esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Defect Detection Methods for Industrial Products: A Review
## 3 Surface Defect Detection Method of Industrial Products Based on Deep Learning
### Unsupervised Method  
In response to the disadvantages of supervised methods, some researchers have begun to look at unsupervised methods. When the input training data are only the data information itself, and there is no label information, the machine learns the pattern of these unlabeled data to obtain some inherent characteristics and connections of the data and automatically classifies the data [53]. Then, when new data are encountered, it can judge which model that new data belong to according to the previously learned model (here, the model refers to the model composed of original data). This process belongs to unsupervised learning.  
Among the unsupervised learning methods, the most commonly used surface defect detection methods mainly include the reconstruction-based methods and the embedding similarity-based methods. For the former, the training of the neural network structure is only used for the reconstruction of normal training images, and the anomalous images are easy to be found because they cannot be reconstructed well; anomaly score is usually represented by reconstruction error. The most common reconstruction-based method is Autoencoder (AE) and Generative Adversarial Network (GAN). For the latter, deep neural networks are used to extract meaningful vectors describing the entire image, and the anomaly score is usually represented by the distance between the embedded vectors of the test images and the reference vector representing normality from the training dataset. Typical algorithms mainly include SPADE [54], PaDIM [55], PatchCore [56], etc. In addition to these two types, Deep Belief Network (DBN) and Self-Organizing Map (SOM) can also be used for surface defect detection.  
This section will first take the four network models of Autoencoder (AE), Generative Adversarial Network (GAN), Deep Belief Network (DBN), and Self-Organizing Map (SOM) as examples to briefly introduce their research status in surface defect detection task. The introduction of three typical algorithms of the embedding similarity-based methods will be shown in Section 5.
# An IoT-Based Non-Invasive Glucose Level Monitoring System Using Raspberry Pi
## 2 Proposed Internet of Things (IoT) Glucose Monitoring System
### IoT Cloud Computing  
#### Model Selection  
Before implementing the whole model, a model selection set of experiments were performed. In order to do this, an ANN was trained using the Tensorflow library in Python programming language. In this regard, experiments for model selection were carried out taking into account the following points:  
* Participants: A total of \(514\) healthy subjects, with age ranging from \(18\)-\(44\) years, and averaging \(28\pm 5.2\) years, were invited on a random basis to be part of this study. The training set is constituted by \(514\) histograms, each representing the average of the \(12\) histograms acquired for all subjects. Participants of this study were of different skin colour for increasing the variability of the sample, thus making the training data set representative of this region.
* ANN structure: The ANN used in this work is composed of \(256\) input neurons--the histogram values--and \(2\) hidden layers, each one composed of \(1024\) neurons, whereas the output layer has only one neuron corresponding to the glucose concentration value. At the end of both hidden layers, a \(0.20\) dropout was considered. The activation functions used in this model were ReLU [44] in all cases.
* ANN configuration: The ANN was trained using the ADAM method for error minimisation [45]. A total of \(100\) epochs with a \(50\) batch size were also considered.  
* Evaluation metrics: With the aim to evaluate the model performance, the mean absolute error and the Clarke error grid analysis [46] were adopted. The mean absolute error (MAE) is computed as in Equation (2) where \(\mathbf{y}_{i}\) represents the reference glucose values, and \(\mathbf{\hat{y}}_{i}\) stands for the estimated values by the algorithm. In the Clarke error grid, the reference glucose concentrations versus the estimated values are plotted and divided into five zones, as shown in Figure 3. Zones A and B represent accurate or acceptable glucose results, respectively; zone C could lead to unnecessary treatments while zones D and E entail potentially dangerous mistreatment.
