esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this study, a novel technique to support smart city planning in estimating and controlling the heating load (HL) of buildings, was proposed, namely PSO-XGBoost. Accordingly, the extreme gradient boosting machine (XGBoost) was developed to estimate HL first; then, the particle swarm optimization (PSO) algorithm was applied to optimize the performance of the XGBoost model. The classical XGBoost model, support vector machine (SVM), random forest (RF), Gaussian process (GP), and classification and regression trees (CART) models were also investigated and developed to predict the HL of building systems, and compared with the proposed PSO-XGBoost model; 837 investigations of buildings were considered and analyzed with many influential factors, such as glazing area distribution (GAD), glazing area (GA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), and relative compactness (RC). Mean absolute percentage error (MAPE), root-mean-squared error (RMSE), variance account for (VAF), mean absolute error (MAE), and determination coefficient (R\({}^{2}\)), were used as the statistical criteria for evaluating the performance of the above models. The color intensity, as well as the ranking method, were also used to compare and evaluate the models. The results showed that the proposed PSO-XGBoost model was the most robust technique for estimating the HL of building systems. The remaining models (i.e., XGBoost, SVM, RF, GP, and CART) yielded more mediocre performance through RMSE, MAE, R\({}^{2}\), VAF, and MAPE metrics. Another finding of this study also indicated that OH, RA, WA, and SA were the most critical parameters for the accuracy of the proposed PSO-XGBoost model. They should be particularly interested in smart city planning as well as the optimization of smart cities.  
energy efficiency; PSO-XGBoost; meta-heuristic algorithm; heating load; smart city +
Footnote †: journal: Applied Soft. 2019, 9, 2714; doi:10.3390/appr9132714
## 5 Results and Discussion
For the hyperparameters of the ISCX-Bot-2014 dataset, the algorithm in KNN (Ball Tree) calculated that the optimal number of nearest neighbors was four because the function weight (Distance) was taken into account to determine them, achieving results of more than 96% in its evaluation metrics. The form-decision function (o-v-r) in SVM, the kernel type (Linear), and the penalty parameter of 0.01 had a positive impact when generating the predictive model, reaching metrics above 80% in the classification of botnet samples. Evaluations in the performance metrics of the predictive model with LR were the lowest of all the algorithms of the used portfolio (below 79% in the classification) despite having good regularization force (0.0001); the penalty rule (\(\ell_{1}\)) affected the results due to the optimization algorithm used (LBFGS). In DT, in the same way as in the CIDDS-001 dataset, the depth of the nodes was nine and their best divisions were 12 because this led to the creation of the best tree with an average performance of 97%. As already mentioned, RF is a derivative of DT, so the depth of its nodes was 9, while its best divisions were seven, taking into account the function to measure its quality (GINI) because counting in unique values is favored and therefore better classification (average of 97%). This being a binary classification problem, leaves were composed of only two samples and a single tree. In future work, a real-time ML-based implementation of SMOTE + GS is proposed by considering the suggestions adopted in [68]. First, a specialized host is indispensable to filter, examine, and transform network packages into useful features; then, a labeling process must map samples through a botnet or intruder category and store them in a knowledge-based database. Furthermore, observations must be trained in an offline fashion for an amount of time; consecutively, an actuator layer is responsible for establishing the ML module to inspect the communication network and force to identity the type of transmitted package. Finally, a handler must decide to mitigate the identified threat, send an alarm to network administrators, or execute a defense mechanism. An important factor for the reproduction of the proposed in real applications is also to gather samples over a four-week timespan because, on average, inactive threats exhibit their functionality. \begin{table}
\begin{tabular}{l l l l} \hline
**Methodology** & **Dataset** & **Algorithm** & **Accuracy** \\ \hline [PERSON] et al. [66] & CIDDS-001 & KNN & 93.87\% \\ \hline SMOTE+GS & CIDDS-001 & KNN & 98.72\% \\ \hline  & & KNN & 93.87\% \\  & & DT & 93.37\% \\ [PERSON] et al. [67] & ISCX-Bot-2014 & Bagging with KNN & 95.69\% \\  & & Ada-Boost with DT & 94.78\% \\  & & Soft voting of KNN and DT & 96.41\% \\ \hline  & & KNN & 98.72\% \\  & & SVM & 97.35\% \\ SMOTE + GS & ISCX-Bot-2014 & LR & 97.89\% \\  & & DT & 98.65\% \\  & & RF & 98.84\% \\ \hline \end{tabular}
\end{table} 
Table 10: Comparative analysis between related works for CIDDS-001 and ISCX-Bot-2014 datasets.
