esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Materials  
The selected dataset for this work was provided by the IEEE Geoscience and Remote Sensing Society (GRSS). It was originally provided to participants of the IEEE GRSS 2018 Data Fusion Contest [20] and acquired by the National Center for Airborne Laser Mapping (NACLM) in February 2017. The dataset depicts approximately 5 km\({}^{2}\) of the University of Houston campus and its surrounding areas. It provides three co-registered data modalities: hyperspectral, multispectral lidar, and high-resolution RGB [20] along with semantic pixel labels.  
The hyperspectral modality was captured by an ITRES CASI 1500 camera system and covers the 380-1050 nm range over 48 bands at a 1.0 m ground sample distance (GSD) [20]. It was provided as a set of 14 spectrally calibrated and orthorectified image tiles, each 4172 \(\times\) 1202 pixels [21]. The multispectral lidar modality was acquired with an Optech Belgrade, MT, USA Titan MW camera system across three wavelengths 1550, 1064, and 532 nm at \(\sim\)0.5 m GSD [20]. Per each spectral channel, there are 14 point clouds, an intensity raster, and four digital elevation models. A DiMAC ULTRA-LIGHT+ camera system acquired the high-resolution RGB data. It was provided as a set of 14 images of 11,920 \(\times\) 12,020 pixels in size at a 5 cm GSD [22]. Finally, the semantic pixel labels were developed from OpenStreetMap data as 20 Land Use Land Cover (LULC) classes at a 0.5 m GSD [20]. Of the total 14 multimodal tiles, these semantic labels only cover four. The other 10 tiles were reserved as the test set for the 2018 Data Fusion Contest itself. In this work, we only utilized the hyperspectral, multispectral lidar, and semantic labels. The high-resolution RGB data was excluded.
## 2 Background
### IoT Challenges
#### 2.3.1 Connectivity Technology  
The Internet of Things (IoT) demands the creation of connections across short, medium, and long distances. IoT solutions are expected to fulfill diverse transmission needs, such as responding quickly (ultra-low latency), repeating tasks (recurrence), operating asynchronously, storing and forwarding data, supporting mobility, and facilitating streaming. To meet these requirements effectively, these solutions may need optimization for specific circumstances [32; 33].
## 2 Bioactive Compounds Present in _Cystoseira_ Extracts for Neurodegenerative Disease Management
### 2.1 _#_  
Among marine algae, species of the Phaeophyceae contain the highest levels of phenolic compounds. Of the phenolic compounds present in _C_#, the group of tannins reveals the strongest bioactivity [57]. They are considered one of the most widely distributed types of natural plant products and are classified into distinct groups, according to their structure. Phlorotannins are restricted to brown algae and can also be divided into different hydrophilic compound groups (fucols, phlorothols, fucophorethols, fuhalols, isofuhalols and exclols), with very different molecular weights, ranging from 126 to 650 kDa [11; 58]. However, it was found that its percentage in algae is quite variable, depending on factors such as the size of the alga, its age, the season, the light intensity and also the salinity and temperature of the water [2; 27]. This may be reflected in differences of anti-inflammatory activity since the potential to reduce inflammatory mediators will be proportional to the content of phlorotannins [59; 60]. Over the years, several biological properties associated with phlorotannins have been discovered, highlighting their capability to absorb UV radiation and avoiding the consequent photo-oxidative stress, but also the antioxidant, antimicrobial, antiallergic and anti-inflammatory properties [61; 62; 63]. The anti-inflammatory activity in vitro of purified extracts of phlorotannins obtained from three different _C_# species (_C_#, _C_#, _C_#  and _C_#, _C_#  and _C_#, respectively)--was demonstrated via an inhibitory effect on the production of NO by RAW 264.7 macrophage cells stimulated by lipopolysaccharidesaccharides (LPS) [60]. LPS is one of the main components of the membrane of Gram-negative bacteria [64] capable of promoting the secretion of pro-inflammatorycytokines [65] and NO [66]. After the incubation period, the phlorotannins extracts of the three C# species were able to considerably reduce NO levels produced by cells, especially C. #i extract which presented the greatest anti-inflammatory potential [60]. Furthermore, the antioxidant activity of purified phlorotannins extracts was also confirmed in the same C# species enunciated above [20]. [PERSON] and collaborators found that these species could eliminate superoxide radicals, avoiding lipid peroxidation.  
