esa-sceva/satcom-chunk-collection huggingface dataset:
# Earth Mover's Distance-Based Tool for Rapid Screening of Cervical Cancer Using Cervigrams
## 3 Results  
The socio-demographic data revealed that, among a total of 398 women enrolled for the current study, a vast majority, 219 (55%), were between 20-40 years of age; 74 (18.6%) of the women were illiterate; and 230 (57.7%) of the women were educated up to the high school level. The majority of these women were housewives, 352 (88.5%), with a family annual income between 1-10 lakhs, 278 (69.9%). Only 39 (9.9%) of the women enrolled in the study had attained menopause; 305 (77.2%) of the women presented with the abnormal menstrual conditions (Table 1). A striking observation was that education (\(p\) = 0.000) and occupation (\(p\) = 0.001) showed statistically significant associations with the health of the cervix (Table 1).  
Furthermore, our analyses indicated that women from lower socio-economic strata belonging to a household with less than INR 100,000 per annum (approximately USD 1300 per annum) had highly unhealthy cervix (\(p\) = 0.000), indicating poor knowledge of adoptive reproductive and sexual health practices (Table 1), which is indeed responsible for increasing the vulnerability of women to cervical cancer, particularly in LMICs such as India. Age and menstrual health also showed statistically significant association with the health of the cervix.  
We believe that the adoption and integration of intelligent decision support systems involving the use of minimally invasive techniques to detect cancerous cervical lesions through automated segmentation of digitized cervigrams on a real time scale would not only form the rationale for the development of effective triage methods towards the early diagnosis of lesions but also prioritize treatment options.  
To this end, an EMD-based measurement scale (OM--The OncoMeter) was developed with a threshold value of 26.77 based on cervigrams obtained from healthy subjects evaluated at BHMRC for being designated as normal. Beyond the threshold value of 26.77 (EMD value), the cervigrams were computationally tagged into different categories (Table 2 and Figure 4).  
The development of an EMD-based measurement scale for binning and categorization of cervigrams based on their EMD distance from the centroid of normal in a quantitative manner is indeed a stellar achievement in the field of cervigram analysis. The scale was designed for large-scale screening of cervical cancer and to act as an adjunct clinical aide for early and timely diagnosis by clinicians. The measurement scale was illustrated per their categorical EMD values obtained after the designation of the centroid value (26.77), an EMD value based upon the cervigrams obtained from the OPD of BHMRC, as well as its comparison with the abnormal cervigrams (Table 2 and Figure 4).
# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
## 4 Use Cases
### Use Case 3: AI-Based Soft Failure Identification  
A use case for failure identification is elaborated in [12]. In addition to the filtering effect of WSS and ASE noise, fiber nonlinearity is also considered. Compared with the previous works, a deep learning algorithm is used and the power spectrum density (PSD) is extracted from a coherent receiver. The overall architecture is shown in Figure 4.  
The SDN agent monitors the physical layer continuously and uploads the PSD to the control layer. Once the anomaly is detected, the CNN embedded in the anomaly identification module analyzes the PSD stored in the database. Finally, the identification results are output to the failure management module and proper actions are taken to restore the optical link.  
The identification results are shown in Figure 5a. The results demonstrate a high accuracy of the proposed method when there exists only one type of anomaly. In the scene when multiple types of anomalies exit, the probability output by the SoftMax layer is utilized to gain insight into their respective influences on the system. The result is shown in Figure 5b. The influences of ASE and  
Figure 3: The estimation performance of the monitoring scheme proposed in [5]. (**a**) The error histogram of monitoring performance without any analytical model input. (**b**) The error histogram of monitoring performance with the incoherent Gaussian noise model (IGN). (**c**) The error histogram of monitoring performance with the coherent GN model (CGN). (**d**) The CDF of three monitoring strategies proposed in [5]. \(SNR^{ANN-EST}_{NL}\) and \(SNR^{EST}_{NL}\) means the nonlinear signal-to-noise ratio (SNR) estimated by methods proposed in [5]. \(SNR^{SSFM}_{NL}\) means the nonlinear SNR estimated by the SSFM. \(\Delta SNR\) means the estimation difference between the proposed method and the SSFM.  
nonlinear interference (NLI) on the system are similar at first since the output probabilities of the two causes are both about 50 percent. Then, with the OSNR increasing, the NLI gradually becomes the dominant cause.
