esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Emotion recognition has been studied by researchers in various fields, such as psychology, sociology, health care, entertainment, advertisement, education, robotics, and computer science [1]. Emotion recognition systems can be used in many applications, such as human-computer interaction, intelligent tutoring systems, surveillance, the psychological state of patients, and lie detection [2]. The ability to recognize facial expressions under masks has become increasingly important for creating realistic and engaging augmented reality (AR) and virtual reality (VR) experiences during the COVID-19 pandemic [3, 4]. It is a challenging problem due to the many variables involved, such as facial features, lighting, and head pose. There are many approaches to solving this problem, but most of them require a substantial amount of training data [5].  
Wearing face masks that cover the mouth and nose during a worldwide pandemic is strongly advised to reduce the risk of infection. This precautionary approach, though, might significantly impact how people interact with one another: only the highest partsof the face, the eyes, and the forehead are visible while one is wearing a face mask, which raises the question of whether doing so would make it harder to identify emotions [6; 7].  
A study has shown that people wearing masks are less likely to be accurately identified because, with a hidden face, certain emotions like happiness, sadness, anger, etc. Due to this, participants are less accurate at identifying emotions when the person is wearing a mask [8]. In addition, the use of a face mask has also impaired people's ability to understand the gestures of others, which can be important for understanding the speaker's intentions [9]. According to the author, this could be because masks conceal the lower half of the face, which is where most of the crucial facial cues are found. Comparing masked targets with unmasked faces revealed a decline in emotion recognition accuracy. Target accuracy was significantly lower for masked faces (48.9 percent) than for unmasked faces (69.9 percent) [10].  
The paper is structured into eight sections. Section 1 provides an introduction to the research problem and the paper's contributions. Section 2 presents a comprehensive literature review of the related work. In Section 3, the proposed solution is explained in detail. Section 4 discusses the implications of the pre-trained model. Section 5 provides a detailed explanation of the feature extraction process from the hidden layers in the neural network. Section 6 examines the various classifiers used in machine learning. Section 7 provides details of the dataset used in the experiments, MLF-W-FER, and explains the experiments conducted on the FC6, FC7, and FC8 layers of the VGG-16 and AlexNet architectures. Finally, the conclusions drawn from the experiments are presented in Section 8.
# Monitoring Autonomous Mowers Operative Parameters on Low-Maintenance Warm-Season Turfgrass
###### Abstract  
Robotic solutions and technological advances for turf management demonstrated excellent results in terms of quality, energy, and time consumption. Two battery-powered autonomous mowers (2 WD and 4 WD) with random patterns were evaluated according to different trampling levels (control, low, medium, high) on a typical warm season turfgrass at the DAFE, University of Pisa, Italy. Data on the percentage of area moved, the distance traveled, the number of passages, and the number of intersections were collected through RTK devices and processed by a custom-built software (1.8.0.0). The main quality parameters of the turfgrass were also analyzed by visual and instrumental assessments. Soil penetration resistance was measured through a digital penetrometer. The efficiency significantly decreased as the trampling level increased (from 0.29 to 0.11). The over-trampled areas were mainly detected by the edges (on average for the medium level: 18 passages for the edges vs. 14 in the central area). The trampling activity caused a reduction in turf height (from about 2.2 cm to about 1.5 cm). The energy consumption was low and varied from 0.0047 to 0.048 kWh per cutting session. Results from this trial demonstrated suitable quality for a residential turf of the Mediterranean area (NDVI values from 0.5 to 0.6), despite the over-trampling activity. Soil penetration data were low due to the reduced weight of the machines, but slightly higher for the 4 WD model (at 5 cm of depth, about 802 kPa vs. 670 kPa).  
precision turfgrass management (PTM); unmanned ground vehicle (UTV); green space maintenance; bermudagrass; dallisgrass; precision agriculture
## 5 Experiment and Results
### Testing the Experiment Prototype  
To ensure the feasibility and integrity of the experiment prototype, we set the criteria for passing/failing the functional and non-functional testing. We conducted a series of test activities. We began with unit testing each component participating in the chain of activities, including encryption key generation, transformation to blinking LED video, securely sending the video to the receiver,  
Figure 13: Translation of a blinking LED into a bit string.  
and transformation of the optical signals back to the key bit string. The testing included test cases for each component, such as checking the correctness of the output after each transformation stage. Then, we prepared system test cases to ensure the correctness and smooth key transmission from the sender's processor to the acceptance of the key by the receiver's processor. The system met the predefined criteria.
