esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Beamforming for Reduction of Inter-Beam Interference in Millimeter-Wave Cellular Systems
## 2 Hybrid Beamforming Techniques for a Multicell Multiuser Environment
Third, the effective channel information transmitted by _MSs_ is shared with neighboring _BSs_ through a backhaul link without any information loss. Figure 2 shows four different types of digital precoding techniques for IBI reduction in mm-wave cellular systems with a hybrid beamformer. In this figure, the lines between _BSs_ and _MSs_ show the directions of interferences as well as the beam directions between the _BS_ and desired _MSs_. Here, solid lines represent the beam directions between the _BS_ and desired _MSs_. The broken and dotted lines represent inter-beam and inter-cell interferences, respectively, when analog beams are formed in the direction of desired _MS_. Figure 2: Four different types of digital precoding techniques for reduction of inter-beam interference (IBI) in mm-wave cellular systems with a hybrid beamformer. (**a**) Type-1; (**b**) Type-2; (**c**) Type-3; (**d**) Type-4.
# Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2 vec and WordNet
## 6 Conclusions and Future Works  
WSD is an important task for many NLP applications. Our method is derived from the intuitive idea that the correct sense most likely has a high similarity between the sense definition retrieved from WordNet and its context. Sense relations are also included to provide more information. Instead of using an overlap-based approach, we constructed sentence vectors from a popular word-embedding approach, Word2 vec, which is capable of effectively capturing semantic and syntactic similarities better than the LSA approach when dealing with a huge corpus of text and using less memory. Cosine similarity was used to compute between those two sentence vectors. If context did not help in disambiguation, the cosine similarity score was below the empirical similarity threshold 0.37 (estimated by the logistic regression model and validated by a human), the probability of sense distribution learned from a large sense-tagged corpus, SEMCOR, was also combined with the result. The possible answer senses were obtained from high scores. The experiments were conducted to verify that the sense relations and sense distribution were useful to help WSD gain more accuracy. Moreover, experiments were conducted in various context sizes and various constructing sentence vector configurations. The results show that the middle context size is recommended and that it gives the same result no matter whether summation or average word-embedding was used to construct a sentence vector; however, idf was not useful in WSD. The size of the corpus that the word-embedding vectors trained from affects accuracy as well. The result shows that our method wsdw2v, (50.9% or 48.7% without the probability of sense distribution) is higher than the baselines (i.e., original, simplified, adapted and LSA Lesk) and outperforms many unsupervised systems participating in the SENSEVAL-3 English lexical sample task. Our method could benefit from using other similarity measures [41] or using other sentences, phrases, or word-embedding approaches [42; 43; 44] and also could benefit from integrating with other lexicon resources such as BabelNet [19].  
Both authors contributed to the article.  
This research is supported by National Key Research and Development Plan. (Grant No. 2016 YFB0801204).  
The authors declare no conflict of interest.
## 2 Principle of FCM Clustering Algorithm Based on Local Regression Feature
### FCM Clustering Algorithm  
FCM clustering algorithm is based on a specific objective function; it divides the set \(X_{i}\) of data monitored during the \(T_{i}\) cycle of the wind speed sensor into \(c\) classes. Assuming that the algorithm sample \(x_{j}\) belongs to class \(i\) with the degree of membership \(u_{ij}\), the objective function and constraints are as follows:  
\[\left\{\begin{array}{l}J=\min\left(\sum\limits_{i=1}^{c}\sum\limits_{j=1}^{ n}u_{ij}^{m}\|x_{j}-c_{i}\|^{2}\right)\\ s.t.\sum\limits_{i=1}^{c}u_{ij}=1,j=1,2\ldots,n\end{array}\right. \tag{6}\]  
In the formula:  
\(J\)--objective function;  
\(c_{i}\)--Class \(i\) sample data center;  
\(m\)--membership factor, which represents the sample's degree of ease, is generally 2;  
\(x_{j}-c_{i}\)--Euclidean distance from the sample \(x_{j}\) to the center \(c_{i}\).  
