esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Microsoft HoloLens Applications over the Past Five Years
## 3 Results
### Applications in Medical and Healthcare Fields
#### 3.1.2 Medical Education and Simulation  
AR technology is beneficial for educating students majoring in medical science and is widely used in medical training and simulation. Furthermore, by using an AR system for telemedicine, providing long-distance medical services, problems in rural areas that lack access to medical services are being improved. Although AR is not necessarily a new technology, it is gaining momentum in medical education and simulation through Microsoft HoloLens [47]. AR technology has the potential to enhance students' understanding of physiology and anatomy, which requires 3D knowledge of human organ systems and structures. Therefore, [PERSON] et al. [48] evaluated and compared the learning effects when delivering lectures through AR and mobile handheld tablet devices. Thirty-eight pre-clinical undergraduate participants were taught in detail about brain physiology and anatomy and pre- and post-intervention tests were performed to evaluate their knowledge. After the activity, participants completed a questionnaire to assess adverse health effects and their perception of the device. However, no significant difference was observed in the test scores for lesson delivery using HoloLens and mobile-based AR. Further, increased dizziness was reported when using HoloLens, but no other adverse health effects, such as nausea, disorientation, or fatigue, were observed. Both modes have been shown to be effective in learning; in particular, AR has been shown to support educators and learners in the fields of health and medical science. [PERSON] et al. [49] performed a virtual autopsy using AR technology (Figure 6) and investigated the possibility of using an AR HMD to enhance autopsy in real-world conditions. They used Microsoft HoloLens as the AR device and five participants performed a virtual autopsy. The participants were able to identify major pathologies in all cases on AR Virtopsy, which took an average of 122 s per case. Further, in AR autopsy, most of the interactions occurred during the first 30 min of the internal examination of the autopsy; it was confirmed that the frequency of interactions decreased and shortened as time passed. AR headsets can help implement image data, such as post-mortem CT, in the autopsy hall and have been shown to be most beneficial during the first 30 min of an internal examination during autopsy. [PERSON] et al. [50] developed a hybrid training system for orthopedic open surgery using HoloLens AR technology and evaluated the potential of MR in the training system. They chose hip arthroplasty (an orthopedic surgery) as the benchmark for evaluating the proposed system. Anatomical 3D models for each patient were extracted from the CT to
# Voice Transformation Using Two-Level Dynamic Warping and Neural Networks
## 1 Introduction  
Voice transformation (VT) refers to the process of changing speech so that speech uttered by one speaker (the source speaker) sounds as if another speaker (the target speaker) had spoken it, for example, transforming from a male voice to a female voice [1]. VT has applications such as text-to-speech synthesis (TTS), international dubbing, health-care, multi-media, language education, music, security-related usage, vocal restoration, speech-to-speech translation, and preprocessing for speech recognition, etc. [2, 3, 4].  
The general framework for VT is shown in Figure 1[5]. In a training stage, performed offline, features are extracted from both source and target speech sources. VT, which maps information from the source speech to target speech, is trained to form conversion rules. In the actual operation, these conversion rules are used to convert features from a source to a target.  
In this work, the spectral features computed using the DFT are transformed using a neural network (NN). Speech signals are aligned using dynamic warping (DW) which aligns both temporally and spectrally to produce training data which is used to train a NN. The NN is trained to produce the spectral mapping functions determined by the frequency-domain warping. Several NN structures (differing numbers of layers, and differing numbers of neurons per layer) were examined to determine which had best performance, as measured by mel-cepstral distortion (MCD). On the basis of MCD, this method was compared with previously developed methods, and is shown to perform well. The test set was based on transformation of a male speaker to a female speaker. Future work will test this architecture transformations between different speakers.
