esa-sceva/satcom-chunk-collection huggingface dataset:
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 5 Results
### The Effect of Reordering Pixels in Images  
For illustration of the effect of reordering pixels in images, we focus on the Extended Yale B face database with CRC as the classifier. Similar experiments have been conducted on the original database and the results are shown in Figure 8c. Therefore, in the current experiment, we emphasize the effect of reordering pixels in images rather than the comparison between different feature extraction algorithms.  
Figure 9 shows the average classification accuracies on the Extended Yale B face database and its six reordered variants. For illustration, only the results when the feature number is smaller than 11 are displayed. As an example of the detailed results, Table 7 shows the accuracies when only the first two features are used to perform classification.  
The accuracies of PCA are very close among the variants; this is due to the fact that PCA does not incorporate 2D image structure during the feature extraction procedure. For the other four algorithms, the results on variants 0-4 are very close to each other while the results on variants 5-6 tend to be quite different; this is because variants 0-4 preserve the original image structure but variants 5-6 only have random image structures. Therefore, reordering pixels in images can affect the classification performances of the 2D algorithms. In other words, the image structure information is incorporated in the feature extraction procedure of the 2D algorithms.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Database** & **Classifier** & **PCA** & **2 DPCA** & **A2 DPCA** & **(2D)2 PCA** & **DB2 DPCA** \\ \hline \multirow{3}{*}{Extended Yale B face} & NN & 0.2716 & **0.6835** & 0.6111 & 0.5621 & 0.6515 \\  & SVM & 0.4922 & 0.9306 & 0.8930 & 0.8171 & **0.9426** \\  & CRC & 0.3316 & **0.9669** & 0.9524 & 0.8302 & **0.9669** \\  & NN & 0.5459 & **0.8517** & 0.8236 & 0.7806 & 0.8378 \\ AR face & SVM & 0.5892 & 0.9745 & 0.9588 & 0.8877 & **0.9870** \\  & CRC & 0.4196 & 0.9531 & 0.9555 & 0.8655 & **0.9738** \\ \multirow{3}{*}{Binary Alphadigits} & NN & 0.5050 & 0.6724 & 0.6643 & 0.6318 & **0.6954** \\  & SVM & 0.4873 & 0.6947 & 0.6805 & 0.5931 & **0.7179** \\  & CRC & 0.3226 & 0.5235 & 0.5089 & 0.5317 & **0.5422** \\ \multirow{3}{*}{PolyU 3D palmprint} & NN & 0.8411 & 0.9693 & 0.9677 & 0.9256 & **0.9739** \\  & SVM & 0.6123 & 0.9650 & 0.9567 & 0.8888 & **0.9707** \\ \cline{1-1}  & CRC & 0.3723 & 0.6949 & 0.7014 & 0.6316 & **0.7034** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The average classification accuracies on four databases by three different classifiers.  
Figure 9: The average classification accuracies on Extended Yale B face database and its six reordered databases with CRC as the classifier. (**a**) PCA. (**b**) 2 DPCA. (**c**) A2 DPCA. (**d**) (2D)2 PCA. (**e**) DB2 DPCA.
# Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems
## 5 Simulation Results
### Hbf-Mo  
Firstly, we present the performance of the HBF-MO algorithm (denoted as _HBF-MO_) for the HD relay system. In this subsection, we will consider a benchmark approach and two other different algorithms, which are the optimal digital processing based on SVD (denoted as _DBF_), the HBF algorithm in [14] (denoted as _HBF_) and the OMP based algorithm in [7] (denoted as _OMP_). Unless otherwise specified, the convergence parameter of the outer loop is set to \(10^{-4}\), and that of the inner loop is set to \(10^{-6}\).  
Figure 3 validates the convergence of SE and MSE with SNR set to 6 dB. To be specific, the number of outer iterations is fixed to 15. As can be observed, our proposed algorithm converges to an optimal solution within about four iterations. SE increases with the growth of iteration number and convergences to almost 5.485 bit/s/Hz, while MSE decreases and maintain at 0.0449 after 15 iterations.  
