esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Two of the most widely used data reductions techniques are the instance selection [23], also known as prototype selection in case of instance-based algorithms [24], and the feature selection or reduction of the data dimensionality [25]. The first technique focuses on finding an optimal subset of samples to optimize the performance of the learner while the main idea of the second technique is to replace the original set of features by a new subset that extract the main information and provide an accurate classification. A few methods are available to approach the instance selection in the MLL domain [26; 27; 28] but, to the best of our knowledge, there are no studies concerning instance or prototype selection reported in LDL. With regard to the feature selection, most of LDL algorithms are built on a simple feature space where all features are used to predict all the labels. However, in real-world applications, an instance is characterized by all labels, but some labels can only be determined by some specific features of their own. Although label-specific feature selection has been widely studied in MLL [29; 30], the studies we can find on this subject for LDL are still scarce [31]. Prototype selection and Label-Specific Feature Evolutionary Optimization for Label Distribution Learning, or from now ProLSFEO-LDL, is our proposal for a pre-processing algorithm adapted to LDL problems and specifically designed to mitigate the handicaps of the AA-kNN method. We provide a novel method to simultaneously address the prototype selection and the label-specific feature selection. Both pre-processing techniques can be considered as a search problem where the search space is huge. Therefore, we have devised a search method based on evolutionary algorithms [32] that allows us to obtain a solution to both problems in a reasonable time. To this end, we have adapted elements of classical evolutionary algorithms to manage LDL restrictions, we have designed a representation of the solution and a way to measure its quality, which allows us to address both problems together. In order to evaluate the proposal proficiency, we will compare an LDL-learner applying our ProLSFEO-LDL algorithm to the raw training set and measuring six aspects of their performance. We will repeat the experiment over 13 real-world datasets and validate the results of the empirical comparisons using Wilcoxon and Bayesian Sign tests [33; 34; 35]. The rest of the paper is organized as follows. First, a brief review and discussion of the foundations of LDL, a description of data reduction techniques and an introduction to the evolutionary optimization process are given in Section 2. The proposed ProLSFEO-LDL method is described in Section 3. Then the details of the experiments are reported in Section 4. Finally, the results and conclusions are drawn in Section 5 and Section 6, respectively.
# A Fast Acquisition Algorithm for Hybrid Signals of 5G and BeiDou B1
## 4 Conclusions
\begin{tabular}{l l} FPGA & Field programmable gate array \\ DSP & Digital signal processing \\ GPU & Graphics processing unit \\ DSPs & Digital signal processors \\ DRAM & Dynamic random access memory \\ MCMM & Mixed mode clock manager \\ PLL & Phase lock loop \\ FIFO & First input first output \\ ADC & Analog-to-digital converter \\ BDS & BeiDou navigation satellite system \\ BPSK & Binary phase shift keying \\ CNR & Carrier to noise ratio \\ CDMA & Code division multiple access \\ DLL & Delay lock loop \\ DSSS-CDMA & Direct-sequence spread-spectrum code division multiple access \\ ELS & Early late slope \\ EKF & Extend Kalman filter \\ FDD & Frequency division duplexing \\ FFT & Fast Fourier transformation \\ FIR & Finite impulse response \\ FLL & Frequency-locked loop \\ Galileo & Galileo satellite navigation system \\ GLONASS & Global navigation satellite system \\ GNSS & Global navigation satellite system \\ GPS & Global positioning system \\ HRC & High-resolution correlator \\ IFFT & Inverse fast Fourier transformation \\ LNA & Low-noise amplifier \\ LPF & Low pass filter \\ MSE & Mean squared error \\ MIMO & Multiple input multiple outputs \\ NCO & Numerically controlled oscillator \\ OFDM & Orthogonal frequency division multiplexing \\ PMF & Partial matched filters \\ PNT & Positioning navigation and timing \\ PPS & Pulse per second \\ PRN & Pseudorandom noise \\ QAM & Quadrature amplitude modulation \\ SA & Selective availability \\ SNR & Signal-to-noise ratio \\ TDD & Time division duplexing \\ TD-SCDMA & Time division synchronous code division multiple access \\ UWB & Ultra wideband \\ WGN & White Gaussian noise \\ \end{tabular}
## 5 Performance Evaluation
### Packet Delivery Ratio  
We then conducted a number of experiments to compare the packet delivery ratio of GPNC with those of DBR and VBF_NC in terms of the number of nodes and the loss rate. Generally speaking, the packet delivery ratio increases with the number of nodes for all the three protocols, however, as shown in Figure 11, the protocols using network coding, which are GPNC and VBF_NC, achieve higher delivery ratios than the DBR protocol, which does not use network coding at all. Network coding can increase delivery rate because it can not only decrease the number of sending packets but also reduce collision and competition between packets. More importantly, GPNC's delivery ratio is about 16% higher than that of VBF_NC when \(N=5\) (GPNC's network delay is 22% lower than that of VBF_NC as shown in Figure 6).  
