esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The purpose of this research is to demonstrate the ability of machine-learning (ML) methods for liver cancer classification using a fused dataset of two-dimensional (2D) computed tomography (CT) scans and magnetic resonance imaging (MRI). Datasets of benign (hepatocellular adenoma, hemangioma, cyst) and malignant (hepatocellular carcinoma, hepatoblastoma, metastasis) liver cancer were acquired at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan. The final dataset was generated by fusion of 1200 (100 \(\times\) 6 \(\times\) 2) MR and CT-scan images, 200 (100 MRI and 100 CT-scan) images size 512 \(\times\) 512 for each class of cancer. The acquired dataset was preprocessed by employing the Gabor filters to reduce the noise and taking an automated region of interest (ROIs) using an Otsu thresholding-based segmentation approach. The preprocessed dataset was used to acquire 254 hybrid-feature data for each ROI, which is the combination of the histogram, wavelet, co-occurrence, and run-length features, while 10 optimized hybrid features were selected by employing (probability of error plus average correlation) feature selection technique. For classification, we deployed this optimized hybrid-feature dataset to four ML classifiers: multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and J48, using a ten fold cross-validation method. MLP showed an overall accuracy of (95.78% on MRI and 97.44% on CT). Unfortunately, the obtained results were not promising, and there were some limitations due to the different modalities of the dataset. Thereafter, a fusion of MRI and CT-scan datasets generated the fused optimized hybrid-feature dataset. The MLP has shown a promising accuracy of 99% among all the deployed classifiers.  
liver cancer; Otsu thresholding-based segmentation; fused optimized hybrid features; multilayer perceptron 2020 10 3134; doi:10.3390/app10093134 www.mdpi.com/journal/applsci
# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 4 Discussion
### Overview of Commercial Platforms Capabilities Available in the Market
Among the case studies reported about its application in Hjorring and Herring [148], DH systems' heat loss reduction of about 10% (from 23 to 20.7%) has been achieved. * Only ENFOR [149] provide an additional tool called MetFor(tm) based on machine learning that is able to optimize the local weather forecast up to 10 days ahead from historical weather data and meteorological models, but also capable of detecting short-term deviations (12 h ahead) using real-time weather data. Another tool that is also emerging in the academic literature is energyPRO [150], which can be used for technical and economic analysis of complex generation systems as well as for short-term operation scheduling. Energy Advice [151] developed a platform that allows for the creation of a thermo-hydraulic model of the network and to use it to improve the control of the DH system. Finally, it is worth mentioning that some companies developed advanced data analytics and visualization tool such as Tango [152] with the aim of getting more insight into information and future trends, but also integrating applications available by third party. A new start-up called Arterial [153] was born recently and intends to differentiate itself from the other companies by developing an innovative platform based on a physical model of the DHC network, thermoeconomics optimization and the assessment of the exergy flows in the DHC system.
## 4 Discussion
### Challenges in Assessing the Degradation  
The main challenge in assessing particle degradation for pneumatic conveyors is the measurement of particle velocity of the bulk material in transit, such as inside of the bends, and the effects of changing operative parameters on the particle velocity, which is the main influential factor on material degradation due to high-speed impacts. This study has identified the assessment technique to measure the particle velocities and the degradation by altering operating conditions and pipeline features, which simulate the pellet delivery mechanisms adopted in industrial practice. It has been observed that there are discrepancies in the same two types of biomass materials (wood pellets) used for sustainable energy  
Figure 6: Velocity profiles for coffee beans at 0.27 kg/s air mass flow rate and 0.2 kg/s solid flow rate.  
generation and therefore handling characteristics of such biomass materials in the same pneumatic conveying system. The difference in the handling characteristics results in a different level of particle degradation. The same challenge is coming to the types of biomass materials. Under the same operation, the degradation levels of the materials are not comparable. For such a large process, degradation of the material is not easily evaluated unless there is a fast approach to measuring the degradation at a controlled concentration for all scenarios. Furthermore, degradation assessment for a pneumatic conveying system must be modeled in a way that integrates the factorial tests for individual test conditions into a prediction for the system, as multiple bends are involved in the system.  
