esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Result
### Results and Discussion
#### 4.3.2 Algorithm Computational Complexity Analysis  
The ILDACP method shows good performance in detecting the operating states, estimating the power contribution, and retrieving the consumption pattern for the considered four appliances. FCM algorithm is adopted iteratively to detect the operating states of the largest contribution appliance. In the clustering phase, multiple states for one appliance are detected respectively, thus the performances are effectively improved. The time complexity of FCM algorithm is O(MNT2) for a problem with M states for each appliance, N appliances and T time instances [37], but FHMM for the same problem attains the time complexity in the order of O(TM2N) on. Obviously, as the number of states and appliances increase, FCM needs less computation than FHMM does (Figure 6). Then, FCM is coupled with SSDTW to estimate the best matching power trace pattern. ILDACP method, the combination of FCM and SSDTW, gets the time complexity O(MNT2+kNT2), where \(k\) is time series \(A^{j}\) length. When combined Figure 6 with the results analysis in Section 4.3.1, we can get the conclusion that our ILDACP method can achieve good disaggregation with a lower computational complexity for the problem with increasing appliances numbers and states number of each appliance than FHMM.  
From the points of disaggregation outputs, we expect to keep improve the disaggregation accuracy. Since cluster validity index for calculating the best cluster number is invalid when only one cluster exists, clustering algorithms have difficulty in detecting turned-on state of normally-open appliance. The problem in our algorithm is solved in correction phase in Section 3.4. However, once more than one kinds of normally-open appliance contribute to the total power consumption, we need to skip the clustering phase when these appliances become the largest contribution appliance.  
Based on the above discussion, the proposed approach has high accuracy on the three metrics and relatively low computational complexity.
# Aluminum-Based Plasmonic Photodetector for Sensing Applications
## 5 Conclusions  
Using the RCWA method, theoretical simulations of the electromagnetic field intensity distribution in a PPPD based on silicon p-n junctions and periodically textured aluminum as the plasmon-carrying metal were performed. This made it possible to optimize the parameters of the plasmon-carrying grating layer in order to increase the sensitivity of the PPPD. Experimental samples of the PPPD with aluminum gratings were manufactured, and their characteristics were studied. For the PPPDs with optimal grating parameters, a polarization sensitivity value of \(\rm I_{p}/I_{s}=8\), an FWHM of the resonance in the photocurrent spectrum ranging from 50 to 100 nm, a sensitivity at the resonance maximum of \(\rm I_{ph}=0.04\)-0.06 A/W, and an angular half-width of the photocurrent resonance of \(\Delta\Theta=5^{\circ}\) were obtained. These characteristics are comparable to the best characteristics achieved with PPPDs using gold gratings. The obtained results pave the way for replacing gold with aluminum in PPPD production, significantly reducing their cost.  
Conceptualization and data curation, S.M., I.I. and Y.L.; methodology, I.I. and S.M.; software, Y.L.; formal analysis and investigation, V.M., V.R., I.M., R.R., M.D., Y.S., V.T., O.S. and D.K.; writing--original draft preparation and writing--review and editing, S.M., I.I. and Y.L.; project administration and funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Research Foundation of Ukraine (Grant No. 2022.01/0126, \"Development and implementation of a dust-insensitive smoke detector based on a plasmon-polariton photodetector\"). S.M. appreciates funding from the Ministry of Education and Science of Ukraine (Project No. 0122U001956).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors are grateful to [PERSON] and [PERSON] for the AFM analysis, and [PERSON] for the spectroscopic ellipsometry measurements.  
The authors declare no conflicts of interest.
