esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Collect and Build Dataset  
As the current research on straw-burning smoke is in its infancy, we need a relevant dataset. The open-source smoke datasets are mainly fire accident smoke datasets and are unsuitable for monitoring straw-burning smoke. We therefore required a dataset for the target detection of straw incineration smoke. As shown in Figure 7a, a 2-megapixel camera with a 10-360 mm zoom lens was designed to capture a straw-burning smoke video at a distance of 3-5 km. The camera model is DS-2 CD5027 EFWD-A, manufactured by Hikvision. It has a \(1/8^{\prime\prime}\) COMS (complementary metal-oxide semiconductor) sensor, a dynamic range of 120 db, supports H.265, and has a resolution of 1920 \(\times\) 1080. The power consumption of the camera is only 6 W, and the operating temperature is \(-30\)\({}^{\circ}\)C to \(-60\)\({}^{\circ}\)C, making it suitable for deployment in the wild. In order to monitor long-distance straw-burning smoke, we equipped the camera with a 10-360 mm zoom lens. The model of the lens is YM36 \(\times\) 10 MAPRF, manufactured by YAMAKO. The lens has a temperature compensation function, with a working temperature of \(-40\)\({}^{\circ}\)C to \(-70\)\({}^{\circ}\)C. The combination of camera and lens can achieve the collection and monitoring of straw-burning smoke within a range of 3-5 km. As shown in Figure 7b, an unmanned aerial vehicle (UAV) with a camera is used to collect smoke from straw burning at close range. The UAV model is DJI MAVIC PRO, manufactured by DJI, with a 12.35-megapixel camera. The camera resolution is 3840 \(\times\) 2160 with a 26 mm lens. The UAV is used to capture straw-burning smoke footage at a distance of 1-2 km.  
To enrich the dataset, we collected straw-burning smoke data in the summer under the background of immature crops and straw-burning smoke data in the autumn after the crops were mature and harvested. Due to the different colors of smoke burned by different crop straws, the smoke from wheat, maize, and rice straws were collected in this study. At the same time, considering the different backgrounds in different geographic environments, the experiment of collecting straw smoke data was arranged in different locations, including Hefei (Anhui Province), Luan (Anhui Province), Huainan (Anhui Province), Shangqiu (Henan Province), and Shenyang (Liaoning Province). Some pictures of training dataset and test dataset in different locations is shown in the Figure 8.  
This study collected smoke videos for about 5 h, and selected 10,000 different smoke pictures as the training dataset and 1000 different pictures not in the training dataset as the test dataset. The number of images in the dataset is shown in Table 2. The different backgrounds of the collected smoke datasets include seasons (spring and autumn), locations (southern and northern regions of China), terrain (plains and hills), crops (rice, wheat, and corn), weather (sunny and cloudy), time (morning, afternoon, and dusk), and distance.  
Figure 7: Zoom camera (**a**) and unmanned aerial vehicle (UAV) (**b**).
# Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios
## 6 Time Complexity Analysis
### LEACH Structure and Time Complexity  
The LEACH protocol is characterised by a distributed approach, where nodes make local decisions without central control. It is designed for energy efficiency, aiming to conserve energy and extend network lifetime by minimising communication and balancing the load across nodes. LEACH effectively manages scalability through clustering, making it suitable for large networks. However, it assumes that all nodes have homogeneous energy levels and does not account for heterogeneous networks with varying energy capacities. The time complexity of the LEACH algorithm depends on the following factors:  
* Initialisation: The time complexity depends on the initial distribution of the number of nodes n, and ranges from \(O(nlogn)\) to \(O(n^{2})\).
* Local Exchange: The local exchange of information to search for the nearest neighbour requires \(O(n^{2})\) per iteration.
* Total Iterations: For I number of iterations, the time complexity is \(O(I)\) The total time complexity of the LEACH algorithm is \(O(I.n^{2}(nlogn(n)\).
