esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Research Gaps  
In summary, the current research can be further improved in the following aspects:  
1. Existing grinding studies mostly focus on specific scenarios, and the selected process parameters vary accordingly. Currently, there is no systematic parameter selection for the case of robotic grinding, where the workpiece is fixed, and the tool serves as the end-effector.
2. At the modeling method level, traditional statistical methods often suffer from insufficient prediction accuracy when dealing with large sample sizes. On the other hand, data-driven methods face challenges such as high computational complexity, long runtime, and overfitting when dealing with small sample sizes. Moreover, existing studies lack considerations for selecting methods based on different data sample sizes, neglecting the training time requirements of the model, which affects data adaptability and the practical feasibility in production environments.
3. Some prediction tasks rely on theoretical modeling, simulation, or are limited to dataset validation, without subsequent parameter optimization and real-machine verification. Theoretical or simulation results may differ from actual production environments, resulting in suboptimal model performance under real working conditions.  
To address these issues, this study proposes an innovative surface-roughness prediction method, referred to as BPNN, along with a corresponding experimental DMV architecture specifically designed for small-batch grinding scenarios. The framework first applies polynomial feature transformation and significance analysis to the proposed parameters. Then, by integrating the Dynamic Central Composite Design-Response Surface Method (DCCD-RSM) with the BP Neural Network (BPNN), a general and adaptive prediction model is established. Finally, through optimization algorithms and real-machine experiments using reverse computation, the proposed method is validated to achieve significant accuracy and practicality in predicting the surface roughness of small-batch workpieces during grinding.
## 2 Antenna Configuration and Performance Analysis
### Performance Comparison  
The performance of the proposed antenna in this work were compared with other wide\(-\)beam antennas in publications, as shown in Table 3. It can be seen that there are few instances of research into broadening HPBW in all cut-planes, especially with end-fire characteristics. Moreover, most of the reported articles could not achieve beam broadening without sacrificing antenna size and complexity. For example, in [14; 17; 19], some interesting designs were presented that could effectively achieve wide beams, but all experienced the disadvantages of increased size, complexity, or both. In this work, based on an ALTSA with an end-fire pattern, two rows of quasi-intersecting subwavelength elements were loaded, realizing a wide beam in all cut\(-\)planes without increasing the antenna size and complexity, and the extensions in different cut-planes were similar. As mentioned above, the proposed antenna meets the demands of wide beam, low cost, and easy fabrication in many applications.  
Figure 5: The performances of antennas with different numbers of subwavelength elements in each row. (a) \(|\)S\({}_{11}\)\(|\). (b) The amplification of HPBW in all cut-planes.  
Figure 6: The current distribution of the proposed antenna.
# Intelligent Ship Scheduling and Path Planning Method for Maritime Emergency Rescue
## 3 Experimental Data  
The intelligent planning of the ship's navigation route mainly needs to consider the time and space relationship between the current position of the ship and the position of the area to be observed, as well as the start time of the mission. The planning parameters of the algorithm are shown in Table 3. The current latitude and longitude of the ship is automatically captured through the ship positioning system, and the position coordinates of the observation area and the start time of the task are obtained through the human-computer interaction interface configuration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Running Stage** & **Algorithm Name** & **Evaluation Metrics** \\ \hline Navigation Planning & \
\begin{tabular}{c} Regular A* algorithm, \\ algorithm in this paper \\ \end{tabular} & Path length, number of turns, average turn Angle \\ \hline Regional Search &
\begin{tabular}{c} Differential evolution algorithm, \\ algorithm in this paper \\ \end{tabular} & Path length, regional coverage rate, point arrival rate, trajectory prediction accuracy \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation indicators of the algorithm proposed in this article.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Types** & **Parameter Name** & **Symbol Name** & **Unit** & **Precision** & **Range of** \\  & & & & & **Values** \\ \hline Ship’s current position & \
\begin{tabular}{c} Vessel longitude \\ Vessel latitude \\ Longitude 1 \\ Latitude 1 \\ Longitude 2 \\ Latitude 2 \\ Mission start \\ \end{tabular} &
\begin{tabular}{c} _Loncp_ \\ _Lanc  
The data used for the creation of this paper were sourced from the raster data of sea and land maps provided by Natural Earth [23] This dataset uses the WGS-84 (World Geodetic System 1984) coordinate system, with a total of about 900 million pixels, each pixel representing a spatial area of 500 m \(\times\) 500 m on the Earth's surface. The rasterized map data are intercepted according to the coordinate values of the starting point and the target point in the exploration mission and inputted into the algorithm to correct the coordinates. A large number of rosters are simplified, and the electronic chart is appropriately trimmed according to the regional scope required by the mission planning, as shown in Figure 3.  
