esa-sceva/satcom-chunk-collection huggingface dataset:
# Biodegradable and Compostable Shopping Bags under Investigation by FTIR Spectroscopy
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
PCA, a widely used method in MVA, often used with spectral data consisting of thousands of variables that necessitate data reduction, was used in the present study for our exploratory purposes.  
In PCA, multi-dimensional data are reduced to a small number of new variables, principal components (PCs), which are orthogonal linear combinations of the original ones that efficiently represent data variability in low dimensions. The information carried out by PCs is expressed as a percentage of explained variance. By definition, PC1 has the largest % explained variance, followed by PC2, PC3, and so on [28; 29].  
PCA is able to identify important spectral regions that differentiate the samples belonging to a test population, and is capable of creating clustering patterns on the basis of the spectral data which depend on the physicochemical characteristics of the samples. PCA provides score plots, where one component (e.g., PC1) is displayed versus another (e.g., PC2), and where samples under study assume specific positions, forming groups of similar compounds. The position taken by each sample, on the selected components, provides reliable predictive information on its physicochemical characteristics.  
In addition, PCA results can be represented also as loading plots. The loading plot graphs the coefficients (loadings) of each variable considered in the PCA (in our case the variables were the wavenumbers in the range 4000-750 cm\({}^{-1}\)) for the first component versus the coefficients for the second component. The loading plots allow us to identify which variables, i.e., which wavenumbers, have the largest effect on each component. Loadings can range from \(-1\) to \(1\). Loadings close to \(-1\) or \(1\) indicate that the variable strongly influences the component. Loadings close to \(0\) indicate that the variable has a weak influence on the component. Evaluating the loadings can also help you characterize each component in terms of the variables.  
The results of PCA performed on all the samples allowed us to cluster compostable and non-compostable films and to visualize which population the new samples supplied by MecPlast belong to. Moreover, it allowed a further separation inside the compostable population and to visualize which samples were more similar to starch. Additional PCAs performed only on M1, M2, and M3 and opportunely selected compostable samples taken as references, allowed to detect differences/similarities among the three samples and to detect the sample film closest to physicochemical composition of Vincotte-S732 (with certified PLA% content \(\geq\)40% and thickness \(<\) 15 \(\upmu\)m).
## 3 Approaches
### Fracture Detection with Artificial Intelligence: Improved Accuracy with Region of Interest Focusing  
This subsection focuses on the work of [PERSON] et al. [24].  
Aim: The aim of this paper is to improve the performance of the system described and already discussed from [PERSON] and [PERSON] (Section 3.5). The original paper discussed the implementation of a deep learning network to recognize whether a bone was fractured or not. The improvement of this work is given by removing unnecessary parts of the image with a semi-automated cropping process.  
Dataset: A total of 11,112 lateral wrist radiographs were used to retrain the network. Plus, a set of 50 fracture and 50 no-fracture radiographs were used in final testing of the model.  
Pre-processing: In an attempt to reduce overfitting, an automated technique was employed to identify the anatomical region of interest and crop the non-necessary image portions. The region of interest was defined using the Python OpenCV [25] matchTemplate() function. The method takes a template image and slides it across every position in the subject image (the wrist radiograph), returning the position in which the closest match was calculated. In this study, the region of interest was the distal radius. The template was, therefore, an anatomical representation of the distal radius. The template was produced by using a representative lateral wrist radiograph and applying a smoothing algorithm,followed by a binary threshold to segment the bone. A scaled template matching approach was adopted to account for different wrist sizes.  
Methods: The network used is the InceptionV3 network.  
Results: This tool was able to crop the image appropriately in 92.4% of cases. The remainder of the cases were cropped manually and, therefore, the process was described as semi-automated. The accuracy of the model was improved when the region of interest focusing was applied, reaching an AUC of 0.978 compared with an AUC of 0.954 without region of interest. Sensitivity and specificity values were 96% and 94%, respectively.  
