esa-sceva/satcom-chunk-collection huggingface dataset:
# Synchronization, Control and Data Assimilation of the Lorenz System
## 2 Master-Slave Synchronization
### Partial Conditional Coupling  
We can naturally generalize the definition of maximum conditional Lyapunov exponents even when we do not have a full coupling, i.e., \(p<1\), by looking for the synchronization threshold \(p_{c}\), for which \(d(p)\) goes to zero.  
From the plots shown in Figure 2, one can see that, for the \(x\) and \(y\) coupling, there is a well-defined synchronization threshold, similar to that obtained for the uniform coupling. For the \(z\) coupling, the maximum Lyapunov exponent is always positive (albeit small), and no synchronization is possible.  
This behavior is shown in Figure 1, where we plot the maximum conditional Lyapunov exponent for different coupling strengths \(p\) and different coupling directions \(C\). For the \(z\) coupling, we also plot the distance \(d(p)\) for some \(p\) (as in Figure 2).  
One can also see that, in the unsynchronized phase, the distance \(d(p)\) exhibits a nonmonotonous behavior, except in the vicinity of the synchronization transitions, as shown in Figure 2. This aspect is analyzed in Section 3.
# Prediction and Analysis of the Surface Roughness in CNC End Milling Using Neural Networks
## 3 Results and Analysis
### Predictive Results of BPNN  
According to Table 12, the lowest RMSE was obtained for experiment 36. Figure 12 illustrates the learning rate curve of each experiment and presents a representation of the convergence process and MSE. The learning rate graph was obtained by calculating the average value of 10 experiments.  
The measured value of the surface roughness instrument used in this experiment is the third place after the decimal point, and thus, the error value is required to be as small as possible. The changes in the target and predicted values listed are shown in Figure 13.  
Figure 11: Number of hidden layer neuron variables for RMSE.  
Figure 12: Learning rate curves (\(g\): 2,000,000, \(\eta\): 0.8, \(a\): 0.5, \(H_{n}\): 20).  
Figure 13: Target and predicted results of BPNN.  
The coefficient (\(R^{2}\)) is used to measure the dependent variable and independent variables in statistical differences and explains the relationship between the target and predicted values. Typically, the value is between 0 and 1, and the value is closer to 1, which indicates excellent predictive power.  
The determination coefficient is as follows:  
\[R^{2}=1-\frac{SS_{res}}{SS_{tot}}=1-\frac{\sum\left(T_{n}-Y_{n}\right)^{2}}{ \sum\left(T_{n}-T\right)^{2}} \tag{14}\]  
where \(SS_{res}\) is the sum of the residual square target and predicted values. \(SS_{tot}\) is the sum of the target and average error. \(Y_{n}\) is the predicted value, \(T_{n}\) is the target value, and \(T\) is the target of the average.  
The predication performance of linear regression was compared with that of BPNN. We substituted an input parameter into the equation to obtain the linear regression equation of surface roughness, which is expressed as follows:  
\[Y=0.203+0.273x_{1}-0.357x_{2}+0.208x_{3}+0.022x_{4} \tag{15}\]  
where \(Y\) is the surface roughness, \(x_{1}\) is cutting depth, \(x_{2}\) is spindle speed, \(x_{3}\) is feed rate, \(x_{4}\) is milling pitch  
Table 13 indicates that the RMSE of the BPNN was less than that of linear regression and that the \(R^{2}\) value reached 0.9995, which is higher than that (0.7794) of linear regression, indicating that the BPNN is more accurate than linear regression.
