esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliable Augmentation and Precise Identification of EPG Waveforms Based on Multi-Criteria DCGAN
## 2 Materials and Methods
### Evaluating Indicators of Generated Data
#### 2.4.3 Visual Discrimination of Generated Data  
To further verify the quality of the generated data, a visual evaluation was conducted by three experts specializing in psyllid EPG signals. The experiment involved three repeated random selections of 175 E1 waveforms from the dataset, including 75 original and 100 E1 waveforms generated by the DCGAN model. The real and generated waveforms were randomly assigned identifiers, with the source of each waveform corresponding to its identifier. The experts were tasked with determining whether each waveform was an E1 waveform and whether it was real or generated. Here, \(ACC^{d}\) represents the accuracy rate of the expert visual discrimination, \(TPR^{d}\) is the proportion of true E1 waveforms correctly identified as real, and \(FPR^{d}\) is the proportion of generated E1 waveforms mistakenly identified as true. The formulas for calculating \(ACC^{d}\), \(TPR^{d}\), and \(FPR^{d}\) are shown below:  
\[ACC^{d}=\frac{TT^{1}+FF^{1}}{TT^{1}+TF^{1}+FT^{1}+FF^{1}} \tag{8}\]  
\[TPR^{d}=\frac{TT^{1}}{TT^{1}+TF^{1}} \tag{9}\]  
\[FPR^{d}=\frac{FT^{1}}{FT^{1}+FF^{1}} \tag{10}\]  
where, if the waveform is identified as E1 and considered real, it is marked as 'True' with the symbol \(T\). If the waveform is identified as generated or not genuinely E1, it is marked as 'False' with the symbol \(F\). T1 and T2 represent the numbers of real and generated E1 waveforms, respectively.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 4 Challenges in Indoor Localization Systems
### NLOS Conditions  
NLOS conditions pose a significant challenge, particularly in military and emergency response operations. In GNSS-denied environments, such as urban warfare zones or collapsed buildings, signals from RFID, UWB, and ZigBee systems can be obstructed by walls, debris, and other obstacles [36]. For example, the Pouet system integrates multiple technologies like GPS, LoRa, and UWB to maintain reliable tracking despite NLOS conditions [36]. In healthcare, NLOS issues arise when tracking patients through dense hospital corridors or rooms, necessitating hybrid solutions to ensure continuous localization [17].
# Watermarking Tiny MLCommons Image Applications Without Extra Deployability Costs
## 7 Results
### White-Box Watermarking Results and Using a Custom Regularizer
#### 7.1.2 Image Classification Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Total** & \multirow{2}{*}{**Scale**} & \multicolumn{2}{c}{**Training Accuracy**} & \multicolumn{2}{c}{**Test Accuracy**} & \multirow{2}{*}{\(b\)**-Length**} & \multicolumn{2}{c}{**Bits**} \\
**Numbers of Epochs** & & & **[\%]** & & **[\%]** & & **\(\
eq b\)** \\ \hline \(1\) & 0.01 & 46.39 & 51.54 & 256 & 79 & 30.86 \\ \hline \(1\) & 0.02 & 47.13 & 54.22 & 256 & 83 & 32.42 \\ \hline \(1\) & 0.05 & 47.77 & 50.13 & 256 & 78 & 30.47 \\ \hline \(2\) & 0.01 & 60.75 & 55.57 & 256 & 72 & 28.52 \\ \hline \(5\) & 0.01 & 71.25 & 65.58 & 256 & 69 & 26.95 \\ \hline \(10\) & 0.01 & 78.65 & 65.52 & 256 & 70 & 27.34 \\ \hline \(30\) & 0.01 & 76.31 & 67.33 & 256 & 59 & 23.04 \\ \hline \(\mathbf{100}\) & **0.01** & **83.78** & **71.02** & **256** & **64** & **25.39** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results using Section 6.1 case with the random type of Section 5.2 case. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Total** & \multirow{2}{*}{**Scale**} & \multicolumn{2}{c}{**Training Accuracy**} & \multicolumn{2}{c}{**Test Accuracy**} & \multirow{2}{*}{\(b\)**-Length**} & \multicolumn{2}{c}{**Bits**} \\
**Numbers of Epochs** & & & **[\%]** & & **[\%]** & & **\(\
eq b\)** \\ \hline \(3\) & 0.01 & 67.58 & 66.84 & 256 & 78 & 34.77 \\ \hline \(3\) & 0.02 & 66.16 & 66.34 & 256 & 84 & 32.81 \\ \hline \(3\) & 0.05 & 66.42 & 66.87 & 256 & 92 & 35.94 \\ \hline \(6\) & 0.01 & 71.72 & 71.47 & 256 & 83 & 24.22 \\ \hline \(15\) & 0.01 & 75.80 & 70.33 & 256 & 78 & 30.47 \\ \hline \(30\) & 0.01 & 87.48 & 66.62 & 256 & 76 & 29.68 \\ \hline \(90\) & 0.01 & 83.53 & 78.70 & 256 & 82 & 32.03 \\ \hline \(\mathbf{100}\) & **0.01** & **94.46** & **74.37** & **256** & **78** & **30.46** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results achieved using the Section 5.1 case with the diff type of Section 6.1.2. Tables 6-8 suggest that the best BER results have been achieved with the watermark matrix initialized with the direct form. Also, in this case, by increasing the number of epochs, an improvement in the training accuracy, test accuracy, and BER metric was observed. Training and evaluation were conducted for 100 epochs using only the scale factor \(k\) equal to 0.01, with the best value aiming to maintain a good balance between the original task and the watermark detectability.
