-loop is the most universal bonding wire form. Compared with the Q-loop, the S-loop has a longer flat distance. The M-loop bond has the most kinking points. Through the finite element analysis and experimental verification, the result shows that the M-loop is 13-75% as good as the S-loop [19], which suggests that the sweep resistance is better than the S-loop relying on bond span and height. This is because the M-loop bond has five kinking nodes. During the transfer molding process, the M-loop bond has higher stiffness to resist the deformation when epoxy compound flows. Keeping the diameter, span and bonding height unchanged, the S parameters of three kinds of loop wires in 1 MHz-30 GHz are simulated and compared. The results of the 1 MHz-20 GHz scan are shown in Figure 11a,b.  
Figure 9: (**a**) S11 of bonding wire with different bonding height; (**b**) S21 of bonding wire with bonding height.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Bonding Height/\(\upmu\)m** & **210** & **230** & **250** & **270** & **290** & **310** \\ \hline Min S21 (Insertion Loss)/dB & \(-8.61\) & \(-8.59\) & \(-8.71\) & \(-8.79\) & \(-8.88\) & \(-9.02\) \\ Corresponding frequency/GHz & 17.58 & 17.33 & 17.38 & 17.28 & 17.43 & 17.38 \\ Passing signal/incoming signal/\% & 37.11 & 37.20 & 36.69 & 36.35 & 35.97 & 35.40 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of S21 extremum under different bonding height.  
The results of the return loss S11 from 1 MHz to 1 GHz are shown in Figure 11a. As shown in Figure 11a, the return loss S11 of three different loop modes has little difference. Figure 11b is the logarithmic coordinate graph of the insertion loss S21. It can be seen that the electrical performance of the S-loop wire is the best, while the Q type is slightly worse than that and the M type is the worst in Figure 11b. For example, the working frequency of S type wire increases by 0.32 GHz (about 8%) compared with M-loop wire at \(-3\) dB. The S21 extreme points of different bonding height were extracted in Table 6. For the S type bonding wire, S21 has the minimum value of \(-8.71\) dB at 17.38 GHz, and the ratio of passing and incoming signal is 36.69%. Compared with the case of the M type wire, the modulus of S21 rises by 0.88 dB (about 10.1%), and the signal transmission quality increases by 3.54%. Therefore, reasonable bonding wire modes should be made according to the process and mechanical reliability requirements comprehensively.
## 4 Risk Analysis for Current Protocols
### NMEA 2000
#### 4.3.3 Replay/Man-in-the-Middle  
We further highlight a recent 2018 research study that discovered that some NMEA 0183 systems on board a ship were susceptible to a man-in-the-middle attack [36]. The attack implements a malware that intercepts and manipulates GPS coordinates that are received from a sensor integrator via the network. The malware can also cause a software crash to an operator station (a mission critical device).