esa-sceva/satcom-chunk-collection huggingface dataset:
# Video-Based Recognition of Human Activity Using Novel Feature Extraction Techniques
## 2 System Overview of Proposed Solution
### Proposed Features Using Motion Vector Phases  
As illustrated in Figure 2, during HEVC coding, the motion vectors of each video frame are stored. These motion vectors are then used for generating feature vectors for the video sequences as follows. The motion vectors are listed in \(V_{x}\), \(V_{y}\) pairs for each video frame in a video sequence. The phases of the motion vectors are computed as \(\tan^{-1}(V_{y}/V_{x})\);  
Figure 2: Flowchart of overall proposed feature extraction.  
thus, each \(V_{x}\), \(V_{y}\) pair is converted into a scalar representing the phase of the motion vector. The resulting lists of phases pertaining to one video frame are then projected using PCA. This results in a list of projected phases with a length equal to the number of video frames in a video minus one (as motion estimation starts from the second video frame). The reason phases of motion vectors are computed is that each human activity is mainly characterized by the phases of the motion as opposed to its the magnitude, as the later depends more on the speed at which an activity is carried out. This arrangement is further illustrated in Figure 3. Once the motion vectors are projected, the generated feature vectors are stored in a feature matrix which can then be used for generating the classification model.
# Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks
## 4 Results
### Model Comparison  
This section uses heterogeneous federated learning, homogeneous federated learning and the centralized LSTM model for comparative experiments.  
* Heterogeneous federated learning: In our federated learning framework, we use Flower [21]. The server is equipped with an Intel i5-13500 CPU and an RTX 4060 GPU to perform model aggregation, while Raspberry Pi 4 Model B (Raspberry Pi Trading Ltd., Cambridge, UK) are utilized as clients for model training.
* Homogeneous federated learning: We utilize a computer equipped with an Intel i5-13500 CPU and an RTX 4060 GPU for model training and aggregation. For the  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Name** & **Explanation** \\ \hline Timestamp & Timestamp of sample \\ \hline Longitude and Latitude & GPS coordinates of mobile device \\ \hline Velocity & Velocity in kph of mobile device \\ \hline Operator name & Cellular operator name (anonymized) \\ \hline CellId & Serving cell for mobile device \\ \hline NetworkMode & Mobile communication standard (2G/3G/4G/5G) \\ \hline DL\_bitrate & Download rate measured at the device (application layer) (kbps) \\ \hline UL\_bitrate & Uplink rate measured at the device (application layer) (kbps) \\ \hline State & State of the download process. It has two values, either I (idle, not downloading) or D (downloading) \\ \hline SNR & Value for signal-to-noise ratio (dB) \\ \hline \multirow{3}{*}{RSRP} & Value for RSRP. Represents an average power over cell-specific reference symbols carried inside distinct RE. RSRP is used for measuring cell signal strength/coverage, and therefore, cell selection (dBm) \\ \hline NRxRSRQ \& NRxRSRP & RSRQ and RSRP values for the neighboring cell \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of dataset features.  
Figure 6: Clients’ Data Distribution, the circles in this figure are the outliers in each clients.  
simulation of federated learning frameworks, we employ the Virtual Client Engine provided by Flower [21].
* Centralized LSTM model [14]: We utilize a computer equipped with an Intel i5-13500 CPU and an RTX 4060 GPU, along with PyTorch, for model training. Long Short-Term Memory (LSTM) networks are primarily employed to address issues related to memory retention and gradient vanishing in long-term sequence training.