## 2 Overview of AC Analysis Methods  
### Discrete Fourier-Transform-Based Methods  
Discrete-Fourier Transform (DFT) is the discrete-time, discrete-frequency counterpart of Fourier-Transform, conceived for use with discrete signals. It aims to decompose the signal into a sum of discrete sinewave signals. Due to its discrete nature, the frequencies of the elementary sinewaves are chosen according to the available frequency bins to which their amplitudes and phases are determined in the transformation process. For instance, given a signal \(x[k]\) with a sampling frequency \(F_{s}\) and length \(N\), the returned complex AC DFT Analysis component \(\underline{X[n]}\) at the nth. frequency bin \(f_{bin}[n]=n\frac{F_{s}}{N}\) is given in (1):  
\[\underline{X[n]} = \sum_{k=0}^{N-1}x[k]\text{exp}\left(\frac{-i2\pi k}{N}n\right) \tag{1}\] \[= \sum_{k=0}^{N-1}x[k]\text{cos}\left(\frac{-2\pi k}{N}n\right)\] \[+ i\sum_{k=0}^{N-1}x[k]\text{sin}\left(\frac{-2\pi k}{N}n\right)\]  
where \(i=\sqrt{-1}\). We recall that \"\(\text{exp}\left(\frac{-i2\pi k}{N}n\right)\)\" and its equivalent Euler cos-sin terms are called DFT coefficients or a twiddle factors. Here the magnitude and angle of \(\underline{X[n]}\) define, respectively, the non-normalized magnitude and the phase of the cosine signal at the nth frequency bin. The extraction of the single-sided amplitude spectra values of the sinewaves at frequency index \(n\) is done by normalizing the DFT amplitude to half the signal length \(N/2\), as shown in (2). The phase spectrum is obtained by the arctangent Formula (3).  
\[A_{k}=2\frac{\left\|\underline{X[k]}\right\|}{N} \tag{2}\] \[\varphi_{k}=atan2(\Im(\underline{X[k]}),\Re(\underline{X[k]}))+ \frac{\pi}{2} \tag{3}\]  
The calculation of DFT components can be performed according to (1). Various algorithms are used to speed up the calculation, such as Fast-Fourier Transform (FFT). Other algorithms embed the DFT in a filter to save memory, such as Goertzel Filter. Both techniques are documented in this section.
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
### Distributed Energy-Efficient Clustering (DEEC)
#### 1.3.2 DEEC Protocol and Machine Learning  
Recent research has explored the integration of ML and intelligent optimisation algorithms with DEEC to further improve energy efficiency and network performance. For example, fuzzy logic systems have been used to factor in additional parameters (e.g., node density and distance to BS) during CH selection, while metaheuristic algorithms like the Firefly Algorithm and Genetic Algorithm have been applied to optimise clustering and routing decisions. These hybrid approaches demonstrate superior load balancing, reduced energy consumption, and increased network longevity [13].  
The DEEC protocol is beneficial for Wireless Sensor Networks facing energy constraints, and it is suitable for networks with heterogeneous nodes with varying energy levels. The residual-energy-based CH selection technique leads to uniform energy consumption distribution, which results in extended network lifetimes because energy consumption is evenly distributed among the nodes. Exploring the use of machine learning and intelligent optimization algorithms in DEEC presents an interesting avenue for future research, as these techniques can improve the decision-making, forecasting, and real-time optimization capabilities of wireless sensor networks.
## 3 Research Framework of Internet of Emergency Services
### Coordination and Communication
#### 3.4.2 Communication Networks  
Communication networks play a critical role in enabling the Internet of Emergency Services (IoES) to function effectively. These networks allow for the collection, transmission, and sharing of data and information between various emergency response agencies and stakeholders in real-time. In this section, we will discuss the use of communication networks for IoES and provide real-world examples of their use in emergency management and response.  
One of the most important aspects of communication networks for IoES is their ability to provide reliable and secure communication channels. This is particularly important in emergency situations where communication breakdowns can have serious consequences. In addition, communication networks can provide a platform for sharing information and coordinating response efforts between different agencies and stakeholders involved in emergency management.  