Conclusion: This extension study has demonstrated that the accuracy of the network to predict fractures can be increased by removing surplus imaging data. The process is still semi-automated, it should be possible to make it fully automated using different techniques, for example using feature matching that tries to match features between the template and the image.
# Chinese Lip-Reading Research Based on ShuffleNet and CBAM
## 2 Lip-Reading Model Architecture
### ShuffleNet  
With the development of deep-learning technology, the concept of the convolutional neural network (CNN) has developed rapidly in recent years. Considered as a fully connected network with multiple layers that can simulate the structure of the human brain, a convolutional neural network can perform supervised learning and recognition directly from images. Convolutional neural networks are mainly composed of the following types of layers: input layer, convolutional layer, ReLU layer, Pooling layer, and fully connected layer. By stacking these layers together, a complete convolutional neural network can be constructed. CNN is great at processing video, which is a superposition of images. It is typically deployed in autonomous driving, security, medicine and so on.  
However, with neural networks becoming deeper and deeper, computation complexity increases significantly as well, which motivated the appearance of the lightweight model architecture design, including MobileNet V2, ShuffleNet V1, ShuffleNet V2, and Xception. Under the same FLOPs, the accuracy and speed of these models are shown in Table 1.  
We can see from Table 1 that ShuffleNet V2 has the highest accuracy and the fastest speed among the four different architectures. Therefore, we utilize ShuffleNet V2 as the frontend of our model. It mainly uses two new operations, namely, pointwise group convolution and channel shuffle, which greatly reduce the computational cost without affecting recognition accuracy.
## 4 SPI Prediction and Application
### Building the PSO-LSSVM Model  
Based on the previous study, the time series of four influencing factors, including the three external triggers (accumulated precipitation, maximum daily precipitation and rainfall days over the past 6-month period) and one internal factor (time), are taken as the input parameters. The SPI is regarded as the output parameter to establish the PSO-LSSVM model. The SPI values from March 2016 to March 2018 (71% of the database) are chosen as the training samples and SPI dates from September 2018 to March 2019 (29% of the database) are chosen as the test samples to verify model reliability. During the sample data preprocessing, all the factors and the SPI are converted to a [0,1] format. Then, the following values of the parameters to be optimized in LSSVM into the solution of PSO are encoded: the regularization parameter is \(\gamma=[0.1,\,1.0\times 10^{6}]\) and the kernel function width is \(\sigma=[0.1,\,100]\). The maximum number of evolutionary iterations is 300, the population of the particle swarm is 30, and the inertia weight is decreased linearly from about 1.0 to 0.4. The optimized parameters, \(\gamma\) and \(\sigma\), for the PSO-LSSVM model are shown in Table 4. To evaluate the predictive performance of the model, the conventional models, including Grid-SVM [25] and GA-SVM [22; 53; 54], are compared with the PSO-LSSVM model. The comparison and accuracy of the prediction values are shown in Figure 8.  
Figure 8: Comparison of the prediction values and the actual values of SPI: (**a**) K2452–K2453 of G105; (**b**) K95–K96 of S223; (**c**) K103–K104 of S270; (**d**) K9–K10 of S356.  
As shown in Figure 8, three models (PSO-LSSVM, Grid-SVM, and GA-SVM) have achieved good SPI prediction results for the four test sites, with the PSO-LSSVM model demonstrating the highest level of accuracy. It was especially accurate in predicting the SPI of the three Provincial Highways, likely because there is more traffic flow on the National Highway than on the Provincial Highways, which increased the difficulty of building the SPI prediction model. In K95-K96 of S223, the SPI declined sharply, and the rate of SPI decline was greater than that of the other three test sites. In relation to the controlling factors, the PSO-LSSVM still showed a good prediction with the MSE of 0.47. Therefore, the PSO-LSSVM model can better reflect the relationship between the impact factors and the SPI, and it is capable of predicting both 'gently decreasing type and rapid decreasing type SPIs.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 3 Proposed Scheme
### Recovery Module  
The objective of this module is to extract the secret voice message, by means of the following blocks: 16-bit conversion, adaptive LSB extraction, bit-to-sample conversion, and descrambling (Figure 2).  
