esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Experimental Environment  
The experiments are performed on the NUMA system, Supermicro A+ 4042G-72 RF4 [18]. The software and hardware specifications are listed in Table 4. This is a 4-node NUMA system with a constant NUMA factor, and each node is installed with one AMD Opteron 6320 processor [19]. This processor has eight cores, so there is a total of 32 cores in the system. The kMLB [4] mechanism and task selection policies are implemented in the Linux kernel. The Linux _numactl_ tool on the system shows that 1.6 times more access is required for remote memory than for local memory.  
Figure 10: A highly exclusive thread group may involve much remote memory access.  
The PARSEC 3.0 [6] is a multithreaded benchmark suite that provides a convenient interface for building and running each benchmark program, as shown in Table 2 of Section 3.2.1. The multithreaded configuration \"gcc-pitreads\" is used to construct most benchmark programs, but the configuration \"gcc-openmp\" is used to construct the benchmark program parsec.freqmine. The input set \"native\" is used for performance analysis on real machines to run benchmark programs. The interface allows each benchmark program to be run with the specified number of threads.
## 4 The Evaluation Results and Discussions
### The Experimental Setup
#### 4.1.3 The DNN Structure for Denoising Feature Extraction  
For each of the utterances in the training and test set, the 69-dimensional FBANK feature stream (23 static FBANKs together with their delta and delta-delta for each frame, with a 20 ms frame duration and a 10 ms frame shift) was created as the baseline feature representation. The presented denoising DNN framework took the FBANK as input to produce the updated feature following the procedures in Section 3.1 for the subsequent recognition. The denoising DNN model was a convolutional neural network (CNN) with four same-size one-dimensional convolutional layers, each following a configuration setting (30, 5, 2), where the setting representation was \"(number of kernel, kernel size, number of padding)\". In addition, these four convolutional layers were followed by the two same fully connected layers with 759 nodes. The activation function for each layer output was the rectified linear unit (ReLU). The training process of this denoising framework underwent 30 epochs with the Adam optimizer and used the log-likelihood loss function.
## 5 Discussion
### Effect of Directional Distance  
Different directional distance can affect the gas extraction performance. Figure 10 illustrates that the residual matrix gas pressure between boreholes increases with increased directional distance. The increased directional distance changed the time needed to complete the extraction and eliminate the outburst risk, as shown in Figure 11. When the directional distance increased from 4 m to 12 m, the extraction time increased by 1.18 times and the zone in which the gas outburst risk is eliminated was enlarged 1.42 times after 284 days of extraction. Therefore, in consideration of working faces alternation and coal seam properties, the required spacing between the fracturing and directional boreholes should be arranged reasonably to complete the extraction process effectively.  
Figure 9: The evolution of hydraulic fracture aperture and temperature in hydraulic fracture at different times.
## 3 Results
### Performance Comparison with Transformer
#### 3.4.2 Results and Analysis  
We conducted experiments on the Yelp Review Polarity, AG's News, Yelp Review Full, and DBPedia datasets to calculate and analyze the results of the Transformer model and the CAC model. We plotted the accuracy and average loss curves of the models across each epoch, as shown in Figure 6.  
By analyzing the curves of accuracy and loss mentioned above, we can further analyze and explain the advantages of the CAC model over the Transformer model in terms of performance and efficiency.  
Firstly, looking at the accuracy curve, our CAC model can converge faster and achieve higher accuracy during the iterations. This indicates that the CAC model is more efficient in learning and adapting to the training data, capturing data features and patterns more quickly. In contrast, the Transformer model has a slower convergence rate and may require more training iterations to reach the same level of accuracy.  
Secondly, the curve of loss values demonstrates the effectiveness of our proposed CAC model during the optimization process. As the model iterates, the loss values of the CAC model gradually decrease and stabilize, indicating that the model can better fit the training data and make more accurate predictions in the classification task. On the other hand, the loss values of the Transformer model may decrease at a slower rate, requiring more training time to reach the same level of loss.  
Taking all the observations into consideration, we can conclude that the CAC model exhibits better performance and efficiency in handling text classification tasks while maintaining accuracy and reducing computational resources and time consumption. The CAC model leverages CNN structures and position embeddings to capture local features and sequence information more effectively, improving the model's performance. Additionally, the use of multi-head attention mechanisms and residual connections enhances the model's learning capacity and generalization ability. Therefore, the CAC model is a superior choice, providing better performance in text classification tasks.