Next, the SE performance versus SNR of our proposed HBF-MO algorithm and three other approaches is investigated in Figure 4. As shown in the figure, the SE of all algorithms increases with growing SNR. Taking the SE of DBF method as a benchmark, the proposed algorithm prevails over two other methods by approaching the DBF algorithm, which is more obvious as SNR becomes higher. When SNR = 10 dB, our HBF-MO algorithm can provide a gain of 7.99% over HBF [14] and 11% over OMP [7].  
By varying the number of relay and destiantion antennas, the SE performance of all algorithms are evaluated when SNR = 6 dB in Figures 5 and 6. Likewise, the DBF method is adopted as a benchmark. Due to the additional antenna gain, the spectral efficiency of all algorithms improves with the number of antennas increasing. Still, our proposed algorithm yields the best performance over the others.  
Figure 4: SE versus SNR for different beamforming algorithms.  
Figure 3: Convergence of SE and MSE for the HBF-MO algorithm.
## 2 Background
### Fuzzy Reward System
Lastly, the defuzzifier converts the fuzzy output back into a crisp reward output. Figure 1: Example of a fully overlapping triangle affiliation function. In this study, we integrated the FRS into the DRL framework to optimize both convergence speed and path efficiency during model training. Specifically, the FRS plays a dual role in the DRL algorithm: firstly, by enhancing the value of rewards based on experience, it increases the efficiency of sample utilization during learning; secondly, it steers the robotic arm towards the targeted goal. Initially, the FRS analyzes the current state of the robotic arm and its environment, including position, speed, and task-specific characteristics, along with the anticipated target state, to generate a fuzzified reward value. This reward not only reflects the effect of the robotic arm's current action but also considers the likelihood of achieving long-term goals. Implementing this fuzzified reward mechanism within the DRL algorithm's learning process enables the robotic arm to gradually learn how to make optimal decisions in complex textile tasks by experimenting with different actions and observing the resultant fuzzy rewards. This approach not only enhances the algorithm's adaptability to uncertain environments but also improves the efficiency of the training process.
# High-Speed GaN-Based Superluminescent Diode for 4.57 Gbps Visible Light Communication
## Materials and Methods
### The Principle of Adaptive Bit-Loading Discrete Multiple Tone (DMT) Modulation Scheme  
On-off keying (OOK) is widely used as a modulation scheme for data transmission experiments, but due to the low spectral efficiency (SE), the data rate is limited. In this work, we use the adaptive discrete multiple tone (DMT) scheme for optical wireless communication and achieve a data rate much higher than those using OOK modulation.  
In the bit-loading DMT modulation scheme, the utilized frequency spectrum is uniformly divided into multiple communication channels with carriers totally separated. The carriers for those separated communication channels are called subcarriers. There is no interference across subcarriers. In the VLC systems, only amplitude modulation is considered. These systems are called intensity modulation direct detection (IM-DD) systems. A structure of Hermitian symmetry is utilized in the DMT modulation scheme to obtain a  
Figure 1: (**a**) Schematic illustration of the superluminescent diode on the GaN substrate; (**b**) the scanning electron microscopy (SEM) image of the fabricated SLD device.  
real-valued signal through inverse discrete Fourier transform (IDFT), then the requirement of IM-DD systems is satisfied [8; 9].  
To better assess the characteristic of the channel for VLC links, a training process is included in the adaptive bit-loading DMT scheme. A 4-QAM training signal is transmitted and then the error vector magnitude (EVM) is calculated, so that the subcarrier signal-to-noise ratio (SNR) can be estimated properly. According to the estimated SNR of the VLC channel, the LC algorithm introduced in [10] is utilized to complete bit loading for each subcarrier. The SE is maximized in this way, which can even approach the Shannon limit. After training, the communication signal is transmitted and then collected at the receiver end (Rx). Further digital signal processing (DSP) is carried out. The received signal is demodulated with posteriori knowledge of the VLC channel, and then the bit error rate (BER) is calculated.
