esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Challenges for the Internet of Things: What Role Can OR Play?
## 3 How can OR Support the Future IoT?
### IoT Robustness Studied Using Reliability Theory  
Related to scalability are robustness and reliability. The IoT will be composed of billions of electronic devices, many of which may be impossible to reconfigure or replace in contrast to one's desktop computer or tablet that requires regular software updates and possibly hardware upgrades for more memory, CPU power, or disk space. As stated by Metcalfe's Law, the more interconnections between independent components or subsystems within a system, the greater the complexity and higher the probability of system failures [39]. The ability of the IoT to operate reliably long term despite hardware and software failures is critical to gain user acceptance and trust. According to [PERSON] et al. [90], bit errors can lead to unmanageable problems in large networks that will characterize the IoT.  
OR techniques such as reliability theory can readily be applied to predict IoT robustness and reliability. [PERSON] and [PERSON] [60] developed an approach using reliability theory for the three IoT subsystems of perception layer, network layer and application layer. Their model assumes that the transmission networks are independent and that the perception and application layers are dependent so that total reliability \(R_{T}\) is given by:  
\[R_{T}=R_{1}\times R_{5}\times(1-\prod\
olimits_{k=2}^{4}(1-R_{k})), \tag{1}\]  
where \(R_{1}\) and \(R_{5}\) are the reliabilities of the perception and application layers, and \(R_{2}\), \(R_{3}\), \(R_{4}\) are the reliabilities of the internet, mobile network, and satellite communication network. This model could be expanded and updated to include other IoT characteristics and features of newer IoT devices as they come into operation, leading to additional terms in the reliability equation.  
Reliability theory could also be applied to other features of the IoT such as the reliability of the data collected [90]. [PERSON] et al. [91] discussed the application of so-called Fog Computing to the IoT while [PERSON] et al. [92] estimated the reliability of this approach by combining the reliability requirements of the grid and cloud with those of the sensors and actuators in the IoT and demonstrate that a reliable system can be achieved.  
The reliability of the IoT software itself may be another significant issue since software reliability can be considered as a special case of reliability theory. Hardware reliability is determined by component or material failure that prevents a system performing its intended function. Software reliability in the IoT is more difficult to assess since software may produce unanticipated results for many reasons such as unusual data coming from another device that was not considered in the design phase. Obsolescence of embedded software in IoT systems that cannot be readily maintained (for example, IoT sensors in a nuclear reactor exposed to radiation) may also affect reliability.
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 5 Experimental Results and Discussion
### Results and Performance Analysis
#### 5.2.2 Overall Performance Analysis
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**Proposed (\(\delta\) =**} & \multicolumn{2}{c}{**Proposed (\(\delta\) =**} & \multicolumn{2}{c}{**[PERSON] et al. [10]**} & \multicolumn{2}{c}{**[PERSON] et al. [34] vs.**} \\  & **100) vs. HM** & \multicolumn{2}{c}{**20) vs. HM**} & \multicolumn{2}{c}{**vs. HM**} & \multicolumn{2}{c}{**HM**} \\ \hline  & \(\Delta\)**T (\%)** & \begin{tabular}{c} **BD-Rate \(\star\)** \\ **(\%)** \\ \end{tabular} & \(\Delta\)**T (\%)** & \begin{tabular}{c} **BD-Rate \(\star\)** \\ **(\%)** \\ \end{tabular} & \(\Delta\)**T (\%)** & \begin{tabular}{c} **BD-Rate \(\star\)** \\ **(\%)** \\ \end{tabular} & \(\Delta\)**T (\%)** & \begin{tabular}{c} **BD-Rate \(\star\)** \\ **(\%)** \\ \end{tabular} & \(\Delta\)**T (\%)** & \
\begin{tabular}{c} **BD-Rate \(\star\)** \\ **(\%)** \\ \end{tabular} & \(\Delta\)**T (\%)** &