Therefore, assessment of biomass materials degradation in pneumatic conveying systems is only feasible in conducting a series of advanced assessments at a laboratory scale with a small number of test samples for all operative conditions. The test conditions must be representable to the impact conditions in the pneumatic conveying system at every single bend for the proposed or existing pneumatic conveying system. The laboratory assessment can become a dedicated tool and be used for industrial applications. Taking the challenges of the degradation assessment for a pneumatic conveyor, an accurate evaluation using a laboratory measurement will be subject to accurate impact conditions in the process, which need to be measured in advance.
## 3 Network Model Construction
### Improvement Based on the Attention Mechanism Model  
Incorporating residual modules into the network enhances training efficiency, as the skip connections between low-level and high-level layers within each residual module facilitate backpropagation without compromising performance. Furthermore, by integrat  
Figure 4: Transfer learning structure diagram.  
ing structures such as the CBAM (Convolutional Block Attention Module) with residual modules (as illustrated in Figure 5), the model can be designed with fewer parameters while maintaining comparable task-specific performance.  
The improved residual model addresses the issues of vanishing gradients and performance degradation--problems in which repeated multiplication during backpropagation can lead to extremely small gradients. Following each residual block, an average pooling operation is applied, and the resulting feature embeddings are fed into a classifier independently, enabling multi-task training.
# Integrating Pareto Optimization into Dynamic Programming
## 4 Experiments
### Putting It All Together
Altogether, our findings do not imply that the SORT strategies are without merit. Although EAGER (unsorted) beats it for Ali3D as an outlier, the good effect of the sorted implementation compared to the standard NOSORT can be seen here, as well. Due to its increased implementation complexity, EAGER (sorted) cannot really be preferred over SORT(7). EAGER (unsorted) is unlikely \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Name** & **Input** & **Front** & **NOSORT** & **NOSORT(B)** & **SORT(1)** & **SORT(7)** & **EAGER(Sort.)** & **EAGER(Uns.)** \\ \hline Ali2D & 509 & 487 & 667.84 & - & 579.77 & 529.98 & **356.11** & 775.77 \\ Gotoh2D & 249 & 209.744 & **29.19** & - & 29.42 & 30.32 & 29.83 & 29.75 \\ Fold2D & 509 & 15 & **154.12** & - & 334.53 & 312.28 & 216.54 & 228.29 \\ Fold2D & 608 & 13 & **631.43** & - & 2220.24 & 1833.31 & 1054.39 & 1279.03 \\ Prob2D & 185 & 9 & **2.05** & - & 3.64 & 3.6 & 2.76 & 2.36 \\ Ali3D & 200 & 446 & 5.43 & 4.93 & 4.97 & 4.88 & 8.04 & **4.85** \\ Fold3D & 404 & 165 & **314.52** & 704.03 & 841.64 & 806.67 & 5810.36 & 813.2 \\ Prob4D & 185 & 2327 & 1774.88 & **1227.13** & 2556.83 & 2449.83 & 14,552.12 & 2535.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of benchmarks of all applications. Input and front sizes are given. We compare the full runtimes in seconds (s) for three strategies in overall six variants: the strategy (NOSORT) using \(\textbf{pf}_{nsort}\), its multidimensional variant NOSORT(B) using \(\textbf{pf}_{bentley}\), SORT(1) using Quicksort and \(\textbf{pf}_{sort}\), the specialized sorted case SORT(7), using Merge-in-place with \(\textbf{pf}_{sort}\), as well as EAGER (Sorted), using Merge 1 and Merge 2, and, finally, EAGER(Unsorted), using Merge 3. No algorithm can win in all cases. to scale well. For Ali3D and similar cases, and still for larger inputs, sorted SORT(7) can be expected to be the best option.
