esa-sceva/satcom-chunk-collection huggingface dataset:
# Feasible Applicability of Deep Learning for Solid Detection in Concrete Wastewater: An Evaluation
## 4 Conclusions  
Based on the investigations outlined in this research, it can be seen that deep learning techniques can be widely exploited for tiny object detection in solutions, for real-time data detection and acquisition, and for production efficiency improvement. Therefore, the conclusions are:  
* The investigation of the use of deep learning practices in general solution concentration detection, metal ion concentration detection, metal element concentration detection, suspended particulate matter feature recognition, and horizontal-scale line detection shows that the technique can achieve high precision and display real-time data, providing a methodological basis for the use of deep learning to detect the concrete solid content of wastewater.
* The deep learning technique can be used to accurately detect the suspending particles in mineral water and the water level of rivers. So, it is feasible to apply the deep learning technique to test the solid content of wastewater, as the sedimentation dividing line in a solid and water is similar to that in the detection of the water level. Also, the OpenCV-based edge detection technique can be used to accurately and efficiently collect the image pattern and process the features. Such algorithms may consolidate the future experimental research on the solid detection of concrete wastewater.
* To overcome the challenges of deep learning applications, such as data availability and generalization, transfer learning, and data augmentation, hybrid approaches and multi-sensor integration techniques have the potential to be utilized to provide a more accurate model performance and adaptations.
* Test tubes can be used for sampling from wastewater sources. They can also be used as the targets for image data achievement when using deep learning, as the particle distribution and solid-liquid surface separation, which are representations of the target wastewater source, can be extracted as features with this method.  
[PERSON] participated in the literature research work and testing method discussion, and wrote the initial draft of the paper. [PERSON] proposed the methodology, analyzed the testing methods, and revised the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the 9 th Group of Key Discipline Projects in Henan Province Mechanical Design, Manufacturing and Mechatronics.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We would like to thank our industrial partners [PERSON], Zhengzhou Xinyuan Mechatronic Engineering Pty. Ltd., Zhengzhou, China, and [PERSON], Henan Miracle Education and Consultation Pty. Ltd., Kaifeng, China, for providing valuable technical and practical information on industrial applications.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
## 3 Deep Learning-Based Studies for CRC Diagnosis
### Tumor Tissue Detection and Classification
A study in [44] segmented and filtered the background area of tumors by using [PERSON]'s thresholding [45] and labeled the tumor area with a self-developed annotation tool before passing it to the CNN model for feature extraction and classification. They built a new model with the combination of DeepLab v2 [46], and ResNet-34 [43] and compared the model's performance with analyses of experienced pathologists. They found that their DL model for the diagnosis of adenoma in CRC was quite similar to the results from the pathologists, where a slide-level accuracy of over 90% and an AUC of 0.92 were obtained. [PERSON] et al. [47] used an approach similar to the one in [44], where data preprocessing was carried out by discarding the unnecessary black regions in the endoscopic image samples via filtering. A transfer learning approach was used where the pretrained weights of various DL models, such as Inception-v3 [48], ResNet-50 [43], and DenseNet-161 [49], were used with 10-fold cross-validation. They evaluated their performance in terms of accuracy, recall, and precision, where they obtained respective values of 92.48%, 99.7%, and 99.2%. Similar studies [50, 51, 52] based on the tumor tissue detection type are listed in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Publication (Year)** & **Objective** & **Method Used** & **Image Types** & **Optimum Results** \\ \hline \multirow{8}{*}{[50] (2020)} & \multirow{8}{*}{To develop a biomarker of the patient outcome by analyzing the scanned H& **Data preprocessing**: Multiple non-overlapping image tiles were selected with 10 \(\times\) and 40 \(\times\) magnification of WSI images. **Model**: Feature extraction and classification used a DoMore v1 network comprising a MobileNetV2 [53] representation network, a noisy-AND pooling function, and an FCL. & \multirow{8}{*}{Hematoxylin and
\begin{tabular}{} \end{tabular} } \\  & & \\ \cline{1-1} \cline{3-4}  & & \\ \cline{1-1} \cline{3-4}  & & \\ \cline{1-1} \cline{3-4}  & & \\ \cline{1-1} \cline{3-4}  & & \\ \cline{1-1} \cline{3-4}  & & \\ \cline{1-1} \cline{3-4}  & & \\ \cline{1-1} \cline{3-4}  & & \\ \cline{1-1} \cline{3-4}  & & \\ \cline{1-1} \cline{3-4}  & & \\ \cline{1-1} \cline{3-4}  & & \\ \cline{1-1} \cline{3-4}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recently published articles on CRC tumor tissue detection and classification using DL techniques.
# Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre
## 3 Modeling Scenarios and Data Processing
### Forecasted Gridded Weather Data  
The second and perhaps more critical file is 'fort.22', or Meteorological Forcing Data [28]. The machine learning model and the ADCIRC numerical model utilize the same input source: a gridded forecasted weather dataset named the North American Mesoscale Forecast System (NAM) [29]. The NAM dataset provides continuous forecasted gridded atmospheric conditions over the continental United States. The model is distributed by the National Centers for Environmental Information (NCEI) and consists of a 12 km-resolution grid with a forecasting range of up to 84 h. This forecasted gridded dataset is utilized in two forms. First, a set of input files for the ADCIRC numerical model is created; at the same time, the dataset is converted into image files to serve as input for the machine learning storm surge model.  
To create both ADCIRC's and the machine learning storm surge model input, the NAM dataset is trimmed to the domain of interest; in this case, the Gulf of Mexico. After the NAM data are trimmed spatially, only the variables that will be used are kept, which includes the U and V components of winds at 10 m elevation and atmospheric pressure. The data is then projected into a regularly spaced grid over the Gulf of Mexico. The resulting grid can be directly used for input in ADCIRC and is also leveraged for the creation of images for their use in the machine learning model. To create the input images, each variable in the data is normalized and its values mapped into the range of an unsigned byte, from 0 to 255, for the creation of PNG stills with three color channels. The resulting composite can be seen below in Figure 1.  
To test the machine learning storm surge forecasting model, a total of five scenarios were prepared from the available data. These five scenarios were selected as representative of the presence and lack of storm surge conditions. Three of the five scenarios are representative of hurricanes that impacted the Laguna Madre directly or caused fluctuations in the ocean levels in the Laguna Madre; the remaining two scenarios provide everyday or normal conditions, meaning no major weather event occurred near the Laguna Madre.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 6 Clinical Applications of Commercially Available TD-NIRS Systems by Japanese Researchers
### Group from Kagoshima University Hospital  
A group from the Kagoshima University Hospital collaborating with Hamamatsu Photonics used TD-NIRS (TRS-10) to monitor cerebral hemodynamics (\(C_{oxy-Hb}\), \(C_{deoxy-Hb}\), \(C_{total-Hb}\) and \(S_{02}\)) of patients during cardiopulmonary bypass surgery [227]. They found that \(C_{total-Hb}\) correlated well with hematocrit measured by a blood gas analyzer. Then, [PERSON] et al. evaluated the occurrence of postoperative cognitive dysfunction (POCD) after cardiopulmonary bypass surgery with hypothermic treatment by \(S_{02}\) measured by TRS-10 and internal jugular vein oxygen saturation (\(S_{\mathrm{jV02}}\)) measured by a conventional method [228]. In the patients with POCD, \(S_{\mathrm{jV02}}\) were found to be significantly larger than \(S_{02}\). The same group also tried to monitor hepatic oxygenation by TRS-10 and found that abdominal \(C_{total-Hb}\) were significantly higher in the liver area than other area in healthy people and that hepatic oxygenation measured by TRS-10 may work for early detection of intestinal ischemia [229]. Recently, monitoring of post-resuscitation encephalopathy by TRS-10 was experimentally tried for pigs with cardiac arrest induced by electrical stimuli for preliminary study toward clinical applications [230].
