esa-sceva/satcom-chunk-collection huggingface dataset:
# Architecting and Deploying IoT Smart Applications: A Performance-Oriented Approach
## 2 Background and Related Work
### Performance and Scalability of IoT Systems  
Different forecasts have been reporting the availability of billions of connected devices in the next years [1; 2]. Since smart applications will typically have to deal with thousands of devices-or considerably more-performance and scalability concerns are key to any successful IoT deployment. Yet, most current reported experiences show: (a) small-scale pilots [7; 44]; (b) simulation-based or analytical results [21; 45]; (c) measurement-based results with limited scope [19; 46]; (d) no quantitative results at all [2; 3; 5; 8; 9; 11; 12; 16; 24; 37; 47]. Also, different architectural and deployment choices for IoT systems affect scalability, and real-time decision-making is possible in an environment composed of thousands of sensors [4; 48]. Therefore, understanding the tradeoffs involved in planning and deploying different software components of a specific scenario on different location infrastructures-such as cloud, fog, or mist--requires careful consideration of the performance and scalability of the solution.  
[PERSON] et al. [19] proposed qualitative and quantitative metrics and evaluated the performance of various IoT platforms. Out of 11 platforms initially analyzed by the qualitative approach, five were selected for the performance analysis. However, since they adopted a generic approach, the authors did not go into the specifics of individual platforms, and they did not evaluate different deployment infrastructures.  
[PERSON] et al. [46] gave a detailed description of a FIWARE testbed architecture configured for precision agriculture, which differs from our approach, mainly because our focus is to test different configurations considering our IoTecture and IoTinuum. Besides, their test application connects directly to Orion with NGSI JSON, while we used an IoT Agent with an IoT sensor simulator that generates synthetic data.  
This paper differs from previous work showing a performance analysis study of six deployment configurations of FIWARE and LoRaWAN/LPWAN for smart agriculture and smart city, involving different placement strategies of components over fog and cloud infrastructures. It extends previous preliminary performance results presented in [4] and [48], which focused on more specific scenarios of smart irrigation.
# Quality Assessment of Banana Ripening Stages by Combining Analytical Methods and Image Analysis
## 2 Materials and Methods
### Image Acquisition  
The goal of this procedure was to assess differences in the banana textures during ripening. For this purpose, the samples of (a) banana peels and (b) banana flesh after iodine testing were photographed with a Sony DSCW800/B digital camera (Sony Europe Limited, Edinburgh, UK), which was performed at 15 cm from the banana sample surface and under appropriate lighting conditions. The images of the banana samples were obtained at a lens aperture of f = 4.6 and a resolution of 1280 \(\times\) 720 pixels, and they were saved in JPEG format. In order to quantify the textural properties of the surfaces of the banana samples, eighteen (18) textural features from each image were calculated. The colored and grayscale versions of the images were used to compute the textural features and to estimate the textural changes that occurred during banana ripening. Three (3) features, namely L*, a* and b*, were calculated from the colored version of the images. Four (4) features, namely mean value, standard deviation (std), skewness, and kurtosis were calculated based on the histogram first-order statistics of the grayscale images. Six (6) features, namelycontrast (con), dissimilarity (dis), energy, homogeneity, correlation, and angular second moment (ASM), were produced based on the co-occurrence matrix second-order statistics of the grayscale images. Five (5) features, including short-run emphasis (SRE), run-length non-uniformity (RLN), long-run emphasis (LRE), gray-level non-uniformity (GLN), and run percentage (RP) were produced from the run-length matrix second-order statistics of the grayscale images [22].
# FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network
## 4 Experiment and Discussion
### Discussion and Results
#### 4.2.2 Effect of the Filter  
Figure 5 illustrates the data distribution in the generator before the MLP extracts user information. Figure 5a displays the data distribution in the user's initial preference vector. The figure reveals that the vector comprises nearly 1750 values concentrated around 0 and displays the characteristics of a peaked and thick-tailed distribution, which differs from the normal distribution fitting curve of the same data. This observation indicates that the model's analysis of the user's initial preference is biased towards the items the user has interacted with in the past, i.e., those items exposed to the user, which cannot mitigate the impact of exposure bias. Conversely, Figure 5b presents the data distribution in the filtered user preference vector. Notably, the figure does not exhibit any apparent high points, and the data conform closely to a normal distribution fitting curve that is similar to the distribution of the same data. Therefore, the use of such user preference data enables the subsequent MLP module to prioritize more interactive items, provide users with additional recommendation options, and effectively mitigate exposure bias.  