# Enhancements and Challenges in CoAP--A Survey
## 3 Applications of CoAP
### Interoperability and Integration
#### 3.1.1 Interoperability with HTTP  
Ref. [9] works on the compatibility of mobile healthcare platforms with internet and world wide web. It devises a smartphone proxy prototype based on CoAP which enables direct compatibility of medical sensors with internet along with interaction with other nodes using RESTful communication. The patient's medical data can be directly communicated from patient's smartphone proxy to doctor's smartphone if it supports CoAP. The medical sensor data can also be transferred to the medical centers or doctors via internet where the smart phone acts as proxy between client(patient's smartphone) and server(doctor's smartphone), implementing HTTP and CoAP conversion in order to increase the compatibility. Moreover, the CoAP observe feature reduces the overhead of constant communication of server with client for gathering medical sensor data. Instead, it only receives periodic responses from the smartphone proxy. CoAP being a lighter IoT protocol also helps in implementing this architecture without much use of CPU, power, and memory resources.
# AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks
## 3 Proposed Splicing Manipulation Detection System Based on Siamese Neural Network  
This section describes the characteristics of the proposed method for detecting image-splicing-manipulated areas. The proposed algorithm uses a Siamese neural network designed for image feature extraction. This neural network architecture efficiently compares the original and manipulated images' features to identify tampered regions. The Siamese neural network analyzes and identifies complex image features and patterns, generating efficient image manipulation detection. This system accurately identifies the manipulations made to the image and determines the altered areas by comparing the extracted features between the original and manipulated images.  
Furthermore, after the feature extraction stage, the detected tampered regions are divided into 8 \(\times\) 8 blocks to realize more precise detection by the K-means algorithm to identify similar areas that are considered manipulated regions. The K-means algorithm clusters similar pixels unsupervised and efficiently analyzes the tampered image regions to locate the manipulated region. Figure 1 presents a general diagram of the proposed technique, illustrating the sequence of steps that comprise the method. This methodology effectively detects image splicing manipulations, highlighting the capabilities of Siamese neural networks and K-means pixel cluster centroid comparison to achieve accurate and robust detection.  
The proposed method has four stages: preprocessing, Siamese neural network feature extraction, image forgery detection, and the identification of regions distorted by splicing manipulations.  
Preprocessing: This stage prepares the image for its analysis and efficiently learns image features.  
Feature extraction using a Siamese neural network: A Siamese neural network extracts significant image features from the original and manipulated images to generate a feature matrix.  
Figure 1: General diagram of the proposed image splicing detection method.  
Detection of manipulated areas: The system determines potentially manipulated areas within the image by comparing the extracted features.  
Identification of manipulated regions: Finally, the detected manipulated areas are divided into small regions to identify and locate manipulated areas using the K-means algorithm.
## 4 Results
### Discussion and Comparison
\begin{table}
\begin{tabular}{c c c c} \hline
**Accuracy of ML Models** & **CCA Results \%** & **SBA Results \%** & **PCA Results \%** \\ \hline
**Bagging Ensemble** & **97.04** & 96.30 & 96.25 \\ \hline Coarse KNN & 95.19 & 93.95 & 93.99 \\ \hline Fine KNN & 95.93 & 96.25 & 95.50 \\ \hline LPBoost & 96.30 & 96.67 & 95.93 \\ \hline Medium KNN & 96.30 & 95.93 & 96.67 \\ \hline RUSBoost & 95.56 & 94.07 & 96.67 \\ \hline SVM & 95.19 & 95.93 & 95.56 \\ \hline \end{tabular}
\end{table} 
Table 26: Comparison of CCA, SBA, and PCA on ML Models. \begin{table}
\begin{tabular}{c c c c} \hline
**Author/Year** & **Methodology** & **Disease** & **Statistics** \\ \hline ([PERSON] 2020 [21]) & ANN \& SVM & ALL & Specificity: 95.31\% \\ \hline ([PERSON] 2020 [22]) & CNN \& SVM & leukaemia & 94.1\% \\ \hline ([PERSON] 2019 [6]) & Active Contours & Leukocytes & 96.5\% \\ \hline ([PERSON] \& [PERSON], 2019 [11]) & Alexnet & Leukocytes & 90.30\% \\ \hline
**(Proposed methodology)** & **Hybrid CNN \& Bagging Ensemble** & **ALL, AML \&MM** & **97.04\%** \\ \hline \end{tabular}
\end{table} 
Table 27: Comparison of Proposed Methodology.
