esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Experiment 3: Comparison with Other Screening Diagnostic Tools  
The purpose of this experiment is to compare the performance of a screening diagnostic tool using a robot with the performance of other existing ADHD screening diagnostic tools. The existing ADHD screening tools for the comparison are K-ARS and CBCL. These screening diagnostic tools are currently extensively used as screening diagnostic tools for ADHD screening. Experiments were conducted by comparing the results of screening diagnostic results of a tool using a robot and the existing screening diagnostic tools. All target children used the two existing screening diagnostic methods prior to the data acquisition procedure of the proposed method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sessions** & **Group** & **Accuracy (\%)** \\ \hline \multirow{3}{*}{Session 1} & HA Group & 95.67 \\ \cline{2-3}  & EA Group & 95.25 \\ \hline \multirow{3}{*}{Session 2} & HE Group & 96.42 \\ \cline{2-3}  & HF Group & 95.71 \\ \cline{2-3}  & EC Group & 92.27 \\ \cline{2-3}  & ED Group & 92.85 \\ \hline Session 3 & HC Group & 97.06 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The performance of the screening diagnostic tool using different environments.  
Figure 15: Data acquisition environments in Session 1 (**left**) and Sessions 2 and 3 (**right**).  
First, the performance of ADHD screening using the existing ADHD screening tools is shown in Table 11. As a result, the sensitivity of ADHD is very low. This results in an inability to properly classify ADHD. Existing screening tools rely on parental surveys; there is no voluntary participation, and parents rarely actively participate in these surveys. This suggests that it is difficult to detect ADHD early because existing screening diagnostic tools rely on parental evaluation.  
The following is the diagnostic performance of the proposed ADHD screening tool using a robot, as shown in Table 12.  
Table 12 shows the results of screening diagnostic tools using a robot. When screening through a game using a tool that uses a robot, the classification sensitivity for the ADHD and ADHD risk groups is higher than that of the existing tools. In other words, this indicates that the robot-based ADHD screening tool can detect ADHD and ADHD risk groups much better than existing diagnostic tools. Although the classification accuracy for the normal group is low, the accuracy for the ADHD and ADHD risk groups is high, which verifies the feasibility of the proposed screening tool using a robot for early ADHD screening.
# UWB Positioning Algorithm Based on Fuzzy Inference and Adaptive Anti-NLOS Kalman Filtering
## 4 Positioning Algorithm Based on Fuzzy Inference and Adaptive Anti-NLOS KF  
### Adaptive Anti-NLOS KF Positioning Algorithm  
The LS positioning algorithm has a small influence on the NLOS errors and strong robustness. In this paper, the adaptive anti-NLOS KF was combined with LS positioning, and an adaptive anti-NLOS KF positioning algorithm was proposed. In this algorithm, the adaptive anti-NLOS KF was first performed on the original ranging data to eliminate the NLOS errors therein. Then, the filtered ranging data were substituted into the LS positioning algorithm for the final estimation of the tag position. The flow chart of the adaptive anti-NLOS KF positioning algorithm is shown in Figure 4.  
Figure 4: Flow chart of the adaptive anti-NLOS KF positioning algorithm.  
Figure 3: Flow chart of the adaptive anti-NLOS KF algorithm.
## 4 Proposed Key Management Infrastructure Design for IACS
### Key Generation  
* Field level devices: Along with polynomial key establishment parameters (polynomial shares), 128-bit random key is generated for field level devices. The generated key is used as a field level group key (\(FGK\)) for field network devices.
* Control level devices: Along with matrix method key establishment parameters (secret rows \(\in\) matrix \(A\) and key seeds \(\in\) matrix \(P\)), 128-bit random key is generated for control level devices. The generated key is used as a control level group key (\(CGK\)) for control network devices.
* Plant level devices: Along with ECDH method key establishment parameters (public and private key pairs), 128-bit random key is generated for plant level devices. The generated key is used as a plant level group key (\(PGK\)) for plant network devices.  
Algorithm 1 depicts the key generation process for IACS devices.  