* Cross-validation model: The whole input data set was divided into train/test (for model selection) and validation subsets. For this purpose a 10-fold cross-validation scheme was randomly applied to the 70% of the whole data set, representing the train/test subsets; the remaining 30% were taken as the validation subset, as illustrated in Figure 2. In the 10-fold cross-validation, data is randomly divided into 10 groups or _folds_ of approximately equal number of observations. The first fold is used as a test subset whilst the remaining nine folds serve to train the classifier. This procedure is repeated ten times until every fold is treated as a test subset [47]. \[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|\mathbf{y}_{i}-\mathbf{\hat{y}}_{i}\right|\] (2)
## 3 Results and Discussion
### Cutting-Path Planning in Five-Axis CAM of Stents and Actual Stent Machining
#### 3.3.1 Cross-Section Height of the Stent  
This study used injection molding to form the tubal structure. During injection molding, heterogeneous internal and external diameters resulted in different cross-section heights of the stent without reaming. The error range was between 0.01 and 0.038 mm and the mean height error was 0.017 mm. Therefore, higher homogeneity in internal and external diameters is necessary. Lathing was used to machine the external diameter of the tubal material to 6 mm; next, reamers (diameter = 5.4 mm) were used for reaming the internal diameter of the tube from 5.2 mm to 5.4 mm. The error range of the cross-section height of the reamed stent was 0.002 to 0.009 mm and the mean height error was 0.004 mm. These results verified that the sidewall thickness of the tube after reaming approached an identical value, thereby increasing the homogeneity of stent heights.  
Figure 16: Differences in the stent structures with and without fillet machining. The height, width, and arc depth are as defined in Figure 8.  
Figure 17: Cross-sectional images of Positions 1–9 at the front end, central part, and rear end of the stent machined with compensation. The height, width, and arc depth are as defined in Figure 8.
## 3 Results and Discussion
### Volatile Compounds Profile Identification  
Figure 3 shows the total ion current (TIC) chromatogram obtained for _sofrito_ 1 used for the optimization procedure by the BBD. Peak identification was based on the comparison of mass spectrum data with spectra in full scan mode (m/z 30-550) present in the Wiley library that matched equal or more than 90% similarity.  
A total of 41 volatile compounds were identified among all the samples analyzed (Table 2). In S1 14 volatile organic compounds (VOCs) were identified meanwhile two more VOCs were encountered in CS1. Some of the volatiles were only detected in the raw sample, those were: 1-octanol (1,3%), 2-hexenal (0.67%), di-2-propenyltrisulfide (3.1%) and 3,4-dihydro-3-vinyl-1,2-ditin (26.3%). The last two compounds are related to garlic and/or onion and represent nearly 30% of the total peak area. It is known that thiosulfonates are very unstable during cooking procedures and they usually rearrange to poly-sulfurs that still have biological activity [38].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Validation Parameter** & **Hexanal** & **Trans-2-Heptnal** & **Octanal** & **Trans-2-Decenal** \\ \hline Linear range & & & & \\ (mg Kg\({}^{-1}\)) & 0.001–1.096 & 0.001–1.760 & 0.002–0.880 & 0.004–0.073 \\ R\({}^{2}\) value & 0.998 & 0.997 & 0.998 & 0.990 \\ Limits of detection (LOD) & & & & \\ (ug Kg\({}^{-1}\)) & 0.89 \(\pm\) 0.06 & 1.03 \(\pm\) 0.53 & 1.55 \(\pm\) 0.07 & 18.48 \(\pm\) 0.65 \\ Limits of quantitation (LQQ) & & & & \\ (ug Kg\({}^{-1}\)) & & & & \\ Intra-day repeatability \({}^{*}\) & 1.9 & 4.2 & 1.4 & 3.5 \\ Inter-day repeatability \({}^{*}\) & 6.7 & 4.9 & 6.5 & 7.9 \\ \hline \hline \end{tabular} \({}^{*}\) Intra-day and inter-day repeatability (peak area relative standard deviation (RSD) %).  
\end{table}
Table 2: Validation parameters for the headspace solid-phase microextraction-gas chromatography mass spectrometry (HS-SPME-GM-MS) optimized method.  
Figure 3: Total ion current (TIC) chromatogram obtained for CS1 used for the optimization procedure by the Box–Behnken designs (BBD) and main volatile compounds identification.
# An Improved Hybrid Transfer Learning-Based Deep Learning Model for PM2.5 Concentration Prediction
## 4 Discussion
### Setting of Hyperparameters  
The main hyperparameters in the proposed model are the batch size of source domain data and target domain data, time step and hidden state size of LSTM. Most of the hyperparameters can refer to our previous work [24] and the related literature [27]. Here, just the time step is discussed, which is the hyperparameter closely related to the proposed model.  
