esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Deviations Produced by Soft Tissue Fitting in Virtually Planned Orthopathic Surgery
## 1 Introduction
The accuracy of these predictions can be influenced by the complexity of the algorithms, as well as the need for continuous improvement. Furthermore, predictability varies considerably between different algorithms. Studies show a wide range of accuracy performance in soft tissue prediction, from 69% to 91%, suggesting that improvements in mesh deformation methods must be considered [21]. Consequently, surgeon criteria and experience could be considered to play a key role in achieving highly satisfied patients and good surgical outcomes, in addition to VSP. While many studies compare different VSP software v12, such as Dolphin, in terms of soft tissue fitting [25; 26], none has considered the functional performance of surgeries or studied the effect of dentofacial deformity class on software prediction performance. Therefore, we decided to evaluate surgical performance using the OQOL questionnaire combined with deviations from VSP produced during surgery. We hypothesized that the surgeon's judgement takes precedence over the VSP's definition of repositioning and that VSP is used merely as a guidance and powerful assessment tool. If so, patients with high deviations from the VSP due to the surgeon's decision should have similar satisfaction levels to those who have undergone repositioning close to the VSP. To test this hypothesis, we conducted an open retrospective study for patients who had undergone orthognathic surgery at the Oral and Maxillofacial Department (OMD) of the Hospital San Pedro de La Rioja.
## 2 Experimental Assessment
### Zone Refining Equipment and Infrared Camera Setup  
The trials were conducted in an industrial-scale horizontal zone refining equipment, equipped with a single induction heater (see Figure 1) capable of generating up to 45 kW with a maximum frequency of 10 kHz. The size of the quartz tube is \(L\) 1800 mm \(\times\)\(D_{int}\) 170 mm, and the heater can travel between 0.01 and 2.00 mm min\({}^{-1}\) along the whole length of the equipment.  
The process was recorded using an Infrared (IR) camera (see Figure 2) with a wide-angle lens of 4.8 mm (Horizontal field of view (HFOV) of 72.5\({}^{\circ}\)) and a pixel resolution of 1280 \(\times\) 1024. This IR camera measures the infrared radiation in the spectral range between 0.78 and 1.1 um, which allows the capture of the radiation passing through the quartz tube of the equipment. The IR camera is mounted on the heater unit and moves along with the induction coil, as seen in Figure 2. The thermography data were then recorded via the software from the IR camera. This software was initially calibrated with the practically obtained emissivity values of a lightly oxidized aluminum sample at a temperature range of 650-700 \({}^{\circ}\)C, as seen in Table 1.  
Figure 1: Horizontal zone refining equipment with a single induction heater (photo by [PERSON]).  
Figure 2: Infrared camera setup focusing on the molten zone region below the induction coil.  
Since the temperature data captured by the IR camera refer to the surface emitted temperature, the calibration data were obtained by measuring the temperature of the metal with the thermocouple (type K) in a position slight below the melt surface. The emissivity data of the camera were later adjusted to match the recorded temperature of the sample. Another calibration necessity is related to the quartz tube layer between the molten surface and the camera. This was performed by testing a 3-mm quartz glass with the same shape as the original one in order to compensate the changes in the emissivity due to the quartz transmissivity effect.