The generator leverages filters for bias reduction and information combination to efficiently extract user information. However, increasing the structure also escalates computational complexity. To validate the filter structure's effectiveness, we train a G and D consisting of a pure MLP network. Table 3 displays the results. The evaluation reveals that our model performs better, demonstrating that incorporating filters enhances the model's efficacy.  
Figure 4: Precision under different embedding dimensions and linear layers. Embed represents the embedding dimension of users and items.
## Appendix B Inverse Laplace Transform: Method of Partial Fraction and Irrational Function  
To obtain the evolution of a function \(f(t)\), given its spectral function \(\overline{F}(s)\), one should solve the inverse Laplace transform. The inverse Laplace transform of functions of a complex argument is defined as follows [63; 64]:  
\[f(t)=L^{-1}\big{\{}\overline{F}(s)\big{\}}(t)=\frac{1}{2\pi j}\lim_{T\to\infty} \int\limits_{\beta-jT}^{\beta+jT}e^{sT}\overline{F}(s)ds \tag{10}\]  
where \(s=\alpha+j\omega\) is complex plane and \(\alpha\) is a real number greater than the real part of all singularities of \(\overline{F}(s)\).  
The integral formula given by Equation (10) is called the Bromwich integral, also known as [PERSON]'s inverse formula or the Fourier-Mellin integral. In many cases, the complex integral can be evaluated using the Cauchy residue theorem.  
In practice, it is not necessary to solve the complex integral given by Equation (10). Instead, existing tables of inverse Laplace transforms can be used [65; 66], along with various methods of representing a complex function in forms for which Bromowich integral solutions are known, such as method partial fractions for rational functions of complex argument \(s\)[34; 65].  
For solving the inverse Laplace transform of the irrational function obtained in this study, the known solutions given in table bellow were used [65].  
\begin{tabular}{c c} \hline
**Complex Function** & **Inverse Laplace Transform** \\ \hline \(\frac{1}{\sqrt{s}+b}\) & \(\frac{1}{\sqrt{\pi t}}-be^{b^{2}t}erfc(b\sqrt{t})\) \\ \hline \end{tabular}  
Additionally, the following properties of the Laplace transform were used [67; 68; 69]:  
\[L^{-1}\big{\{}\overline{F}(s+a)\big{\}}=e^{-at}f(t) \tag{11}\]  
\[L^{-1}\big{\{}\overline{F}(s)\overline{G}(s)\big{\}}=\int\limits_{0}^{t}f(t-t ^{\prime})g(t^{\prime})dt^{\prime} \tag{12}\]
## 3 State-Of-The-Art
### Government and Regulatory Agencies
Cellular V2X (C-V2X) has been standardized since 3G/UMTS and 4G/LTE proved to be useful (i.e., up to 2 km, 1.5-3.5 s, and 75-300 Mbps) but not versatile for time-critical scenarios. In this sense, the Third Generation Partnership Project (3 GPP) has been a standardization body especially active in the development of 5G technology. It has regulated C-V2X from releases 14 and 15 in early 2016 and early 2019, respectively [23; 24]. According to the previous scenario, although the major shift lever for the development of ITS applications has been the regulation of the radio spectrum and the technology standardization by the various regulatory bodies, there are still some limitations that hinder worldwide adoption. On the one hand, the locations of the radio bands are unique to each country and not interoperable across different territories (e.g., the Japanese ITS system in the 760 MHz band overlaps with the 4G/LTE mobile network operating in New Zealand) [25]. On the other hand, the degree of investment required by some other applications (e.g., V2I) will take time to be implemented due to the numerous infrastructures existing [26]. This means a different adoption rate of LTE versus DSRC-based solutions. For all the above, an increase in the efforts of governments, regulatory bodies, industries, and scientific community is still necessary due to the ambiguity of the communication technologies, lack of supporting infrastructures, overall implementation cost and attention to certain technical challenges, such as the improvements in cybersecurity [27].