Reasonable setting of the time step has a great influence on experimental accuracy and speed. The larger the value of time step is, the more the characteristics of the sample it contains. However, the influence of past data on the current PM2.5 concentration will become weaker and weaker with the increase of the time step. If the time step is too large, it will lead to the reduction of the experimental accuracy. At the same time, the time of experimental training will increase with the increase of the time step. To get an appropriate setting of the time step, some experiments are conducted. The experimental results corresponding to different time steps are shown in Figure 7. It can be seen from the figure that the comprehensive performance of the proposed model is the best when the time step is 8. Thus, the time step is set as 8 in this study.  
Figure 6: Prediction of real and predicted PM2.5 concentrations at Guangzhou station using different models.
## 3 Results and Discussion
### Thermophysiological Comfort of the Surgeon  
Figure 4 summarizes the results for the PMV index, computed for a surgeon, working in the OR.  
The three clothing ensembles are compared and four different graphs for the relative humidity values in the range 30%-60% are shown. The simulated temperatures were between 16 and 30 \({}^{\circ}\)C, different from the temperature range used in the predictions for the thermophysiological comfort of the patient (23-32 \({}^{\circ}\)C). The temperatures went below 23 \({}^{\circ}\)C to reach climatic conditions category 5 of the indoor environment and to discover at which temperature it turns to climatic conditions category 4.  
Figure 5 presents the results for the PPD index and Figure 6 the climatic conditions of the OR from the point of view of the surgeon.  
Figure 4: PMV index for the surgeon, dressed in three clothing ensembles with different clothing insulation: (**a**) relative humidity (RH) of the air 30%; (**b**) RH 40%; (**c**) RH 50%; (**d**) RH 60%.  
Figure 3: Category of the climatic conditions for the patient under anaesthesia.  
The predicted values for the PMV and PPD show that the thermophysiological comfort of the surgeon, dressed in different clothing ensembles is assured in the following intervals:  
* For clothing ensemble with 0.96 clo: from 18 \({}^{\circ}\)C, 30% RH (PMV = \(-\)0.48) to 23 \({}^{\circ}\)C, 40% RH (PMV = \(+\)0.48).
* For clothing ensemble with 1.01 clo: from 17 \({}^{\circ}\)C, 60% RH (PMV = \(-\)0.49) to 23 \({}^{\circ}\)C, 30% RH (PMV = \(+\)0.47).
* For clothing ensemble with 1.05 clo: from 17 \({}^{\circ}\)C, 40% RH (PMV = \(-\)0.49) to 22 \({}^{\circ}\)C, 60% RH (PMV = \(+\)0.49).  
The comfort temperature interval for the surgeon is much closer to the prescribed comfort indoor environmental temperature (20-22 \({}^{\circ}\)C) [2], compared to the comfort temperature interval for the patient. The results confirm the suggestions in [10], the temperature in the OR to be lower than 18 \({}^{\circ}\)C to assure the comfort of the surgeons in heavy protective gowns.  
The results in Figures 5 and 6 confirm the conclusions from the prediction of PMV index for the surgeons. The graphs in Figure 6 allow the comfortable environment for the surgeons to be quickly assessed, using the studied protective clothing: all combinations between air temperature and relative humidity, where the climatic category is computed as \"5: comfortable, no health risk\" (Table 3).  
Figure 5: PPD index for the surgeon, dressed in three clothing ensembles with different clothing insulation: (**a**) relative humidity (RH) of the air 30%; (**b**) RH 40%; (**c**) RH 50%; (**d**) RH 60%.
