esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Fluorescence Spectroscopy to Detect Rot in Fruit and Vegetable Crops
###### Abstract  
The potential of the method of fluorescence spectroscopy for the detection of damage and diseases of fruits and vegetables was studied. For this purpose, the spectra of fluorescence of healthy and rotten apples and potatoes have been investigated. Excitation of samples was carried out using a continuous semiconductor laser with a wavelength of 405 nm and a pulsed solid-state laser with a wavelength of 527 nm. Peaks in the region of 600-700 nm in rotten samples were shifted towards shorter wavelengths for most samples in both modes of spectroscopy. The differences in the fluorescence spectra of a healthy and rotten apple surface have been revealed to be in the spectral range of 550-650 nm for 405 nm continuous excitation. When exposed to a laser in a pulsed mode (527 nm), the contribution of the 630 nm peak in the spectrum increases in rotten samples. The observed differences make it possible to use this method for separating samples of healthy and rotten fruits and vegetables. The article paid attention to the influence of many factors such as sample thickness, time after excitation, contamination by soil and dust, cultivar, and location of the probing on fluorescence spectra.  
Keywords:fluorescence; rot detection; apple; potato +
Footnote †: journal: Appl. Sci. 2022, 12, 3391. [[https://doi.org/10.3390/app12073391](https://doi.org/10.3390/app12073391)]([https://doi.org/10.3390/app12073391](https://doi.org/10.3390/app12073391))
# A Novel Multipath Transmission Scheme for Information-Centric Networking
## 3 Design
### Overview of Transmission Process
The MPSID does not appear in the header of the packet, and the edge node can decide whether to use the MPSID according to the preference field. Locators and identifiers are separated, which is one of the characteristics of ICN. In the process of multipath transmission, the MPSID acts as an identifier, and each NA bound to an MPSID acts as a locator, which conforms to the basic characteristics of ICN. In this paper, we refer to the design pattern of flat IDs from MobilityFirst [7], and use a 20-byte flat ID to name network entities, including the MPSID. In addition, the NRS plays a vital role. Firstly, the data source can obtain the NAs of these relay ICN routers by querying the NRS with an MPSID to discover multiple paths. Secondly, the relay ICN routers can query the NRS through the destination ID of the data packets to obtain the NA of the receiver device. Existing ICN multipath transmission mechanisms either rely on its flexible forwarding plane or on the multi-homing feature of devices. To the best of our knowledge, unlike existing mechanisms, we are the first to regard multipath transmission as a service and use an ID to identify it. At the transport layer, we use the previously proposed transport protocol [35], which has congestion control and a retransmission mechanism, to ensure efficient and reliable transmission by adjusting the request sending rate within a single chunk.
## 2 Materials and Methods
### Feature Extraction  
It is not appropriate to use FRF experimental data directly as the basic data for machine learning. This is because the experimental data are the energies for frequencies in the FRF graph, and although they can be compared in the natural mode as a graph when all the data are connected, not every data point with manufacturing or environmental error is significant. Therefore, feature extraction was performed to derive quantitative values from the FRF graph. The feature extraction process is shown in Figure 4, and a total of 12 features were derived using area, RMS, moment, centroid, kurtosis, and skewness; these were used as the basic dataset for machine learning. The meanings of the feature values extracted from the FRF curves are as follows: the area is the lower area of the FRF curve, root mean square (RMS) is the effective output of the FRF data, moment is the geometrical moment of area based on the origin, center is the median of the FRF curve, kurtosis is the sharpness of the FRF curve, and skewness is the degree of non-symmetry of the FRF curve.
# LungVision: X-ray Imagery Classification for On-Edge Diagnosis Applications
## 3 Materials and Methods
### Evaluation Metrics  
In general, several metrics can be utilized to evaluate the performance of image classifying. The metrics used in this paper are summarized below.  
The first metric is accuracy, which determines an algorithm's ability to make accurate predictions, measured by the ratio of true positive (_TP_) and true negative (_TN_) predictions combined, and divided by the total number of predictions.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
The second metric is specificity, which measures the proportion of true negatives that are correctly identified by the model. It is calculated by dividing the number of true positive predictions (_TP_) by the sum of true positives and false positives (_FP_).  
\[Specificity=\frac{TP}{TP+FP} \tag{2}\]  
The third metric is sensitivity, which determines an algorithm's capability in recognizing samples, determined by dividing the true positives (_TP_) by the total of true positives (_TP_) and false negatives (_FN_).  
\[Sensitivity=\frac{TP}{TP+FN} \tag{3}\]  
The fourth metric is the F1-Score, which considers both the sensitivity and specificity described earlier in this paper. It provides a more balanced evaluation to the model performance.  
\[F1=\frac{2\times Specificity\times Sensitivity}{Specificity+Sensitivity} \tag{4}\]  
We also examine the inference time, which refers to the duration it takes for a machine learning model to process input data and generate predictions or outputs. It is a critical metric for assessing the real-time performance and efficiency of models when the speed of prediction may impact the users experience.