## 4 Results and Discussion  
### Performance Analysis of Residual Neural Network  
The results in Table 3 show that the data for female subjects yields better performance, 4.1% higher, compared to the data for male subjects, which is due to the fact that the amount of data available for the former is four times more than the later. The amount of data is critical for the construction of good machine learning models particularly deep learning models, which have significantly more parameters to tune. Similarly, the confusion matrices for this experiment are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sex**} & \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Training Samples (75\%)**} & \multirow{2}{*}{**Test Samples (25\%)**} \\ \cline{3-3} \cline{5-8}  & & **Training (75\%)** & & & **Validation (25\%)** \\ \hline \multirow{3}{*}{**Female**} & 1 & 3150 & 1050 & 1400 & 5600 \\ \cline{2-8}  & 2 & 3150 & 1050 & 1400 & 5600 \\ \cline{2-8}  & 3 & 3150 & 1050 & 1400 & 5600 \\ \hline \multirow{3}{*}{**Male**} & 4 & 788 & 262 & 350 & 1400 \\ \cline{2-8}  & 5 & 788 & 262 & 350 & 1400 \\ \cline{2-8}  & 6 & 788 & 262 & 350 & 1400 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of datasets used for training and testing the ResNet.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sex**} & \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**F1 (\%)**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Sex-Based**} & \multirow{2}{*}{**Final**} \\ \cline{3-3} \cline{5-8}  & & **Wake** & **REM** & **N1** & **N2** & **N3** & **(\%)** & & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline \multirow{3}{*}{**Female**} & 1 & 85.2 & 87.5 & 83.6 & 82.6 & 83.1 & 84.4 & & & \\  & 2 & 89.7 & 89.5 & 88.4 & 88.3 & 89.4 & 89.1 & 87.8 & & \\  & 3 & 89.4 & 90.4 & 88.4 & 90.6 & 91.0 & 89.9 & & & 85.8 \\ \hline \multirow{3}{*}{**Male**} & 4 & 80.5 & 78.8 & 83.3 & 80.9 & 82.4 & 81.1 & & & \\  & 5 & 84.5 & 87.5 & 83.0 & 85.6 & 83.6 & 84.8 & 83.7 & & \\ \cline{1-1}  & 6 & 85.8 & 84.7 & 84.6 & 86.6 & 84.6 & 85.3 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of the classification accuracy.  
Figure 5: Confusion matrix for different datasets, i.e., (**a**) dataset 1, (**b**) dataset 2, (**c**) dataset 3, (**d**) dataset 4, (**e**) dataset 5, and (**f**) dataset 6.  
For the purpose of this experiment, the ResNet is trained for 1000 epochs with a 75/25 train/validation ratio to tackle the overfitting-underfitting problem. Moreover, during training, two different optimization techniques: i.e., stochastic gradient descent (SGD) and Adamax are considered for loss function optimization. Adamax shows better convergence (learning rate) for all the datasets compared to SGD as shown in Figure 6.
# Off-Line Camera-Based Calibration for Optical See-Through Head-Mounted Displays
###### Abstract  
In recent years, the entry into the market of self contained optical see-through headsets with integrated multi-sensor capabilities has led the way to innovative and technology driven augmented reality applications and has encouraged the adoption of these devices also across highly challenging medical and industrial settings. Despite this, the display calibration process of consumer level systems is still sub-optimal, particularly for those applications that require high accuracy in the spatial alignment between computer generated elements and a real-world scene. State-of-the-art manual and automated calibration procedures designed to estimate all the projection parameters are too complex for real application cases outside laboratory environments. This paper describes an off-line fast calibration procedure that only requires a camera to observe a planar pattern displayed on the see-through display. The camera that replaces the user's eye must be placed within the eye-motion-box of the see-through display. The method exploits standard camera calibration and computer vision techniques to estimate the projection parameters of the display model for a generic position of the camera. At execution time, the projection parameters can then be refined through a planar homography that encapsulates the shift and scaling effect associated with the estimated relative translation from the old camera position to the current user's eye position. Compared to classical SPAAM techniques that still rely on the human element and to other camera based calibration procedures, the proposed technique is flexible and easy to replicate in both laboratory environments and real-world settings.  
augmented reality; calibration; head mounted displays; optical see-through display; computer vision Article
## 5 Attacks and Countermeasures
### Attacking MANETs
#### 5.3.1 Malicious Behavior during Route Discovery and Maintenance  
In order to transmit packets to nodes which are not in direct transmission range of the source node, intermediate nodes have to operate as routers to forward the packets to their destination. For this, the MANET routing algorithm has to rely on the cooperation of the intermediate nodes. Yet, the required cooperation can not be assumed without restrictions in real-world environments. Misbehaving nodes may tamper with the routing algorithm by, e.g., forging, redirecting, or dropping routing messages.  
A wide range of routing attacks was introduced in the recent years. The _Blackhole_ attack is one of the most aggressive routing attacks on MANETs. _Blackholes_ manipulate the routing algorithm to redirect network traffic. For this, the routing metric is misused to increase the probability that the traffic is routed via the _Blackhole_. As a result, the malicious node is capable of analyzing the transmitted data or of denying the service by dropping the received packets.  
