esa-sceva/satcom-chunk-collection huggingface dataset:
# Large Scale Implementations for Twitter Sentiment Classification
## 2 Related Work
### Machine Learning Techniques  
In the proposed manuscript, we utilized three classification algorithms in order to implement the Sentiment Analysis Tool. The three algorithms utilized are Naive Bayes, Logistic Regression and Decision Trees.  
Naive Bayes is a simple multiclass classification algorithm based on the application of Bayes theorem. Each instance of the problem is represented as a feature vector, and it is assumed that the value of each feature is independent of the value of any other feature. One of the advantages of this algorithm is that it can be trained very efficiently, as it needs only a single pass to the training data. Initially, the conditional probability distribution of each feature given class is computed, and then Bayes theorem is applied to predict the class label of an instance.  
Logistic Regression is a regression model where the dependent variable can take one out of a fixed number of values. It utilizes a logistic function to measure the relationship between the instance class and the features extracted from the input. Although widely used for binary classification, it can be extended to solve multiclass classification problems.  
Decision Trees make up a classification algorithm that is based on a tree structure whose leaves represent class labels while branches represent combinations of features that result in the aforementioned classes. Essentially, it executes a recursive binary partitioning of the feature space. Each step is selected greedily, aiming for the optimal choice for the given step by maximizing the information gain.
# An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks
## 3 Preliminaries
### Cluster Maintenance  
After the initial formation of clusters, dynamic topology changes frequently occur in FANETs. Thus, it is significant to enhance the robustness of the clustering network in dynamic environments. In this work, cluster maintenance is proposed to keep the stability of the cluster structure and improve the network performance. The following analyzes the cluster maintenance in different situations.  
To maintain the clusters effectively, the CHs broadcast their HELLO messages to their CMs periodically with a period \(T\), and a CM replies with an ACK (acknowledgement) message to its CH, immediately after receiving the HELLO message. If the CH cannot receive an ACK from the CM for \(\beta\) periods, the CM is considered to not be in the transmission range and is depleted from the CM list. If a CH leaves the cluster, a CH re-selection process is triggered.  
If a new node (including a newly added node and the node that has left the original cluster) wishes to join the clustering network, it sends its HELLO message to the nearest CH. The CH checks whether the remaining energy of the new node is greater than the threshold \(E_{th}\), which is set to 20% of the initial energy in the paper. If it meets the requirements, the CH replies with an ACK to the new node. The pseudocode of cluster maintenance is listed in Algorithm 3.  
```
1:if ACK from \(CM_{j}\) is not heard by \(CH\)
2: deplete \(CM_{j}\) from CM list
3:endif
4:if CH\({}_{i}\) leaves the cluster
5: execute Algorithm 2
6:endif
7:if a new node requests to join the cluster
8: the new node sends HELLO message to \(CH_{i}\)
9:if (remaining energy of the new node \(>E_{th}\))
10:\(CH_{i}\) reply ACK and agree to join
11:else
12: ignore the request
13:endif
14:endif
```  
**Algorithm 3**Cluster Maintenance
## 2 Method
### Trajectory Replanning Using Gradient-Based Optimization
#### 2.2.1 Collision Cost Function  
The collision cost function aims to guide the trajectory away from moving obstacles based on their predicted motion.  
At the \(i\)-th time step, the state of a moving obstacle is predicted as \(\hat{\xi}_{i}^{o}\), and the replanned waypoint is denoted as \(\xi_{i}\). The distance between the replanned trajectory and the moving obstacle \(d_{i}\) can be computed:  
\[d_{i}=\sqrt{(\xi_{i}-\hat{\xi}_{i}^{o}-\Delta_{\varphi})^{T}(\xi_{i}-\hat{\xi} _{i}^{o}-\Delta_{\varphi})} \tag{21}\]  
where \(\Delta_{\varphi}\) is used to ensure that the difference in the third component \(\varphi\) is zero, focusing our calculation on the distance between the 2D positions.  
