esa-sceva/satcom-chunk-collection huggingface dataset:
# Key Technologies for 6G-Enabled Smart Sustainable City
## 2 Recent Trend of Smart Sustainable City  
As greenhouse gas emissions increase globally, cities are currently responsible for more than 70% of global energy consumption and greenhouse gas emissions. Furthermore, due to continuous urbanization, it is expected that the urban population in 2021 (i.e., 56% of the total population) will reach 68% of the total population by 2050 [3]. However, urbanization leads to various problems, such as environmental pollution, inefficient resource utilization, traffic congestion, and social inequality. To overcome these challenges, it is necessary to develop a more advanced urban model capable of surpassing the limitations of current city infrastructure. A smart sustainable city refers to an innovative city where ICTs are used primarily to improve the quality of life and efficiency of city operations and services, while ensuring the need for current and future generations in economic, social, environmental, and cultural terms [4]. Moving to the post-pandemic world, the way we live and communicate with each other has changed rapidly. It is noted that people all over the world use various form factor devices such as smartphones, smart watches, augmented reality (AR) glasses, and virtual reality (VR) headsets to join online meetings and connect with their family and friends. Consequently, it is necessary for current cities to get smarter by exploiting innovative technologies. The emergence of smart cities is growing and has attracted increasing interest from industry. As shown in Figure 1, the amount of revenue from global smart city projects is expected to grow from USD 43.7 billion in 2020 to USD 165.8 billion in 2028 [5]. Within the last decade, several countries have established many smart city policies. Smart city policies are implemented in various ways by countries, but the use of innovative technologies and the implementation of advanced services are commonly considered to address economic and environmental challenges in cities. In Table 1, we present some smart city policies from various countries. Recently, the draft of the international mobile telecommunications (IMT)-2030 (6G) recommendation has been completed and six major usage scenarios have been defined [10]. Three of these usage scenarios are an advanced version of the IMT-2020 (5G) usage scenarios (i.e., enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), ultra-reliable and low-latency communication (URLLC)) [11]. That is, immersive communication, massive communication, and hyper-reliable and low-latency communication (HRLLC). Three new usage scenarios include ubiquitous connectivity, AI and communication, and integrated sensing and communication. With the development of 6G communication systems, 6G candidate technologies such as non-terrestrial networks (NTN), artificial intelligence (AI), and integrated sensing and communication (ISAC) can be exploited in upcoming policies for SSC.
# Attention-Gate-Based Model with Inception-like Block for Single-Image Dehazing
## 3 Proposed Method
### Loss Function  
The loss function setting of the image-dehazing CNN usually adopts the mean square error (_MSE_), as follows:  
\[L_{MSE}(P)=\frac{1}{N}\sum_{p\in P}(x(p)-y(p))^{2}, \tag{9}\]  
Treating _MSE_ as a loss function means that only the difference in pixel values between the dehazing image and clear image is considered during training. The _MSE_ is related to the peak signal-to-noise ratio (_PSNR_).  
\[PSNR=20\log_{10}\bigg{(}\frac{MAX}{\sqrt{MSE}}\bigg{)}, \tag{10}\]  
where _MAX_ is the maximum possible pixel value. In the case of an 8-bit image, _MAX_ is 255.  
Considering that using only _MSE_ may lead to visible speckle artifacts, this study introduces the structural similarity (_SSIM_) to reveal the degree of similarity between dehazing and clear images, including luminance, contrast, and structure, which can improve the learning efficiency and quality of image dehazing. When the _SSIM_ is closer to 1, the two images are more similar.  
\[SSIM(P)=l(x,y)^{\alpha}\cdot c(x,y)^{\beta}\cdot s(x,y)^{\gamma}, \tag{11}\]  
Figure 6: The effect of selecting four random feature maps before and after passing through AG 1.  
where \(\alpha=\beta=\gamma=1\), \(l(x,y)\) is the similarity of luminance, \(c(x,y)\) is the similarity of contrast, and \(s(x,y)\) is the similarity of structures. The details of \(l(x,y)\), \(c(x,y)\), and \(s(x,y)\) are as shown in Equation (12).  
