esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Analysis and Optimization
### Average Output SNR and Amount of Fading
#### 5.2.2 Amount of Fading  
The AoF is the critical performance measure indicating the severity of fading for the wireless communication system, which is typically independent of the average fading power and can be utilized to parameterize the distribution of the SNR of the received signal. Based on the definition, the AoF of SSC over Fisher-Snedecor \(\mathcal{F}\) fading channels is given as follows:  
\[\alpha{}F=\mu_{\gamma_{SSC}}(2)/\mu_{\gamma_{SSC}}^{2}(1)-1. \tag{67}\]  
By setting \(q=1\) and \(2\) in (40) and (41), respectively, then inserting them in (38), the analytical expression of AoF can be deduced over Fisher-Snedecor composite fading channels. Similarly, the analytical expression of AoF for SEC and SECps can also be easily obtained. The AoF can also be used to study the spectral efficiency and the channel quality estimation index.
# Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks
## 4 Results
### Experimental Results
In about 3 h, the transmission rate of the SPMBM mobile model reached the maximum value, about 0.67, the transmission ratio of the RWP model reached the maximum value at 4 h, about 0.625, and the transmission rate of the RM model reached the maximum value at 3 h. The value is 0.587 and then floats around 0.53. After simulation, this research found that the best delivery rate is the FABD algorithm under the SPMBM mobile model. Figure 11: The relationship between the end-to-end delay and the cache. Figure 10: The relationship between energy consumption and cache. Figure 13 shows the average overhead of the FABD algorithm under the three mobility models. From the simulation results, it can be seen that the routing cost of the FABD algorithm is less affected by the mobility model. The routing cost of the three models is in the range of 110 to 118, this is because in the process of node selection, due to the continuous progress of calculations, the community has formed a large number of tasks for nodes to share information in the process of information transmission. Figure 14 shows the energy consumption of FABD under different mobile models. Because the FABD algorithm has a firm transmission function, when the model changes, the method will not change greatly. This view has been verified to be correct in the simulation. The simulation results of the three models have little difference in energy consumption. Figure 12: The receiving rate of the FABD algorithm in different mobile models. Figure 13: The routing cost of the FABD algorithm under the three mobility models. Figure 15 shows the average delay of the FABD algorithm under the three mobile models. The simulation results show that the delay difference between the three community models is not obvious. The average delay under the SPMBM mobile model will be slightly lower, and the average delay of RWP will be slightly higher. The delays of the three models are all within the range of 180-215.
# FCN-Based 3D Reconstruction with Multi-Source Photometric Stereo
## 2 Materials and Methods
### Network Architecture  
We converted the solution of the mapping relationship from image to depth in the multi-source Photometric Stereo method into an end-to-end optimization process with a large number of parameters. The FCN with encoder-decoder architecture has an outstanding performance in the problem of the pixel-level classification of images; its skip structure combined with the results of different depth layers ensures the long-distance dependence between pixels and the robustness and accuracy of the network and improves the accuracy of the feature extraction. Meanwhile, the network structure of the FCN determines that it can perfectly adapt to any size of input, which is exactly what we needed. Therefore, on the basis of the FCN network structure, we adopted U-net as the basis of our network design.  
The architecture of the proposed network is shown in Figure 1. The U-shaped network structure could fully combine the simple features of shallow layer in the decoder stage, so it could also adapt to our small dataset. Our network contained twenty-nine layers, including twenty-one convolution layers, four pooling layers and four up-sampling layers. The activation function of all the convolution operations in the network was ReLU, and we took multiple RGB images from different light source directions containing different degrees of shadow and brightness information as the input of the network. In addition, the network outputted the original RGB images synthesized by the proposed network while outputting the predicted depth--that is, the output of the network was a multi-channel output.
# Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies
## 2 Related Works
### Wake-Up Receiver  
Wireless sensor nodes that are equipped with wake-up receivers listen permanently to the wireless channel for incoming wake-up calls. In case they detect a valid wake-up message, they usually provide a signal to the microcontroller of the wireless sensor node that transfers from an energy-saving sleep state to an active state to perform a predefined task. Figure 2 illustrates the main blocks of a wireless sensor node with a wake-up receiver that consists of an envelope detector and a correlator. The matching network provides impedance matching of the antenna and detector. The microcontroller controls an antenna switch and changes, upon reception of a wake-up message, the signal path to a second radio, called the communication or main radio.  
The envelope detector demodulates the high-frequency carrier signal to achieve a low-frequency wake-up signal. To ensure ultra-low-power consumption, the demodulator often consists only of  
Figure 2: Schematic of the wireless sensor node including a wake-up receiver consisting of the envelope detector and correlator.  
passive components like diodes and capacities. Figure 3 illustrates a low-frequency wake-up signal on-off keying modulated on a high-frequency carrier signal.  
The above-described wake-up receiver has addressing capability provided by the correlator. The correlator prevents the wake-up receiver from activating the main controller of the sensor node if a wake-up message does not correspond to a pre-configured address. Instead, it resides in ultra-low-power listening state. Examples of wake-up receivers with addressing capability are introduced for example in [2; 8; 20; 21; 22; 23].  
Some wake-up receivers can also be configured to be sensitive to a certain wake-up frequency without the requirement of an address embedded in the wake-up message. Then, the wake-up receiver activates the main controller in all cases that it detects a valid frequency at its input and does not correlate the signal to a stored pattern. An advantage of this method is comprised by short and simple wake-up messages, as they do not require a certain embedded pattern. However, without addressing, wake-up receivers are prone to false positive wake-ups, as shown for example in [8]. Further discussion on wake-up receivers can be found for example in [24; 25].
## 2 Proposed Modular Cell Balancing Circuit
### Analysis of the Previous Cell Balancing Circuits
#### 2.1.1 Active Cell Balancing Circuit Using the MWT
However, in this study, the different voltages among the cells are small. Therefore, the circuit does not need to limit current and protect circuits. \(V_{Cell1}\) and \(V_{Cell2}\) are higher than \(V_{\text{aver}}\), \(V_{Cell3}\), and \(V_{Cell4}\). Therefore, currents \(i_{1}\), \(i_{2}\) flow from the cells (i.e., \(Cell_{1}\) and \(Cell_{2}\)) to the transformer \(T\), and \(i_{3}\) and \(i_{4}\) flow from \(T\) to the battery cells (i.e., \(Cell_{3}\) and \(Cell_{4}\)). This means that the electric charges in \(Cell_{1}\), \(Cell_{2}\) are transferred to \(Cell_{3}\), \(Cell_{4}\) through \(T\). Mode 2 [\(t_{1}\), \(t_{2}\)] (see Figure 2c): At \(t_{1}\), four switches are turned off simultaneously, as shown in \[V_{TPi}=V_{aver}=V_{Celli}-L_{iki}\frac{d_{i}}{dt} \tag{1}\]2 The voltage across the \(L_{m}\) becomes negative, \(V_{Lm}\). Therefore, the magnetizing inductance is reset through the diode, resulting in a decrease in the \(i_{Lm}\) to zero level. \[V_{TPi}=V_{aver}=V_{Celli}-L_{iki}\frac{d_{i}}{dt} \tag{1}\]3  
Mode 3 [\(t_{2}\), \(t_{3}\)]: Mode 3 starts when the \(i_{Lm}\) is equal to zero. No current flows in the MWT from \(t_{2}\) to \(t_{3}\). At \(t_{3}\), this mode is completed and changes to mode 1 occur in the next switching period. This circuit has several advantages [17] such as short equalization time, simple control scheme, repudiation of the voltage sensor. However, if the SCBS is extended, the MWT requires a large number of windings. Meanwhile, this MWT is difficult to manufacture due to the parameter matching for the turns ratio and the leakage inductances. Equations (5)-(8) show that larger leakage inductances result in a smaller cell current. In other words, the balancing time is longer. In particular, the leakage inductances of the transformer are not uniform. Therefore, this causes a charge imbalance problem by itself. In order to reduce this problem, the number of windings of the transformer needs to be reduced [28]. This requirement motivates our proposed cell balancing circuit to solve these issues.