Our goal is to maintain a distance between the UAV and obstacles that exceeds \(d_{safe}\). Thus, the collision cost function can be designed as follows:  
\[\mathbb{J}_{c}=\sum_{i=1}^{n}\exp\left(-\alpha_{c}(d_{i}-d_{safe})\right) \tag{22}\]  
where \(\alpha_{c}\) is a positive scaling factor that determines the sensitivity of the cost to the distance. The exponential function is used to penalize distances that are close to or less than the safe threshold \(d_{safe}\), with the cost rapidly increasing as the distance decreases. This formulation ensures that trajectories maintaining a safe distance from obstacles are favored, effectively minimizing the risk of collision.
# Harnessing the Potential of Emerging Technologies to Break Down Barriers in Tactical Communications
## 6 Conclusions  
The utilization of 5G and prospective 6G networks poses challenges to military communications in tactical situations. This study carefully chose and examined a set of cutting-edge technologies that facilitate the advancement of tactical networks. The implementation of network virtualization through 5G plays a crucial role in ensuring enhanced security. These advancements are pivotal in enhancing the practicality of military applications that truly embrace network-centric operations. Software-defined networks, the IoT, blockchain, AI, semantic communications, and neuromorphic processors all contribute to the enhancement of communication performance. These technologies notably improved eleven traditional military use cases. This study serves as a foundational analysis, providing a starting point for new researchers to explore fresh avenues of inquiry. It is not simply a compilation; rather, it serves as an analytical framework for guiding future research initiatives.  
Conceptualization, methodology, investigation, validation, resources, writing--original draft preparation and visualization, L.C.S. and V.M.B.; writing--review and editing, L.C.S. and V.M.B.; supervision, V.M.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflict of interest.
# A Cache Placement Strategy with Energy Consumption Optimization in Information-Centric Networking
###### Abstract
Considering the wide application of information and communication technology in daily life, the energy consumption of telecommunication networks accounts for 5% of the total energy consumption, and is increasing at a rate of 10% per year. Therefore, the issue of network energy consumption must not be ignored. In view of the importance of energy conservation, how to reduce network energy consumption is a very meaningful study. Our paper is organized as follows. The background and significance of research questions are briefly introduced in Section 1. Related work is introduced in Section 2. The energy consumption model is constructed, and a cache placement strategy with energy consumption optimization is proposed in Section 3. The simulation results are given in Section 4, and the advantages of the strategies proposed in this paper are analyzed. The work and research results of this paper are summarized in Section 5.
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 6 Results and Discussion
### Anns  
Given that in the development of DL techniques, no dedicated steps are performed for feature scoring and dimensionality reduction of the dataset, the evaluation focused on comparing the results obtained by the two ANNs. As mentioned in Section 5.4.2, the performance of both developed ANNs was very similar. The achieved accuracies were identical (97.84%), and the other metrics obtained from the confusion matrix showed minimal differences between the two models. For example, a slightly higher _Recall_ was observed for the _Scikit-Learn_ ANN (14.98%), but both models indicated poor effectiveness in error detection. However, the _Keras_ ANN produced higher _Precision_ and therefore, a lower probability of false alarms (72.17%).  
Furthermore, the evaluation methods provided by _Scikit-Learn_ (_score()_) and _Keras_ (_evaluate()_) were applied to both ANNs. In the first case, only the accuracy value of the model was obtained, while in the second case, the loss function was also provided. As expected, the results remained similar and, although the differences between the two models were minimal; if the effectiveness in detecting real errors was prioritized, the _Keras_ ANN would be chosen. However, a higher _Recall_ was observed in some simpler MLmethods (such as RF when applying RFECV) compared to these more sophisticated DL methods, which may be attributed to the different levels of effectiveness of dimensionality reduction techniques used in ML models and the internal feature scoring performed by ANNs.