Solving the above equation, the _Lagrange_ multiplier method transforms the constrained optimization into the unconstrained optimization problem [29]. Then, let the partial derivatives of \(u_{ij}\), \(c_{i}\), \(\lambda_{j}\) and other variables in the function be 0. Finally, the iterative formulas of variables \(u_{ij}\) and \(c_{i}\) are as follows:  
\[u_{ij}=\frac{1}{\sum_{k=1}^{c}\left(\frac{\|x_{i}-c_{j}\|}{\|x_{i}-c_{k}\|} \right)^{\frac{2}{m-1}}} \tag{7}\]  
\[c_{i}=\frac{\sum_{i=1}^{N}u_{ij}^{m}\cdot x_{i}}{\sum_{i=1}^{N}u_{ij}^{m}} \tag{8}\]
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Operation Related Problems in WSNs
#### 3.2.5 Monitoring  
Wireless sensor networks are being used for monitoring and surveillance applications in various practical scenarios including warehouse monitoring, cargo fleet monitoring, home monitoring, human activity monitoring, health monitoring, industrial process monitoring and infrastructure monitoring [94; 95; 96]. A multi-objective optimization strategy has been proposed in [97] for dynamic monitoring of the bridge. The authors applied the proposed scheme on the dynamic monitoring of a bridge in Quzhou, China. The experimental results complemented the ideal information acquired by means of ANSYS simulation. A pervasive health monitoring system using body area sensor network has been discussed in [98], where authors have suggested an optimal resource allocation technique for sustainable power supply and guaranteeing the quality of service to support data streams. Accurate localization of sensor nodes is critical in many applications namely, remote patient monitoring, people and goods tracking, environment monitoring and wildlife habitat monitoring. In [99], a multi-objective optimization algorithm has been suggested to accurately localize the sensor nodes so as to measure data having more geographical relevance.
# A Multi-Hop Clustering Mechanism for Scalable IoT Networks
## 3 Multi-Hop Clustering for IoT Networks
### The Proposed Mechanism
The optimal solution and proposed mechanism are compared for a small size network of 25 m \(\times\) 25 m area with 20 to 70 randomly distributed nodes across the network area following the uniform random distribution. Furthermore, the proposed mechanism is experimented with varying values of parameters that are supposed to affect the number of selected coordinators, that is, the transmission range \(TR\), the maximum hop count constraint \(H\), and the node density of the network. A higher transmission range increases the connectivity among nodes and, as a result, the number of nodes that may be covered by a coordinator tends to grow. A larger maximum hop count constraint \(H\) also extends the coverage of a coordinator, which may lead to a smaller number of selected coordinators. Higher node density increases the connectivity of the network and lowers the ratio of selected coordinators to the entire set of nodes in the network. The behavior of the proposed mechanism is analyzed to confirm that it conforms to that of the optimal solution. The parameters of the two kinds of experiments explained above are summarized in Table 1.

### Numerical Results

For the two kinds of experimental settings explained in Section 4.1, the number of selected coordinators and the average hop counts from a node to its coordinator are measured. Every result represents the average of 10 measurements with varying node distributions generated according to the uniform random distribution. #### 4.2.1 Experiments with Small-Scale Networks

Figure 1a shows the average number of selected coordinators for varying the number of nodes in the network with the experimental setting (1) of Table 1 and 0.5 to 2.0 more coordinators are selected by the proposed mechanism than by the optimal solution on the average. It shows, furthermore, the number of selected coordinators converges to a certain value instead of continuing to increase for a given geographical area in the proposed mechanism, as well as in the optimal solution. In other words, not only in the optimal solution but also in the proposed mechanism, it is shown that relatively
# Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
## 3 Methodology
### Complex Trajectory Gesture Recognition
#### 3.2.2 Bidirectional LSTM Fusion Neural Network  
The bidirectional LSTM fusion neural network mainly consists of three-dimensional convolutional layers and a bidirectional LSTM neural network. The schematic diagram of the neural network is shown in Figure 15. The three-dimensional convolutional layer is mainly used to extract image features, while the bidirectional LSTM network is mainly used to extract temporal features from input data.  
