esa-sceva/satcom-chunk-collection huggingface dataset:
# Infrared Multi-Scale Small-Target Detection Algorithm Based on Feature Pyramid Network
## 2 Materials and Methods  
Although many current methods employ various techniques to enhance detection performance, there is still the issue of small targets disappearing in the deep network in many methods. Therefore, considering the limitations of the current detection algorithm, this paper proposes a dual attention fusion mechanism based on a pyramid network for infrared multi-scale small-target detection. It is mainly divided into three parts: pyramid segmentation-connection (PSC), context semantic expression (CSE), and dual attention fusion (DAF) modules. The context semantic representation module includes a global semantic relevance (GSR) module and a local semantic stitching (LSS) module.
# A Study of EEG Feature Complexity in Epileptic Seizure Prediction
## 3 Results and Discussion
### Analysis of the Complexity of the Pre-Ictal and Inter-Ictal Features
Indeed, the maximum Fisher discriminant ratio is 0.0245 for epilepsy data, 31.19, 49.94, and 4.27 for the Versicolor-Virginica, Setosa-Virginica, and Setosa-Versicolor, respectively, from the Iris data, 0.9 for the Letter dataset, 2.66 for the human activity recognition (HAR) dataset, and 1.7 \(\times\) 10\({}^{-5}\) and 1.66 \(\times\) 10\({}^{-5}\) for the two random datasets. Similarly, the maximum feature efficiency of easy classification problems from Iris and Letter data, and for a relatively complex classification problem, such as human activity recognition, given the HAR dataset, is higher than 0.25, unlike the epilepsy data having a low value of 0.003 and the two random data having, respectively, a maximum feature efficiency of 0.007 and 1.66 \(\times\) 10\({}^{-5}\). Hence, the maximum feature efficiency of the epilepsy prediction problem is not as high as the comparatively easy problem nor as low as the intrinsically complex random noise problem. Therefore, this shows evidence that the epilepsy data does contain learnable structures, yet the classification problem using the extracted features from the EEG records is highly complex.
## 3 Materials and Methods
### Feature Transformation and Reduction Approaches  
#### 3.2.1 Discrete Wavelet Transform  
The DWT is a prevalent method for transforming a signal or photo into its wavelet representations, facilitating analysis in both temporal and frequency domains [48]. The process encompasses several kinds of wavelets, principally classified as orthogonal and biorthogonal. Orthogonal wavelets, originally developed by Hungarian mathematician [PERSON] [49], are particularly significant. According to [50], the DWT mechanism involves the transformation of data (which could be a signal or photo) \(D\) using a series of filters. In the beginning, \(D\) is subjected to a low-pass filter characterised by an impulse response \(P\), leading to a convolution procedure represented as:  
\[DWT[m]=(D*P)[m]=\sum_{q=-\infty}^{\infty}X[m]L[m-q] \tag{1}\]  
where the instance is denoted by \(q\) and the decomposition step by \(m\).  
The data undergoes processing through a high-pass filter \(H\mathrm{i}_{\mathrm{t}}\) resulting in a pair of elements: detailed coefficient from the high-pass filter and approximation coefficients from the low-pass filter [51]. The Nyquist rule dictates that fifty percent of the input signal's spectrum of frequencies is discarded. As a result, the result obtained from the low-pass filter is down-sampled by a value equal to two and subsequently analyzed by sending it across successive high-pass and low-pass filters. The low-pass filter output \(L\)o and the high-pass filter output \(H\mathrm{i}\) are both decreased by a factor of 2, as illustrated here:  
\[DWT_{low}[m]=\sum_{q=-\infty}^{\infty}X[m]Lo[2m-q] \tag{2}\]  
Figure 1: Thermal IR photos of the breast from the DMR-IR dataset.  