Below, each block from the receptor module is explained.
# Extraction of City Roads Using Luojia 1-01 Nighttime Light Data
## 6 Conclusions  
Nighttime light imagery, with its high correlation with human activities, has been widely used in the study of social economy and environmental issues [51; 52; 53]. The Luojia 1-01 data source, with a much higher resolution compared with DMSP and VIIRS data sources, will provide new thoughts on the study of urban planning. This paper investigated the potential of the Luojia 1-01 data source in the extraction of city roads. The main work can be concluded as follows:  
1. We proved the possibility of extracting city roads through a nighttime lighting data source, which provides more thoughts in the study of the extraction of ground objects.
2. An unsupervised neural network-PCNN was established in the extraction of road networks. To improve the extraction precision, the urban regions were extracted through a threshold method. We also adopted a series of optimizing methods to enhance the image contrast and eliminate the residential regions along the roads. The method we proposed takes consideration of the negative effects and do not need enormous training data, which gave it potential in the road extraction project.
3. The results showed that the extraction quality of city centers was lower than suburban areas, which indicated that there existed a great similarity of light intensity in city centers.  
In general, the extraction quality of Wuhan reached 83.2%, which proved the possibility of using Luojia-01 data to extract city roads. In this study, we just adopted one single image in the experiment, which may limit the improvement of extraction accuracy. In future work, we will adopt a more stable Luojia-01 data set through the observation of a long time, and use more external datasets, such as high-resolution images and Landsat data, to obtain more precise results.  
[PERSON], [PERSON] and [PERSON] conceived and designed the main idea and experiments. [PERSON], [PERSON] and [PERSON] helped with the data processing. [PERSON] and [PERSON] performed the experiments. [PERSON] wrote the paper. [PERSON], [PERSON] and [PERSON] made revisions. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 72001046, 917462006, 42001389) and the National Key Research and Development Program of China (Grant Nos. 2017 YFB0503601).  
**Institutional Review Board Statement:** Not applicable.
## 4 Numerical Simulation  
### Model Establishment  
The following assumptions are made for the finite element method calculation [20]: (1) The soil is a homogeneous isotropic body and an ideal elastic-plastic body; (2) the thrust force acting on the excavation surface soil is circular, uniformly distributed load; (3) pipe segment joints are not considered in the model calculation; (4) the friction resistance value of bentonite mud is equally distributed along the pipeline; and (5) the time effect of soil is not considered during pipe jacking. Through the boundary sensitivity analysis [21], it is determined that the transverse (X axis) length of this calculation model is 40 m, the length of the parallel pipe jacking (Y axis) is 40 m, and the soil burial depth (Z axis) is  
Figure 6: Horizontal lateral deformation curve. (**a**) Horizontal lateral deformation curve of CX1 at different construction stages; (**b**) horizontal lateral deformation curves of CX1, CX2 and CX3 at \(L_{1}=6\) m.  
30 m. The finite element software Midas GTS NX 2021 was used to establish the 3D numerical analysis model of the vertical double-layer tube. The software was developed by POSCO of South Korea, and the Chinese version was developed and maintained by Beijing Midas Technology Co., LTD. The model is divided into a total of 111,900 cells and 115,860 grid nodes.  
In the numerical model, the soil and pipe and grouting layers are simulated by solid elements. Each entity is connected to each other through Boolean operation, and the mesh size is controlled during mesh partitioning to ensure the coupling of nodes between layers. The grouting layer can well simulate the interaction between pipe jacking and soil, and the mesh is divided by the mixed hexahedron mesh. Gravity loads are applied to the model and the boundary conditions are set by limiting the displacement. The boundary conditions are set as follows: The left and right side boundaries are fixed in the horizontal direction to limit the horizontal displacement in the X direction, and the horizontal displacement in the Y direction of the front and rear boundaries is limited. The ground is fixed with displacements in three directions, and no boundary conditions are set for other boundaries. The computational model grid is shown in Figure 7.
