esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Egress Model for Multiplex Buildings under Fire Based on Artificial Neural Network
## 3 Artificial Neural Network (ANN) Model for Estimating Available Safe Egress Time (ASET)
### Training Results  
The weight and bias of the ASET prediction algorithm for the fire compartment and nonfire compartment were derived through the ANN training. Table 8 represents the weight and bias values at the fire compartment, and Figure 12 shows the training results of the proposed ANN model. The ANN model was found to be well trained because the COV (coefficient of variation) of the analysis results for both the fire compartment and nonfire compartment were 0.2 or less. Later, the reliability of the ANN model was verified using 15 fire simulation data that were not used in the training process. As shown in Figure 13, the COV for the ratio of the ANN analysis results to the FDS analysis results was 0.173, and this result suggests that the proposed model provides ASET, which is similar to the fire simulation.  
Figure 11: ANN model at the fire compartment.  
Figure 12: ANN training results.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Hidden** \\ **Neurons** \\ \end{tabular} } & \multirow{2}{*}{**Bias 1 (\(b_{1}\))**} & \multicolumn{5}{c}{**Weight1 (\(W_{1}\))**} & \multicolumn{5}{c}{**Weight2 (\(W_{2}\))**} \\ \cline{2-9}  & & **Temperature**, & **CO, ppm** & **CO\({}_{2}\), \%** & **Time after** & **O\({}_{2}\), \%** & **Visibility**, **m** & **ASET, s** \\ \hline
1 & 2.827 & \(-\)0.210 & 2.907 & 0.855 & \(-\)3.355 & \(-\)0.889 & \(-\)0.169 & \(-\)2.264 \\ \hline
2 & 0.991 & \(-\)1.0
## 4 Results and Discussion
### Validation of CPC  
3.1 Experimental Determination of Efficiency, \(\mathrm{F_{R}}\), and \(\mathrm{U_{L}}\) Values of the CPC  
The experimental procedure was based on the testing procedure for collectors prescribed in the (ASHRAE). Figure 9 shows the experimental setup. The experiment was carried out on 19 May-22 May 2022. The CPC and blower were connected in series with the exit of the blower to the inlet of the CPC, and inlet suction to the exit of the CPC, making it a closed loop system with a steady mass flow rate. The pressure drop was measured using the digital manometer across the orifice plate positioned at the inlet and exit of the collector. The K-type thermocouples were positioned for recording the inlet, exit, and ambient temperatures. The data were recorded at an interval of 30 min from 11:30 a.m. to 13:30 for specific mass flow rates for four consecutive days as mentioned in Table 9, after bringing the system to a steady-state condition. Table 9 shows the average values of each variable recorded, with a specific mass flow rate for each day for four consecutive days. Figure 10 shows the curve obtained against \((\mathrm{T_{fluid\ out}-T_{ambient}})/\mathrm{I_{T}}\) and efficiency (\(\eta\)). The efficiency of the collector was calculated using Equation (18).  
The above line is in the form of y = mx + c, Compared with Equation (17), we wet  
\[\text{y}=-4.7247\text{x}+0.5199\]  
\[\text{F}_{\text{R}}\tau\alpha=0.5199\]  
\[\tau\alpha=0.85\]  
\[\text{F}_{\text{R}}=0.60\]  
\[\frac{\text{U}_{\text{I}}}{\text{C}}\text{F}_{\text{R}}=4.72\]  
\[U_{\text{I}}=24\Big{(}\text{W}/\text{m}^{2}\text{K}\Big{)}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{2}{c}{**Value**} & \multirow{2}{*}{**Ta**} & \multirow{2}{*}{**Tin**} & \multirow{2}{*}{**Tout**} & \multirow{2}{*}{**\(\Delta\)P**} & \multirow{2}{*}{**\(\mathbf{I_{T}}\)**} & \multirow{2}{*}{**V**} & \multirow{2}{*}{**Q**} & \multirow{2}{*}{**M**} & \multirow{2}{*}{**\(\eta\)**} & \multirow{2}{*}{**Qin**} \\  & & & & & & & & & & & & \\ \hline \multicolumn{10}{c}{**CPC performance data on May 19**} \\
11:30 & 0.25 & 46 & 46.2 & 63.5 & 23 & 942 & 3.82 & 0.152 & 0.18 & 0.35 & 4647.86 \\
12:00 & 0.25 & 46.1 & 47.2 & 63 & 35 & 944  
The maximum efficiency is recorded as 0.58 with a mass flow rate of 0.36 kg/s at 12:40 p.m., while the minimum efficiency is recorded as 0.32 with a mass flow rate of 0.17 kg/s at 12:40 p.m. as shown in Table 9.