## 5 Results and Discussion
### Velocity Streamlines  
Figure 9 depicts the velocity streamline of blood flow in the left coronary artery for rigid and FSI models during the cardiac cycle's systolic and diastolic phases. The higher velocity of blood flow can be found across the restricted regions (stenosis) in the left coronary artery.  
Figure 8: Projected displacement patterns on wall of left coronary artery (LCA) at systolic flow for different views—(**a**) front side (**b**) back side, and diastolic flow (**c**) front side (**d**) back side.  
Figure 9: Velocity streamlines of the blood flow field in the left coronary artery (LCA) during the systolic and diastolic phase of cardiac cycle for (**a**,**c**) rigid and (**b**,**d**) FSI models.  
It was also observed that a significant increase in the velocity was found for the FSI model compared to rigid model, across the stenosis region. The higher velocity varied within 0.78-1 m/s for the rigid wall while a range of 0.45-0.9 m/s was encountered in the FSI model, along the systolic phase of the cardiac cycle. Similarly, the maximum velocity in the range from 1.8-2.25 m/s for the rigid model and 1.81-2.26 m/s for the FSI model was observed during the diastolic phase. A prominent recirculation region at post stenosis region followed by a bifurcation, was observed.
## 4 Defines and Clarifies the Problems--Why Is Biomechanical One Different from Other Biofeedback?  
### Unique Aspects of Biomechanical Feedback  
Physiological, neurological, and biochemical feedback present information related to one's physiological variation, muscle tension, physical condition, and thought processes. Such information is conserved across human motor skills, i.e., across different movement forms. Therefore, feedback devices monitoring these parameters can be universally applied to all activities [40; 41].  
In contrast to physiological, neuronal, and biochemical feedback, biomechanical feedback mainly provides information that is related to the limb control of human motor skills, which directly accelerates motor skill learning and optimization, but must be tailored to the activity being examined [42; 43; 44; 45]. In other words, biomechanical feedback is thus a more useful tool but complicated for its development.  
Several studies in the past decades confirmed the importance of real-time biomechanical feedback, showing up to 100% improvement with its application [46; 47; 48; 49]. However, the development of biomechanical feedback is still in its infancy. While the real-time biofeedback of the first three types (i.e., physiological, neurological, and biochemical feedback) has been well developed for the past decades and is now a routine application (successfully transferred from lab-based to training and/or competition environments), the studies and applications of biomechanical one are still rare. After reviewing 666 publications between 1960's and 2010's, [PERSON] and his colleagues found that therewere only seven studies using real-time biomechanical feedback for physical training in laboratory environment [2]. Supplementary, the current state has not shown a considerable change, especially in sports and arts performances (Tables 1 and 2). The rarity could be caused by the numerous hitches that must be overcome during the development of the real-time biomechanical feedback tools. The obvious one is that biomechanical feedback must always be tailored to an activity (i.e., non-generalizable), requiring different design parameters for different motor skills. Thus, to develop a biomechanical feedback device, one must first obtain a thorough understanding of the selected motor skill in order to select the useful parameters for monitoring. Additionally, the devices must not interfere with the motor skill being executed. This technical limitation alone has proved to be a major hindrance to the development of biomechanical feedback devices in motor learning and training.  
In short, biomechanical wearables still require much more researches before it can become an impactful tool in the real world.
