esa-sceva/satcom-chunk-collection huggingface dataset:
# Acceleration of OCT Signal Processing with Lookup Table Method for Logarithmic Transformation
## 3 Results  
The calculation error with the lookup table method was defined as the maximum difference between the lookup table calculation results and the actual logarithmic values. The calculation error decreased with the size of the lookup table as shown in Figure 3, however, at the cost of more memory of the computer and more lookup time. Considering a balance between processing time, memory and calculation error, a table of 1000 components was chosen to calculate the logarithmic function with the maximum error of 0.0043.  
The processing time of logarithmic transformation for B-scan imaging (1024 \(\times\) 1000 pixels) was reduced from 13.3 ms to 9.3 ms with the lookup table method. As shown in Figure 4, the real-time processing A-rate considering the data transferring and FFT processing was improved from 50 kHz to 62 kHz corresponding to a B-scan rate of 62 frames/second. A shorter processing time results in a higher display rate that could help physicians with better diagnosis and treatment.  
Figure 3: Calculation error as the function of the table length, the error was calculated by subtracting the lookup table calculation from the actual logarithmic value.  
Figure 2: The calculation process of the lookup table method for logarithmic transformation based on a table of 1000 components.  
To test the accuracy of the lookup table method, a mirror was used as a sample. As shown in Figure 5, the A-line signals that are respectively reconstructed from logarithmic function and lookup table calculation are identical.  
Furthermore, in vivo human fingernail was imaged to demonstrate the lookup table method in real-time processed OCT imaging. Figure 6A,B illustrate OCT images reconstructed by the logarithmic function calculation and lookup table method, respectively. In order to evaluate the image quality obtained by the lookup table method, the mean square error (MSE) between two images that are  
Figure 4: Processing A-rate of calculation with lookup table (red) and logarithmic function (blue).  
Figure 5: A-line signals reconstructed from logarithmic function (**blue**) and lookup table (**red**) calculation.  
reconstructed from logarithmic function and lookup table calculation was calculated. The MSE calculation is widely used in digital image processing for imaging quality evaluation and is defined as:  
\[MSE=\frac{1}{MN}\sum_{i=1}^{m}\sum_{j=1}^{N}(x(i,j)-y(i,j))^{2}\]  
where \(x(i,j)\) and \(y(i,j)\) represent the pixel values of the original image and the image that needs to be evaluated, respectively. Considering the pixel values of a grayscale image are from 0 to 255, an MSE of 0.019 between the two images shown in Figure 6A,B is quite small, demonstrating that our method meets the required accuracy of OCT imaging display.
###### Abstract  
Previous research has investigated non-pharmacological digital therapeutic interventions to improve compliance and reduce side effects in attention-deficit/hyperactivity disorder (ADHD) medication treatments for children. This study focuses on validating the effects of game-based intervention content for enhancing working memory and concentration. It tracks quantitative changes to evaluate improvements in concentration and working memory when digital game-based content is used as adjunct therapy alongside medication for children with ADHD. Thirty children participated; one group received digital therapeutic intervention based on game content alongside medication (experimental) and the other group received conventional treatments (control). The study results show that children with ADHD in the experimental group, who use digital game-based content, exhibit a reduction of 8.13 \(\pm\) 6.71 points in the K-ARS total score at the fourth week compared to baseline, while the control group shows a reduction of 7.14 \(\pm\) 8.73 points. Inattention decreases by 36.84% in the experimental group and 28.56% in the control group, while hyperactivity-impulsivity decreases by 50.71% in the experimental group and 34.00% in the control group. All the results are analyzed using a paired \(t\)-test between baseline and the fourth week. Significant decreases in the K-CBCL total problem behavior score and internalizing and externalizing behaviors are consistently observed at 28 days compared with baseline. The FAIR attention-concentration test results show significant differences between the experimental and control groups in the Q-percentile and Q-standard scores, with repeated measures ANOVA results showing \(p\) = 0.006 and \(p\) = 0.007, respectively. Digital content was shown to influence digital therapeutic intervention--a non-pharmacological treatment for ADHD.  
digital intervention content; ADHD; attention; concentration; game content 202515788
## 4 IoT Deployment Opportunities in Developing Countries
### Leveraging Unlicensed LPWANs for IoT Deployments
#### 4.2.2 Modulation Scheme  
The LoRa modulation scheme is based on the chirp spread spectrum (CSS) whereby the chirp signal varies linearly with the frequency [52]. By applying different spread factors (SFs), a compromise may be achieved between the power consumption or transmission distance for a data rate according to Equation (1). The advantages of this type of modulation scheme include constant envelope, scalable bandwidth, resistance to Doppler effect, interference and multipath fading, and improved range, network and geolocation capabilities [12]. It also reduces the complexity of the receiver design since the time and frequency offset are identical. An adaptive data rate (ADR) is another mechanism employed to reduce the consumed RF power and the data rates of LoRa UEs.
