esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
This transformation, controlled by sophisticated algorithms and cryptographic keys, ensures that unauthorized entities cannot access the data, providing _data confidentiality_. In distributed data storage systems, combining hashing and encryption offers a robust framework for data security, effectively tackling two critical aspects of data security: integrity and confidentiality. Indeed, hashing values can be used to provide a unique fingerprint for the data that allows one to check data integrity without the need to decrypt data and compare the original contents. Conversely, the sole use of encryption techniques requires the decryption of the retrieved data, namely, the inverse transformation from ciphertext to plaintext, exposing private keys and posing a significant security risk. The solution proposed in this paper integrates IPFS with blockchain technology to solve all the problems above. In particular, we propose to exploit the decentralized and efficient architecture of IPFS for large data storage, significantly reducing costs while improving scalability. We propose a dual-layer security mechanism that combines hashing and encryption to ensure robust data security and privacy. We propose a novel approach for verifying data integrity, which detects and handles any modifications to the data by generating and comparing hashes of the retrieved and stored information. Furthermore, we eliminate the need for decryption during data retrieval and querying, thus mitigating the risks of exposing private keys and improving overall system security. The proposed approach is developed with reference to a real-world scenario in which a set of IoT devices periodically produces several information records that need to be stored in an immutable and tamper-proof way, as well as preserving their confidentiality. The remainder of the paper is organized as follows: Section 2 summarizes some previous results in combining IPFS, blockchain, and encryption techniques for distributed data storage and security. Section 3 formalizes the considered problem, while Section 4 illustrates the proposed solution. Section 5 discusses the implementation of the solution and the performed experiments. Finally, Section 6 concludes the work.
# Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization
## 4 Experimental Results and Discussions
### Analysis of CFA Strategies
#### 4.2.3 Multi-Start Strategy for Swarm Rebuilding  
Multi-start is a diversification strategy which terminates an ineffective trajectory search and reinitiates a new one from an under-explored region. Our CFA applies the adaptive multi-start strategy which monitors the performance of the incumbent firefly swarm. If the program stops improving the performance for a threshold number of consecutive iterations, part of the swarm is rebuilt by positioning some fireflies on diversified locations using the path-relinking technique. The performance stagnation threshold is made adaptive according to the result of the landscape analysis such that the frequency of the multi-start activation depends on the regional function profiles under exploration. It can be seen from Table 5 that the multi-start strategy effectively improves the mean best function value and the standard deviation obtained by CFA.
## 3 Results
### Sensitive Region Mapping  
According to the spatial distribution of the sensitivity to rocky desertification (1-km grid), the sensitivity index of rocky desertification can be obtained for the county unit, as shown in Figure 5. The sensitive areas of rocky desertification in China are mainly distributed in Guizhou Province, Guangxi Province and Yunnan Province. There are also a small number of rocky desertification-sensitive areas in Sichuan, Chongqing, Hubei, Hunan, Jiangxi and Guangdong. Among them, areas with moderate or greater sensitivity to rocky are mainly distributed in central and western Guizhou, eastern Yunnan, northwest and western Guangxi, northern Guangxi, western Sichuan and other regions. At the same time, there are a small number of distributions in the border areas between Hubei and Chongqing and between Hunan and Yunnan. Areas with moderate or greater sensitivity to rocky desertification included 117 counties or cities, with a total area of 273.2 thousand km\({}^{2}\).  
Specifically, the areas with very high sensitivity were mainly distributed in some counties in Bijie, Liupanshui and Guiyang of Guizhou Province and some counties of Baise, Chongzuo and Guilin in Guangxi Zhuang Autonomous Region. High sensitive areas were mainly distributed on the periphery of very high sensitive areas and were often interwoven with them. They were mainly distributed in Bijie, Liupanshan, Anshun, Southwest Guizhou Autonomous Prefecture, Buyi and Miao Autonomous Prefecture of QianNan, some counties of Qian  
Figure 5: The County-based Rocky Desertification Sensitivity Index in China.  
Guizhou Province, as well as some counties in Hechi, Chongzuo and Guilin of Guangxi Province. The moderately sensitive areas were distributed in Zhaotong, Kunming and Hani-Yi Autonomous Prefecture of Honghe in eastern Yunnan, some counties of central and western Guizhou (Gan Liang, Shi Bing and Tantan) and some counties and cities in northwestern Guangxi Province.