###### Abstract  
A reliable high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method was established for the simultaneous detection of zeralenone-14-glucose (ZEN-14G) and its metabolite, zeralenone (ZEN), in the plasma, urine, and various tissues of rats. The performance of the developed method was validated by determining the selectivity, linearity (\(R^{2}>0.99\)), sensitivity (lower limit of quantification, 0.1-1 ug/L), recovery (80.7 \(\pm\) 3.0-112.3 \(\pm\) 3.1%), precision (0.6-16.5%), and stability (81.7 \(\pm\) 1.7-104.1 \(\pm\) 3.9%). Through use of the methodological advances, the subsequent kinetics and distribution after administration of ZEN-14G by gavage were thoroughly investigated. ZEN-14G and ZEN exhibited similar trends in the plasma, and reached their peak concentrations at 10 min and then rapidly decreased. ZEN-14G could be quantified in the stomach, small intestine, and large intestine 24 h after administration, while ZEN was detectable in all tested tissues. Interestingly, ZEN-14G (7.6 \(\pm\) 3.0 ug/L) and ZEN (977.5 \(\pm\) 98.0 ug/L) were also detected in the urine 24 h after administration, indicating that ZEN-14G was prone to be slowly and continuously hydrolyzed into ZEN to be absorbed into the plasma and distributed to various tissues, thus leading to a cumulative exposure. Continuous attention should be paid to the co-exposure of ZEN and ZEN-14G, which might pose additional health risks to humans and animals.  
Keywords:zeralenone-14-glucoside; zeralenone; HPLC-MS/MS; rat plasma; urine; tissues : +
Footnote †: journal: Applied Sciences
## 5 Safe Packaging against Electromagnetic Interference  
Metals as shielding materials and packaging materials have become potential candidates for electroveticals to avoid interference-induced abnormal conditions. The primary advantage of using metallic shielding is that it simply reflects the unwanted or disturbing electromagnetic fields. However, the biological interactions between the metallically shielded device and the surrounding biological tissues/media can cause the failure of shield, consequently, communication interruption or a device failure due to short circuits and loss of device' properties [146; 147; 148]. Therefore, packaging must be biocompatible and electromagnetically compatible within the biological environment. Well-designed packaging within the availability of material resources is essential for those devices that must survive under biological environments. Hermetically compatible materials could be reliable candidates for implantable electroveticals. Hermetically sealed metallic encapsulation is a significant protective layer and reflects the incident electromagnetic fields of all frequencies. Thus, there are no electromagnetic field interactions with the internal circuits/chips, no interference during operating conditions of the electrovetical device [146; 147; 148]. In addition to the significant shielding capability, it also exhibits biocompatibility and corrosion resistance behavior to sustain the electrovetical device within the biological environment.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 3 Impulsive Noise Measurements
### Description of the Setup  
Measuring impulsive noise becomes essential for evaluating the characteristics that are considered in the design of a communication system. For the laboratory measurements, the main components are a generator bar and a Universal Software Radio Peripheral (URSP) radio. The setup is described in the following.  
* The generator bar is used in controlled laboratory conditions under different voltages. Hydro-Quebec researchers use this device to evaluate the partial discharges localization on the voltage phase. In the middle of the bar, the insulation is covered by a conductor made with epoxy-mica insulated by a shield, as depicted in Figure 3. The principal characteristics of the dimensions are summarized in Table 1. When a high voltage generator (1-18 kV) is connected, an electromagnetic field is emitted radially by the conductor that the isolator tries to isolate. The generator bar is an excellent candidate for partial discharge generation, and it controls the power supply accurately.
* USRP N210 [29] connected to an antenna is used for recovering the signal from the generator bar. Indeed, we implement some blocks to save the measures issued from the impulsive noise generator. More information on the functioning of the USRP are given in Section 4.  
Figure 3: The plane of a generator bar with the designed air cavities [28].
# Mutual Authentication Protocol for Role-Based Access Control Using Mobile RFID
## 4 Security Analysis  
Below is an overview of the security analysis performed for the mechanism proposed in this paper.  
1. User privacy: As the encrypted data sent was calculated using the random numbers generated from the electronic tag and reader, r\({}_{1}\) and r\({}_{2}\), the values were untraceable. Moreover, attackers cannot have the tag key to determine the tag identity. Data is anonymous and untraceable, providing user privacy.
2. Non-linkability: Tags create different random numbers for each reading. Thus, all response values are different, making it impossible for attackers to determine whether data was sent from the same tag.