Figure 12 further shows that the packet delivery ratio decreases with the increase of packet loss rate of transmission link for all the three protocols. However, the protocols using network coding, which are GPNC and VBF_NC, again achieve much higher delivery ratios than the DBR protocol (GPNC and VBF_NC are 45% and 26% better than DBR respectively) because with network coding, data packets do not need to be retransmitted even if some data is lost in transmission. More importantly, GPNC's delivery ratio is about 19% higher than that of VBF_NC as GPNC employs multiple sink nodes for efficient data transmission.  
Figure 11: Packet delivery ratio with the number of nodes when \(N=5\).  
Figure 10: Energy consumption with the number of nodes when \(N=5\).
## 1 Introduction
Finally, the performance of the ML models was compared in terms of accuracy and area under the receiver operating characteristic (AUROC) to determine whether feature selection had an impact. Explainable AI [17] was employed to interpret the ML models' decisions. Explainable artificial intelligence (AI) is a set of techniques and strategies that allow humans to understand and trust the results and output of ML algorithms. For the purpose of explaining ML models, Shapley additive explanation (SHAP) was used in this study. The followings are the contributions of this research:* This work broadens the field of mortality prediction in the ICU for heart failure patients by examining the impact of several nature-based feature selection methods on prediction;
* The role of features in the prediction process was analyzed using SHAP in this study, providing insight into the determinant features that decide mortality in the ICU.
# A Review of Air Pollution Monitoring Technology for Ports
## 2 Detection Methods for Air Pollutants in Ports
### PM Detection Methods  
According to the emission inventory in Table 3, TSP (Total Suspended Particulate) and PM (Particulate Matter) are the particulate pollutants that have a great impact on the ambient air in the port, so this section will mainly discuss the detection methods of TSP, PM2.5, and PM10.
# A Study of Model Iterations of Fitts' Law and Its Application to Human-Computer Interactions
## 4 Some Studies of Fitts' Law in HCI Scenes
### Handheld Devices  
A mouse's main purpose is to give users a tangible way to enter data into a computer by enabling them to click and move the cursor across the screen. The mouse gain, which is the ratio of the distance on the screen to the distance the mouse moves, affects the sensitivity and speed of the pointer. To improve the efficiency of HCIs, a great deal of research is being conducted to find the ideal mouse gain and integrate Fitts' law. Cursor behavior is affected differently by varying gains; a high gain allows for faster but less accurate cursor placement, whereas a low gain allows for regulated accuracy but slower cursor movement. It is imperative to strike the ideal balance between precision and speed, which calls for a trade-off between high and low gain settings [71]. [PERSON] et al.'s study looked at the relationship between movement time (MT) and different gains and found that the optimal gain depends on the size of the movement [72]. [PERSON] et al. also found that, in addition to target size and distance, the angle of approach has a major impact on target selection when using a mouse, which is consistent with the results of touchscreen pointing tasks [73]. Fitts' law, which takes into account the angle of approach as well as the best mouse gain data--which has been demonstrated through research--can be used in computer interface design to facilitate effective mouse operation.