Figure 14: Schematic diagram of using sliding window to capture time series for different gestures.  
In the proposed neural network, the input data are a 10-frame 3D data cube containing information about distance, azimuth, and elevation. A 3D convolutional neural network is employed to extract features from this input data cube. Subsequently, the extracted gesture features are fed into the bidirectional LSTM neural network via a fully connected (FC) layer and a sequence unfolding layer to capture temporal features of the gestures. Finally, an FC layer is utilized to classify and recognize the features that are output by the bidirectional LSTM.  
Due to the correlation between gestures during the movement, it is difficult to infer the trend of future trajectories solely based on the gesture data before the current moment, as different gesture actions may have similarities within the same time period. For example, the two types of gestures set up earlier, the check and cross marks, all start with the hand waving downwards. The changes in distance, azimuth, and elevation are consistent. Therefore, in addition to the current moment of hand movement information, more gesture movement information before and after that moment is needed to determine whether the gesture belongs to the check or cross gesture. The incorporation of bidirectional LSTM neural networks into gesture recognition can effectively address the aforementioned issues by leveraging data from both before and after gesture movements simultaneously, which enhances the accuracy of gesture recognition.
## 4 Analysis and Results
### Communities Formation Criteria within the Networks  
We calculate the average of clustering coefficients per degree [54] with our own Python code. We plot this curve and fit it to a power law using _curve_fit_. We use the same scale as the degree function, although, here, the y axis' range is one order of magnitude smaller. Figure 5 reveals that the power law fit is greater than in the previous cases when we plotted the degree. This means that communities at different levels are formed according to an identical law showing a fractal behaviour, i.e., a hierarchical network [54]. The values of \(\alpha\) are very close to 2 for both HVGs, where we identify an initial scale-free behaviour and around 1.85 for the VGs. In this case, we do not plot this fit using the _powerlaw_ module by [PERSON] since the fit we observe with _curve_fit_ shows a less heavy-tailed distribution than in the degree graphs.  
Figure 4: Both empirical and fit probability density functions (PDF) and complementary cumulative distribution functions (CCDF) using the _powerlaw_ module by [PERSON] et al. [55].
# Electrospinning of High-Performance Nanofibres: State of the Art and Insights into the Path Forward
## 3 Electrospinning of High-Performance Polymers
### Meta-Aramid
The electrospinning parameters are a voltage of 11 kV, a distance of 20 cm, and a flow rate of 0.025 mL/h. The nanofibre diameter is in the range of 105–155 nm. much higher voltages. [PERSON] et al. [44] found that 20-25 kV was optimal to electrospun a similar concentration of m-aramid fibres (14 wt%) with diameters of 175 nm. In most instances, optimal voltages for electrospinningming m-aramid polymers were between 15 and 25 kV [10; 11; 12; 43; 44]. Variations in the polymer concentration, salt type, distance, flow rate, and ambient parameters may explain similar fibre diameters reported at different voltages. Flow rates for the electrospinning of m-aramid fibres are variable, with values between 1.6 and 25 \(\mu\)L/min being used to supply sufficient amounts of polymer solution to the spinneret [9; 10; 11; 12]. Distance also varied, with a maximum distance of 20 cm being used for a needle-based electrospinninging setup [10]. The mechanical strength of m-aramid nanofibres has been widely investigated in the literature, for both as-spun m-aramid nanofibres and treated electrospun nanofibres. The tensile strength of m-aramid nanofibrous membranes electrospun at three different concentrations (9, 11, and 13 wt%) in a DMAc/LiCl solvent system was compared by [PERSON] et al. [11]. It was found that the 11 wt% solution produced the nanofibrous membrane with the highest elongation at break and modulus, with a breaking strength of 1.5 mPa. At a higher concentration of 13 wt%, it was theorized that the viscosity of the solution was too high, slowing down the jet, leading to weaker conglutinated interactions among fibres. This resulted in a breaking strength of 0.25 mPa. The orientation of the nanofibres in the membrane was also found to have an effect on the overall strength of the membrane [43]. Aligning the nanofibres using a rotating collector was found to increase the elastic modulus by five times for the aligned nanofibrous mat compared to the randomly oriented mat (5000 and 1000 mPa, respectively). However, when measuring the oriented membrane in the transverse direction, the mat was so weak that it could not be properly attached to the testing frame.