## 2 Related Work  
### Context Categorization Schemata  
The context can be identified and categorized in many different ways. One of the first efforts to categorize context uses three common questions to determine context: where you are, who you are with and what is nearby [29]. [PERSON] et al. [5] categorize context into primary and secondary, which is still one of the most used schemata. In their seminal work on ubiquitous computing, [PERSON] and [PERSON] [30] indicate five questions as the minimum set that corresponds to every contextual observation, the 5 Ws, namely, who, what, when, where, and why. [PERSON] et al. [31] categorize context into physical and cultural, while [PERSON] et al. [32] categorize it into physical and logical. [PERSON] [33]distinguishes context according to source and mutability in (i) sensed context, i.e., data directly collected from sensors, (ii) static context, i.e., immutable data, (iii) profiled context, i.e., data that change infrequently and (iv) derived context, i.e., computed information. Direct and indirect context types are used by [34]. [PERSON] and [PERSON] [35] propose a distinction between objective and cognitive context. [PERSON] et al. [36] categorize context as either observable or non-observable. Temporal categorization (active and past) is proposed by [37]. Physical and virtual (derived) context is proposed in [38]. Finally, contextual data may be event-driven or time-driven [12], depending on the triggering condition. [PERSON] et al. [6] highlight that each schema has its strengths and weaknesses, thus an ideal middleware solution should employ a combination of different schemata. The authors also argue that most categorization schemata fall exclusively under the conceptual or operational type, each of which alone is not sufficient to represent context from all perspectives. On this premise, the current work goes beyond the state of the art by proposing a combination of existing categorization schemata that satisfies both the conceptual and implementation perspective.
# Developing Analytical Tools for Arabic Sentiment Analysis of COVID-19 Data
## 2 Related Works
### Arabic COVID-19 Corpus  
Since December 2019, social media platforms, and Twitter in particular, have proven to be indispensable for sharing information and discussing the COVID-19 pandemic. To facilitate the analysis of these discussions, a large volume of raw datasets were made available. The majority of the COVID-19 datasets are unlabeled tweet collections, such as as as [12], that present a large Arabic tweet dataset that contain keywords relevant to COVID-19. The dataset was collected using the Tweepy Python library and Twitter streaming API from 1 January 2020 to 15 April 2020. Similarly, ArCOV-19 [10] is an Arabic dataset collected by searching Twitter over the course of one year from 27 January 2020. The dataset contains approximately 2.7 million tweets, along with the propagation networks of their most popular subset.  
In addition to unlabeled data, ref. [18] manually annotated tweets in English (containing 504) and Arabic (containing 218). The dataset was annotated for various purposes, including harmfulness to society, the relevance of tweets to policymakers or governments, and fact-checking. The authors in [19] obtained a total of 1M unique Arabic tweets about COVID-19 from December 2019 to April 2020. The authors cluster tweets into five by using the K-means algorithm. The dataset was annotated for fact detection tasks. The authors in [11] annotate the 10-thousand English and Arabic dataset, called SenWave. The dataset annotated for fine-grained SA task. ArCovidVac is the largest manually annotated Arabic tweet dataset developed by [20] The dataset includes 10,000 tweets. The dataset annotated for various tasks consists of distinguishing informative tweets from uninformative ones; stance detection utilizing transformer architectures; and fine-grained multi-class tweet classification.  
As a summary, the publicly available datasets are either unlabeled or manually annotated, whereas our dataset is automatically annotated using the COVID-19 lexicon. The dataset contains larger than 42K tweets annotated with the three labels positive, negative, and neutral.