\begin{tabular}{c} **BD-Rate \(\star\)** \\ **(\%)** \\ \end{tabular} \\ \hline Kimono & 72.60 & 2.32 & 81.06 & 5.92 & 80.74 & 4.13 & 70.50 & 2.54 \\ Basketball Pass & 52.68 & 0.56 & 72.15 & 5.99 & 51.84 & 1.21 & 54.55 & 2.80 \\ BQTerrace & 56.17 & 0.91 & 72.94 & 7.52 & 52.03 & 0.80 & 56.78 & 1.95 \\ Traffic & 61.71 & 0.56 & 78.59 & 6.45 & 49.48 & 0.98 & 59.02 & 2.35 \\ RaceHorses & 40.92 & 0.47 & 62.75 & 4.82 & 49.07 & 1.04 & 53.98 & 2.36 \\ BlowingBubbles & 31.78 & 0.28 & 40.21 & 2.81 & 31.33 & 0.41 & 31.59 & 1.93 \\ Johnny & 56.61 & 2.22 & 70.46 & 6.02 & 71.99 & 2.94 & 71.35 & 4.28 \\ KristenAndSara & 59.41 & 1.68 & 66.74 & 4.89 & 62.14 & 2.21 & 68.78 & 3.18 \\ PeopleOnStreet & 49.59 & 2.36 & 75.61 & 13.65 & 44.42 & 1.17 & 56.49 & 2.25 \\ PartyScene & 42.37 & 0.58 & 52.91 & 3.40 & 29.68 & 0.30 & 44.72 & 2.23 \\ \hline Average & 52.38 & 1.19 & 67.34 & 6.15 & 52.27 & 1.52 & 56.78 & 2.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Coding efficiency and complexity reduction performance (All Intra main). The high visual quality degradation and fixed time gains of [34] can be attributed to the rigid models used in the approach. Similarly, the fixed time complexity reductions in [10] are a result of the offline and fixed-threshold models, that do not continuously adapt to the changing nature of the video content. The proposed method, on the contrary, makes use of content-adaptive models with the complexity control parameter \(\delta\) that can be tuned by the user. This allows higher time complexity reductions, while also keeping the encoding quality intact. Furthermore, it gives the flexibility to the user to trade-off the computational complexity with the encoding efficiency according to individual application requirements. In addition, the RD performance graphs for the bit streams generated by the proposed algorithm depicted in Figure 6 demonstrate similar coding efficiency performance to that of the bit streams generated by the HM16.1 reference encoder implementation.
## 5 Challenges, Open Research Problems, and Future of WBANs
### Bandwidth Problems  
The bandwidth available for data transmission for WBANs is usually low. Although, the new sensor nodes can operate at 250 Kbps, due to duty cycling mechanisms for lowering the power consumption lowers the actual available bandwidth. If the sensor nodes operate with a duty cycle of 10%, then they are actually transmitting data only 10% of the time. This situation may pose difficulties especially when there is huge amount of data like in the case of transmission of medical diagnostic imaging data, which may require up to Mbit/s level capacities. For this reason the efficient compression algorithms must be developed for multimedia data transmission [84-88]. These compression methods should also be lightweight enough for running on a sensor node that has no high performance processing and memory capabilities.
# Preddiagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound
## 4 Results
When the ST-MFCC dataset is used, the four classifiers are ranked in the following order: Xception, ResNet50, VGG19, and AlexNet, with AUC values of 0.988, 0.983, 0.976, and 0.951, respectively. The effect of unsegmented KS signal characteristics on HF prediagnosis was investigated. Tables 4 and 5 show the performance of the four neural networks on the UST-CWT and UST-MFCC datasets. As shown in Table 4, when the UST-CWT dataset is used for classification, Xception got AUC (0.979), Acc (94.3%), Se (95.7%), and Sp (92.4%). Resnet50 came in second, with an AUC of 0.974, Acc of 92.2%, Se of 90.5%, and Sp of 93.4%. The AUC, Acc, Se, and Sp obtained by VGG19 were 0.968, 91.1%, 96.4%, and 83.6%, respectively, and the AlexNet got AUC (0.966), Acc (09.9%), Se (94.9%), and Sp (85.2%). The average Tc for each model was as follows: Xception (55 min), ResNet50 (6 min), VGG19 (9 min), and AlexNet (3 min). Table 5 demonstrates that, when the UST-MFCC dataset is applied for HF prediagnosis, Xception obtained AUC (0.989), Acc (96.0%), Se (97.5%), and Sp (93.8%). ResNet50 got \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **Acc (\%)** & **Se (\%)** & **Sp (\%)** & **AUC** & **Tc (min)** \\ \hline AlexNet & 90.9 & 94.9 & 85.2 & 0.966 & 3 \\ VGG19 & 91.1 & 96.4 & 83.6 & 0.968 & 9 \\ ResNet50 & 92.2 & 90.5 & 93.4 & 0.974 & 6 \\ Xception & 94.3 & 95.7 & 92.4 & 0.979 & 55 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of four types of neural networks on the UST-CWT datasets. Figure 8: Performance comparison of different neural network models. (**a**) ST-CWT dataset, (**b**) ST-MFCC dataset. AUC (0.988), Acc (95.4%), Se (92.0%), and Sp (97.8%). The classification performance of VGG19 ranked third, with an AUC of 0.979, Acc of 93.0%, Se of 97.5%, and Sp of 86.5%. The AUC, Acc, Se, and Sp obtained by AlexNet were 0.956, 89.3%, 93.9%, and 82.8%. The average Te for AlexNET, VGG19, ResNet50, and Xception was 3 min, 9 min, 6 min, and 55 min, respectively. Figure 8b shows the ROC curves of four neural networks when classifying HF using UST-CWT and UST-MFCC data sets. Using the UST-CWT dataset, the four classifiers ranked as follows: Xception, ResNet50, VGG19, and AlexNet, with AUC values of 0.979, 0.974, 0.968, and 0.966, respectively. When the UST-MFCC dataset was used, the four classifiers performed in the following order: Xception, ResNet50, VGG19, and AlexNet, with AUC values of 0.989, 0.988, 0.979, and 0.956, respectively.
# Rural Healthcare IoT Architecture Based on Low-Energy LoRa
## 4 System Architecture
### IoT Devices  
In the proposed architecture, the IoT devices collect data. In the topological sense, they are part of the fog network as they connect to the healthcare gateways through a network link. These nodes consist of a microcontroller, optional communication chips and timers, and sensors. A diagram of the IoT node components is presented in Figure 5. The sensors are part of the device's enclosure, or in some cases, are connected by wire, depending on the type of measurement conducted. To reduce cost, the number of sensors on a node is maximized. However, each sensor could independently be turned on or off to facilitate energy-saving schemes. While data processing is primarily done on the gateways, the IoT nodes also reprocess data. Decision-making for simple logic that can be programmed on the microcontroller is also done directly on the node. For example, in the environmental safety user story, a water shutoff is automatically initiated by a node that monitors water usage if certain limits are reached. Such actions should always be reported to the healthcare gateways, with the possibility to receive overrides. The UI on IoT nodes should be limited to only servicing interfaces and alerting the care receivers, such as with the medicine dispenser.  
IoT devices in healthcare might not have predetermined patterns for energy usage, especially when they measure events related to the person who receives the care. For example, devices that measure sleep-related parameters will consume energy proportional to the number and duration of monitored activities. The same applies to other aspects of activities of daily living (ADL). Thus, different behavior in ADL could lead to a different energy profile of the devices. Proper battery management is needed to estimate and adjust the activity periods to extend the battery life to the expected period. A battery level measurement could be conducted to receive feedback of battery consumption, and battery usage could be estimated from the duration and energy profile of different stages of the IoT device, such as boot-up, data collection, data processing, and communication. The energy consumption should be measured for each IoT device and a detailed energy profile created. This energy profile will then be stored in the persistent memory of the device itself. The battery capacity profile should also be established [39]. Different device profiles should be created depending on the speed of battery depletion to compensate and extend  
Figure 5: IoT node components.  
the battery life. Techniques must be customized depending on the device function but could reduce sensor sampling frequency, reduce communication windows, and increase the threshold for data collection. For example, the ML models such as decision trees should be pre-programmed or received from the network. They should be structured to use minimal computing and battery power, for example, by using data structures with lower precision and, consequently, lower memory implications.
