esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Oversampling Technique  
The oversampling techniques have been continuously studied to solve the class imbalance problem. By using linear interpolation, it is possible to regenerate a small number of existing sample classes into a new balanced dataset. Thus, the oversampling technique could be able to expand to its original role while protecting important information in the original dataset. SMOTE, as introduced by [PERSON] et al. (2002) [22], remains one of the most widely adopted techniques for addressing class imbalance, while [PERSON] et al. (2018) [23] emphasize the importance of combining oversampling and undersampling for robust performance.  
Synthetic minority sampling technique (SMOTE) algorithm oversampling the minority class, thus generating synthetic examples instead of simple random oversampling. Since SMOTE generates synthetic data points in the feature space, the overfitting problem in the minority class generation process can be avoided. The newly generated synthetic data are extracted through interpolation, and it can be said that the original dataset still has significance. In addition, for each minority class instance, values are interpolated using the k-nearest neighbor technique, and attribute values are generated for new data instances. A new synthetic data point is created for each minority sample data by taking the difference between the minority sample class feature vector and the nearest neighbor belonging to the same sample class and multiplying it by a random number between 0 and 1 and then adding the results back to the minority sample class feature vector. This creates a random line segment between every pair of existing features. This results in a new instance generated within the dataset [24]. The cycle is replicated for the remaining minority sample data.  
The ADASYN algorithm is an extended approach of the SMOTE algorithm, and was proposed by [PERSON] in 2008 for the purpose of learning unbalanced data [25]. ADASYN adds the concept of weighting according to the density of majority-class samples around the sample when selecting a minority-class sample. The main idea of the ADASYN algorithm is to use a density distribution. On the other hand, the SMOTE algorithm generates an equal number of synthetic data samples for each minority data sample class chosen with equal chances. Therefore, the ADASYN algorithm could automatically evaluate the number of synthetic data samples to generate for each example of minority-sample class data.
## 2 Overview of Multi-Link Operation in IEEE 802.11 be WLANs
### Multi-Link Transmission and Reception
#### 2.2.1 One-to-One TID-to-Link Mapping (Flow-Level Aggregation)  
In this mapping strategy, each TID is mapped to a single link, meaning that traffic with a given TID will be transmitted using the associated dedicated link only. Distributing individual traffic flows defined by different TIDs to different links in this way will lead to increased throughput and decreased latency. However, since TID-to-link mapping updates may not be frequent, the effect of the throughput increase achieved with this strategy is somewhat limited; even if the amount of traffic associated with a particular TID is large and the corresponding link queue becomes congested, other links cannot be used for this TID. For the same reason, however, this mapping strategy is beneficial for high-priority TIDs because it physically isolates the associated link resources from other lower-priority TIDs.  
Figure 1: Multi-link system architecture.
## 3 Results and Discussion
### Validation of Statistical Analysis  
In this section, the lateral stress increases in the study are compared with the independent dataset (15 lateral stresses) obtained from the experimental setup, as shown in Figure 13. Here, the independent dataset was substituted into Equation (5), and a validation set was created. The results were similar and compatible.  
To test the reliability and validity of the experimental lateral stresses obtained in this part of the study, the results from Equation (5) were compared with the formulation presented in Frazee (2021) [44] When the experimental results were compared with the results in Figure 14a, it was observed that the lateral stress values were compatible for Dr = 10% Figure 14b. In the formulation given for the finite area load in Frazee (2021) [44], it was stated that \(\
u\) = 0.5 was used, and no formulation was provided for other values of the Poisson ratio. Therefore, compatible results were obtained only for a relative stiffness value of 10%.
# The Big Picture: An Improved Method for Mapping Shipping Activities
## 2 Background  
In this section, we provide important definitions regarding vessel movement and density map visualization. Moreover, we provide a brief review of past work concerning vessel trajectory reconstruction and vessel identification through satellite imagery.
## 4 IRS-Aided Wireless Network Performance and Deployment  
An IRS improves the performance of a wireless network by transforming the propagation channel into a controllable radio environment, thereby realizing better SE and EE [12]. The ability to control the radio environments changes the design and optimization of wireless networks. Manipulation of the EM waves with the use of software overcomes the highly probabilistic nature of EM propagation. An IRS can prevent the effect of multipath fading by coherently combining the reflected, refracted, and scattered radio waves at the desired destination [90].