# CAL: Core-Aware Lock for the big.LITTLE Multicore Architecture
###### Abstract  
The concept of \"all cores are created equal\" has been popular for several decades due to its simplicity and effectiveness in CPU (Central Processing Unit) design. The more cores the CPU has, the higher performance the host owns and the higher the power consumption. However, power-saving is also one of the key goals for servers in data centers and embedded devices (e.g., mobile phones). The big.LITTLE multicore architecture, which contains high-performance cores (namely big core) and power-saved cores (namely little core), has been developed by ARM (Advanced RISC Machine) and Intel to trade off performance and power efficiency. Facing the new heterogeneous computing architecture, the traditional lock algorithms, which are designed to run on homogeneous computing architecture, cannot work optimally as usual and drop into the performance issue for the difference between big core and little core. In our preliminary experiment, we observed that, in the big.LITTLE multicore architecture, all these lock algorithms exhibit sub-optimal performance. The FIFO-based (First In First Out) locks experience throughput degradation, while the performance of competition-based locks can be divided into two categories. One of them is big-core-friendly, so their tail latency increases significantly; the other is little-core-friendly. Not only does the tail latency increase, but the throughput is also degraded. Motivated by this observation, we propose a Core-Aware Lock for the big.LITTLE multicore architecture named CAL, which keeps each core having an equal opportunity to access the critical section in the program. The core idea of the CAL is to take the slowdown ratio as the matric to reorder lock requests of these big and little cores. By evaluating benchmarks and a real-world application named LevelDB, CAL is confirmed to achieve fairness goals in heterogeneous computing architecture without sacrificing the performance of the big core. Compared to several traditional lock algorithms, the CAL's fairness has increased by up to 67%; and Its throughput is 26% higher than FIFO-based locks and 53% higher than competition-based locks, respectively. In addition, the tail latency of CAL is always kept at a low level.  
lock; fairness; the big.LITTLE multicore architecture
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 2 Theory and Methodology
### Selection of Sensor Evaluation Indicators  
#### 2.1.1 Sensitivity  
Good sensitivity is the prerequisite for obtaining effective response data of the sensor to the target gas. The sensitivity of a sensor is determined by its sensitive materials. When the sensor is exposed to the target gas, its resistance value will be changed. After the detected gas disappears, the sensor resistance will gradually return to its baseline value. The sensitivity is defined as the ratio of the response value of the sensor in air to the value in the target gas, and the calculation equation is as follows:  
\[S=\frac{R_{0}}{R_{s}} \tag{1}\]  
where \(R_{0}\) is the response value of the sensor in air, and \(R_{s}\) represents its response value in target gas. The larger the value \(S\), the stronger the response of the sensor to the target gas.
# Design and Analysis of High-Accuracy Telecentric Surface Reconstruction System Based on Line Laser
## 3 System Calibration
### Motor Direction Calibration  
The motor direction of the 1D displacement platform in camera coordinate system is another system structure parameter required, which is described in Figure 6. The calibration procedure is realized by fixing a calibration board on the motor desk of the displacement platform, and controlling the desk to move a certain distance. The camera is used to obtain the image capture before and after the movement, respectively.  
Let \(P_{j1}=\left(x_{cj1},y_{cj1},z_{cj1}\right)^{T}\) and \(P_{j2}=\left(x_{cj2},y_{cj2},z_{cj2}\right)^{T}\) be the corresponding coordinates of the control points before and after the movement in the camera coordinate system, respectively, in which \(j\) is the count of the control points. From Equation (5), \(\left(x_{cj1},y_{cj1}\right)\) and \(\left(x_{cj2},y_{cj2}\right)\) can be determined by the corresponding image coordinates \(\left(u_{j1},v_{j1}\right)\) and \(\left(u_{j2},v_{j2}\right)\). Meanwhile, the certain distance \(d\) of \(P_{j1}P_{j2}\) is obtained from the displacement platform.  
\[d=\|P_{j1}P_{j2}\|=\sqrt{\left(x_{cj1}-x_{cj2}\right)^{2}+\left(y_{cj1}-y_{cj 2}\right)^{2}+\left(z_{cj1}-z_{cj2}\right)^{2}}. \tag{10}\]  
Figure 6: Motor directions calibrated in structure calibration.  