## 1 Introduction  
Over the past few decades, the versatility and synthetic value of \(\alpha\)-haloketones in organic synthesis have been amply showcased in the literature. The existence of two adjacent electrophilic centres, namely the \(\alpha\)-halocarbon and the carbonyl group, transforms these reactive carbonyl compounds into highly valuable building blocks for the construction of more complex structures. In that context, a wide variety of \(N\), \(S\), and \(O\)-heterocycles have been accessed using protocols involving \(\alpha\)-haloketones [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. These compounds have also proven to be important intermediates in the synthesis of various organometallic species. [12; 13; 14]. Other applications have demonstrated their utility as key synthetic intermediates for pharmaceutical blockbuster compounds (Figure 1) [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27].  
In recent years, a significant body of work has been dedicated to the design and development of new synthetic protocols to access \(\alpha\)-haloketones. Even though these processes have been reviewed in the literature, a thorough and comprehensive survey is still lacking. For example, recent reviews have dealt with the \(\alpha\)-bromination [28; 29] and \(\alpha\)-iodination [30] of carbonyl compounds; however, these were very specific and only involved pathways towards a very limited range of \(\alpha\)-haloketones. In 2018, [PERSON] and co-workers reviewed the synthetic access to \(\alpha\)-iodocarbonyl compounds [31]. Although a broad range of iodinated products were considered, the work was restricted to direct iodination routes. In 2003, [PERSON] and co-workers reported a survey involving the synthetic access to and application of \(\alpha\)-haloketones [32]. Here, again, synthetic protocols yielding the desired ketones were only briefly discussed and the authors relied on the work published by [PERSON] and [PERSON] [33]. An up-to-date review of the synthetic access to \(\alpha\)-haloketones is clearly needed. Therefore, the present contribution aims to describe and evaluate the diverse protocols leading to \(\alpha\)-haloketones that have been published over the past two decades. Since the number of reports in the literature is quite significant, this review will be limited to methods generating carbonyl compounds carrying one halogen atom at their  
Figure 1: \(\alpha\)-Haloketones as versatile building blocks in organic synthesis.  
\(\alpha\)-position, and will mainly focus on aromatic \(\alpha\)-haloketones compounds, as these appear to have the highest synthetic value [34; 35; 36].
## 3. Experimental Results and Discussion
### Simulation Validation Experiments  
Multi-factor simulation experiments were conducted under the optimal pressurized tube structural parameters. After processing the simulation results, the seed velocity, airflow velocity, pressure loss, distribution uniformity coefficient, and the coefficients of variation for row discharge consistency were obtained. The specific data are presented in Table 7.
# A Secure IoT-Based Irrigation System for Precision Agriculture Using the Expeditious Cipher
###### Abstract
The following points summarize the main contributions of this article. 1. The main contribution of this article is the integration of a lightweight cryptography layer to the IoT ecosystem for smart agriculture that meets the requirements of constrained devices used in smart agriculture in general and specifically, in our proposed IoT-based irrigation system. 2. The article investigates the deployment of a lightweight encryption protocol (Expeditious Cipher (X-cipher)) to create a secure channel between the sensing layer and the broker in the MQTT protocol as well as a secure channel between the broker and its subscribers in smart irrigation systems (our case study). 3. The proposed model is evaluated through simulation to validate the lightweight property of the chosen encryption protocol in terms of power consumption, execu  
Figure 1: MQTT architecture. tion time, and memory usage. Moreover, a performance comparison is carried out between the Expeditious Cipher (X-cipher), AES, and PRESENT cipher (lightweight standard protocol) in terms of power consumption, execution time, memory usage, and average throughput. * The security requirements of IoT-based agriculture systems and the potential attacks against them are discussed. * A state-of-the-art lightweight security architectures proposed for securing MQTT protocol is reviewed after highlighting the concept of lightweight cryptography. The remainder of the article is organized as follows: Section 2 presents our proposed secure smart irrigation system after briefly reviewing the state-of-the-art proposed lightweight security architectures for securing MQTT protocol. Section 3 highlights and discusses the performance evaluation results of our selected lightweight encryption algorithm versus AES, in addition to a performance comparison between X-cipher and the PRESENT cipher. Section 4 summarizes the findings of the article and highlights our suggestions for future work.