Neuroprotective functions of sterols extracted from marine organisms have already been confirmed [27; 80], although there are few studies with seaweed extracts.
## 1 Introduction
For example, principal component analysis (PCA) was used to outline of the similarities and differences among 16 algal species and to classify 40 wine samples based on their high-performance thin-layer chromatography (HPTLC) fingerprints [18; 19]. It is well known that each plant species has a special, complex mixture of bioactive natural products, in which each component contributes to its overall bioactivity. Related botanical families often contain similar types of biologically active secondary metabolites, and an understanding of the systematic position of a medicinal plant species allows some assumptions to be made about compounds that are present. Plants from the Asteraceae and Lamiaceae family have been historically used to cure various diseases. This is because they produce a wide range of secondary metabolites with potent antibacterial, antioxidant, anti-inflammatory, antimicrobial, antiviral, and anticancer activities. The Lamiaceae family plant extracts have a higher content of phenolics (and flavonoids) than Asteraceae extracts and contain more known medicinal plants than the Asteraceae family [20; 21]. The main aim of the current study was to investigate the effects of plant taxonomy or botanical origin and purity of essential oil based on their spectral fingerprint in the mid-infrared region. For that purpose, attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy was combined with pattern recognition techniques such as PCA and cluster analysis.
## 1 Introduction
The method first demodulates the received signal to reduce the baseband signal waveform to determine the symbols transmitted in each code element cycle and then segments the received signal, with the length of each segment being one symbol cycle. After segmentation, the initial phases of different symbols are balanced to ensure that the phase difference between each symbol waveform is caused by the motion process. Finally, the two-dimensional matrix composed of all the symbol waveforms is vertically transformed by FFT to find out the speed of the moving target. This is the structure of the remainder of the paper: The model of the generalized MPSK radiated source signal is presented in Section 2. A description of the velocity estimate procedure for inter-symbol processing is then given in Section 3. Moreover, simulations in Section 4 confirm the viability of the strategy. Finally, Section 5 concludes this research study.
## 1 Introduction
* We tried to address the shortcomings in the literature and avoid them in our experiments, as well as comparing our results against them. * We used several evaluation metrics to provide more detailed analysis and a full view of the proposed models' performance. * In the final analysis, our results show an improvement in performance metric scores, as shown in Section 5. The rest of this paper is organized as follows: Section 2 discusses related works. Section 3 presents the necessary background information. In Section 4, we show the proposed methods and experimental design. Section 5 presents the results. Section 6 discusses our experimental results and compares them with those in the related literature. Finally, Section 7 concludes this study, followed by future research directions.