## 2 Literature Review
For malware detection, an optimizing feature-based database was utilized to train the presented hybrid of bi-directional long short-term memory (BiLSTM) and a merged sparse autoencoder (MSAE) with a softmax DL technique. [PERSON] et al. [21] examine a MAPAS, a malware detection scheme that accomplishes a higher accuracy and an adjustable usage of computing resources. The MAPAS examines the performances of malicious applications using API call graphs with an exploiting CNN. [PERSON] and [PERSON] [22] introduced a model dependent upon LSTM for malware detection that is allowed to not only distinguish between malware and benign instances, but among the malware to identify and detect novel and unseen families. According to our understanding, this is the first time that traffic data were designed as an order of flows and that the sequential-based DL approach was exploited. In [23], the authors establish eight Android malware detection approaches dependent upon ML and DNN that are then examined for their robustness against an adversarial attack. With this intent, the authors generated novel variations of malware utilizing Reinforcement Learning (RL) that is misclassified as benign when using the current Android malware detection approaches. Though several malware detection models are available in the literature, only a limited number of works have involved both FS and hyperparameter tuning processes. For the design of malware detectors, ML models can be employed to construct classifiers that are effective for discerning whether the application is benign or malware. Training this classifier requires large quantities of labeled datasets, which comprise categorized benignware and malware Android applications, characterized by a collection of features enable to define their behaviors. Even though various solutions have been put forward for the recognition of Android malware, feature selection approaches must be utilized in Android malware detection mechanisms. On the other hand, most of the existing Android malware detectors do not focus on the automated hyperparameter tuning process, which significantly affects their overall classification performance. More specifically, hyperparameters, which include learning rate selection, epoch count, and batch size, play a vital role in accomplishing effective outcomes. As the trial-and-error hyperparameter tuning process seems to be difficult, an automated hyperparameter optimizer needs to be employed. In summary, the design of automated Android malware detectors is yet to be well explored.
# Offline Identification of a Laboratory Incubator
## 2 Materials and Methods
### Model Structures
#### 2.5.1 Auto-Regressive Exogenous (ARX) Model  
The input-output relationship of a system can be described by using a linear difference equation, as in Equation (5):  
\[y(t)+a_{1}y(t-1)+\dots+a_{n_{u}}y(t-n_{a})=b_{1}u(t-1)+\dots+b_{n_{b}}u(t-n_{b} )+e(t) \tag{5}\]  
Figure 5: LTI system with an additive disturbance signal.  
The linear difference equation, Equation (5), has autoregressive and exogenous parts. A model structure having these two parts is called an ARX model and is represented in Equation (6):  
\[A(q)y(t)=B(q)u(t)+e(t) \tag{6}\]  
If the parameters in Equation (4) are written with the polynomial functions given in Equations (7) and (8):  
\[A(q)=1+a_{1}q^{-1}+\cdots+a_{n_{q}}q^{-n_{a}} \tag{7}\]  
\[B(q)=b_{1}q^{-1}+\cdots+b_{n_{b}}q^{-n_{b}} \tag{8}\]  
then Equation (4) corresponds to Equation (3) with the use of Equation (9):  
\[G(q,\theta)=\frac{B(q)}{A(q)},\quad H(q,\theta)=\frac{1}{A(q)} \tag{9}\]  
Therefore, the predictor for the system output can be written as the following equation, Equation (10):  
\[\hat{y}(t\mid\theta)=B(q)u(t)+[1-A(q)]y(t) \tag{10}\]
# Heart Rate and Rhythm Changes in Dogs Treated in a Hyperbaric Oxygen Chamber
## 2 Materials and Methods
### Treatment Procedure  
The patients underwent HBOT in a single-place chamber with 100% oxygen saturation in a hyperbaric chamber at a pressure of 1.5 ATA. Holter monitoring was performed using a Holter device Aspel AsPEKT 712 v. 301 (Aspel, Poland). Due to the high randomization in terms of reported cases, it was decided to group individuals during data analysis. The individuals were divided into three groups: (1) individuals with degenerative mitral valve disease (DMVD), (2) individuals with diseases other than cardiac issues, and (3) healthy individuals qualified for the HBOT procedure. The ECG recording was divided into four periods: (1) a 30 min recording before the start of the procedure, (2) 4 min of pressure increase in the chamber (compression), i.e., achieving appropriate atmospheric and oxygen conditions, (3) HBOT treatment lasting 90 min, and (4) 1 min decompression of the hyperbaric chamber. Heart rate and potential arrhythmias were assessed. Due to the possibility of an explosion inside the chamber [19], metal collars and leashes were removed from the patients. Additionally, no electrical appliances or toys were allowed during the treatment except for dog beds to ensure psychological comfort. Seven electrodes were placed in the area from the sternum to the posterior wall of the scapula, three on the left side and four on the right side, following the oblique position standards [20]. The device and electrodes were carefully secured with veterinary adhesive bandages to prevent the risk of sparks and fire.  