\[\left\{\begin{array}{l}l(p)=\frac{2\mu_{x}\mu_{y}+C_{1}}{\mu_{x}^{2}+\mu_{y}^ {2}+C_{1}}\\ c(p)=\frac{2\
u_{x}\
u_{y}+C_{2}}{\sigma_{x}^{2}+\sigma_{y}+C_{2}}\\ s(p)=\frac{\sigma_{xy}+C_{3}}{\sigma_{x}\sigma_{y}+C_{3}}\end{array}\right., \tag{12}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the mean values of the signals \(x\) and \(y\), respectively, \(\sigma_{x}\) and \(\sigma_{y}\) are the standard deviations of the signals \(x\) and \(y\), and \(C_{1}\), \(C_{2}\), and \(C_{3}\) are constant values.  
In this study, the loss function is used in combination with the _MSE_ and _SSIM_, as follows:  
\[Loss=0.2\cdot L_{MSE}+0.8\cdot L_{SSIM}, \tag{13}\]  
In addition to the pixel value difference between the image generated by the proposed network and the ground truth, the difference in the similarity between image patches is also considered.
## 2 Overview of Developed IAQMS System
The typical electrochemical sensors consist of chemical reactants (electrolytes or gels) and two terminals--an anode and a cathode. The anode is responsible for an oxidization process and the cathode is responsible for a reduction process. As a result, current is created by way of positive ions flowing to the cathode and the negative ions flowing to the anode. The output is directly proportional to the concentration of the sample gases. The calibration of each gas sensor has been carried out in our previous manuscript [35]. For validation, self-developed sensor nodes were validating with the commercial device. The validation procedure had been carried out to make sure that the data collected by the sensor was similar to the data collected by a commercial sensor. The discussion of this procedure was limited only to the NO2, temperature and RH sensors. High NO2 levels in the outdoor environment, originating from local traffic or other combustion sources, influences the NO2 level in the indoor environment. Exposure to an excessive level of NO2 could be fatal [4]. The sensor validation was carried out with an Aeroqual Ltd. (Auckland, New Zealand) Series 500 portable indoor monitor device (a professional grade air quality measurement system) which had been pre-calibrated [36]. Three sensor nodes and the Aeroqual device were placed in a clear, sealed glass container of 100 cm \(\times\) 40 cm \(\times\) 30 cm which was completely sealed. Then, the gas concentration inside the sealed container was varied by injecting the particular gas of interest. The outputs of the sensors were recorded continuously for 1 h and plotted (Figure 2). Figure 2: _Cont._  
Figure 2a shows the result of the NO\({}_{2}\) sensor when 35 ppb of NO\({}_{2}\) gas concentration was injected to the sealed container. It can be observed that the value for all sensor nodes, including the Aeroqual device, gave relatively similar readings for a one hour period. During the experiment, the same room temperature setting 25 \({}^{\circ}\)C was applied as shown in Figure 2b, while Figure 2c shows the readings for RH. For validation purposes, means and standard deviations for all three parameters were calculated as shown in Table 2 below. Also shown in Table 2, the mean value for NO\({}_{2}\), Temp and RH of three nodes (Node 1, Node 2 and Node 3) did not differ substantially from that of Aeroqual (a pre-calibrated device). This shows that the data measured for each developed sensor modules provide a similar response with the pre-calibrated device. On the other hand, the mean value for those three parameters is within an acceptable range or exposure limit as suggested by IAQ authorities such as [10]. The standard deviation (SD) from Table 2 shows how the data differed from the mean value for each node. Overall, it shows that the developed system provides reliable data.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 4 The Proposed Clustering Protocol
### Markov Chain Model for Resolving the Optimal Cluster Formation and CH Selection Problems
Consequently, we exclude the cases with no CH selected in the network and we get these statistical properties given as:  
\[Avg_{non-zero[CH]}=E[CH|CH>0]=\frac{Avg(CH)}{1-p(CH=0)} \tag{7}\]  
The SD and COV for the number of CHs are given as:  
\[SD_{non-zero[CH]}=\sqrt{\sum_{L=1}^{N}\left(\frac{p(CH=L)}{1-p(CH=0)}\right) \cdot L^{2}-(Avg_{non-zero[CH]})^{2}} \tag{8}\]  
\[COV_{non-zero[CH]}=\frac{SD_{non-zero[CH]}}{Avg_{non-zero[CH]}} \tag{9}\]  
By utilizing the Markov chain model, we perform a stage-based stochastic analysis of our model to analyze the performance of MOCHs with better granularity. The conditional PMF of the number of CHs for a stage \(x\) is given as:  
\[Pr(x,L)= \begin{cases}P_{(0,N)\rightarrow(1,N-L)}&x=0,L\in[0,N]\\ \sum_{i=L}^{N}f_{xi}\cdot P_{(x,i)\rightarrow(x+1,i-L)}&x\in[1,n-2],L\in[0,N]\\ f_{(n-1)L}&x=n-1,L\in[0,N]\end{cases}  \tag{10}\]  
By using the Equation (10), we are able to estimate the SD, and the COV for the optimal number of CHs at each step.