# Non-Parametric Model-Based Estimation of the Effective Reproduction Number for SARS-CoV-2 +
## 1 Introduction
In this study, we present a function-class-free approach that simultaneously estimates the time dependencies of all the dynamical parameters in the state-space representation of an SIRD model, including the effective reproduction number. We employ the Ensemble or Augmented Kalman Smoother (AKS) method, treating dynamic parameters as states to estimate their time dependencies [47]. Unlike recent approaches that use the Augmented Kalman Filter for estimating the time-dependent reproduction number in COVID-19 data [48] based on \"trial and error\", our method combines the Augmented Kalman Smoother with an Expectation-Maximization (EM) algorithm [49; 50]. Notably, we demonstrate that our approach is capable of describing real-world COVID-19 data while providing time-dependent estimates for all the model parameters. Importantly, our method requires no additional prior knowledge about the remaining model parameters, total population numbers or initial values of individual compartments. We apply this algorithm to SARS-CoV-2 incidence data in Germany from winter 2020 to summer 2021, obtaining time-dependent estimates for all the model parameters and real-time estimates of the effective reproduction number \(\mathcal{R}_{t}\). The code for the AKS method and the analysis scripts used in this work are freely available on GitHub at [[https://github.com/vandensich/Augmented-Kalman-Smoother](https://github.com/vandensich/Augmented-Kalman-Smoother)]([https://github.com/vandensich/Augmented-Kalman-Smoother](https://github.com/vandensich/Augmented-Kalman-Smoother)) (release v1.0, published on 14 November 2023) and are published under the MIT license. This work extends a previously published conference paper that addresses the same scientific core content [51]. The introduction and methods sections have been rephrased, and two substantial parts have been added. Sections 3.1-3.3 present the results of an extensive simulation study, analyzing the method's performance on generated data sets. Additionally, Sections 3.5 and 5 include the results of a successful validation, where the estimated parameter time courses were used as inputs for an ODE model predicting the model states.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 2 Technology Background of Physical Layer
### Parameters and Basic Structure of 5G PHY Layer
#### 2.2.4 PHY Layer Signals  
The following signals in the physical layer of 5G NR are critical to enable effective communication between UE and the network:  
* Primary Synchronization Signal: There are three possible sequences of the PSS. The Primary Synchronization Signal (PSS) is based on maximum length sequences (m-sequences), contrary to Zadoff-Chu sequences in LTE. There are 127 consecutive subcarriers in the frequency domain, contrary to 72 in LTE. The frequency position of the PSS in NR can vary in order to adopt more flexibility in the deployment, contrary to the fixed scenario in LTE. The PSS is linked to the cell identity group \(N_{ID}^{2}\).
* Secondary Synchronization Signal: This is based on a Gold sequence of length 127 mapped to 127 subcarriers, which is formed by combining two m-sequences. Due to low cross correlation of signal, the UE can distinguish between neighboring base station. The number of possible Secondary Synchronization Signal (SSS) variations is 336, contrary to 168 in LTE. The SSS is linked to the cell identity group \(N_{ID}^{1}\), and the signal length is the same as that of the PSS. Unlike LTE, the NR SSS does not change depending on which subframe it is transmitted from. Both PSS and SSS are related to the Physical Cell ID (PCI) by the following formula: \[PCI=3\times N_{ID}^{1}+N_{ID}^{2}\] (1) resulting in 1008 possible PCIs (also referred to as NCellIDs). This differs with respect to the LTE, where only 504 combinations are possible.
* DeModulation Reference Signals: These aid in the channel estimation for the coherent demodulation of PDSCH and PBCH, as outlined in [57]. DeModulation Reference Signals (DM-RS) symbols are mapped to specific resource elements within an RB. The structure depends on the network configuration, such as localized or distributed mapping and SCS. Contrary to LTE, the DM-RS are separated for the PDSCH, PUSCH, PDCCH, PUCCH, and PBCH.
* Phase Tracking Reference Signal: This is introduced in 5G PDSCH and PUSCH to help in the phase tracking process and to mitigate Common Phase Error (CPE) effects in the mmWave, caused by phase noise from local oscillators, ensuring system performance [57].  
* Sounding Reference Signal (SRS): This is used for channel sounding in the UL. The signal is transmitted periodically by the UE and can utilize frequency hopping to avoid interference.
* Channel State Information Reference Signal (CSI-RS): This is transmitted by gNB to estimate DL radio channel quality. CSI-RS is used in beamforming to determine the best beam based on the channel's state and to maximize the spectral efficiency in MIMO transmission [57].
## 4 Experimental Results
### Comparative Evaluation  
Additionally, the comparative investigation between the proposed and the existing models are indicated in Table 7. [PERSON] et al. [10] created a novel intracranial hemorrhage detection system based on a synergic deep learning model and a Grab cut-based segmentation algorithm. Hence, the developed model has achieved 95.73% of classification accuracy, 97.78% of specificity, and 94.01% of recall on the benchmark intracranial hemorrhages detection database.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**OGRU-CSO Model with 80:20\% Training and Testing of Data**} \\ \hline
**Features** & **Precision (\%)** & **Recall (\%)** & **F-Measure (\%)** & **MCC (\%)** & **Specificity (\%)** & **Accuracy (\%)** \\ \hline HoG & 92.52 & 91.09 & 93.02 & 92.87 & 92.43 & 93.36 \\ LBP & 94.55 & 93.43 & 93.94 & 93.55 & 94.08 & 93.42 \\ LTP & 93.72 & 94.88 & 96.77 & 94.20 & 94.60 & 96.90 \\ Hybrid & 99.86 & 99.25 & 99.34 & 99.67 & 99.40 & 99.36 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation results of the proposed model by varying the features with 80:20% training and testing of data.  