## 4 Simulation Results Analysis
### Analysis Results of Case 1 (with the Emergency Stairwell in the Middle of the Corridor)
Therefore, installing an opening as in Figure 11 allows the smoke to be expelled even if a vortex is created, enhancing visibility. Figure 10: Time-based simulation results for Case 2. (**a**) Temperature. (**b**) Visibility distance. Figure 9: Comparison of smoke amount in Case 2 (2 added). Figure 11c shows that because the protruding wall blocks the spray angle of the stairwell air curtain and its location at the end of the corridor, the inflow into both the corridor and stairwell appears stronger. Similarly, as shown in Figure 11d, installing an opening within the stairwell allows the drawn smoke to be expelled, proving beneficial for maintaining visibility. The installation of an opening showed that Case 2-h-35-2 had about 19% less smoke than Case 2-h-35, and Case 2-V-35-2 exhibited approximately 65% less smoke than Case 2-V-35. With horizontal air curtains, there was a tendency for smoke to stagnate inside the stairwell owing to the rotating flow. However, vertical air curtains facilitated the flow of smoke outwards through the open wall structure more efficiently than horizontal air curtains. In terms of visibility, only Case 2-V-35-2 managed to maintain a visibility of more than 5 m.  
Figure 12 visually presents the modeling of the openings of the emergency staircase. (a) illustrates a partially opened emergency staircase, while (b) shows the complete opening excluding the handrails. Figure 11: Vector velocity caused by air curtains when the entrance door is open. (**a**) Horizontal air-curtain vector with stairwell wall closed. (**b**) Horizontal air-curtain vector with stairwell wall closed. (**d**) Vertical air-curtain vector with stairwell wall open. Analysis of Case 3 (Where the Emergency Stairwell Is Located at the End of the Corridor with Its Shorter Side Facing the Corridor)  
In Case 3, the stairwell is located at the end of the corridor, with the external wall of the stairwell open for ventilation. In Figure 13, in scenarios without air curtains, the stairwell's soot mass fraction exceeded 98%, and Case 3-V-10 had less smoke than Case 3-H-35. By examining the visibility and temperature in Figure 14, similar to Case 1, only Case 3-None, without air curtains, exceeded the human occupancy safety standard. With air curtains installed, all cases showed values that were below the standard, with vertical air curtains demonstrating a higher smoke control performance than horizontal ones. Figure 15 illustrates the vectors of airflow generated by the air curtain when the entrance door is open. Figure 12: Modeling of stairwell opening installation. (**a**) Partially open stairwell wall, (**b**) fully open stairwell wall. Figure 13: Comparison of smoke amount in Case 3.
# Feedback-Forward Neural Networks for Failure Mechanics Problems
## 3 Neural Network (NN) Based Elasticity
### Data Collection
#### 3.1.2 Isotropic Elasticity  
For isotropic elastic material behavior the Hookean strain energy is assumed to be a quadratic function as follows:  
\[\psi(\varepsilon)=\frac{\lambda}{2}\ \text{tr}^{2}[\varepsilon]+\mu\ \text{tr}[ \varepsilon^{2}]\, \tag{13}\]  
where \(\lambda>0\) and \(\mu>0\) are the elastic Lame constants defined in terms of [PERSON]'s modulus \(E\), [PERSON]'s ratio \(\
u\) and the shear modulus \(G\) as follows:  
\[\lambda=\frac{vE}{(1+\
u)(1-2\
u)}\qquad\text{and}\qquad\mu=G=\frac{E}{2(1+ \
u)}\, \tag{14}\]  
Following the Coleman-Noll procedure, the stress tensor is obtained from the energy function \(\psi\) in (13) for isotropic material behavior as follows:  
\[\sigma=\partial_{\varepsilon}\psi(\varepsilon)=\lambda\ \text{tr}[ \varepsilon]\ \mathbf{1}+2\ \mu\ \varepsilon\, \tag{15}\]  
hereby, both the stresses \(\sigma\) and strains \(\varepsilon\) are symmetric tensors. For the 2D case, the inputs of the model and their outputs are chosen as the strain and stress components, respectively.  