## 3 Test Results and Discussion
### Load-Displacement Curve  
The axial load-displacement curves of specimens are shown in Figure 6.  
Figure 5: Failure patterns of specimens.  
Figure 6: \(Cont.\)  
(1) Figure 6a shows that the failure process can be divided into four stages. (a) Elastic stage (OA): At the initial stage of loading, no cracks were found on the surface of the concrete, and the load and displacement presented a linear relationship. (b) Elastic-plastic stage (AB): With the increase in displacement, when the longitudinal strain reached about 3.3%, cracks appeared and continued to extend, and the curve gradually showed a nonlinear change. (c) Descending stage (BC): After reaching the peak load, the bearing capacity decreased, and the rate of decline was steep. (d) Residual stage (CD): As the displacement continued to increase, the bearing capacity of the specimen decreased significantly, the curve became flat, and the specimen was in the residual stage when the longitudinal strain reached about 10.0%.  
(2) Figure 6b shows that the influence of heating temperature on the load-displacement curves was obvious. With the increase in heating temperature, the slope of the curve in the elastic stage gradually became smaller, that is, the initial stiffness decreased. The residual stage of the curves at \(T<600\)\({}^{\circ}\)C was not obvious, whereas it was the opposite at \(T\geq 600\)\({}^{\circ}\)C. The main reason is the outer RC subjected to high temperature was seriously damaged, and the load was mainly transferred by the core CFST columns.  
(3) Figure 6c,d indicate the influence of the steel tube diameter and concrete cover thickness on the load-displacement curves. There was no significant difference in the slope of the descending section of the curve with different steel tube diameters at ambient temperature as the diameter of the steel tube increased. However, when exposed to high temperature (\(T=600\)\({}^{\circ}\)C), the descending section of the curve gradually flattened. The curves of specimens with different concrete cover thicknesses basically coincided at the initial stage but decreased slowly as the concrete cover thickness increased.
## 4 Conclusions  
Based on the results obtained, the following conclusions have been formulated.  
1. An empirical database of images of facing bricks has been created, which has various violations of the structure and appearance which have arisen at the production stage during technological processing, packaging, transportation, and storage.
2. We developed, optimized, and tested a computer vision model based on the YOLOv5s convolutional neural network. By applying a custom augmentation algorithm, synthetic images were created for the training dataset. The application of the developed algorithm in practice became possible with the help of a model trained on its own set of data obtained by simulating the shooting conditions, the angle of rotation, object deformation, and light distortion by image processing methods.
3. The developed algorithm detects defects both in images taken under normal shooting conditions and in the presence of color/light distortions. In the work, it was taken into account that defects that are small in size can be missed by a specialist when marking and, therefore, will not be taken into account in the process of training a neural network. Careful markup of these objects is required, which was performed in this study, which made it possible to achieve an accuracy of mAP0.50 = 87% for the \"inclusion\" class.
4. The developed algorithm has a high accuracy in the problems of detection and classification of defects: mAP0.50 = 87% and mAP0.50:0.95 = 72%.
5. Behind the conclusion about the quality of products based on intelligent control, there is always a specialist who can adequately respond outside the machine protocol, taking into account the real factors of production, transportation, and storage of products.  
The prospect of improving the model is to expand the visual variability in the generation of images in the process of augmenting the original data set, as well as to adapt software tools for intelligent online control of surface defects in conveyor production products.  
The developed defect detection and classification model can also be applied to other facing materials and products, such as stone, clinker facade tiles, facade panels, and plaster for more complete control of the degree of defectiveness of each product. The developed model is quite versatile and can be used for most building materials and products that are subject to increased requirements for appearance.
# Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol
## 4 Proposed Methodology
### Setup Phase
#### 4.1.2 Steady-State Phase  
Following the sending of a petition message from ordinary nodes, the cluster heads execute the Time-division multi-access (TDMA) procedure for every node. The TDMA scheduling gives every member node a timescale and ensures that each node is permitted to send in its particular time frame only or is permitted to wait and go into sleep mode, by which the set-up phase is completed.  