## 6 IEC's Market Drivers
### Gaming, AR and VR  
The integration of 5G wireless systems and mobile computing is recognized as a driving force for the development of immersive content for virtual and augmented reality content. Based on the data aggregated by IoT, the human is more able to interact with technologies through augmented reality, virtual reality or mixed of them [111]. AR is technology based on dropping virtual objects and information into the real user environment to provide additional information whereas, VR is based on projecting real objects in a virtual environment. Users can deal with AR information and objects through several devices, including portable ones, such as smartphones, wearable devices such as glasses and lenses [6]. All of these devices use a tracking system that provides accurate forecasting. As defined by the European Telecommunications Standards Institute (ETSI), IEC is considered to be sufficient method for low latency in AR and VR applications and services, especially when dealing with physical reality [90]. Additionally, IEC platforms have the ability to provide high capacity and low latency wireless coverage for big places such as smart cities with an enormous density of people enjoying AR and VR experiences.
# A Blockchain-Based Decentralized Public Key Infrastructure Using the Web of Trust
## 7 System Evaluation and Limitations
### Scalability Evaluation  
The rapid increase in participants within a system leads to blockchain network expansion, which underscores the scalability challenges in existing blockchain technologies. The blockchain trilemma [46] highlights a fundamental conflict among scalability, security, and decentralization within a public blockchain with numerous participants due to the trade-off between decentralization and scalability. As blockchain technology evolves, research is increasingly focused on enhancing scalability across its different layers.  
However, ongoing development and research are addressing these scalability issues. Initiatives like ScalaCert [47] aim to improve scalability by integrating revocation data into certificates, eliminating the need for additional storage for CRLs. The Lightweight Scalable Blockchain (LSB) [48] employs a Distributed Time-based Consensus (DTC) algorithm for a faster mining process and features a distributed throughput system for adaptive scaling. Additionally, CBPKI [16] leverages cloud services like AWS for hosting PKI servers, addressing auto-scaling challenges.  
Our study did not specifically address scalability issues in large networks, but certain adjustments can mitigate initial challenges. Ethereum 2.0 introduces a multi-phased enhancement plan. The first phase moves from the energy-heavy proof-of-work (PoW) model to a greener proof-of-stake (PoS) model and introduces sharding [49]. Through key infrastructure changes, the plan aims to significantly improve the network's scalability, energy consumption, and security. Additionally, scalability can be approached through horizontal scaling (adding more nodes), vertical scaling (upgrading individual node capabilities), and sharding (partitioning data for parallel processing). These strategies can benefit from existing cloud computing technologies for greater flexibility and efficiency.  
While our system does not directly tackle scalability, it remains a critical area for future work, as we aim to develop a scalable, industry-standard decentralized PKI.
## 6 Discussion and Recommendations
### Discussion
In conclusion, when dealing with wideband interference(here, OQPSK-DSSS), using 2\(\times\) repetition provides a theoretical 3 dB advantage, but that advantage is overcome by the bandwidth reduction that provides a higher PSD. Another interesting result comparing the performance of the SUN-OFDM and OQPSK-DSSS physical layers against interference is that the degradation in terms of PDR caused by the increment of the packet length (going from 20 bytes to 120 bytes) is not equal for both modulation types. For SUN-OFDM, the SIR increase to maintain a PDR \(>\) 80% is bounded to 2 dB, whereas, for OQPSK-DSSS, the SIR increase is larger than 3 dB. This is thanks to time and frequency diversity, which allows for recovering random errors caused by multi-path propagation and external interference. The SIR spread between operating in ideal conditions (PDR \(>\) 80%) and degraded conditions (PDR \(<\) 20%) for all SUN-OFDM modulations requires SIR = 3 dB regardless of the packet length. In contrast, the SIR spread between operating in ideal conditions and degraded conditions for OQPSK-DSSS depends on packet size and interference type. When dealing with SUN-OFDM interference, transitions require SIR = 2 dB for 20-byte packets, and SIR = 5 dB for 120-byte packets. In contrast, when dealing with OQPSK-DSSS interference, the transition requires SIR = 2 dB regardless of packet length. Comparing OFDM1-MCS1 to OQPSK-DSSS, we observe that, for a PDR \(>\) 80%, it provides an advantage between 5-10 dB, depending on interference type and packet length. In particular, it provides an advantage of 5 dB for OFDM3-MCS3 interference with packet length of 20 bytes, and an advantage of 10 dB for OFDM1-MCS1 interference with packet length of 120 bytes. This is in contrast with the fact that OFDM1-MCS1 uses a channel separation of 1.2 MHz, whereas OQPSK-DSSS uses a channel separation of 