## 3 Results and Discussion
### Numerical Analysis Using Wave Equation  
In addition to analysis of the view factor, the propagation of plasma can be studied by numerical methods using the wave equation. In the analysis of the view factor, only the change in projection shape detected by the detector was considered. In the analysis using the wave equation, the radiation energy at the surface of the plasma was regarded as an electromagnetic wave radiating in all directions. When the electromagnetic wave generated by plasma is propagated from a finite-area source rather than a point source, the intensity value of the electromagnetic wave differs depending on the location of the detector. In reality, the shape of the plasma changes according to the flow conditions, and the detector is fixed. In the simulation, only the relative values of the two bodies were important, so for modeling convenience the plasma signal size was calculated by fixing the plasma and changing the position of the detector (Figure 9).  
Commercial software (Multiphysics; COMSOL) was used to confirm the propagation of electromagnetic waves, and to calculate the magnitude of the electromagnetic field signal at a specific location. The simulation was conducted by setting the electric field source corresponding to the plasma at the left boundary of the quadrant and emitting an electric field from the source (Figure 10a). The magnitude of the superimposed wavelength of the electric field wave at nine positions, separated by a zenith angle of 1/4 arc of the  
Figure 8: View factor according to the change of the zenith angle.  
Figure 9: Schematic diagram of the relationship between plasma deformation and detector position in simulation.  
electric field radiated from the source, was calculated. The governing equation used in the simulation was as follows:  
\[\left(\frac{1}{\mu\epsilon}\
abla^{2}-\frac{\partial^{2}}{\partial t^{2}}\right) \overset{\rightarrow}{E}=0, \tag{2}\]  
where \(\mu\) is relative permeability, \(\epsilon\) is relative permittivity, \(t\) is time, and \(E\) is the electric field. Perfect electric conductor and scattering boundary conditions were chosen. In the quarter hemisphere, the symmetrical interface was set as a perfect electric conductor. The condition for this boundary can be expressed by the following Equation:  
\[\overset{\rightarrow}{n}\times\overset{\rightarrow}{E}=\overset{ \rightarrow}{0} \tag{3}\]  
In the remaining region, the scattering boundary  
was not considered in the analysis of the radiation of the plasma; the deviation was not quantified. Although this simulation had clear limitations, it is still meaningful as it allowed assessment of plasma energy propagation according to the relative positions of the plasma and detector, where the relative shape change of plasma in such a simulation can be interpreted as a function related to the flow of gas. This can then be confirmed by comparison with experiments.
# Coordinated Mitigation Control for Wideband Harmonic of the Photovoltaic Grid-Connected Inverter
## 5 Simulation Case Study  
### Simulation Model and Parameters  
A simulation model has been built to verify the resonance suppression and the dead zone compensation methods. The main topology of the simulation is shown in Figure 1, including a PV grid-connected inverter operating at maximum power point (MPP), LCL  
Figure 15: Flowchart of parameter correction.  
As shown in this figure, the control system has lost the ability to achieve stable current tracking, and the resonant current amplitude is close to 3000 A. Obviously, the resonance point of the LCL filter greatly threatens the operation safety of the PV grid-connected inverter. The resonance frequency with another group parameter is 1160 Hz, and the FFT result is shown in Figure 18. Except for the component at the resonant frequency increasing substantially, other frequency components within the low-frequency band are also amplified to some extent. FFT results demonstrate the accuracy of the LCL filter's resonance point, which is calculated by the closed-loop transfer function method.  
These experiments show that, no matter where the LCL filter's resonant point is, the system loses stability as long as the grid current contains resonant frequency components. In the simulation experiment, the outer loop is used in the constant voltage method, and the reference voltage is 750 V. The next section verifies the influence of the capacitive current feedforward method on resonance suppression.  
Figure 17: Time domain waveform in steady state.  
Figure 18: FFT result of Parameter 2 in Table 1.