# Protecting Data at Risk of Unintentional Electromagnetic Emanation: TEMPEST Profiling
## 4 From van Eck Phreaking to TEMPEST Profiling  
Whenever an electronic device is used to process information, the various components can unintentionally emit EM radiation that may pose a security risk. Intercepted and recorded emissions can be analyzed, and the information processed by the device can be recovered [60]. EM leaks can be monitored in different ranges, depending on the environmental conditions. In most cases, the signal can be detected and observed even hundreds of meters from the device. If the signal is, however, transmitted over a conductive medium such as a transmission line, monitoring can be performed over a much greater distance (several kilometers) [85]. In 1985, [PERSON] first presented a type of eavesdropping, known as van Eck phreaking, or van Eck radiation. This activity involves the interception and decoding of EM radiation emitted from video displays, with the aim of unauthorized access to information. His research revealed that this method of information theft could be easily accomplished using a standard television receiver, specialized equipment, advanced technical expertise, and proximity to the target device [86]. Countermeasures against [PERSON]'s phreaking include shielding devices to minimize or distort EM emission, using specialized software and hardware, and maintaining physical security measures to prevent potential attackers from being near the target device for executing such an attack.  
The protective area of the device against EM leakage can be determined based on the distance to the device location and the boundaries of the controlled area. The TEMPEST profile, or footprint, is the point from the processing device to the farthest point where unwanted EM radiation can be detected or exploited. The footprint measures were first identified by Bell Laboratories during World War II. Bell Laboratories proposed shielding against radiation through space and magnetic fields, filtering of conducted signals on power and signal lines, and masking of spatial radiation or conducted signals [7]. TEMPEST uses a combination of distance, shielding, filtering, and masking techniques to protect target devices. Devices must be installed at certain distances, including additional shielding; wires transferring sensitive data must be separated from those transferring unclassified data (RED/BLACK separation); and overlapping jamming signals can be used for masking real data. The United States National Security Agency (NSA) established regulations for EM emissions, as outlined in National Communications Security Memorandum (NACSIM) 5100A [87]. Electrical devices and equipment that meet NSA standards are considered secure. Surveillance and countersurveillance investigation tools are overseen by the government and a small number of NACSM 5100A-licensed companies. Since certification for private sector needs is extremely expensive, this has led to the development of zoning regulations that provide more cost-effective alternatives but with a slightly lower level of security.
# Cellulose Nanofibril (CNF) Films and Xylan from Hot Water Extracted Birch Kraft Pulps
## Materials and Methods
### TEMPO Oxidation and Fibrillation of Pulps  
The initial bleached kraft pulp and the water-extracted pulps were oxidized in a TEMPO/NaBr/NaClO system prior to their fibrillation. Briefly, the pulps were first suspended in water at 1% consistency, together with 0.13 mmol of TEMPO and 1.0 mmol of NaBr per dry gram of pulp. The pH was adjusted to 10 by addition of 1 M NaOH, and the suspension was stirred for over 1 h to allow swelling of the pulp. The TEMPO-mediated oxidation was then conducted by gradually adding amounts of NaClO (total amount was 2.5 mmol per dry gram of pulp) while maintaining the pH at 10 by addition of NaOH. Once the pH remained constant (i.e., no consumption of NaOH), the pulps were filtered through nylon filter bags and then washed. To facilitate the washing, the pulps were acidified by re-diluting in water and adjusting the pH to 2.5-3 by addition of 1 M HCl. Thereafter, the pulps were washed three times with water.  
Mechanical fibrillation of the TEMPO-oxidized pulps was performed in a high-pressure fluidizer (Microfluidizer EH110, Microfluidicies Corp, Westwood, MA, USA) after diluting in water to 1.5% consistency and adjusting the pH to neutral. The water-extracted pulps were first homogenized by passing through a 400 \(\upmu\)m chamber. Subsequently, all the pulps (i.e., kraft pulp and water-extracted pulps) were passed twice through a 200 \(\upmu\)m chamber and once through a 100 \(\upmu\)m chamber. The higher viscosity of the CNF dispersion from kraft pulp made the fluidization at 1.5% consistency rather difficult, particularly during the last pass through the 100 \(\upmu\)m chamber. Although the consistency of such CNF should have been lower for practical purposes, in this study it was not adjusted to allow for direct comparison with the other CNF samples. After fibrillation, the resulting CNF dispersions were stored in a refrigerator at 4 \({}^{\circ}\)C until further use. Microbial contamination during storage was not observed.