## 1 Introduction
The first aim of this sensory evaluation is to define VOOs' quality grade, to identify and quantify the main perceived off-flavors (defects), as well as their fruity scents [7]. However, sensory analysis is generally imprecise and unreproducible. Furthermore, it is time and cost consuming, it requires trained panels and it is therefore not always available in every context. Analytical approaches, mainly represented by chromatographic (high-performance liquid chromatography, gas chromatography-mass spectrometry, gas chromatography-ion mobility spectrometry), vibrational (FTIR, MIR, NIR and Raman spectroscopy), spectroscopic (ultraviolet-visible, nuclear magnetic resonance, mass spectroscopy, fluorescence spectroscopy, CO\({}_{2}\) laser infrared optothermal spectroscopy, dielectric spectroscopy, visible spectroscopy, ultraviolet-ion mobility spectrometry) and thermal techniques (differential scanning calorimetry, thermogravimetric analyzer), provide a more precise indication of olive oil composition [8,9]. However, even in these cases some important limitations are present: these techniques require expensive and bulky instruments, high-purity gas carriers, complex and often long sample preparation and, lastly, trained personnel, therefore limiting their use only to qualified laboratories. Additionally, the internal concentration of specific compounds does not always provide information on the resulting organoleptic sensation and, therefore, it cannot predict consumers' sensory evaluation. An alternative approach that has been proposed to overcome these drawbacks is represented by the E-nose [10; 11; 12; 13]. The E-nose allows fast and untrained evaluation of aroma profile, very similar to that obtained from the human nose. In this context, the present review sought to investigate the current trends regarding the usage of human panels and E-nose in the specific field of VOO SL determination, comparing the performances between the two different approaches. To better discuss the topic, a state-of-the-art analysis about the sensory SL concept, together with the mechanism of human olfactory determinations and E-nose functioning, were preliminary addressed, and a short review of the recent literature about the main applications of the E-nose in the agri-food sector was also included.
# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 5 EIT Clinical-Based Applications
### Head Imaging
In head impedance imaging, because of the high degree of non-linearity and deficiency of the perfect head models, the image reconstruction might be unsuccessful. Currently, a nonlinear difference imaging method has been presented to alleviate modeling error. This technique depends upon unconstrained modeling that permits tissue conductivity values to be unrealistically negative. Therefore, substantial image artifacts are probably conducted. In [82] two techniques of constrained modeling that can significantly reduce artifacts and increase localization performance are presented. The simulation result showed that the new images attain good localization performance as compared to those using unconstrained modeling
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
## 3 Decision Function
### Definitions
#### 3.1.2 Terminology  
Verify_Time outputs a binary response: 0 if the tested sample is considered genuine, 1 otherwise. Throughout Section 3.2, we challenge Verify_Time with genuine and relayed samples and analyze its efficiency using the following terminology:  
* False positive: Verify_Time outputs 1 to a genuine sample
* False negative: Verify_Time outputs 0 to a relayed sample
## 2 Materials and Methods
### Photoplethysmographic (PPG) and Electrocardiographic (ECG) Signals Pre-Processing  
Raw PPG signals from brachial and tibial arteries were converted into optical densities (OD) according to the equation [37; 40]:  
\[\mathrm{OD}=-\ln(\mathrm{I(t)}/\mathrm{I_{o}}) \tag{1}\]  
where I(t) is the time dependence of the recorded signal intensity and \(\mathrm{I_{o}}\) is its average value.  
Raw OD and ECG were downsampled by a factor of 10, down to 100 Hz, and filtered employing zero-lag, 4 th order, band-pass Butterworth digital filters. For the PPG data, the cut-off frequencies were set at 0.2 Hz and 10 Hz; the ECG data cut-off frequencies were set at 0.2 Hz and 50 Hz. The R-wave peaks identification was performed by considering the local maxima on a filtered and normalized (z-scored) Lead I ECG signals, defining some constraints (minimum value of the peak: 3; interpeak distance: 600 ms). This procedure provided an accuracy in the R-peaks identification of 100%. Single pulse PPG was estimated for the brachial and tibial locations in a time window from 0.3 s prior to 1.5 s after the R-wave peak. The duration of the time-window of 1.8 s allowed to investigate a large range of pulsation frequencies (down to a minimum value of \(\sim\)30 beats/min). In order to exclude possible noisy periods of PPG signal from the analysis, a trim-mean approach for the evaluation of the average signal was applied by excluding single pulses with at least a value below or above the 25 th or 75 th percentile of the sample population. Thanks to the high-quality of the signals and the reduced presence of motion artifacts, the described analysis guaranteed a stable estimation of the pulse average PPG. Figure 3 reports the preprocessing steps and an example of pulse average ECG and PPG and associated standard errors (shaded areas), showing the high signal to noise ratio of the recordings.  