## 4 Results and Discussion
### Data Analysis
#### 4.2.6. Insolation  
As shown in Figure 17, the average values of insolation data were recorded as 89.1 W/m\({}^{2}\), 97.0 W/m\({}^{2}\), 104.3 W/m\({}^{2}\), and 119.2 W/m\({}^{2}\) for respective months. The average insolation displayed a consistent increasing trend from 89.1 W/m\({}^{2}\) in June to 119.2 W/m\({}^{2}\) in September. Throughout the specified months, the minimum insolation levels remained constant at 0 W/m\({}^{2}\). On the other hand, the maximum insolation levels were observed to be 567.5 W/m\({}^{2}\), 674.5 W/m\({}^{2}\), 923.1 W/m\({}^{2}\), and 830.8 W/m\({}^{2}\), respectively, with August recording the highest maximum insolation.
# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 2 Model Architecture
### Mixture Alignment Encoder
#### 2.2.2 Predictor Architecture  
The duration predictor is composed of a two-layer 1D convolutional architecture, featuring a ReLU activation layer, a normalization layer, and a dropout layer following each 1D convolutional layer. Subsequently, an additional linear layer transforms the hidden features into a scalar corresponding to the number of characters, indicating the number of frames in which each character is pronounced. Inputs to the duration predictor include the hidden features from the character encoder, while the outputs are the actual pronunciation durations of each character. The structure of the duration predictor is shown in Figure 3c.  
In addition to the duration predictor, this paper introduces pitch and energy predictors structured similarly to the aforementioned duration predictor. The primary goal of the pitch predictor is to accurately forecast contour changes in fundamental frequency (F0) within speech signals, which effectively convey different emotional states. To extract F0, data processing from the raw audio waveform is employed using a hop size identical to that of the target Mel spectrogram to ensure alignment between F0 time frames and Mel  
Figure 3: Overview of mixture alignment encoder and its components. (**a**) Mixture alignment encoder. (**b**) Encoder architecture. (**c**) Predictor architecture.  
spectrogram frames. Initially, F0 is computed at each time frame and then quantized into 256 possible values representing different pitch levels. Subsequently, the quantized F0 is transformed into a sequence of one-hot vectors for further processing.  
The energy predictor determines the volume of sound signals by computing the L2 norm of the short-time Fourier transform (STFT) for each frame. This method converts each frame of sound signal into a numerical value reflecting its intensity or loudness, which is quantized into 256 continuous scale values.  
During the training phase, pitch and energy information calculated directly from the original speech are utilized. The predictors' outputs are optimized by comparing their mean squared errors with real F0 and energy values. In the inference phase, trained pitch and energy predictors are used to predict F0 and energy for each time frame.  
The introduction of these predictors enables our end-to-end system to capture pitch and energy variations within speech signals more accurately, thereby enhancing the naturalness and expressiveness of speech synthesis. Future research will continue to refine these predictors, exploring more effective feature extraction and model training strategies to further improve the quality and performance of speech synthesis.
## 3 Results
### VPD Thresholds Affecting Crop Growth  
VPD is a critical driver of atmospheric water demand for crops, and elevated VPD levels are known to adversely affect crop growth and yields [30; 74]. In the agroecosystem of the Yellow River Basin, a significant correlation was found between the VMFDI and both SIF and SM (Figure 9a). Furthermore, causal analysis revealed a stronger influence of the VMFDI on SIF and SM than the reverse (Figure 11b), indicating that reductions in  
Figure 11: Causalities between the monthly VMFDI and other corresponding variables. \(T_{i\to j}\) is the rate of the information flow from i to j. * represents a 95% significance level.  