Horizontal and vertical grid lines are used to divide the task area delivery into grid cells of the same size. According to the selected task area, the human-computer interaction simulation platform extracts the sea, land, and water depth information in the task area by traversing and sets the land, islands, and areas with shallow water depth as obstacle areas. After determining the position of the obstacle area, the grid map of the obstacle grid, the free grid (navigable area), and the initial position grid (the initial position of the ship) are established. According to the iff files of different precision, they are converted into the corresponding rasterized data, as shown in Figure 4.
## 4. Discussion  
Quantitative methods for studying the impact of climate change on compound floods in coastal cities include statistical models, numerical models, and machine learning models. Statistical models are widely used to evaluate the frequency (return period) of compound floods based on long-term climate data and flood observation data. The numerical simulation method analyzes the dynamic flood evolution process, including flood intensity and impact range, by inputting data reflecting climate change and urbanization into a compound flood model. Machine learning, a branch of artificial intelligence (AI), enables computers to perform specified tasks like detecting spatial flood extents and predicting flood risks through remote sensing (RS) and historical flood data [92].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Study Area** & **Study Objective** & **Numerical Model** \\ \hline [PERSON] et al. [78] & Continental US & Precipitation response to urban development & WRF \\ [PERSON] et al. [84] & European Suburbs & Urbanization exacerbated rainfall & WRF \\ [PERSON] et al. [80] & China & The impact of land use/cover change on urban pluvial floods & SWAT \\ [PERSON] et al. [85] & Beijing, China & Urban land-use impacted urban precipitation & METRAS \\ [PERSON] et al. [86] & Shanghai, China & Land use change and sea level rise effect on storm surge floods & LISFLOOD-FP and FLUS \\ \hline \hline \end{tabular}
\end{table}
Table 4. Numerical methods for the impact of impervious surfaces on urban flood disasters.
###### Abstract  
With the anticipated growth of the internet of things (IoT) market, many low-power wide-area (LPWA) technologies have been introduced to connect a wide range of IoT devices with varying performance requirements. The narrowband internet of things (NB-IoT) is a 3 rd generation partnership project (3 GPP) standardized LPWA technology that meets most IoT service requirements. In this paper, the design and implementation of the physical uplink transmitting chain as well as the physical downlink receiving chain of the NB-IoT user equipment (UE) are presented. Both chains' main blocks are designed to follow the 3 GPP NB-IoT LTE standard Release 14 (Rel-14). The whole design is experimentally implemented on the Virca-7 (VC 709) Connectivity kit, and all performance metrics are reported. Moreover, an NB-IoT base station is implemented and integrated with the two prototyped UEs to set-up an NB-IoT system, which is employed to send data from one UE to another UE through the NB-IoT base station using two FPGAs (one to implement the sending UE and the other one to implement the receiving UE) and three universal software radio peripherals (USRPs) B200 (one to implement the RF front-end of the transmitting UE FPGA, one to implement the base station RF front-end, and one to implement the RF front-end of the receiving UE FPGA). Experimental results show that the implemented NB-IoT system is working successfully, as the two NB-IoT UEs communicate together successfully through the NB-IoT base station and exchange the data properly.  