## 2 Materials and Methods
### Land Cover Classification
#### 2.3.1 Data Acquisition
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor** & **Capture Date** & **Capture Time** & **Visible Islands** & **Cloud Cover (\%)** \\ \hline Sentinel-2 & 21-February-17 & 11:33:01 AM EST & Isabela, Santa Cruz, & 0 \\  & & & Floreana & 0 \\ Sentinel-2 & 10-March-19 & 12:23:11 PM EST & San Cristobal & 22 \\
4-Band PlanetScope & 10-March-18 & 10:47:46 AM EST & Floreana & 0 \\
4-Band PlanetScope & 10-March-18 & 10:46:47 AM EST & Floreana & 0 \\
4-Band PlanetScope & 11-April-18 & 11:52:07 AM EST & Isabela & 0 \\
4-Band PlanetScope & 11-April-18 & 11:52:08 AM EST & Isabela & 0 \\
4-Band PlanetScope & 11-April-18 & 11:52:09 AM EST & Isabela & 0 \\
4-Band PlanetScope & 9-March-18 & 10:48:35 AM EST & Santa Cruz & 1 \\
4-Band PlanetScope & 9-March-18 & 10:48:36 AM EST & Santa Cruz & 1 \\
4-Band PlanetScope & 9-March-18 & 10:48:37 AM EST & Santa Cruz & 0 \\
4-Band PlanetScope & 10-March-18 & 10:47:34 AM EST & Santa Cruz & 0 \\
4-Band PlanetScope & 10-March-18 & 10:47:35 AM EST & Santa Cruz & 0 \\
4-Band PlanetScope & 10-March-18 & 10:45:18 AM EST & San Cristobal & 2 \\
4-Band PlanetScope & 10-March-18 & 10:45:19 AM EST & San Cristobal & 1 \\
4-Band PlanetScope & 31-March-18 & 11:44:57 AM EST & San Cristobal & 1 \\
4-Band PlanetScope & 31-March-18 & 11:44:58 AM EST & San Cristobal & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Multispectral images used for land cover classification of the Galapagos agricultural zone. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Island** & **Number of UPAs Surveyed** & **UAV Survey Area (ha)** & **Percentage of Study Area** \\ \hline Santa Cruz & 26 & 566.89 & 1.92 \\ San Cristobal & 38 & 503.37 & 2.92 \\ Isabela & 12 & 124.18 & 0.98 \\ Floreana & 7 & 57.85 & 3.35 \\ \hline All islands & 83 & 1252.29 & 2.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Extent of surveyed area per island.
## 4 Performance Validation
### Performance Metrics of Scheduling Algorithm
#### 4.1.3 Context Switching  
Context switching is defined as switching from one process to another. It measures how often the scheduler must switch between processes and can determine the overhead of a scheduling algorithm. Context switching can be costly in terms of both time and resources and can impact the overall performance of a system.  
To sum up, evaluating the performance of scheduling algorithms requires assessing three key performance measures: waiting time, turnaround time, and context switching [8; 9; 10]. They can determine a scheduling algorithm's efficiency, fairness, responsiveness, and overhead. In the next section, we will compare and contrast the results of various scheduling algorithms based on these performance measures.
