esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Baseline Classification  
To create a baseline for this database that utilises spatial information whilst maintaining a level of parity with the MFCC-Gaussian mixture model (GMM) baseline typically used in DCASE challenges [5; 6; 25], the audio was filtered into 20 mel-spaced frequency bands (covering the frequency range up to 12 kHz) using a bank of bandpass finite impulse response (FIR) filters. The filters each used 2048 taps and were designed using hamming windows. Estimate direction of arrival (DOA) estimates to be used as features were extracted from each band using directional audio coding (DirAC) analysis [35; 36; 37] as follows:  
\[\mathbf{D}=-\mathbf{P}\mathbf{U} \tag{1}\]where \(\mathbf{P}\) contains the 20 mel-filtered versions of the zeroth-order Ambsonic channel (W) of the recording and \(\mathbf{U}\) contains the filtered versions of the first-order Ambsonic bi-directional X, Y and Z-channels in a three-dimensional matrix. Resultant matrix \(\mathbf{D}\) contains instantaneous DOA estimates for each frequency band. Mean values of \(\mathbf{D}\) were calculated over a frame length of 2048 samples, with 25% overlap between frames. Angular values for azimuth \(\mathbf{\theta}\) and elevation \(\mathbf{\phi}\) were derived from this as follows [38]:  
\[\mathbf{\theta}=\arctan\left(\frac{\mathbf{X}}{\mathbf{Y}}\right) \tag{2}\]  
\[\mathbf{\phi}=\arccos\left(\frac{\mathbf{Z}}{||\mathbf{D}||}\right) \tag{3}\]  
where \(\mathbf{X}\), \(\mathbf{Y}\), and \(\mathbf{Z}\) are the X, Y and Z channel matrices extracted from \(\mathbf{D}\). These angular values were used as features. Diffuseness values were also used as features, and were calculated as follows [36]:  
\[\mathbf{\psi}=1-\frac{||-\mathbf{D}||}{c\{\mathbf{E}\}} \tag{4}\]  
where \(\{.\}\) represents the mean-per-frame values described previously, \(c\) is the speed of sound, and:  
\[\mathbf{E}=\frac{1}{2}\rho_{0}\left(\frac{\mathbf{P}^{2}}{Z_{0}^{2}}+||\mathbf{U}||^{2}\right) \tag{5}\]  
where \(Z_{0}\) is the characteristic acoustic impedance and \(\rho_{0}\) is the mean density of air.  
The database was split into four folds for cross-validation. In each fold, six location class recordings were used for training, with the remaining two used for testing. The extracted DirAC features from each frame of the training audio were used to train a bank of 10-component GMMs (one per scene class). The test audio was cut into 30-s segments (40 segments in total for testing). Features were extracted from these segments, and each GMM gave a probability score for the frames. These scores were summed across frames from the entire 30-s segment, with the segment classified according to the model which gave the highest total probability score across all frames.
# RJ-RRT: Improved RRT for Path Planning in Narrow Passages
## 4 Simulation and Result
### Complex Environment  
Figure 8 shows the performance of the three algorithms in a complex environment. When testing the Basic-RRT algorithm, due to random sampling in the entire configuration space, the final generated random tree occupies almost the entire space. However, although the final tree generated by the RRV algorithm produces fewer nodes than RRT, it still expands in a lot of useless space, and PCA takes a lot of time to calculate. The average number of samples required by the RRV to plan a path is 29% less than that of the Basic-RRT, but the final average planning time is 51% higher than that of the Basic-RRT. The RJ-RRT has the best performance in this environment. The experimental results in the figure show that only a few redundant nodes are generated during the sampling process, and the planning time is the shortest. The specific statistical results of the three algorithms are shown in Table 1. It can be seen that with the setting of the maximum number of iterations, the three algorithms can find the path 100% in the test. In terms of planning time, RJ-RRT saves  
Figure 7: Three different maps in the experiment. (**a**) Complex; (**b**) Narrow passage; (**c**) Bug Trap.  
87% compared to Basic-RRT, and 92% compared to RRV. The memory consumption of the generating node is 96% lower than that of Basic-RRT and 94% lower than that of RRV.