Figure 2: (**a**) Homemade multi-channel Photoplethysmographic (PPG) and Electrocardiographic (ECG) system; (**b**) PPG probe composed by a light emitting diode at 940 nm of wavelength and a silicon photomultiplier (SiPM) detector; (**c**) example of PPG probes and ECG electrode placement on a subject arm and schematic locations of the PPG probes and ECG electrodes on a body template.
# MFCosface: A Masked-Face Recognition Algorithm Based on Large Margin Cosine Loss
## 3 Proposed Methods  
### Dataset Preprocessing  
Since deep learning models usually require large-scale datasets for training, the lack of masked-face datasets makes it difficult for a model to learn the feature mapping when a face is occluded by a mask, resulting in a poor recognition rate. To solve this problem, we used a masked-face image generation algorithm based on the detection of key facial features to construct a dataset. We generated realistic masked-face images for model training through face detection, the detection of key features, and mask coverage analysis. The process of masked-face image generation is shown in Figure 1.  
1. Face detection: We used a multi-task cascaded convolutional neural network (MTCNN) [25] for preprocessing, and got an image containing only faces. The result is shown in Figure 1. MTCNN is mainly composed of three convolutional neural networks, cascaded. First our system involves resizing the image and generating image pyramids of different scales; then we send them to P-Net to generate many candidate frames containing faces or partial faces; then we filter out a large number of poor candidate frames through R-Net and perform regression on the candidate frames to optimize the prediction results; finally, we use O-Net to regress the features and output the positions of the key features.
2. Key feature detection: MTCNN can only detect the key points of the eyes, nose, left mouth, and right mouth, and it is difficult to generate a more realistic masked-face image using only 5 key points, so we used HOG features to detect 68 key points of the face [42]. This method is more detailed in the detection of key points. Note that we used the Dilb library to implement this process.
3. Mask coverage: To generate a mask, we calculate the distance and structure information based on the relative positions of the chin and the bridge of the nose, get the coordinates of the mask, use common mask templates (surgical mask, KN95, etc.) to cover the face, and generate the masked-face image. The result is shown in Figure 1. [PERSON] et al. [43] also used a method for generating masked-face images for model training, but their method needs to collect mask templates from different angles, which has significant limitations. Our method uses only a front image of the mask, analyzes the relative positions of the key points of the face, and distorts the mask to generate a more realistic masked-face image.
## 3 Experimental Results
### Construction of Analysis Data Using Image Processing Technique  
In this study, aerial photography was performed using a UAV equipped with a laser point model to estimate the location of cracks on the exterior wall of a building without a reference object. In order to correct the distortion of the aerial image obtained using aerial photography, four laser points identified in the raw aerial photography image were selected, and image correction was performed using a homography matrix. The four points and corresponding points set for homography matrix conversion are shown in Figure 5.  
Correspondence points were set for the distorted laser points of the raw image using model design information, and image distortion correction was performed for the original images acquired using aerial photography using a homography matrix. The following Figure 6 is the result of image correction using the homography matrix.  
Figure 5: Four points and corresponding points are set for homography matrix.  
Figure 6: Result of image correction using homography matrix.  
Next, feature point detection and matching were performed using the SIFT algorithm to confirm the availability of the distortion-corrected image, and the results are shown in Figure 7.  
As a result of the experiment, it was confirmed that the image generated using the image distortion correction and feature point extraction method proposed in this study can be sufficiently utilized for image processing techniques.
###### Abstract  
This study examined the effects of incorporating processed (hydrolysed) (POP) and unprocessed (non-hydrolysed) (UOP) orange peels into the diets of lactating ewes on the proximate composition, fatty acid profile, antioxidant properties, physicochemical characteristics, and sensory attributes of traditional sheep milk yoghurt. Thirty-six Chios breed ewes were divided into three dietary groups: POP, UOP, and a conventional control diet (Control). Yoghurt produced from the UOP and POP diets had higher protein content (5.93 and 5.53%, respectively) and fat content (6.79 and 6.24%, respectively) compared to the Control (5.42% for protein and 6.06% for fat). Additionally, there were no significant differences in the nutritional indices of yoghurt fat. The determination of antioxidant activity showed no significant differences among the groups using the ABTS, DPPH, and FRAP methods, whereas significant differences were observed when measured with the Folin-Ciocalteu method. Textural analysis revealed that UOP yoghurt had greater firmness and cohesiveness. The whiteness index of yoghurt across all treatments remained consistent during storage, maintaining the desirable visual characteristics of traditional sheep milk yoghurt. Syneresis levels were similar across all groups, indicating no negative effects on yoghurt stability. Sensory evaluation confirmed the high acceptability of yoghurt produced from both experimental diets. These findings highlight the potential of orange peels as a sustainable feed additive, with no adverse effects on the nutritional, functional, or sensory properties of dairy products, while contributing to effective waste valorisation.  