5 MHz. That is, the spectral efficiency of OFDM1-MCS1 is 0.166 bits/Hz (or 200 kbps/1200 kHz), whereas the spectral efficiency of OQPSK-DSSS is 0.05 bits/Hz (or 250 kbps/5000 kHz). Hence, OFDM1-MCS1 provides an increased robustness level while offering a higher spectral efficiency that enables having denser deployments (a 333% capacity increase). From these results, we can observe that the SIR required to provide PDR \(>\) 80% for OQPSK-DSSS largely depends on the packet length (4 dB difference). In contrast, the SIR required to provide a PDR \(>\) 80% for the robust SUN-OFDM modulations remains constant (\(<\)1 dB) regardless of the packet length and the interference type. This is owing to the fact that OQPSK-DSSS does not use any physical layer mechanism to enhance robustness except for the processing gain provided by DSSS. Hence, the error probability increases as the packet length is increased. For SUN-OFDM-based modulations, time and frequency symbol repetition reduces the probability of having an erroneous bit.
###### Abstract  
Most research activities that utilize linear matrix inequality (LMI) techniques are based on the assumption that the separation principle of control and observer synthesis holds. This principle states that the combination of separately designed linear state feedback controllers and linear state observers, which are independently proven to be stable, results in overall stable system dynamics. However, even for linear systems, this property does not necessarily hold if polytopic parameter uncertainty and stochastic noise influence the system's state and output equations. In this case, the control and observer design needs to be performed simultaneously to guarantee stabilization. However, the loss of the validity of the separation principle leads to nonlinear matrix inequalities instead of LMIs. For those nonlinear inequalities, the current paper proposes an iterative LMI solution procedure. If this algorithm produces a feasible solution, the resulting controller and observer gains ensure robust stability of the closed-loop control system for all possible parameter values. In addition, the proposed optimization criterion leads to a minimization of the sensitivity to stochastic noise so that the actual state trajectories converge as closely as possible to the desired operating point. The efficiency of the proposed solution approach is demonstrated by stabilizing the Zeeman catastrophe machine along the unstable branch of its bifurcation diagram. Additionally, an observer-based tracking control task is embedded into an iterative learning-type control framework.  
polytopic uncertainty; stochastic disturbances; robust control; observer design; linear matrix inequalities; optimization Article  
1  
Abbildung 1
# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
## 2 Methods  
In this section, we introduce the main implementation principles of the IOF-Tracker multi-object tracking method and demonstrate a two-stage multi-object tracking framework based on the fusion of temporal and spatial features (R-IoU and optical flow feature fusion model). The implementation principles of this tracking method are illustrated in Figure 1.  
The input video frames are first processed by a detector to divide the detected multiple object bounding boxes into high-scoring detections and low-scoring detections. The information within the high-scoring detections is input into a deep appearance extractor (SBS-S50) [12] to extract appearance features, obtaining the deep features of the target images (ReID) [13]. We then use a Kalman filter with camera motion compensation to estimate the predicted bounding boxes and extract the average optical flow vectors of the pixels within the detection and prediction boxes. By matching the optical flow vectors in these two boxes, we generate an optical flow feature similarity matrix. Next, we calculate the non-overlapping degree of the detection and prediction boxes to obtain the R-IoU feature similarity matrix. We combine these two features with weighted fusion to create a fused R-IoU and optical flow feature similarity matrix, which is then used in the two-stage association tracking process. When the tracking enters the association phase, during the first association, we match the target information between the high-scoring detections and predicted boxes using the fused R-IoU and optical flow features, combined with ReID. The first association produces three results: matched tracklets, unmatched detections and remaining tracklets. The matched tracklets proceed to the tracklets updating, unmatched detections build new tracklets, and the remaining tracklets are associated with low-scoring detections for a second association based on the fused R-IoU and optical flow features. The second association generates two results: matched tracklets and re-remaining tracklets. The matched tracklets proceed to the tracklets updating, while the remaining tracklets undergo a similar process. If they remain unmatched for 30 consecutive frames, the re-remaining tracklets are placed in the lost tracklets. We will provide detailed introductions to the two-stage tracking framework and the fused R-IoU and optical flow feature model in subsequent sections. Specifically, our approach is shown step by step in the pseudocodes (Algorithm 1). Figure 1: IOF-Tracker multi-object tracking method.
