esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Classification of the Tympanic Membrane Using a Convolutional Neural Network
## 3 Results  
### Accuracy  
The classification accuracy was calculated as the number of correctly classified instances divided by the total number of considered instances and multiplied by 100. In the training phase, the accuracy  
Figure 3: Class activation maps (CAM). CAM is a colored representation of data where different values are represented as different colors with values ranging from blue to red (blue, cyan, green, yellow, red). The red regions in the overlay image represent the most specific discriminative parts of the image by the deep neural network. The blue regions represent the non-specific part. (**A**) Right side tympanic membrane (TM) image with perforation. (**B**) Left side TM image without perforation.  
Figure 2: Anatomy of the tympanic membrane (TM). (**A**) Right normal TM (**B**) Left TM with perforation. (a) Malleus short process, © Handle of malleus, © Umbo, © Cone of light, © Perforation margin, © Middle ear, © Annulus of TM, \(\bigstar\) Anterior of external auditory canal, \(\blacktriangle\) Superior of external auditory canal.  
of the CNN model was 98.7% for the TM side classification and 87.20% for the detection of perforation presence or absence. In the test phase, the accuracy of detecting the side of the normal TM was 99.2% for the right side and 96.5% for the left side. The accuracy of detecting the side of the TM with perforation was 99.5% for the right side and 95.6% for the left side. The accuracy of detecting presence or absence of perforation was 94.5% for the right side and 91.2% for the left side. The accuracy of finding no perforation was 91.7% for the right normal TM and 89.1% for the left normal TM. The accuracy of detecting perforation was 94.5% for the right TM with perforation and 91.2% for the left TM with perforation. The overall accuracy of the model was 98.7% for detecting the side and 91% for detecting the presence or absence of perforation (Table 1). Table 2 show the performance of the CNN model. The sensitivity and specificity of the model were 97.9% and 96.3% in the detection of side. In the detection of perforation, the sensitivity and specificity were 91.0% and 90.5%. The positive predictive value (PPV) and negative predictive value (NPV) were 96.9% and 99.1% for the detection of side and 98.0% and 72.3% for the detection of perforation.
## 2 Materials and Methods
### Earthquake Catalog  
The utilization of an earthquake catalog that is as complete and accurate as possible constitutes a fundamental element in PSHA. However, this task could be challenging, mainly due to the heterogeneous history of earthquake reporting and magnitude estimates. The instrumental earthquake catalog of [PERSON] et al. [4] was employed to reliably calculate the seismicity parameters necessary for PSHA. In order to expand the original timespan of the catalog, which covers the period from 1900 to 2009, and to include more recent earthquakes with M\({}_{\mathrm{w}}\geq 4.1\), the catalog was uniformly extended to 2019, resulting in the inclusion of 6918 additional earthquakes that were used for the present seismic hazard assessment. The decision to exclude earthquakes that occurred after 2019 was based on the use of the reviewed events from the International Seismology Centre (ISC) catalog, as documented in [PERSON] et al. [4]. It should be noted that since the probabilistic approach is time independent, the earthquakes included in the catalog should lack any spatiotemporal correlation. Consequently, a declustering procedure could be conducted to exclude aftershocks and foreshocks and retain only the \"parent\" earthquakes. However, it is important to acknowledge that ground motion exceedances may occur randomly during any event and the application of a declustering procedure may result in the omission of a significant amount of data. This could potentially impact the results, as annual exceedance rates for the magnitude of completeness would be lower, leading to the underestimation of the maximum expected ground motion parameters [58; 63; 64]. Therefore, the use of a non-declustered earthquake catalog following a Poissonian process was preferred, given that ground motion at a given site may surpass a certain level due to either foreshocks, mainshocks, or aftershocks [58; 63; 64].