# Study on Penetration Sensing Method Based on Pool Oscillation and Arc Voltage during Pulsed GMAW
## 2 Materials and Methods
### Penetration Status Definition  
In the production process of welding, penetration status cannot be classified by the geometry of the weld cross-section. The shape of the bead on the back of the weld was the main criterion to determine the penetration status. In this paper, penetration status is classified into partial penetration, critical penetration and full penetration, as shown in Figure 3. The shape of the bead on the back of the full penetration weld was stable and uniform, as shown in Figure 3a. The shape of the bead on the back of the critical penetration weld was discontinuous and constructed by discrete fusion points, as shown in Figure 3b. The back of the partial penetration weld was the continuous unfused groove, as shown in Figure 3c. The penetration status of the weld beads in this paper are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Materials** & **C** & **Mn** & **Si** & **P** & **S** & **Ni** & **Cr** & **Mo** & **V** & **Other** \\ \hline S235 JR & 0.17 & 1.40 & 0.3 & 0.035 & 0.035 & - & - & - & - & N \\  & & & & & & & & & 0.012 \\ \hline ER70S-6 & 0.06–0.15 & 1.4–1.8 & 0.8–1.2 & 0.025 & 0.035 & 0.15 & 0.15 & 0.15 & 0.03 & Cu 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical compositions (in wt%) of base metal and filler wire (Fe balance).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Welding Parameters** & **Value** \\ \hline Mean welding current (A) & 60 A. \\ Mean arc voltage (V) & 19.5 V. \\ Wire feed rate (m/min) & 2.2 m/min. \\ I\({}_{\text{p}}\) and I\({}_{\text{b}}\)/A & I\({}_{\text{p}}\) = 495 A; I\({}_{\text{b}}\) = 18 A. \\ Pulsing frequency & 40 Hz. \\ Welding travel speed (cm/min) & 11, 13.75, 16.5, 19.25, 24.75, 30.25, 35.75. \\ Arc height (grade displayed on the power) & 0, 3, 6, 9. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Welding process parameters.  
Figure 2: Experimental system and image processing system.
# Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols
### Dynamic MANET on Demand Routing Protocol (DYMO)  
DYMO is an innovative reactive (on-demand) routing protocol that is currently being developed in a simpler design. This helps to minimize the system requirements of participating nodes and makes it easier to implement the protocol. DYMO maintains the tried-and-true techniques of previously investigated routing protocols, such as the use of sequence numbers to ensure the absence of loops. At the same time, DYMO expands the purview of the MANET working group that is part of the Internet Engineering Task Force (IETF). DYMO makes use of the knowledge gained from prior methods of reactive routing, most notably the routing protocol AODV. It intends to provide path accumulation and cover all potential scenarios involving a gateway between a MANET and the internet as part of its features.
# A Cell Counting Framework Based on Random Forest and Density Map
## 3 Method
### Detection Framework
#### 3.2.3 Density Map Generation  
The last step of the detection framework is averaging all detection maps predicted in the previous step. This step is very key to refine the count result because it benefits from the overall CCF including the well-prepared training data, the ensemble of RFs, and the detection based on the probability map.  
Next, we will explain the reason why the average can refine the count. Four cases are listed for discussion in Figure 9. The red circle represents the true cell region, the box represents the local region belonging to different detection maps and the colorful patch is the corresponding detected cell. The left of Figure 9a-d is the stack of detection maps, and the right is the averaged detection result which is denoted as the lighter colors. Assuming there are 4 detection maps, and each detected cell in the averaged detection map denotes 0.25 cells. (a) All the detection maps have a detected cell in the cell region, so the result is that there is a cell. (b) Three detection maps have a detected cell in the cell region and the result is 0.75 cells. (d) Two detection maps have a detected cell in the cell region and the result is 0.5 cells. (c) Only one probability map detected a cell in the region, which means the prediction is disapproved by most probability maps. Therefore, there are only 0.25 cells and the detection is suppressed.  
Figure 8: The illustration of the detection map generation. (**a**) The detection map generation. (**b**) Cell image. (**c**) Cell annotations.  
Figure 7: The eigenvalues of the Hessian matrix. (**a**) The ground truth, (**b**) The \(\lambda_{1}\) of Hessian matrix, (**c**) The \(\lambda_{2}\) of the Hessian matrix.  
In summary, the density map generation process can be described as:  
\[D_{avg}=\frac{1}{N}\sum_{i=1}^{N}D_{i} \tag{5}\]  
\[density\;map=G(\sigma)\otimes D_{avg} \tag{6}\]  
where, \(N\) represents how many RFs the proposed CCF contains, \(D_{i}\) denotes the detection result of the \(i\)-th RF and \(D_{avg}\) denotes the averaged detection result, and they all maintain the same resolution as the input image. \(G\) is the Gaussian function and \(\sigma\) controls the degree of smoothness. \(\otimes\) means the convolution operation.  
The training and testing of RFs are independent. Therefore, the proposed model can be running in parallel to improve computational efficiency.
# Adaptive Deterministic Vibration Control of a Piezo-Actuated Active-Passive Isolation Structure
## 2 Analysis of a Piezo-Actuated Active-Passive Isolation Structure
### Adaptive Active Vibration Control System Formulation  
As shown in Figure 2, an adaptive active vibration control system is designed and applied to the piezo-actuated active-passive isolation structure. The deterministic residual vibration signal \(w_{d}\) of the primary path causes the error signal \(e\) which characterizes the vibration of the payload, and the control signal \(u\) of the adaptive regulator drives the actuator to act on the second path to compensate the \(w_{d}\) actively.  