# Reputation Consensus Mechanism for Blockchain Based on Information-Centric Networking
## 4 Design
### Hybrid Consensus Mechanism  
To address the inefficiency of existing consensus mechanisms in handling network heterogeneity, we introduce a hybrid consensus mechanism based on a reputation model. The hierarchical design of the hybrid consensus module is illustrated in Figure 6. This mechanism builds upon the Casper FFG and the the Latest Message Driven Greedy Heaviest Observed Subtree (LMD GHOST) fork-choice rule. By incorporating a reputation-based consensus mechanism for checkpoint blocks, we enhance the checkpoint block proposal process in Casper FFG. Simultaneously, traditional proposal mechanisms (PoW/PoS) are employed for regular blocks. This design overlays an additional layer of reputation-based  
Figure 5: Evolution of the reputation value over time under different initial conditions.  
consensus on existing proposal mechanisms, effectively improving consensus efficiency while ensuring the fairness of the blockchain system.  
The block structure of the specific consensus mechanism is depicted in Figure 7. In Ethereum's Casper FFG, time is divided into multiple epochs based on the number of blocks, with each epoch containing multiple blocks. In each epoch, the block created in the first slot serves as a checkpoint. If a checkpoint receives votes from more than two-thirds of validators by the end of the epoch, it is finalized. In this mechanism, Proof of Work (PoW) is used for producing regular blocks, while PoS is employed for producing checkpoint blocks to achieve network finality. Validators vote to confirm checkpoint blocks, aiming to leverage the efficiency and environmental friendliness of PoS while maintaining the high security and decentralization provided by PoW. However, this consensus mechanism does not resolve the inefficiency caused by network heterogeneity; nodes with lower network capabilities become bottlenecks in block production and propagation, thereby reducing consensus efficiency.  
In the block structure of the hybrid consensus mechanism proposed in this study, PoW/PoS is used for producing regular blocks, while Proof of Reputation (PoR) is utilized for producing checkpoint blocks to achieve network finality. The reputation-based consensus mechanism selects ICN consensus nodes with superior network service quality and performance as block proposers responsible for creating checkpoint blocks. Unlike Casper FFG, which selects proposers through a random algorithm, our method employs the PoR consensus mechanism to reduce the likelihood of proposer nodes experiencing network latency fluctuations. By utilizing ICN consensus nodes as proposers, we can leverage ICN's multicast and caching technologies to accelerate block dissemination, thereby expediting network finality and enhanc  
Figure 6: Hierarchical design of the reputation-based hybrid consensus mechanism.  
Figure 7: Block structure of the consensus mechanism.
# A Time-Varying Filter for Doppler Compensation Applied to Underwater Acoustic OFDM
## 3 Doppler Compensation for Multicarrier Modulation
### Compensation at the Receiver
The OFDM block duration is \(T_{b}=1.9\) s.  
Figure 6: An example of linear search for time-scaling factor estimation in a packet of five OFDM blocks. The points for which the energies are maximized show the optimum time-scaling factors. Since the time-scaling factor changes continuously in time, the received packet needs to be compensated with a time-varying resampling process. In this work, linear interpolation of estimates between the OFDM samples is adopted. Once the time-varying time-scaling factor over the packet is approximated by interpolation, the passband received signal can be resampled in a time-varying manner using the TVFDL. An example of the approximation of the time-varying delay is shown in Figure 7. In this channel simulation, the relative velocity between the receiver and transmitter is increasing continuously from \(-1\) m/s to \(2.5\) m/s. The negative speed shows a positive delay and the positive speed creates a negative delay. The delay varies with time following a quadratic function because the acceleration rate remains constant for the packet duration. The time-scaling estimator accuracy is limited. As shown in Figure 7, the linear interpolation can effectively approximate the time-varying time-scaling factor and consequently time-varying delay. However, this approximation has a residual error that can create considerable frequency-dependent frequency shifts after resampling. These residual frequency shifts can be modeled as a common frequency shift for all sub-carriers, if they are smaller than the frequency separation between the sub-carriers, \(\Delta f\). This means that they can be canceled out by an opposite frequency shift. By this assumption, the maximum frequency shift is defined by  
\[\mu_{max}f_{max}=|(1-\alpha_{max})|f_{max}<\Delta f, \tag{30}\]  