\[DWT_{high}[m]=\sum_{q=-\infty}^{\infty}X[m]Hi[2m-q] \tag{3}\]
# Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks
## 2 Related Work
[59] was 40 Hz, while the WISDM [26] and Actitracker datasets [27] were sampled at 20 Hz. According to [60], ninety-eight percent of the power for the walking activity was contained below 10 Hz, and ninety-nine percent was contained below 15 Hz. Furthermore, no amplitudes higher than 5% of the fundamental existed after 10 Hz. Regarding on-device implementations, the main frequencies were lower than 18 Hz when a mobile device was carried in a hip location [61]. Lower sampling rates can reduce battery usage and memory needs. Much work has been directed toward finding the optimal window size, but most implementations use window sizes in the 1-10 s range. Most implementations use fixed window sizes of 1.28 s [53], 1.8 s [52], 2.5 s [50; 51], 2.56 s [37; 43; 44], 3.2 s [46], 7.5 s [59], 10 s [47; 48; 54; 26; 49; 54], 2 s and 5 s [55], while [PERSON] et al. tested their implementation in the 1 to 10 s window size range [35]. An overview of the works presented in this section can be seen in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Method(s)** & **Window Size** & **Dataset Used** \\ \hline
[34] & AdaBoost, J48, SVM, Random Forest & Variable & Custom \\ \hline
[26] & J48, Logistic Regression, MLP & 10 s & WISDM \\ \hline
[36] & PSO k-NN & 5 s & Daily and Sports Activities Dataset \\ \hline
[35] & Random Forest, k-NN, CNN & 1–10 s & WISDM, UCI HAR \\ \hline
[37] & SVM & 2.56 s & UCI HAR \\ \hline
[38] & NB, BN, J48, MLP, ALR, Bagging & Variable & Custom \\ \hline
[3] & RT, Bagging, J48, BN, KNN, DT, CGHAR & 2.56 s & UCI HAR \\ \hline
[39] & CNN, LSTM & 1 s, 5.12 s, 1 s & Opportunity, PAMAP2, DFoG \\ \hline
[40] & CNN, LSTM & 0.5 s & Opportunity, Skoda \\ \hline
[43] & CNN & 2.56 s & UCI HAR \\ \hline
[44] & CNN & 2.56 s & UCI HAR \\ \hline
[45] & CNN & 10 s, 2.56 s & WISDM, UCI HAR \\ \hline
[46] & CNN & 3.2 s & WISDM \\ \hline
[47] & DBN, HMM & 10 s & WISDM, DFoG, Skoda \\ \hline
[48] & LSTM & 10 s & WISDM, Custom \\ \hline
[49] & LSTM, RNN & 10 s & WISDM \\ \hline
[52] & CNN & 1.8 s & WISDM \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summarization table.
## 4 Simulation Results
### Analysis on Field of View  
Figure 11 shows the effect of FOV on the average system throughput. To meet the coverage requirements of the proposed algorithm, the deployment density of APs is increased to 0.284 APs/m\({}^{2}\). The performance of the four methods decreases as FOV increases. This is because when terminals  
Figure 10: The average ratio of satisfied terminals versus average blocking probability.  
Figure 9: The average system throughput versus average blocking probability.  
are within FOV, the first derivative of the optical concentrator gain with respect to \(\varphi_{FOV}\) satisfies \(g^{\prime}(\varphi_{FOV})=-2n^{2}\cos\varphi_{FOV}/\sin^{2}\varphi_{FOV}\), which is less than 0 according to Equation (2). Therefore, an increase in FOV will lead to a decrease in \(g(\varphi_{FOV})\). The channel gain will decrease as \(g(\varphi_{FOV})\) decreases according to Equation (1). Therefore, the FOV angle can be appropriately reduced to improve the average system throughput. However, an excessive reduction of FOV will cause the shrinkage of the coverage area of APs and the formation of communication blind zones. Thus, a tradeoff is needed between the coverage efficiency and throughput performance. The decreasing rates of the four curves decreases as FOV increases. This is because the second derivative of the optical concentrator gain with respect to \(\varphi_{FOV}\) satisfies \(g^{\prime\prime}(\varphi_{FOV})=2n^{2}\big{(}1+2\cos^{2}\varphi_{FOV}\big{)}/ \sin^{4}\varphi_{FOV}\), which is greater than 0. Compared with the RDR-PA method, the performance improvement ratio of the proposed algorithm is stable at 17.2%.  
In Figure 12, the average ratio of satisfied terminals is shown as a function of FOV. The proposed algorithm has a similar performance to the inter-point method and outperforms the RDR-PA method and the uniform method. The performance decreases as FOV increases. This is because an increase in FOV will lead to a decrease in the average system throughput.  
Figure 11: The average system throughput versus the field of view.  