## 3 Results and Discussion
### Improve  
The results indicate that the most significant deviations occurred in those products where the analyzed component was supplied in a solid form and in large quantities of liquid, i.e., P1 and P3, respectively (P2 showed negative deviations, Tables 5 and 6). Therefore, the improvement activities were focused on products P1 and P3. The main objective was to reduce the variability in the composition of P1 and P3 by 50%. This reduction can be achieved by minimizing the variability in product density, which is directly correlated with the loss of raw materials. Table 7 displays the target values required to achieve a 50% reduction in product variability.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{P_{i}}\)} & \(\mathbf{P_{T}}\) & \(\overline{\mathbf{D_{iT}}-\mathbf{D_{IR}}}\) & \(\Delta\mathbf{P_{T}}\) & \(\overline{\mathbf{Y_{ijR}}}\) & \(\Delta\mathbf{Y_{ij}}\) & \(\Delta\mathbf{MP_{ij}}\) \\  & \(\mathbf{L}\) & \(\mathbf{kg/L}\) & \(\mathbf{kg}\) & \(\mathbf{kg}\) & \(\mathbf{kg}\) & \(\mathbf{kg}\) & \(\mathbf{kg}\) \\ \hline P1 & 193,440 & \(-\)0.022 & \(-\)4255.68 & 0.054 & \(-\)229.81 & \(-\)919.23 \\ \hline P2 & 72,160 & 0.003 & 216.48 & 0.598 & 129.46 & 199.16 \\  & & & & 0.030 & 6.49 & 25.98 \\ \hline P3 & 130,080 & \(-\)0.007 & \(-\)910.56 & 0.046 & \(-\)41.89 & \(-\)598.43 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Total product loss (\(\Delta\)PT) and raw material loss (\(\Delta\)MPij).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mathbf{P_{i}}\) & \(\overline{\mathbf{x}}\) & \(\Delta\overline{\mathrm{x}}\) (\%) & \(\mathbf{LI}\) & \(\Delta\)LI (\%) & \(\mathbf{LS}\) & \(\Delta\)LS (\%) & \(\sigma\) \\ \hline D1R & 1.222 & 1.83 & 1.208 & 0.67 & 1.234 & 2.83 & 0.007 \\ Y11R & 0.054 & \(-\)1.82 & 0.047 & \(-\)14.55 & 0.057 & 3.64 & 0.002 \\ D2R & 1.327 & \(-\)0.23 & 1.300 & \(-\)2.26 & 1.336 & 0.45 & 0.007 \\ Y21R & 0.598 & \(-\)0.33 & 0.582 & \(-\)3.00 & 0.610 & 1.67 & 0.005 \\ Y22 RConsequently, the density target values for the P\({}_{1}\) and P\({}_{3}\) were 1.211 and 1.154 kg/L, respectively. Manufacturing products with these densities will result in a 50% reduction in the composition variability of both products.
# MODIS Cloud Detection Evaluation Using CALIOP over Polluted Eastern China
## 3 Results and Discussion
### Cloud Detection Differences between MODIS and CALIOP as a Function of Cloud Properties
Previous studies also showed that the cloud detection from MODIS and CALIOP agreed more than 87% of the time, where most of the discrepancies were largely associated with the optically thin clouds (COT \(<\) 0.4) that were undetected by MODIS but readily observed by CALIOP [17; 33]. That can be at least partly expected due to the well-known larger CALIOP sensitivity to thin cloud layers than MODIS [11; 15; 16]. The distribution of r_MODalone and r_CALalone along with SZA and CTH over dark, bright, and water surfaces are shown in Figure 3. The 86% to 95% of r_MODalone was less than 0.01 at altitudes \(>\) 4 km, while it increased to 0.1-0.3 at altitudes \(<\) 1 km and SZA \(<\) 40 deg, and rapidly increased to 0.4-0.9 at low altitudes (\(<\)1 km) and high SZA (\(>\)50 deg). That suggested that MODIS observed clouds at low altitude and high SZA that were not detected by CALIOP, which is consistent with Figure 2a. The r_CALalone was larger than 0.3 for most SZA and CTH intervals, suggesting that when MODIS did not observe clouds, CALIOP observed clouds at all CTH values, as Figure 2c shows. The r_CALalone was almost larger than 0.5 at CTH \(>\) 15 km over dark and water surfaces, indicating that MODIS missed more clouds (\(>\)50%) for high-level clouds. Figure 3: The r_MODalone as a function of solar zenith angle (SZA) and MODIS cloud top height (CTH) over (**a**) dark, (**b**) bright, and (**c**) water surfaces, and the r_CALalone as a function of SZA and CALIOP CTH over (**d**) dark, (**e**) bright, and (**f**) water surfaces.
## 3 Results and Discussion
### Energy Dissipation Capacity  
The energy dissipation capacity of the structure plays an essential role in determining its seismic performance, especially in the elastoplastic stage. In general, a larger hysteresis loop contributes to better energy dissipation capacity. The average between the hysteresis loops at each drift level was used to calculate the cumulative energy dissipation capacity, as shown in Figure 12.  
Figure 11: Stiffness degradation curves: (**a**) BMRC1 and NEWRC1–5; (**b**) BMRC1 and NEWRC1; (**c**) NEWRC1, 2, and 3; (**d**) NEWRC1 and 4; (**e**) NEWRC1 and 5; (**f**) NEWRCF.  
Figure 12 also shows the comparison between the cumulative energy dissipation for all the specimens. The energy dissipation values increasingly vary with the increasing drift ratio. Here, it can be noted that all the NEWRC specimens have a distinctive pattern compared to BMRC1. Additionally, in Figure 12, the energy dissipation between BMRC1 and NEWRC1 is compared; under the same drift ratio, BMRC1 has a higher capacity than NEWRC1. The results suggest that the material properties had a significant influence on the energy dissipation capacity of the columns.  