## 3 Results
### Qualitative Analysis and Milk Classification
The absence of lactose lowers the value of the conductance. Figure 4: (**a**) Imaginary part of the admittances of different types (whole, semi-skimmed, fat and lactose-free, lactose-free) of milk from the same brand; (**b**) Detail of Figure 3a. Figure 5: Imaginary part of the admittances of different types (whole, semi-skimmed, fat-free) of milk from the same brand. The height of the peak is inversely proportional to the fat content. Although the admittance measurement is a very interesting method for the characterization of milk whose composition is known, it could fail as a blind detection technique. Indeed, as can be easily seen from Figures 4 and 6, errors could arise in the distinction between the semi-skimmed milk (blue curves in the figures) and the semi-skimmed, fat-free and lactose-free milk (light blue curves), or even between the whole milk (red curves) and the lactose-free milk (green curves). In order to perform a more quantitative analysis, that can dispel any doubts, the equivalent circuit for the electrical description of the milk's behavior was obtained from the curves and, as described in the next section, the values of the electronic components were used for the purpose.
## 1 Introduction
#### Novelty of the Proposed Approach  
The novelty of the proposed scheme is listed below:  
* We propose a new RDH scheme suitable for the transmission of electronic patient reports along with medical images.
* The final image in the proposed process is in encrypted form, which ensures the security of the medical image and the patient reports.
* We introduced a bit plane compression-based RDH scheme, which can provide a high embedding rate with a 0 bit error rate during image recovery and data extraction.
* The well-known Elias gamma encoding process is modified to embed additional bits during the encoding process. The remaining sections of this manuscript discuss related works, the proposed algorithms, the illustrative example, the experimental setup, and the results.
# Federated Learning-Based Analysis of Human Sentiments and Physical Activities in Natural Disasters
###### Abstract
In addition to these problems, an additional challenge exists for FL based on online active learning, it needs a wider array of manually labeled seeding training data that is distributed to an entire row of client nodes during the training of the models. Two fascinating applications are used to assess the proposed approach: (1) the analysis of human visual sentiments in the social media imagery of natural disasters, and (2) the physical activity connected with the natural catastrophe response. In addition to the novel methodology, this study also investigates a distinct perspective of the applications when compared with the current studies on the subject, comprising our prior contributions, as discussed in detail in Section 2. This study provides a solid foundation for future research on this topic. The following are the most significant contributions of this study to the ongoing research in this domain:  
* We investigate the viability of autonomous annotation of training data for FL using a novel methodology for generating a global model from locally available unlabeled data (on each node device). * We demonstrate that AL can achieve equivalent outcomes in federated and centralized learning without requiring human intervention to label the data. * We demonstrate that the efficacy of FL may be adversely influenced by a lower amount of data for the training of local models. Nevertheless, AL may be effective in such cases to acquire relevant data without any manual annotation. * In two distinctive contexts, we assess the performance of two AL techniques based on a pool with five alternative disagreement sampling approaches under centralized and FL, in which the training of a model is performed by uploading all participating nodes' data to a cloud aggregator. The rest of the paper is structured as follows: Section 2 covers published studies in four distinct areas, namely, FL, AL, human sentiments, and associated physical activities in disasters based on ML, notably deep learning. Section 3 covers the suggested approach for active learning-based FL using the human sentiment accompanying an associated physical activity analyzer in disaster-related visual imagery. It examines the experimental outcomes, including assessment trade-offs. Section 5 provides valuable lessons from the experimentation. Section 6 summarizes the aims and addresses future research prospects in this research area.