Figure 12: The average ratio of satisfied terminals versus the field of view.
# Automatic Segmentation of Mandible from Conventional Methods to Deep Learning--A Review
## 3 Results
### Methodology
#### 3.4.1. SSM-Based, ASM-Based and AAM-Based Methods
The active shape model (ASM) [109] is the one of the commonly used image segmentation techniques based on the SSM approach. In 2006, [PERSON] et al. [40] used a vanilla SSM-based segmentation method to extract the anatomical variability of developed mandible shapes in the training stage and match the statistical mandible shape to a given CT dataset via a deformable model approach in the testing stage. [PERSON] et al. [42] developed a segmentation method based on an SSM and a Dijkstra-based optimization for reconstructing the mandible including the course of the alveolar nerve. [PERSON] et al. [41] adopted a multiatlas-based segmentation to obtain the coarse segmentation for the OARs and then employed the active shape model (ASM) to finely segment the OARs based on the results from coarse segmentation. The active appearance model (AAM) [110] is an extension of SSM and ASM to further statistically model the texture information of the object. Two statistical models of shape and texture are further merged into an appearance model [110]. [PERSON] et al. [44] applied the AAM approach to automatic mandible and brainstem segmentation. [PERSON] et al. [43] utilized the groupwise image registration method-based minimum description length approach [111] and AAM [110] built from manually segmented objects in CT images. Moreover, several publications on SSM-based segmentation methods have been used in combination with other strategies to enhance the segmentation outcomes. Hybrid methods have demonstrated good results [18; 37; 38; 39]. [PERSON] et al. [39] used SSM adaptation for mandible segmentation and then utilized the graph cut algorithm for fine segmentation in the MICCAI 2019 challenge [112]. [PERSON] et al. [37] applied the SSM method with optimized correspondence for mandible segmentation. The authors established correspondence by optimizing a model-based cost function. Moreover, the authors introduced a relaxed SSM method for mandible segmentation [38]. [PERSON] et al. [18] proposed a framework based on SSM and fast marching for automatic segmentation of the mandibular bone and canal. Moreover, the authors utilized low-rank decomposition for preprocessing. Table 3 presents the segmentation performance using SSM-based, ASM-based and AAM-based approaches.
## 3 Evaluation of Process Precision and Accuracy
### Process Quality Evaluation  
Then, the process quality is evaluated based on 25 groups of subsample data in statistical process control. Since \(Z_{0.0025}=2.807\), \(\chi_{0.0025250}^{2}=191.802\), \(\chi_{0.9975250}^{2}=317.362\) and based on the above data in the control charts, we have  
\[\delta_{L}=\overline{\delta}-\frac{Z_{0.0025}}{\sqrt{275}}\times\sqrt{\frac{25 0}{\chi_{0.0025250}^{2}}}\overline{\gamma}=0.443-\frac{2.807}{\sqrt{275}} \times\sqrt{\frac{250}{191.802}}\times 0.196=0.405\]  
\[\delta_{R}=\overline{\delta}+\frac{Z_{0.0025}}{\sqrt{275}}\times\sqrt{\frac{25 0}{\chi_{0.0025250}^{2}}}\overline{\gamma}=0.443+\frac{2.807}{\sqrt{275}} \times\sqrt{\frac{250}{191.802}}\times 0.196=0.481\]  
\[\gamma_{L}=\sqrt{\frac{250}{\chi_{0.9975250}^{2}}}\overline{\gamma}=\sqrt{\frac {250}{317.362}}\times 0.196=0.174\]  
\[\gamma_{R}=\sqrt{\frac{250}{\chi_{0.0025250}^{2}}}\overline{\gamma}=\sqrt{\frac {250}{191.802}}\times 0.196=0.224\]  
According to Section 3, the process was required to reach a quality level of six-sigma, the accuracy index was required to be \(-0.25/k\leq\delta\leq 0.25/k\), and the required precision index was \(\gamma\leq 1/6\). Thus, on the statistical test, the null hypothesis (\(H_{0}\)) and the alternative hypothesis (\(H_{1}\)) are expressed as follows:  
\[H_{0}:\delta\in A_{\delta}\;\text{and}\;\gamma\in A_{\gamma} \tag{44}\]  
\[H_{1}:\delta\
otin A_{\delta}\;\text{or}\;\gamma\
otin A_{\gamma} \tag{45}\]  
where \(A_{\delta}=\{-0.25\leq\delta\leq 0.25\}\) and \(A_{\gamma}=\{\gamma\leq 1/6\}\). According to the testing rules,  
1. If \([\delta_{L},\delta_{R}]\cap A_{\delta}=\phi\), then reject \(H_{0}\). It is concluded that the process accuracy needs to be improved.  