```
1: CKMI server: Generates the security parameters for various crypto methods to be used.
2:if Key generation is for field level devices then
3: Choose Polynomial method for key establishment
4: Generate \(\lambda\)-degree polynomial equation (\(P\))
5: Evaluate polynomial shares by substituting unique IDs in \(P\)
6: Generate 128-bit field level group key (\(FGK\))
7:elseif Key generation is for control level devices then
8: Choose matrix method for key establishment
9: Generate secret symmetric matrix \(S\) and public matrix \(P\)
10: Perform matrix multiplication \(A=SXP\) and transpose to get \(A^{T}\)
11: Generate 128-bit control level group key (\(CGK\))
12:elseif Key generation is for plant level devices then
13: Choose ECDH method for key establishment
14: Generate elliptic curve domain parameters (\(p\), \(a\), \(b\), \(G\), \(n\), \(h\))
15: Generate private keys \(d_{i}\)\(\in\)n and public keys \(Q_{i}=d_{i}\cdot G\)
16: Generate 128-bit plant level group key (\(PGK\))
17:endif
```  
**Algorithm 1** Key generation for IACS devices.
# Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design
## 8 Results and Discussion
### Key Performance Indicators
#### 8.1.1 System Efficiency  
The efficiency was measured in terms of time, before and after the implementation of the architecture and prototype. As mentioned in Section 4, before the implementation, the S/Os were manually sent to the next department. After production, the storing of each carton in the inventory required verifying the order contents. Similarly, the S/O completion was performed after one shift; till then, the order was out for delivery, and there was no way to track or make up for any mistakes made in the order. However, after the installation of the scanners at each node according to the model in Figure 4, there was a decrease of 79% in the time taken in storing the goods in the inventory, and the time to update the records after the completion of a S/O was reduced to a few minutes (Figure 12). Overall, while it increased the efficiency, it also helped reduce the complaints of stakeholders to almost zero, as the orders were checked and verified at every node and updated instantly.
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 3 Methodology
### Particle Filter
#### 3.4.4 Particle Resampling  
Let \(M_{th}\) be the threshold of a valid number of particles. Then, by the \(k+1\)-th step, the valid number of particles is  
\[M_{k+1}=\frac{1}{\sum_{i=1}^{m}(\overline{w}_{k+1}^{(i)})^{2}}. \tag{20}\]  
If \(M_{k+1}<M_{th}\), it implies severe degeneracy. To mitigate the effects of the degeneracy, sampling importance resampling (SIR) is applied whenever the degeneracy problem is detected. The resampling stage aims to remove the samples with a small value of the weight and to focus on the samples that have a large value of weight. Specifically, \(m\) particles are chosen with replacements with respective weights as the probabilities. Then the weights are reset to \(1/m\) equally.
# Aggression Detection in Social Media from Textual Data Using Deep Learning Models
## 3 Methodology
### Feature Selection  
Overfitting [51] remains one of the major problems in many deep learning and machine learning algorithms. Feature selection is a process of finding the right combination of different features to build classifiers useful for solving a specific problem. Feature selection seeks to remove extraneous features that may be irrelevant, redundant because they are already modeled adequately by other features, or not needed by the end-user. We used the mutual information selection method to obtain the best-selected feature set among all combined features. The author in [52] presented a detailed review of the mutual information method. Moreover, they also discussed some of the variants of Mi. The author in [53] presented a new feature selection method based on mutual information, called the mutual information with correlation coefficient (CCMI). The mutual information and correlation coefficient is used to identify the relation between two features. [54], used the mutual information method along with the correlation coefficient for the intrusion detection system. Mutual information is a statistical measure of the relationship among two variables, Features \(X\) and target \(Y\) (usually represent random variables), which quantifies the amount of information obtained about \(Y\) from knowing the value of \(X\)[55]. Mutual information can be calculated by  
\[I(X;Y)=\int_{X}\int_{Y}P(x,y)\log\frac{P(x,y)}{P(x)P(y)}dxdy \tag{6}\]Here, \(P(x,y)\) shows the probability density function of variable \(X\) and variable \(Y\), and the marginal density functions are denoted by \(P(X)\) and \(P(y)\), respectively. The mutual information quantifies the similarity of joint distribution \(P(x,y)\) to the marginal distributions product \(P(x)\)\(P(y)\). If \(X\) and \(Y\) are independent (unrelated) then \(P(x,y)\) will be equal to the product of \(P(x)\), and \(P(y)\) and their integration will be 0. In feature selection, we want to make that the most predictive subset of features \(XS\) are relevant to the target variable \(y\). We can obtain the best subset of features \(XS\) by maximizing the mutual information between the feature and the target \(Y\).  