Thus, the displacement on the \(Z_{C}\) axis is achieved, and the sign ambiguity can be avoided with the distribution of the camera and displacement platform. Suppose \(z_{cj1}<z_{cj2}\), then the unit motor direction of the displacement platform \(n_{d}\) in the camera coordinate system is expressed in Equation (11).  
\[n_{d}=\frac{p_{j1}^{\rightarrow}p_{j2}}{\|p_{j1}p_{j2}\|}=\Bigg{(}\frac{x_{cj2} -x_{cj2}}{d},\frac{y_{cj2}-y_{cj2}}{d},\frac{\sqrt{d^{2}-\big{(}x_{cj2}-x_{cj2} \big{)}^{2}-\big{(}y_{cj2}-y_{cj2}\big{)}^{2}}}{d}\Bigg{)}^{T}. \tag{11}\]  
\(n_{d}\) can be determined with single corresponding image points ideally. Several pairs of image points are employed to obtain a mean value in practice. Take the value as an initial value to carry out a non-linear optimization with the following optimization function, in which \(p_{ji}\) is the image coordinate of \(P_{ji}\), and \(\widetilde{p}_{ji}\) is the projection calculated with the calibration parameters and the corresponding image coordinate of the other point in a pair.  
\[G=\sum_{j}\Big{(}\|p_{j1}-\widetilde{p}_{j1}\big{(}p_{j2},m,d,n_{d}\big{)}\|^{ 2}+\|p_{j2}-\widetilde{p}_{j2}\big{(}p_{j1},m,d,n_{d}\big{)}\|^{2}\Big{)}. \tag{12}\]
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 8 Performance Analysis
### Computational Efficiency  
The protocol's computational performance is evaluated across multiple dimensions, with execution times summarized in Table 3. The results demonstrate sublinear time complexity (\(\mathcal{O}(d^{1.2})\)) as dimensionality increases, where the 8D Yeast dataset requires \(128.6\pm 3.7\) s--1.78\(\times\) longer than its 4D counterpart (\(72.3\pm 2.4\) s). Larger datasets impose greater computational burdens, with Yeast (1484 samples) taking 6.4\(\times\) longer than Wine (178 samples) for 4D clustering (72.3 s vs. 20.1 s). Table 4 breaks down the per-iteration costs, revealing three key phases: (1) encryptions (\(\mathcal{O}(n)\)), scaling linearly with sample size;  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Dataset** & **Samples** & **Features** & **Dimensions Tested** & **Clusters (k)** \\ \hline Wine & 178 & 13 & 2D–4D & 3 \\ Breast Cancer & 569 & 30 & 2D–4D & 2 \\ Ionosphere & 351 & 34 & 2D–4D & 2 \\ Yeast & 1484 & 8 & 2D–8D & 4 \\ \hline \end{tabular}
\end{table}
Table 2: Dataset specifications and experimental configuration.  
Figure 4: Experimental results.  
Figure 5: Yeast data clustering experimental results.  
(2) distance calculations (\(\mathcal{O}(nk)\)), dependent on both samples and clusters; and (3) homomorphic multiplications (\(\mathcal{O}(nkd)\)), exhibiting the highest computational overhead. The 8D case doubles operation counts compared to lower dimensions, directly impacting runtime. These results validate the protocol's dimension-aware scalability while highlighting its practical viability for moderate-scale privacy-preserving clustering tasks. The measured sublinear scaling outperforms theoretical bounds for naive homomorphic implementations, suggesting optimization benefits from our algorithmic design.
## 4 Research Methodology  
The primary goal of the study was to analyze and evaluate the system features, usability, and performance when deploying a containerized SmartSAT mobile web application [30; 39; 46] on three different serverless cloud platforms: Google's Cloud Run, AWS's App Runner, and Microsoft Azure's Container Apps. Based on the advantages of using serverless cloud services discussed in Section 2: Literature Review, these three serverless platforms were chosen to conduct the comparative analysis in deploying the SmartSAT application. The evaluation analyzed the results of the system features, usability, and performance when running the application on each of the serverless platforms. The study aimed to guide the implementation of the SmartSAT application by addressing three specific research questions:  
* RQ1: What are the key differences in system features between serverless computing services provided by AWS, GCP, and Azure, as determined by comparing their service configurations, pricing model, and memory and CPU specifications?RQ2: Which cloud provider among GCP, AWS, and Azure offers the most beginner-friendly documentation and learning resources for serverless computing services, as accessed for a usability study?