The chamber had a built-in window that allowed the patient's condition and reactions to be monitored in the event of adverse reactions. The dogs were observed continuously during compression and decompression, during which the risk of adverse effects is greatest [11]. During a 90 min session in the hyperbaric chamber, the dogs were observed five times (every 15 min) for 30 s.  
The hyperbaric chamber was operated by a person properly trained to use the entire system following the current guidelines.  
Analysis of the Holter recordings was performed by an experienced veterinarian specializing in veterinary cardiology using licensed ASPEL HTL-712 v.301 Holter ECG recorder with ASPEL HLT HOLCARD-712 v.301 ALFA software. The heart rate, morphology of individual ECG waves, and the occurrence of possible arrhythmias before and after the start of the HBOT procedure were evaluated. Comparisons were also made between the voltages of selected waves in terms of the change in amplitude of each QRS complex as well as the P and T waves. Comparisons were made at each stage of the procedure and were made possible by the software mentioned above.  
Side effects that occurred during 90 min of hyperbaric chamber therapy at a pressure of 1.4 ATA were also observed. Possible side effects were divided into minor and major adverse effects according to the formula used in another study [11].
# Warpage Behavior on Silicon Semiconductor Device:
## 3 Warpage Measurements and Physical Analysis
### Results  
The measurement outputs considered for the presented work are the warpage maps shown in Figure 4, captured at different time of temperature profile, during both the heating and cooling phases.  
Figure 3: Schematic of experimental setup for warpage measurement.  
In particular, Figure 4 shows the acquisition for the first cycle of \"W/o plateau\" sample; meanwhile, similar analyses are performed for other test vehicles. Starting from the warpage maps, they are extracted warpage profiles along a specific vertical cutline, as depicted in Figure 6.  
Figure 4: Warpage maps at different temperature for temperature profile “W/o plateau”. On the right, the considered sign convention in Figure 5 is shown.  
Figure 5: Measured warpage during temperature profiles. Negative values are reached during the heating, while positive ones during the cooling phase. Dotted line represents the warpage versus temperature profile that occurred during a second temperature cycle.  
Warpage-temperature behavior is plotted in Figure 5, following the sign convention declared in Figure 4 and reporting the maximum warpage resulting in cutline extrapolation for each map, explained in Figure 6. It is arbitrarily chosen to consider the warpage positive in case of crying (convex) shape and a negative value in case of smiling (concave) shape. Analysis highlights that annealing process modifies warpage at 50 \({}^{\circ}\)C in all temperature profiles. Warpage is \(-10\) um at the start, whereas it becomes around 35 um at the end of first cycle considering profiles \"W/Plateau\" and \"W/o Plateau\", while it becomes around 20 um for \"150 \({}^{\circ}\)C\" profile. A possible explanation could be grain size growth is \"freeze\" and not completed in \"150 \({}^{\circ}\)C\" profile with respect to other temperature profiles with maximum temperature of 250 \({}^{\circ}\)C. Looking to the second cycle, warpage behaves as a closed loop because there is no warpage modification between the start and the end of cycle at 50 \({}^{\circ}\)C. Warpage is modified during 30 min plateau at 250 \({}^{\circ}\)C from around \(-30\) um at the start of plateau to \(-10\) um at the end of plateau. However, no significant difference is carried out after the cooling at room temperature. It is observed the first part of warpage-temperature diagram in the second cycle (between 50 and 90 \({}^{\circ}\)C) has a linear trend. The incremental ratio between warpage variation and temperature in this range is considered to benchmark the different warpage calculation methods presented in following paragraph. The warpage trend in temperature during the heating phase shows a change in slope that occurs at 150 \({}^{\circ}\)C, at which warpage reaches its maximum. This observed behavior could indicate an irreversible phenomenon in copper layer: the dedicated physical analysis will help to better understand the phenomenology as reported in Section 3.3.