If \(\gamma_{L}=0.174>1/6\), then reject \(H_{0}\). It is concluded that the process precision needs to be improved.
# Performance of Solar Control Films on Building Glazing: A Literature Review
## 5 Discussion of Existing Studies on the Performance of Solar Control Films
### Comfort Assessment  
A minority of research studies analyze the impact of SCFs on occupants' thermal [34; 43; 46; 50; 53; 55; 57] and visual [19; 43; 46; 50; 53] comfort, considering different standards and metrics.  
Existing studies usually analyzed thermal comfort through the PPD and PMV thermal indexes developed by [PERSON] [63]. Few studies [46; 55; 57] use indoor operative temperature levels to assess thermal comfort. No existing research study considers adaptive thermal comfort models to assess indoor thermal comfort in the presence of SCFs.  
In terms of visual comfort, existing studies used the useful daylight illuminance (UDI) metric [64; 65] to determine the percentage of working hours with useful illuminance levels (between 0.1 and 3.0 klx). The assessment of the indoor UDI levels performed by the existing studies is rather limited since they only consider up to two indoor points to compute and compare illuminance levels with and without SCFs. On the contrary, [PERSON] et al. [19] conducted an experimental study that focused on the impact of SCFs on daylight availability under clear sky conditions (measurements from winter and summer solstice) using small scale-models. The authors mapped the horizontal daylight distribution inside the models, considering the absolute illuminance values and the ranges of the UDI metric, concluding that SCFs decreased illuminance levels below critical values and provided a more extensive spatial distribution of acceptable daylight availability levels compared with the clear single-glazing without a film.  
Still, regarding visual comfort, only the study conducted by [PERSON] et al. [46] assessed indoor glare in the presence of SCFs. The authors used the daylight glare index (DGI) metric and concluded that highly reflective SCFs could significantly reduce potential glare and promote comfortable glare levels (DGI \(<\) 22) during 43% of working hours. However, recent studies suggest that the daylight glare probability metric is more suitable to assess glare in daylight-dominated indoor spaces.  
Since people spend a significant amount of time indoors [66], the assessment of the impact of SCFs on indoor thermal and visual comfort becomes of extreme importance. For this reason, future research on the topic is strongly advised and should help fill the identified research gaps.
## 1 Introduction
In particular, it is assumed that the data are obtained with a portable LIDAR scanner with a short scanning time and generating a relatively low number of points for each scan. * The focus of the algorithm was on minimizing the computational load. Consequently, the total processing time of the proposed workflow is short enough to allow processing of the scans in real time with low latency. * The proposed solution is purposely limited to the data from a single sensor. The proposed solution is expected to become a component of a future multi-sensor system of navigation dedicated to a mobile robot for idler roller inspection.
## 2 Related Work
### Mechanical Signal Transfer through Human Body Parts  
There have been numerous application systems for transmitting acoustic signals through human body parts [11; 12; 13; 14] by utilizing the fact that ultrasonic waves can be propagated through the human body (e.g., skin and bone [15] ). As a previous study described, it is worth noting that human body, which is composed of around 65% of waters, is not good medium for transmitting the RF electromagnetic waves [16].  