## 3 Methods
### Multi-Scale Residual Block  
We utilize a multi-scale residual block structure, as illustrated in Figure 2. The input feature \(F_{in}\) undergoes a pre-activation layer and two consecutive 3 \(\times\) 3 convolutions, resulting in the output feature \(F_{1}\). Subsequently, \(F_{1}\) is fed into two separate branches: the Hourglass Block (HB) [27] and the Efficient Structure Extraction Module (ESEM), facilitating multi-scale feature extraction in both spatial and channel dimensions. The spatial dimension emphasizes key facial parts, such as the nose and eyes, which are rich in feature information. On the other hand, the channel dimension focuses on extracting structural and edge features from the face image. To mitigate artifacts in the reconstructed faces, additional 3 \(\times\) 3 convolution layers with a stride of 2 are introduced in the downscale stage for downsampling and fusion of shallow features. Similarly, an additional 3 \(\times\) 3 convolution layer is used in the upscale stage for upsampling and reconstruction of deep features. The resulting multi-scale features \(F_{scale}\) are multiplied with the convolutional extracted features and then added to the original features \(F_{in}\) to obtain the final features \(F_{out}\).  
The Hourglass structure was originally introduced in human pose estimation networks [27]. Its exceptional multi-scale feature extraction capability has yielded impressive results in various studies [12; 15; 16]. Recognizing the importance of independent facial components, such as the eyes and nose, in facial tasks, and considering the hourglass structure's ability to extract richer facial feature information and preserve high-frequency details, we adopt hourglass blocks for feature extraction of key facial parts. The facial features \(F_{in}\) are extracted in depth through a series of successive 3 \(\times\) 3 convolutional layers, with each layer focusing on a different facial structure. To enhance convergence and prevent gradient explosion, normalization layers and LeakyReLU activation functions are incorporated. Additionally, hopping connections are employed to facilitate the fusion of feature information from different layers, mitigating information loss during layer-by-layer feature transfer. Finally, the output features \(F_{out}\) are obtained for further processing.
## Materials and Methods
### Total Phenolics and Antioxidant Activity  
Total phenolic content was determined by using the Folin-Ciocalteu reagent (Sigma, St. Louis, MO, USA) based on colorimetric reduction [32]. The phenolic compounds are oxidized to phenolates by the reagent at alkaline pH in a saturated solution of sodium carbonate resulting in a blue complex. About 1 mL of Folin-Ciocalteau (10%, \(w/v\)), is added to 0.2 mL of prepared pomegranate juice, followed by the addition of 1.2 mL of aqueous Na\({}_{2}\)CO\({}_{3}\) (7.5%, \(w/v\)). The mixture was left in the dark for 90 min. The absorbance of the blue color solution was monitored at 760 nm on a UV visible spectrophotometer (Shimadzu, Kyoto, Japan), against blank (distilled water). The total phenolics content (TPC) was assessed by plotting the gallic acid calibration curve and expressed as mg of gallic acid equivalents (GAE)/100 ml juice. The antioxidant activity (AA) of pomegranate juices was evaluated applying the ABTS radical cation decolorization assay [33]. ABTS\({}^{+}\) was prepared by reacting of ABTS with potassium persulfate. Samples were analyzed at five different dilutions, within the linearity range of the assay, as previously described by [PERSON], et al. [34]. TAA was expressed as mg of Trolox equivalent (TE)/100 mL juice. All measurements were repeated three times. All measurements were repeated three times.
## 5 Simulation Results and Analysis
### Simulation 2: Performance of 2D DOA Estimation with Respect to the Number of Sensors  
The second simulation shows performance with respect to the number of sensors. It varies from 5 to 15, while the SNR is fixed at \(-6\) dB and the number of snapshots is fixed at 500. As shown in Figures 5 and 6, the two estimators with known waveforms have almost the same performance, which also meet with CRB. That means our method can acquire the best performance when the number of sensors ranges from 5 to 15, which covers most situations in reality. Moreover, the results in Figures 3 and 5 show that the performance of the estimators with known waveforms outperforms the estimator without known waveforms, especially for lower SNR and fewer M.
## 1 Introduction
Optically controlled RTD-basedphoto-detectors can have interesting applications in generation of microwave stable low-phase noise signals for clock recovery systems, low-power miniature atomic clocks as time references [16], and synchronization in communication networks [17]. Because of the fast response of RTDs [18], this technology has the potential to deliver Gb/s wireless transmission [19] which matches the data capability of current fiber optics, and considerably enhances radio-over-fiber communications [20].