One example of the use of communication networks for emergency management is the Emergency Services IP Network (ESInet) in the United States. ESInet is a dedicated high-speed, fiber-optic network that provides a secure, reliable, and interoperable infrastructure for emergency communications. ESInet supports emergency call centers and enables real-time communication and data exchange between emergency responders and other stakeholders. Another example is the use of satellite communication networks in disaster response situations where traditional communication networks may be damaged or disrupted. Satellites can provide a reliable means of communication and can be quickly deployed to areas affected by disasters. For example, in the aftermath of Hurricane Maria in 2017, the Federal Emergency Management Agency (FEMA) used satellite communications to provide internet and phone services to emergency responders and affected communities in Puerto Rico.  
Communication networks can also be used for crowd management and control during emergency situations. For example, during large-scale events such as concerts or sports games, communication networks can be used to coordinate security and emergency response personnel and ensure that crowds are managed effectively.  
In conclusion, communication networks are a critical component of IoES, providing reliable and secure communication channels for emergency response agencies and stakeholders. Real-world examples such as ESInet and satellite communication networks demonstrate the importance of communication networks in emergency management and response. It is important that communication networks are designed to be resilient and scalable, ensuring that they can function effectively in times of crisis.
# Distributed Global Function Model Finding for Wireless Sensor Network Data
## 3 Function Finding Algorithm by Using Gene Expression Programming for Wireless Sensor Networks Data
### Adaptive Population Generation Strategy Based on Collaborative Evolution of Sub-Population  
In GEP, in order to better evolve, gene diversity in the initial population is required so that the GEP algorithm can evolve from different directions. At present, the strategy of initial population generation is simple and occupies fewer system resources. However, the diversity of the population generated by the strategy is limited. With the increasing of fitness value of an individual, it is easy to stop the population evolving and fall into local optimum. In theory, the greater the population space, the more diverse the individual, the greater the probability of searching the global optimal solution. However, increase of population space will increase the computational complexity and reduce the convergence speed. Thus, in order to prevent the population from falling into local optimum, this paper presents an adaptive population generation strategy based on collaborative evolution of sub-population (APGS-CESP). In APGS-CESP, the probability of searching the global optimal solution is increased by raising the diversity of the individuals in the population. The flow of APGS-CESP is shown as follows.  
```
Input: Pop, \(P_{s}\), \(P_{m}\), \(P_{t}\), \(P_{r}\), popSize; Output: new Population; Begin {
1. for (int \(i=1\); \(i<=\) MaxGen; \(i++\)){
2. \(F_{\max}[i]\) = MaxFitness (Pop [i]);
3. \(SumF_{\max}=0\); \(SumF_{\max}+=F_{\max}[i]\);
4. if (\(i\bmod 10==0\)){
5. if (\(sumF_{\max}\bmod 10==0\)){
6. \(subPop\gets RandomInitPop(subPopSize)\);
7. \(Pop\gets Insert(subPop,Pop)\);
8. \(EvalueFitness(Pop)\);
9. \(Pop\gets Select(P_{s},Pop,popSize)\);
10. \(Pop\gets Mutate(P_{m},Pop)\);
11. \(Pop\gets ISTransposition(P_{t},Pop)\);
12. \(Pop\gets RISTransposition(P_{t},Pop)\);
13. \(Pop\leftarrow GeneTransposition(P_{t},Pop)\);
14. \(Pop\leftarrow OnePointRecombination(P_{r},Pop)\);
15. \(Pop\leftarrow TwoPointRecombination(P_{r},Pop)\);
16. \(Pop\leftarrow GeneRecombination(P_{r},Pop)\);
17. \(newPopulation=Pop\);
18. Return newPopulation;}
```  
**Algorithm 1**APGS-CESP (\(Pop\))  
Generally, Algorithm 1 enriches diversity of the individuals in the population, and expands the scope of the global optimal solution. However, size of the population has not increased and time complexity of the algorithm changes from \(O(popSize)\) to \(O(popSize+\ subPopSize)\).
