esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Section
### Metrics
#### 4.1.2 Effectiveness of the Cluster Formation Technique  
The second metric is concerned with the performance of the distributed cluster formation techniques, and more specifically, their ability to adapt to the movement of Mobile Nodes through the tracking zone. Since the WSN investigated in this research is composed of stationary sensor nodes (Sink Nodes and Anchor Nodes), this form of adaptability involves \"virtual mobility\" of these stationary tracking nodes. In other words, as a Mobile Node traverses the tracking zone, the clusters adapt to the tracking task by assigning Anchor Nodes with the closest proximity to the Mobile Node tracking task.  
As a metric to measure how effective a given cluster formation is and to compare the performance of the cluster formation techniques in deploying a proper set of Anchor Nodes, we propose using the average distance each Mobile Node is at any point in time from the estimating Anchor Nodes during the course of WSN operation, _i.e.,_  
\[\begin{array}{c}f^{cc}=&\frac{\sum_{\forall t}\sum_{\forall i}\sum_{\forall j }d_{i,j}^{t}}{TNM}\ i\in\{deployed\ mobile\ nodes\}\,j\\ &\in\{estimating\ anchor\ nodes\}\,\ and\ t\\ &\in\{quantized\ operation\ time\}\end{array} \tag{2}\]  
where \(d_{i,j}^{t}\) is the distance from Mobile Node \(i\) to estimating anchor \(j\) at quantized time \(t\), \(T\) is the WSN/WAN operation period, \(N\) is the number of Mobile Nodes in the wireless network, and \(M\) is the number of distance estimating Anchor Nodes.
# A Critical Examination of the Beam-Squinting Effect in Broadband Mobile Communication: Review Paper
## 2 Broadband Communication
### Millimeter Waves and Massive MIMO
#### 2.1.3 Hybrid Beamforming  
Recently, interest in the hybrid analog/digital beamforming array has increased [32; 33]. This type of array increases the efficiency of analog beamforming while reducing the complexity of digital beamforming [34]. The hybrid beamformer is composed of two parts: an analog and a digital component [35]. The RF chains that compose the digital portion share a common structure with the many plans under discussion. For each user on each subcarrier, a hybrid beamforming's digital component can be used, just like in traditional fully digital beamforming. In contrast, this is not true of the hybrid beamforming analog network or analog component. Since the transmitted signals for all users are combined by the digital beamformers and analog RF beamforming is a post-IFFT (inverse Fast Fourier Transform) operation, all users and subcarriers in an analog network share the same RF component [36]. Hybrid beamforming's fundamental architecture is depicted in Figure 4.  
Beam squint is a phenomenon caused by the frequency dependence of the magnitude of the phase shift in analog beamforming [37]. This can cause issues with wideband communication. The Digital Signal Processing (DSP) logic and digital beamforming algorithms can both use actual time delay. As a result, a phased array architecture with every element digitalized would naturally lend itself to solving the frequency dependency problem as well as offer the greatest degree of programmable flexibility. However, issues with this solution's strength, size, and price can arise.  
A time delay among elements occurs as a wavefront approaches an array of elements, and it depends on the wavefront angle in relation to boresight. The beam steering can be achieved with a single frequency by substituting a phase shift for the time delay [38]. This is true for narrowband waveforms, but in wideband waveforms when a phase shift is used to provide beam steering, the beam's direction can change with frequency. As a result, the needed phase shift varies with frequency for a given beam direction. Alternately, the beam  
Figure 4: Hybrid beamforming [29].  
Figure 3: Digital beamforming [31].  
direction varies with frequency for a given phase shift. Beam squint is the effect where the beam angle changes depending on the frequency [39]. Figures 5 and 6 shows the effect of beam squint.  
Figure 5: The effect of beam squint [9].  
Figure 6: Beam squint effect for a phased array antenna using electrical phase shifters operating at frequencies in the range of 10–20 GHz [39].  