## 2 Traditional Jitter Modulation  
This section describes the traditional method of jitter generation by analog modulation and compares it with the new method proposed in this paper.  
As shown in Figure 1, the jittery clock is generated by two signal generators. The signal generator#1 serves as a modulation source to generate a modulated signal, and the signal generator#2 serves as a clock source to generate a jittery clock. It is used as the clock that generates the pattern to implement the jittery data pattern.  
In order to facilitate the calculation, the sine wave is used for derivation in this paper. The final output signal is as follows:  
\[s(t)=A_{c}\cos[w_{c}t+\theta(t)] \tag{1}\]  
where \(A_{c}\) is the amplitude of output signal, \(w_{c}\) is the angular frequency of carrier, and \(\theta(t)\) is modulation signal. According to the nature of the frequency modulation, the modulation signal is as follows:  
\[\theta(t)=2\pi D_{f}\frac{A_{m}}{2\pi f_{m}}\cos(2\pi f_{m}t-\frac{\pi}{2}) \tag{2}\]It is assumed that the modulated signal is a sinusoidal function, where \(A_{m}\) is the amplitude of the modulation signal, \(f_{m}\) is the frequency of the modulation signal, and \(D_{f}\) is the frequency offset constant. Then the peak-to-peak value of phase offset is  
\[\theta(t)_{p-p}=2*2\pi D_{f}\frac{A_{m}}{2\pi f_{m}}=\frac{2D_{f}A_{m}}{f_{m}} \tag{3}\]  
The magnitude of the jitter obtained according to the definition of jitter is (in UI)  
\[A_{jitter}=\frac{\theta(t)_{p-p}}{2\pi}=\frac{D_{f}A_{m}}{\pi f_{m}}[U\!I_{p-p}] \tag{4}\]  
According to Equation (11), it can be seen that the factors affecting the amplitude of the jitter generated by the analog modulation are \(D_{f}\), \(f_{m}\), and \(A_{m}\); this means that adjustable jitter amplitude is limited by jitter frequency. When the jitter frequency increases, the jitter amplitude decreases. The jitter amplitude is at least 0.1 Uipp (at a jitter frequency of 20 M) in [19], and jitter amplitude is at least 0.01 Uipp (at a jitter frequency of 2 M) in [21], but, in this design example, the jitter amplitude can be adjusted freely and its range of 10 ps-10 ns, a step of 2 ps or 10 ps, is independent of jitter frequency and data rate, as shown in Table 1. The frequency resolution of jitter using analog modulation is \(\pm\)50 ppm; however, the jitter frequency resolution in this design example is \(\pm\)30 ppm in the actual test, as shown in Table 1.
# Discrete Environment-Driven GPU-Based Ray Launching: Validation and Applications
## 2 The DED-RL Algorithm
### GPU Parallelization  
Since the RL approach is inherently fit to parallel computing [16; 21], the whole algorithm, including visibility preprocessing, has been parallelized for nVIDIA GPUs using the CUDA (Computer Unified Device Architecture) language extension, thus further reducing computation time with respect to traditional implementations. The CUDA platform is a software layer that gives direct access to the GPU's computational elements (CUDA cores) and virtual instruction set for the execution of sets of parallel instructions (CUDA \"kernels\") on nVIDIA GPU architectures.  
CUDA processors are usually programmed in CUDA C/C++, which is basically the standard C/C++ programming language, with some CUDA extensions to handle parallel computation on multiple cores and threads [29].
## 5 Test Results
### Internal Force of Secondary Lining
#### 5.2.1 Arch Sections 1-6 in Cases 1 and 2  
Figure 17 shows the internal force curves of Sections 1-6 in the bilateral slide surface immersion cases (cases 1 and 3). Figure 17a,b illustrates the axial force and bending moment curve of arch six sections under the bilateral arch foot slide surface immersion case (case 1), respectively. It can be seen from Figure 17a that the entire lining arch was compressed during the test. The axial force increased linearly, the same as their change trend. During the second loading stage (loading step 10-15), the axial force of the lining arch changed little.  