Inputs: \[\left(\varepsilon_{xx},\varepsilon_{yy},\varepsilon_{xy}\right)\] (16) Outputs: \[\left(\varepsilon_{xx},\varepsilon_{yy},\varepsilon_{xy}\right)\] (17)  
The stresses as output data can be computed using (15) accordingly. The neural network is trained until the stopping criteria is reached. After training, the model is saved. The NN-based elasticity model reads as follows:  
\[\sigma^{NN}=FFNN(\varepsilon,W,b)\, \tag{18}\]  
where \(\sigma^{NN}\) is the predicted stress by the FFNN.  
As a representative example, we choose the following material parameters for the isotropic-elastic model in the training data collection: [PERSON]'s modulus \(E=21\ \text{kN}/\text{mm}^{2}\) and [PERSON]'s ratio \(\
u=0.3\). The data set is split into training (70%), validation (15%), and test (15%) subsets. A feed-forward neural network with an architecture of \(3-10-10-3\)  
Figure 4: Analytically generated data set. (**a**) Input data—2D strains \(\varepsilon\) and (**b**) output data—2D stresses \(\sigma\) [\(\text{kN}/\text{mm}^{2}\)].  
is applied. It consists of three neurons for the input and output layer each, and 10 neurons for each of the two hidden layers. The Levenberg-Marquardt algorithm [54] is chosen as the training optimizer. Figure 5 depicts the performance of the neural network model throughout training, where the model performance reaches its optimum at epoch (iteration) 1400 over the scaled data set. This is based on the termination criteria introduced in Section 2. The mean squared error is decreased to \(2.2\times 10^{-16}\), which costs time of 14 m 48 s; see Table 1.
## 5 Service Scenario  
Figure 5 illustrates the scenario in which services are provided to users in daily life using the self-organized device network in this study. We experimented in a test environment with three rooms  
Figure 4: Estimating walking speed.  
prepared for patients with mental illness in a hospital. However, since the smart devices (tag and stationary) in the proposed system can be seamlessly installed and extended, a test environment consisting of many rooms (as depicted in Figure 5) would also be applicable. Each indoor unit space is equipped with one stationary device and multiple tag devices for measuring the usage of home appliances. A family member who wants to measure ADL wears a wearable device that includes a unique ID to identify the individual.  
The wearable device records the information of a user entering and leaving a specific zone in its internal memory and the usage information of home appliances in each space. Consider a family member with a wearable device who performs the following activities in a bathroom: enter the bathroom, flush the toilet, turn on the faucet, turn off the faucet, and leave the bathroom. The wearable device can receive this series of actions from the stationary device and record these activities with time data in its internal memory. This measurement and recording procedure can be performed without any user intervention.  
The proposed system can collect the user's ADL data and determine whether the user's activities were successful or abnormal, providing the user with a notification and risk warning service based on the measured location and activity data. For example, if a user turned on a gas stove in the kitchen and stayed in another room for a long time, the user's wearable device can alert him or her to a dangerous situation. Moreover, when the user tries to exit the space where he or she stayed, the wearable and stationary devices search for appliances that have not been turned off and notifies the user. For example, if a user washes his or her hands in the bathroom and tries to leave without turning off the faucet, the stationary device announces that something needs to be turned off by ringing its buzzer. This is possible because the stationary device collects all activities occurring in that space in real-time. The wearable device can identify abnormal behaviors and the forgetfulness of the user during daily living over a long duration.  
The recorded data (simple ADL, abnormal behavior, forgetfulness, and moving ability) in the user's wearable device can be transmitted to a smartphone through BLE communication and delivered to a PC or other network. A doctor may predict the mental health and physical ability of the patient more accurately by referring to his or her ADL and faulty activities in daily life than through an interview with the patient or guardian.  
Figure 5: Self-organized activities of daily living (ADL) collection service scenario using three types of portable devices.