Figure 11: Graphical comparison of dissimilar features with 50% training and 50% testing of data.  
Figure 12: Graphical comparison of dissimilar features with 80% training and 20% testing of data.  
[PERSON] et al. [15] have combined a pre-trained CNN model named ResNeXt-101 with a bidirectional long short-term memory network for recognizing intracranial hemorrhages in 3D CT scans. The experimental results showed that the individual ResNeXt-101 model achieved 97.54% of classification accuracy, 60.79% of recall, and 99.32% of specificity. However, the ResNeXt-101 with bidirectional long short-term memory network has achieved better specificity of 99%, recall of 72.86%, and classification accuracy of 97.83% on the RSNA 2019 brain CT hemorrhage database. [PERSON] et al. [16] developed a novel deep learning model, which integrates two sequence models and 2D CNN to achieve precise acute intracranial hemorrhage detection. However, the presented model attained specificity of 94.85%, recall of 95.84%, and classification accuracy of 95% on the benchmark intracranial hemorrhages detection database. Related to these comparative models, the developed OGRU-CSO model achieved superior results in intracranial hemorrhage detection by utilizing specificity, recall, and accuracy on the RSNA 2019 brain CT hemorrhage database. In addition to this, the selection of the optimal features by the CSO algorithm reduces the model complexity to linear. The computational time of the proposed model is 33.28 s, which is superior to other optimizers and classifiers.
## 6 WSN Wireless Charging-Use Case  
The main purpose of this section is to demonstrate a use-case scenario for WC in the context of WSN. As previously discussed, SNs are normally distributed on the AoI and grouped into clusters. To achieve this, the Unsupervised Machine Learning \(K\)-means algorithm is utilized. The algorithm divides the SNs into \(K\) mutually exclusive clusters, such that the nodes that belong to the same cluster are as close as possible to each other, and as far as possible to the other clusters' nodes. In this work, we used \(K\)-means algorithm to cluster the nodes into \(K\) clusters, where the centroid of each cluster specifies the location of the WC UGV. However, in order to ensure that the UGV location achieves the maximum energy charging for all the nodes within each cluster, the number of clusters \(K\) is chosen such as the nodes' distances to their cluster centroids are less than a threshold (\(\alpha\)), where its value is determined from the conducted experiments. For example, in our tests, the time needed to charge a capacitor differed according to the distance and the capacitor capacity. For instance, according to the results depicted in Figure 11, charging a 1F Super capacitor that can be used to charge the SN at a 6 m distance took around 15 h. If such a configuration is suitable for the WSN application under consideration, then \(\alpha\) will be set to 6 m, and \(K\) will be varied from 1 till \(K^{*}\) in a unit step, where \(K^{*}\) is the minimum number of clusters considered in order to ensure that all of the distances between all cluster nodes and the centroids is less than \(\alpha\). For example, in Figure 12, we demonstrate how 80 nodes randomly distributed in an AOI were grouped into three clusters \(K^{*}=3\) using \(\alpha=6\). Once the number of clusters and their centroids are determined, the UGVs equipped with the wireless charging stations that supports high charging batteries will be sent to the centroids locations of each cluster to mutually collect the sensed data by the sensor nodes and perform the wireless charging process. Furthermore, since the UGV nodes will have two roles (data gathering as a CH and wireless charging station), it is important to ensure that there will be no interference between the signal sent by the SNs to the CHs, and the charging radio signal sent from the CH to the SNs. To achieve this, the SNs as well as the CHs are designed to have two separate radios, one for data transmission and the other one for the wireless charging process. Each radio module operates on different channels, and thus, the interference is avoided.