## 3 Results
### Industry 4.0 & Key Performance Indicators
#### 3.2.6 Key Performance Indicators for Assembly  
Key Performance Indicators (KPIs) are employed widely to assess the outcome of assembly systems. New concepts for novel assembly systems need to use KPIs to evaluate their potential performance. In most cases, traditional KPIs are used [96]: cost (investment, labor), quality (first pass yield, final yield) [97; 98; 99], throughput time, quantity and lot size; inventory costs [100], line productivity (e.g., OEE--overall equipment effectiveness) [101], energy consumption, cycle time and service level [102; 103]. Integrating KPIs that link design, production, and quality goals through the product & process development has proven useful to limit late engineering changes, which delay the assembly system development [104]. A combination of economic and structural KPIs can be used to evaluate the adaptability of reconfigurable manufacturing systems [105]. [PERSON] et al. propose that KPI selection for the smart automation of manufacturing systems needs to be company and location-specific and that the KPIs variation and sensitivity to the introduction of new Industry 4.0 technology needs to be a key driver for developing a strategy for smart assembly automation [106]. For evaluating the performance of Line-less Mobile Assembly Systems (LMAS), [PERSON] et al. developed a set of 11 specific KPIs, 6 of which are adapted from conventional KPIs to account for the wide variety of products being made in the assembly system, and 5 are specific to LMAS (e.g., overall traveled distance, number of station configuration reconfigurations) [107].  
**In conclusion**, to evaluate assembly systems, standard KPIs need to be adapted in order to include both traditional metrics (e.g., cost, quality, throughput, inventory, lead time, productivity) and new indicators that are specific to the products, operations context and business goals.
# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 5 Swarm Based Solution
### Path Planning Problem
#### 5.1.1 Pheromone Calculation  
The pheromone value is updated using the environmental impact metric of the new location since the mobile sink needs to move on a safe path away from the dangerous area. The selection of the new cluster head location with maximum environmental impact is required to lower the influence of environmental factors on the mobile sink movement. At the same time, it helps choose cluster head locations in favorable surroundings and prevent harmful places. Thus, the pheromone increasing density is calculated by the final environmental impact metric of cluster head \(x\) in zone \(i\) as follows:  
\[\Delta\tau_{ux}^{1}=EIM_{x}^{i}(t). \tag{86}\]  
The pheromone trail value is updated as each ant \(k\), reaches its new cluster head position according to the following rules:  
\[\tau_{ux}^{1}t)=(1-inch\rho_{1})\tau_{ux}^{1}(t-1)+inch\rho_{1}\Delta\tau_{ux} ^{1} \tag{87}\]  
where \(inch\rho_{1}\in(0,1)\) is the evaporation factor [33].
###### Abstract  
Additive manufacturing (AM) offers many advantages for the mechanical design of metal components. However, the benefits of AM are offset to a certain extent by the poor surface finish and high residual stresses resulting from the printing process, which consequently compromise the mechanical properties of the parts, particularly their fatigue performance. Ultrasonic impact treatment (UIT) is a surface modification process which is often used to increase the fatigue life of welds in ship hulls and steel bridges. This paper studies the effect of UIT on the fatigue life of Ti-6 Al-4V manufactured by Direct Metal Laser Sintering (DMLS). The surface properties before and after the UIT are characterized by surface porosity, roughness, hardness and residual stresses. Results show that UIT enhances the fatigue life of DMLS Ti-6 Al-4V parts by suppressing the surface defects originating from the DMLS process and inducing compressive residual stresses at the surface. At the adopted UIT application parameters, the treatment improved the fatigue performance by 200%, significantly decreased surface porosity, reduced the surface roughness by 69%, and imposed a compressive hydrostatic stress of 1644 MPa at the surface.  
fatigue life improvement; materials characterization; additive manufacturing; ultrasonic impact treatment; DMLS +
Footnote †: journal: Applied Sciences
# Optimization of Screen-Hole-Clearing Devices for Mechanized Residual Film-Impurity Separation
## 3 Results and Discussion
### Parameter Optimization and Experimental Validation  
To maximize the clearing effect of a screen-hole-clearing device, the regression equation model of the influencing factors and performance indexes were optimally solved by applying the optimization-seeking function of Design-Expert software [27]. The screen hole cleaning device was used to clear screen hole clogging to improve the clearing effect of the residual film-impurity wind separator. Therefore, the ratio of impurities in the residual film should be appropriately increased during the optimization process. The optimal parameter values for each influencing factor were calculated: the distance of the nozzle from the screen surface was 102 mm, the wind speed of the fan was 24 m/s, and the number of airflow pipes was 4. The optimal value of the performance index corresponding to these parameters was 7.3% for the ratio of impurities in the residual film and 6.6% for the ratio of the blockage of the screen holes.  