the VMFDI are associated with declines in both SIF and SM. Additionally, the NDVI, GPP, and LAI were significantly affected by the VMFDI (Figures 10 and 11b). The VMFDI, in turn, was primarily driven by variations in VPD, with a significant causal relationship observed between VPD and the VMFDI ( \(|\mathrm{T}_{\mathrm{VPD}\rightarrow\mathrm{VMFDI}}|>|\mathrm{T}_{\mathrm{VMFDI }\rightarrow\mathrm{VPD}}|\)) (Figures 9a and 11b). In this study, VPD-induced drought significantly affected the NDVI, GPP, and LAI of the agroecosystem. Importantly, a critical threshold was identified in VPD, beyond which the NDVI, GPP, and LAI shifted from positive to negative responses (Figure 12a). The VPD threshold varied across crops, ranging from 7.11 to 7.17 ha in the Yellow River Basin agroecosystem. While, in maize and wheat planting systems, the thresholds were 8.03 to 8.57 ha and 7.15 ha, respectively (Figure 12a). These findings suggest that a modest increase in VPD may enhance photosynthesis in vegetation by increasing transpiration and CO\({}_{2}\) uptake, but excessive VPD could weaken stomatal conductance and photosynthesis, even leading to leaf rolling and further limiting plant growth [75]. Notably, maize, as a C\({}_{4}\) plant, exhibits higher tolerance to heat and drought compared to wheat, which is reflected in the higher VPD thresholds for maize.
# Machine Learning-Based Prediction and Optimisation System for Laser Shock Peening
## 2 Laser Peening Residual Stress Data
### Prediction Using Artificial Neural Networks  
ANNs are abstract computational models connected layer-by-layer to form a network that replicates the behaviour of biological neurons in the human brain [34]. The multilayer perceptron (MLP) model [35] is a commonly used architecture consisting of interconnected neurons between the input, hidden and output layer that can be used to learn complex relationships existing in the input and output data. Figure 4 shows a schematic representation of the network architecture showing the input parameters and the output. All input parameters were normalised to a value ranging between \(-1\) and \(+1\) using a simple mathematical transformation. Residual stresses in the longitudinal and transverse directions (output parameters) were normalised between \(-1\) and \(+1\) by dividing them by the yield strength of the material. The ANNs work by adapting the network parameters (synaptic weights and bias) to minimise an error function using the backpropagation (BP) algorithm [36]. The network continues to fit the data to converge at a minimal state of the error function. The sum of the squares error \(E(x,w)\) is defined as the sum of squares of the actual (\(a_{k}\)) and predicted (\(p_{k}\)) output vectors:  
\[E(x,w)=\frac{1}{2}\sum_{k=1}^{N}\{a_{k}-p_{k}\}^{2} \tag{3}\]  
where \(x\) is the input matrix, \(w\) the weight matrix, and \(N\) the total number of measured residual stress data points at a given depth.  
Additionally, different statistical indicators were used to compare the performance of different models, and optimisation of neurons in the hidden layer were primarily based on the measure of root-mean-square error (_RMSE_). Performance indicators such as _RMSE_, absolute fraction of variation (\(R^{2}\)) and mean absolute percentage error (_MAPE_), as expressed in (4)-(6), were used for the evaluation of network performance. The performance indicators for different numbers of hidden neurons for developing prediction models of residual stresses using two datasets are summarized in Table 3. The best performance was achieved with 12 hidden neurons for the first dataset and 13 hidden neurons for the second dataset.  
\[RMSE=\sqrt{\frac{1}{N}\underset{i}{\sum}(a_{i}-p_{i})^{2}} \tag{4}\]  
Figure 3: Schematic of the incremental centre hole-drilling technique (ICHD).