## 3 Formulation of the EBRO Model  
As mentioned above, evidence theory uses a pair of probabilistic values \([Bel,Pl]\) to measure the robustness of the performance value related to the given threshold. However, engineers generally tend to adopt conservative strategies to deal with uncertainties in the product design process. Thus, the robustness objective of EBRO can be established as \(\max Bel(f\leq v)\). Meanwhile, in order to improve product performance, the performance threshold is minimized. The EBRO model is formulated as a double-objective optimization problem:  
\[\begin{array}{l}\min v,\ \max Bel(f(\mathbf{d},\mathbf{X},\mathbf{P})\leq v)\\ \text{s.t.}\ \mathbf{d}^{l}\leq\mathbf{d}\leq\mathbf{d}^{u},\ \overline{\mathbf{X}}^{l}\leq\mathbf{X} \leq\overline{\mathbf{X}}^{u}\end{array} \tag{8}\]  
where \(\mathbf{d}\) is the \(n_{d}\)-dimensional deterministic design vector; \(\mathbf{X}\) is the \(n_{X}\)-dimensional uncertain design vector; \(\mathbf{P}\) is the \(n_{P}\)-dimensional uncertain parameter vector; the superscripts of \(l\), \(u\) represent the value range of a design variable; and \(\overline{\mathbf{X}}\) represents the nominal value of \(\mathbf{X}\). Note that the threshold of \(v\) is usually difficult to give a fixed value to, while it should be treated as a deterministic design variable.  
The proposed model is an improvement on the existing model [24] because it can handle more types of uncertainty, such as the perturbations of design variables resulting from production tolerances, and the variations of parameters due to changing operating conditions. As for the solving process, the EBDO involves the nested optimization of the double-objective optimization in the outer layer and the robustness assessment in the inner layer. Due to the discreteness introduced by the evidence variables, each of the robustness analyses need to calculate the performance extrema of all focal elements. Essentially, extremum evaluation is an optimization problem involving the performance function based on time-consuming simulation models, and therefore the robustness analysis bears a high computational cost. More seriously, the double-objective optimization in the outer layer requires a large number of robustness evaluations in the inner layer. Eventually, the EBDO solving becomes extremely inefficient.
# Design of an Open Remote Electrocardiogram (ECG) Service
## 5 Experimental Results
### Data Acquisition  
We instrumented the code (see Listing 1) so that the levels of two defined ports indicate respectively data input and serial output: the aim is to visualize every interference between such activities in the Arduino MCU. Figure 7 shows the output of the two pins as displayed on a 2-traces oscilloscope: the lower trace represents the timing of data input from the ADC, while the upper trace shows data output on the serial line. There is no interference between the two operations, and their timing is very regular, driven by the timer-based interrupt. The envelope of acquisition cycles (not shown) is within the 0.1 ms. There is an idle time of 2 ms between the end of data delivery, and the next data acquisition interval. Data preparation, after acquisition and before delivery, takes 0.8 ms. We conclude that there is no interference, and that the Arduino CPU is idle approx. 50% of the time.  
Figure 6: Power consumption of a patient’s device. To obtain the trace, we measured voltage drop on a 1 Ohm shunt resistor in series with batteries: 1 mV on the trace corresponds to 1 mA.
## 2 Materials and Methods
### Analyzing Vegetation Indices  
#### 2.3.1 Vegetation Indices  
Three vegetation indices (NDVI, EVI, and SAVI) are widely used to identify vegetation variability, based on the green, blue, red, and NIR bands of MODIS (Table 2). These VIs were used to identify mean monthly (from January to December) and mean annual vegetation variability from 2000 to 2019.