The steady-stage phase begins after the setup stage ends, identically as in the LEACH protocol. However, in our proposed protocol, we added a feature to prevent redundant data from being sent to the base station. As in this, common nodes collect data from the local surroundings. Each node then transmits the collected data to the CH, which was previously selected in the setup phase and is in its own allocated TDMA time slot. The steady-stage phase begins after the setup stage ends, identically as done in the LEACH protocol. However, in our proposed protocol, we had added a feature to preclude surplus data [23; 24] from being sent to the base station. The CH combines collected data from its various parts, and a data compression algorithm is executed to commingle the data. Now, on the compressed data, an essential XOR operation is executed on the data sensed by the neighbor nodes to prevent the CH from sending duplicate data to the BS. In the XOR procedure, the scrambling of data in bits is done. In this, the data that produces the result as 'True' are considered duplicate, and the opposite with a 'False' outcome case. The duplicate collected data will not be sent if the data sent out are identical as compared bit by bit [23]. Thus, it avoids sending duplicate data again and also decreases the amount of data sent to BS. Hence, as a result, the entire network will use less energy. The energy consumed for data transmission is determined through the cluster head involvement and estimated by equation [24; 25; 26; 27; 28] below.  
\[Total_{ER}(Packet_{data},d)=Ec*(Packet_{data})+E_{fst}*(Packet_{data})*d^{2} \tag{4}\]  
\(Total_{ER}(Packet_{data},d)\) is the reduction of energy, which is carried out while sending packet data, \(Ec\) is the amount of energy reduced through the transmitter and receiver circuit, \(E_{fst}\) is the amplifier parameter for transformation constituting the free-space technique, and \(d\) is the distance between the CH and the ordinary node (Euclidean distance).  
The degree of reduction in energy is calculated by the equation mentioned below:  
\[Total_{ER}(Packet_{data},d)\ =\ Ec*(Packet_{data})+\epsilon mp\ (Packet_{data})*d^{2} \tag{5}\]  
\(\epsilon mp\) is the amplifier parameter for transformation constituting the multi-path fading model.
# A Robust Dual-Platform GMTI Method against Nonuniform Clutter
## 2 The Dual-Platform GMTI Method against Nonuniform Environment
### Problem Formulation  
Assuming that a uniform linear array is composed of \(N\) array elements, the number of coherent pulses in the time domain is \(K\), the platform moves along the axis direction of the array, the speed is \(V\), the interval of the array element is \(d\), the azimuth angle and pitch angle from the clutter block to the antenna phase center are \(\theta\) and \(\varphi\), respectively, the wavelength of the electromagnetic wave emitted by the radar is \(\lambda\), and the first array element is used as the phase reference center. The earth observation geometry is shown in Figure 1.  
Figure 1: The earth observation geometry of an airborne radar system.  
The spatial steering vector of the array, denoted by \(\mathbf{s}_{s}(w_{s})\in\mathbb{C}^{N\times 1}\), is calculated as  
\[\mathbf{s}_{s}(w_{s})=\Big{[}\begin{array}{cc}1&e^{j\mathbf{s}_{s}(\theta,\varphi)} \quad\cdots&e^{j(N-1)\mathbf{s}_{s}(\theta,\varphi)}\end{array}\Big{]}^{T}, \tag{1}\]  
where \(w_{s}(\theta,\varphi)=2\pi d\cos\theta\cos\varphi/\lambda\) is the spatial angular frequency of the target. The temporal steering vector, denoted by \(\mathbf{s}_{t}(w_{t})\in\mathbb{C}^{K\times 1}\), is similarly defined as follows:  
\[\mathbf{s}_{t}(w_{t})=\Big{[}\begin{array}{cc}1&e^{j\mathbf{w}_{t}(\theta,\varphi)} \quad\cdots&e^{j(K-1)\mathbf{w}_{t}(\theta,\varphi)}\end{array}\Big{]}^{T}, \tag{2}\]  
where \(w_{t}(\theta,\varphi)=2\pi f_{d}/f_{r}\) is the time-domain angular frequency of the target, \(f_{d}\) is the Doppler frequency of the target, and \(f_{r}\) is the pulse repetition frequency. The space-time sampling signal of the unit amplitude point target clutter is expressed as a vector, and the space-time two-dimensional steering vector \(\mathbf{s}\in\mathbb{C}^{NK\times 1}\) is defined as  