## 4 Performance Evaluations  
### Simulation Environment  
We have performed extensive simulations to evaluate the performance of LPMC in NS-2 [25]. We have considered a network with an area of 200 m \(\times\) 200 m and 250 nodes placed in a uniform random distribution. We have set the transmission power in such a way that the interference range becomes  
\begin{table}
\begin{tabular}{|c|c||c|c|} \hline \hline Parameter & Value & Parameter & Value \\ \hline Link bit rate & 250 Kbps & Packet size & 32 Bytes \\ PHY Header & 192 \(\mu\)s & MAC Header & 224 bits \\ ACK Packet & 112 bits & Slot Time & 20 \(\mu\)s \\ SIFS & 10 \(\mu\)s & DIFS & 30 \(\mu\)s \\ Min CW & 32 & \(R_{req}\) & 0.95 \\ No. of channels & 6 & Switching delay & 200 \(\mu\)s \\ \(\alpha\) & 0.12 & \(\beta\) & 0.10 \\ \hline \end{tabular}
\end{table}
Table 1: System parameters used in simulation.  
only 1.5 times the transmission range. In our experiment, the transmission and interference ranges of the nodes are set to 30 m and 45 m, respectively. Actually, this communication model is typically used to simulate the RF model of the CC2420 radio that operates on multiple-channels [11]. The link bandwidth is set to 250 kbps, and 6 orthogonal channels are used. Though the CC2420 radio chip used in Micaz motes provides 16 orthogonal channels, not all of them can be used in parallel because of close channel interference [26]. We use CSMA/CA as the MAC protocol with a maximum of 4 retransmissions. We compare the performance of LPMC with DM-MAC [10] and TMCP [11]. The required reliability (or success rate) is set to 0.95 for DM-MAC and LPMC. However, we set the required reliability of all the mechanisms to 1 when end-to-end reliability is considered. We have performed three different sets of experiments to evaluate the performance of the compared protocols. We show the impact of increasing the offered loads and varying the node densities in the first two set of experiments. Finally, we also show the impacts of the channel quality and external interference. All of the simulations were run 50 times, and the average results are plotted in the graphs. The other system parameters used in the simulation are summarized in Table 1.
## 2 Phase Spectrum Simulation by Modified Fractional Brownian Motion
### Concept of Modified Fractional Brownian Motion  
To simulate the phase difference with power law expressed by Equation (8) as a stochastic process we need probability density function of the phase difference \(p\left(\Delta\psi\right)\). The simplest way to define \(p\left(\Delta\psi\right)\) is applying the normal distribution expressed by  
\[p\left(\Delta\psi\right)=\frac{1}{2\pi\left(\Delta\omega\right)^{H}\sigma_{0}} \exp\left(-\frac{\left(\Delta\psi\right)^{2}}{2\left(\Delta\omega\right)^{2H} \sigma_{0}^{2}}\right) \tag{9}\]  
[PERSON] [33] discussed a possibility that the phase difference process with the probability density function expressed by Equation (9) can be simulated by fBm developed by [PERSON] and [PERSON] [35]. However, if we used the simple fBm to simulate an earthquake motion phase, we could not satisfy the constraint expressed by Equation (5), then, we modify the original fBm to satisfy this constraint. Because the basic concept for deriving a continuous formula of the modified fBm is given in Sato [36] we only express here the discretized formula of incremental modified fBm at the \(j^{th}\) discrete point that is expressed by  
\[\frac{d\psi_{j}}{\left(d\omega\right)^{H}} = \gamma_{b}\left\{\sum_{m=1}^{L}a_{m}Z_{j-m}+Z_{j}+\sum_{m=1}^{L}a_ {m}Z_{j+m}\right\} \tag{10}\]  
where \(d\psi\) is the phase difference, \(d\omega\) is the circular frequency interval, \(Z_{j}\) is random number sequence generated under the identically independently distributed assumption from \(N\left(0,1\right)\) and \(L\) is a measurement of the weighting vector dimension and is obtained from the following equation:  
\[L =\exp\left(\left(\frac{1}{2\left(1-H\right)}\right)\left[-\ln\left( \varepsilon\right)-\ln\left(3-2H\right)+\ln\left(H-\tfrac{1}{2}\right)^{2} \right]\right) \tag{11}\]  
where \(\varepsilon\) is an index controlling the sum of terms in the equation. The proper value of \(L\) can be defined by selecting a small value for \(\varepsilon\), such as \(0.0002\). \(a_{m}\) is the weighting vector with \(L\) dimensions defined as a sequence given by the following equation:  
\[a_{m}=\frac{1}{2}\left\{\left(m+1\right)^{\beta}-\left(m-1\right)^{\beta}\right\} \tag{12}\]  
where \(m\) is an integer that increases from \(1\) to \(L\) and \(\beta\) is:  
\[\beta=H-\tfrac{1}{2} \tag{13}\]  
\(\gamma_{b}\) is defined by using the variance \(\sigma_{0}^{2}\) of the phase difference, for which a regression equation is defined in the next section, and uses the following relationship to be obtained:  
\[\gamma_{b} = \frac{\sigma_{0}}{A} \tag{14}\]  
where \(A\) is defined by Equation (15):  
\[A = \sqrt{\left\{2\sum_{i=1}^{L}a_{i}^{2}+1\right\}} \tag{15}\]  
According to the above equations, the information needed to simulate a phase difference is the Hurst index \(H\) and the variance \(\sigma_{0}^{2}\) that controls the amplitude of the modified fBm process as well as the discrete frequency interval \(d\omega\). A flowchart for simulating the phase difference is shown in Figure 3.