## 3 Results and Discussion
### Prediction of Response Variables by SVM and RF  
In this research, two distinct regression-based machine learning models were employed to forecast the response values derived from a bone drilling procedure. The dataset was divided randomly, with 70% allocated for model training and the remaining 30% for testing and validation purposes. The models utilized three key input variables--the spindle speed, feed rate, and drill bit diameter--to predict the temperature generated during drilling. To mitigate potential errors arising from differences in the units of these input parameters, both the training and testing datasets were normalized using one-hot encoding. Furthermore, cross-validation was conducted to evaluate and optimize the models by employing a grid search CV to guarantee the selection of the most appropriate parameters while simultaneously considering both training and testing errors.  
The temperature values for bone drilling were derived from 27 experiments utilized for both training and testing. Table 4 displays the varied predictions from the support vector regression (SVR) and random forest regression (RFR) models. Table 5 lists the testing and training errors, assessed using three performance indicators to gauge the prediction accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experimental Temperature (\({}^{\circ}\)C)** & **RFR Model Predicted Temperature (\({}^{\circ}\)C)** & **SVR Model Predicted Temperature (\({}^{\circ}\)C)** \\ \hline
35.7 & 38.442 & 42.360 \\
49.9 & 51.764 & 45.013 \\
54.5 & 52.77 & 45.013 \\
36 & 36.976 & 42.360 \\
36.4 & 38.538 & 42.360 \\
36.8 & 37.726 & 42.360 \\
42.9 & 43.051 & 43.585 \\
43.9 & 44.299 & 42.361 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Predicted bone drilling temperature values from different machine learning algorithms.  
Figure 6: Input parameter optimization to achieve minimum response temperature.
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 3 The Modulation Recognition Method Based on Deep Learning  
Deep learning is a powerful artificial intelligence technology that can learn features from a large amount of data and fit nonlinear networks, so it is widely used in the fields of computer vision, natural language processing, and speech recognition, and has achieved tremendous success. Since mobile communication networks are able to generate large amounts of different types of data at a very fast pace, relevant researchers have applied deep learning to the field of communication [35], bringing opportunities for the development of communication technologies. For example, signal modulation identification in wireless communication can be done using deep learning techniques, and deep learning-based modulation identification methods have better robustness than traditional AMR methods and higher accuracy rates.  
Essentially, the modulation recognition algorithm based on deep learning is a feature-based recognition method that needs to extract the features of the signal. However, deep learning can automatically extract and classify the features of signals, replacing the steps of relying on expert experience to identify features in traditional recognition algorithms, and the recognition accuracy is higher. The modulation recognition model based on deep learning can be represented by Figure 5.  
There are many excellent neural networks in deep learning, such as CNN, RNN, etc. Among them, CNN is good at processing image data and RNN is good at processing sequence signals. CNN and RNN are widely used in AMR. In this section, we will introduce the application of these neural networks to deep learning in detail.
# Protecting Data at Risk of Unintentional Electromagnetic Emanation: TEMPEST Profiling
## 4 From van Eck Phreaking to TEMPEST Profiling
### RED/BLACK Separation and Filtering
In the study of [94], the authors investigate the application of an air-gap strategy as a preventive measure against potential security breaches in computer networks that store or handle sensitive and confidential data. Air-gapped networks are designed to be completely isolated from public networks and to keep data safe and protected. This approach is particularly useful when the information stored or generated by the system is considered highly sensitive and any risk of data leakage is unacceptable. Attackers exploit the emanations emitted by various computer components to exfiltrate data from such networks, thus establishing covert communication channels with external entities operating outside regular data transmission channels. The author of [95] introduced a new approach to compromising isolated air-gapped workstations. Air-gapped computers can still be vulnerable to attacks, despite the lack of wireless connectivity. The authors showed that attackers can use a Serial Advanced Technology (SATA) cable commonly used for data transmission as a wireless antenna. This technique enables radio signal transmissions in the 6 GHz frequency range. The results show that even highly secure air-gapped computers can wirelessly leak sensitive information to a nearby receiver over a SATA cable. It can be concluded that the implementation of RED/BLACK separation significantly reduces the risk of unintentional data leakage and information disclosure. The success of this approach depends on several factors, including effective cable management, careful equipment selection, proper configuration, and a robust network architecture.