## 3 Proposed Sparse SLMS Algorithms
### Second Proposed Algorithm: SLMS-Rza  
A stronger sparse penalty function can obtain more accurate sparse information [19]. By devising an improved sparse penalty function RZA, we can develop an improved LMS-RZA algorithm. Its cost function can be constructed as  
\[G_{RZA}\left(w_{i}(n)\right)=\frac{1}{2}e^{2}(n)+\lambda_{RZA}\sum_{i=0}^{N-1 }\log\left(1+\varepsilon_{RZA}\left|w_{i}(n)\right|\right) \tag{20}\]  
where \(\lambda_{RZA}>0\) is a positive parameter. By deriving Equation (20), the update equation of SLMS-RZA is obtained as  
\[w_{i}(n+1) =w_{i}(n)+\upmu\frac{\partial G_{RZA}(w_{i}(n))}{\partial w_{i}( n)} \tag{21}\] \[=w_{i}(n)+\upmu e(n)\mathbf{x}(n-i)-\frac{\rho_{RZA}\text{sgn}(w_{i} (n))}{1+\varepsilon_{RZA}\left|w_{i}(n)\right|}\]  
where \(\rho_{RZA}=\upmu\lambda_{RZA}\varepsilon_{RZA}\). By collecting all of the coefficients as the matrix-vector form, Equation (21) can be expressed as  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\upmu e(n)\mathbf{x}(n)-\frac{\rho_{RZA}\text{sgn}\left( \mathbf{w}(n)\right)}{1+\varepsilon_{RZA}\left|\mathbf{w}(n)\right|} \tag{22}\]  
where \(\varepsilon_{RZA}=20\)[7] denotes reweighted factor. By inducing sign function to constraint \(e(n)\) in Equation (22), the stable SLMS-RZA algorithm is proposed as  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\upmu\text{sgn}\left(e(n)\right)\mathbf{x}(n)-\frac{\rho_{ RZA}\text{sgn}\left(\mathbf{w}(n)\right)}{1+\varepsilon_{RZA}\left|\mathbf{w}(n) \right|} \tag{23}\]
# Optimization of Linear Quantization for General and Effective Low Bit-Width Network Compression
## 4 Experiment
### Accuracy Comparison between Linear Quantization and K-Means  
In this section, this paper compares the impact of different clustering methods on the accuracy of the model. As mentioned above, linear quantization in this paper is treated as a combination of clustering and scaling. Its clustering function is based on uniform partition. Note that cluster centroids are not quantized. The number of cluster centroids is obtained by the bit-width.  
According to the experimental results in Tabel 3, it can be found that the accuracy of uniform-partition and K-means at 6, 7, and 8 bits are almost the same. When the bit-width is further reduced, the difference in model accuracy is obvious. The experimental results show that the reason for the decrease in accuracy is consistent with the analysis in this paper.  
The effect of clustering will directly affect the accuracy of the model. Because the distribution of weights is bell-shaped, the disadvantage is that the weights cannot be accurately distinguished by uniform partition when the bit-width is under 5 bits. By comparing the results of uniform partition and clustering, it can be concluded that if the weights can be accurately clustered, high accuracy can be maintained at low bit-widths.
# A New Method to Lightweight Magnesium Using Syntactic Composite Core
## 3 Results and Discussion
### XRD Analysis  
Figure 3 shows the X-ray diffraction (XRD) patterns of as-cast pure Mg and Mg/Mg-20 GMB hybrid composite. The XRD pattern in Figure 3a for as-cast pure Mg shows only peaks corresponding to the \(\alpha\)-Mg matrix, as expected. Mg-based hybrid composite peaks corresponding to Mg and Mg\({}_{2}\)Si phases were observed in the case of as-cast Mg/Mg-20 GMB hybrid composite (Figure 3b). Interfacial reactions between the Mg matrix and GMB particles in the Mg-20 GMB core result in the formation of Mg\({}_{2}\)Si  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Theoretical Density** & **Experimental Density** & \multirow{2}{*}{**Porosity (\%)**} \\  & **(g/cc)** & **(g/cc)** & \\ \hline as-cast pure Mg & 1.738 & 1.712 \(\pm\) 0.009 & 1.50 \\ Mg-20 GMB & 1.537 & 1.492 \(\pm\) 0.016 & 2.93 \\ as-cast Mg/Mg-20 GMB & 1.680 & 1.651 \(\pm\) 0.013 & 1.73 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of density and porosity measurements of monolithic and Mg/Mg-20 GMB hybrid composite.  
secondary phases. This conclusion is also supported by SEM observations and will be discussed in detail in the subsequent sections (Figures 4 and 5).