# Effects of Aft-Body Extension Design on the Fundamental Characteristics of HWB Civil Aircraft
## 2 MDO Optimization Platform  
The optimization planform in this paper uses NSGAII (Nondominated Sorting Genetic Algorithm II) as the optimization algorithm, and the initial population is set to 120. NSGAII is a multi-objective optimization algorithm, and the optimization objectives in this paper are to maximize the cruise lift-to-drag ratio and minimize the maximum takeoff weight (MTOW). The whole optimization architecture is shown in Figure 1 and contains five modules with three constraints. The five modules are described in detail in later sections, and here we introduce the three levels of optimization constraints: (1) The cabin area, with reference to the cabin density of conventional TAW civil aircraft [21], should have a lower limit set to 300 m\({}^{2}\) for a 400-seat HWB civil aircraft. (2) There should be a clean configuration at cruise altitude and Mach number, meaning that the total aircraft pitch moment should be kept to zero when each control surface is in neutral position during cruise flight. (3) The stability and control constraint should be applied, which contains two aspects: One is the static stability margin constraint, which will largely determine the planform of the HWB, so by controlling the range of values of this constraint, different layouts of HWB configurations will be obtained. The other one is the basic control constraint, which is used to ensure that the resulting configuration is basically maneuverable. This constraint is determined by short-period frequency, \(\omega_{nsp}\), and the control anticipant parameter, \(\frac{\Delta n}{\Delta a}\), of the cruise state. The design has an acceptable maneuvering performance or not depending on whether the point \(\left(\omega_{nsp},\frac{\Delta n}{\Delta a}\right)\) lies within the envelope of Class 2 flight quality.  
In this paper, the inputs to the MDO design platform are nine aircraft planform geometry parameters that can determine the main features of the aircraft's plan layout, and the lift-to-drag ratio, MTOW, and static stability margin of the design were set as output parameters. The entire MDO design program consists of five modules: geometry module, weight module, aerodynamic module, trim module, and stability and control characteristics calculation module. According to the Figure 1, the data flow unidirectionally in the above five calculation programs; that is, only the result of the planform planning of the HWB will determine the inertial characteristics of the aircraft, while the planform  
Figure 1: Program structure of the MDO design platform.  
and inertial characteristics will jointly determine the aerodynamic characteristics and S&C characteristics of the aircraft.
###### Abstract
Although the RF solves the classification problem of the linear fractionable data set, its solutions are infinite, which leads to a lack of generalization ability in the model [49]. Further, the increase in the size of the pixel-based RF classifier can improve the generalization ability and stability of the model, while also increasing the required storage capacity, resulting in a significant decrease in classification prediction speed [11; 49], especially for high-resolution remote sensing image classification. Although extensive research based on machine learning has been carried out on glacier classification, no single study exists that has shown the best performance. Therefore, relying on only one classifier--whether it be a shallow classifier based on pixels or a deep classifier based on contextual semantics--cannot achieve the best classification results [51; 52; 53]. Cross-validation is a method of model selection. In the process of model building, the parameters of the model are continuously adjusted by verifying the data to establish the optimal model and prevent overfitting. Using the concept of cross-validation to determine the weights of different classifiers can make the assignment of the weight coefficients of a single classifier more accurate, simultaneously reducing the complexity of the compound classifier selection algorithm [36; 54]. [PERSON] et al. [55] used the CNN model to extract the surface temperature, combining it with other features of the SAR image, and effectively determined glacier information using the RF algorithm. Therefore, the respective advantages of RF and CNN [40] can be leveraged to enhance the classification results through decision fusion strategies. This demonstrates the potential for improving the final classification performance, so as to build a more reasonable RF-CNN composite classifier for glacier classification. In this paper, the extraction of information regarding a debris-covered glacier is realized by constructing a composite classifier of the RF-CNN. Through qualitative and quantitative comparative research on historical glacier inventory data, the advantages of glacier extraction based on machine learning methods are explored. Furthermore, this research method can provide decision-making guidance for future glacier boundary extraction.
