esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Dual-Polarization Synthetic Aperture Radar
###### Abstract  
Compact polarimetry for a synthetic aperture radar (SAR) system is reviewed. Compact polarimetry (CP) is intended to provide useful polarimetric image classifications while avoiding the disadvantages of space-based quadrature-polarimetric (quad-pol) SARs. Two CP approaches are briefly described, \(\pi\)/4 and circular. A third form, hybrid compact polarimetry (HCP) has emerged as the preferred embodiment of compact polarimetry. HCP transmits circular polarization and receives on two orthogonal linear polarizations. When seen through its associated data processing and image classification algorithms, HPC's heritage dates back to the Stokes parameters (1852), which are summarized and explained in plain language. Hybrid dual-polarimetric imaging radars were in the payloads of two lunar-orbiting satellites, India's Earth-observing RISAT-1, and Japan's ALOS-2. In lunar or planetary orbit, a satellite equipped with an HCP imaging radar delivers the same class of polarimetric information as Earth-based radar astronomy. In stark contrast to quad-pol, compact polarimetry is compatible with wide swath modes of a SAR, including ScanSAR. All operational modes of the SARs aboard Canada's three-satellite Radarsat Constellation Mission (RCM) are hybrid dual-polarimetric. Image classification methodologies for HCP data are reviewed, two of which introduce errors for reasons explained. Their use is discouraged. An alternative and recommended group of methodologies yields reliable results, illustrated by polarimetrically classified images. A survey over numerous quantitative studies demonstrates HCP polarimetric classification effectiveness. The results verify that the performance accuracy of the HCP architecture is comparable to the accuracy delivered by a quadrature-polarized SAR. Four appendices are included covering related topics, including comments on inflight calibration of an HCP radar.  
radar astronomy; synthetic aperture radar (SAR); compact polarimetry; Stokes parameters; Lunar Reconnaissance Orbiter; Earth observation; SAR applications; calibration +
Footnote †: journal: Remote Sensing, 11, 1521; doi:10.3390/rs11131521
# Three-Stage Deep Learning Framework for Video Surveillance
## 3 Proposed Method
### Proposed Overall Architecture
#### 3.2.3 Third Stage  
For the utilization of data processed through the BiLSTM and multi-head attention layers as input for the Transformer encoder layer, it is essential to perform a position embedding operation on the data. Typically, data with a temporal dimension, such as videos, are subjected to absolute position embeddings to encode unique positional information. However, in this study, relative position embeddings are employed. Relative position embeddings are particularly beneficial for video data processing, as they better grasp the dynamic context by considering the relative temporal distances between frames. This approach is suitable for videos of varying lengths, given its basis in relative relationships. Additionally, it has the advantage of effectively capturing the interactions between adjacent and distant frames, enhancing the understanding of the inter-frame dynamics.  
The data passed through the relative position embedding layer were used as input to the Transformer encoder architecture, specifically tailored to anomaly detection, as proposed in this paper, and the Transformer encoder architecture is shown in Figure 5.  
The output from the Transformer encoder layer structure undergoes further refined processing. The global maxpooling layer plays a crucial role in reducing the complexity of the data by extracting the most significant information from each feature map while preserving key features. This is followed by a dropout layer to prevent overfitting and enhance the model's generalization ability. Finally, a sigmoid operation is employed to determine whether the input video data are normal or exhibit an anomaly.  
The introduction of a fully connected layer is pivotal in effectively classifying any anomalies within the input video data based on the output of the proposed architecture. This layer simplifies complex features and focuses on critical information for classification tasks, achieving high accuracy and efficiency. This structural approach is key to the precise analysis of video data and the reliable detection of anomalous states.  
This framework was trained independently for each video, taking into account the correlations between the features of frames within each video. Such an approach enables the more precise and effective analysis of complex and diverse video data, offering potential applications in various real-world scenarios.