Further, in Figure 12, a comparison between NEWRC1, NEWRC2, and NEWRC3 is made to investigate the influence of transverse reinforcement spacing on the energy dissipation capacity. It can be observed that until the 4% drift ratio, the energy dissipated  
Figure 12: Cumulative energy dissipation: (**a**) Energy in the hysteretic loop; (**b**) BMRC1 and NEWRC1-5; (**c**) BMRC1 and NEWRC1; (**d**) NEWRC1, 2, and 3; (**e**) NEWRC1 and 4; (**f**) NEWRC1 and 5; (**g**) NEWRC7.  
by all three specimens is similar. However, after 4%, the energy dissipated by NEWRC3 is slightly higher, followed by NEWRC2 and NEWRC1. It is evident that the transverse reinforcement spacing only had a minor influence on its energy dissipation capacity under the same drift level.  
The energy dissipation curves for NEWRC1 and NEWRC4 are also shown in Figure 12. It can be observed that under the same drift ratio, the energy dissipated by NEWRC4 is slightly higher than that of NEWRC1. Hence, it can be concluded that the increase in the axial load ratio can increase the energy dissipation of the specimen. The replacement of HSR transverse reinforcement strength with NSR had no apparent influence on the energy dissipation capacity of the columns, as the energy dissipated by NEWRC1 and NEWRC5 is approximately the same, as shown in Figure 12.  
Figure 12 further shows the energy dissipation curve for NEWRCF. The frame's energy dissipation capacity increased linearly with the increase in drift cycles, which indicates that the NEWRCF has a strong energy dissipation capacity under the cyclic loading.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 4 Q-Learning-Based Position-Aware Routing Protocols in FANETs
### QL-Based Routing Protocol for FANET (Q-Fanet)  
[PERSON] et al. [24] proposed an improved QL-based routing protocol for FANETs (Q-FANET), which consists of two sub-modules: QMR [22] and Q-noise+ [53]. In typical QL, the Q-values are updated based on the reward of the most recent episodes. In contrast, considering the mobility in FANETs and dynamic channel conditions, Q-FANET updates the Q-value more precisely by considering the weighted reward for the finite number of last episodes and the SINR level of the selected link. While updating the Q-value, a higher weight is assigned to the recent episodes to obtain a more accurate Q-value. Q-FANET uses the QMR module to select the relay node according to the maximum PTS and to solve the routing hole problem (local minimum). When the routing hole problem occurs, Q-FANET uses the QMR penalty mechanism and allocates the minimum reward to that relay node. Otherwise, a QL module that performs random action (random relay selection) or exploration according to the maximum Q-value is used by adopting the \(\in\)-greedy policy.  
_Advantages:_ Q-FANET provides low delay and jitter for a better quality of service in highly dynamic FANETs. It also allocates minimum reward for bad action to overcome the local minimum, routing loop, and node failure-state in FANETs.  
_Limitations:_ Q-FANET uses the \(\in\)-greedy policy with random action (relay UAV selection) to balance between exploration and exploitation. However, random actions without proper guidance may provide less reward and produce higher retransmission in FANETs. Additionally, storing the Q-value for the last finite number of episodes requires extra memory consumption for each UAV.
## 3 System Model
### Energy Model  
Our proposed algorithm follows first order radio model [32; 33; 34; 35] to handle energy dissipation as shown in the Figure 3. The distance between receiving and transmitting \"\(l\)\" bits of data is presented in the Equations (1)-(3) respectively.  
\[E_{i,CH}=lE_{elec}^{Ta}+l\varepsilon_{fs}d^{2},d<d_{0}, \tag{1}\]  
\[E_{CH,sink}=lE_{elec}^{Ta}+l\varepsilon_{mp}d^{4},d\geq d_{0}, \tag{2}\]  
\[E_{Ra}(l)=lE_{elec}^{Ra}. \tag{3}\]  
Figure 3: Dissipation model of radio energy.  
\(E_{elc}^{Ra}\) and \(E_{elc}^{Ta}\) are the consumed energy via receiver and transmitter circuits per bit. Similarly, \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) are multipath and free space radio modes amplification factor. Also, threshold distance \(d_{0}\) can be obtained as:  
\[d_{0}=(\frac{\varepsilon_{fs}}{\varepsilon_{mp}})_{2}^{1} \tag{4}\]  
Next, for the data bits \"\(l\)\", the aggregate energy is \(E_{A}\) is calculated as  
\[E_{A}=NIE_{1}, \tag{5}\]  
where \"\(N\)\" is the nodes in the cluster, \"\(l\)\" represent bits number and \(E_{1}\) represent the aggregate energy of one bit.  
The total energy consumed by a node \(E_{c}\) is calculated as follows:  
\[E_{c}=E_{elc}^{Ra}+(E_{i,CH})+E_{m}+(E_{CH,sink})+E_{A}, \tag{6}\]  
where \((E_{i,CH})\) and \((E_{CH,sink})\) represent the required transmitting energy from \"\(l\)\" members of the cluster to CH and from CH to sink (Base-station). \(E_{A}\) is the aggregate energy of a data, and \(E_{m}\) is mobility energy.  