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 4 Experiments
### Image Recognition  
To compare the recognition performance of DB2 DPCA with the aforementioned four algorithms, we employ these algorithms to extract features and then apply a specific classifier on the extracted features to perform classification. Since recognition performance is largely determined by the classifier, we try three different classifiers in order to highlight the effects of the feature extraction methods. The classifiers chosen are the Nearest Neighbor (NN) classifier, linear Support Vector Machine (SVM) [62; 63] and Collaborative Representation Classifier (CRC) [64; 65]. These classifiers are widely employed to evaluate the recognition performance of unsupervised learning algorithms.  
Ten-fold cross validation is adopted for performance evaluation--that is, all images are randomly separated into ten folds, wherein nine folds are for training and the remaining  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Database** & **Image Size** & **No. of Images** &
\begin{tabular}{c} **No. of Images** \\ **per Class** \\ \end{tabular} & **No. of Classes** \\ \hline Extended Yale B face & \(48\times 42\) & \(2414\) & \(64\) & \(38\) \\ AR face & \(50\times 40\) & \(3120\) & \(26\) & \(120\) \\ Binary Alphadigits & \(20\times 16\) & \(1404\) & \(39\) & \(36\) \\ PolyU 3D palmprint & \(32\times 32\) & \(8000\) & \(20\) & \(400\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The statistics of the four databases used in the experiments.  
Figure 2: **(a)** Twelve images of the first subject in the Extended Yale B face database. **(b)** The first twelve images of the first subject in the AR face database. **(c)** The first image from twelve different classes in the Binary Alphadigits database. **(d)** The first image from twelve different classes in the PolyU 3D palmprint database.  
one fold is for testing. This procedure is repeated ten times and the average classification accuracy is calculated.  
For PolyU 3D palmprint database, we perform classification on images from the first 100 subjects to reduce the computational time. For the other three databases, all images are used.  
For the two bilateral algorithms, we extract the same number of features from both literals. To implement linear SVM, we use LibSVM [62; 63] with default parameters. As recommended in [62], prior to classification by linear SVM, the training and testing data are linearly scaled. Specifically, each attribute in training data is scaled to [0,1]; then, the same parameters are applied to the testing data. For CRC, the regularization parameter \(\lambda\) is set to be \(0.001*n/700\) as in [64], where \(n\) is the number of training samples.
## 3 Case Study: Purse Seiner in the Adriatic Sea
### LCA Models for Different Power Systems
#### 3.2.7 The LCA of an LBG-Powered Ship  
LBG, known as bio-LNG or renewable LNG, can be produced from various sources. LBG production from animal waste is considered in this study.  
Since LBG and LNG are interchangeable, LBG is used like LNG, in a dual-fuel engine with diesel as the pilot fuel. Specific fuel consumption and equations for the calculation of fuel consumption are presented in Section 3.2.2. The processes included in the LCA of an LBG-powered ship are presented Figure 13.  
The environmental impact of the engine is the same as that of the diesel engine in Section 3.2.1, while TTW emissions are calculated based on Equation (13), with emission factors for LBG and diesel presented in Table 3. The methane slip in the TTW phase is considered in the same manner as for LNG. The WTT phase of LBG includes the anaerobic digestion of a feedstock (animal waste), the process of biogas upgrading (biogas purification process, during which methane is separated from CO\({}_{2}\) and other gases to form biogas), liquefaction of biogas, and its transportation to storage in the port via tank truck for 2000 km.
## 4 Methodology
### Metis  
In this approach, the program _gpmetis_ from METIS was used to automatically partition LeNet and compare the results with our approaches. The reason to choose METIS is that it is considered a widely known state-of-the-art tool used to automatically partition graphs for general purpose.  