## 3 Results
### B. subtilis CU1 Is Able to Grow on IMOSs as a Unique Carbon Source  
The growth kinetics of probiotic _B. subtilis_ CU1 were determined in minimal medium with IMOS as a unique carbon source and compared to glucose as the control carbon source. Figure 2A represents OD measurements at 595 nm, and Figure 2B represents CFU/mL determination during the growth of the strain. The results show that _B. subtilis_ CU1 is able to grow in minimal conditions on both glucose and IMOS containing medium (Figure 2A,B). The growth kinetics are similar on both carbon sources, but with a higher generation time on IMOS (85.2 min on IMOS against 63 min on glucose estimated from the maximum specific growth rate). The general shape of the growth curves obtained by bacterial numeration on agar plates supports the results obtained by OD measurements, with a final bacterial concentration at 12 h of about 10\({}^{9}\) CFU/mL with each substrate (Figure 2B). No growth was observed on the minimal medium without a carbon source (data not shown).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Minimal Medium** & **Glucose** & **Inuline** & **FOS** & **GOS** & **XOS** & **AXOS** & **Lactulose** & **SOS** & **IMOS** \\ \hline _B. subtilis_ CU1 & - & ++ & + & ++ & ++ & ++ & + & - & ++ & ++ \\ _B. subtilis_ 2-4/2 & - & ++ & + & ++ & ++ & + & + & - & ++ & ++ \\ _B. licheniformis_ BL31  
Figure 1: Structure of commercial IMOS determined by MS. (**A**) Mass spectra of ion \(m/z\) 1013.3 (DP6) obtained by ESI-MS/MS in positive mode with zoom on spectral sections corresponding to intracyclic fragmentations. (**B**) Structure of IMOSs composed of glucose oligomers with \(\alpha(1\to 4)\) and \(\alpha(1\to 6)\) linkages.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(m/z\) & **Intensity** & **Structure** & \(m/z\) & **Intensity** & **Structure** \\ \hline  
& 5.09 & Hexose DP56 [M + Na]\({}^{+}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of IMOSs by MS in linear mode.  
Figure 2: _B. subtilis_ CU1 grown on commercial IMOS as sole carbon source via the secretion of \(\alpha\)-glucosidase enzyme. Growth curves for _B. subtilis_ CU1 in minimal medium with glucose (control) or IMOS as sole carbon sources determined by **(A)** OD measurement at 595 nm and **(B)** bacterial count on agar plate. **(C)** IMOS consumption (DP1, DP2 and DP3) during growth of _B. subtilis_ CU1 determined by liquid chromatography. The data presented are from one representative experiment among three. **(D)** Determination of \(\alpha\)-glucosidase and \(\beta\)-glucosidase activities in the culture medium or the cytosol of _B. subtilis_ CU1 after 12 h of culture on glucose or IMOS as sole carbon sources. **(E)** Production of lactic acid and **(F)** acetic acid in the culture medium of CU1 determined by HPLC. The data from panels **(A,B,D–F)** are presented as means \(\pm\) SD and are from one representative experiment performed in triplicate among three. * \(p\) \(<\) 0.05, **_p_ \(<\) 0.01, ***_p_ \(<\) 0.001 or ***_p_ \(<\) 0.0001 were used to indicate statistical significance according to Mann-Whitney test.
# On Beamforming with the Single-Sideband Transform
## 4 Comparisons and Simulations
### Comparison for Different Observed Frames  
In this simulation, we compare our filters for the accurate and estimated RFR for a range of considered observed frames \(L_{Y}\), in which \(L_{Y}=1\) reduces to the MTF model. The results are in Figure 2. For all metrics except DI, the accurate results are consistently better than those achieved for an estimated RFR. Also, the SSBT-A results are, overall, worse than the accurate STFT-A ones, but not by too large a margin (around 3-4 dB for all metrics). This is also the case between the SSBT-E and STFT-E, but to a higher degree since the SSBT-E beamformer also led to a higher desired DSDI, which considerably adds to its performance loss of the former compared to the STFT-E for all metrics.  
Figure 2: Output metrics for the beamformers over time for varying values of \(L_{Y}\).  
The best STFT-E results are obtained for \(L_{Y}=1\) in this scenario. With \(L_{Y}=2\), there is a notable increase in DSRF and a sharp decrease in gSER, with the latter increasing again for larger values of \(L_{Y}\) but never achieving the same performance. This is due to errors in the RFR estimation for frames other than \(l=0\), given that these windows carry less information, and these errors interfere more than adding new frames helps. Also, the estimate with \(L_{Y}=1\) already takes into account some information about the different windows' correlations with regard to the desired signal (see Equation (27)), explaining the STFT-E results being better than the STFT-A results in this case.
## 4 Results and Discussion  
This section describes the simulation outcomes for achieving the study's specific goal. The simulation results and discussions on how the proposed channel estimator performs in comparison to the current estimate for the CF M-MIMO uplink system based on the SE, EE, and area throughput parameters under the Weichselberger Rician fading channel are provided. Table 1 is a list of the simulation parameters that are used in this paper to compare the proposed channel estimator's performance and complexity to the previous work channel estimator for a CF M-MIMO UL system. The simulations are carried out in the MATLAB R2022a software with the computer having 16 GB of RAM. Also, Concept Draw is used as a drawing tool.  