## 1 Introduction
Therefore, the motivation of this work was to develop a small absolute optical low-cost encoder with a significant higher resolution, such as state-of-the-art encoders with comparable small dimensions. To overcome the physical limitations for optical state-of-the-art encoders, a new approach was developed using a reflective blazed grating as an encoder disc and a specific photo detector with a circular array of photodiodes. In this way, the diameter of the encoder disc was no longer the limitation for the resolution because, on the encoder disc, there are no longer any Gray code patterns. In this way, a centric optical scanning of the encoder disc was possible. This achieved a higher integration density because the optical device could also be arranged centrally. Of course, the circular array of photodiodes was now the limitation factor. By comparison, state-of-the-art encoders need an eccentric arrangement of the optical device and, therefore, more construction space. With this novel approach, an optical encoder with a resolution of 1024 increments within only 1 cm\({}^{3}\) was developed. A demonstrator was built and tested successfully. The main advances of the proposed encoder are the small dimensions with a comparatively high resolution and an adjustment-free assembling of the encoder disc. Another advance is the manufacturing method of the encoder disc itself. While traditional optical encoders with high resolution need expensive encoder discs made of glass with chromium patterns and an additional adjustment process of the encoder disc, the proposed encoder needs only an injection-molded encoder disc with a simple blazed grating. The optical set-up is also relatively simple because the optical path is in a reflection order and there is no additional adjustment process necessary. A further advantage is the modular design concept. The same optical module and grating can be used for different encoder diameters. In summary, this results in a low-cost encoder with high resolution and small dimensions.
## 3 Research Model and Hypothesis Development
### The Moderating Effect of Experience of Privacy Violations  
In this study, we defined the experience of privacy violations as the degree to which SNS users worry about SNS-based leaks of personal information or violations of privacy [12]. The issue of personal privacy has become important in social science research, regardless of age. Indeed, in the Web 2.0 environment where SNSs proliferate, the problem of personal information security has become a particularly important social issue. SNS-related privacy concerns continually arise in relation to personal profiles, uploaded content, and messages [42]. In addition, SNSs are environments where one is forced to disclose personal information to some extent, which may lead to inadvertent leaks. Leaked information can, moreover, spread quickly and harm individuals. [PERSON] et al. [32] argued that the more anxiety one has about privacy violations, the stronger one's negative attitude toward SNSs. Furthermore, [PERSON] and [PERSON] [30] claimed that the likelihood of personal information leaking has increased on SNSs, fueling fatigue and reduced SNS use intentions. Users with living disorders are less likely to use SNSs, and this relationship will be further strengthened if an additional negative effect is added to this relationship through the experience of privacy violations. In this regard, [PERSON] et al. [43] claimed that individuals' attitudes toward using SNSs become negative once users experience privacy violations and their anxiety levels or burdens increase. Based on these claims, we proposed the following hypothesis.  
**Hypothesis 7**.: _The experience of privacy violations moderates the causal relationship between living disorder and reduced SNS use intention._
# The Use of Autogenous Teeth for Alveolar Ridge Preservation: A Literature Review
## 1 Introduction  
A tooth is indicated for extraction when it is no longer possible to restore or maintain in acceptable conditions considering its health, function and/or aesthetics [1]. The extraction of a tooth triggers a series of events that further result in the decrease of height and width of the alveolar process, particularly on the buccal side and horizontally [2, 3, 4, 5, 6, 7]. After extraction, this resorptive event occurs during the first three months of healing until one year, with potential aesthetic and functional consequences for prosthetic rehabilitation [2, 8].  
Due to the fallouts of alveolar ridge resorption after tooth extraction, a socket-filling procedure is frequently required when dental implants are planned to rehabilitate function, aesthetics and comfort [9]. To this end, alveolar ridge preservation (ARP) in post-extraction sockets is a well described surgical technique able to prevent bone resorption partially but not completely [10, 11].  
Several graft materials have been advocated in ARP including bone substitutes, such as allografts, xenografts, alloplasts and autografts (i.e., autogenous bone) [4]. Bone graft materials must have three main properties: osteochond (the ability to provide scaffold for bone regeneration), osteinduction (the capacity to recruit primitive, undifferentiated and pluripotent cells that are developed into having a bone-forming capacity) and osteogenesis (presence of cells that promote bone regeneration) [12, 13]. Autogenous bone is widely accepted as the gold standard bone graft material as it contemplates all three characteristics [9]. Nonetheless, autogenous bone has limited intra-oral availability, causes high donor site morbidity and presents elevated resorption rates [9, 12].  