Keywords:NPDSCH; NPUSCH; NB-IoT; LTE; 3 GPP; FPGA; USRP; PHY layer; IoT; LPWA +
Footnote †: journal: Electronics and Communications Engineering
# Multi-Scale Temporal Convolutional Networks for Effluent COD Prediction in Industrial Wastewater
## 2 Materials and Methods
### Fourier Transform  
The Fourier transform is a mathematical method that can convert the time-domain signal \(f(t)\) into the frequency-domain signal \(F(\omega)\) and is widely applied in fields such as physics, engineering, and digital signal processing [26]. Since any periodic signal can be represented as a sum of simple oscillating functions of different frequencies, complex signals that meet the Dirichlet convergence condition can be decomposed into the superposition of infinitely many discrete sine or cosine waves using Fourier series.  
The period of a continuous signal is T\({}_{1}\), then its Fourier expansion is defined in Equation (1) as follows [27]:  
\[f(t)=a_{0}+\sum_{n=1}^{\infty}a_{n}\cos(n\omega_{1}t)+\sum_{n=1}^{\infty}b_{n} \sin(n\omega_{1}t) \tag{1}\]  
where \(\omega_{1}\) is the fundamental frequency of \(f(t)\), and each decomposed quantity is an integer multiple of \(\omega_{1}\). While \(a_{0}\) represents the direct current component, \(a_{n}\) and \(b_{n}\) are the amplitudes of the cosine and sine signals, respectively. In [\(t_{0}\), \(t_{0}+T_{1}\)], they can be calculated as follows:  
\[a_{0}=\frac{1}{T_{1}}\int_{t_{0}}^{t_{0}+T_{1}}f(t)dt \tag{2}\]  
\[a_{n}=\frac{2}{T_{1}}\int_{t_{0}}^{t_{0}+T_{1}}f(t)\cos(n\omega_{1}t)dt \tag{3}\]  
\[b_{n}=\frac{2}{T_{1}}\int_{t_{0}}^{t_{0}+T_{1}}f(t)\sin(n\omega_{1}t)dt \tag{4}\]  
However, many variables sampled from the wastewater treatment process in practice have no significant periodicity, in which case it can be equivalent to applying the Fourier transform to a function with an infinitely large period. Thus, the value of \(\omega_{1}\) approaches zero, and the Fourier transform can be described as follows:  
\[F(\omega)=\int_{-\infty}^{\infty}f(t)e^{-i\omega t}dt \tag{5}\]  
where \(e^{-i\omega t}\) is a rotation vector for time-domain transformation and \(i\) in it is a unit of the complex number [28]. Each frequency component \(F(\omega)\) contains the information of amplitude and phase, which determine the height and position of the wave, respectively. According to the top \(k\,F(\omega)\) with the largest amplitudes, the most predominant \(k\) periods in the time series can be identified.  
Thereby, the original data can be reconstructed across different time scales utilizing these periods obtained by the Fourier transform approach, which is essential for extracting specific features at each time scale and then enhancing the prediction accuracy.
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 1 Introduction  
### Modern Coffee Supply Chain in a Global Context  
Coffee is known to be one of the most significant commodities after crude oil and plays a crucial role in a country's economy, especially for coffee producing countries [1]. A coffee supply chain is an extremely complex multi-stakeholder process that includes farm-level production and harvesting, cooperative sorting, export and import logistics, roasting, retail distribution, and finally consumer delivery. Under contemporary conditions of globalization, a single bag of coffee can easily pass through at least five or more independent entities before finally reaching the end consumer [2]. In spite of this long and complexsupply chain process, most transactions and quality checks remain opaque and/or paper-based [3]. As a result of this lack of transparency, questions regarding the origin of coffee beans, ethical sourcing, bean quality, and handling necessary conditions are difficult to answer with certainty, especially from the consumer's perspective.  
For example, a consumer purchasing a bag of \"single-origin organic Ethiopian coffee\" has no way to confirm if it was actually manufactured from a certified organic farm in Ethiopia, or if the beans were mixed with other lower-quality batches along the way. Likewise, exporters have little or no transparency into whether the beans were properly stored before roasting. Similarly, farmers may never receive a fair compensation due to a lack of proper coffee data lineage and due to the absence of the traceable documentation. These kinds of pressing issues pose ethical, operational, and financial risks to all the parties involved in the supply chain, from coffee farmers to end consumers.  