Figure 4: Flow chart of the proposed channel estimator based on element-wise MMSE and QR decomposition with the precoding matrix at the UE side.  
While QR factorization and user-side precoding can significantly improve system performance and reduce receiver complexity, they introduce notable limitations related to computational demands, power consumption, hardware requirements, channel state information acquisition, signaling overhead, implementation complexity, and scalability. These factors must be carefully considered when designing practical systems, especially for resource-constrained user devices.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 6 Evaluation and Results
### Comparison Against Related Research
#### 6.8.1 Dataset and Performance Metrics  
The reported performances for the systems presented in the related research chapter are heavily dependent on the used dataset. Some of the used datasets, for instance, rely on horizontal and vertical touch inputs only, while others use a combination of tap and slide gestures. Our system, on the other hand, tries to use every possible form of touch input for the classification. Only inputs with a single data point are disregarded since they do not contain information for how the internal states would chance in the HMM. To also consider those inputs, another HMM classifier could be employed, which predicts on a sequence of such inputs, and is used together with the default model.  
Furthermore, not every research paper uses the EER metric for comparing the performance of the created classifier. Some papers, for example, are only reporting FAR and FRR like [1], while others measure the performance in regard to standard accuracy, but do not report the AUC which is considered a better measure for learning algorithms [35]. Our implementation relies on the AUC and EER values of a classifier since most other work described in Section 3 also relies on the AUC and the EER. Moreover, the EER can be calculated from the FAR and FRR, and gives a better insight over the actual performance since it combines the FAR and FRR into a single, easy to compare metric.
# A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals
## 5 Performance Evaluation  
To evaluate and validate the proposed algorithm, the tracking algorithms discussed above are implemented on a software Global Positioning System (GPS) receiver that is based on a Visual C++ platform. The receiver directly processes the IF signal, which is read from a data file.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 6 Nanoparticle Toughening Method
### Summary  
The advantages of nanoparticle-toughened epoxy resin lie in their large specific surface area and strong interfacial effects, which significantly enhance the fracture toughness and impact resistance of the material. Meanwhile, hybrid epoxy nanocomposites achieve further breakthroughs in performance through the synergistic combination of nanofillers and original modifiers. This collaboration optimizes the strength-toughness balance, providing innovative material solutions for aerospace, electronic packaging, and other fields, which integrate high mechanical performance with process feasibility.
## 1 Introduction  
### Electrochemotherapy in Veterinary Oncology  
Electrochemotherapy (ECT) is a local cancer treatment that has gained popularity in both human and veterinary oncology since European Standard Operating Procedures (ESOPE) were published in 2006 [1]. The treatment consists of the combination of reversible electroporation with an appropriate electric pulse duration and electric field intensity leading to enhanced cytotoxicity of a chemotherapeutic drug inside the tumor [1, 2]. The effectiveness of ECT was demonstrated in several tumor histotypes: melanoma, planum cell carcinoma, mast cell tumors, sarcomas, and others [3]. Most studies focus on the application of ECT in treating skin or head and neck tumors; however, new research also investigates its usefulness in internal organs, i.e., the liver and pancreas [4, 5, 6, 7]. The way of drug administration can be achieved by local or systemic injection and dependson the species, tumor size, and the type of chemotherapeutic [2,3,8]. One of the best-studied substances commonly used in ECT treatment is bleomycin (BLM). This is an antitumor antibiotic derived from _Streptomyces verticillus_, whose mechanism of action relies on sequence selective DNA binding and cleavage, resulting in DNA degradation [9]. BLM is a very potent cytotoxic drug once inside the cell; however, its large size and hydrophilic structure ampere transport through the plasma membrane and hinder its high concentrations in the cytosol [10]. This limitation was conquered when the use of electroporation to increase BLM uptake into the cells was introduced [11]. Significant potentiation of BLM cytotoxicity can be obtained even in BLM-insensitive tumors [11]. The ECT is described as an easy and uncomplicated technique [12]. Among factors such as size, histology, and exposure to previous oncological treatments, a good outcome relies on proper tumor infiltration with chemotherapeutic and electroporation of the whole tumor mass. Although the determination of electric field distribution in the targeted tissue may be predicted, the distribution of the injected drug is still difficult to detect. The authors of this study suggest combining indocyanine green (ICG) with bleomycin in ECT to attain control over drug distribution and allow for detection.