This tool offers several parameters that can be modified by the user like the number of partitions, the number of different partitionings to compute, the number of iterations for the refinement algorithms at each stage of the uncoarsening process, the maximum allowed load imbalance among the partitions, and the algorithm's objective function. The number of partitions corresponds to the maximum number of devices allowed to be employed in each setup described in Section 4.2. Thus, as METIS attempts to balance all the constraints, it always employs the maximum number of devices in each experiment. All the other parameters listed here were varied in our tests and, for our inference rate maximization objective function, the maximum allowed load imbalance among the partitions parameter was substituted for the maximum allowed load imbalance among partitions per constraint,  
Figure 6: Partitionings using the inference rate greedy approach: (**a**) LeNet 2:1 for the 11-device experiments; (**b**) LeNet 2:1 for the 56-device experiments; (**c**) **LeNet 1:1** for the 56-device experiments; (**d**) LeNet 2:1 for the 63-device experiments.  
which allows using different values for memory and the computational load. It is worth noting that, for the objective function parameter, both functions were used: _edgecut_ minimization and total communication volume minimization. These parameters are detailed in the METIS manual [47].  
For the locked-input experiments, the LeNet graph had the vertices from the input layer removed to run METIS with a small difference between the constraints proportion (target weights in METIS) related to the amount of memory and computational load that the input layer requires. After METIS performs the partitioning, the input layer is plugged back into the LeNet graph and we calculate the cost (inference rate or amount of transferred data) and if this partitioning is valid.
# Investigation on the Application of Artificial Intelligence in Prostodontics
## 3 Result
### Diagnostic Accuracy  
All subsequent therapies and suggestions were based on the results of the diagnostic accuracy examination. A thorough evaluation of the patient's teeth, musculature, and associated tissues are required. Challenges with missing teeth, complications with replacement, and arch diagnosis for implant surgery are necessary. AI significantly increases diagnosis accuracy.  
The improvement in diagnostic accuracy explains the improvement in diagnosis during prosthodontic rehabilitation and showed that the use of AI was directly proportional to accuracy. Here, diagnosis includes the determination of the problem, the decision on the type of prosthesis, and finalizing the design and related components in removable and fixed prostheses.  
AI and machine learning emerged as powerful tools for assisting diagnosis, determination of the type of prosthesis requirement, the development and positioning of clasps in RPD, designing of connectors and pontics, etc. Given this vast amount of data being generated, dentists/prosthodontists today are faced with an overwhelming amount of information when working to rehabilitate even a single patient. AI has the potential to provide these professionals with the ability to speed up and improve their rehabilitative capabilities by helping to extract clinically relevant insights from the wealth of information available. AI technologies are making great strides in medical imaging. Studies showed that the use of AI may be able to enable earlier disease detection, while also enhancing the workflows. AI can review r vast numbers of images and then, quickly and regularly identify patterns, including variations that humans cannot. This may improve patient rehabilitative treatment outcomes. This adds to the huge potential of AI to support clinical decisions in time-critical situations or when there is a lack of expert knowledge available. AI has incredible potential for analysis and diagnosis. Most of the time and effort in a lab is spent on pre- and post-analytical processes. AI could help bring significant improvements to the workflow and operations, saving time, labor, and costs.
## 3 Detailed Description of the Existing Protocols
### Hydrocast  
In this routing protocol, a recovery mechanism is proposed to tackle the void hole problem. Moreover, the next forwarder node is elected on the base of its depth difference. The priority of a node for next forwarder node will be high, if its depth is lower than its neighbor nodes. This forwarder node selection minimizes the packet drop ratio and shows high performance during a dense network. However, in the sparse network, this strategy leads towards long recovery path as in Figure 3. This figure highlights the issue of the long path (during a sparse network). For example, the source node sends the DP towards sink via relay nodes (by avoiding the void hole region). This strategy results in longer recovery paths, i.e., (from node I to node VII). The probability of a longer recovery path increases during sparse network. Due to the aforementioned reasons, the network faces high E2E delay and high EC. This analysis shows that this protocol needs to be enhanced. Therefore, Co-Improved Hydrocast protocol is proposed. The detailed description of this protocol is presented in the next subsection.
## 3 Mathematical Model
### Problem Description and Assumption
In Figure 2, b1 represents the station storage process corresponding to the cargo from the consignment time to the start loading time, b2 represents the loading process corresponding to the cargo from the start loading time to the departure time, b3 represents the inter-station transportation process of the cargo following the train k1 from A to C, b4 represents the dwell process of the cargo following the train at C, and b5 represents the inter-station transportation process of the cargo following train k1 from C to D. b6 represents the unloading process after cargo arrives at the final arrival station D. The en route transportation time is 7 h, and the total time is 10 h.  