## 4 Simulation Results and Discussion
### Superior Optimization Method  
In this section, optimal energy scheduling and management of EH with the aim of maximizing profit are presented in the day-ahead markets in EGHNs by satisfying network operation constraints and hub constraints using IELA's new meta-heuristic algorithm. The performance of the proposed algorithm has been compared with conventional FLA, PSO, and MRFO. The convergence curve obtained from solving the problem by different algorithms is shown in Figure 7. Here, the value of the objective function represents the profit of the EH. According to the minimization in Matlab software and the objective of profit maximization, a negative sign is included in the objective function. As can be seen, the proposed method based on IFLA has obtained the optimal global solution with a higher profit value, higher convergence speed, and lower convergence tolerance in fewer convergence iterations compared to other methods. The numerical results of EH scheduling based on the proposed methodology are given in Table 4. As is noted, the suggested approach based on IFLA achieved a profit of $472.13 in the 30 th iteration, while other methods are stuck in the local optimum and are not able to achieve the global optimum. It can also be seen that the performance improvement of the conventional FLA method based on [PERSON]'s direct rotational (RDR) method prevents the premature convergence of its improved version, while the traditional FLA method with a profit value of $435.34 has experienced premature convergence. Furthermore, in a statistical analysis of the performance of different algorithms according to Table 5, the superior capability of the proposed algorithm has been confirmed in terms of achieving better statistical indices. The difference in the IFLA results compared to other methods is to achieve more profit of hub energy in the day-ahead electricity market. The proposed method has been able to get more profit from energy exchange with more accurate planning between energy resources and storage sources of the energy hub. The results showed that the proposed scheduling framework based on the IFLA based on a meta-heuristic algorithm has the ability to create a favorable balance between the phases of exploration and exploitation and is less able to determine the optimal solution in the convergence iteration. The results show that the use of a powerful optimization method can guarantee the optimal economic operation of the EH by satisfying the many constraints of the operation and the EH.  
Figure 7: Convergence behavior of various algorithms when applied to solve the problem.
# Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis
## 2 Materials and Methods
### Study Domains and Datasets
SAR has much higher resolution than SMAP and CYGNSS, as it operates at a higher frequency (5.4 GHz, compared to 1.4 for CYGNSS). The dataset is provided at a resolution of 3 km grid cells; however, it is available at a 500 m resolution.
## 4 Young-Type Interference with Gauss-Maxwell Beams
From the corresponding total electric and magnetic fields arising from the superposition (their expressions are provided in Appendix B), it is shown that the intensity distribution at a distance \(z\) from the input plane is proportional to the density \[|\psi|^{2}=|\psi_{1}|^{2}+|\psi_{2}|^{2}+2\text{Re}\left[\langle P_{2}|P_{1} \rangle\psi_{1}\psi_{2}^{*}\right], \tag{108}\]  
which involves the factor  
\[\langle P_{2}|P_{1}\rangle = \alpha_{1}\alpha_{2}^{*}+\beta_{1}\beta_{2}^{*} \tag{109}\] \[= \cos\theta_{1}/2\cos\theta_{2}/2e^{-i(\delta_{1}-\delta_{2})/2}+ \sin\theta_{1}/2\sin\theta_{2}/2e^{i(\phi_{1}-\phi_{2})+i(\delta_{1}-\delta_{2 })/2}.\]  
This factor leads to a reduction of the fringe visibility as the polarization states \(|P_{1}\rangle\) and \(|P_{2}\rangle\) become more different, with the maximum loss, with \(\langle P_{2}|P_{1}\rangle\), when these states are orthogonal (e.g., horizontal vs. vertical polarization, or left-handed vs. right-handed circular polarization), in agreement with the aforementioned Arago-Fresnel laws. By inspecting (108), we readily notice that, again, the fringe distribution is along the \(x\) coordinate. In the case of the horizontally polarized beams discussed above, we could get an idea of why this was the behavior: leaving aside the scalar fields themselves, the total electric and magnetic fields explicitly depended on only one transverse coordinate, namely the electric field on the \(x\)-coordinate and the magnetic field on the \(y\)-coordinate, as it can be seen in Equations (100) and (101), respectively. This is in clear contrast with the fields described by Equations (A12) and (A13), which both depend on both coordinates (in their \(z\)-component). That is, not only coordinates and polarization coefficients are mixed up, but there is also a coupling between different field components, which is going to play an interesting role from the point of view of the topology displayed by the corresponding rays. Such a mixture of coordinates becomes more apparent from the time-averaged Poynting vector or the corresponding ray equations along the \(x\) and \(y\) directions in terms of the longitudinal coordinate \(z\) (see expressions in Appendix B). The latter equations reduce to Equations (103) and (104), respectively, in the particular case \(\alpha_{1}=\alpha_{2}=1\) and \(\beta_{1}=\beta_{2}=0\) (i.e., two input beams horizontally polarized at the input plane \(z=0\)). However, unlike the case described by Equations (103) and (104), now the generality of the current equations makes difficult to get a clear clue on what is going on with the dynamics they describe. Nonetheless, still we notice that there is a correlation between transverse coordinates (mediated, in turn, by the coupling with the polarization state) that may induce out-of-plane dynamics (i.e., dependence of one transverse component of the rays on the other, and vice versa), which are not present in the case of either horizontal or vertical polarized input beams [8].