A recently proposed material was autogenous teeth, commonly seen as dental waste after dental extractions [14]. Chemically, dentin is very similar to bone, with an osteeconductive and osteoinductive matrix, and therefore is a viable candidate for bone grafting [15; 16]. Autogenous teeth have fair intra-oral availability and may be obtained through standard procedures with low morbidity [17]. Nonetheless, it is important to bear in mind that the amount of dentin graft is dependent on the condition of the discarded teeth [12].  
Ever since, several protocols have been proposed for ARP using autogenous teeth as a graft material and, so far, three different methods of dentin processing have been developed: demineralized dentin matrix (DDM), partially demineralized dentin matrix (PDDM) and undemineralized dentin (UDD) (Figure 1) [12; 14]. However, these different methods present clinical pros and cons that deserve attention. For this reason, this review summarizes the available evidence on autogenous teeth as graft material, its different types and its applicability in ARP.
## 2 Results and Discussion
### Extraction Efficiency for Non-Polar Target Compounds  
The correlation of the surface wettability for the sorbent and the hydrophobicity of the target compounds in the sample matrix was further investigated. Superhydrophobic materials were expected to be the ideal sorbent for the enrichment of organic species with the least amount of polarity [40; 41; 42]. Herein, the lowest peak areas for five PAHs were obtained using the pristine hydrophilic wire (a) in Figure 9. In comparison, both superhydrophobic wires (b,d) exhibited similar enrichment abilities. It should be noted that the superhydrophobic wire (c) had the highest extraction efficiency to the non-polar compounds of five PAHs. It demonstrated that the surface wettability of the sorbent resulted in different effects for the preconcentration of target compounds. Most artificial superhydrophobic surfaces usually present a rough structure in the micro- or nanoscale and have poor mechanical and chemical stability. Herein, the fabricated superhydrophobic Ti wires can endure a long-time rinse from the mobile phase without a decrease in hydrophobicity and enrichment ability. In order to confirm the truth further, a superhydrophobic Ti mesh was immersed in the mobile phase solution for one week and sonicated discontinuously. The highly hydrophobic property still remained for the dried mesh. It indicated our fabricated superhydrophobic wire with strong mechanic and chemical stability could be used as a robust sorbent for in-tube SPME.  
In general, the enrichment capability is positively related to the absorption or adsorption capability of the coating material [17]. Herein, we first systematically elucidated the relationship between the surface wettability of the sorbent and the polarity of the target analytes by selecting a smart material with switchable surface wettability. It is similar to the theory of \"like dissolves like\", primarily determining the selectivity of a self-made coating by the hydrophobic/hydrophilic interaction.
# A Deep Learning Approach for Credit Scoring Using Feature Embedded Transformer
## 3 Theory and Method
### Evaluation Metrics  
In order to test the validity of the model, we choose two commonly used indicators of credit scoring to evaluate the performance of the model: area under the receiver operating characteristic curve (AUC) and Kolmogorov-Smirnov (KS).  
Let TP be the real status of the customer classified as non-default and who is judged to be non-default. FN is the real status of the customer classified as non-default and who is judged to be default. TN is the real status of the customer who is judged to be default. FP is the actual status of the customer classified as default and who is judged to be non-default. Define the True Positive Rate (TPR) as the number of TPs divided by the total number of positive customers, and define the False Positive Rate (FPR) as the number of FPs divided by the total number of negative customers; the formulae of TPR and FPR is as follows:  
\[\text{TPR}=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100\% \tag{8}\]  
\[\text{FPR}=\frac{\text{FP}}{\text{FP}+\text{TN}}\times 100\% \tag{9}\]  
Taking TPR as the abscissa and FPR as the ordinate, we draw the receiver operating characteristic (ROC) curve of the model. The closer the ROC curve is to the upper left corner, the better the performance of the classifier. However, since \"close to the upper left corner\" is only an intuitive description of the graph, it is generally only chosen to calculate the AUC value to better quantify the degree of proximity. AUC also considers the model's ability to discriminate between defaulting customers and non-defaulting customers, avoiding the problem of model evaluation criteria failure caused by sample imbalance. The larger the AUC value, the stronger the ability of the model to Identify defaults.  