The proposed adaptive active vibration control method does not require the model of the vibration source signal and the primary path so that the above system is simplified as shown in Figure 3. The \(K(Q)\) represents the controller corresponding to the shadow region in Figure 2 and the \(P\) represents the second path. The error signal \(e\) is used as the feedback signal, and the control signal \(u\) acts directly on the \(P\), then the system can be further simplified.  
Figure 3: Simplified block diagram of the adaptive active vibration control system.  
Figure 2: The adaptive active vibration control system.  
The discrete-time system model \(G\) represented in state space can be written as:  
\[G\;:\;\left\{\begin{array}{l}x(k+1)=Ax(k)+Bu(k),\quad x(0)=x_{0}\\ e(k)=C_{e}x(k)+w_{d}(k)+v(k)\end{array}\right. \tag{1}\]  
where \(v(k)\) is the measurement noise, and the unknown multifrequency or time-varying deterministic residual vibration signal wd can be described as:  
\[w_{d}(k)\;=\;\sum_{\ell=1}^{n}A_{\ell}(k)\cos(\omega_{\ell}(k)k\;+\;\phi_{ \ell}(k)) \tag{2}\]  
with unknown or time-varying amplitude \(A_{\ell}\), frequency \(\omega_{\ell}\) and phase \(\phi_{\ell}\), and \(\ell=1\cdot\cdot\cdot\cdot n\).
# Application-Specific Integrated Circuit of an Inter-IC Sound Digital Filter for Audio Systems
## 1 Introduction  
Filters and equalizers are essential blocks of audio systems, both in their input and output stages [1]. For example, most modern cars are equipped with discrete audio equalizers in order to enable the user to configure the audible sound output with the audio parameters of preference. Usually, in digital audio systems, the main problem is the computational complexity and the latency; for these reasons, the signal processing is handled by digital signal processors, which have better performance than general-purpose processors and an ad hoc digital hardware architecture, but they are very expensive devices. This problem is accentuated by the growth of streaming services and the need to enhance audible sound quality.  
With this in mind, by offloading these essential audio-processing tasks to an application-specific integrated circuit (ASIC), the system is able to streamline processes and redirect resources to other critical tasks. Furthermore, from the digital design perspective, it is possible to downgrade the employed microcontroller, which results in a reduction of the overall cost of the audio system.  
This applied research proposes a hardware implementation of a filter processing unit (FPU) based on a filter bank integrated with an audio equalizer with a finite impulse response (FIR) and an adaptive filter, which can be used as a noise canceler working with the well-known least-mean-squares (LMS) and normalized least-mean-squares (NLMS) algorithms. Given that the core element of these algorithms is an FIR filter, small modifications such as a predefined filter coefficient bank and a user-defined filter were added to deliver afull audio system, which consists of a configurable high-resolution equalizer and a noise canceler compliant with a left-justified inter-IC sound (I2S) bus and controlled through an inter-integrated circuit (I2C) bus. The system-on-chip (SoC) prototyping approach was implemented on field-programmable gate array (FPGA) technology and the physical ASIC in TSMC 180 nm technology.  
The paper is organized as follows. Section 2 gives an overview of the implemented noise-canceling algorithms and describes the proposed architecture for the digital filtering. Section 3 shows the results of the SoC implementation on FPGA technology and the manufactured ASIC with the resulting layout. This section also provides the post-silicon validation and presents the printed circuit board (PCB), which enables the ASIC and the test environment; finally, a discussion of the results is provided. Section 4 draws some conclusions.