## 2 Status of Research and Objectives in Radiant Tube Lifetime Modeling
### Objectives of the Present Study  
None of the previous investigations, however, took the effects of the tube's radiation exchange with the surroundings on its thermomechanical behavior into account. The authors of the present work developed the first coupled heat transfer (CHT) model [35] to consider the effects of surrounding radiation on the lifetime of radiant tubes in a CGL, characterized by their creep deformation and corresponding thermomechanical stresses. The CHT model was validated against tube temperature measurements on a P-type radiant tube, operating with a 120 kW self-recuperative burner, in an in-house test bench. This model was used for P-type radiant tubes as extensive experimental data, and validations were available [27; 28; 30; 31; 32]. The steel strip, the neighboring tubes, and the remaining parts of the furnace incorporated as the furnace chamber were considered as the surroundings in the CHT model. The results showed an increase in creep deformation of the tube when the surroundings were considered. The highest creep deformations were observed for setups with a strip, even though these cases showed the lowest maximum tube temperatures. However, the results of this first CHT model were too simplistic as they did not involve assessing the effects of the complex furnace operation.  
The present series of investigations was an extension of this CHT model. The case studies were performed for vertically-mounted P-type radiant tubes over a processing time of 400 h. The specific objectives of this work were:  
* To investigate the effects of the absence or presence of a (wide or narrow) strip with respect to a reference case of an isolated tube.
* To demonstrate the influence of three different setups of the neighboring tube arrangement on a tube's creep deformation and stresses.
* To study the influence of the choice of burner operation (full-load firing, synchronous, and asynchronous on/off firing) on radiant tube lifetime.
* To understand the effect of changing strip temperatures across an annealing furnace on the tube's thermomechanical behavior.
* To examine the correlation between the obtained creep deformation and thermomechanical stresses with the furnace operating conditions.
## 6 Experimental Results and Analysis
### Influence of V-Rib Attack Angles (\(\beta\))
#### 6.2.2 Friction Factor Results  
Variations in the friction factor (\(f\)) with the Reynolds number for the tested tubes are presented in Figure 11. From the experimental results, \(f\) for tubes with CVRTs having attack angles (\(\beta\)) of 15\({}^{\circ}\), 30\({}^{\circ}\), and 45\({}^{\circ}\) are 383, 373, and 352% greater than a plain tube, respectively. At a given Reynolds number, the friction factor shows an identical trend as the angle variation. Regardless of the Reynolds number, a CVRT with \(\beta\) = 15\({}^{\circ}\) generates maximal friction losses. This suggests that within the range of heat transfer augmentation reported in this paper, increased heat transfer augmentation inevitably leads to greater pump work losses. The friction coefficient corresponding to the twisted tape with \(\beta\) = 15\({}^{\circ}\) is 6.4% and 12.1% higher than those corresponding to CVRTs with \(\beta\) = 30\({}^{\circ}\) and 45\({}^{\circ}\), respectively. This shows that a twisted tape with a smaller attack angle has a stronger capability to promote and disturb fluid flow, which elevates the fluid velocity and flow resistance. It also explains the variation in the Nusselt number.  
Figure 10: Effect of V-rib attack angle (\(\beta\)) on the heat transfer rate.
## 3 Materials and Methods  
The research evaluated the technical possibilities of measuring and recognizing the movement of the experimenter's hand, potentially monitoring the progress of rehabilitation or other medical process. At this stage, in order to check the precision, it was chosen to measure not only the movement of the whole hand or another large part of the body but more precise scenarios, that is, to examine the movement that is performed by the fingers. A total of 7 gestures were performed, namely: (a) raising the index finger, (b) raising the middle finger, (c) raising the ring finger, (d) raising the little finger, (e) thumbs up, (f) palm at rest with all fingers extended, and (g) victory sign displayed.  