c 2025 15 3641 1
## 2 Materials and Methods
### Performance Evaluation Method  
The performance of the proposed HRR prediction method was evaluated using experimental fire data released from the NIST fire calorimetry database [37]. Six different fire experiment datasets were selected based on fuel type and fire scale to ensure a diverse evaluation. Figure 4 shows that the selected datasets include two box fire experiments (one and four boxes filled with paper), two wood-crib fire experiments (one and four wood cribs), and two corn oil pool fire experiments (corn oil in 20 cm Calphalon(r) and aluminum pans). Each video of the fire experiments was converted into a frame-based image dataset. The final dataset included 191, 463, 539, 713, 35, and 37 frames for the one box, four boxes, one wood crib, four wood cribs, corn oil (Calphalon(r) pan), and corn oil (aluminum pan) fires, respectively. Although the NIST fire dataset used in this study provides experimentally validated HRR data and is widely adopted in fire research, the limited number of image frames may constrain the evaluation of the model's performance in predicting transient fire HRR behavior. In specific cases with few frames, such as the corn oil fire experiments, the evaluation of the overall HRR trend prediction remains feasible; however, the reduced temporal resolution may hinder accurate assessment of the model's performance in capturing transient HRR behavior.  
Figure 3: Architecture of YOLOv8 segmentation [36].  
The proposed method estimates the HRR by detecting flame regions in the images and extracting key frame parameters such as fire diameter and flame height. Therefore, two aspects were evaluated: (1) flame segmentation performance, which is essential for accurately masking the flame region, and (2) HRR prediction accuracy based on the extracted flame dimensions. The IoU metric, which measures the similarity between the predicted and actual flame regions, was used to assess the flame segmentation performance. The IoU is defined as follows:  
\[\text{IoU}[\%]=\frac{P_{i}\cap G_{i}}{P_{i}\cup G_{i}}\times 100 \tag{3}\]  
where \(P_{i}\) and \(G_{i}\) represent the predicted flame region in the \(i\)th image and actual flame region in the image, respectively.  
The MAE, which provides an intuitive measure of the difference between the predicted and actual HRR values, is used to evaluate the HRR prediction performance. A lower MAE indicates higher accuracy. The MAE is calculated as follows:  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}\lvert y_{i}-\hat{y}_{i}\rvert \tag{4}\]  
where \(y_{i}\), \(\hat{y}_{i}\), and \(N\) represent the actual HRR, predicted HRR, and total number of image frames, respectively.  
In addition, \(R^{2}\) is used to assess the correlation between the predicted and actual HRR values. It is determined as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{N}(y_{i}- \overline{y}_{i})^{2}} \tag{5}\]  
where \(\overline{y}_{i}\) represents the mean of the actual HRR values.