## 4 A Typical Application: Wuhan Huoshenshan Hospital  
With the support of CITIC General Institute of Architectural Design and Research Co., Ltd., Central-South Architectural Design Institute Co. Ltd., Beijing Construction Engineering Group, Beijing Institute of Architectural Design, and China Construction Science and Industry Co. Ltd., the proposed simulation method in this study was applied to the construction of the Wuhan Huoshenshan Hospital (new construction), Wuhan Leishenshan Hospital (new construction), Beijing Xiaotangshan Hospital  
Figure 12: Comparison between the wind profiles of the experiment [37] and the FDS model.  
Figure 13: Distributions of mean concentration inside the street canyon at three different heights (center line).  
(reconstruction), Beijing Ditan Hospital (retrofitting), and The Third People's Hospital of Shenzhen (new construction), respectively, during the period from 23 January 2020, to 16 February 2020. The Wuhan Huoshenshan Hospital is selected to demonstrate the workflow and advantages of the proposed simulation method.  
The proposed high-efficiency simulation framework was applied over multiple design phases of the Wuhan Huoshenshan Hospital, which is the first temporary hospital built in China for the centralized treatment of COVID-19 patients. This hospital, located in the Caidian District of Wuhan City, has a construction area of approximately 33,900 m\({}^{2}\) and can accommodate 1000 patients. In terms of the HVAC design of the hospital, the ventilation system for each ward functions 12 times per hour. And the designed volume of the air outlet and fresh-air intake is 600-750 m\({}^{3}\)/h and 500-550 m\({}^{3}\)/h, respectively.  
The design of the hospital commenced on 23 January 2020. The design scheme of the hospital was completed on 24 January, and the construction was finished on 2 February. It took only 10 days from the commencement of the design to the completion of the construction. During the design and construction period of the hospital, approximately 100 FDS simulation cases were performed by the authors. To make the paper clear enough, 54 FDS simulation cases (20 cases in Section 4.2, 14 cases in Section 4.3, and 20 cases in Section 4.4) are introduced in detail here to demonstrate the application of the proposed method in the design of the Huoshenshan Hospital.
# Arabic Gloss WSD Using BERT
###### Abstract  
Word Sense Disambiguation (WSD) aims to predict the correct _sense_ of a word given its context. This problem is of extreme importance in Arabic, as written words can be highly ambiguous; 43% of diacritized words have multiple interpretations and the percentage increases to 72% for non-diacritized words. Nevertheless, most Arabic written text does not have diacrited marks. Gloss-based WSD methods measure the semantic similarity or the overlap between the context of a target word that needs to be disambiguated and the dictionary definition of that word (gloss of the word). Arabic gloss WSD suffers from a lack of context-gloss datasets. In this paper, we present an Arabic gloss-based WSD technique. We utilize the celebrated Bidirectional Encoder Representation from Transformers (BERT) to build two models that can efficiently perform Arabic WSD. These models can be trained with few training samples since they utilize BERT models that were pretrained on a large Arabic corpus. Our experimental results show that our models outperform two of the most recent gloss-based WSDs when we test them against the same test data used to evaluate our model. Additionally, our model achieves an F1-score of 89% compared to the best-reported F1-score of 85% for knowledge-based Arabic WSD. Another contribution of this paper is introducing a context-gloss benchmark that may help to overcome the lack of a standardized benchmark for Arabic gloss-based WSD.  
WSD; BERT; Arabic; context gloss Article  
[PERSON], [PERSON]; [PERSON]; [PERSON] Arabic Gloss WSD Using BERT. _Appl. Sci._**2021**, _11_, 2567. [[https://doi.org/10.3390/appl11062567](https://doi.org/10.3390/appl11062567)]([https://doi.org/10.3390/appl11062567](https://doi.org/10.3390/appl11062567))