# GNSS Signal Acquisition Algorithm Based on Two-Stage Compression of Code-Frequency Domain
## 4 Simulation Results  
The GPS L1 signal is adopted to evaluate the performance of the proposed method. Set the data rate \(R_{\mathrm{b}}=50\) bps, the code rate \(R_{\mathrm{c}}=1.023\) Mcps, the sampling rate \(f_{\mathrm{s}}=2R_{\mathrm{c}}\), the code phase \(\tau=400\), the code length \(N=1024\), the measured length \(M=768\), and the Doppler frequency offset range is \(\pm 80\) KHz.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acquisition Algorithm** & **Complexity** \\ \hline Serial acquisition algorithm & \(O(N^{2}\cdot I\cdot L)\) \\ PMF-FFT acquisition algorithm & \(O(N\cdot(L\cdot\log_{2}(L)+N))\) \\ CC acquisition algorithm & \(O(N\cdot(\log_{2}(N))^{2}\cdot I\cdot L)\) \\ CFC acquisition algorithm & \(O(N\cdot(\log_{2}(N))^{2}\cdot I)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of algorithm complexities.
## 4 Implementation and Experiment Results
### Parameter Analysis
#### 4.3.4 Other Datasets  
Except for the Netflix datasets, two more popular datasets are experimented with to show the performance of the proposed CS item recommender. The two datasets are Amazon All Beauty and Amazon CDs & Vinyl [40]. Table 9 shows the features of the two datasets in which they have very sparse density. Figure 8a,b shows the MAE under different DACR settings with NNCF4 model for All Beauty dataset and CDs & Vinyl dataset, respectively. Note that DACR0 means no DACR generator is applied while DACR\({}_{1}\) means that the DACR generator will generate the ratings for CS items that receive no greater than one rating in the dataset. Figure 8a shows All Beauty dataset reveals similar trends with Netflix datasets in which when \(\alpha\) increases, the MAE decreases. Figure 8b, however, illustrates when \(\alpha=50\), the MAE increases. It indicates that fifty similar non-CS movies (neighbors) might be too many for rating generation. Selecting an appropriate number of neighbors when conducting a DACR generator is critical for better performance.
## 3 Intersection Delay Fairness Analysis
### Delay Model Fairness Evaluation  
Cross-intersection delay fairness mainly manifests in the consistency of average delay across phases. Descriptive parameters typically include variance, standard deviation, and, to eliminate the influence of scales and dimensions, the coefficient of variation. However, the ranges of these parameters are uncertain, posing difficulties in effectively integrating multiple objectives. Information entropy can also describe data consistency, and for specific problems, its range of values is fixed. Therefore, this study selects information entropy as the descriptive parameter for cross-intersection delay fairness.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 4 SMTrust Design and Analysis
### Rank and Trust Computation  
Rank computation in SMTrust is adopted from the IETF RFC 6550 RPL specification [12] and as implemented in ContikiRPL default OF. Trust computation assumes that all nodes are trustworthy when joining the network. As ContikiRPL assumes that nodes overhear neighbour nodes and their packet transmission. The same assumption is made by SMTrust. Trust metrics are quantified in combination with the specification of rank for the selection of parent in routing. The OF specifies the routing decisions, including the preferred parent selection, and rank computation, according to the normal operations of RPL [65].  