* RQ3: How do serverless computing services on AWS, GCP, and Azure differ in the performance of running a containerized web application, as determined by measuring container request latency, API response time, and CPU utilization?
# Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens
## 2 Materials and Methods
### Machine Learning Methods
#### 2.4.6 Extreme Gradient Boosting (XGBoost)  
Extreme Gradient Boosting (XGBoost) represents an optimized distributed gradient boosting library meticulously crafted for the efficient and scalable training of machine learning models [35]. Functioning as an ensemble learning method, XGBoost amalgamates predictions from multiple weak decision trees, culminating in a robust prediction. The algorithm generates a series of weak decision trees, each incrementally grown on the weighted data derived from the preceding tree. The assignment of weights is achieved through the optimization of a function that gauges the alignment of model coefficients with the underlying data, commonly referred to as the loss function. The prediction yielded by the final ensemble model is the weighted sum of predictions derived from the preceding tree models.
# A Software Framework for Heterogeneous Wireless Sensor Network Towards Environmental Monitoring
## 3 Proposed WSN System Architecture
### Software Framework  
As an indispensable block in WSN systems, a software framework includes software components in Figure 2, such as Simple-Device, Simple-Message, Simple-Communication, Application-Point-Server, Data-Management-Application, and SimpliciTi Application Programming Interface (API). In this section, we will introduce and describe each of these software components in detail.  
Figure 1: Hardware components of a wireless sensor module and an application point module.  
Figure 2: Block diagram of system software framework. API: Application Programming Interface.  
Simple-Device object: It stores all relevant node device information into a single packaged object. This object contains device information related to device voltage, device temperature, and ADC conversion for each signal I/O pin. The reference voltages for ADC conversion are dynamically selected in the software. Moreover, after the device reads its battery voltage, this battery voltage will be used as the upper bound reference voltage for every ADC conversion. This reference voltage is always updated before new data is acquired from each pin.  
Simple-Message object: It leverages an underlying SimpliciT API to facilitate the transfer of device data. The Simple-Device object is passed to a Simple-Message object, which wraps all device data before transfer. After sending a data packet, the receiving device unpacks the packet into usable data using a Simple-Message object.  
Simple-Communication object: It allows easy communication with the host operating system via a UART port. Data is transferred using a JSON format and allowed to be easily parsed in the host operating system. This software module is located on the application point node, where the node is connected to the host operating system through a physical UART port.  
Application-Point-Server host: It uses a Node.Js server to listen for incoming data transfers from an application point network, which is connected to a host computer. Once an incoming transfer request is received, the data is transformed according to user-specified conversion and data label mapping. Later, this data will be posted in an online database.  
Data-Management-Application: This is a setting application that allows users to configure data conversion functions for each environmental sensor. In addition, the received data can be mapped to the desired range via this type of a user-friendly interface. This module also allows users to view recently collected data from each sensor in the network.  
SimpliciT Application Programming Interface (API): A low-complexity software has been provided by Texas Instruments to facilitate communication between CC2500 chips. In this software framework, the API supports node communications in a peer-to-peer or star network topology. In addition, if an end device node is too far away to communicate with the application point, repeaters may be brought into the network for the signal relay.
## 1 Introduction
While many methods offer benefits in evaluating concrete surfaces, an assessment of the quality of concrete surfaces cannot be provided in a simple manner on-site and by non-specialized operators. Also, previous studies have not considered the case in which there are many different surface defects on the same concrete surface. However, this situation occurs frequently on concrete surfaces. Moreover, these methods face limitations in terms of cost, complexity, time requirements, resolution constraints, and  
Figure 2: Bughole scale(CIB): Comparison of bughole percentages across Surfaces 1 to 7, with Surface 1 having the lowest percentage of bugholes, while Surface 7 has the highest percentage of bugholes [19]. subjectivity. Additionally, variations in the complexity of defects pose challenges for precise measurements and effective evaluation. The main purpose of this study is to develop a novel Surface Defect Index (SDI) method for defect quantification. The proposed index is intended to quantify the percentage of the volume occupied by all types of defects found simultaneously on the same surface, regardless of the complexity of their shape and size, using the properties of liquid penetration. Furthermore, the developed method is used for the quantification of randomly distributed defects and the non-uniform distribution of their surfaces and depths, thus enhancing objectivity in surface quality evaluation. This novel method mainly focuses on assessing the impacts of volume parameters (length, width, and depth) on the quality of concrete surfaces. This is because the volume of defects can significantly impact the ingress of harmful elements, such as moisture, aggressive chemicals, and microorganisms, into the concrete matrix. Deeper defects allow pathways for these detrimental elements to penetrate deeper into the concrete, potentially compromising its structural integrity over time. Therefore, accurately quantifying the volume of defects is essential for identifying potential areas of vulnerability in concrete structures and implementing appropriate maintenance and repair strategies to enhance their long-term durability and performance. The efficacy of the method presented was validated using controlled samples, and it demonstrated successful application and effectiveness when implemented on real concrete surfaces.