In addition, keep in mind that there is no phase shift between the elements at boresight (\(\Theta\) = 0\({}^{\circ}\)); hence, there is no way to induce any beam squint. As a result, both the frequency variation and the angle must be functions of the amount of beam squint [38]. Beam squint can seriously affect performance since mmwave communications depend heavily on accurate beam alignment between the transmitter and the receiver [40].
# A BIM-Based Smart System for Fire Evacuation
## 4 Conclusions  
This study presented a comprehensive management system for fire evacuation that can (i) detect fire early; (ii) collect and analyze the environmental data provided by sensors; (iii) locate occupants; and (iv) provide users with the optimal evacuation paths. The system is based on the combination of several technologies and simulation tools. IoT and smart technology are used to detect a fire early and reduce false detection. FDS is used to simulate fire scenarios and ABS for crowd simulation and evacuation path generation. Both FDS and ABS provide a database used for the smart selection of evacuation paths in real time using AI. Moreover, this study highlighted the importance of BIM tools for (i) visualizing environmental data in a 3D model; (ii) tracking occupants in real time; and (iii) alerting occupants and providing them with the optimal evacuation paths.  
The novelties of this study are:  
* Using AI and previous simulations to learn and predict the best evacuation routes for occupants during a fire via the BIM environment.
* The system's power in visualizing fire and evacuation simulation outputs simultaneously in the BIM environment. It provides the evacuation route with accurate information regarding the distance needed to evacuate safely and the emergency exit to be taken.
* The capacity of occupants to interact with the system using a mobile application.  
The system's capacity was illustrated through its application to a research building of Lille University. Two fire scenarios showed how the fire's location, type, and severity impact the occupants' selection of evacuation routes.  
Future work will establish a dataset on the best evacuation paths during fire events in critical locations of the building. This dataset will be used with ML techniques for the real-time determination of the best evacuation paths in complex buildings.  
Conceptualization, R.W. and I.S.; Data curation, R.W.; Formal analysis, R.W.; Investigation, R.W.; Methodology, R.W. and I.S.; Project administration, I.S.; Software, R.W.; Supervision, I.S.; Validation, R.W. and I.S.; Visualization, R.W.; Writing--original draft, R.W.; Writing--review and editing, I.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable, the study does not report any data.  
The authors declare no conflict of interest.  
Figure 14: Occupants’ information.
# Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning
## 3 Materials and Methods
### Modelling Using the GRU Network  
GRU was proposed by [PERSON] et al. in 2014 [29]. It is a model with an RNN architecture designed to capture temporal dependencies in sequential data processing. While GRUs share a similar structure with LSTM networks, they differ in terms of the number of gates within the hidden units and the signal flow mechanism (Figure 7).  
A GRU hidden unit consists of two primary gating mechanisms known as _update gate_ and _reset gate_ that sum the input, \(x_{t}\), the previous hidden state, \(h_{t-1}\), and the bias, \(b\). These mechanisms alleviate the vanishing gradient problem, enabling more effective training over deeper and longer sequences. Despite having a simpler architecture than LSTM, a GRU network can learn time dependencies effectively.  
Initially, at \(t=0\), the output vector is \(h_{0}=0\). At time \(t\), the activation of GRU, denoted as \(h_{t}\), is a linear interpolation between the previous activation \(h_{t-1}\) and the candidate activation as given in Equation (10).  
\[h_{t}=(1-z_{t})\odot h_{t-1}+z_{t}\odot\widetilde{h_{t}} \tag{10}\]  
Here, \(h_{t}\) is the output vector, and \(\widetilde{h_{t}}\) stands for candidate activation, which is calculated as in Equation (11).  
\[\widetilde{h_{t}}=\tanh(W_{h}x_{t}+U_{h}(r_{t}\odot h_{t-1})+b_{h}) \tag{11}\]  
_Update gate (\(z_{t}\))_: This mechanism regulates how much \(h_{t}\) is influenced by \(h_{t-1}\). It is defined using a sigmoid activation function, \(z_{t}=\sigma(W_{z}x_{t}+U_{z}h_{t-1}+b_{z})\), where  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Layer** & **Configuration** & **Parameters** \\ \hline Output & 1 unit & Regression layer: Loss function = MSE \\  & & Initial learning rate: 0.03 \\  & & Learning rate: piecewise (drop factor: 0.5, drop period: 100 epochs) \\ Training algorithm (Solver) & ADAM & Gradient explosion threshold: 0.8 \\  & & L2 regularisation: 0.01 \\  & & Shuffle: every epoch \\ Weight initialisations & \(W,R,b\) & He initialiser [28] \\ Epochs & Iterations per epoch = 1 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 7: Signal flow in a GRU hidden unit.  