Further, the routing algorithm may be misused for _Resource Consumption_ attacks. We consider for example a reactive routing protocol and a malicious node that initiates a high number of route requests. As the bandwidth of MANETs is strongly limited and request messages are forwarded by each neighbor of the malicious node, those attacks can affect large parts of the network. Further, by forging the source field of the request messages, benign nodes can be accused for misbehavior and countermeasures can be evaded.  
Besides an intended misbehavior, a selfish behavior can also affect the functionality of a MANET. Selfish nodes refuse to cooperate in acting as routers for other nodes in order to save resources. Since the functionality of MANETs is based on the cooperation of nodes, selfish nodes affect the availability of the network.
## 3 Numerical Simulations and Discussion
### Effect of Sensor Resolution
#### 3.4.2 Discussion
In addition, the identification results are improved for g1.4, g1.1,h1.4 and h1.1, in which only two bearings are included. It is indicated that the identification results will be improved when the two methods are used for simple rotors. The reason is that the solution of the inverse matrix of H\({}_{1}\) and H\({}_{3}\) in the two algorithms is more accurate when the algorithms are used for simple rotors g1.4, g1.1, h1.4 and h1.1. When the two algorithms are used for complex rotors, the inverse matrix of H\({}_{1}\) and H\({}_{3}\) may be inaccurate and they might not even be solved where the measured errors of unbalance responses are too big, which is caused by low resolution.
## 3 SHM Using Guided Ultrasonic Waves in Laminated Composites
### Sensor Technologies  
The accurate and reliable monitoring of composite structures using ultrasonic waves necessitates careful sensor selection and sophisticated signal processing. The primary sensors employed in ultrasonic SHM are piezoelectric transducers, which convert electrical signals into mechanical waves and vice versa. These sensors can be permanently mounted on the structure to provide continuous monitoring capabilities. Polyvinylidene fluoride (PVDF) and PZT are the two most widely utilized piezoelectric materials, each offering specific advantages in terms of sensitivity, frequency response, and durability [70]. Recent advancements include the development of miniaturized and flexible sensors such as Micro-Electro-Mechanical Systems (MEMSs) and piezoelectric water active sensors (PWASs), which can be embedded within composite materials for more integrated and less intrusive monitoring solutions [21]. Additionally, the use of IDTs has been explored for their capability to generate and receive guided waves with specific modes and frequencies. Table 2 highlights the key types of sensor technologies utilized in guided-ultrasonic-wave-based structural health monitoring, detailing their operational principles, advantages, challenges, and common applications.
# An Approach for Anomaly Detection in Network Communications Using _k-Path_ Analysis
## 4 Three-State Markovian Approach  
This section presents a framework for anomaly detection in an active _k-path_, based on the observed Markov model and the generalized likelihood ratio.
# Assessment of Thermal Comfort in an Electric Bus Based on Machine Learning Classification
## 2 Materials and Methods
### Comparison of PMV-PPD Model and Thermal Sensation  
To compare the ML classifiers with the PMV-PPD model, the PMV is employed as parameter. This can be accomplished because PMV predicts the thermal sensation on the ASHRAE scale (see Table 2). The difference to TS is that PMV is a rationale number while  
Figure 4: Parameters pairplot, were blue represents the passengers belonging to group 1 and orange to group 2 as defined in Section 2.2.  
TS is an integer. Since only the PMV value is used, in the following, the PMV-PPD model is referred to as PMV model. In order to assign a passenger to a group based on the PMV values as explained in Section 2.2, a threshold needs to be defined. Based on the grouping described in Section 2.2, one could use 1.5 as a threshold, as it lies exactly between the 2 values that were used to divide the groups. Passengers with a PMV value equal or above 1.5 will be rounded up to 2 and are placed in group 2 while the remaining passengers are placed in group 1.  
According to [12], _\"PMV had a mean absolute error of one unit on the thermal sensation scale\"_ when applied in more realistic environments characterized by dynamic conditions. Hence, Figure 5 shows the distribution of TS, which was given by the passengers in the survey, and compares it with the distribution of the calculated PMV values. A clear leftward shift can be seen. This means, the predicted thermal sensation of the PMV model is generally colder than the actual thermal sensation from the surveys obtained in warm conditions. Hence, in order to match TS given by the passenger, a shift needs to be added to the calculated PMV.  