## Materials and Methods
### Numerical Simulations  
To quantify the EMF absorption, the specific energy absorption rate (SAR) values were calculated in a multi-layer ellipsoidal model of the human head (involving skin, fat, skull bone and brain layers).  
The simulations were carried out by Sim4 Life software (Zurich Med Tech, Zurich, Switzerland) using finite-difference time-domain (FDTD) solvers and the finest resolution of 0.005 mm was set for the antenna, and 1 mm was set for the numerical model of the head and other elements of exposure scenarios. The resolution of the numerical model of the head was finer than the minimum resolution established for the evaluation of SAR required by the International Electrotechnical Commission (IEC) standard 62232-2011--the resolution of the human body model was better than 1/15 of the wavelength in tissues [37]. The uncertainty of the numerical simulations was estimated as not exceeding \(\pm\)(20-25)% (K = 1) and was in the range compliant with state-of-the-art in SAR numerical simulations [37; 38; 39].  
The SAR values were calculated in the numerical model of a head exposed to RF EMF emitted by the ESP32-WROOM-32 RF module, averaged over the whole head or averaged over any 10 g of continuous tissue (localised) [22; 23; 30; 38]. The averaging algorithm according to IEC/IEEE 62704-1:2017 was used to obtain localised SAR values [38]. This algorithm is appropriate for use in a compliance assessment with the SAR limits established by IEEE C95.1 and ICNIRP 1998 [22; 24].
## 2 Materials and Methods
### Determinations of Recovery Coefficients Using the NEMA/IEC Phantom  
A NEMA/IEC NU2 (Figure 2b, Table 2) phantom was then used to assess the recovery coefficient (\(RC_{j}\)) in spherical objects as a function of the sphere size. Six spherical inserts of 10, 13, 17, 22, 28, and 37 mm diameter were filled with an activity concentration (\(C_{sph}\)) of 1.74 MBq/mL in a cold background. For each spherical insert (\(j=1\dots\) 6) the \(RC_{j}\) were defined by four different ROI delineation methods according to the following equations:  
\[RC_{j,max}=\frac{\epsilon_{sph,j,max}}{C_{sph}}\]  
\[RC_{j,50\%}=\frac{\epsilon_{sph,j,50\%}}{C_{sph}}\]  
\[RC_{j,PE}=\frac{\epsilon_{sph,j,PE}}{C_{sph}}\]  
\[RC_{j,V}=\frac{\epsilon_{sph,j,V}}{C_{sph}} \tag{4}\]  
where:  
* \(\epsilon_{sph,j,max}\) is the measured maximum voxel value (in terms of activity concentration) for a given spherical insert (\(j=1\) to 6);
* \(\epsilon_{sph,j,50\%}\) is the average voxel value for each hot insert volume of interest (VOI) defined by a 3D iso-contour at 50% adapted for background as recommended by the EANM Guidelines for FDG tumor PET imaging [23].
* \(\epsilon_{sph,j,PE}\) is the average voxel values for each hot insert VOI assessed with the PET-Edge (PE) tool, a gradient-based technique that detects the steepest drop off in SUV values to create the contour boundary automatically.
* \(\epsilon_{sph,j,V}\) is the average voxel values for each VOI corresponding to the physical volume of each sphere. In this work we used a semi-automatic segmentation method to obtain 3D spherical contours (2D/3D brush tool) adopting the physical diameter of the inserts
###### Abstract  
Torsional vibrations pose a serious challenge in drilling operations and can lead to effects such as stick-slip phenomena, tool wear, and reduced drilling efficiency. While previous research has been conducted on torsional vibrations, there is a notable gap in comparative studies that assess the scalability of downscale models to real-world drilling conditions. This study fills this gap by systematically comparing torsional vibrations in downscale and upscale drill strings under different torque conditions at three different depths, shedding light on scaling effects in drilling vibrations. Numerical simulation was carried out taking into account non-linear interactions, damping effects, and torque variations. The laboratory set-up was for a well length of 15 m and was geometrically scaled to represent an upscale well of 450 m. Certain operational parameters such as rotation speed, torque, density, and friction coefficients were modified to keep realistic dynamic behavior, and all models were run at an identical speed of rotation to enforce consistency. The results show that both the upscale and downscale models exhibited stick-slip behavior, but differences in vibration intensity and stabilization trends point out how scaling affects torsional dynamics. Notably, the upscale bit first faced higher torsional oscillation than the set rotation speed after overcoming stick-slip before stabilizing, whereas the downscale bit went through prolonged stick-slip instability before synchronization. This study enhances the understanding of scaling effects in torsional drilling vibrations, offering a foundation for optimizing experimental setups and improving predictive modeling in drilling operations.  