## 1 Introduction
To further elucidate the electrotherapeutic potential for synovial cell-mediated repair, study 2 investigated the effect of EF stimulation via galvanotaxis to direct bovine FLS migration within a collagen hydrogel matrix. The benefits of electrotherapeutic strategies on cartilage and synovium explants within an in vitro defect-repair model have also not been investigated. Therefore, studies were translated into the three-dimensional tissue environment to track FLS migration from healthy bovine synovium to injured cartilage explants. By developing and validating a novel tissue-scale bioreactor capable of applying DC EFs in sterile culture conditions to 3D constructs, study 3 investigated the recruitment of synovial repair cells via galvanotaxis from intact synovium explants to the site of a cartilage wound injury. Similarly, study 4 characterized the effect of PEMF stimulation on modulating the migration of endogenous and/or exogenous FLS from the bovine synovial membrane into the adjacent cartilage defect. Limited studies characterizing the functional outcomes and intrinsic biochemical properties of both synovium and cartilage explants under electrotherapeutic stimulation have been performed [37]. Therefore, study 5 assessed the biochemical composition of bovine synovium explants and injured cartilage constructs via DNA, GAG, and collagen content following PEMF treatment. Histological characterization of both explants was evaluated in order to confirm differences observed from the functional biochemical assays. Gene expression of cartilage and synovium ECM constituents, specifically collagens, were assessed in order to characterize the recruitment of chondrocytes and FLS into the bovine cartilage defect region following PEMF stimulation. PEMF and galvanotaxis DC EF modulation are potential complementary strategies that may enhance FLS migration, providing alternative nonoperative methods to accelerate cartilage repair following injury [43; 44]. The purpose of the current study is to investigate the potential of both techniques in initiating an intrinsic healing response by stimulating FLS movement toward injured articular cartilage lesions, which may lead to peripheral cellular integration along the defect site and ultimately repair the joint surface with similar biochemical properties and mechanical characteristics as healthy hyal cartilage [45].
# Enhancing Voice Cloning Quality through Data Selection and Alignment-Based Metrics +
## 6 Evaluation Results and Discussion
### Evaluation of HQ Speakers Trained on 3 h  
This subsection presents the results of the evaluation performed on the models trained with a random 3 h partition and their corresponding ablations as described in Section 5.1.  
The metrics regarding the two MOS estimators NISQA and MOSnet on the eight different training setups corresponding to the these subsets are displayed in Figure 6.  
As can be observed from the results displayed in Figure 6, there was no significant change in the estimated MOS values when comparing the models that use more data with those for which some audio files were removed. In addition, the speaker _Hi-Fi TTS 92_ tended to have a higher estimated MOS value than speaker _[PERSON]_. Both speakers had an estimated MOS value between 3 and 3.5 for NISQA and between 2.3 and 3.2 for MOSnet, concluding that NISQA is more generous in term of MOS estimation than MOSnet for these two speakers, just the opposite that happened with speaker _[PERSON]_. In any case, it can be stated as the main conclusion of this graph that the impact of having more variable audios in the training dataset did not impact the quality of the models significantly, and therefore, adding a set of more-variable audio files to the training set did not necessarily guarantee a better final result.  
In terms of sentence and character alignments, the results of the models trained on the random 3 h partitions are portrayed in Figure 7.  
Similar to the speaker _[PERSON]_, the percentage of aligned sentences using MFA approached 100% in the early stages of training, which confirmed that this metric is not the most-suitable for this particular task. Regarding character alignments, however, an improving evolution can be observed as the training progressed. Even though removing  
Figure 6: Estimated MOS values obtained from NISQA (**above**) and MOSnet (**below**) for the audio generated for each iteration for the HQ speakers _[PERSON]_ (**left**) and _Hi-Fi TTS 92_ (**right**) on the random 3 h partitions.  
audios with a lower SNR had a noticeable impact on the number of characters aligned for _Tux_, having less audios with more variability in terms of utterance speed did not influence the number of aligned characters significantly.  