It can be seen from Figure 17b that the bending moment trend at the symmetrical position of lining is the same, and the magnitudes are not much different. In the second loading stage (loading step 10-15), the bending moment of the vault and the arch waist decreased first and then increased, and finally bent in the opposite direction, while the bending moment of the arch shoulder always increased. The bending moments of the Sections 1 and 2 of the vault, and the Sections 5 and 6 of the arch waist, began to decrease and develop in the opposite direction due to the loading step 14 (immersion width 4 m). No cracks appear in the arch during this stage.  
Figure 17c,d shows the axial force and bending moment curve of arch six sections under the bilateral waist slide surface immersion case (case 3), respectively. It can be seen from Figure 17c that all the six sections of the arch are under compression during the test, and the changing trend of axial force is similar. The axial force of Sections 5 and 6 at the lower end of the arch is greater than other positions. During the second loading stage (loading step 10-15), the axial force of the arch always increased. When the loading step was 13 (immersion width 3 m), No. 1 crack on the inner surface of the vault appeared, and the increased rate of the axial arch force decreased. It can be seen from Figure 17d that Sections 1 and 2 of the vault were always bent outwards, the Sections 5 and 6 of the arch waist always bent inwards, and Sections 3 and 4 of the arch shoulder bent from inwards to outwards in the third loading stage. It is worth noting that when the loading step was 13 (immersion width 3 m), the bending moment of Section 6 suddenly increased, while No. 1 crack on the inner surface of the vault appeared. Afterward, the increasing rate of the bending moment of the vault and the arch waist slowed down, and the bending moment of the arch shoulder began to decrease.
# Prefix Data Augmentation for Contrastive Learning of Unsupervised Sentence Embedding
## 4 Unsupervised PrdSimcse
### Main Results  
In Table 4, we showcase the performance on the STS tasks. We replicated the results of SimCSE and, through comparison, we found that PrdSimCSE-BERTbase substantially outperformed SimCSE-BERTbase on STS13-STS16, STS-B, and SICK-R in terms of Spearman correlation coefficient, notably by 2.01% on STS-B. On STS12, PrdSimCSE performed consistently with SimCSE, producing an increase in the average Spearman correlation coefficient from 76.25% to 77.33%. Compared with the unsupervised models, PrdSimCSE outperformed CT-BERT by 5.28% and IS-BERT by 10.75% in terms of the average Spearman correlation coefficient. For the RoBERTa model, PrdSimCSE-RoBERTbase also improved upon SimCSE-RoBERTbase's 76.57% to 77.10%, particularly outperforming SimCSE by 4.81% on SICK-R.  
In Table 5, we present the results for transfer tasks. Compared with SimCSE-BERTbase, PrdSimCSE-BERTbase performed better on five of the datasets. However, on the TREC task, the accuracy of PrdSimCSE was 7.89% lower than that of SimCSE--a substantial difference that requires further consideration. TREC is a widely used dataset in the field of information retrieval, employed for evaluating the performance of information retrieval systems. The Text REtrieval Conference (TREC) Question Classification dataset contains 5500 labeled questions in the training set and another 500 in the test set. The dataset has 6 coarse class labels and 50 fine class labels. During testing, each sentence is assigned a label. We speculate that the reason for this discrepancy is because PrdSimCSE uses prefix data augmentation, resulting in a large number of identical sentence prefixes in the training corpus, which leads to considerable semantic bias when encoding sentences. This was further discussed in the ablation experiments.  
In Table 6, we present the evaluation results of PrdSimCSE on the two Chinese transfer task datasets. Because our model was trained on English corpora, direct evaluation using Chinese datasets resulted in very poor performance. We used Google Translate to translate all the data into English before conducting the evaluations. PrdSimCSE-BERT\({}_{\text{base}}\) outperformed SimCSE-BERT\({}_{\text{base}}\) by 1.63% on the BQ dataset and by 2.17% on LCQMC. PrdSimCSE-RoBERT\({}_{\text{base}}\) exceeded SimCSE-RoBERT\({}_{\text{base}}\) in performance by 0.85% on the BQ dataset and by 1.37% on LCQMC.