3. Confidentiality: In the proposed mechanism, each tag's key is shared with the back-end database. If the reader is not authorized, it cannot read the electronic tag because it does not have the tag key.
4. Data integrity: The tag reduces the number of reads based on the data sent from the reader and uses the shared key to encrypt this data before sending it to the reader. Readers must send a certificate to the back-end database to verify its legality. The time stamp and number of reads are only sent to the reader after verification to ensure data integrity.
5. Protection against replay attacks: As the data sent from the tag M\({}_{n}\) is calculated using the random numbers generated from the reader r\({}_{2}\) and the tag r\({}_{1}\), and r\({}_{2}\) is different during each read, even if an attacker captures data sent from a tag, they are unable to resend the captured data.
# Low-Frequency Expansion Approach for Seismic Data Based on Compressed Sensing in Low SNR
## 1 Introduction
proposed an inversion method using logging data to improve the resolution and the signal-to-noise ratio (SNR) of seismic records [23]. Firstly, the initial reflection coefficient model is constructed using logging, seismic and geological information. Then, the reflection coefficient is convoluted with the estimated seismic wavelet to obtain a synthetic seismic record. Finally, the correlation between synthetic records and actual seismic records is analyzed, and the reflection coefficient model is modified repeatedly to maximize the correlation coefficient between synthetic records and actual seismic records, thereby obtaining the optimal reflection coefficient. This method has a relatively large dependence on the accuracy of the initial reflection coefficient model during inversion. If the initial model has a wider frequency band, the final reflection coefficient will be more accurate. [PERSON] et al. proposed a multivariate interpolation method based on logging data to improve the accuracy of low-frequency prediction by estimating the layer velocity, layer depth, and layer thickness of seismic data [24]. This method can significantly improve the wave impedance information above 2-8 Hz. Due to considerations of the geological background, the modeling process of this method is more complicated. In order to improve the accuracy of low-frequency compensation and avoid the dependence of the well-constrained seismic trace inversion method on the accuracy of the initial model, this paper develops low-frequency protection and frequency expansion processing technology of well-constrained seismic data under the framework of CS and uses the well data to constrain the sparse inversion process so as to realize low-frequency compensation for noisy seismic data.
# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 2 Related Work
### Zero-Shot Dehazing Methods  
In learning-based dehazing methods, a large-scale hazy image dataset is required for training, which is time-consuming and labor-intensive during the data collection. Therefore, some researchers propose zero-shot dehazing methods that use a single hazy image to train the network.  
For example, [PERSON] et al. [14] propose a zero-shot dehazing (ZID) neural network that regards the hazy image as an entanglement of different components in the haze imaging model. They also propose another unsupervised and untrained dehazing network called You Only Look Yourself (YOLY) [13]. However, the ZID and YOLY may suffer from color distortion in the dehazed images. [PERSON] et al. [35] design a powerful image restoration framework from the viewpoint of image decomposition, called the Double deep image prior (Double-DIP), inspired by the deep image prior (DIP) [36]. The Double-DIP approach is effective in various restoration tasks such as dehazing, watermark removal, segmentation, and more. However, the Double-DIP produces unsatisfactory results when processing dense hazy images. In another approach, [PERSON] et al. [16] propose a zero-shot single-image restoration method that uses controlled perturbation of [PERSON]'s model. [PERSON] et al.'s [16] method requires no training data and can restore images with various types of degradation, such as haze, rain, and snow; however, the enormous training time is a major issue. [PERSON] et al. [15] propose a three-branch RS image-dehazing network regulated by a re-degradation haze imaging model to achieve zero-shot learning; however, this model is less effective in non-uniform hazy conditions.  
Although zero-shot dehazing methods have excellent generalizability, they must train the network on each degraded image in order to infer the haze-free image. Training time complexity is therefore significant. Existing zero-shot dehazing methods, however, train the model from the original hazy input, and the network architecture is complex, resulting in low training efficiency. Therefore, we proposed the pre-dehazing _DCP_ module and lightweight refining network to decrease training time without sacrificing dehazing performance.
# Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model
## 2 ICEEMDAN-MPE-AWT Denoising  
### ICEEMDAN  
The ICEEMDAN algorithm is a further improvement on CEEMDAN. Traditional EMD decomposition results in mode mixing, while CEEMDAN partially eliminates mode mixing by adding white noise [21]. However, the introduced white noise is difficult to remove, resulting in increased signal reconstruction errors. In order to solve the problem of reconstruction error, ICEEMDAN was proposed. Unlike CEEMDAN, the white noise added by ICEEMDAN is the \(k\)th IMF component after EMD decomposition, that is, \(E_{k}(w^{(i)})\). By introducing adaptive noise adjustment, ICEEMDAN can better deal with aliasing and pseudo-signature problems that may exist during signal decomposition and improve the precision and dependability of signal analysis. The ICEEMDAN algorithm can be described as follows.  
\(x\) is defined as a vibration signal containing noise; \(E_{j}(\cdot)\) is defined as the \(j\)-th IMF component obtained after EMD decomposition; \(w^{(i)}\) is defined as the \(i\)th Gaussian white noise following the \(N\)(0,1) distribution (\(i=1,2,3,\ldots i\)); \(M(\cdot)\) is defined as the local mean of the solution signal; and the coefficient \(\beta_{k}\) is defined as the SNR of the \(k\) stage, when \(k<1\), \(\beta_{0}=\varepsilon_{0}\frac{\sigma(x)}{\sigma(E_{1}(w^{(i)}))}\), \(k\geq 1\), and \(\beta_{k}=\varepsilon_{0}\sigma(r_{k})\), where \(\varepsilon_{0}\) is the amplitude set in advance, \(\sigma(\cdot)\) is the mathematical expectation operator and \(r_{k}\) is the residual of the \(k\) stage. The specific calculation process of the ICEEMDAN algorithm based on the above definition is as follows.  
(1) Add \(E_{1}(w^{(i)})\) to the original signal after multiplying the coefficient \(\beta_{0}\)  
\[x^{(i)}=x+\beta_{0}E_{1}\Big{(}w^{(i)}\Big{)}. \tag{1}\]  
The residual of the first stage can be obtained by separately calculating the \(i\)-th local mean in Equation (1) using the EMD method  
\[r_{1}\ =\Big{\langle}M(x^{\ (i)})\Big{\rangle} \tag{2}\]  
where \(\langle\cdot\rangle\) is the operator for calculating the average value throughout the process.  
(2) Subtract the residual difference between the original signal and the first stage to obtain the first component of the signal, denoted as IMF\({}_{1}\):  
\[\text{IMF}_{1}=x-r_{1}. \tag{3}\]Calculate the local mean of \(r_{1}+\beta_{1}E_{2}(w^{(l)})\) to obtain the residual of the second stage  
\[r_{2}=\Big{\langle}M(r_{1}+\beta_{1}E_{2}(w^{(i)})\Big{\rangle}. \tag{4}\]  
Subtract Equation (4) from Equation (2) to obtain the second component of the signal, denoted as IMF\({}_{2}\)  
\[\text{IMF}_{2}=r_{1}-r_{2}=r_{1}-\Big{\langle}M(r_{1}+\beta_{1}E_{2}\Big{(}w^{-( i)}\Big{)}\Big{\rangle}. \tag{5}\]  
When \(j=3,\ldots,j\), the residual of the \(j\)-th stage can be written as  
\[r_{j}=\Big{\langle}M(r_{j-1}+\beta_{j-1}E_{j}\Big{(}w^{-(i)}\Big{)}\Big{\rangle}. \tag{6}\]  
The \(j\)-th component of IMF can be calculated as follows:  
\[\text{IMF}_{j}=r_{j-1}-r_{j}. \tag{7}\]  
Return to step (4) to calculate the next \(r\) and IMF.