In order to better understand this shift behavior, the shift of each passenger was analyzed and calculated as  
\[shift=TS-PMV \tag{2}\]  
Equation (2) is equivalent to  
\[TS=PMV+shift \tag{3}\]  
The analysis of the shift behavior helps identify the threshold in order to better match passengers' TS and calculated PMV.
## 4 Experiment and Results
### Progressive Learning for Food Category and Ingredient Classification
#### 4.6.2 Multi-Label Classification for Ingredient Detection  
The study evaluated three food datasets with ingredient information for this experiment, VireoFood-172, Ingredient Food101 (Full), and Ingredient Food101 (Simplified). After feature extraction and selection, we implemented the incremental learning protocol proposed by [PERSON] et al. [41] and [PERSON] et al. [42] to measure performance. We categorize the unique set of labels into groups and set the kernel parameter to 0.1, and the learning rate in PKELM is set through manual search. In the base session, there are two groups. Following that, it learns a new group of ingredients in each session.  
Experimental analysis in Table 6 shows that PKELM (RBF) has superior performance in contrast to InceptionResNetV2 and ResNet-50 for all datasets. For PKELM with RBF kernel, the difference in Precision, Recall, and F1-score for Food101 as compared to InceptionResnetV2 Model is (8.17%, 11.27%, 9.64%), Food101(Simplified) is (6.79%, 7.04%, 6.77%), and VireoFood-172 is (17.57%, 16.23%, 16.34%) respectively. When chi-square kernel is used in PKELM, the difference in Precision, Recall, and F1-score for Food101 as compared to InceptionResnetV2 Model is (6.35%, 6.70%, 5.61%), Food101(Simplified) is (7.42%, 1.48%, 2.46%), and VireoFood-172 is (19.10%, 13.05%, 14.99%), respectively. Figure 21 shows results of ingredient detection on randomly selected images.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Datasets** & **Precision** & **Recall** & **F1-Score** \\ \hline
**Food101** & & & \\ ResNet-50 & 83.42 & 59.34 & 69.35 \\ InceptionResNetV2 & 79.39 & 76.54 & 77.94 \\ PKELM (RBF) & 87.56 & 87.81 & 87.58 \\ PKELM (chi-squared) & 85.74 & 83.24 & 83.55 \\
**Food101(Simplified)** & & & \\ ResNet-50 & 72.17 & 68.53 & 70.30 \\ InceptionResNetV2 & 76.10 & 76.41 & 76.25 \\ PKELM (RBF) & 82.89 & 83.45 & 83.02 \\ PKELM (chi-squared) & 83.52 & 77.89 & 78.71 \\
**VireoFood-172** & & & \\ ResNet-50 & 70.38 & 70.93 & 70.65 \\ InceptionResNetV2 & 70.40 & 70.94 & 70.67 \\ PKELM (RBF) & 87.98 & 87.18 & 87.02 \\ PKELM (chi-squared) & 89.51 & 84.00 & 85.67 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of final measures of food ingredient recognition for Food101 and VireoFood-172 datasets.  
Figure 20: VireoFood-172 classification results (**a**) Worst performing classes (**b**) Confused classes (**c**) Best performing classes.
###### Abstract  
This paper proposes an optimal control scheme based on a synchronized phasor (synchronosp) for power system secondary voltage control. The framework covers voltage stability monitoring and control. Specifically, a voltage stability margin estimation algorithm is developed and built in the newly designed adaptive secondary voltage control (ASVC) method to achieve more reliable and efficient voltage regulation in power systems. This new approach is applied to improve voltage profile across the entire power grid by an optimized plan for VAR (reactive power) sources allocation; therefore, voltage stability margin of a power system can be increased to reduce the risk of voltage collapse. An extensive simulation study on the IEEE 30-bus test system is carried out to demonstrate the feasibility and effectiveness of the proposed scheme.  
PHASOR measurement unit; power system control; synchronized phasor; secondary voltage control +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### VI Selection  
The measured Vis/NIR spectral reflectance data from leafy vegetables can be used as VIs [35]. In the Vis/NIR region, because photosynthetic pigments of leafy plants, such as chlorophyll a, b, and carotenoids, convert light energy to chemical energy in the blue and red regions (450-470 nm and 660 nm), they are known as crucial indicators representing vegetation vigor and growth of agricultural crops [36; 37]. The main multispectral bands used for VI analysis of agricultural crops are blue (450-510 nm), green (510-580 nm), red (630-690 nm), and NIR (770-895 nm) [38]. Therefore, VIs created using Vis/NIR spectral and/or imaging data can provide valuable phenotyping features and vegetation properties, such as the spatial distribution of vegetation [39], drought and water stress monitoring [30], forecasting crop yield [30], and precision farming applications [40].  