## 4 Theoretical and Experimental Analyses
### Reversibility  
#### 4.4.1 PSNR and SSIM  
Reversibility refers to whether the original image can be completely recovered and the extra data can be extracted without loss. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) can be used to evaluate the reversibility, which is calculated as follows:  
\[PSNR=10\log_{10}\frac{2^{8}-1}{\frac{1}{X\times Y}\sum\limits_{i=0}^{X-1}\sum \limits_{j=0}^{Y-1}(P(i,j)-P^{\prime}(i,j))} \tag{14}\]  
\[SSIM(X,Y)=\frac{2M_{X}M_{Y}+Cons1}{U_{X}^{2}+U_{Y}^{2}+Cons1}\times\frac{2C_{X} C_{Y}+Cons2}{C_{X}^{2}+C_{Y}^{2}+Cons2}\times\frac{C_{X_{Y}}+Cons3}{C_{X}C_{Y}+ Cons3} \tag{15}\]  
where X is the original image, Y is the comparison image, \(P(i,j)\) is the original image pixel, \(P(i,j)\) is the comparison image pixel, \(M_{X}\) and \(M_{Y}\) are the mean values of X and Y, respectively, \(C_{X}\) and \(C_{Y}\) are the standard deviations of X and Y, respectively, \(C_{XY}\) represents the covariance of X and Y, and \(Cons1\), \(Cons2\) and \(Cons3\) are constants. The larger the PSNR is, the closer the SSIM is to 1, which indicates greater reversibility. If \(\text{PSNR}=+\infty\) and \(\text{SSIM}=1\), the scheme is fully reversible. Furthermore, when \(\text{PSNR}>35\) dB, there is no visual difference. The experiments were conducted on five test images, and the results are shown in Table 2. The PSNR values are all \(+\infty\), and the SSIM values are all 1, which indicates that the proposed scheme is fully reversible. Since the proposed scheme uses Lagrange interpolation to reconstruct the image, its binary polynomial coefficients can be recovered, so the carrier image pixels and embedded extra data can be recovered correctly. To better highlight the advantages of the proposed scheme, we conducted a comparative analysis of the PSNR between our scheme and other high-performance schemes. The comparison results are illustrated in Figure 10. The PSNR of schemes [18,23,26,28] decreases with increasing ER, which indicates that the PSNR is greatly affected by the ER and that the  
Figure 8: Pixel correlation in the four directions of the first share.  
Figure 9: Pixel correlation in the four directions of the reconstructed image with only two shares when \(T=3\).  
carrier image is not completely reversible. In contrast, for schemes [29; 34] and our scheme, which utilize polynomial secret sharing to encrypt the image, the PSNR is infinite and is not affected by the ER, which indicates that our scheme is fully reversible and has more stable reversibility.
# Sound Event Detection Using Derivative Features in Deep Neural Networks
## 4 Experimental Results
### Evaluation Metrics  
The CRNN computes the posterior probability for each class in every time frame and identifies a sound event when this probability exceeds 0.5. To improve reliability, a median filtering is applied to the probabilities across the frames before the final decision.  
The performance of the CRNN is measured by the F-score and error rate (ER) using an event-based analysis [23], which compares the output of the CRNN with the ground truth table when the output indicates that an event has occurred. The initial decision comprises three different types: true positive (TP), false positive (FP), and false negative (FN). A TP indicates that the period of a detected sound event overlaps with that from the ground truth table. In the decision, a 200 ms onset collar and a 200 ms or 20% of the event length offset collar are allowed. An FP implies that there is no corresponding overlap period in the ground truth table, although the CRNN output indicates an event. An FN implies that there is an event period in the ground truth table, but the CRNN does not produce the corresponding output.  
The F-score (F) is computed based on the initial three decisions and is the harmonic average of the precision (P) and recall (R). They are computed as follows.  
\[\mathrm{P}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}},\ \ \mathrm{R}=\frac{ \mathrm{TP}}{\mathrm{TP}+\mathrm{FN}},\ \ \mathrm{F}=\frac{2\mathrm{PR}}{\mathrm{P}+\mathrm{R}} \tag{3}\]  
The error rate is computes as  
\[\mathrm{Error\ rate}=\frac{\mathrm{S}+\mathrm{D}+\mathrm{I}}{\mathrm{N}} \tag{4}\]  
where N is the total number of sound events active in the ground truth table. Sound events with correct temporal positions but incorrect class labels are counted as substitutions (S), whereas insertions (I) are sound events present in the system output but not in the reference, and deletions (D) are the sound events present in the reference but not in the output [23].
# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 4 Field Survey Results and Discussion
### Service Life Expectancy and Performance Level under Standard Conditions
Within a 25-year span, it is anticipated that the performance level will decline below the required MRCP\({}_{60\%}\) threshold, indicating significant deterioration. Consequently, this type is generally not recommended for intensive occupancy conditions (\(\mu>4\)), due to its reduced durability and functional lifespan in such conditions. In the case of hollow concrete blocks, the projected service life expectancy (SLE) only falls below 50 years in intensive occupancy conditions (\(\mu>4\)). Additionally, for a span of 25 years, these partitions maintain performance above the MRCP\({}_{60\%}\) threshold, demonstrating resilience and a slower rate of deterioration. Thus, it can be concluded that hollow concrete block partitions exhibit higher durability and are generally more suitable across the range of conditions observed in this study. Figures 8 and 9 compare service life expectancy (SLE) across partition types and occupancy conditions, finding that hollow concrete block partitions have the longest service life cycle of all examined partitions. Within the category of lightweight partitions, aerated concrete block partitions exhibit a marginal advantage in durability and sustained performance over time, when compared to gypsum board counterparts.
## 5 Numerical Results
### Primary User Interference  
The PU interference is given by Equation (12). The values of total channel interference for each channel are not necessarily the maximum values \(I_{max}^{i}\) for each channel. In this section, we look at the total channel interference values for the different network sizes and packet size values. The results are given in Figure 5.  
It can be observed from Figure 5a,b that both network size and packet size increase with total interference. As the network size (number of SUs) increases, the chances of interference with PUs also increases. This explains the general increase of PU interference with network size. In addition, an increase in packet size leads to an increase in SU activity, which in turn leads to an increased chance of interference with a PU on the channel.  
Figure 4: The effect of changing the number of Cognitive Radio Sensor Network (CRSN) nodes on the optimal solution of (17).
## 1 Introduction  
Cartographer is a SLAM method developed by Google, which utilizes grid-based mapping together with a Ceres based scan matcher to reconstruct environment across various sensors configuration [1]. Initially, it was developed for portable applications to map unknown areas with a person using the inertial measurement unit (IMU) and LiDAR installed in a backpack. However, since the codes have been made public, Cartographer is now managed by an open source community, and various improvements have been made to widen the scope of applications including more substantial map support, more sensor integration, and other technological improvements which support intelligent robots. SLAM methods are not new research and are beyond the focus of this paper. However, this paper contributes to the improvisation of the current Cartographer or SLAM by using an adaptive multistage distance scheduler to reduce computational load while maintaining accuracy. The main contributions of this paper are as follows:  
1. Optimizing Cartographer by using an adaptive multistage distance scheduler (AMDS) to increase time computation performance up to 20% while maintaining the pose generation and map accuracy similar to that of standard Cartographer;
2. Integrating Cartographer by using a car for online world mapping, which is crucial for the future autonomous vehicle navigation systems;3. Investigating the performance of the proposed method by comparing it to the popular SLAM methods (LiDAR odometry and mapping (LOAM) and normal distribution transform (NDT) SLAM), in the actual world environment.
# Creep Model of Weakly Cemented Soft Rock Considering Damage and Secondary Development in FLAC3D
## 2 Experimental Study on Weakly Cemented Soft Rock
### Creep Test Protocol  
Creep tests on weakly cemented mudstone were conducted using the RLJW-2000 rock rheometer, following a step-loading method. Stress levels were designed based on uniaxial and triaxial test data, corresponding to 55%, 63%, 71%, 87%, and 95% of the peak strength, resulting in six stress levels (specific values are detailed in Table 2). Both confining and axial pressures were applied uniformly at a rate of 0.005 MPa/s. Once each target stress level was reached, the axial load was maintained for 6 h or until axial deformation stabilized, after which the next stress level was applied. This iterative process continued until the specimen experienced creep failure.