\[S=argmax\ I(XS;y),\ \ \ s.t.|S|=k \tag{7}\]  
Among all embedding features, the 25 best features were selected, which was then combined with 8 emotional features before feeding to machine learning and deep learning algorithms.
## 4 Results
### Characteristics of Pore Development
#### 4.2.2 Selective Dissolution  
Anhydrite concretion selective dissolution is the secondary reservoir space of the Majiagou Formation in the study area, and each small layer is developed. Anhydrite concretion selective dissolution is the pore formed by the dissolution of the anhydrite concretion (Figure 4A-C). The diameter is generally between 0.06 and 5 mm, mainly developed in fine-powder dolomite. The selective dissolutions were unfilled (Figure 4D), half-filled (Figure 4B,C), and full-filled. According to the different karst strengths, the filling conditions from top to bottom are different. The upper part is not half-filled, while the lower part is full-filled, with a face rate of 20% and uniform distribution. The aperture is about 2 mm. Most of the fillings are dolomite and gypsum, followed by calcite filling. The rock karstification is weak, the dissolution is mainly concentrated in the middle and upper parts, and a small amount of fractures are developed.  
Crystal-selective dissolutions are also referred to as crystal-selective dissolutions (Figure 4D-F). The number of halite crystal selective dissolutions is far less than that of columnar and needle-like gypsum crystal selective dissolutions. The pore morphology is  
Figure 4: Microphotographs of mold pores in the Majiagou Formation in the Ordos Basin. (**A**) L89, 3564.33 m, gypsum selective dissolution; (**B**) T42, 3605.67 m, gypsum mold pore (BSE); (**C**) T79, 3696.52 m, gypsum selective dissolution; (**D**) Sanchuanhe section, needle-shaped mold pore; (**E**) T79, 3696.52 m, cylindrical selective dissolution; and (**F**) T79, 3696.52 m, cylindrical selective dissolution.  
relatively regular, and the size is mostly between 0.3 and 0.6 mm. When there is local filling, it is mainly filled with calcite, followed by dolomite and a small amount of authigenic quartz (Figure 4E). The long axis of the pores is 0.3-0.5 mm, the short axis is 0.02-0.05 mm, and the pore length/width ratio is generally greater than 0.1. When there is local filling, it is mainly filled with calcite, followed by authigenic quartz (Figure 4D,F). This kind of pore has good reservoir performance, but it is less developed in the region. The selective dissolutions can increase the porosity of the reservoir and form additional oil storage space.