# Adaptive Relay Selection Scheme by Using Compound Channel
## 3 The Conventional Relay Selection Schemes
### The MIMO Capacity-Based Selection Scheme  
Among the relay selection schemes, the scheme using MIMO relay channel capacity is frequently used [38; 39; 40]. The MIMO channel capacity of the \(k\)-th relay is as follows,  
\[C_{k}=\log_{2}(\det(\mathbf{I}+\frac{\rho}{N_{l}}\mathbf{H}_{k}\mathbf{H}_{k} ^{H})), \tag{5}\]  
where \(\mathbf{I}\) denotes the identity matrix. \(\rho\) denotes average SNR at each antenna of the relay as \(\frac{P_{l}}{N_{0}}\). \(P_{t}\) is transmitting power of the signal and \(N_{0}\) is noise power. \(\mathbf{H}_{k}^{H}\) means the Hermitian conjugate transpose matrix of \(\mathbf{H}_{k}\).  
According to (3)-(5) can be approximated as follows,  
\[C_{k}=\sum_{i=1}^{N_{l}}\log_{2}(1+\sigma_{i}^{2}\rho). \tag{6}\]  
As a \(\sigma_{i}^{2}\) increases, available channel capacity also increases. The MIMO capacity-based selection scheme selects relay with the largest capacity using (5) or (6).
## 4 Discussion  
In [PERSON]'s research [32], he used a SegNet convolutional neural network to segment rice images, and extracted texture features, morphological features and color features from the segmented images. He used the selected features to build a linear regression model to predict the fresh weight and dry weight of rice, and the \(R^{2}\) of the model was 0.812 and 0.772. The characteristic parameters used in this paper are almost the same as those in [PERSON]'s paper, and the planting conditions of rice are also the same. However, fractal dimension is not paid attention to in [PERSON]'s paper, which may be the reason why the effect of [PERSON]'s model is inferior to that in this paper. Therefore, when establishing the prediction model of rice biomass, we can consider adding the fractal dimension into the model, which may improve the model effect. In [PERSON]'s research [33], she proposed the idea and method of establishing a mathematical model for accurate prediction of rice yield based on texture analysis and fractal theory. She obtained the texture features and fractal dimension by analyzing and calculating the binary image of rice, and then used principal component analysis to obtain the variables for establishing the model. She used linear regression to establish the yield prediction model, and the \(R^{2}\) only reached 0.494, and the model had a poor effect in the posterior error test. [PERSON]'s use of the fractal dimension to establish the model is innovative, but she ignored the traditional characteristic parameters of rice, which may be the reason for the poor final result of the model. By combining the fractal dimension with the traditional characteristics of rice, the prediction model obtained in this paper has a better effect. Compared with their research, this paper improved the shortcomings of their research and obtained a simple and effective model for predicting rice biomass. The model is based on images of rice and can play a role in the field of smart agriculture.  
The multivariate linear regression model is very different from other machine learning algorithms. The model obtained through machine learning belongs to the black box model, and we cannot know the specific structure of the model, nor the role of each explanatory variable in the model, so the model has a low explanatory ability. However, the linear regression model has good explanatory properties, the expression of the model can be obtained and the effect of each variable in the model can be clearly understood through the expression. In the study of rice, researchers often pay close attention to the influence of variables which can guide the improvement in varieties. Therefore, in the prediction of rice biomass, if the effect of the linear regression model is not far from that of the machine learning model, it is suggested to give priority to the linear regression model.
# Design and Fabrication Technology of Metal Mirrors Based on Additive Manufacturing: A Review
## 4 Challenges Faced in the Development of AM Metal Mirror
### Porosity  
Porosity is one of the most important criteria for evaluating AM products. Fusion errors, gas pores, and shrinkage pores are three types of pores in AM parts. PBF can be seen as a micro-welding, which causes sizeable residual stress inside the part during the unique process of rapid melting and solidification. Improper selection of laser power, scanning speed, or other parameters can result in cracking, holes, low density, and poor forming quality [80]. Other pores are attributed to an insufficient supply of liquid metal during the solidification process. As shown in Figure 31, the mirror's holes are obvious under the light, which affect surface accuracy. Therefore, selecting the best processing parameters and carrying out densification for the forming parts are essential. Surface modification is required if necessary. Moreover, the standard methods used to test porosity are scanning electron microscopy (SEM) and computed tomography (CT).  
Figure 31: An AM mirror under the light.