represents the weights of \(x_{t}\). \(U_{z}\) represents the weights of \(h_{t-1}\). \(b_{z}\) is the bias term in the update gate. The sigmoid activation function, \(\sigma\), is employed in this gate. It compresses values to the [0, 1] interval, enabling the gate to operate in an on/off manner, thereby allowing the GRU to selectively pass on information.  
_Reset gate (\(r_{t}\)):_ This mechanism determines how much \(h_{t-1}\) should influence \(h_{t}\). It has a sigmoid activation function, \(r_{t}=\sigma(W_{r}x_{t}+U_{r}h_{t-1}+\mathrm{b_{r}})\), where \(W_{r}\) and \(U_{r}\) represent the weights of \(x_{t}\) and \(h_{t-1}\), respectively. \(b_{z}\) is the bias in the reset gate.  
Since the GRU network is a type of LSTM network, the LSTM network's parameters given in Table 2 are used, as well. Apart from these, tanh is used as the state, while sigmoid is used in the gates.
## 5 Simulation Results and Performance Analyses
### Performance Comparisons
By dynamically evaluating the BER of links and the buffer state of nodes, the CLCQ can effectively avoid congested areas and select more reliable links, thereby reducing the number of retransmissions in data transmission and the delay caused by congestion. Therefore, the design of the CLCQ improves packet transmission efficiency while reducing end-to-end delay, which validates its contribution to improving communication performance in dynamic network environments. Figure 16 illustrates the PDR of each routing protocol under varying node counts. As the number of nodes increases, the PDR of all protocols improves. For GEDAR and QELAR, an increase in node count means that more nodes participate in the routing process, which, in turn, improves the PDR. Nevertheless, the protocol proposed in this paper exhibits a more pronounced advantage in terms of PDR. This advantage is not solely attributable to the increased route selection opportunities afforded by the increase in the number of nodes. It also benefits from the cross-layer mechanism of protocol design. In the event of a high-quality channel between nodes, there is a greater probability of the nodes being selected as the next hop, thereby reducing the occurrence of transmission failures due to unreliable channels. At the same time, the CLCQ effectively avoids congested links and further improves the PDR. For instance, at a node count of 90, the PDR of QELAR is 78.71%, while that of GEDAR is 86.93%. When \(\varphi_{q}\) = 0.9, the PDR of the CLCQ is 97.02%. Because GEDAR has shortcomings in network congestion management, QELAR's assessment of channel quality is not comprehensive enough, and the CLCQ is better at solving these problems. Figures 13-16 provide a comprehensive evaluation of the four key indicators of the CLCQ, with a comparison to QELAR and GEDAR. In terms of energy consumption, once the network node count surpasses 60, the CLCQ's energy consumption advantage becomes apparent, especially in large-scale networks. Although the CLCQ initially shows a marginally higher average end-to-end delay compared to QELAR, this gap narrows as the number of nodes increases, as illustrated in Figure 15. At a certain point, the CLCQ reaches the lowest average end-to-end delay. Additionally, the CLCQ presents outstanding results with respect to the crucial metric of the PDR, with a significantly higher success rate than that of QELAR and GEDAR. Overall, the protocol proposed in this paper is the most balanced of the three protocols. Figure 15: Average end-to-end delay of different protocols.