## 2 Spatiotemporal Characteristics of Diversified Loads in Multiple Functional Areas
### Characteristics of the Loads Suitable for Internal Transmission in Functional Areas
#### 2.1.2 Characteristics of the Cooling Loads  
The temporal characteristic of the cooling systems is similar to that of the heating systems. There is large time inertia constant and a certain 'energy storage' capacity in the cooling systems. Its 'energy storage' capacity is affected by the inertia of the cooling systems. The indoor temperature of the current period is affected by the indoor temperature, the outdoor temperature, and the cooling power of the previous several periods. The cooling systems can change the cooling loads from a curve to an interval on the time scale by utilizing the inertia of the cooling systems.  
Unlike centralized heating, centralized cooling has low efficiency, and it just can be efficiently transmitted within 1 km [23]. Considering that the relative distance between the inner parts of each functional area has exceeded the distance suitable for centralized cooling, the cooling loads are usually supplied by distributed cooling systems and balanced within each functional area. There are two main kinds of refrigerators which are common cooling loads: electric refrigerators and lithium bromide absorption refrigerators. Lithium bromide absorption refrigerators can directly utilize excess heat or waste heat to refrigerate. However, when the waste heat energy of heating network is less and does not necessarily cover the demand area of cooling loads, and renewable energy is dispersed and has surplus output, an electric chiller is suitable to realize distributed cooling. The cooling loads of different functional areas also have different characteristics. In this paper, the cooling loads of winter are mainly considered; therefore, cold storages and warehouses in the commercial area and the industrial area are the main cooling loads. A cooling load also has a certain elastic range for its environmental temperature requirements, and the requirements of different functional areas are different. For example, the temperature of commercial cold storage of meat and aquatic products should be \(-10^{\circ}\mathrm{C}\sim-18^{\circ}\mathrm{C}\), and that of industrial warehouse of goods should be \(-15^{\circ}\mathrm{C}\sim-20^{\circ}\mathrm{C}\).  
The dynamic temperature characteristics of cooling systems [24] can be described by the Equivalent Thermal Parameters (ETP) model as  
\[\widetilde{T}_{\mathrm{n,t}}=\widetilde{T}_{\mathrm{w,t}}-Q_{\mathrm{L,t}}D- [\widetilde{T}_{\mathrm{w,t}}-Q_{\mathrm{L,t}}D-\widetilde{T}_{\mathrm{n,t-1 }}]\cdot\mathrm{e}^{-\frac{\Delta t}{\beta\mathrm{C}}} \tag{6}\]  
It shows that the indoor temperature of a cooling building in a certain period of time is the result of the combined action of cold supply of the cooling system in the past several periods.
## 4 Simulation and Evaluation
### Performance Evaluation
#### 4.2.1 Energy Efficiency  
In WSNs, there are \(N\) nodes. Residual energy model \(e_{i}\) in node \(i\) is denoted by Equation 1:  
\[e_{i}=UI_{i}t \tag{1}\]  
There \(t\) is the left work time of node \(i\), so the average residual energy of WSNs \(\overline{E}\) can be obtained from Equation 2.  
\[\overline{E}=\frac{1}{N}\sum_{i=1}^{n}e_{i} \tag{2}\]  
Figure 2: 1000-Nodes Random Network  
For different routing protocols, lager \(\overline{E}\) means more energy efficiency after WSNs experience same time under the same conditions.  
Figure 3 shows the comparison of average energy consumption over 10000 seconds, where node number N=100, \(k\)=9. As a result, it is obvious that LQER can save more energy than MHFR and MCR, especially with time passed. This is because it is not enough to consider only cost and hop count in case the link quality is poor. If the poor link is chosen to deliver the data, loss rate will be high and retransmission will cause extra energy consumption, at the cost of lifetime of WSNs.
## 4 Implementation and Discussion
### The Network Performance Result  
This section highlights the simulation results based on the network performances. The delays for the transmission and processing of messages and also the signaling overhead are described here in detail. The scenarios of suburban and urban are considered to evaluate the performances of the network.