where \(f_{max}=f_{c}+(N/2-1)\Delta f\) is the maximum frequency component of the OFDM signal in passband. Then, the maximum tolerable Mach factor is  
\[\hat{\mathbf{x}}=\tilde{\mathbf{A}}_{\alpha}^{\dagger}\mathbf{D}_{\alpha}^{H} \mathbf{r}. \tag{25}\]0  
This indicates that in a mobile UWA communication system design, choosing the optimum frequency separation and central frequency as well as the optimum sub-carrier allocation should be considered carefully. In the next section, the performance of the proposed Doppler compensation to track the time-scaling is presented for two realistic datasets. \[\hat{\mathbf{x}}=\tilde{\mathbf{A}}_{\alpha}^{\dagger}\mathbf{D}_{\alpha}^{H} \mathbf{r}. \tag{25}\]1 An example of time-varying delay estimation. The delay of the strongest path changes with time following a quadratic function.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 3 Analysis of the Characteristics of Relative State Measurement Technology
### Composite Relative Measurement Technology of Spacecraft Formation  
GPS measurement technology is mature, small in size, and light in weight, and can provide spacecraft speed and position information [57]. RF measurement technology has high real-time accuracy and high coverage [49], but RF and GPS signals are easily blocked and interfered with. Laser measurement has high accuracy and offers system robustness [58], but the disadvantage is that the beam is narrow and requires other measurement units for auxiliary guidance. Visible-light visual measurement technology [59] is only suitable for close-range formation navigation since it needs to avoid interference from factors such as strong light and space radiation. Infrared technology [33] offers good concealment but can only provide angular information. It cannot be used as an independent measurement method. Various measurement methods offer their own advantages. In order to achieve complementary advantages in principle, more laboratories have begun to study combined measurement methods, which has greatly boosted the development of multi-sensor fusion measurement technology.
# Road-Side Unit Anomaly Detection
## 2 State of the Art
### Clustering Algorithms
#### 2.3.2 Partitioning-Based Algorithms  
The k-means [28] algorithm is used to select at random k (k is a number defined by the user) data points, to consider them as a cluster and to regroup the other points in the closest cluster until all points are inside a cluster. The center of the cluster is the mean of all the data points of the cluster and it is updated after a new point is added to the cluster. It is light and efficient for many small datasets.  
The K-Medoids [29] algorithm works as the k-means algorithm but does not rely on an average of a cluster center but on the most representative data point of the cluster.
## 1 Introduction
Nevertheless, previous studies have focused on using one or more DL or ML methods to predict the same air quality dataset, and most studies have focused on predicting the air quality index (AQI). The novelty of this study is in applying four DL and two ML methods in the same work and performing the daily predictions of air pollution, which is more difficult to predict due to higher variability compared to AQI prediction. Due to the lack of a reliable emission inventory in Macau, developing an accurate air pollution prediction model in recent years has been very difficult. With the advancements in machine learning algorithms, a reliable and precise air pollution prediction method was developed in this work, which is also the novelty of this study. The air pollution and weather variables were considered in this work because it is known that weather conditions significantly contribute to the levels of air pollutants. Also, this is the first study in the Macau region combining DL and ML methods in air pollution predictions using Macau's dataset. As an effort to ensure the excellent health and well-being of the residents (in compliance with SDG 3: Good Health and Well-Being), construct a sustainable, safe, and resilient city (in compliance with SDG 11: Sustainable Cities and Communities), and take urgent action to combat climate change (in compliance with SDG13: Climate Action), a reliable and accurate air pollution prediction model must be built to warn the residents ahead of an air pollution episode, which ensures that vulnerable groups of people with pre-existing health conditions such as heart or lung diseases avoid outdoor activities during poor air quality days and is also an effort to create safe and sustainable cities for Macau. By accurately identifying and reducing air pollution problems, the effects of climate change may also be mitigated for a double win. Hence, using computational ML methods may be a solution for accurate air pollution predictions in Macau, and this study will investigate the efficiency and accuracy of the proposed models.