The Kolmogorov-Smirnov (KS) is a commonly used credit score evaluation index, and it is mainly used to measure the model's ability to distinguish default users. After the model predicts the default probability of all samples, we sort the samples according to the predicted default probability, calculate the cumulative TPR value and the cumulative FPR value under each default rate, and then calculate the sum of the two values under each default rate. Then, after obtaining the absolute value of the difference, we take the maximum value of these absolute values as the KS value. The larger the value of KS, the better the ability of the model to distinguish between defaulting borrowers and on-time borrowers.
## 4 Proposed Compression Approach  
The compression approach used in this paper is composed of two main steps: model pruning and quantization. The flowchart of the compression approach is provided in Figure 2.
## 4 Design of MIM-QSMS Decoder for RC-QC-LDPC Codes
### Proposed SMIM-DE
#### 4.1.1 Channel Quantization  
As shown in the literature, e.g., [9; 11; 12; 16], the design of the FAIDs is conducted under a discrete additive white Gaussian noise (AWGN) channel with a properly selected noise standard deviation. Therefore, we first present the channel quantization considered by the SMIM-DE, which is used to discretize a continuous AWGN channel at the beginning of the design process. Assume that \(X\) is modulated by binary phase-shift keying (BPSK). Given a preset bit width \(q_{m}\), we first uniformly quantize the AWGN channel into a discrete memoryless channel with \(Y\) outputs, where \(Y\gg 2^{q_{m}}\), and we set \(Y=2002\) in this paper. Then, we adopt DP to find an optimal SDQ [21] for a \(q_{m}\)-bit channel output, which aims to maximize the mutual information between \(X\) and \(L\). In this way, we can obtain the conditional pmf \(P_{L|X}\) and the corresponding quantization LUT of the SDQ, denoted by \(\Gamma_{ch}\). The constructed quantization LUT is essentially a threshold set \(\Gamma_{ch}=\{\gamma_{k}:k=1,2,\ldots,2^{q_{m}}-1\}\) with \(\gamma_{1}>\gamma_{2}>\cdots>\gamma_{2^{q_{m}}-1}\), which operates as  
\[\Gamma_{ch}(x)=\begin{cases}0,&x\geq\gamma_{1},\\ 2^{q_{m}}-1,&x<\gamma_{2^{q_{m}}-1},\\ k,&\gamma_{k}>x\geq\gamma_{k+1}.\end{cases} \tag{9}\]  
Based on \(P_{L|X}\) and \(\Gamma_{ch}\), we next discuss the node updates of the SMIM-DE.
# Research on Routing Algorithm of Construction Robot Cluster Enhanced Ad Hoc Network
## 1 Introduction
### Motivations, Contributions, and Limitations  
In the construction industry, the application of construction robots is gradually becoming popular [29]. Cluster construction robots will no longer be satisfied with independently completing a particular part of the work. Instead, it is necessary to jointly complete industrial tasks through coordinated control and achieve the goal of improving work efficiency [30]. With the continuous development of the intelligent process, people's demand for mobile communication is not limited to daily life, and the demand for work is alsobecoming stronger and stronger. The characteristics of the distribution of construction robot clusters in industrial sites meet the requirements of distributed networking, so the research on construction robot cluster networking aligns with the current social science and technology development trend. However, it should be noted that the development and application of construction robots is increasing. There is an urgent need for coordinated communication among multiple robots. The complex environment of the construction site has seriously affected the connectivity of the construction robot ad hoc network [31]. The complex environment refers to the interference caused by metal building materials and walls on the construction industry site to the communication between construction robots. For the current situation of weak connectivity of coordinated communication between building robots, in this case, this paper proposes an enhanced ad hoc network algorithm (Ad hoc Cluster Enhanced Routing, ACER) for construction robot clusters. Firstly, by integrating the connectivity rate feedback mechanism and the routing path quality index to improve the routing path quality and network reliability, compared with other algorithms, this algorithm has a more significant effect in the construction environment, which significantly interferes with node communication. Regarding coverage area, the movement of construction robot nodes is adjusted based on the task priority, which reduces the impact of node movement on network coverage, further improves network connectivity, and improves the stability of the communication network of cluster construction robots. Finally, this was verified by simulation experiments. The validity and feasibility of the proposed algorithm are presented. The main contributions of this work are as follows:  
1. We designed a new route discovery method, which selects the relay nodes by calculating the node connectivity contribution to ensure the reliability of the communication network;
2. We optimized routing path selection rules to quantify routing node performance;
3. The topology control strategy based on link maintenance time and node priority was designed according to construction robots' different mobility and construction sequence. The network's connectivity in the construction environment is improved from the perspective of topology control, and the communication process between nodes is improved.