# A Comprehensive Methodology for Investment Project Assessment Based on Monte Carlo Simulation
## 3 Application of the Methodology on a Virtual Investment Project
### Simulation of NPV by Monte Carlo Method
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Unit** & **Statistical Characteristics** & **Distribution Function** \\ \hline \multicolumn{4}{c}{**Revenue Variables**} \\ Price A & EUR/t & Likewise 1300, Min. 1250; Max. 1330 & Triangular \\ Price B & EUR/t & Likewise 1600; 5\% 1500; 95\% 1700 & Triangular \\ Price C & EUR/t & Likewise 1800; Min. 1720; Max. 1880 & BetaPERT \\ \multicolumn{4}{c}{**Cost Variables**} \\ Variable costs A & EUR/t & Likewise 370; 5\% 350; 95\% 380 & Triangular \\ Variable costs B & EUR/t & Likewise 550; Min. 495; Max. 605 & Triangular \\ Variable costs C & EUR/t & Likewise 1800; Min. 1720; Max. 1880 & BetaPERT \\ Personal costs & EUR & Likewise 40,000; Min. 39,000; Max. 43,000 & BetaPERT \\ Other fixed costs & EUR & Mean 123,500; 90\% 135,000 & Normal \\ Investment costs & EUR & Likewise 1,339,690; 5\% 1,248,086; 95\% & Triangular \\ \multicolumn{4}{c}{**Time Variables**} \\ Average production time A & h/t & Mean 6; Std. Dev. 0.2 & Normal \\ Average production time B & h/t & Mean 7.6; Std. Dev. 0.76 & Normal \\ Average production time C & h/t & Mean 7.5; Std. Dev. 0.3 & Normal \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Distribution function of input variables. The price of product A contributes the most to the uncertainty of NPV--almost 40%. It is mainly due to the highest share of product A in the total produced volume and also because of the higher volatility of the price of this product. Then comes the value of the invested capital, i.e., the price of the line and the cost of its acquisition, while next in line are the variable costs of product A. The price and variable costs of products B and C are significantly lower contributors to the overall uncertainty, i.e., even weaker risk factors.
# An Improved RAIM Availability Assessment Method Based on the Characteristic Slope
## 2 Technical Background  
Before discussing the RAIM availability assessment methods, some technical backgrounds need to be stated, including the derivation of the integrity risk requirement for the single-satellite fault mode, the definition of the vertical characteristic slope and the specific meaning of RAIM being available.
# Mobility, Citizens, Innovation and Technology in Digital and Smart Cities
## 3 Cities Transportation Trends
### Blockchain for Managing Cities' Transportation Data and Contracts
#### 3.5.3 The Potential of 5G and V2X  
While Blockchain contracts may operate ofchain [179] and then be published on a public or private chain, the need for a more robust and efficient internet connection is evident [180]. In this sense, we highlight how the concept of Vehicle-to-Everything (V2X) intersects with 5G evolution as a core component of cities' communication [181; 182].  
By decentralizing transportation, more sophisticated communication paradigms may emerge, in which information can be accessed throughout a swarm of devices. Aligned with Dedicated Short Range Communications (DSRC) [183], this mesh of a vehicular network can surely contribute with the core concepts of Blockchain consensus mechanisms, in which an MAS of vehicles, drones and smartphones would interact with the network. An intersection of these concepts is the Cellular V2X protocol [184], which appears to be suitable for several real applications in the cities' transportation scenario [185]. Besides the efficiency and robustness that such systems may offer, DSRC technology can be more cost-effective than the traditional concept of expanding cellular communication networks [186]. Blockchain technology enabled via V2X DSRC can provide to users a new level of experience in making business and interacting with the financial world. For instance, Jaguar Land Rover recently commented about a partnership with IOTA for cryptocurrency earning when driving their cars [187] via cars' smart wallets. This kind of incentive is interesting and can also be applied on self-organized social systems, such as embedding on platforms such as Waze, Maps.me, among others. In this sense, these same kinds of incentives may appear in such self-organized systems focused on the relationship between users when navigating throughout the cities.