In [14], a recent work proposed an ultrasonic transmission and a multiple access technique in the context of intra-body area networks. They validated the feasibility of the proposed method by experimentally demonstrating a communication between two nodes through a human-kidney phantom. In their follow-up study [17], they further proposed U-Wear architecture for enabling wearable medical devices to efficiently communicate with each other based on the near-field ultrasonic sounds. In another recent study, bone-conducted sound signals were utilized for measuring the touched position and angle of the elbow joint [11]. In their proposed passive sensing scenario, two microphones, located at the styloid process of the ulna and olecranon to capture bone-transmitted acoustic signals in a robust way, are used to measure the signal generated by touch. On the basis of the fact that the amplitude of the two signals is dependent only on the distance from the source (_i.e._, finger taps) to the sensor, the system is able to estimate the touched position. In the active sensing scenario, two sensors measure reflected sinusoidal signals of 800 Hz, which are transmitted from the attached speaker, and analyze the magnitude of the signal. Considering that the amount of reflection is proportional to the elbow angle (_i.e._, the more the elbow is bent, the less the signal is transmitted to the upper arm), they could estimate the elbow's bending angle on the basis of the measured signal intensity. Recently, [PERSON] _et al._ proposed a touch sensing system based on transdermal ultrasound propagation [12]. In one configuration, a finger-mounted receiver measures ultrasonic vibration transmitted from an armband-mounted transmitter on the contralateral arm. By utilizing frequency-based coding (35-50 kHz), the system could identify which body parts (e.g., forearm, anterior and posterior) are currently being touched. In addition, measured intensity is used for calculating the touched distance from the armband. Another recent project used the human body for transmitting acoustic signals for the purpose of resolving the location of finger taps on the forearm [13]. Their novel idea is mainly focused on utilizing the infinitesimal mechanical waves produced by finger impacts on skin for estimating the location of the on-body finger taps.
# Electrochemical Degradation of Crystal Violet Using Ti/Pt/SnO\({}_{2}\) Electrode
## 3 Results and Discussion
### Depollution in Terms of COD Removal and Current Efficiency (ICE)  
In order to study the potentialities of this electrode for depollution in terms of COD removal, electrolysis was carried out on a CV solution with an initial COD of 76 mg L\({}^{-1}\) at 50 mA cm\({}^{-2}\) and the electrolyte concentration (NaCl) was set at 0.01 M. Figure 9a shows the COD removal, calculated from Equation (9), as a function of electrolysis time using the Ti/Pt/SnO\({}_{2}\) anode.  
One can observe that the initial COD concentration of 76.41 mg L\({}^{-1}\) has been reduced to 9.16 mg L\({}^{-1}\) corresponding to an elimination of 82%. To detoxify the solution, the removal of the color is very often not sufficient. Indeed, 20 min of electrolysis are enough to reach the decolorization of the solution while there is still 60% COD. In these conditions,  
Figure 8: CV abatement rate as function of (**a**) time and (**b**) electrical charge in C/(mgL\({}^{-1}\)). [CV]\({}_{0}\) = 10 mg L\({}^{-1}\), initial pH = 7, i = 50–60 mA cm\({}^{-2}\) and temperature = 25 \({}^{\circ}\)C.  
Figure 9: Temporal variation of COD and CV removal rate during electrolysis on Ti/Pt/SnO\({}_{2}\) anode. (**a**) Instantaneous current efficiency (**b**). Operating conditions: [CV]\({}_{0}\) = 10 mg L\({}^{-1}\), 50 mA cm\({}^{-2}\), initial pH = 7, temperature = 25 \({}^{\circ}\)C.  
it is useless to continue beyond one hour because a plateau has been reached in the COD removal.  
Afterward, the instantaneous current efficiency (ICE) during crystal violet degradation process was calculated by the following Equation (10):  
\[\%COD=\frac{COD_{i}-COD_{t}}{COD_{i}}\times 100 \tag{9}\]  
\[\%ICE=FVs\frac{(COD_{0}-COD_{t})}{8 IAt}\times 100 \tag{10}\]  
with \(COD_{i}\): \(COD\) value (mg O\({}_{2}\) L\({}^{-1}\)) at the initial time, \(COD_{t}\): \(COD\) value (mg O\({}_{2}\) L\({}^{-1}\)) at time \(t\), both given (g O\({}_{2}\) L\({}^{-1}\)); t is the time in (s); \(F\) is the Faraday constant (96,487 C mol\({}^{-1}\)); \(V_{s}\) is the volume of the electrolyte (L); and \(I\) is the current (A).  
The ICE during the electrolysis is shown in Figure 9b. The initial value close to 55% confirms that the oxidation of the organic matter is not the only process. Indeed, part of the applied current used for the water discharge increases as the concentration of CV and by-products decreases. This exponential decrease is due to the limitation of mass transfer. To increase ICE values, an adjustment of the applied current must be done during the electrolysis.