###### Abstract  
Ectopic eruption of the first permanent molar in the maxilla, generally characterized by the wrong axial positioning during eruption, is reported to occur in about 2-6% of children. Its incidence is surely under-estimated as it frequently remains undiagnosed and, additionally, becomes irreversible, resulting in posterior crowding in such cases. This retrospective study aims to investigate the cephalometric features of patients affected by ectopic eruption of the maxillary first molar in comparison to a control group of patients. From a total of 1935 subjects, 13 patients were enrolled in the study group, while 26 patients were randomly selected as a control group. Lateral cephalometric teleraliographs obtained at the time of diagnosis were used to measure cephalometric values between study patients and controls. The prevalence of ectopic eruption of the first maxillary permanent molar in our patients was 1.14%. The mandibular angle (_SN-GoMe_) was found to be significantly greater in the study group than in control. The same statistical trend was observed for the inter-maximly angle (_SN9-GoMe_); also, the distance _SN-Me_ was greater in the study group. Differences between groups were statistically significant for the following distances: _Snp-Sn_, _Bu-S_ and _SOR-Sn_, which were found to be, overall, greater in the study group. Ectopic eruption of the first permanent molar in the maxilla seems to be associated with morphogenetic characteristics of patients, mainly with a dolichocephalic pattern.  
ectopic eruption; first maxillary molar; dental anomaly; cephalometry 24 August 2021 26 August 2021 26 August  
Several investigators have studied the possible etiologic factors of EEFPMM. Small dental arches and lack of bony growth in maxillary tuberosity, [1] as well large teeth, an abnormal path of eruption of the permanent molar or its premature eruption [9] have been considered as etiological factors. Also, [PERSON] [10] suggested a combination of etiologic factors including the posterior position of the maxilla to the cranial base and the delayed calcification of the permanent first molar in the maxilla.  
In addition, the higher prevalence of EEFPMM in children with adjunctive or-facial or dental anomalies leads us to consider a possible association with genetic factors [11].  
The diagnosis of EEFPMM is essentially based on the clinical and radiographic findings [3] identifiable at a routine radiographic examination in a patient clinically missing the permanent first maxillary molar, or with the eruption of the distal cusps exclusively [12].  
Literature reports several staging methods of the severity of EEFPMM, generally based on the width of the impacted marginal ridge [7; 9], the magnitude of the deciduous second molar distal resorption [9; 12] or the amount of impaction measured as the distance from the maximum convexity of the permanent molar mesial contour to a tangential plane, to the distal surface of the deciduous one [12].  
The present retrospective study aims to investigate the cephalometric features of patients affected by ectopic eruption of the maxillary permanent first molar compared to a control group.
## 4 Simulation and Performance Evaluation  
We ran simulation over APT cyber attack cases using the proposed modeling method and analysis system. A few years ago, a massive APT attack aimed at paralyzing major press and corporate networks occurred in this country. Several tens of thousands of systems were infected with BotNets, causing serious damages such as corruption of disk information including data, boot area, and the system. If a civilian damage occurs on a large-scale military tactical network, the results of the simulation can be analyzed through the simulation methodology proposed herein. Additionally, the performance of the proposed analysis system was quantitatively evaluated.
# Document Image Shadow Removal Based on Illumination Correction Method
## 1 Introduction
Therefore, we proposed the dark illumination correction network (DICNet) to improve the performance of shadow removal for document images, which can accurately simulate the optical principle, thus removing shadows based on light correction while maintaining high-quality images. DICNet acquires a soft shadow mask via the designed shadow detection module to label the shadow areas with an accurate shadow intensity of each pixel in the images. Furthermore, DICNet learns the required illumination intensity correction via the dark light correction module with a soft shadow mask. By correcting shadow area illumination, the network can produce a shadow-free document image. Overall, through these two pivotal branches of shadow detection and light correction, the DICNet model ensures an accurate and high-quality removal effect, achieving state-of-the-art (SOTA) results on different datasets. Our primary contributions are as follows:  
(1) Proposing our DICNet model. It can utilize the optical principle of shadow formation and achieve better performance on different datasets. (2) Designing a novel dark-illumination correction module. The module can learn the required illumination intensity correction. (3) Designing a shadow detection module. It can acquire an accurate shadow intensity of each pixel in the document images. (4) Constructing a simplified shadow-corrected attention block. It enables the model to pay attention to both the global channel and local spatial information to capture richer and more complete feature representation.