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
###### Abstract
* Originality/value: the simulation method of three controllers is proposed through performance comparisons with trajectory tracking graphs and performance indicators. Our study primarily aims to obtain the dynamic model and parameterization of a 3-DoF Scrobot-type robotic arm to enable the design, simulation, and comparison of three advanced controllers meeting speed, practicality, adaptability, and scalability requirements. The selected robotic system resembles the Scrobot robot intended for mining industry operations, with simulation providing an ideal method to evaluate potential real operation scenarios under complex conditions. The approach remains applicable to any robotic arm. While practical implementation faces time and resource limitations, simulation offers significant advantages in safety assurance through preliminary controller evaluation. The research aims to provide a valuable guide for researchers and professionals, highlighting the significance of controllers in robotic arm applications.
## 3 Results and Discussion
### Total Phenolic Content (TPC) and Antioxidant Activity
The DPPH assay is used to ascertain antioxidant activities through a mechanism in which antioxidants act to inhibit lipid oxidation, scavenging DPPH radical, and therefore determine free radical scavenging capacity. The method is widely used due to the relatively short time required for the analysis [36]. The DPPH free radical method is an antioxidant assay based on electron transfer that produces a violet solution in ethanol [37]. Figure 1 presents the DPPH scavenging activity of the sausages with added seaweeds. The TPC in the control sample was low. The DPPH assay result increased as more seaweed were added into the chicken sausages (\(p<0.05\)). Chicken sausages with 6% _C. lentitifirna_ (CL) showed the highest DPPH scavenging activity (50%), while 2% CL sausages had the lowest antioxidant activity of all seaweed sausages. The DPPH scavenging activity was more active in the _C. lentitifirna_ (CL) sausage samples compared to the other formulations. This could be due to its high detectable phenols as shown in the TPC value result, which indicated that CL has the highest total phenolic compound (Table 4). The phenolic compounds are a large and diverse molecule group. The group includes a variety of aromatic secondary metabolite families in plants [38]. The major active phenolic compounds in different seaweed are fucoxanthin and phlorotannins [26].
## 2 Optimized Hybrid Energy System for High-Speed Rail Integrating Carbon-REC Trading
### Models of System Components
#### 2.2.4 REC Model  
This study employs a real-time integration method to calculate the REC generation. First, the PV generation during the \(i\)-th time interval, denoted as \(\text{PV}_{\text{generation}}\), is calculated in kWh. Where \(t[i]\) represents the duration of PV generation within the \(i\)-th 10 min high-speed rail operation interval, and \(P_{\text{PV}}[i]\) denotes the PV power output in kW. The cumulative total PV generation, \(\text{REC}_{\text{generation}}\), is measured in kWh. Equations (16) and (17) are presented below.  
\[PV_{generation}=\sum_{i=1}^{k}\frac{P_{PV}[i]\times t[i]}{60} \tag{16}\]  
\[REC_{generation}=PV_{generation} \tag{17}\]  
The hybrid high-speed rail power system in this study is a semi-islanded microgrid, where the solar energy absorbed by PV panels is primarily stored by ESS devices and supplied to the system. Increasing the capacity and power of ESS can enhance the PV accommodation rate, thereby reducing the curtailment rate. Therefore, considering the synergistic amplification effect of ESS configuration on REC, a coupling coefficient \(\psi\) is introduced. Equations (18) and (19) are presented below.  
\[REC_{\text{revenue}}=-REC_{generation}\times Price_{REC}\times\psi \tag{18}\]  
\[\psi=1+0.29\times\frac{Eb}{100}+0.29\times\frac{Pb}{100} \tag{19}\]The \(\psi\) coefficients of 0.29/kW (\(Pb\)) and 0.29/kWh (\(Eb\)), derived from Spearman correlation analysis (\(p<0.01\)), demonstrate that increasing the ESS capacity \(Eb\) or rated power \(Pb\) by 100 units enhances REC revenue by 29%. The REC price is set to 0.036 USD/kWh [44], with REC revenue consistently treated as a negative cost in economic calculations. The assumption of guaranteed REC monetization at fixed prices with 100% purchase rates represents an idealized market scenario adopted for computational tractability. While such simplifications allow clear attribution of economic benefits in our framework, it constitutes a methodological limitation given empirical evidence of substantial REC price volatility.