It is quite noticeable, nonetheless, that removing audio files with a lower SNR and with more variable utterance speed had a better impact on the training than removing audio files with a lower SNR only for both speakers. This could be due to the fact that these two datasets had relatively low values of noise, and therefore, the audio files whose SNR value was lower than 25 dB did not necessarily increase the variability of the data, in contrast with the utterance speed, with a similar impact as having a lower quantity of training data.
# Sand and Dust Storms: Impact Mitigation
## 4 SDS Hazards and Their Mitigation
### SDS Source Mitigation
#### 4.1.1 Controlling Wind Erosion on Cropland
However, windbreaks occupy space that might otherwise be used to grow crops and require maintenance to preserve their effectiveness, so they are sometimes removed to make way for irrigation developments or modern agricultural machinery. In practice, many of these techniques to control erosion on cropland are used in combination. In a survey of villages across the southern Sahelian zone of Niger, [PERSON] et al. [79] recorded the use of at least 10 different low-cost wind erosion control technologies and noted that all of the techniques used also had advantages in addition to erosion control.
## 4 Experiments
### Ablation Study  
We employ the CNN branch and the Transformer branch, both branches running in parallel. Then, we fuse feature maps from both branches by the TcFusion module. To assess the usefulness of the branch-parallel approach to designed, we conduct experiments where each branch is removed separately from GPNet. Additionally, we replace the TcFusion module with a simple summation of feature maps to highlight the impact of the TcFusion module. We evaluate the manipulation localization performance on NIST and CASIA datasets.  
The quantitative results are presented in Table 5. It is evident that when using only the CNN branch, both the F1 score and AUC decreased by 13.2% and 8.7%, respectively, on the NIST dataset and the CASIA dataset. Conversely, when employing only the Transformer branch, the AUC scores decreased by 5.3% on CASIA and 4.3% on NIST. Furthermore, without the TcFusion block, the F1-score and AUC experienced a decline of 6.8% and 7.1%, respectively. These deteriorations in the quantitative results demonstrate that the adoption of the parallel-in-branch design and the TcFusion module effectively contributes to the performance improvement of our method.
# Recognition and Classification of Handwritten Urdu Numerals Using Deep Learning Techniques
## 6 Conclusions and Future Work  
In this paper, we proposed two approaches to classify novel datasets of Urdu numerals. The first approach extracts features using convolution layers and uses Softmax activation followed by fully connected layers for classfication. The second approach applies an SVM classifier to the features extracted from the convolution layer. All the models give best results in terms of accuracy where the first approach provides a validation accuracy of 98.41%, while an accuracy of 99.0% is achieved by the second approach. The accuracy of 96.4% on ResNet and 95.61% on GoogLeNet is achieved on this novel dataset. We tested these models on Pakistani currency to see their reliability in real-world application after being trained on our dataset. To implement this, we developed another dataset from Pakistani currency notes and evaluated our proposed models with it. Our handwritten Urdu numerals dataset is unique and any such dataset is not available publicly. This hinders research in the domain of the Urdu language. Moreover, our dataset is refined and is collected along the lines of the MNIST dataset, so it provides the best results with real-life problems as shown in our paper.  
In the future, we plan on increasing the Urdu numeral dataset and then making it publicly available so as to motivate researchers to work in this field. Increasing this dataset will also increase the accuracies of all models. Additionally, our dataset and CNN can help develop a system to identify and count currency notes. Since the performance of deep learning algorithms in real-world applications is of utmost importance, we plan on testing it on other applications such as recognizing the Surah numbers of The Holy Quran and numbers on Pakistani postage stamps. The sole motivation of this paper is to bring our mother language Urdu to a competitive level with all the latest research.  
Conceptualization, W.K. and S.K.; methodology, A.A. ([PERSON]) and A.B.; software, A.B.; validation, W.K., A.u.R., B.K. and A.A. ([PERSON]); data curation, A.B., A.A. ([PERSON]) and W.K.; writing--original draft preparation, A.B.; writing--review and editing, A.A ([PERSON])., W.K., A.u.R. and B.K.; visualization, A.A. ([PERSON]) and S.K.; supervision, A.A. ([PERSON]) and S.K.; funding acquisition, A.u.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 16: Bar plot for comparison of accuracies on real-life example.