## 6 Conclusions  
This paper presented a framework for fatigue assessment of structural details in the steel bridges integrating multi-scale coupled dynamic analysis of the TTB system and LEFM theory. The multi-scale FE model of the Baihe Bridge including a refined local shell model located in the area around the fatigue-critical details was established and validated. Based on the multi-scale FE model and the wheel-rail contact relationship, the dynamic equation of the TTB system was formulated to acquire the bridge stresses, which were compared against field measurements. Fatigue stresses obtained from the multi-scale coupled dynamic analysis were used in conjunction with the LEFM to investigate the fatigue crack propagation of the stringer-to-floor-beam bracket. The following conclusions can be drawn:  
1. Fatigue stresses of the critical bridge details are successfully calculated through multi-scale dynamic analysis of the TTB system. The incorporation of the multi-scale bridge FE model into the train-bridge interaction model enables accurate and efficient fatigue stress computation of both the bridge primary members and structural details with the coupled dynamic effects of the TTB system also considered.  
2. The integration of multi-scale coupled dynamic analysis of the TTB system and LEFM provides an effective tool for enhanced fatigue analysis of the Baihe Bridge, allowing insights into the fatigue crack propagation of structural details in steel bridges.  
3. Fatigue life of the critical bracket is extended from the year 2004 to the year 2082 when the track condition of the Baihe Bridge is upgraded to class 6, as prescribed by the U.S. Federal Railroad Administration. The effects of track irregularities on the fatigue crack propagation life of structural details in the bridge deck system can be characterized using the presented framework.  
4. Fatigue crack propagation life of the analyzed stringer-to-floor-beam bracket ranges from a few years to more than 100 years depending on the operating train speeds. Train speeds greatly impact the stress range amplitude and stress cycle count associated with the bracket. The effects of the train speed are well captured by multi-scale dynamic analysis of the coupled TTB system.  
Conceptualization, H.L. and G.W.; methodology, H.L.; formal analysis, H.L.; data curation, H.L.; writing--original draft preparation, H.L.; writing--review and editing, G.W.; project administration, G.W.; funding acquisition, G.W. and H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China Grant Nos. 51708112 and 51525801.  
The authors declare no conflict of interest.
# Design General Cam Profiles Based on Finite Element Method
## 7 Case Study
### Influence of Parameters \(\epsilon\) and \(\mu\)  
Next, the influence of penalty parameters \(\epsilon\) and \(\mu\) is discussed in this section. The accuracy of the cam profile is estimated by parameter \(\epsilon\) as shown in Figure 6a. It is clearly seen that an accurate solution is obtained for a sufficiently large \(\epsilon\). However, it should be noted that for the augmented method, a much smaller penalty parameter is already sufficient for the comparative accuracy due to the extra correction loop.  
Figure 6b further shows the convergence of the finite element calculation with mesh refinement for three minimum values of the parameter \(\mu=50\), \(25\), and \(10\). It is observed from the figure that the rate of convergence increases with decreasing the parameter \(\mu\). On the other hand, for the fine meshes, parameter \(\mu\) affects the convergent rate significantly, i.e., the smaller stabilization parameter, the more accuracy is obtained.
## 3 System Architecture and Service Process
### System Services  
#### 3.2.1 Environmental Monitoring Service  
The environmental monitoring service is a service to store in the database environmental and soil information collected from the WSN sensors installed on the outdoors, and allow users to monitor environmental information about the planted crops via the Web server anywhere in real time.  
Looking at the detailed operation of this service, the WSN sensors installed in the outdoors collect environmental and soil information and periodically sent the sensing data to the sensor manager, then the sensor manager parses and analyzes the received sensing data to extract each sensing value and convert its format to store it into each table of the database.  
The web server fetches environmental and soil information on the outdoors stored in the database to send it to a GUI at regular intervals, and users can monitor the outdoors environment information on via the GUI. Figure 2 shows the operating process of the environment monitoring service.