## 3 Shield Tunneling Anomaly Detection and Scenario Identification
### Anomaly Detection Model Based on Dynamic Threshold
#### 3.2.3 Online Detection  
In the online detection phase, the trained DT_VAE-LSTM model is used to reconstruct the time-series data to be tested, and the reconstruction error is used to represent the anomaly score. When detecting whether the data at time \(t\) is abnormal, the sub-sequence ending at time \(t\) is used as the test data. When using \(x_{test}\) to represent data at time \(t\), the DT_VAE-LSTM model can generate the reconstructed data denoted as \(\hat{x}_{test}\) and calculate the reconstruction error \(er_{t}\) between the reconstructed time series and the original time-series data. Finally, \(er_{t}\) is chosen as the anomaly score at time \(t\). The anomaly detection result is determined by comparing \(er_{t}\) with the dynamic threshold \(\epsilon\), and \(y_{t}\in\{0,1\}\) is used to represent the result. When \(er_{t}>\epsilon\), \(x_{t}\) will be detected as an anomaly and \(y_{t}=1\).
# Static Wind Reliability Analysis of Long-Span Highway Cable-Stayed Bridge in Service
## 2 Reliability Theory of Static Wind Instability Analysis for Bridges
### Aerostatic Instability Analysis Theory for Bridges
#### 2.1.1 Static Wind Load Calculation on Bridges
Static wind load on piers and pylons  
Under the action of static wind, the piers and pylons have a small deformation due to their high flexural rigidity along the longitudinal direction of the bridge. Therefore, only the effect of the transverse wind on the piers and pylons is considered, and the calculation formula is the same as Equation (2), where \(A_{n}\) is the projected area of the corresponding bridge components in downwind direction, \(C_{H}\) is the drag coefficient of the components. The drag coefficients of the piers and pylons can be determined according to Table 5.4.2-1 in the \"Wind-resistant Design Specification for Highway Bridges\" (JTG/T 3360-01-2018) [22] in China, but if the section of the piers and pylons are complex, the coefficient should be got by boundary layer wind tunnel test or computational fluid dynamic simulation.
# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 9 Exercises
### 5G Cryptomining Attack Detection Scenario  
In this section, we describe the cryptomining attack detection scenario that occurs in the control plane of a 5G network. First, we introduce the cryptomining attack. Next, we present details on the integration of the ML/DL models into the training scenario for this particular attack. Then, we describe the proposed exercise to train cybersecurity professionals in the use of traditional and more advanced ML/DL-based methods to detect cryptocurrency attacks on a 5G network in real-time.
# Multimodal Unsupervised Speech Translation for Recognizing and Evaluating Second Language Speech
## 3 Proposed Fluency Scoring and Automatic Proficiency Evaluation Method
### DTW-Based Feature Extraction for Fluency Scoring
#### 3.1.1 DTW-Based Phoneme Alignment  
Dynamic time-warping is a well-known technique for finding an optimal alignment between two time-dependent sequences by comparing them [15]. To compare and align two phonemic sequences uttered by learner and native speaker (reference), we compute a local cost matrix of two phonemic sequences defined by the Euclidean distance. Typically, if a learner and a native speaker are similar to each other, the local cost matrix is small, and otherwise, the local cost matrix is large. The total cost of an alignment path between the learner's and native speaker's phonemic sequences is obtained by summing the local cost measurement values for each pair of elements in two sequences. An optimal alignment path is the alignment path having minimal total cost among all possible alignment paths, and the goal is to find the optimal alignment path and align between two phonemic sequences with the minimal overall cost.  
Figure 5 shows an example of DTW-based phonemic sequence alignment and stress patterns' computation results. Error phonemes caused by phoneme mismatch are marked in red, and the stress value of error phonemes was set to 3, which is not a standard stress  
Figure 4: Block diagram of the proposed dynamic time-warping (DTW)-based stress and rhythm scoring method.  
Figure 3: Block diagram of the proficiency evaluation model training and evaluating system for automatic proficiency evaluation. (**a**) Flow of the proficiency evaluation model training. (**b**) Flow of the automatic proficiency evaluation.  
value indicated by 0 (no stress), 1 (secondary stress), or 2 (primary stress) in order to compute the deducted stress score according to the presence of the phonemic errors. By tagging error phonemes, the proposed method evaluates the learner's utterance more accurately and helps L2 learners practice their English pronunciation.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 2 Wi-Fi Basics
### PHY in Wi-Fi  
Recent Wi-Fi standards utilize OFDM modulation, MIMO technology, and modulation techniques for increasing data rates. We introduce these techniques in the following subsections.