###### Abstract  
Linear approximate segmentation and data compression of moving target spatio-temporal trajectory can reduce data storage pressure and improve the efficiency of target motion pattern mining. High quality segmentation and compression need to accurately select and store as few points as possible that can reflect the characteristics of the original trajectory, while the existing methods still have room for improvement in segmentation accuracy, reduction of compression rate and simplification of algorithm parameter setting. A trajectory segmentation and compression algorithm based on particle swarm optimization is proposed. First, the trajectory segmentation problem is transformed into a global intelligent optimization problem of segmented feature points, which makes the selection of segmented points more accurate; then, a particle update strategy combining neighborhood adjustment and random jump is established to improve the efficiency of segmentation and compression. Through experiments on a real data set and a maneuvering target simulation trajectory set, the results show that compared with the existing typical methods, this method has advantages in segmentation accuracy and compression rate.  
Keywords:trajectory segmentation; trajectory compression; particle swarm optimization; feature point selection; update strategy +
Footnote †: journal: Computer Vision and Pattern Recognition
# Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing
## 5 Experiment and Results Analysis
### ACTS-LB Algorithm Shortcomings  
The ACTS-LB algorithm, however, can still be further improved, which requires further optimization and in-depth study in the following aspects:  
(1) Because the ant colony algorithm has a slow convergence rate in the initial stage, it can easily fall into the local optimum. Therefore, the ACTS-LB algorithm also has some shortcomings. In the subsequent algorithm improvement, we will draw on other heuristic algorithm advantages, such as the simulated annealing (SA) algorithm and genetic algorithm, etc. The comprehensive application of these algorithms' different characteristics, and the fact that their advantages and disadvantages complement each other, will aid us in improving the algorithm's overall task scheduling performance.  
(2) The ant colony algorithm parameter setting is very important. If it is not set properly, it will slow down the solving speed and affect the quality of the results. In the next SWIM task scheduling study, we will consider the task dynamics and the service quality of the system. We plan to dynamically adjust the corresponding parameters in the algorithm and study the impact of ant colony parameters and local search methods on the overall performance of the system, so as to continuously improve and perfect the task scheduling strategy.
## 1 Introduction  
The explosive growth in communication devices in the last decade pushed the industry towards exploiting the existing communication spectrum. The recent mass-deployment of Internet of Things (IoT) devices paired with increasing demand for quality in the streaming and entertainment industries made satisfying these requirements using the same spectrum particularly challenging. To ensure that the huge spectrum capacity required to maintain a stable communication system is met, the industry started shifting with the design of 5G networks to millimeter-wave (mmWave) frequency bands. For indoor communication, mmWave bands are already in use, e.g., IEEE 802.11 ad [1]. However, mmWave channels are fundamentally different from Ultra High Frequency channels used for existing cellular communication protocols, they have higher propagation loss in air, and low penetration for construction materials and foliage [2]. The high path loss in mmWave frequencies makes it necessary to implement methods to increase the received power at the mobile terminal (MT) in order to maintain an acceptable signal-to-noise ratio (SNR) to decode the signal. One way of increasing the SNR is by increasing the gain at the base station (BS). Therefore, beamforming became one of the essential technologies for 5G mmWave communication. The usage of a large number of radiatingelements at the antennas will allow them to emit signals in fast-varying, high-gain beams focused on the desired MT, while simultaneously decreasing the interference to other MTs.  
Exposure compliance assessments are performed by manufacturers and operators to ensure that emitting devices are compliant with safety regulations concerning human exposure to electromagnetic fields. Safety regulations concerning human exposure to EM fields have been specified by International Commission on Non-Ionizing Radiation Protection (ICNIRP) [3] and IEEE [4]. Multiple studies have been made over the years to characterize the exposure in current and legacy communication systems which are based on assuming the maximum theoretical power emitted for the antenna. However, these characterizations struggle to stay accurate for 5G and future technologies due to the fundamental differences between the systems. In [5; 6], it has been shown that the actual power contributing to the electromagnetic field exposure is much lower than the theoretical maximum. This is due to the uncertainty in the values of the emitted and received power from a massive MIMO antenna deploying beamforming. The spatio-temporal variation of the antenna pattern, and its dependency on the MT distribution and channel characteristics make the transition to statistical methods for the exposure estimation a necessity if accurate estimations are desired. Statistical analysis of cellular networks' exposure has been a case study for the last couple of years, using simulations to model the behavior of the channel and the transmission e.g., in [7], but accurate analytical analysis is yet to be conducted.
## 3 Materials and Methods
### Determination of OTA Concentration
#### 3.6.3 Method Validation  
The developed method was validated based on the evaluation of the following indices: limit of detection (LOD), limit of quantitation (LOQ), linear equation, linear range, correlation coefficient (r2), precision, accuracy, and matrix effect (ME). The standard curves were obtained by spiking different OTA concentrations in a solvent mixture (99:1 acetonitrile/formic acid). The OTA-free Merlot berries were considered as the blank matrix for the method validation.  