\[\mathbf{s}=\mathbf{s}_{s}\otimes\mathbf{s}_{t}. \tag{3}\]  
Assuming that the number of independent clutter blocks in a range gate is \(N_{c}\) and the range ambiguity number is \(N_{r}\), the clutter sampling signal of a space-time snapshot, denoted by \(\mathbf{x}_{c}\in\mathbb{C}^{KN\times 1}\), is expressed as follows:  
\[\mathbf{x}_{c}=\sum_{i=1}^{N_{c}}\sum_{k=1}^{N_{c}}\alpha_{ik}\mathbf{s}_{ik}. \tag{4}\]  
The clutter covariance matrix, denoted by \(\mathbf{R}_{c}\in\mathbb{C}^{KN\times KN}\), can be expressed as  
\[\mathbf{R}_{c}=E\Big{[}\mathbf{x}_{c}\mathbf{x}_{c}^{H}\Big{]}. \tag{5}\]  
The weight vector of the space-time two-dimensional optimal processor, denoted by \(\mathbf{w}_{opt}\in\mathbb{C}^{KN\times 1}\), can be obtained by the following formula:  
\[\mathbf{w}_{opt}=\mu\mathbf{R}^{-1}\mathbf{s}. \tag{6}\]
# On the Design and Implementation of a Mixed-Platform Collaborative Virtual RAS Planner
## 3 Materials and Methods
### Architecture  
Our collaborative system is designed to be simultaneously accessible from multiple types of computing platforms: PC, tablet, VR, and XR. VR devices, such as Meta Quest 2 [24] or HTC Vive [25] allow an immersive experience but prevent interaction with real elements. XR devices such as Microsoft HoloLens 2 [26] also allow navigating through the virtual world, but the experience is not as immersive, allowing the real world to also be seen.  
All devices must maintain a stable connection to the Internet during the use of the application. In the case of VR and XR devices, the OpenXR standard is used to increase the compatibility of the system with the largest number of devices. Unity3D [27] was chosen for development and visualisation.  
Regarding the communication system, Photon Engine [28] technology is used for the creation of the shared space. The system is based on the serialization of the states of the objects of the virtual world, being updated only when any alteration has occurred. This architecture reduces the communication and data processing load, favouring a high frame rate and low latency (crucial aspects in VR and MR devices). Figure 2 summarizes the architecture. As can be seen, the system includes local storage where the customised segmented 3D model is stored alongside all the annotations that surgeons may make. This local case data storage will also be used to save information about the planning process generated by the surgeons. The collaborative experience is managed through a collabo  
Figure 1: The proposed virtual operation room for RAS planning.  
rative Photon On-Premises server application. The server requires a standard PC with a Windows 8 (or higher) operating system and a reliable internet connection. This structure ensures that clinical data never leaves our network, as it may contain sensitive patient information. The server is configured for load balancing up to a maximum of 10 attendees per room. If the maximum number is reached, the user will be directed to an anteroom where he/she can wait until the main room is free or create a duplicate of the main room where the case can be analysed in parallel. During remote collaboration sessions, the same server is responsible for activating and managing the voice channel that broadcasts conversations between users. To ensure correct transmission of voice messages, the system can filter messages from white noise to transmit only the speech sections. This implementation favours overall load balancing on the server. Finally, the system can be accessed from different devices, so that each user has a different avatar and controller system. The collaboration is asymmetric because not all the devices have the same interaction capabilities, as explained in the next section.
## 3 Classification of Optical Waveguide Sensors
### Cascaded Structured Optical Waveguide Refractive Index Sensors
#### 3.3.2 Double Microring Cascade Structure  
To achieve high-performance sensing devices, devices with multiple coupled or cascaded ring resonators are typically designed using two coupled rings, with the circumference of the second ring being 1-2 times that of the first ring. Compared with the structure, the fineness is enhanced, and the \(Q\) value can be 3 \(\times\) 10\({}^{4}\) or higher.  