# The Role of Cryptogams in Soil Property Regulation and Vascular Plant Regeneration: A Review
## 3 Effects of Cryptogam on Vascular Plants Regeneration
### Alleplopathy  
The allelopathic effect of cryptogams on vascular plants has been documented extensively. The effect of water extracts on vascular plant regeneration is often manifested as a high concentration of extracts inhibiting vascular plant regeneration, while a low concentration has the opposite effect. Moreover, lichens usually have a negative allelopathic effect on vascular plant regeneration. For example, [PERSON]'s study found that high concentrations (5-10%) of moss water extracts inhibited seed germination and seedling root growth, but low concentrations of moss water extracts promoted seed germination [33]. The low concentration of moss water extract (0.5-5 mg\(\cdot\)mL\({}^{-1}\)) improved the germination rate of _Pica crassifolia_ seeds by inhibiting fungal infection of seeds, whereas the high concentration (50 mg\(\cdot\)mL\({}^{-1}\)) significantly inhibited seed germination [105]. In [PERSON]'s study, lichen water extracts significantly inhibited seed germination in three species of vascular plants (_Pinus rigida_, _Schizachrium scoparius_, and _Vaccinium pallidium_). The water extract of moss had no effect on seed germination [36]. There are many allelopathic substances in the extracts of cryptogam. For example, [PERSON] et al. extracted 7 flavonoids from 5 species of moss and found that these 7 flavonoids inhibited the root growth of _Raphanus sativus_ and led to changes in root morphology [106]. Using 3-Hydroxy-beta-ionone, an allelopathic substance found in _Rhynchosegium pallidifolium_, we discovered that 1-3 umol\(\cdot\)L\({}^{-1}\) of this substance resulted in a 46-64% reduction in the hypocotyl and root growth of _Lepidium sativum_[107]. The usnic acid present in _Cladonia_ spp. lichen extracts also had significant allelopathic effects on vascular plants, having also been found to significantly inhibit the regeneration of _Abies balsamea_[38]. It also significantly inhibited the above-ground and below-ground growth of half-year _Pinus banksiana_ seedlings and still inhibited the root development of 2-year seedlings [108]. Phenolic substances are also widely found in cryptogams [109], in which phenolic acids exert significant allelopathic effects on vascular plants. When the concentration of phenolic acids (including p-hydroxybenzoic acid, protocatechic acid, syringic acid, vanillic acid, gallic acid, salicylic acid, ferulic acid, p-coumaric acid, and caffeic acid, etc.) was higher than 20 mmol\(\cdot\)L\({}^{-1}\), seed germination was significantly inhibited. However, 5-20 mmol\(\cdot\)L\({}^{-1}\) phenolic acid had no significant effect on seed germination [110]. In soil, phenolic acids above 50 umg\(\cdot\)g\({}^{-1}\) begin to inhibit vascular plant regeneration [111].
# Using AI-Based Tools to Quantify the Technical Condition of Bridge Structural Components
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & **Advantages** & **Disadvantages** \\ \hline \multirow{2}{*}{Data Analysis} & Increased accuracy and speed of processing & Dependence on data quality, difficulty in \\  & large amounts of data & interpreting results \\ \hline \multirow{2}{*}{Detection} & Early detection of damage through the & \multirow{2}{*}{Need for a large amount of data for training} \\  & detection of anomalies and patterns & \\ \hline \multirow{2}{*}{Automation} & Automation of routine tasks, freeing up & High implementation cost (hardware, \\  & engineers’ time & software, training) \\ \hline \multirow{2}{*}{Prediction} & Remaining resource forecasting and & Need for qualified specialists for \\  & optimizing scheduling/budgeting & development, implementation, and support \\ \hline \multirow{2}{*}{Inspection} & Monitoring in hard-to-reach places using & Cybersecurity issues and \\  & drones and robots & vulnerability to cyberattacks \\ \hline Human Factors & Reducing the impact of human error & Need for highly qualified personnel \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of using AI tools in the system of research and assessment of the technical condition of bridges [36; 37; 38]. tion and assessment of the technical condition of the structure to take timely measures to ensure the safe operation of the structure under changing operating conditions. The study aims to develop an AI-based model for assessing the technical condition of bridge structural components based on data obtained from the survey. To achieve this goal, the following tasks were solved: existing approaches to the inspection and assessment of bridges were analyzed, and experiences in the use of AI in bridge assessment were examined (Section 2); the process of creating a dataset and the modeling process using AI (Section 2) were presented as well as the results of using AI to assess the technical condition of bridge components (Section 3); Section 4 presents a discussion of the results obtained; Section 5 presents the conclusions of the study.