Among the various VIs, NDVI, RE/R, and PRI were calculated from sample images. As the reflective spectra of leafy crops vary with environmental conditions, NDVI, RE/R, and PRI are the major indicators representing the productivity and vegetation vigor of agricultural crops [30; 31; 32].  
NDVI is primarily used to quantify vegetation greenness and retrieve the biophysical characteristics of vegetation by measuring the difference between NIR (which vegetation strongly reflects) and red regions (which vegetation absorbs) [41]. RE/R, which includes data from the red edge and red bands (700-740 nm), is also an effective indicator of the chlorophyll content in agricultural crops [32] and is known to increase the sensitivity of biomass estimation in moderate to high vegetation [32]. Moreover, RE/R values can be affected by moisture deficit stress, heat stress, and changes in various environmental factors [42; 43]. The PRI has been widely used as an indicator of photosynthetic efficiency in leafy plants [31].  
Therefore, these calculated VIs were used to analyze the discriminant performance of the newly developed PLS-DA and LS-SVM models. To accomplish this, multiple comparison analysis (MCA) and Bonferroni correction were used based on the spectral information obtained from Chinese cabbage grown at different temperatures. These methods allow comparison of the calculated values of VIs with PLS-DA results and correct errors that may occur in multiple comparisons. For detailed information about the MCA and Bonferroni correction, readers can refer to the following articles [44; 45].
## 2 Literature Review on Noise Criteria and Regulations  
### Korea  
In Korea, national regulations and recommendations for indoor noise levels with respect to water supply and drainage noises in multi-residential buildings are currently absent, although a KS (Korean Standard) [6; 7] that is related to the method for measurement and evaluation has been issued. On the other hand, national regulations on acceptable indoor noise levels are implemented for floor impact noise and airborne noise from neighbors [8; 9]. The minimum requirement for the indoor noise level from external traffic noise is also managed by a relevant national regulation [10]. As described in Table 1, the regulations for the indoor noise level in \(\mathrm{L_{eq}}\) (equivalent continuous A-weighted sound-pressure level) during the daytime ranges between 43 dBA and 45 dBA, while the 5 dBA for night-time is stricter. Impulsive noise such as floor-impact noise is regulated at less than 57 dBA in \(\mathrm{L_{max}}\) (maximum A-weighted sound-pressure level) during the daytime.  
The G-SEED is a Korean certification system that was created to accomplish the construction of environmental-friendly buildings. Within G-SEED, the measures for the reduction of water supply and drainage noises are assessed according to four tiers, as described in Table 2. The tier in Table 2 is determined by checking whether building elements and construction methods that generate low noise levels have been applied or not. The highest point is four, and is given to the construction method called \"in-floor type\", whereby the pipes on the slab are installed at the same floor.  
In Korea, the field measurements of water supply and drainage noises are carried out based on KS F 2870 (supply noise) and KS F 2871 (drainage noise). The standards describe factors such as the location and height of microphones, room conditions, and background-noise correction. Alternatively, KS F 2872 [11] is the rating method for water supply noise based on the NC-curve and A-weighted SPL (sound-pressure level).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Indoor Noise Level (dBA)**} \\ \cline{3-4}
**Type of Noise Sources** & **Noise Index** & \multicolumn{2}{c}{**Daytime**} & \multicolumn{2}{c}{**Night-Time**} \\  & & **(06:00 to 22:00)** & **(22:00 to 06:00)** \\ \hline Structure-borne noise (e.g., floor-impact noise) & \(\mathrm{L_{eq}}\) & 43 & 38 \\  & \(\mathrm{L_{max}}\) & 57 & 52 \\ Airborne noise (e.g., TV noise from neighbor) & \(\mathrm{L_{eq}}\) & 45 & 40 \\ Road traffic noise & \(\mathrm{L_{eq}}\) & & 45 \\ Water supply and drainage noise & None & None & None \\ \hline \hline \end{tabular}
\end{table}
Table 1: Korean regulation on acceptable indoor noise level for different noise sources.