These issues make us aware of how the current generation of supply chain system lacks real-time monitoring, auditability, interoperability, and accountability at each stage of the supply chain. Today, emerging technologies such as blockchain and the Internet of Things (IoT) offer powerful tools and features as solutions, allowing these supply chain issues and limitations to be addressed immediately [4]. When these two technologies are used together synergistically, they allow for an entirely new class of supply chain solutions that are verifiable, automated, and mainly transparent from one end to another.
## 1 Introduction
### Contributions of This Study  
This study proposes a miniaturized MTS-based leaky-wave MIMO antenna optimized for CubeSat communication systems across microwave and millimeter-wave bands. The antenna features a compact, dual-port MIMO design using a modified phased MTS with coffee bean-shaped arrays. The MTS arrays were embedded on a substrate with a different permittivity, which was separated by a cavity layer to have improved propagation. This specific construction can offer beamsteering abilities with frequency and phase dependency, which is vital for CubeSat communications. The antenna operates over three bands, 3.75-5.25 GHz, 6.4-15.4 GHz, and 22.5-30 GHz, and is designed to fit within the standard 10 \(\times\) 10 \(\times\) 10 cm\({}^{3}\) CubeSat unit, preserving space for other critical systems, like solar panels. In addition, its high gain and radiation efficiency of 12.5 dBi and 85%, respectively, and also its circular polarization can assure low power consumption and a robust signal reception, which are essential for low Earth orbit (LEO). Overall, the antenna meets the stringent size, weight, and power (SWaP) requirements of modern CubeSats while offering enhanced performance and reliability.  
The MIMO capability supports high data rates, catering to data-intensive applications, such as Earth observations, scientific experiments, or inter-satellite networking, where rapid and voluminous data transfer is essential. The proposed antenna's beamsteering ability was obtained without any electrical and mechanical sophistication in the entire antenna system, improving the CubeSat to ground station communication. By having characteristics such as a low profile and high gain and efficiency, the proposed leaky-wave antenna can also be a reliable candidate in a harsh environment, like space, especially for the next generation of CubeSat communications. Furthermore, having a CP feature can degrade the signal interference between the CubeSats.  
After giving a thorough introduction and explaining the state of the art in Section 1, the detailed design of the proposed antenna is given in Section 2, followed by the results in Section 3. Finally, Section 4 offers the conclusions of this work.
## 4 Results
### Datasets  
We employed the COVID-19 radiography images dataset [60,61] for the proposed CovidDetNet approach, produced by a group of academics from the University of Dhaka, Bangladesh and Qatar University, Qatar, to detect COVID-19. This COVID-19 chest radiograph image database is developed and created in collaboration with medical doctors con  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**S. No** & **Classification Layers** & **Kernels** & **Size** & **Padding** & **Strides** \\ \hline
1 & Input & & & & \\
2 & Convolutional-layer-1 (LeakyRelu + Cross channel Normalization) & 128 & \(11\times 11\) & \(0\times 0\) & \(4\times 4\) \\
3 & Max pooling & & \(3\times 3\) & \(0\times 0\) & \(2\times 2\) \\
4 & Convolutional-2 (LeakyRelu + Cross channel Normalization) & 512 & \(5\times 5\) & \(2\times 2\) & \(1\times 1\) \\
5 & Max pooling & & \(3\times 3\) & \(0\times 0\) & \(2\times 2\) \\
6 & Convolutional-3 (LeakyRelu) & 384 & \(3\times 3\) & \(1\times 1\) & \(1\times 1\) \\
7 & Convolutional-4 (LeakyRelu) & 256 & \(3\times 3\) & \(1\times 1\) & \(1\times 1\) \\
8 & Convolutional-5 (LeakyRelu) & 256 & \(3\times 3\) & \(1\times 1\) & \(1\times 1\) \\
9 & Max pooling & & \(3\times 3\) & \(0\times 0\) & \(2\times 2\) \\
10 & Convolutional-6 (Batch normalization + ReLu) & 960 & \(1\times 1\) & same & same \\
11 & Convolutional-7 (Batch normalization + ReLu) & 960 & \(3\times 3\) & same & same \\
12 & Convolutional-8 (Batch normalization + ReLu) & 320 & \(1\times 1\) & same & same \\
13 & Convolutional-9 (Batch normalization + ReLu) & 1280 & \(1\times 1\) & same & same \\
14 & Average pooling layer & & & & \\
15 & FC layer & & & & \\
16 & Softmax & & & & \\
17 & Classification & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: CovidDetNet architecture parameters.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**CovidDetNet Parameters** & **Values Given** \\ \hline Learning rate & 0.001 \\ Validation frequency & 30 \\ Optimization algorithm & SGDM \\ Shuffle & Every epoch \\ Iterations per epoch & 82 \\ Maximum epochs & 60 \\ Verbose & false \\ Activation function & Leaky ReLu + ReLu \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected parameters for CovidDetNet classifier.  