## 1 Introduction
We feed the session graphs to the embedding layer that provides each graph's embedding that is applied as input to the graph convolutional neural network. Figure 1 shows the product IDs (P9, P3, P1, P45, P10) representing the click sequences of user n at a particular session and how the session graph is generated according to the click pattern. The double circle indicates the product brought by the user in that specific session. We also consider the number of paths to an item and give weightage to a product with multiple paths, indicating that the product must be of user interest. The methodology is repeated for each user and session. Therefore, our model captures complex user behavior within multiple sessions and for various users. After processing the data and generating the session graphs, our primary focus in the proposed work was to obtain the probability distribution and sample the users of similar behavior. We measure the similarity through distance metric learning and KL divergence, and finally calculate the similarity score. We regenerate and update the graphs based on the similarity scores and train our graph convolutional neural network model for prediction and recommendation. The main contributions of the work can be summarized as follows:  
* We propose a GCNN model for product recommendation that is based on the similarity among different nodes. * We use the item click probability of a user to find their similarity with other users. * We perform neighborhood sampling based on the similarity scores calculated through the KL divergence probability distribution formulation. * The GCNN model is simplified by using neighbor modeling as a preprocessing step. The same neighbor modeling and embeddings learned are propagated through all the layers rather than the recursive aggregation of the neighbor's data. * We conducted experiments on two different datasets and used other proposed models to verify our proposed GCNN model's performance. The results show that the proposed model performs better than the existing GCNN models and some traditional recommendation systems. The rest of this paper includes related work in Section 2, the proposed method in Section 3, a data introduction in Section 4, the results in Section 5, the evaluation setup in Section 6, and the conclusions in Section 7.
## 1 Introduction
### Contributions  
Until recently, the selection of sites has tended to be driven primarily by the suitability of the local environment for the target species and by historical inertia in areas where aquaculture is well-established [17].  
Over the past three decades, a number of studies have described the use of different types of geotechnology, such as Geographic Information Systems (GIS), Remote Sensing (RS), Global Navigation Satellite System (GNSS), and digital cartography, for the selection of sites for aquaculture ponds [18]. Remote Sensing (RS) and GIS are considered to be important tools for the evaluation of areas destined for aquaculture (e.g., [19; 20; 21]).  
Geophysical tools are one other valuable option for the description of the quality of soils. This technology is non-invasive, non-destructive, and has excellent spatio-temporal resolution (e.g., [22; 23; 24]), and can provide valuable information on the characteristics of the subsoil, including its lithology, stratigraphy, bedrock, water table, and the presence of depressions, faults, and fractures (e.g., [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]).  
Electrical and electromagnetic methods are the techniques used most frequently (e.g., [22; 23; 39; 40; 41]). The most important of these methods in the context of the present study are Ground Penetrating Radar (GPR) and Electromagnetic Induction (EMI), which have been employed successfully in studies of soils to determine their properties and spatial-temporal variability [42].  
In the present study, the application of two geophysical tools (GPR and EMI) permitted the systematic evaluation of the subsoil of sites earmarked for the installation of aquaculture ponds. The acquisition and operation of the GPR and EMI do not demand an excessive investment and are cost-effective, given their durability and low maintenance costs, while also contributing to the preservation of the environment. The surveys can be conducted by teaching institutions, local administrations, government agencies that provide technical support in rural areas, and even farming cooperatives and associations. We combined these geophysical tools with the sedimentological analysis (rapid field tests) of the study terrains and present an important alternative approach for the verification of the viability of sites earmarked for the installation of fish farming ponds.
## 2 Materials and Methods
### Computational Complexity Measurement Metric  
Computational complexity of information fusion function is an important measure that determines the feasibility of the function in practice. Ultralow power microprocessors are commonly used in IoT end nodes and they have low computational capabilities. The eight-bit Almega1281 is one of the popular microprocessors used in many nodes such as IRIS, MicaZ and Mica2. These microprocessors support multiple clock frequencies. The number of CPU cycles to execute the information fusion function is a measurable parameter. Thus, we use the time taken to execute the information fusionfunction (\(T_{A}\)) as the performance metric to measure the computational complexity. It is expressed by Equation (6) as  
\[T_{A}=\frac{Number\ of\ CPU\ Cycles}{Clock\ Frequency} \tag{6}\]  
Note that the impact of \(T_{A}\) on energy consumption cannot be simply neglected.