# \"In Vivo\" Validation of 3D-Printed Innovative Surgical Template for Lumbar Spinal Arthrodesign.
## 3 In Vitro Tests
### Clinical Trial: In Vivo Tests  
Sixty vertebrae (30 with template and 30 without template), for which a spinal arthrodesis was required, were treated to test the proposed template.  
For each pair of screws inserted, an intra-operative X-ray has been taken for evaluating the position of the Kirschner wire and, thereafter, the position of implanted pedicle screws.  
Qualitative evaluation parameters: the evaluation of the correctness of the trajectories was expressed by the surgeon based on the good standards present in the medical literature. The X-ray shots were used to provide a good surgery rating.  
Quantitative and qualitative evaluation parameters: each screw insertion was evaluated through the following parameters:1. accuracy of screw insertion based on three geometrical parameters: centring in the pedicle cross-section, alignment with vertebral plate, screws convergence (the evaluation criterion was class 5 (no violation): the screw is placed along the planned trajectory; class 4 (precise): the screw axis deviates by \(<\)2 mm away the planned trajectory; class 3 (normal accuracy): the screw axis deviates by \(>\)2 mm but \(<\)4 mm away; class 2 (deviated): the screw axis deviates by \(>\)4 mm; and class 1 (not conform): the screw axis presents critical deviation);
2. surgery duration for each verteb (in minutes);
3. number of X-ray shots per verteb (n);
4. Kirshner wires repositioning times (n); and
5. pedicle screw repositioning times (n).  
All procedures and criteria for the clinical trial were in line with the 1964 Helsinki Declaration, performed in accordance with the ethical standards of current Italian law and approved by the Ethical Review Board of the San Giovanni di Dio e Ruggi d'Aragona University Hospital in Salerno, where the trial/tests were conducted. The trial itself and the proposed procedure have been approved by the Ethical Committee of the Italian Health Ministry (Campania regional district). All participants were informed about the experiment protocol, the nature of the medical implements used, and risks and benefits linked to the new surgical device. Informed consent was signed by the patients.
## 3 Security Vulnerabilities
### Surface-Associated Vulnerabilities
#### 3.2.2 Communication Vulnerability  
**Vulnerable Multi-Protocol Connectivity**: An IoT system comprises various types of networking protocols and communication links, including wireless, wired, intranets, and Internet [66; 67]. A vulnerability in one networking protocol can be propagated to another protocol during cross-protocol communications. Moreover, the privacy and confidentiality of sensor information can be breached during the protocol translation performed by the Gateway. A Gateway device translates IPv6 packets to IPv4 packets when an IoT device exchange messages with a cloud server. A malicious Gateway device can learn about the payloads while performing the protocol translation. As a result, communication between the heterogeneous networks is vulnerable to various attacks that can be a threat to the violation of information privacy and confidentiality.  
**Network Service Vulnerability:** Security features, such as Firewall, to configure network services with proper security settings may not be present in the IoT operating systems. The settings may include MAC address, IP address, and port filtering. For example, Contiki [32] and RIoT [33] operating systems do not have options to configure firewall settings. As such, ports that are used for device maintenance can be exposed to adversaries [82].  
**Vulnerable Cryptography**: IoT devices may not adopt strong cryptographic schemes, such as larger keys, to encrypt communications because they have limited storage capacity, network bandwidth, and CPU power. Adversaries may find it easy to learn the communications as they are encrypted using weak encryption methods.