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 1 Introduction
### Existing Surveys
A comparative overview of existing surveys on different 5G coverage enhancement techniques are shown in Table 2. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Authors \& Ref. No.** & **Small Cells** & **CA** & **D2D** & **NOMA** & **5G Optimization** & **MIMO** \\ \hline [PERSON] et al. [10] & ✓ & X & X & X & X & X \\ \hline [PERSON] et al. [11] & ✓ & X & X & X & X & X \\ \hline [PERSON], [PERSON] et al. [12] & ✓ & X & X & ✓ & X & ✓ \\ \hline [PERSON] et al. [13] & X & X & X & X & ✓ & X \\ \hline [PERSON], [PERSON] et al. [14] & X & X & X & X & ✓ & X \\ \hline [PERSON] et al. [15] & X & X & X & X & ✓ & X \\ \hline [PERSON] et al. [16] & X & X & X & X & ✓ & X \\ \hline [PERSON] et al. [17] & X & X & X & ✓ & X & ✓ \\ \hline [PERSON] et al. [18] & ✓ & X & X & ✓ & X & ✓ \\ \hline [PERSON]. et al. [19] & X & X & X & ✓ & X & X \\ \hline [PERSON] et al. [20] & X & X & ✓ & X & X & X \\ \hline [PERSON], [PERSON] et al. [21] & X & X & ✓ & X & X & X \\ \hline [PERSON] et al. [22] & X & X & ✓ & X & X & X \\ \hline [PERSON] et al. [23] & X & X & ✓ & X & X & X \\ \hline [PERSON] et al. [24] & X & X & ✓ & X & X & X \\ \hline [PERSON] et al. [25] & X & ✓ & X & ✓ & X & X \\ \hline [PERSON], [PERSON] et al. [26] & X & ✓ & X & X & X & X \\ \hline [PERSON] et al. [27] & X & ✓ & X & X & X & X \\ \hline [PERSON], [PERSON] et al. [28] & X & ✓ & X & X & X & X \\ \hline [PERSON] et al. [29] & X & X & X & X & ✓ & ✓ \\ \hline Our Survey & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular} Note: In Table 2, ✓ denotes covered topics and X denotes uncovered topics.  
\end{table} 
Table 2: A comparative overview of existing surveys on 5G coverage enhancement techniques.
# Vector Signals and Invariant Systems: Re-Tooling Linear Systems Theory
## 5 Frequency Domain Analysis for LRITI Systems
### Synopsis  
So far in Section 5, we developed frequency-domain analysis for VV LRITI systems and compared it with traditional CV LTI system theory. While the frequency response defined in (64) may be useful for bivector exponential signals, the utility for analysis on a general signal which has not been decomposed into bivector exponentials is not clear. This is because as noted in _Remark_16 the FT we have defined in (45) does not provide a decomposition for a general VV signal. To address this challenge, we propose a decomposition approach based on the set of TOPs in (33), which provides an alternate approach to the frequency response of LRITI systems.  
Figure 5: Block diagram illustrating the eigenfunction property of LRITI systems.  
Now, using the frequency spectrum in (69), which uses TOPs for the input signal in (70), gives  
\[\mathbf{x}(t)\stackrel{{\mathcal{F}_{\mathbf{I}_{k}}}}{{ \longleftrightarrow}}\{\mathbf{X}_{\mathbf{j}_{k}}(\omega)\} = \{\mathbf{X}_{\mathbf{j}_{1}}(\omega),\,\mathbf{X}_{\mathbf{j}_{2} }(\omega)\} \tag{72a}\] \[= \{\mathbf{e}_{1}2\pi\delta(\omega-\omega_{0}),\,\mathbf{e}_{4}[ \pi\delta(\omega-\omega_{1})+\pi\delta(\omega+\omega_{1})]\}. \tag{72b}\]  
In example 3, we discussed that the FT in (45) does not provide a decomposition for the given VV signal, \(\mathbf{x}(t)\). Thus, to perform the frequency-domain analysis for a given \(\mathbf{x}(t)\), we used a decomposition method utilizing a set of TOPs. We showed that the TOPs provided the frequency-domain analysis for the given VV signal in a finite number of planes, \(\mathbf{j}_{k}\), and provided the FT of each decomposed signal in terms of their planar spectra, \(\mathbf{X}_{\mathbf{j}_{1}}(\omega)\), \(\mathbf{X}_{\mathbf{j}_{2}}(\omega)\), respectively.  
**Theorem 9** (Convolution Property of FT with respect to TOPs).: _The Fourier transform of the convolution of two VV signals is given by the following relation_  
\[\mathcal{F}\{\mathbf{x}(t)*\mathbf{h}(t)\} = \sum_{k=1}^{M}\mathcal{F}_{\mathbf{j}_{k}}\{\mathbf{h}_{k}(t)\} \mathbf{e}_{2k-1}\mathcal{F}_{\mathbf{j}_{k}}\{\mathbf{x}_{k}(t)\}\] (73) \[\quad+\  
signal (47a). On the other hand, the second set of definitions, (67) and (69), utilizes a decomposition method based on a set of TOPs (33), which is particularly useful for performing frequency-domain analysis for general VV signals. Finally, we provide a convolution property for the FT with respect to TOPs that unifies the time-domain and frequency-domain analysis for VV signals.