The 8 channel Mindrove armband (Mindrove, Budapest, HU) was used to capture the EMG data. The wearable equipment was placed on the forearm and used to measure different types of muscle groups in real time.  
1. The carpi radials of the muscle flexor are responsible for the flexion and radial deviation of the wrist.
2. Musculus flexor pollicis longus, which is responsible for the flexion of the pollicis.
3. Musculus flexor digitorum, which is responsible for flexion of the fingers.
4. The carpi ulnaris muscle flexor, which is responsible for flexion and ulnar deviation of the wrist.
5. Musculus extensor pollicis longus at brevis, which is responsible for the extension of pollicis.
6. The carpi ulnaris muscle extensor is responsible for extension and ulnar deviation of the wrist.
7. Musculus extensor digitorum, that is responsible for extension of the fingers.
8. Musculus extensor carpi radials, which is responsible for extension and radial deviation of the wrist.  
According to accepted scientific practice, all gestures were recorded with EMG sensors pressed on the forearm to measure arm muscle activity (see Figure 1). The EMG time signal was recorded for up to 5 s while each participant performed a required gesture. A 5 s segment of the signal was analysed using the statistical estimates discussed in the previous section. An experimental study was conducted to determine which statistical estimates of the temporal signal can be used as motion-discriminating features and suitable for developing a classification algorithm. A proprietary software approach was used to process the captured data based on the Pareto optimized principles described below.  
The study analysed nearly 10,500 individual measurements of the specified gestures (approximately 1500 for each class). All measurements are assigned to a specific movement class, which is numbered from M1 to M7, as explained above. The number and distribution of classes is shown in the figure below, where the measurement number values are plotted on the horizontal axis, and the motion class numbers are plotted on the vertical axis. The sample of data used is assumed to be balanced.
# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 5 Conclusions  
According to the application scenarios of signal blind detection, we first propose a high-performance target detection model combined with STFT time-frequency image feature extraction to test the effect of different SNRs on target detection in a Rayleigh fading channel + Non-smooth undulating noise environment. Subsequently, we improved the YOLOv5 model, which combines BiFPN and EIOU to accomplish the target detection task for signal blind detection of STFT time-frequency images. According to the requirements of practical applications, we detect four modulation types, AM, FM, MFSK, and MPSK, and then compare the performance of these four types of detection with that of the baseline model detection to check the differences between the improved model and the original model. Finally, through comparative experimental studies, we demonstrate the superiority of our model. Therefore, we strongly believe that this important area will be a fruitful research direction, but we have just touched the tip of the iceberg. We have provided a novel strategy to process signals and hope to attract some attention from the relevant area.  
In our future work, we will consider blind detection on broadband communication signals in more complex environments, such as Multiple Input Multiple Output (MIMO) communication systems [34; 35]. We will consider model compression and acceleration using the Filter Pruning via Geometric Median (FPGM) algorithm and TensorRT framework.  
Figure 12: Experimental results of false alarm probability (**a**) and average error of signal parameters (**b**) at different signal-to-noise ratios.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **YOLOv5 + BiFPN + EIOU** & **SSD** & **FRCNN** \\ \hline mAP & 92.46\% & 84.11\% & 77.10\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the mAP of YOLOv5 and other methods.  
**Author Contributions:** Conceptualization, S.G. and Z.L.; methodology, S.G.; software, S.G. and J.Y. ([PERSON]); validation, Z.L., S.G. and P.W.; formal analysis, S.G.; investigation, S.G.; resources, J.Y. ([PERSON]); data curation, P.W. and W.W.; writing--original draft preparation, S.G.; writing--review and editing, S.G., W.W. and Z.L.; visualization, P.W.; supervision, Z.L.; project administration, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the 2020 Science and Technology Innovation Team from the Universities of Fujian Province and the NSF of China (Nos. 61871132, 62171135).  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not Applicable.  
**Data Availability Statement:** Not Applicable.  
**Acknowledgments:** Not Applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.