## 3 Results
In order to determine the back-reflection power, a monitor was positioned in the input waveguide to capture all the light that was returning from the MMI coupler, as shown in Figure 8. Table 3 displays the back-reflection losses that were determined by FDTD simulation. As anticipated, the SiN MMI coupler waveguides achieved a decreased back reflection in the C-band spectrum without the need of a special angled MMI. For all FDTD simulations, the ideal x, y, and z axis grid sizes were set to 10 nm to achieve acceptable mesh convergence. In order to highlight the benefits of SiN MMI technology over existing demultiplexer designs, comparisons of our design's essential properties to those of previously published studies were made. The SiN MMI demultiplexer design suggested in this work is compared to various types of demultiplexers in Table 4. Insertion losses, crosstalk, bandwidth, band range, back-reflection loss, and device footprint are the main factors that were compared. Table 4 suggests that our solution outperforms existing demultiplexer designs in three categories: lower insertion losses, improved crosstalk, and increased bandwidth. Additionally, the majority of studies do not account for back-reflection loss. Furthermore, our design uses SiN waveguide technology, which is anticipated to have a lower back-reflection loss and can be used with lasers susceptible to the back-reflection phenomenon. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(\lambda\) (nm)} & **1530–1540** & **1540–1560** & **1530–1560** & **1530–1570** \\  & **(10 nm Spacing)** & **(20 nm Spacing)** & **(30 nm Spacing)** & **(40 nm Spacing)** \\ \hline L\({}_{\text{MMI}}\) (\(\mu\)m) & 4154 & 2067 & 1374 & 1030 \\ \hline IL (dB) & 0.974 & 0.915 & 0.909 & 0.902 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Coupling length behavior for different wavelength spacing channels. Figure 8: Monitor placement illustration in FDTD simulation.
## 2 Materials and Methods
### Performance Evaluation  
The trained CNN-LSTM was evaluated on a hold-out test set, to which no data augmentation operations were applied. The hold-out test set also featured the original class imbalance, where only approximately 20% of images were positive labels. We evaluated our model's performance using precision, recall, F1-score, accuracy, and the AUC by comparing the models predicted label to the ground truth label of each CFP. The metrics were determined as follows: precision (number of true positives/total number of true and false positives), recall (total number of true positives/total number of true positives and false negatives), F1-score (2 \(\times\) precision \(\times\) recall/[precision + recall]), and accuracy (number of true positives and true negatives/total number of entries); AUC was calculated as the area under the receiving operator curve using the sci-kit learn Python module [47].  
Additionally, Grad-CAM [44] was used to visualize and explain the model's learning process. Grad-CAM uses the gradients entering a specific layer of a model to generate a heatmap, highlighting areas in an input most important for predicting the output. The layers used to generate the heatmaps were the \"expand_conv_depthwise\" and \"out_relu\" layers, which correspond to the fifth and third-to-final layers, respectively, in the CNN model. All layers present in MobileNetV2 are attached in the Supplementary Materials in Table S1.
# Experimental Study on Two PCM Macro-Encapsulation Designs in a Thermal Energy Storage Tank
## 1 Introduction
In particular, the PCM tank is used in the system as a thermal buffer to store the solar energy at low-grade temperature (\(15\pm 5\,^{\circ}\)C) to be supplied to a novel seasonal TES based on selective water sorbent materials. To the best of the authors knowledge, very few experimental studies on PCM tanks with rectangular slabs were published in the literature. One of the first papers was published by [PERSON] et al. [31] in which the performance of a TES tank filled with commercial PCM encapsulated in rectangular slabs was compared with the same tank filled with water. The results showed that the energy storage capacity of the tank filled with PCM was increased by 35.5% compared with the same tank filled with water. Another study published by [PERSON] and [PERSON] [32] reported the results of experimental tests performed to study the behavior of a real-scale PCM storage at different operating conditions. One of the main conclusions from the study was that the PCM hysteresis and sub-cooling effects deviate the expected behavior from the experimental results. [PERSON] et al. [33] used the experimental results obtained from the testing of a PCM tank filled with rectangular slabs containing a PCM with a sub-zero melting temperature (\(-26.7\,^{\circ}\)C) suitable for refrigerated transport, and glycol as heat transfer fluid. The developed model was based on a one-dimensional approach considering the temperature variations along direction of the heat transfer fluid showing a good agreement with the test. All experimental studies mentioned were carried out using a fixed design of the PCM tank without changing any boundaries related to the geometry or the configuration of the storage tank. However, the geometrical design of the PCM encapsulation has a large influence on the thermal behavior of the PCM affecting the melting and the solidification process, and consequently the heat transfer [34]. In the case of rectangular shapes, the aspect ratio (height to width ratio) is a parameter that has to be taken into account in the design of TES tanks [21]. This paper shows for the first time a comparison based on experimental results of the thermal behavior of two different designs of macro-encapsulation of rectangular PCM slabs. The behavior of a thermal energy storage tank was analyzed using commercial PCM slabs with different thicknesses. The comparison of the two designs was done in terms of temperature profile, heat transfer rate, and energy obtained during the discharging process. The main results obtained from the experimental tests reported in this paper can be used as a reference for institutions and manufacturers to optimize future designs of PCM tanks.