taining COVID-19 positive samples, pneumonia, and healthy samples. The COVID-19 chest radiograph images database, which was recently released, includes 3616 chest radiographs of COVID-19 infected people, 10,192 chest radiograph images of healthy people, 6012 Lung Opacity, and 1345 images of pneumonia. The images in the dataset are 1024 \(\times\) 1024 pixels in size. The images were resized to fit the need of each model. It is a standard Kaggle dataset that is freely available for research purposes. This dataset's radiograph images are grayscale and have the exact dimensions. A few example image samples from the SIRM dataset are displayed in Figure 3, whereas Table 3 provides statistical information about the dataset.
## 3 Results and Discussion
### Volatile Compound Analysis of Lafun Samples and Raw Cassava Roots
#### 3.3.1 Gas Chromatography-Mass Spectrometry Analysis
As pointed out by [PERSON] and [PERSON] [37], the enzymatic oxidation of lipids yields a wide range of carbonyl compounds, among which are acetone, pentanal, hexanal, heptanal, octanal, nonanal, (E)-2-penteal, (E)-2-hexanal, (E)-2-heptenal, (E)-2-octenal, (E)-2-nonenal, (E)-2-decenal, (E)-2-undecenal, (E,E)-2,4-heptalenal, and (E,E)-2,4-nonadienal. All of these were also found in this study (Tables 3 and 4). Hexanal was particularly high in the sample of bitter cassava produced with the combination of _W. koreensis-1_ and _L. lactis_. Hexanal is one of the many well-documented aromatic compounds in the literature. It contributes to flavour and aroma, and it is used as a measure of the oxidative status of foods and antimicrobial properties [38; 39]. Like pentanal and 3,5-octaleni-2-one, hexanal is a product of autoxidation. Hexanal has been found to be formed during the termination phase of specific fatty acids oxidation (linoleic and arachidonic acids). Therefore, it is used as a measure of oxidative stability [40]; however, it is the alkadienals that are often the most potent and give a rancid note. The oxidation of lipids is the cause of rancidity in food and thereby its reduction in quality [41; 42]. The lipid content of lafun from fortified cassava is generally higher than those of the other two varieties, as noted in the proximate analysis, but their hexanal, pentanal, and 3,5-octaleni-2-one are much lower. Therefore, lipid oxidation is not related to lipid content, but to the lipid profile of the sample, the oxidative status of the sample, or the antioxidant activity of vitamin A. In this study, the most likely cause is related to the antioxidant activity of vitamin A. Among the organic acids identified, acetic acid had the highest concentration in lafun. This could be related to the high content of acetic acid produced during fermentation [43]. Phenols were only found in the Sango market sample (Table 5). p-Cresol can be formed by the degradation of tyrosine and has a characteristic medicinal or coal-tar smell. However, it is also released into the environment through fuel combustion (wood and trash burning and vehicle exhausts). A small number of cresols have been detected in foods such as tomatoes, asparagus, butter, and some drinks such as coffee, brandy, black tea, and wine [44]. The presence of cresols in the market sample could be due to the process used during the drying stage. Some indigenous producers dry lafun by spreading it on tarred roads that could contain cresol and where automobile exhaust emissions are heavy. Cresols are toxic but unlikely to cause harm at such low levels.