# Early Detection of Residual/Recurrent Lung Malignancies on Post-Radiation FDG PET/CT
## 2 Materials and Methods
### CNN Model  
The CNN model, on the other hand, used the 3D patches of regions of interest from PET and CT images, each sized at 48 \(\times\) 48 \(\times\) 48, for prediction. These patches, including the 3D-contoured potential residual/recurrent lesions and the surrounding voxels, served as inputs for the CNN model. Unlike the radiomics model, the CNN model was designed to learn features from the input 3D patches and make predictions automatically. The proposed CNN architecture consists of eight 3D convolutional layers, three max-pooling layers, one global-average-pooling layer, and two fully connected layers. Each convolutional layer was equipped with ReLu activation and batch normalization. In addition, a 11 kernel regularizer with a weighting factor of 0.01 and a dropout rate of 0.01 was also embedded in each convolutional layer to reduce the potential of overfitting. The architecture was designed by alternating convolutional layers (two stacked layers each time) with the max-pooling layer to gradually capture the local to global features. The feature volumes of each two-stacked convolutional layer from the beginning to the end are 3, 6, 12, and 24, respectively. After the global max-pooling layer, two fully connected layers were used to reduce features and finally generate the predicted probabilities for two different categories: benign or malignant.  
To compensate for the large scale of the CNN architecture, we implemented data augmentation through 3D image rotation. The rotation angle was randomly selected within the range of [-10,10] degrees along one of the x, y, or z axes. Specifically, for each training sample, 20 augmented samples were generated by rotating the images along a random axis. An example of the augmented sample is shown in Figure 3. For CNN model training, binary cross entropy was utilized as the loss function, and Adam was the optimization algorithm. The models with the best prediction results on the validation dataset were selected for testing.
# Blockchain-Based Cloud-Enabled Security Monitoring Using Internet of Things in Smart Agriculture
## 7 Performance Evaluation  
Monitoring system performance: The end-to-end system performance needs to be evaluated to estimate the solution's effectiveness. The network latency and throughput are the indicators seen in the literature as performance factors for blockchain-based applications. The time taken to receive the sensor alert when an anomaly occurs in the sensor environment directly indicates the network latency. Our test results on Rinkeby show that  
Figure 10: Smart-contract web application—frontend GUI.  
Figure 9: Smart-contract web-application frontend—alert notifications.  
the network latency is a few seconds. The network throughput was not tested using our implementation due to the infura API free-access limitation.  
Performance comparison with existing works: Our solution performance is compared with the existing works using blockchain in smart contracts. Although none of the existing works implemented the end-to-end solutions with AWS cloud and smart contracts, we included the closely related smart-contract implementation for smart agriculture. Table 3 depicts the message network latency comparison of our work with existing works. The authors [38] implemented Ethereum-based smart contracts to update the IoT sensing data to the blockchain and evaluated the network latency of issuing a transaction in the blockchain. The authors reported a total network latency of 16.55 s. This work is closely related to our work in terms of adding the IoT sensor data into the blockchain. Our solution performed much better than the work [38] because we used real-time implementation applications, including IoT core and smart contracts using Infura API. The additional latency in [38] can also be caused by the blockchain node running in the virtual machine. The work [27] performed simulations to test the IoT devices sending updates to the blockchain and estimated the network latency. They considered 4G as a communication medium to model the communication link and obtained less than 0.2 s latency. We utilized the home WiFi to perform the experiments and obtained the matchable performance with [27]. The authors authors [34] also used Ethereum to build the agriculture smart contract. The authors reported that it took 272 s to complete one transaction. The high network latency may be caused by the usage of the real Ethereum network. Our solution reported a total network latency of 0.11 s, which is real-time alert reporting. We also determined the mean time to detect (MTTD) when the 95% confidence interval is used. The MTTD is reported as 0.115 with a margin of error of 0.00919 and a standard deviation of 0.016.
## 3 Results and Discussion
### SIMCA Validation
These results suggest that it is possible to establish a reference library for a bacterial species of interest and to build a SIMCA calibration model that is robust enough for species level detection as a presumptive screening tool, effectively reducing the amount of time and reoccurring cost associated with traditional detection methods. Microorganisms of interest to the food industry, such as _Listeria_, _Campylobacter_, or _Staphylococcus aureus_, could have HMI reference libraries established and validated. Here, the _Salmonella_ model can be tuned over time to incorporate the addition of more serotypes and wild type bacteria isolated from field trials, and it could eventually be tested in industry settings for the early and rapid presumptive screening of pathogenic microorganisms.