## 2 Materials and Methods
### Accuracy Evaluation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Artificial Buildings** & **Rice** & **Water Area** & **Vegetation** & **Total** & **User Accuracy (\%, UA)** \\ \hline Artificial Buildings & 175 & 9 & 6 & 10 & 200 & 87.50 \\ Rice & 9 & 162 & 6 & 23 & 200 & 81.00 \\ Water area & 1 & 2 & 189 & 8 & 200 & 94.50 \\ Vegetation & 10 & 16 & 5 & 169 & 200 & 84.50 \\ Total & 195 & 189 & 206 & 210 & 800 & \\ Mapping accuracy (\%, PA) & 89.74 & 85.71 & 91.75 & 80.48 & \\ Overall accuracy (OA) = 86.87\% & & Kappa coefficient = 0.8249 & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classification accuracy of time-series SAR and coherence coefficients. In cloudy regions, the application of optical image is restricted. The SAR image can penetrate through clouds and mists and therefore make up for the limitation of optical image. Hence, the use of optical image or SAR image should be determined according to the characteristics of the study area, research purpose, and research methods in rice monitoring and other fields. In this selection, the accuracy difference in the rice extraction result between optical image and SAR image should be analyzed under different cloud covers to obtain a scale. This scale can be used to determine which data source should be selected efficiently and quickly.
# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 5 Experiment
### Experiment Results
#### 5.2.3 User Activity for Different Length of Sequences  
In this paper, a sequence refers to the continuous check-ins of users over a certain period. Trajectories of varying lengths contain different amounts of information, especially those that are too short, such as those with only two or three check-ins. Such short sequences can also affect the models' performance. In this study, the length of sequences is categorized into three types: long, middle, and short. The 30% of sequences with the longest lengths are classified as long, the 30% with the shortest lengths are classified as short, and the remaining 60% are classified as medium-length. The results are shown in Table 4.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & & NYC & & \multicolumn{2}{c}{TKY} \\ \hline Sequence Length & Model & Acc@1 & MRR & Acc@1 & MRR \\ \hline Short & STHGCN & 0.2703 & 0.3783 & 0.2787 & 0.3710 \\ \hline Middle & STHGCN & 0.2545 & 0.3795 & 0.2923 & 0.3850 \\ \hline Long & STHGCN & 0.3184 & 0.4401 & 0.3116 & 0.4246 \\ \hline Short & ours & 0.2689 & 0.4072 & 0.2364 & 0.3269 \\ \hline Middle & ours & 0.2942 & 0.4328 & 0.3524 & 0.4851 \\ \hline Long & ours & 0.2862 & 0.4271 & 0.2905 & 0.3938 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of different sequence length.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & & NYC & \multicolumn{2}{c}{TKY} \\ \hline User groups & Model & Acc@1 & MRR & Acc@1 & MRR \\ \hline Inactive & STHGCN & 0.1460 & 0.2247 & 0.2164 & 0.3053 \\ \hline Normal & STHGCN & 0.3050 & 0.4265 & 0.2659 & 0.3596 \\ \hline Active & STHGCN & 0.3085 & 0.4402 & 0.3464 & 0.4618 \\ \hline Inactive & ours & 0.2599 & 0.3986 & 0.2753 & 0.3974 \\ \hline Normal & ours & 0.2780 & 0.4208 & 0.3192 & 0.4254 \\ \hline Active & ours & 0.2917 & 0.4315 & 0.2984 & 0.4173 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of different number of check-ins.  
Table 3 compares the results of our model with STHGCN on NYC and TKY datasets. As the table shows, our model significantly increases the performance on middle-length sequences, with 15.59% and 20.5% improvements on the NYC and TKY datasets, respectively, which implies our superior capability when dealing with these. The relatively lower results regarding the short or long sequences suggest we should leverage more valuable information with less noise. But the increase in MRR values indicate that our model can make more accurate recommendations in a broader range.