###### Abstract  
Satellite communications are a well-established research area in which the main innovation of last decade has been the use of multi-carrier modulations and more robust channel coding techniques. However, in recent years, novel advanced signal processing has started being developed for these communications due to the increase in the signal processing capacity of transmitters and receivers. Although signal processing capabilities are increasing, they are still constrained by large limitations because these techniques need to be implemented in real hardware, thus making complexity a matter of critical importance. Therefore, this paper presents the design and implementation of a transmitter with adaptable coding and modulation on a field-programmable-gate-array (FPGA). The main motivation came from the standard CCSDS 131.2-B-1 which recommends that such a novel transmitter which has to date not been implemented in a real system The system was modeled by MATLAB with the purpose of being programmed in VHDL following the AXI-stream protocol between components. Behavioral simulation results were obtained in VIVADO and compared with MATLAB for verification purposes. The transmitter logical circuit was synthesized in a FPGA Zynq Ultrascale RSoC ZU28 DR, showing low resource consumption and correct functioning, leading us to conclude that the deployment of new communication systems in state-of-the-art hardware in satellite communications is justified.  
FPGA; RTL; VHDL; DSP; SCCC turbo code; constellation diagram; puncturing; interleaver; pseudo-randomizer +
Footnote †: journal: 2021  
10.2476. [[https://doi.org/10.3390/electronics10202476](https://doi.org/10.3390/electronics10202476)]([https://doi.org/10.3390/electronics10202476](https://doi.org/10.3390/electronics10202476))
# Multi-Level Split Federated Learning for Large-Scale AIoT System Based on Smart Cities
## 4 Experiment
### Performance of Multi-Level SFL, FL, and Centralized Learning  
Centralized learning and multi-level federated learning serve as benchmarks for testing our proposed multi-level split federated learning. Multi-level federated learning and multi-level split learning are, respectively, tested in the AloT system of four levels. The end layer is set with 50 nodes, which are divided into two groups according to geographical location, and the edge layer and fog layer are set with 2 nodes, which are, respectively, responsible for aggregating the model weights of the two groups of end nodes. The cloud has one node responsible for performing the FedAvg algorithm to aggregate the model weights of the edge nodes. Table 1 summarizes the accuracy of distributed collaborative learning over 50 global epochs with a batch size of 32 (Fashion MNIST) or 1024 (HAM10000) for each local epoch.  
As shown in Table 4, multi-level split federated learning and multi-level federated learning perform well in experimental settings, and there is no significant difference between centralized learning and multi-level federated learning. Although centralized learning on both machine large learning tasks (ResNet18) and small machine learning tasks LeNet) has slightly better convergence results than multi-level split federated learning and federated learning, only a few accuracy losses are negligible. Moreover, we compare multi-level SFL and FL, both of which do not overfit on the HAM10000 and Fashion  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Training Samples** & **Testing Samples** & **Image Size** \\ \hline Fashion MNIST [37] & 60,000 & 10,000 & 28 \(\times\) 28 \\ HAM10000 [38] & 9013 & 1002 & 600 \(\times\) 450 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training and testing of dataset.  
MNIST datasets, because multi-level SFL and FL execute the FedAvg algorithm on multiple upper layers, so the client can reduce the number of local model updates. In addition, as shown in Table 4, multi-level SFL performs better than multi-level FL in the HAM10000 dataset. Because the sample size of the HAM10000 dataset is unbalanced and multi-level SFL inherits the properties of SL, machine learning performance is better under the unbalanced dataset.  
In Figure 4, the X-axis represents the epoch of training, the Y-axis on the left represents the accuracy of training, and the Y-axis on the right represents the training loss. It can be seen from the experimental results that both multi-level SFL and FL can converge after 50 epochs on the Fashion MNIST dataset, reaching an accuracy of 88.9% and 88.6%. We note that multi-level SFL and FL converge first, while centralized learning begins to converge later, presumably because the dataset each client trains on is too small and the data type is large due to the random allocation of the dataset. Furthermore, the convergence rate of multi-level SFL is faster, and the model began to converge at about 20 epochs. This shows that multi-level SFL can reduce the communication overhead during training.
# Gateway-Assisted Retransmission for Lightweight and Reliable IoT Communications
## 3 Gateway-Assisted CoAP (GaCoAP)
The RTT and the RTO occupy two extra bytes of the PUBLISH message and the ACK, respectively. Note that the RTT and the RTO are only delivered between the device and the gateway (and are thus unknown to the server).