# Seismic Coherent Noise Removal of Source Array in the NSST Domain
## 4 Discussion  
As described in Sections 3.1 and 3.2, we used synthetic data examples and field data examples to analyze the denoising performance of the proposed method in this article. We mainly used the difference in the distribution of noise and signal in the scale and direction through NSST, a multi-scale and multi-directional analysis method, to suppress the noise. The denoising results show that this method can effectively remove the coherent noise from the source array seismic data, especially surface wave, and it can greatly protect the effective information of the seismic data from being lost and greatly improve the SNR of the seismic data. The proposed NSST method is not only a method to suppress noise, but also a beneficial tool for signal analysis. The accurate data analysis, targeted strategy, and sparse representation make NSST have more desirable denoising results. There are some limitations to the proposed method: we need to artificially select the scale, direction, and threshold zone according to the seismic data, which is not adaptive enough, and then we will study further to simplify the process.  
Figure 10: The results of applying f-k transform in the original seismic data and the denoised seismic data. (**a**) The original data. (**b**) The seismic data after the NSST. (**c**) The seismic data after the FK filter.  
Figure 9: Waveform and amplitude spectra of the seismic data. (**a**) The waveform of trace 80 of the original seismic data (the green curve), the denoised seismic data by the NSST (the blue curve), and the denoised seismic data by the FK filter (the red curve). (**b**) The amplitude spectra of trace 80 of the original seismic data (the green curve), the denoised seismic data by the NSST (the blue curve) and the denoised seismic data by the FK filter (the red curve). (**c**) The average amplitude spectra of the original seismic data (the green curve), the denoised seismic data by the NSST (the blue curve) and the denoised seismic data by the FK filter (the red curve).
## 1 Introduction
MTS is a popular multisystem pattern recognition technique that has proven to be effective in medical diagnosis, early warning, product identification, fault analysis, market administration, and systematic assessment. According to [PERSON] et al. [8], MTS is also used to classify and optimise large samples of data or imbalanced data. Costing systems have progressed from a traditional method concentrated mostly on overhead allocation and product costing to the most comprehensive study of an organization's cost model, assessment, and strategic cost management. Likewise, traditional cost accounting (TCA) is incapable of effectively determining the cost of various complexity of cost items since their determination is based primarily on volume measurements for costing designations. TCA is also less costly to use and more straightforward to comprehend, making it suitable for reporting purposes. As mentioned by [PERSON] et al. [9], Activity-Based Costing (ABC) involves the cost of producing, distributing, or advertising commodities, according to activity. It was developed in response to discontent with traditional management accounting procedures, which focus on volume tactics used to assign overheads to goods. [PERSON] et al. [10], on the other hand, stressed that ABC is a cost analytical tool that allocates resource costs to resource consumption-based activities. Nevertheless, [PERSON] and [PERSON] discovered that ABC has two major flaws. To begin with, establishing an ABC programme might be quite costly, especially if the present accounting system does not support ABC information compilation. Next, it is critical to upgrade the system on a regular basis, which raises the overall cost. These disadvantages motivated [PERSON] and [PERSON] to create the TDABC method, a redesigned ABC version that describe these issues while not compromising the benefits [11]. TDABC is a cost allocation method based on total activity time. The TDABC model may be assessed and developed quickly since only two factors are required: the practical capacity of committed resources and their cost, and unit time frames for conducting transactional tasks. TDABC systems may thus be implemented and upgraded faster, cheaper and easier than traditional ABC systems [11]. TDABC model is applicable in a variety of areas such as product and service operation. The goal of this work was to determine the ideal system integration for a production setting. Several approaches, including conventional, MTS, ABC and TDABC were evaluated. As a consequence, crucial tasks may now be performed more successfully and at lower costs. In order to describe the relationship between cost and quality, the organization should integrate its systems. Four different types of integration: integration A (conventional-ABC), integration B (conventional-TDABC), integration C (MTS-ABC), and integration D (MTS-TDABC), were characterized as outcomes of system integration. Integration has a connection to cross-functional cooperation among all entities, claimed [PERSON] et al. Furthermore, the integration of information systems creates a shared platform that guarantees the accuracy of the data [12].
# Weed Detection Method Based on Lightweight and Contextual Information Fusion
## 2 Materials and Methods
### Enhanced YOLOv5 Model  
#### 2.2.1 YOLOv5  
The YOLO series of algorithms have seen extensive application in target detection tasks within the field of agricultural production. Currently, the most widely used YOLO variant is the YOLOv5 series, known for its fast detection speed and high recognition accuracy. The YOLOv5 series is divided into YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. This paper focuses on improvements made to YOLOv5s.  