## 3 Methodology
### Classification Experiments
#### 3.5.2 Fine-Tuning  
The fine-tuning process in this study involves adapting the pre-trained models for multi-class emotion classification using a custom-labeled dataset. The models are initialized with six output labels corresponding to the emotion categories: _joy, fear, surprise, sadness, disgust_, and _anger_. The emotion dataset is pre-processed by mapping textual emotion labels to numerical values and tokenizing the text inputs using the corresponding BERT and BART tokenizers. Tokenization ensures uniform sequence lengths with truncation and padding to a maximum length of 128 tokens. The dataset is split into training and validation subsets for supervised learning, where the model is fine-tuned using a cross-entropy loss function to minimize the difference between predicted and true labels. The training configuration includes a learning rate of \(2\times 10^{-5}\), a batch size of 4 for both training and evaluation, and weight decay for regularization. The training process spans three epochs, with the model evaluated after each epoch based on accuracy. The trainer API from Hugging Face's Transformers library manages the optimization, evaluation, and saving of the best-performing model checkpoint. This approach leverages the model's pre-trained language understanding while adapting its classification head to the specific task of emotion recognition, resulting in a domain-specific fine-tuned model.  
The models were then fine-tuned with the following datasets:  
* Baseline synthetic dataset (general English)
* Region-specific synthetic datasets with general prompts
* Region-specific synthetic datasets with detailed prompts  
Fine-tuning the models with these different datasets allows us to assess how regional language variations and prompt specificity affect model performance. This step is crucial for testing our hypothesis that region-specific synthetic datasets lead to better emotion detection. Cross-dataset classification was chosen as it presents a more challenging task and better simulates the practical application of emotion detection in online environments, where classification is often performed on previously unseen data samples.
# Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model
## 5 Conclusions  
This study proposed eight input models based on the variance and correlation coefficients of meteorological factors combined with the cosine and sine values of the date. To address the shortcomings of the commonly used PSO-ELM model for ET\({}_{0}\) prediction, the Kmeans-QPSO-MKELM model was proposed. The optimal number of clusters was selected in the experiment, followed by ablation and comparison experiments. The main conclusions are as follows:  
1. Compared to the PSO-ELM model, the Kmeans-QPSO-MKELM model, with improvements in the optimization algorithm, reduces the probability of PSO falling into local optima and has a higher chance of finding a better solution. Introducing multiple kernel functions in the ELM model improves its ability to handle complex nonlinear problems, especially when input factors such as wind speed, which exhibit a more nonlinear relationship with ET\({}_{0}\), are included. When K-means clustering is applied, and the number of clusters is appropriately chosen, classification training followed by weighted prediction significantly reduces the model training time while maintaining similar prediction accuracy.
2. For all models in this study, adding the cosine and sine values of the date as input significantly improved prediction accuracy. It is, therefore, recommended that the cosine and sine values of the date be included in the input features.
3. Comparison experiments show that the prediction accuracy of Kmeans-QPSO-MKELM and QPSO-MKELM is significantly higher than that of other models under all eight input scenarios, especially when the date cosine and sine values are included. The Kmeans-QPSO-MKELM model runs slightly slower than the RF model but faster than all other models in the comparison. Therefore, it is strongly recommended to use the Kmeans-QPSO-MKELM model combined with the date cosine and sine values as inputs for ET\({}_{0}\) prediction.  
Conceptualization, C.Z.; methodology, C.Z. and M.Y.; software, C.Z.; validation, C.Z.; formal analysis, C.Z.; data curation, C.Z.; writing--original draft preparation, C.Z.; writing--review and editing, C.Z. and M.Y.; visualization, C.Z.; supervision, M.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding authors upon reasonable request.