# Evolutionary Features for Dynamic Link Prediction in Social Networks
## 3 Network Datasets and Experimental Settings
### Datasets
#### 3.1.3 Co-Authorship Networks  
This study also considered two collaboration networks of authors of scientific papers in arXiv. Two sections were considered to build dynamic networks, namely, (i) high energy physics-phenomenology (Hep-ph) and (ii) high energy physics-theory (Hep-th). In these networks, a link between two authors represents a common publication that both authors have co-authored. \(G_{ph}\) and \(G_{th}\) are two symbols used to denote these two networks. Since the temporal granularity of these networks is a year, this study sampled dynamic networks considering yearly duration (i.e., \(\tau=365\) days) as the window size. In \(G_{ph}\), yearly networks for the duration 1992-1998 were analysed in the training phase to predict the links in the year 1999 and, likewise, in \(G_{th}\), the training phase was the duration 1993-1998, and the test phase was the year 1999. Table 2 sets out the basic and different temporal statistics for each type of network dataset. In this table, we explicitly describe the training and test duration for each dataset where the link prediction mechanism explores the link structure to predict the possibility of links during the test phase. In this study's context, the network within the training interval is split into smaller network snapshots known as short-interval networks (SIN) using three different sampling window sizes for each network but the co-authorship networks. In the latter case, we only used yearly window size. In the case of the undirected networks, we emulate daily, weekly and monthly networks by considering one-, seven-, and thirty-day sliding window sizes to sample dynamic networks and generate temporal network snapshots. On the other hand, in the case of the directed retweet network, we consider the hourly window as a sampling granularity. In the table, we observe that the cut-off time for this directed network is 4:00 a.m. Considering the timestamps, we selected 6, 12 and 24 h sampling window sizes to split this network and generate SINs. Considering this different sampling window size to aggregate links, each network dataset had different numbers of SINs in each dataset.
###### Abstract  
Background: There is a consensus among combat sports researchers regarding the important role of evaluating the comprehensive special fitness and performance of athletes. This is due to the specific structure of tasks performed in these sports as they require a comprehensive and integrated function of broadly understood physical fitness. The present study aimed to develop and check the Special Brazilian Jiu-Jitsu Fitness Test: Takedown Zone (SBJJFT-TZ), Gi formula's reliability. The major task of this sport-specific tool is to illustrate the comprehensive special fitness and performance of professional Brazilian jiu-jitsu athletes. Methods: The study covered 27 BJ] athletes (age in years: 25.36 \(\pm\) 2.99; height: 175.04 \(\pm\) 5.70 cm; weight: 76.56 \(\pm\) 8.59 kg; BMI: 24.96 \(\pm\) 2.30; 6.33 \(\pm\) 2.51 years of training) with a high sports skill level. A coaches' ranking of the athletes studied according to sports achievement was developed. An experimental approach to the problem was employed, with the SBJJFT-TZ assessment, including throws and specialized locomotion, performed on two dates (seven days apart). The test parameters were recorded and, using a specialized formula, an index showing comprehensive special fitness was calculated. To verify the test's validity and reliability, using statistical procedures, the results were comparatively analyzed (Student's _t_-test for the dependent variables), and the relationships between rankings were examined (Pearson's r linear correlation). The level of statistical significance was set at \(p\) < 0.05. Results: The analyses showed statistically significant relationships with very high strength between coach ranking and SBJJFT-TZ parameters for both dates (R = 0.73-0.88; \(p\) < 0.001). A similar trend of significant correlations was noted between the first and second SBJJFT-TZ dates (R = 0.96-0.98; \(p\) < 0.001), and the strength of the coexistence was almost complete. The test-retest procedure for SBJJFT-TZ showed similar levels for the parameters studied, without significant variation (_p_ > 0.05) but with a weak effect profile (d = 0.03-0.06). Internal variation in the variables was very low (HR CV \(<\) 10%) and moderate (Throws and Index CV = 20.82-32.25%). Significantly different throwing performance between sets was shown, with an advantage in the first round of work compared to the second, and a moderate effect (_p_ < 0.05; d = 0.38-0.39). Conclusions: The identification of relationships, the comparison, and the test-retest procedure showed the high application value of SBJJFT-TZ. The proposed tool provides a reliable cross-sectional evaluation of BJ] athletes' special fitness and performance in the stand-up fighting plane, as well as at the moment of achieving an advantage over the opponent, and favors implementation in field conditions.  