For recovery studies, grapes of the Cabernet Gernischet, Cabernet Sauvignon, and red globe varieties were used as the representative matrices. Matrix-matched calibration curves were obtained using an OTA-free Merlot berries matrix. The linearity was determined using five matrix-matched calibration points for OTA. The LOD and LOQ were calculated using signal-to-noise ratios of 3 and 10, respectively. The precision and accuracy of the OTA method were assessed using relative standard deviation (RSD) and recovery [13].  
The recovery was assessed at three spiking levels of quality control, with five replicates within the linear range of the calibration curve, i.e., 1.5 \(\upmu\)g/kg, 15 \(\upmu\)g/kg, and 40 \(\upmu\)g/kg. The precision was determined from triplicate assays of grape berries spiked at three concentration levels (low, middle and high load of contamination), and was expressed as a percentage of RSD (%). The impact of the matrix effect (ME) was determined using the following equation: ME (%) = [(matrix-matched calibration curve slope--solvent curve slope)/solvent curve slope] [13; 47].
## 5 Field Testing
### Lower-Level Controller Verification
#### 5.1.2 Driving Controller Verification  
To validate the trained neural network, the speed tracking experiments are carried out under no-load and full-load conditions, respectively. In the experiments, the step desired speeds of 1 m/s, 2 m/s, and 3 m/s are given every five seconds. The experimental results are shown in Figure 14, where the vehicle tracks at the desired speed in a shorter time and maintains it afterward. In addition, it should be noted that the controller has undershoot performance at a desired speed of 1 m/s and some overshoot performance at a desired speed of 3 m/s. This is due to the fact that the entire drive system (including the extended engine, torque converter, and transmission) is a nonlinear system.  
Figure 12: The layout of hardware architecture and communication.  
Figure 13: Experimental verification results of steering controller: (**a**) no load; (**b**) full load.
## 4 Results and Discussion
### Discussion  
The above simulations assumed a fixed SNR (=10 dB) situation to permit a comprehensive evaluation. When we assume a total transmission power of 10 dBm, 0 dBi antenna gain, \(-174\) dBm\(/\)Hz noise density and free space propagation, average reception SNR in the SISO case is about 9 dB in the simulation environment. Of course, the actual value depends on system parameters and deployment environment and the received signal strength significantly impacts the capability of the proposed CSI estimation scheme. To discuss the application region, Figure 11 plots average beamforming gain versus SNR where 1 st eigenmode transmission is compared to the proposed scheme. It additionally plots the result of the proposed scheme with CSI estimation error due to additive noise. Under the total transmission power constraint, reducing the training subcarriers from 2048 to four strengthens the transmission power density per subcarrier, per antenna element, by 10 log\({}_{10}\)(2048/4/16) = 15.1 dB.  
Figure 10: Average system throughput versus number of multiplexed UEs.  
Figure 9: Average system throughput versus UE speed.  
In addition, thinning out the training subcarriers also reduces PAPR and thus the transmission power can be raised thanks to easing IBO; this reduction is assumed to be 6 dB or so [35]. As seen by the results, even with CSI estimation error, the proposed scheme retains comparable beamforming gain to the ideal CSI case in the low SNR region around 0 dB. Though the practical gain \(15.1+6=21.1\) dB is much smaller than the beamforming gain 36 dB, the lack of the gain does not cause serious degradation on our proposed scheme, even in low SNR region. This validates the throughput performance evaluations in Section 4.2, which assumed ideal CSI.  
As a result, our proposal can eliminate the link budget shortfall for CSI estimation in the millimeter waveband. It should be noted that assuming OFDM, timing extraction for fast Fourier transform (FFT) windowing for the CSI estimation on the reduced subcarrier is a critical issue since it is performed in the time domain, i.e., the gain provided by the reduced number of training subcarriers cannot be obtained. Meanwhile, millimeter wave small cells can be supported by control signaling from macro cell using micro wave band [36]. It will be possible to know the FFT windowing timing by synchronizing the macro cell system.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Computation Complexity
#### 3.8.2 Encryption-Decryption Complexity  
Image encryption algorithms should be fast and efficient enough to encrypt decryption images quickly, especially in real-time applications. Nevertheless, the algorithms should have enough complexity to resist all possible attacks. An image encryption algorithm's encrypt decryption complexity involves operations such as substitution-permutation networks, XOR operations, and modular operations.
# Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland-Urban Interfaces
## 2 Materials and Methods  
### The System Architecture  
The architecture and the components of the system that were developed are shown in Figure 4, which also illustrates the flow of data across the system. It all starts with a designated area, which is defined by the end user, by drawing a polygon on a WebGIS interface based on Mapbox GL v3. This polygon represents the area that the user wants to evaluate.  