To verify the feasibility of the optimization results, a real machine test was conducted based on the predicted values. According to the optimal working parameters of the original residual film-impurity wind separator, the distance between the nozzle of the screen-hole-clearing device and the screen surface was 102 mm, the fan wind speed was 24 m/s, and the number of airflow pipes was 4. The minimum air velocity value was 15.8 m/s, greater than the theoretical air velocity required to clear the screen hole plugging. The test was conducted three times to obtain the average value, as illustrated in Table 4. The results indicated that the average ratio of impurities in the residual film in the validation test was 5.86%, which was 2.42% lower than the ratio before optimization. The ratio of the blockage of the screen holes was 5.41%, which was 1.92% lower than the preoptimization rate. The relative error with the predicted value was less than 19.7%. The test results were close to the predicted value and thus verified the accuracy of the model.  
The device continued to operate, and the ratio of the blockage of the screen holes and the ratio of impurities in the residual film were recorded after 1 h, 3 h, and 5 h of operation. The results were basically consistent with the test results. This indicated that the optimized screen-hole-clearing device had stable working performance and effectively reduced the ratio of the blockage of the screen aperture of the residual film-impurity wind separator, thereby improving the screening performance and reliability of the device.
# Image Classification of Pests with Residual Neural Network Based on Transfer Learning
###### Abstract  
Agriculture is regarded as one of the key food sources for humans throughout history. In some countries, more than 90% of the population lives on agriculture. However, pests are regarded as one of the major causes of crop loss worldwide. Accurate and automated technology to classify pests can help pest detection with great significance for early preventive measures. This paper proposes the solution of a residual convolutional neural network for pest identification based on transfer learning. The IP102 agricultural pest image dataset was adopted as the experimental dataset to achieve data augmentation through random cropping, color transformation, CutMix and other operations. The processing technology can bring strong robustness to the affecting factors such as shooting angles, light and color changes. The experiment in this study compared the ResNeXt-50 (32 \(\times\) 4d) model in terms of classification accuracy with different combinations of learning rate, transfer learning and data augmentation. In addition, the experiment compared the effects of data enhancement on the classification performance of different samples. The results show that the model classification effect based on transfer learning is generally superior to that based on new learning. Compared with new learning, transfer learning can greatly improve the model recognition ability and significantly reduce the training time to achieve the same classification accuracy. It is also very important to choose the appropriate data augmentation technology to improve classification accuracy. The accuracy rate of classification can reach 86.95% based on the combination of transfer learning + fine-tuning and CutMix. Compared to the original model, the accuracy of classification of some smaller samples was significantly improved. Compared with the relevant studies based on the same dataset, the method in this paper can achieve higher classification accuracy for more effective application in the field of pest classification.  
agriculture; insect pest; pest recognition; transfer learning; convolutional neural network; image processing; data augmentation +
Footnote †: journal: Applied Sciences
# Key Issues on Integrating 5G into Industrial Systems
###### Abstract
With the development of 5G technology, during the last few years, many authors started exploring the application of 5G and above networks in Industrial Systems. Ref. [2] paper first analyzes the development status of smart factories and understands that there are problems, such as slow communication speed of equipment, insufficient level of production information acquisition, and inability to guarantee equipment information security, and gives the application Strategy of IoT in the Smart Factory under the Background of Industry 4.0 and 5G Communication Technology. There is no mention of the paths for the integration between 5G and industrial systems in this paper. [PERSON] et al. [16] present the state-of-the-art 5G architecture, transformative technologies, and recent design trends. They also identify several research challenges in these promising design trends that beyond-5G systems must overcome to support the rapidly unfolding transition in creating value-centric industrial wireless networks. On a more advanced level, [PERSON] et al. [11] present a practical implementation of IIoT over 5G composed of different infrastructure and application components. From the infrastructure perspective, the implementation includes a 5G Internet of Things (IoT) end device that collects sensing data from shop floor assets and the surrounding environment and makes these data available over an industrial 5G Network. Application-wise, the implementation includes an intelligent assistant that consumes such data to generate valuable insights that allow for the sustainable operation of assets. Despite these efforts, most solutions mentioned above are replacing enterprise communication networks and control networks with 5G. The focus of this article is to study the 5G replacing control-layer communication network, which requires low latency and high reliability in real-time networks. To summarize, the little existing literature has systematically explored the integration schemes, key technologies, and feasibility assessments of 5G with industrial systems. This study is dedicated to closing this gap by proposing certain paths and key technologies for the integration between 5G and industrial systems. Consequently, the outcomes garnered from this research will significantly drive the implementation of 5G technology across vertical industries.