## 2 Materials and Methods
### Rotating Polarization-MPSK (RP-MPSK) Modulation
#### 2.1.1 RP-MPSK Modulation  
RPW is made feasible by equipping the transmitter in Figure 3 with two baseband PSK modulators of frequency \((\omega_{r}=2\pi f_{r}=2\pi/T_{r})\), but orthogonal to each other for horizontal and vertical polarizations. A data symbol \(s_{m}\) is simultaneously input to both modulators to generate \(h_{t}(t)\) and \(v_{t}(t)\), where the subscript \(t\) corresponds to transmitted signal.  
\[\left.\begin{array}{l}h_{t}(t)=\sqrt{\frac{2E_{t}}{T_{r}}}\cos\left(\omega_{ r}t+\frac{2\pi\pi}{M}\right)\\ v_{t}(t)=\sqrt{\frac{2E_{t}}{T_{r}}}\sin\left(\omega_{r}t+\frac{2\pi\pi}{M} \right)\end{array}\right\}\quad m\in\{0,1,2,\ldots,M-1\} \tag{3}\]  
Figure 2: RP-QPSK Transmitter.
## 2 Materials and Methods
### Experimental Procedure  
Three evaluation methods were performed over three days considering the cognitive and physical load of the participants. The participants performed the three evaluations in different orders. For example, one participant performed RE on the first day, AE 1 on the second day, and AE 2 on the third day; and another participant performed AE 2 on the first day, RE on the second day, and AE 1 on the third day. For minimizing contextual contamination in a large number of evaluations, some strict guidelines were set up so that the participants should follow. In the beginning of each evaluation, a brief explanation about the purpose and steps of the evaluation was given to all participants to ensure their understanding of the items of the questionnaire. Furthermore, they were given guidance on how to evaluate each semantic pair one by one, and they rated each semantic pair after completing a specific interaction. For example, they immediately rated the semantic pair of \"Hard-Soft\" on the questionnaires immediately after tapping the samples. In particular, researchers had observed and monitored the whole process of all participants' evaluations, confirming whether they met a minimum number (at least once per semantic pair) of the right interaction (e.g., seeing, touching, rubbing, pressing, and tapping). In terms of the group of participants, two participants were joined in one session of an evaluation, however, samples were given in a counter-balanced order across participants to avoid interrupting each other's evaluations as well as to prevent ordering effects of samples. In order to maintain the tactile sensitivity of the participants over a certain level, a rest period of 5 to 10 min was provided in the middle of the evaluation. In addition, samples which were located in the same place side by side were cleaned before each experiment to eliminate any possible influence of contamination. In terms of the group of samples, six samples were recommended by experts in the field as the most popular ones: each of samples was presented in random orders in AE 1; each list of six samples per semantic pair was provided in AE 2; each pair of two samples was presented in RE so that six samples could be paired once each other. After one affective evaluation each day, the participants were asked directly in the questionnaire about the perceived distinguishability of the samples for each semantic pair. The participants evaluated how easy or difficult it was to distinguish between the samples in terms of the seven semantic pairs by a seven-point Likert scale. For example, one point indicated that they felt it was very difficult to distinguish between the samples whereas seven points indicated that they could distinguish between samples very easily. An example of the questionnaire is presented in Figure S4.  
Figure 5: Flowchart of three semantic differential (SD) methods in affective evaluation.