The designated area is sent to another module, the drone's mission planner, which calculates an optimised flight path over the designated area, considering the requirements of each bundle; for example, image resolution and overlapping. After the flight over the designated area using the waypoints calculated by the mission planner, the mosaic of high-resolution images is then processed to generate a high-resolution orthophoto map and a 3D point cloud file, which are required by the CHAMELON bundles.  
Bundle BC1 (vegetation monitoring and census) is used to identify individual trees and extract their traits, namely the crown projection area, allowing the measurement of the distances between tree canopies. Bundle BC3 (continuity of vegetation) is used to identify clusters of connected trees in the designated area.  
After the bundles process the input data, the generated results are sent to a post-processor module, which merges all the results and determines if the designated area complies with the parameterised vegetation density and minimum distances between tree crowns. In case of non-compliance, the post-processor displays a WebGIS interface with the designated area, a layer showing the zones that do not comply, and a report describing the reasons for the nonconformity.  
Figure 4: System architecture and components.
## 3 ITS Standards and Projects
### ITS Research Projects
Some notable examples are as follows: (i) COMeSafety2 project (EU/FP7) [41], whose objective is to facilitate the development and deployment of cooperative ITS safety applications and to promote their benefits towards industrial stakeholders and authorities; (ii) Preparing secure v2x communication systems project (PRESERVE - EU/FP7) [42], whose main goal is to design, develop and evaluate secure and scalable V2V and V2I communication systems in realistic deployment scenarios; (iii) Advanced cellular technologies for connected cars project (CellCar - Qatar National Research Fund) [43], whose goal is to propose new strategies to combine the IEEE 802.11p standard with LTE to improve the network performance and enable delay-tolerant services; (iv) Cooperative systems for smart mobility services and solutions project (CosMob - Qatar National Research Fund) [44], whose objective is to enhance road traffic efficiency by proposing new cooperative and real-time traffic data collection and dissemination concepts, as well as their analysis and delivery to the vehicles' drivers; (v) Security and safety modelling project (SESAMO - EU/FP7) [45], whose main goal is to better investigate, understand and model the relations between functional safety and security mechanisms in embedded systems in multiple domains; and (vi) Engineering security and performance aware vehicular applications for safer and smarter roads (SafeITS - Qatar National Research Fund) [46], whose main objective is to design an adaptive and context-aware ITS applications framework enabling the dynamic adaptation of the quality of service and security features to ensure the safety of the ITS users and entities.
# Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
## 2 Semantic Segmentation Models for Multi-Damage Detection
### Context Guided Network (CGNet)  
[PERSON] et al. [42] proposed a context-guided network (CGNet) as a lightweight semantic segmentation model. CGNet contains only 0.5 million trainable parameters, which saves memory footprint and is considered suitable for implementation on ECDs. The performance of CGNet was evaluated using the Cityscapes and CamVid datasets, and it outperformed the other lightweight models, in terms of accuracy and computational cost. The architecture of the CGNet is simple and constitutes many CG blocks. Figure 2 presents the hierarchy of the CG block used for feature extraction. The CG block comprises two primary stages. In the first stage, floc(*) and fsur(*) are used to learn the local features and the relevant surrounding context. The floc(*) is a 3 \(\times\) 3 depth-wise convolutional layer was employed for learning the local features from the eight adjacent feature vectors. Meanwhile, fsur(*) is a 3 \(\times\) 3 depth-wise dilated/atrous convolutional layer for a large receptive field to efficiently learn the surrounding context. The depth-wise convolutional layer in both floc(*) and fsur(*) helped to significantly reduce the number of parameters in CGNet. Furthermore, fioi(*) extracts joint features from the outputs of floc(*) and fsur(*). The fioi(*) is simply designed as a concatenation layer, followed by the BN and PReLU operators. Stage two involves fglo(*), which consists of global average pooling, followed by two fully connected layers that extract the global context to improve the joint features. Joint features  
Figure 1: (**a**) ENet initial block; (**b**) ENet bottleneck module.  
are refined channel-wise by applying the global context as a weighted vector to enhance relevant features and minimize irrelevant features.  
Figure 3 represents the entire CGNet architecture. The encoder part comprises three stages for downsampling the input image resolution, as well as many CG blocks. In stage 1, the downsampling ratio is half, constituting three standard convolutional (3 \(\times\) 3) layers, followed by the BN and PReLU operators. In stage 2, the input image passes through three CG blocks that downsample the resolution to 1/4. The dilation rate for fsur(*) in the CG blocks of stage 2 was 2. In stage 3, the input image is passed through 21 CG blocks and further downsampled to 1/8. The dilation rate for fsur(*) in the CG blocks of stage 3 was 4. Finally, the decoder part, to visualize the prediction of CGNet, employs an upsampling layer to resize the prediction into its original input resolution.