## 4 Experiments
### Results
#### 4.3.3 Detection-Based MOT Pipeline
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**YOLO**} & \multirow{2}{*}{**Tracking Method**} & \multicolumn{8}{c}{**Evaluation Metrics**} \\ \cline{3-11}  & & **\% MOTA\(\uparrow\)** & **\% MOTP\(\uparrow\)** & TP\(\uparrow\) & FP\(\downarrow\) & **FN\(\downarrow\)** & **IDs\(\downarrow\)** & **\% MT\(\uparrow\)** & **\% ML\(\downarrow\)** & **FM\(\downarrow\)** & **FPS\(\uparrow\)** \\ \hline \multirow{9}{*}{\(Y_{\text{M1}}\)} & SORT (\(IoU\)) & **23.30** & **78.65** & 13,345 & **9665** & 1373 & 1078 & 42.6 & **6.6** & 167 & 47 \\  & SORT (\(A\)) & 22.39 & 78.61 & **13,384** & 9847 & 1350 & **1062** & **44.6** & **6.6** & 168 & 48 \\  & SORT (\(W_{\text{M}}\)) & 22.66 & 78.61 & 13,358 & 9778 & 1348 & 1090 & **44.6** & **6.6** & 166 & **49** \\  & Deep-SORT (\(IoU\)) & \(-\)2.27 & 78.12 & 13,029 & 13,387 & 1338 & 1429 & 39.1 & 7.0 & 157 & 27 \\  & Deep-SORT (\(E_{D}^{IoU}\)) & \(-\)10.16 & 78.08 & 13,090 & 14,695 & 1236 & 1470 & 40.3 & 7.4 & **109** & 28 \\  & Deep-SORT (\(A\)) & \(-\)10.66 & 78.20 & 13,100 & 14,784 & **1218** & 1478 & 43.0 & 7.0 & 114 & 28 \\ \hline \multirow{9}{*}{\(Y_{\text{M2}}\)} & SORT (\(IoU\)) & **41.93** & 81.03 & 14,191 & **7567** & 926 & **679** & **58.9** & **3.9** & 123 & **49** \\  & SORT (\(A\)) & 41.59 & 80.99 & **14,222** & 7652 & **895** & **679** & 58.5 & **3.9** & 114 & **49** \\  & SORT (\(W_{\text{M}}\)) & 41.54 & 81.00 & 14,203 & 7642 & 906 & 687 & **58.9** & **3.9** & 120 & **49** \\  & Deep-SORT (\(IoU\)) & 21.50 & 81.14 & 13,942 & 10,546 & 991 & 863 & 55.8 & 4.3 & 124 & 28 \\  & Deep-SORT (\(E_{D}^{IoU}\)) & 14.90 & 81.04 & 14,043 & 11,689 & 899 & 854 & 55.8 & 4.7 & **81** & 28 \\  & Deep-SORT (\(A\)) & 15.65 & **81.20** & 14,032 & 11,560 & 917 & 847 & 56.6 & 4.3 & 87 & 28 \\ \hline \multirow{9}{*}{\(Y_{\text{M3}}\)} & SORT (\(IoU\)) & **65.43** & **81.64** & 14,623 & **4288** & 858 & **315** & **66.7** & **5.4** & 64 & **50** \\  & SORT (\(A\)) & 65.21 & **81.64** & 14,633 & 4333 & **833** & 330 & **66.7** & **5.4** & 71 & **50** \\  & SORT (\(W_{\text{M}}\)) & 65.40 & **81.64** & **14,644** & 4313 & 835 & 317 & 66.3 & **5.4** & **63** & **50** \\ \cline{1-1}  & Deep-SORT (\(IoU\)) & 53.75 & 80.33 & 14,406 & 5916 & 975 & 415 & 61.2 & 5.8 & 101 & 30 \\ \cline{1-1}  & Deep-SORT (\(E_{D}^{IoU}\)) & 49.28 & 80.32 & 14,430 & 6646 & 897 & 469 & 60.5 & 6.2 & 83 & 30 \\ \cline{1-1}  & Deep-SORT (\(A\)) & 50.01 & 80.44 & 14,450 & 6551 & 890 & 456 & 62.4 & 6.2 & 75 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of the detection-based MOT pipeline on the ISR200 sub-dataset. All data association cost matrices used the class gate formulation.
# Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI
## 2 Literature Review
### Artificial Intelligence Algorithms For Building Energy Prediction  
#### 2.1.1 Acausal and Reverse Modeling  
Acausal modeling and reverse modelling are a set of opposite modeling methods used in building energy analysis. Acausal modeling completely follows the engineering principles for energy consumption prediction. This method does not need to be combined with the actual energy consumption data of the building, so it is widely used in the design phase of new buildings [3]. Reverse modeling is a data-driven method. The historical operation data of the building is obtained through a series of technical means in the early stage. Then, the data classification is used as the data set, and a certain algorithm is used to establish the analysis model and prediction model of the target building to simulate the future energy consumption of the building. Common reverse modeling methods include black box method, gray box method, and so on.  
The black box method is to simplify the research object to a black box with a specific algorithm and to input the front-end data through operations, analysis, processing, transformation, and transmission, and finally output the results. It is an effective research method. In recent years, artificial intelligence algorithms for building energy prediction are mostly black box methods or its extension techniques. This chapter will focus on several mainstream black box methods: artificial neural network and support vector machine.