## 3 Experiments
### Experimental Results
#### 3.5.2 Comparison with Other Methods  
To further evaluate the classification performance of HoRFNet on multi-magnification images, this study conducts a systematic comparison with representative CNN methods from recent years. The comparison includes accuracy at both the image level and the patient level, as shown in Table 4. In particular, SeONet and RFNet are included as reference models to further assess the improvements brought by HoRFNet. Based on Table 4, we conclude that HoRFNet demonstrates a significant competitive advantage over previously proposed models. Specifically, on the 200\(\times\) dataset, HoRFNet achieves an optimal image level accuracy reaching 99.50%, while the performance at the patient level attains 99.23%. Compared with studies [43; 44; 45; 52; 54], HoRFNet improves accuracy at the image level by 8.50%, 14.60%, 10.40%, 11.02%, 7.05%, and 5.70%, respectively, and at the patient level by 8.23%, 15.13%, 9.43%, 10.03%, 6.95%, and 4.93%, respectively. Additionally, SeONet yields 98.01% accuracy for image level classification and 97.89% for patient-level prediction on the 200\(\times\) dataset, while RFNet achieves 98.34% and 98.44%, respectively. Both models significantly outperform those in studies [43; 44; 45; 46; 49; 52; 53]. However, HoRFNet reaches 97.66% accuracy on the 40\(\times\) dataset in terms of image-level performance, which is slightly 0.33% lower than BreastNet proposed by [PERSON] et al. [22]. Nevertheless, at other magnifications, the proposed model outperforms BreastNet in recognition accuracy. Compared with the models in recent studies [53; 54; 55], HoRFNet shows consistently better results under all magnification settings of the BreakHis dataset. For instance, [PERSON] et al. [54] employ Inception-V3 combined with image segmentation and fusion strategies to enhance BCHI classification accuracy. However, its average performance is approximately 5.70% and 4.93% lower than that of HoRFNet on image level and patient level evaluations, respectively. According to the above analysis results, compared with the network model in recent years, our HoRFNet has considerable advantages.
## 6 Conclusions  
Determining the optimal number of MAs and their itineraries in MIP has a direct impact on the overall performance of the data gathering task in WSNs. However, the previous MIP algorithms were based on a general assumption such that the sink node is the starting and ending point of each MA's itinerary. In this paper, a spawn multi-mobile agent itinerary planning (SMIP) approach has been proposed. The main aim of the proposed SMIP was to determine the optimal number of MAs and their itineraries in MIP. The idea of the proposed SMIP approach was based on agent spawning, where the sink node dispatches only one main MA to the assigned partition in the network, and this main MA has the ability to spawn one or more new MAs with different assigned tasks. Furthermore, the proposed SMIP approach used x-means clustering algorithm to partition the network such that the number of clusters is efficiently achieved. Subsequently, extensive simulations have been carried out to evaluate the performance of the proposed SMIP. The results shown that SMIP has achieved significant improvements in terms of energy and task duration. Additionally, the proposed SMIP approach has achieved a better performance (EDP) compared to the previous MIP algorithms. Future research needs to consider fault tolerance (such as sensor's failure and battery depletion) during the determination of the MA itinerary, so that the MA can dynamically migrate to the next hop node.  
This work is partially supported by Universiti Putra Malaysia and the Ministry of Higher Education (Universiti of Al-Anbar), the State of Iraq.  
[PERSON] conceived the main idea of this work, carried out the numerical simulations and analysed the results, while [PERSON], [PERSON] and [PERSON] equally served as an advisor to [PERSON]. They discussed the main idea, the whole simulations, assisted in performance evaluation and aided in the manuscript preparation.  
The authors declare no conflict of interest.