# Lamb Wave-Based FDM-PPM Method Data Transmission Scheme in Plate Structures
## 5 Conclusions  
This study proposes a Lamb wave-based FDM-PPM data transmission scheme in plate structures, leveraging the unique dispersive characteristics of Lamb waves. Compared with ASK, PPM, and FDM, the proposed FDM-PPM method effectively enhances the  
Figure 14: Comparison of bit number differences between FDM-PPM signals and other modulation methods when \(N_{s}=2\): **(a)** ASK, **(b)** PPM, and **(c)** FDM.  
Figure 15: Bit number differences in symbol signals with different frame numbers when the number of subcarriers is 2: **(a)** ASK, **(b)** PPM, and **(c)** FDM.  
system's communication rate. It reduces the signal energy consumption at the excitation end. Experimental results demonstrate that the Lamb wave communication based on FDM-PPM can achieve a maximum rate of up to 50 kbps with a bit error rate as low as 90.7%. Moreover, when the condition of the monitored component changes, the scheme can adapt to the new channel conditions and continue data transmission by reacquiring Lamb wave symbol signals with different carrier combinations and updating the demodulation atom library. The proposed method effectively improves the transmission rate of Lamb wave communication systems, providing new guidance for low-power Lamb wave sensing network communication. Additionally, this scheme is universal, applicable to Lamb wave communication, and easily extendable to electromagnetic wave communication, showing great potential for application.  
Further research will focus on addressing communication issues when structural defects occur, aiming to develop more robust adaptive equalization compensation algorithms. These algorithms will dynamically adapt to channel variations and optimize signal transmission quality. Simultaneously, further research will be conducted on joint control strategies for multi-sensor node communication using Lamb waves to achieve efficient multi-node communication. Additionally, integrating structural defects and damage identification results in a Lamb wave-sensing network that combines defect detection and data transmission capabilities, comprehensively improving the system's overall performance and application value.  
Conceptualization: [PERSON], [PERSON], [PERSON] and X.G.; methodology: T.X.; experimental setup: T.X.; validation: T.X.; writing--original draft preparation: T.X.; writing--review and editing: [PERSON], X.L., J.L. and X.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China under Grant 12272014, and in part by the State Administration for Market Regulation's Science and Technology Plan Project under Grant 2024 MK075.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
# Automatic Era Identification in Classical Arabic Poetry
## 4 Experiments and Results
### Experiment 4: Era Classification by Poet
As an example, consider [PERSON], for whom there are 35 poems from the pre-Abbsaid period, where the F1 for era identification is 0.99. Other examples are [PERSON] (200 poems) and [PERSON] (200 poems), representing two different periods, for which the F1 results are 0.94 and 0.91, respectively--all of these results are higher than the general average of Experiment 1. Figure 4 visualizes the F1 results for all of the poets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Isolated Feature Set** & **P** & **R** & **F1** \\ \hline
**Statistical features only** & & & \\ \hline
**Abbasid** & 0.765 & 0.767 & 0.766 \\ \hline
**Pre-Abbsid** & 0.769 & 0.767 & 0.768 \\ \hline
**Average** & 0.767 & 0.767 & 0.767 \\ \hline
**Lexical features only** & & & \\ \hline
**’Abbsid** & **0.885** & **0.863** & **0.874** \\ \hline
**Pre-Abbsid** & **0.868** & **0.889** & **0.878** \\ \hline
**Average** & **0.876** & **0.876** & **0.876** \\ \hline
**Syntactic features only** & & & \\ \hline
**’Abbsid** & 0.674 & 0.683 & 0.678 \\ \hline
**Pre-Abbsid** & 0.682 & 0.673 & 0.677 \\ \hline
**Average** & 0.678 & 0.678 & 0.678 \\ \hline
**Domain-specific features only** & & & \\ \hline
**’Abbsid** & 0.738 & 0.712 & 0.725 \\ \hline
**Pre-Abbsid** & 0.725 & 0.75 & 0.737 \\ \hline
**Average** & 0.731 & 0.731 & 0.731 \\ \hline
**Topics only** & & & \\ \hline
**’Abbsid** & 0.659 & 0.612 & 0.634 \\ \hline
**Pre-Abbsid** & 0.641 & 0.686 & 0.663 \\ \hline
**Average** & 0.65 & 0.649 & 0.649 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of Experiment 3b—individual feature contributions.