## 2 Materials and Methods
### Instruments
#### 2.3.2 Kinematic Data  
The joint position of the shoulder, forearm, wrist, head, and trunk segments were assessed using an optoelectronic system, the Qualisys Motion Capture System (Qualisys AB, Goteborg, Sweden) [23], for concurrent validation. The spatial position of the reflector markers, placed on the participant, were collected using twelve infrared cameras, eight Oqus 500 and four Miqs M3, connected to the Qualisys USB Analog Acquisition interface, at a sampling frequency of 100 Hz. Qualisys Track Manager software version 2021.2 (Qualisys AB, Goteborg, Sweden) [23] was used to display, acquire, and analyze kinematic data.  
The smartphone-based video analysis was conducted by a smartphone application, designed for Android devices, that leverages the power of machine learning to facilitate shoulder rehabilitation exercises. The app uses the device's 2D camera to capture video footage of patients performing their exercises. This footage is then processed in real-time using the MediaPipe library [24], a state-of-the-art machine learning solution for computer vision tasks, which recommends a minimum resolution of 640 \(\times\) 480 pixels and 30 frames per second. Employing pose detection, a computer vision technique that identifies and tracks human body parts, the app analyzes users' movements during shoulder rehabilitation exercises. Despite the 2D nature of the camera, the app extracts valuable information such as shoulder position, arm angles, movement patterns, and gesture recognition from the detected poses. By analyzing the spatial relationships and temporal dynamics of key body landmarks, the application intelligently interprets the user's movements, offering a nuanced understanding of shoulder rehabilitation exercises beyond the limitations of a traditional 2D perspective. The key feature of the app is its ability to provide immediate feedback to the user. By analyzing the video footage, the app calculates a metric that reflects how similar the performance of the exercise is when non-guided compared to the exercise guided. This score is based on various factors such as the accuracy of the movements and the completion of the exercise.  
MediaPipe is a powerful tool that uses machine learning to process video footage in real-time. One of the key features of MediaPipe is its ability to detect and track landmarks on the human body. These landmarks are specific points of interest that are identified in each frame of the video footage. When a patient performs an exercise, the app uses MediaPipe to identify these landmarks on the patient's body. Once the landmarks are identified, it tracks their movement across multiple (video) frames. This allows the app to analyze the patient's movements and calculate a score representing movement quality. For this study, landmarks from the shoulder, forearm, wrist, head, and trunk segment were considered.
# CycleDiffusion: Voice Conversion Using Cycle-Consistent Diffusion Models
## 3 Proposed Method
### Comparison to Similar Works  
The concept of cycle consistency for DMs was presented in a document enhancement task [28]. Nevertheless, it did not employ the cycle consistency loss for DM training. Rather, two independently trained DMs--one in the noisy document domain and the other in the clean document domain--were linked at the latent space during inference, enabling cyclic conversion. The cycle consistency loss for DM training was initially explored in [29] and applied to image-to-image translation tasks. CycleDiffusion differs from [29] in that it uses score-based diffusion models, which are a continuous-time version of DMs, and is applied to voice conversion tasks; whereas [29] used denoising diffusion probabilistic models, which are a discrete-time version of DMs. To the best of the authors' knowledge, CycleDiffusion is the first work employing cycle consistency loss for score-based DM training in voice conversion tasks.