## 3 Results
### Statistical and Geostatistical Analysis  
The results of the descriptive statistics are given in (Table 1). The mean values of the particulate matter observed in the study area during most days of the year exceed the National Ambient Air Quality Standards (NAAQS). The correlation coefficient shown in Table S4 shows strong-to-moderate correlation between particulates, CO\({}_{2}\), CO, and NO\({}_{2}\) and indicates the same source of pollution. Factor analysis (PC) helped in identifying four factors having eigen values of >1. The four factors explain 82.046% of the total variance, as shown in Table S5. PC1 accounts for 41.2% of the data variability and shows significant factor loadings of PM1, PM2.5, PM4, PM10, CO, and NO\({}_{2}\), as shown in Table S6. These variables have strong-to-moderate correlation, as shown in Table S4. PC2 represents 14.5% of the total variability and has significant factor loadings of SO\({}_{2}\) and CO\({}_{2}\). PC3 and PC4 represent 13.6% for O\({}_{3}\) and VOCs and 12.756% for H\({}_{2}\)S and NO, respectively. Ordinary kringing maps were built, as shown in Figure 8, for the particulate matter and gases, except O\({}_{3}\) because there was no significant spatial variation, as seen in the semivargrom model shown in Table S7. The distribution of all variables was lognormal for different types of the chosen model, depending on the least mean square error. High concentrations of pollutants and particulate matter are in Al-Misfat, the Second Industrial City, and along the highway. The concentration of PM varies in the study area, depending on land use and working conditions. However, a high concentration of coarse particulate matter (PM10) covers large areas, and its mean (214 ug/m\({}^{3}\)) exceeds the standard limit (150 ug/m\({}^{3}\)). Fine particulates (smaller than 2.5) are more critical and may cause more respiratory problems. They are concentrated in specific areas around Al-Misfat and the Second Industrial City. These fine particulates, derived mostly from combustion, may remain suspended for weeks and drift for many kilometers [39]. Moreover, spatial distributions of particulates are similar to those of CO and NO\({}_{2}\) and consequently considered to be within PC1. A high variation in the concentration of oxides, CO\({}_{2}\), NO\({}_{2}\), and SO\({}_{2}\), is observed in the study area. The highest concentrations are around the Second Industrial City and Al-Misfat, where industries and automobile emissions are considered as the major sources of NO\({}_{2}\)[20]; however, emissions remained below standard at all places. VOC emission is concentrated in Al-Misfat and in the southeastern part of the Second Industrial City. The oil and natural gases sector is the main source of VOCs. H\({}_{2}\)S is concentrated in the urban area in the northern part of the study area and near Al-Misfat. The main sources of H\({}_{2}\)S are petroleum refineries, Al-Misfat, and the sewage system. Ad-Dar Al-Baida suffers from the leakage of an old sewage system, and the population suffers greatly from stagnant sewage pools and bad smells.
## 6 Implementation and Evaluation
### Power Consumption of Tag Device  
Since the tag device of the proposed device network is battery-powered, its power consumption should be considered. It is normally in a standby state and is activated when a user enters a unit space where a stationary device is installed. Figure 9 illustrates the power-consumption waveform according to the state of the tag device (BLE advertising, standby, or active), and Table 1 lists the amount of current consumed by the tag device in each state. In the BLE advertising state, the tag device emits advertising packets to continuously connect it to the stationary device. In the standby state, the tag device is connected to the stationary device, but there is no user in the unit space. When a user with a wearable device enters the unit space, the tag device enters the active state by enabling its built-in sensors to measure the usage of home appliances. The tag device consumes 1.751 mA in the active state and 0.895 mA in the standby state, on average. We tested 10 tag devices with a 500 mAh LiPo battery, and the tag device remained in the standby state for approximately 20 days. When an average user performed his or her daily activities, the lifetime of the tag device was 15 days on average; the duration depends on the frequency of use of an item to which the tag device is attached. The tag device is not always active for measuring the usage information of the attached appliance. Instead, it usually disables its internal sensors to consume less power in the standby state and only enables sensors upon receipt of an activation message from the connected stationary device. The stationary device in a unit space detects the user's entry and controls the state (standby or active) of tag devices in  
Figure 8: Preparation time of tag devices when user enters a specific indoor unit space.  
its space. We effectively reduced the standby power of the tag device by disabling its internal sensors when the user was not in the space.