As illustrated in Figure 2, YOLOv5s-7.0 is divided into four main components: Input, Backbone, Neck, and Head. After the dataset is fed into the network, preprocessing steps are applied to the input data, including adaptive image scaling, Mosaic data augmentation, and adaptive anchor boxes. The Backbone is responsible for feature extraction from the images and consists of CBS (Convolutional, Batch Normalization, and Activation) layers, CSP1_X, and SPPF. CBS layers perform feature extraction and information propagation, CSP1_X modules enhance feature extraction, improving the model's receptive field, and the SPPF module utilizes pooling at different scales to enhance the transmission of contextual information.  
Figure 1: Examples of images from the dataset.  
Figure 2: YOLOv5s Architecture Diagram.  
The Neck section primarily focuses on feature fusion within the main network. It is composed of the Feature Pyramid Network (PFN) and the Path Aggregation Network (PAN). PAN constructs a pyramid of feature maps at different levels within the main network, enabling the capture of rich feature information at various scales for improved detection of both small and large targets. PAN introduces a top-down path within the network, facilitating effective information transfer and feature fusion.  
Finally, the Head section is responsible for object localization and classification in the detection process.
# An Operational Image-Based Digital Twin for Large-Scale Structures
## 3 Drone-Based Image Acquisition and Pre-Processing
These figures show a smaller section of the tower where the SIFT and SURF keypoints, respectively, were positioned at a section with paint defects/damages. The SIFT algorithm also placed keypoints on the welding seam. Figure 3c shows the 100 strongest-matched SURF points in two images that were captured subsequently during the drone flight. Figure 2: Drone-based image acquisition. (**a**) Drone flight planning, with blue dots showing the drone locations for image acquisition. (**b**) The actual drone flight around the transition piece. Information that possibly leads to the identification of the TP owner is removed from the images. The possibility of tracking keypoints from one image to the next is very important for a good reconstruction. The calculation of image keypoints does not take a long time. This calculation makes it possible to estimate, in the field, how well suited the images are for performing 3D reconstruction; few keypoints on the tower and in the image would hinder a good 3D reconstruction of the tower. Image keypoints, such as SIFT and SURF, can also be used in the field to estimate where the paint defects/damages are placed on the tower. Images that only show the tower, and not the surroundings, would have image keypoints mostly in the areas of the paint defects/damage. The SURF algorithm was better suited for our purposes because it finds the paint damages and not other non-essential features, such as the welding seams. Figure 4: (**a**) The green plus signs show the SIFT keypoints in the image. Compared to the SURF keypoints, a higher number of points are placed on the TP. (**b**) A smaller section of the image where paint defects/damages are located. The SIFT algorithm places keypoints both on the paint defects/damages and on the welding seams. This explains the higher number of SIFT keypoints. Figure 3: (**a**) SURF keypoints in the image shown as green plus signs. There are a large number of image keypoints on the highly textured ground but only a few on the TP. These keypoints are placed on edges, light shadows, and paint defects/damages. (**b**) A smaller section of the image where paint defects and damages are located. The green SURF keypoints are placed on the paint defects/damages. (**c**) The 100 strongest-matched SURF points in two images. The yellow lines connect the matched SURF image keypoints in the two images.
## 3 Methodology
### Structural Health Monitoring Systems
#### 3.1.1 Low-Cost Structural Health Monitoring System
According to the function scheme, the Raspberry microprocessor Pi 3B+ sends the data acquisition request to the ADXL355 accelerometer for the analog signal acquisition. Then, the signal conditioning adapts and addresses the analog signal through the ADC converter. In this regard, the accelerometer FIFO buffer arranges and saves the digital signal in a 20-bit integer format. Simultaneously, the Raspberry Pi 3B+ solicits the data acquired through the interface bus communication and saves the information temporarily. Once the testing finishes, the stored data are communicated remotely from the base station via VNC Connect. The user at the base station adequately addresses this data transmission inquiry. After the base station receives the information from all measuring nodes of the WSN, it is possible to process and analyze the dataset in order to characterize the dynamic properties of the structure monitored.