# mmS-TCP: Scalable TCP for Improving Throughput and Fairness in 5G mmWave Networks
## 6 Conclusions  
In this paper, we proposed mmS-TCP to improve TCP performance over mmWave links. Existing TCP does not provide sufficient throughput due to low link utilization in the highly variable mmWave channel. Depending on the queue management scheme applied to mmWave base stations, throughput may increase, but RTT will also increase, so the design goal of 5G cannot be achieved.  
Therefore, in order to achieve the 5G design goal of high delivery rate and ultra-low latency at the same time, the existing TCP congestion control algorithm was redesigned to increase link utilization, and the AQM scheme was adopted to lower end-to-end delay. First, by modifying the congestion window increase/decrease mechanism of S-TCP, we improved the stability of TCP flow while providing high throughput. In addition, the CoDel queue management scheme was applied to mmWave base stations to prevent the bufferbloat problem, thereby lowering end-to-end delays.  
We implemented several simulation scenarios based on NS-3 to evaluate the performance of the redesigned mmS-TCP in the mmWave networks. The simulation results showed that mmS-TCP reduced the total number of duplicated ACKs to half compared with S-TCP and reduced the number of duplicated ACKs to 1/4 in the slowstart phase. In addition, mmS-TCP guaranteed throughput greater than 80% of the available capacity of mmWave links.  
In the evaluation of fairness between protocols, S-TCP, which showed a fairness index of 0.819, greatly overwhelmed CUBIC, whereas mmS-TCP competed relatively fairly with CUBIC with a fairness index of 0.9733. Even in the evaluation of intra-protocol fairness between five identical protocols, mmS-TCP simultaneously improved average total throughput and intra-protocol fairness. In future work, we plan to implement a CUBIC-based congestion control algorithm that can provide sufficient performance in mmWave networks to compensate for the shortcomings of the ACK-based algorithm.  
[PERSON] proposed the idea, conducted the experiments, and wrote the manuscript. [PERSON]-[PERSON] supervised the entire research. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Education, 2018R1A6A1A03025109, and was funded by the Korean government (MSIT), 2019R1A2C1006249.  
Not applicable.  
This research was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2018R1A6A1A03025109), and by the National Research Foundation of Korea (NRF) grant funded by the Korean government (No. NRF-2019R1A2C1006249).  
The authors declare no conflict of interest.
## 3 Materials and Methods
### GC-MS Analysis  
An Agilent Technologies (Palo Alto, CA, USA) gas chromatograph model 7820A equipped with a mass selective detector (MSD) model 5977E (Agilent Technologies, Palo Alto, CA, USA) was used. An HP-5 MS capillary column (Agilent J & W GC column, Palo Alto, CA, USA; 5%-phenyl-methylpolysiloxane; 30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m film thickness) was used for GC-MS analysis. The GC conditions were as follows: split ratio, 1:50; oven programmed, 2 min at 70 \({}^{\circ}\)C, with the temperature increased at the rate of 3 \({}^{\circ}\)C/min to 200 \({}^{\circ}\)C and held isothermal for 15 min; injector temperature, 250 \({}^{\circ}\)C; detector temperature, 300 \({}^{\circ}\)C; carrier gas, He (velocity: 1 mL/min). The MSD (EI mode) was operated at 70 eV, and the mass range was 30-300 average mass units (amu). Firstly, 1 \(\upmu\)L of diluted essential oil (10 \(\upmu\)L of the oil in 1 mL of pentane) was manually inserted with a syringe into the GC injector. The identification of the VOCs was based on the comparison of their retention indices (RIs) determined relative to n-alkanes (C9-C25) with those reported in the literature and their mass spectra were determined via comparison with the spectra from Wiley 9 (Wiley, New York, NY, USA) and NIST 17 (D-Gaithersburg) mass spectral libraries. The percentage composition of the samples was computed from the GC peak areas using the normalization method (without correction factors). The average component percentages were calculated from three GC-MS analyses