## 2 System and Signal Characterization
_Receiver System and Signal Modeling_  
It is well-established that ZF and MMSE receivers are computationally intensive, as they require channel inversion for each frequency component, which results in significant battery consumption. ZF receivers offer the benefit of effectively mitigating intersymbol interference. However, they also suffer from increased noise levels under moderate to high noise conditions, leading to a degradation in performance. In contrast, MRC and EGC receivers avoid such complexity, resulting in a simpler receiver architecture that is particularly advantageous in LIS scenarios with a very large number of antenna elements. However, MRC and EGC receivers introduce some interference during the decoding process, which can be mitigated using iterative receivers. Considering a non-iterative receiver, the estimated frequency domain data symbols \(\widetilde{\mathbf{S}}_{k}=\left[ \begin{smallmatrix}\widetilde{\mathbf{S}}^{(1 )}&\ldots&\widetilde{\mathbf{S}}_{k}^{(R)}\end{smallmatrix} \right]^{T}\) are defined as follows:  
\[\widetilde{\mathbf{S}}_{k}=\mathbf{B}_{k}^{H}\mathbf{\gamma}_{k}. \tag{2}\]  
Depending on the algorithm, \(\mathbf{B}_{k}\) can be computed differently [20]. Using a ZF receiver, \(\mathbf{B}_{k}\) becomes \(\mathbf{B}_{k}=\mathbf{H}_{k}^{H}\left(\mathbf{H}_{k}\mathbf{H}_{k}^{H} \right)^{-1}\), while \(\mathbf{B}_{k}\) becomes \(\mathbf{B}_{k}=\left(\mathbf{H}_{k}\mathbf{H}_{k}^{H}+\beta\mathbf{I}\right)^ {-1}\mathbf{H}_{k}^{H}\) for the MMSE, where \(\beta=\frac{\sigma_{\widetilde{\mathbf{S}}}^{2}}{\sigma_{\widetilde{\mathbf{S} }}^{2}}=\frac{\frac{\varepsilon\left[\left\|\mathbf{h}_{k}^{H}\right\|^{2} \right]}{\varepsilon\left\|\mathbf{h}_{k}^{2}\right\|}}{\frac{\varepsilon \left\|\mathbf{h}_{k}^{2}\right\|}{\varepsilon\left\|\mathbf{h}_{k}^{2}\right\|}}\) and where \(\mathbf{I}\) is an \(R\times R\) identity matrix. Using the MRC receiver, \(\mathbf{B}_{k}\) becomes \(\mathbf{B}_{k}=\mathbf{H}_{k}^{H}\) and \(\mathbf{B}_{k}\) becomes \(\mathbf{B}_{k}=\mathit{exp}\left\{j\arg\left(\mathbf{H}_{k}^{H}\right)\right\}\) for the EGC. Iterative block decision feedback equalization (IB-DFE) [19] is a highly efficient receiver for SC-FDE, utilizing feedforward and feedback coefficients for an enhanced performance. While MRC and EGC are computationally simple, they generate residual interference, especially with moderate signal-to-noise ratio (SNR) values. This can be countered by iterative receivers, similar to IB-DFE, implementing the following function:  
\[\widetilde{\mathbf{S}}_{k}=\mathbf{B}_{k}^{H}\mathbf{\gamma}_{k}-\mathbf{C}_ {k}\overline{\mathbf{S}}_{k} \tag{3}\]  
where the interference cancellation matrix, \(\mathbf{C}_{k}\), is computed as \(\mathbf{C}_{k}=\mathbf{A}_{k}^{H}\mathbf{H}_{k}-\mathbf{I}\)[21], and \(\mathbf{I}\) is an \(R\times R\) identity matrix. Figure 1: An LIS serving four 6G users. As an alternative to this iterative receiver, we propose an interference cancellation process applied at the output of the MRC/EGC receiver (with a single iteration), based on a neural network, as defined in the following.
# A Review on Observer Assistance Systems for Harvested and Protected Fish Species
## 3 Proposed Electronic Monitoring System  
The focus of EM is to be able to collect a high-quality image and to be able to transmit these data in real time from the ship. Due to the long voyage time of ordinary deep-sea fishing vessels and national flag vessels, data collection, learning, and estimation may not be smooth. Therefore, the system presented in this study transmits a 4 k video over land in real time, it learns based on the transmitted data, and finally, it performs the estimation. In addition, the currently required conditions are the presence or absence of illegal catches and the total amount of the catch in the video taken during operation.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 7 Land-Based Systems Implementation-Evolution: Early Rail Defect Detection Capability
### Signal Pre-Processing
#### 7.3.3 Dispersion Compensation  
Even if a mode characterized by a small dispersion is selected, the effects of this phenomenon can be appreciable in the presence of a long propagation range. Dispersion compensation can be performed by exploiting the algorithm developed by [PERSON] [72] and the dispersion data evaluated by means of the SAFE method in the considered scenario. The compensation process allows also for converting the received signals from the time domain to the distance domain. The correctness of this conversion can be verified by comparing the location of the welds on field and that inferred from the reflections appearing in the compensated signals.