## 4 Results
### Model Performance and Evaluation
#### 4.3.4 Gravel  
The maintenance model for a bridge deck with a wearing surface is influenced by deterioration, along with location attributes such as deck area, longitude, and latitude, which play pivotal roles in predicting maintenance for a bridge deck with Gravel wearing surface. Additionally, it is evident that bridge decks with Gravel wearing surfaces exhibit a wide range of deterioration scores. Intervened bridge decks have a lower median deterioration score of \(-0.095\) (faster deterioration) than non-intervened bridges (\(-0.076\)). The distribution of longitude indicates that intervened bridges have slightly median higher values (\(-97.43\)) than non-intervened bridges (\(-97.67\)), as seen in Figure 10. Furthermore, considering the deck area reveals that the median deck area is higher within the intervened distribution compared to the non-intervened group.  
The two-PDP demonstrates the interaction between deck area and deterioration, the two top influential variables in modeling bridge decks with Gravel wearing surfaces, as shown in Figure 11. The PDP suggests higher deterioration score (low deterioration rate) values are associated with no intervention, whereas lower deterioration score (higher deterioration rate) values are associated with the intervention of the bridge deck; darker shades and lower contour values represent this. Specifically, there is a significant intervention pattern with respect to low deterioration scores for deck areas higher than 50, as represented by lighter shade and higher contour values. Overall, the relationship between the deck area and deterioration across all ranges of values is complex.  
Figure 10: Distribution of influential variables with respect to repair and non-repair groups in predicting bridge deck maintenance with Gravel wearing surface. Deterioration, Longitude, and Deck Area are the most influential variables.
# Incrementally Mining Column Constant Biclusters with FVSP Tree
## 3 Experiments
### Experimental Results
#### 3.2.3 Testing on the Mushroom Dataset  
Table 7 shows the experimental results on the mushroom dataset. Mining CCB in an incremental way costs less in terms of runtime and memory than mining in a batch way on all minimal support rates.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Minimal Support Rate**} & \multirow{2}{*}{**Methods**} & **Runtime (s)** & **Memory (MB)** \\  & & **(Mean \(\pm\) std)** & **(Mean \(\pm\) std)** \\ \hline \multirow{2}{*}{0.01} & Incremental & **15.76 \(\pm\) 0.88** & **56.62 \(\pm\) 0.68** \\ \cline{2-4}  & Batch & 28.72 \(\pm\) 1.07 & 96.72 \(\pm\) 0.04 \\ \hline \multirow{2}{*}{0.008} & Incremental & **15.96 \(\pm\) 0.22** & **57.22 \(\pm\) 0.13** \\ \cline{2-4}  & Batch & 28.60 \(\pm\) 0.83 & 97.06 \(\pm\) 0.11 \\ \hline \multirow{2}{*}{0.006} & Incremental & **16.41 \(\pm\) 1.50** & **57.54 \(\pm\) 0.08** \\ \cline{2-4}  & Batch & 28.94 \(\pm\) 1.05 & 97.26 \(\pm\) 0.11 \\ \hline \multirow{2}{*}{0.004} & Incremental & **16.98 \(\pm\) 0.32** & **58.4 \(\pm\) 0.14** \\ \cline{2-4}  & Batch & 29.71 \(\pm\) 0.35 & 98.04 \(\pm\) 0.11 \\ \hline \multirow{2}{*}{0.002} & Incremental & **23.15 \(\pm\) 1.17** & **59.92 \(\pm\) 0.24** \\ \cline{2-4}  & Batch & 35.06 \(\pm\) 0.22 & 99.38 \(\pm\) 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results on the chess dataset. (bold means better result)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Components**} & \multicolumn{4}{c}{**Different Combinations of Components**} \\ \hline FVSFP tree & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) \\ CCB mining & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) \\ \hline Runtime (s) (mean \(\pm\) std) & 39.39 \(\pm\) 0.81 & 25.11 \(\pm\) 0.93 & 28.72 \(\pm\) 1.07 & **15.76 \(\pm\) 0.88** \\ Memory (MB) (mean \(\pm\) std) & 120.11 \(\pm\) 1.25 & 79.25 \(\pm\) 0.87 & 96.72 \(\pm\) 0.04 & **56.62 \(\pm\) 0.68** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study about the components. (bold means better result)
# Contrast Analysis of Polarization in Three-Beam Interference Lithography
## 3 Contrast in Three-Beam Interference Patterns
### Co-Directional Component Polarization
The maximum value of the interference field intensity gradually decreases to half of the theoretical maximum value. This change in the maximum intensity value undoubtedly increases the difficulty of determining the exposure time in the photolithography process.