[PERSON] et al. proposed a high-sensitivity sensor based on a cascaded reflective MZI and MRRs [119]. As shown in Figure 19a, the sensor is composed of a microring and cascaded MZI. In Figure 19b, the microring chip was fabricated on an SOI platform. The thickness of the surface silicon layer here was 220 nm, and the thickness of the buried oxide was 2 \(\upmu\)m. Embedded in the trench, the sensing chip was covered with SU8 upper cladding. The microring's straight waveguide has a width of 0.5 \(\upmu\)m, while the ridge height, ring diameter, and ring width are 0.22, 124, and 0.5 \(\upmu\)m, respectively. The gap between the straight waveguide and the ring is designed to be 0.5 \(\upmu\)m. The reflective MZI consisted of a fiber coupler, with the two arms of the MZI coated with 200 nm Au on the output fiber ends. Compared with a traditional bipolar MRR, the sensor has an average power increase of 13 dB and can achieve a high sensitivity of 1892 dB/RIU.  
[PERSON] et al. proposed a refractive index sensor utilizing the vertical coupling between two SOI waveguides [120]. A schematic diagram of the proposed sensor structure is shown in Figure 20, which consists of a planar single-mode SOI waveguide interacting with another planar single-mode SOI waveguide of length \(L\). Thus, it consists of three planar waveguide sections: the I and III sections are identical single-mode silica-covered SOI waveguides, whereas the middle section II is a directional coupler consisting of a single-mode silica-covered SOI waveguide (WG1) and another single-mode SOI waveguide (WG2) with the covering medium as the analyte. Using optimized waveguide parameters for aqueous coverings, the bulk sensitivity of 7000 (3400) nm/RIU for a refractive index of 1.33 was achieved for TE and TM modes propagation in the optical communication band. As a result of its high bulk and thin-layer sensitivity, this sensor has potential for use in biosensing applications.  
Figure 19: Schematic diagram of the microring and MZI cascading; (**a**) overall structure; (**b**) MRR.
# Application of a Real-Time Visualization Method of AUVs in Underwater Visual Localization
## 4 Discussion  
The experimental results showed that the algorithm was highly robust in underwater low-texture environments due to the inclusion of line segments. At the same time, the algorithm achieved a high accuracy of location effectively, which means that it can be implemented in the navigation and path planning of AUVs in the future. The current positioning accuracy was 2.98%. The experimental environment simulated a shallow water area well, and also verified that the visual positioning method can be applied in shallow water areas. However, there were some problems with the experiment. The added line features undoubtedly increased the complexity of the algorithm, which is more time-consuming than ORB-SLAM. Secondly, it was difficult to calibrate the underwater cameras, especially using the stereo underwater calibration model, however this fact is beyond the scope of our discussion. Due to the fact that high distortion cannot be neglected in underwater environments, the matching of line features occasionally failed. However, the ORB-SLAM algorithm also often fails due to the small number of point features in an underwater environment. The PL-SLAM algorithm combined with the point line feature method had a high success rate. Finally, the accuracy of the pose measured by the AR-marker is not very high and therefore can only be used as a reference.
# Review of Automatic Estimation of Emotions in Speech
## 3 Acoustic Analysis of Speech
### More Advanced Spectral Envelope Measures  
A frequent way to analyze speech spectrally is the MFCCs; they resemble FBEs (with the Mel scale), but convert data to an efficient, reduced set of parameters; 13 are enough to handle telephone speech to discriminate the center frequencies of formants to within 100 Hz, which is a range accurate enough to discriminate close vowels (e.g., /i-e/). The name \"cepstral\" corresponds to the mapping (with logarithm) of multiplication of spectra (i.e., VT filtering of the VT excitation) with linear addition terms for the excitation and the envelope transfer function; this thus permits separation of the filter and excitation of speech, and is useful because these two are handled distinctly in both speech perception and in common speech coding such as LPC.  
LPC is a common spectral analysis method, and uses an all-pole autoregressive (AR) model of the speech spectrum [46], which thus directly focusses on VT resonances (AR corresponds to the digital feedback speech synthesis model, used for reconstruction of coded speech, as it represents resonances well). LPC analysis is directly related to speech, as it assumes sounds from a VT, unlike MFCCs or FBEs. Basic LPC, as found in speech telephony for decades, uses ten parameters for the narrow bandwidth (300-3200 Hz) of commercial networks. During the 1970s, ASR commonly used LPC, and then used MFCCs until recently. Modern SER systems do not use LPC, as MFCCs or FBEs (or other models) perform better; LPC is fine for coding, where its 10 parameters model spectral envelope well, but have little detail relevant for emotion estimation.