## 2 Problem Description and Modelling
### Parameterization and Decision Variables  
The parameters of the variables involved in the model are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Instructions** & **Unit** \\ \hline Q & Total container requirements at the yard & t \\ N & Number of coal preparation plants & size \\ D\({}_{\text{i}}\) & Distance from the coal preparation plant to the freight yard & km \\ C\({}_{\text{i}}\) & Capacity of coal preparation plant i & t/h \\ S\({}_{\text{i}}\) & Beginning stockpile at coal preparation plant i & t \\ M & Number of containerised vehicles in the yard & Number of vehicles \\ L\({}_{\text{j}}\) & Maximum load of container transport vehicle j & t \\ V\({}_{\text{j}}\) & Speed of container transport vehicles j & km/h \\ P\({}_{\text{j}}\) & Unit transportation cost of container transport vehicles j & RMB/(t\(\times\)km) \\ P\({}_{\text{2}}\) & Waiting cost per unit of time for container transport vehicles & RMB/h \\ K & Number of vehicles hauling coal for external transportation & \\ G\({}_{\text{k}}\) & Coal beneficiation plants visited by outbound vehicles k & \\ T\({}_{\text{k}}\) & Time of arrival of outbound vehicles k at the coal preparation plant & time slot \\ qk & Coal hauled by outbound vehicles k & t \\ s & Coal preparation plant loading speed & t/h \\ b & Coal unloading speed at the yard & t/h \\ T\({}_{\text{i}}^{\prime}\) & Container transport vehicles j in t missions, time of departure from the yard & time slot \\ T\({}_{\text{2}}^{\prime}\) & Container transport vehicles j at t mission, arrival time at coal processing plant & time slot \\ T\({}_{\text{j}}^{\prime}\) & Container transport vehicles j at task t, time of start of coal loading & time slot \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of variables.
## 5 Experimental Results
### User Survey
#### 5.1.1 Usability  
This category of the survey mainly focused on the evaluation of convenience in using the system, as well as its learnability. Of the total participants, 53% strongly agreed that the proposed authoring system was very easy to understand, while 41% just agreed, and 6% were neutral (mean (M) = 4.47, standard deviation (SD) = 0.082).
## 4 Results and Discussion
These challenges highlight areas for potential refinement and improvement in future iterations of the model. Ultimately, a three-tiered interface was developed, leveraging the best-performing image captioning models created. This interface allows users to upload an image of an accessory and generate a corresponding description. Illustrated in Figure 3, the interface offers descriptions of varying complexity (see Section 3). Let us consider an example image of a gold necklace with a pendant featuring a sapphire gemstone. The image is uploaded to the system, and the generated descriptions at different complexity levels are as follows:  
* Basic description: 'A necklace with a pendant'. * Normal description: 'A gold necklace with a sapphire pendant'. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CNN** & **RNN** & **Neurons** & **Val. CCR** & **Val. Loss** & **Test CCR** \\ \hline InceptionV3 & LSTM & 512 & 0.9307 & 0.0615 & 0.8439 \\ VGG-16 & LSTM & 512 & 0.9270 & 0.0881 & 0.9036 \\ InceptionV3 & GRU & 256 & 0.9483 & 0.0921 & 0.8354 \\
**VGG-16** & **GRU** & **256** & **0.9633** & **0.0706** & **0.9345** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Best model for image captioning. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accessory** & **Precision** & **Recall** & **F1-Score** \\ \hline Necklaces & 0.9452 & 0.9087 & 0.9131 \\ Rings & 0.9276 & 0.9173 & 0.9343 \\ Earrings & 0.9452 & 0.9675 & 0.9674 \\ Bracelets & 0.9420 & 0.8298 & 0.8807 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Individual class classification quality. * Complete description (after several iterations): This exquisite gold necklace showcases a beautiful sapphire pendant, adding a touch of elegance and sophistication to the accessory'. From a linguistic perspective, the descriptions exhibit a progressive level of detail, ranging from a simple identification of the accessory to a more elaborate narrative that captures specific attributes, such as the material and gemstone, and an emotional tone. This iterative process in the complete description phase involves multiple iterations to incorporate the embellishment of the jewelry with superlative adjectives, further enhancing the richness of the generated descriptions. This meticulous approach aims to provide potential customers with a more nuanced and compelling understanding of the featured jewelry. For the sake of reproducibility, the jewelry identification models, multilevel description generation models, and database of jewelry images, along with the necessary code, are available at Jewelry Linguistics Github [[https://github.com/jewelryling/jewelry_linguistics/](https://github.com/jewelryling/jewelry_linguistics/)]([https://github.com/jewelryling/jewelry_linguistics/](https://github.com/jewelryling/jewelry_linguistics/)) (accessed on 12 May 2025).