**Keywords: combat sports; Brazilian ju-jitsu; throws; special fitness; reliability; validation**
## 4 Results
### Breast Ultrasound Report Classification Model Ablation Experiment
#### 4.3.4 Impact of Generative Model on Classification Performance  
In the actual breast ultrasound image classification task, the generative model is used to denoise the Photo and obtain the GenPic image. Then, both Photo and GenPic are input into the breast ultrasound report classification model. To validate the performance  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input Type** & **Dataset** & _Accuracy_ & _Recall_ & **Specificity** & \(F1\) & **AUC** \\ \hline single input & Photo & 0.8874 & 0.8747 & 0.8614 & 0.8642 & 0.9355 \\ single input & GenPic & 0.8809 & 0.8872 & 0.8729 & 0.8801 & 0.8851 \\ dual input & Photo + GenPic & 0.8931 & 0.8865 & 0.8957 & 0.8942 & 0.9453 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of experimental results between single input and dual input.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & _Accuracy_ & _Recall_ & **Specificity** & \(F1\) & **AUC** \\ \hline Baseline & 0.8801 & 0.8753 & 0.8534 & 0.8706 & 0.9302 \\ Baseline + CBAM & 0.8832 & 0.8763 & 0.8723 & 0.8776 & 0.9365 \\ Baseline + CA & 0.8931 & 0.8865 & 0.8957 & 0.8942 & 0.9453 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental results of different attention modules.  
improvement of the classification model with GenPic images, this section tested different input images, and the experimental results are shown in Table 8.  
By comparing with Table 6, it can be seen that the dual-input Photo and GenPic present a slight improvement in most indicators compared with single-input. The dual-input uses two feature extraction modules to extract image features, so when classifying, the model has relatively more feature information available for use. When the input images are changed to Photo + GenPic, there is a significant improvement compared with only inputting Photo or GenPic. This is because the channel attention module can filter the original features contained in Photo and the image features restored by GenPic, selecting significant feature channels to enhance the classification model's performance.
## 3 Results
Figure 5: COF and applied load during scuffing load experiments. The friction mapping experiments allowed the measurement of the COF for the various lubricants with a complete run-in, fully lubricated contact at varying sliding and entrainment speeds. A comparison of the lubricant blends with additives to the baseline, unadditivized DoD-PRF-85734 oil is given in Figure 6 as the difference in COF between the lubricant blends and COF of the baseline oil for all of the sliding conditions (UE/slip %). The DoD-PRF-85734 + 1 wt% IL + 0.02 wt% NG blend is excluded from these results due to scuffing that occurred during the experiment. Negligible differences in COF were observed between the DoD-PRF-85734 + 0.02 wt% NG blend and the baseline oil across all operational speeds, as indicated by the low values in the figure. However, the DoD-PRF-85734 + 1 wt% IL oil showed a slight increase in COF below entrainment speeds of about 4 m/s. The difference in COF appeared to increase towards the low entrainment speeds and high slip % conditions and is largest at 100% slip and 0.35 m/s entrainment speed where the difference in COF is 0.004. After complete run-in, the surface roughness within the wear track was Sa = \(70\pm 10\) nm for DoD-PRF-85734, Sa = \(120\pm 30\) nm for DoD-PRF-85734+ 0.02 wt% NG, and Sa = \(110\pm 10\) nm for DoD-PRF-85734+ 1 wt% IL. The COF during the loss-of-lubrication experiments in a reciprocating contact is shown in Figure 7 for the various lubricant blends. During these experiments the COF quickly decreased to a stable value during the initial run-in. The COF remained mostly stable until the scuffing event, which resulted in a large increase in friction. The time to the onset of scuffing is taken from the time the oil is cut to the time the COF exceeds 0.3. The time to scuffing initiation was determined to be 12.65 min for DoD-PRF-85734, 25.55 min for DoD-PRF-85734 + 1 wt% IL + 0.02 wt% NG, and 143.11 min for DoD-PRF-85734 + 3 wt%  
Figure 6: Difference in COF under different contact velocity conditions between the lubricant blends and baseline lubricant. **(a)** 85734 and 85734 + 1 wt% IL, **(b)** 85734 and 85734 + 0.02 wt.% nanoscale graphene (NG). II + 0.02 wt% NG. The COF never exceeded 0.3 with DoD-PRF-85734 + 5 wt% IL + 0.02 wt% NG for the 304 min of operation before the experiment was stopped.