upscale; downscale; stick slip; torque; numerical simulation Article
# A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product
###### Abstract  
Balancing adaptability, reliability, and accuracy in vision technology has always been a major bottleneck limiting its application in appearance assurance for complex objects in high-end equipment production. Data-driven deep learning shows robustness to feature diversity but is limited by interpretability and accuracy. The traditional vision scheme is reliable and can achieve high accuracy, but its adaptability is insufficient. The deeper reason is the lack of appropriate architecture and integration strategies between the learning paradigm and empirical design. To this end, a learnable viewpoint evolution algorithm for high-accuracy pose estimation of complex assembled products under free view is proposed. To alleviate the balance problem of exploration and optimization in estimation, shape-constrained virtual-real matching, evolvable feasible region, and specialized population migration and reproduction strategies are designed. Furthermore, a learnable evolution control mechanism is proposed, which integrates a guided model based on experience and is cyclic-trained with automatically generated effective trajectories to improve the evolution process. Compared to the \(1.69\lx@math@degree\),\(55.67\) mm of the state-of-the-art data-driven method and the L28\(\lx@math@degree\),\(77.67\) mm of the classic strategy combination, the pose estimation error of complex assembled product in this study is \(0.23\lx@math@degree\),\(23.71\) mm, which proves the effectiveness of the proposed method. Meanwhile, through in-depth exploration, the robustness, parameter sensitivity, and adaptability to the virtual-real appearance variations are sequentially verified.  
machine vision; complex assembled product; camera pose estimation; virtual-real matching; viewpoint evolution; learnable evolution control Article  
1
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 5 Immersive Audio-Visual Experiments
### Source Stimuli  
The UnB-AV dataset [47] was used for this study (this dataset is available for download from the site of the University of Brasilia at www.ene.unb.br/mylene/databases.html, accessed on 22 June 2021). This dataset contains one-hundred and forty (140) video clips (with accompanying audio) distributed in three sub-sets and used in three different perceptual experiments: Experiment 1 (60 sequences), Experiment 2 (40 sequences) and Experiment 3 (40 sequences). Pristine versions of these video clips were pre-processed to standardize some video and audio characteristics. For the video component, the spatial and temporal resolutions were set to 1280 \(\times\) 720 (720p) and 30 frames per second (fps), plus the color space configuration was set to 4:2:0. As for the audio component, the sampling frequency and bit-depth were fixed to 48 kHz and 16 bits, respectively. Note that all videos considered for the study had characteristics equal or above the ones set in this pre-processing phase. Video clips were 19 to 68 s long, with an average duration of 36 s. Sample frames of 18 video clips grouped by content genres are presented in Figure 1. Content material was selected based on the recommendations found in [16] and the Final Report on the validation of objective models multimedia quality assessment (phase 1) of the Video Quality Experts Group (VQEG) [57]. A scatter-plot showing the distribution of the spatial and temporal information, defined by [PERSON] and [PERSON] [58], for all video clips is presented in Figure 2.  
Figure 1: Sample frames of the videos from the UnB-AV Database. The database include different genres contents like: Sports, TV Commercials, Interviews, Music, Documentaries, and Cartoons [47].