# Seismic Exploration Methods for Structural Studies and for Active Fault Characterization: A Review
## 4 Seismic Attributes
### Instantaneous Frequency  
The instantaneous frequency is a seismic attribute that measures the time rate of change of the instantaneous phase of a seismic trace [92; 93; 94]. Instantaneous frequency is widely used for identifying specific seismic events, such as abnormal attenuation and thin bed tuning [87] based on its frequency content. It is one of the important seismic attributes related to the complex seismic trace. Although the definition itself is clear in  
Figure 6: Seismic amplitudes (**A**) from a 2D reflection profile over Centennial Valley (USA) compared with the coherence attribute (**B**), energy (red) and dip-angle attribute (blue if south-dipping or green if north-dipping.) in (**C**,**D**) interpreted section (from [PERSON] et al. [88]). The authors interpreted faults by matching consistent reflectors offset in the amplitude plot (**A**) with low-coherence (i.e., black) attribute (**B**), discontinuity in the energy attribute and high values of the dip attribute (**C**). On the other hand, the energy attribute is an excellent indicator of unconformities.  
theory, in practice, the calculation can vary considerably and deviate from the theoretical aim [95]. The instantaneous frequency is influenced by both wave propagation effects and depositional characteristics. This makes it a useful discriminator with a range of applications by identifying frequency anomalies [96]. For example, an unconsolidated sand can be recognized as a low-frequency anomaly, and this effect can be amplified by oil/gas saturation. Instantaneous frequency can also serve as an indicator of fracture zones, as fractures may also show up as lower-frequency zones especially if they are filled by fluids. Additionally, frequency can reveal bed thickness, with higher frequencies indicating sharp interfaces such as those found in thinly laminated shales, while lower frequencies are indicative of more massive bedding.
## 1 Introduction
To address this limitation, the integration of automatic optimization control is proposed as a means to reduce computational complexity. The case study involving an ammonia reactor illustrates that the combined use of ESC and automatic optimization enables effective compensation for unmeasured disturbances and plant-model discrepancies, thus improving overall system robustness. The methodology presented in this work aims to ensure zero steady-state error in response to various reference and disturbance signals, including step, ramp, and second-order polynomial (parabolic) inputs. The proposed control scheme combines the incremental state and incremental-N state modeling frameworks with the Takagi-Sugeno (T-S) fuzzy representation to address nonlinear system dynamics. To improve the controller tuning process, Extremum Seeking Control (ESC) is integrated into the design. The effectiveness of the approach is validated through a case study involving a thermal tank system, selected due to its inherent nonlinear behavior and multivariable characteristics. The structure of the paper is as follows: Sections 2-4 introduce the incremental state-space model, its fuzzy extension via Takagi-Sugeno rules, and a nonlinear parameter identification method. Sections 5 and 6 address discrete LQR control and state estimation. Sections 7 and 8 detail the construction of incremental-2 and generalized incremental-N models, including their fuzzy formulations. Section 9 presents the Extremum Seeking Control (ESC) algorithm for automatic LQR tuning. Section 10 applies the proposed fuzzy control strategy to a nonlinear thermal mixing tank, evaluating its performance and robustness across various reference signals and disturbance scenarios.
# Iterative MLFMA-MADBT Technique for Analysis of Antenna Mounted on Large Platforms
## 3 Formulation
### Initial Beams Generation and Visibility Determination  
A novel and efficient way proposed in this paper is creating the initial beams according to the geometry of the BT region. As illustrated in Figure 3, the initial radiation beams are generated by directly connecting the point source with mesh triangles in the BT region. Thus, the initial beams in this paper are pyramids emitted from a point source.  
Please note that only the mesh triangles visible to the point source can be used to create the initial beams. The visibility determination is implemented by progressive processing to minimize the calculation.  
First, a sketchy filtering with a back-face culling based on the normal [20] is presented in (6).  
\[\hat{n}\cdot(\mathbf{r}_{M}-\mathbf{r}_{P})<0 \tag{6}\]  
where \(\hat{n}\) denotes the normal of the triangle, \(\mathbf{r}_{P}\) and \(\mathbf{r}_{M}\) are the locations of the point source and the center of the triangle.  
Then, the triangles satisfying (6) will be verified more precisely by sending a test ray from the point source to the center of the triangle. The test ray of a visible triangle should not intersect with other triangles before arriving the destination. The searching of the triangles near the test ray can be accelerated by kd-tree algorithm [21].