Once the topology is constructed, and the neighbour list is established, the trust values are computed according to the Equations (1) to (6) and the trust index is calculated accordingly. When DIO messages are received from neighbour nodes, the information communicated is used to update the routing table. The trust values of its neighbours are calculated using the SMTrust trust computation mechanism. A set of trusted potential parents is then selected for an optimal path to the root node. This process is followed by the neighbouring nodes for the construction of DODAG. Afterward, the DODAG maintenance relies on the trickle timer, which limits the frequent transmission of control messages. The trust value is updated periodically, using the trickle timer in RPL, and reactively, when there is a change in the node's behaviour.
## 2 Materials
### UAV-Based HI  
At present, the HI is usually acquired by a low-altitude remote-sensing platform UAV, such as an unmanned fixed-wing aircraft, unmanned helicopter or unmanned airship equipped with a hyperspectral imaging spectrometer. Compared with the unmanned fixed-wing aircraft and the unmanned helicopter, the unmanned airship has the outstanding advantages of a long continuous flight time and high suspension and load abilities [21]. In our study, the HI (Figure 2) was collected by the UAV hyperspectral remote sensing system consisting of an unmanned airship, an automatic rudder system, a task device and a ground control system. The automatic rudder system consisted of a global positioning system (GPS), a computer and a gyroscope, while the task device consisted of a triaxial platform and a near-infrared hyperspectral imaging spectrometer, HyperspecTM VNIR (Headwall, MA, USA) with a 400-1000 spectral range, 325 bands and the EMCCD detector generating a 1004 \(\times\) 1002 spatial image with 8 \(\upmu\)m pixel size. The hyperspectral remote-sensing system communicated with the ground control system wirelessly.  
Figure 1: The study site location. (**a**) The map of China with Chongqing City in white color; (**b**) The map of Chongqing city with Fuling district in red color; (**c**) The UAV image of the study area. The purple rectangle in (**b**) and red rectangle in (**c**) represent the field plot’s location and the hyperspectral flight area, respectively.  
The survey shows that PWD usually spreads fast during May to December, and the physiological characteristics of infected pine trees change obviously [29]. Therefore, the HI data were collected for 30 min (4:30-5:00 PM) on 14 September 2020. The flight altitude was 112 km, and the ground resolution was 0.1 m. A total of 325 spectral channels from visible to near-infrared regions (400-100 nm) were included in the HI. In the preprocessing, radiometric correction was conducted using a diffuse reflection board and a Compact Hyperspectral Data Processing Unit (CHDPU) of HyperspecTM VNIR (Headwall, Bolton, MA, USA). Orthophotos, atmospheric and reflectance correction were also performed in the CHDPU.
# Multi-Objective Production Scheduling of Perishable Products in Agri-Food Industry
## 3 Agri-Food Scheduling Problem Description
### Objective Function  
The objective of this study is to find the best solution with the best optimizing criterion. This problem corresponds to a flow-shop scheduling problem, and the algorithm must find an order of execution of the various jobs in a way that it minimizes \(C_{max}\) and reduces the perishability product function. The value of this criterion depends on the makespan value that equals the completion time of the last job executed.  
Our objective is to minimize the makespan (\(C_{max}\)) of these products while respecting these specific constraints bound to this type of industry. The \(C_{max}\) is calculated as follows:  
\[C_{max}=Max(\alpha_{ij}),i=\{1,\ldots,n\},j=\{1,\ldots,m\}. \tag{1}\]  
The perishability product function, represented by the number of expired products, is calculated as follows:  
\[Pp_{min}=\left\{\begin{array}{ll}\sum{(\alpha_{ij}-v_{ij})}/(\alpha_{ij}-v_ {ij}),\text{if}&\alpha_{ij}\geq v_{ij}\\ 0&,\text{otherwise}.\end{array}\right. \tag{2}\]  
If \(Pp_{min}\leq 0\), then we do not have any perishable products. Otherwise, there is at least one perishable product.