# Position-Based Formation Control Scheme for Crowds Using Short Range Distance (SRD)
## 2 Related Work
### Dynamical Systems and PBD  
One of the traditional techniques for controlling crowds is the application of the dynamical system. Basically, the dynamical system controls the movement of crowds by first setting the physical properties of individuals and then applying the appropriate forces and integrating their accelerations and velocities [7]. However, finding the force to move a user to a desired position is recognized as a difficult problem in dynamical systems.  
[PERSON] et al. introduced the PBD as an alternative to the dynamical system [7]. Unlike traditional dynamics models, the PBD does not use the relationship between force and acceleration to derive the velocity of an object, but instead focuses on an instantaneous position change, which has the advantage of being fast and controllable. It also avoids the overshooting problem that occurs during explicit time integration in conventional dynamical models, and it can completely solve the problem of penetrations between objects. These advantages further improve the stability of the simulation. In light of these advantages, [PERSON] et al. introduced a crowd simulation in real time using PBD [4]. The crowd simulation using PBD is redesigned to use the Power-Law technique [5] that expresses the movement of the crowd based on the existing explicit method as a constraint in the form of an implicit method. This redesigned constraint is defined as the long-range collision constraint. This algorithm works in an iterative fashion until agents arrive at their final destinations while avoiding collisions and penetrations. However, such crowd simulations using PBD only focus on crowd collision avoidance, not on controlling crowds that move in formation. In this paper, we extend the current method so that we can control the formation of a crowd while utilizing the advantages of PBD. Figure 2 compares the two methods.
## 5 Verification by Simulations
The performance of the MPC-based 3D cooperative guidance law and time cooperative guidance law are shown in Figures 17 and 18, respectively. It can be observed that, when the missile flies with the time cooperative guidance law, although the impact time was similar to the flight time of the leader missile, there was still a significant gap between the terminal attack attitude angle and the ideal impact angle. After iterative optimization using the MPC algorithm, both the trajectory inclination \(\theta_{\text{0L}}\) and the trajectory deflection angle \(\psi_{\text{0L}}\) achieved the impact angle constraints within the specified time. The MPC algorithm is based on the principle of optimization and was designed according to performance indicators to minimize the total energy in the guidance process under the condition of meeting terminal constraints. In every iteration of the MPC algorithm, the deviation at the terminal of the trajectory was evenly distributed to each step of the entire trajectory by a controlled adjustment. Therefore, the control command exhibited a smoother transition than the initial control command, and the amplitude was smaller, which was easier to implement in engineering. Figure 8:0 Remaining time error of MPC. Figure 8:1 Comparison of the trajectory inclination curves of each follower missile between the proposed guidance scheme and the time cooperative guidance scheme: (**a**) Time cooperative guidance law; (**b**) MPC-based cooperative guidance law. Figure 8:2 Comparison of the trajectory deflection curves of each follower missile between the proposed guidance scheme and the time cooperative guidance scheme: (**a**) Time cooperative guidance law; (**b**) MPC-based cooperative guidance law. From Figures 19 and 20, it can be observed that the time cooperative guidance strategy required greater control capabilities, especially at the terminal. In order to achieve the consistency requirements of the time cooperative guidance, a greater overload is required, and the required overload exceeded that which the missile can provide. Therefore, this method has very high requirements for the maneuverability of the missiles. In contrast with the MPC-based cooperative strategy, the control amount can be controlled within a certain range through the strategy of rolling optimization, and the requirements for the maneuverability of the missile are not high. Furthermore, it can not only achieve the time cooperation, but also achieve the requirements of impact angle at the terminal. Thus, it proved to be a better cooperative guidance strategy. The results demonstrated that the cooperative strategy of tracking the distance between the missile and the target based on the dynamic inverse design was very effective. The MPC algorithm added the impact angle constraints based on the impact time constraints, thereby achieving good effects and a better guidance performance, along with an easy implementation of the instructions.