As shown in Figure 3, {b7, b8, b9, b11, b12, b13, b14, b15, b16} is a transit path in the space-time-state network, which corresponds to path four of the alternative transportation scheme in Table 1, indicating that the cargo chooses this alternative for transportation. As shown in Figure 3, path 4, compared with path 1, has more transit processes in B. b9 represents goods following train k2 after arriving at B. b10 is in the storage layer after the unloading process of b10 at B. In addition, b11 represents goods stored at station B, and b12 represents goods loaded at B to train k3, followed by train k3 transportation. The transit time en route is 11 h, and the total time is 14 h.  
The following model assumptions are made in this paper:  
* In this paper, we only consider the transportation process between different cities on the high-speed rail line and do not consider the short-distance intra-city transportation process, including express collection and delivery between the two ends of the OD from where the freight demand occurs to the high-speed rail station and between the high-speed rail station and the freight destination. * Before making transportation plan decisions, the express cargo flow of different OD zones, different product categories, and different shipping periods can be projected in advance. * The physical network and train schedules of HSR do not change and can be known in advance during the decision-time cycle. * The existence of a large high-speed rail station in each target city for express freight operations, with the operational facilities and conditions required in both high-speed rail confirmation and piggyback transport modes. * Each express cargo flow can find the corresponding cargo service arc segment (including the virtual arc segment) to complete the network flow allocation, and the same cargo service arc can serve different cargo flows. * Assume that the operational process and efficiency in the process of loading and unloading, transit connection, etc., are relatively stable without considering the relevant perturbations of external conditions on the transportation organization.
# Biodegradable and Compostable Shopping Bags under Investigation by FTIR Spectroscopy
## 3 Results and Discussion
### Essential Technical Details
In Italy, TUV Italia issues product certifications through the laboratory activities carried out by TUV SUD Product Service, which is an entity recognized all over the world [24]. TUV SUD Product Service allows companies to stamp their certified products with the octagonal TUV SUD's mark known and recognized worldwide (Figure 2b). The mark certifies that the product, here in particular the shopping bags, has been manufactured in compliance with all safety requirements and that it is subjected to periodical checks. TUV SUD Product Service certification requires the execution of several rigorous tests and the maintenance of consistent quality production standards over time [24]. The assessment of the conformity of the product to the relevant standards, is made by carrying out laboratory tests and verifying the related documentation [25]. In order to maintain the certification and thus ensure that the certified products are constantly \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Minimum Renewable Raw Material (PLA) Content (\%) 1**} & & & \\ \cline{2-5}  & **Current** & **By 1 Jan 2020** & **By 1 Jan 2021** & **Thickness 2** & **Other Features** \\ \hline
**Shoppers for bulk foods *** & 40 & 50 & 60 & 15 \(\mu\)m &
\begin{tabular}{c} Composable \\ Biodegradable \\ Suitability for food use 3 \\ Distribution charged \\ Prize visible on the ticket \\ \end{tabular} \\ \hline N.R. = not requested;* bags used as primary packaging for bulk foods or supplied for hygien purposes (fruit and vegetable departments, bakery, delicatessen, bucther, fish market; * transport bags, used for example at the checkout of the market; 1; determined according to the UNI CEN/TS standard; 2 depending on the cocktail ingredients involved in the production strictly associated with their suitability for food use; 3 obtained by the manufacturer from specific analysis laboratories. & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Main requirements for shopper bags intended for the transport of packaged food or as primary packaging for bulk food [23]. Figure 2: The “OK compost” certification mark issued by TUV AUSTRIA (**a**); the TUV SUD Italy octagon conformity mark, globally known and recognized, enabled by TUV SUD Product Service (**b**). manufactured according to the required quality and safety standards, the experts carry out periodic checks of the physicochemical properties of the production [25]. In this context, FTIR spectroscopy was herein considered as a fast and easy analytical technique to perform a simulative investigation on samples of shopping bags, and to obtain information on their physicochemical properties and possible conformity.