## 3 Materials and Methods
### Monitoring Device Architecture: Embedded Application
\begin{table}
\begin{tabular}{c c c c} \hline
**Quantity** & **Description** & **Limits for Trigger Alarms** & **LED** \\ \hline \multirow{2}{*}{Illuminance level} & Classified as 1—dark, 2—dim, 3—light, 4—bright, & & \\  & and 5—very bright. & - & - \\ \hline Dust & Total dust (particles/m3). & \textgreater{}1,415,000 & Red \\ \hline Noise & Noise (dBA) & \textgreater{}85 & Red \\ \hline \multirow{2}{*}{UV} & UV radiation in standard erytmal dose (SED)—total amount per day. SED is checked every ten minutes, and & \textgreater{}1.3 & Red \\  & its sum is registered in a file. & & \\ \hline Temperature & Temperature (\({}^{\circ}\)C). & \textless{}10 or \textgreater{}30 & Yellow \\ \hline Humidity & Relative humidity (\%). & \textless{}50 or \textgreater{}70 & Yellow \\ \hline Flammable gases & The total gas concentration is measured every minute. & \textgreater{}1000 & Blue \\ \hline \end{tabular}
\end{table} 
Table 1: Monitored parameters and limits. protocol commonly used by smart sensors and other IoT devices to transmit data. MQTT supports device-to-cloud and cloud-to-device messaging. The MQTT protocol follows the publish/subscribe model. In traditional network communication, clients and servers communicate directly with each other. MQTT uses a publish/subscribe pattern to decouple the message sender (publisher) from the message receiver (subscriber). A third component called a message broker filters messages received from publishers and distributes them to subscribers [45]. To address privacy issues, MQTT communication is secured by transport layer security with a pre-shared key (TLS-PSK). Messages sent by a device are encrypted and signed using a secret key shared between the equipment and the server (broker in MQTT). The same key is used for the decryption and authentication of messages by the destination. The pre-shared keys (PSKs) are 256-bits long, and they are configured between each device and the MQTT broker, as recommended by [46]. The keys were automatically generated by using the OpenSSL [47]. On the monitoring device side, the keys are fixed in the code. In the MQTT broker, the keys are configured in a broker configuration file specific for this purpose. MQTT topics are composed of the device ID (a four-digit number) together with the name of the monitored quantity, such as \"6068/dust\", \"6070/humidity\", etc. IDs are unique to allow for association with the employees in the applications running on the server.
# Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment
## 5 Performance Verification  
### Experimental Setup  
In this paper, we build a device-free localization system using Intel 5300 NIC in the time-varying indoor environment. We use a miniPC with Intel 5300 NIC as the receiver and a commercial router (i.e., TP-Link WDR5300) as the transmitter. The transmitter is equipped with two antennas, while the receiver has three antennas, so each data packet contains \(3\ \times\ 2\ \times\ 30\): a total of 180 CSI values. In order to collect the raw CSI, we use CSITOOL [34] under the Linux system to collect the signal of the target at different reference points. We divide the cells in the monitoring area and select the reference location uniformly. The reference points are set to the center of each cell as the blue dots and the testing points are the centers of the reference point as the red squares, where the cell size is 1 m by 1 m. The layout of the floor plan of the experimental environment is shown as Figure 7, where the transceiver is located at the place with the mark \"TX\" and the receiver is located at the place with the mark \"RX\". Since the fingerprint is highly dependent on the target being trained, we have consistently used the same volunteer as the target for the experiments. During the offline phase, we build a fingerprint database by collecting CSI data when the target is located at the reference points, which is indicated by the blue points. During the online phase, we build the testing set by collecting CSI data when the target is located at the testing points, which is indicated by the red points in Figure 7. In the paper, the experiments are post-processing after the data have been collected.
# Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security
## 1 Introduction
* In addition, we provide a comparative performance evaluation of quantum and classical RNGs in terms of randomness quality, runtime performance, and memory usage to validate the feasibility of QRNG integration under real-world constraints. Our evaluation results demonstrate that the QTPM can significantly harden IoT security against emerging quantum computing threats. The remainder of this paper is organized as follows: Section 2 provides an overview of key concepts, including TPMs, randomness, randomness assessment, PRNGs, and QRNGs. Section 3 delves into the design and implementation of the proposed QTPM. Then, Section 4 introduces the QEaaS framework, which supports legacy IoT systems and devices while enhancing their security. Section 5 evaluates the performance of the QTPM by comparing the QRNG with traditional PRNGs. Finally, Section 6 concludes the paper by summarizing the key findings and contributions.
# 2D Winograd CNN Chip for COVID-19 and Pneumonia Detection
## 2 Materials and Methods
### Methods
#### 2.2.4 Automatic Layout and Routing  
After adding the test circuit, the next step was automatic layout and routing. We needed to prepare the IO placement of the chip in advance, design the SDC of various timing constraints in the chip, and the Netlist after DFT. Here we used Cadence's Innovus for automatic layout [37]. Winding, after the winding is completed, the GDS file for the subsequent DRC and LVS will be output, and the process of automatic layout and winding APR is roughly divided into IO placement, floorplan, power plan, placement, CTS, and routing. IO placement is our beginning. For the written IO placement, after placement, we needed to set the ratio that the chip needed to use and the distance to the pad. These settings affected the subsequent process, so it was very important. Here we used a ratio of 0.7; the higher the ratio, the larger the space used by the chip, but this would compress the space for the subsequent wiring, and the timing would be more compact. After the above floorplan was completed, a power plan was performed. This project is mainly to set the power supply to the chip, respectively, from the power ring to the power stripe and finally to the pad pin. Placement mainly carries out the placement of components. CTS (Clock Tree Synthesis) mainly allows our timing to reach each component in an average time. Buffer is used to achieve balance, and routing is to confirm that all the routings are correct. At this point, the automatic layout routing is over. The flow chart is shown in Figure 4, and the next step is the verification of DRC & LVS.  
After the automatic layout and winding are completed, it is the verification of DRC and LVS. In this verification, it will be checked whether the winding part conforms to TSMC's process regulations, such as wire-to-wire distance and metal area. When there was a violation, it was because there was a process error that prevented smooth offline, and LVS compares the GDS file generated after APR with the Schematic circuit diagram, mainly to verify whether the circuit is consistent, and NanoSim verification can be performed after the DRC and LVS are completed [38].  
Figure 4: The proposed entity makes a flow chart of the architecture of the verification mechanism.  
After passing the DRC and LVS verification, we needed to use Nanosim to replace the components in the original GDS with the real Layout file. On Nanosim, we used typical nMOS and typical pMOS (TT), fast nMOS and fast pMOS (FF), and slow nMOS and slow pMOS (SS), representing three different speeds to simulate the waveform of TT. The transition time is an ideal state, the waveform transition of FF will be earlier, and SS will be delayed, but the power consumption of FF is large, and the power consumption of SS is small. This is because when designing, designers often consider finding the best and worst case.
# A Multi-Resolution Approach to GAN-Based Speech Enhancement
###### Abstract  
Recently, generative adversarial networks (GANs) have been successfully applied to speech enhancement. However, there still remain two issues that need to be addressed: (1) GAN-based training is typically unstable due to its non-convex property, and (2) most of the conventional methods do not fully take advantage of the speech characteristics, which could result in a sub-optimal solution. In order to deal with these problems, we propose a progressive generator that can handle the speech in a multi-resolution fashion. Additionally, we propose a multi-scale discriminator that discriminates the real and generated speech at various sampling rates to stabilize GAN training. The proposed structure was compared with the conventional GAN-based speech enhancement algorithms using the VoiceBank-DEMAND dataset. Experimental results showed that the proposed approach can make the training faster and more stable, which improves the performance on various metrics for speech enhancement.  
speech enhancement; generative adversarial network; relativistic GAN; convolutional neural network +
Footnote †: journal: Applied sciences