The distance between two nodes in the network is given by  
\[d_{i}=\sqrt{(x_{i+1}-x_{i})^{2}+(y_{i+1}-y_{i})^{2}}, \tag{7}\]  
where \(x_{i}\) and \(y_{i}\) are node \"\(i\)\" coordinates, while \(x_{i+1}\) and \(y_{i+1}\) are sink or neighbour node coordinates. The parameters considered for CH election are mobility speeds of nodes, the distance between nodes and the base station, and the nodes' remaining energy.
# An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring
## 8 Results and Discussion
### The Results Measured from the Brand Board  
The circuit connected on the bread board was tested using ECG signals with different heart rates in beats per minute (BPM) in the range of (30-300) BPM. The input ECG signals were generated from the commercial Patient Simulator and the measured signal was displayed on an Oscilloscope. As shown in Figure 6, the circuit was able to measure all the necessary Peaks i.e., (P, Q, R, S, T) waves at all heart rates.
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 2 Motivation and Background
### Background on 5G AKA and Cryptographic Primitives
#### 2.2.4 Key Encapsulation Mechanism (KEM)  
Because symmetric-key cryptography is more efficient than public-key cryptography, the general cryptographic practices use the public-key encryption (PKE) for the key encapsulation mechanism (KEM) to share and establish the symmetric key (which can then be used to encrypt the communications and messages). KEM/PKE solves the problem by securely transmitting the symmetric key while encrypting the symmetric key with public-key cryptography. Hence, the KEM enables two communicating parties to derive a shared key by leveraging public-key cryptographic encryption. This section fills the gap between the SUCI calculation and our implementation design.  
The KEM consists of the following three algorithms. The details of KEM inclusion in SUCI calculation are described in Section 5.2.  
**KEM.KeyGen:** A probabilistic key generation algorithm, _KEM.KeyGen_, that generates a public-private key pair.  
**KEM.Encapsulation:** A probabilistic encapsulation algorithm, _KEM.Encapsulate_, that takes the public key of the receiver as input. Then, it selects a shared key randomly, encrypts the shared key using the receiver public key, and outputs the ciphertext, which is also known as an encapsulated shared key.  
**KEM.Decapsulation:** A deterministic decapsulation algorithm that takes as input a ciphertext (encapsulated shared key) and private key of the receiver to generate the shared key or report a failure.
# An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection
## 3 Results and Discussion
### Experiment on the Effectiveness of Adapter
Furthermore, the yellow box in Figure 5 marks a cold front, whereas the corresponding region in Figure 5 contains both a cold front and a warm front. This occurs because the MRCNN network without the adapter fails to effectively resolve conflicts between meteorological features and frontal properties caused by special weather conditions or geographical factors, leading to the extraction of erroneous features. In contrast, the network with the adapter can accurately determine whether multi-meteorological features under such conditions represent frontal characteristics. Special weather conditions include scenarios such as differing radiation conditions, evaporation-condensation dynamics, vertical motion variations, or the movement of cold air masses and cold fronts from land to sea. These conditions often weaken the temperature gradient across the frontal zone, making thermal features less pronounced. The adapter's weighting layer enhances these weak frontal features by assigning higher weights. Special geographical locations, such as constant temperature zones near coastlines, can exhibit \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Network Category** & _ACC_ & \(R\) & _F_**1** & _mIoU_ \\ \hline CNN & 73.0\% & 65.0\% & 66.4\% & 47\% \\ Adapter + CNN & 76.7\% & 70.2\% & 69.7\% & 52.3\% \\ FCN & 75.1\% & 74.5\% & 74.9\% & 56\% \\ Adapter + FCN & 78.5\% & 79.7\% & 82.0\% & 60.1\% \\ UNet & 77.3\% & 83.3\% & 83.3\% & 63\% \\ Adapter + UNet & 80.9\% & 85.9\% & 85.8\% & 68\% \\ MRCNN & 79.2\% & 84.1\% & 81.6\% & 72\% \\ AD-MRCNN & 83.5\% & 86.8\% & 85.1\% & 77\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The scores of the adapter effectiveness experiment across multiple evaluation metrics. strong temperature differences due to varying underlying surface properties, often accompanied by wind disparities. Similarly, areas where plateaus meet plains may show temperature discrepancies due to differing station elevations. For such non-frontal regions with strong thermal signals, the adapter's weighting layer assigns low weights to their meteorological features, enabling correct identification as non-frontal zones. Moreover, the adapter's fusion layer integrates multi-meteorological features, facilitating the network's accurate learning of composite characteristics for different frontal types. This is evident in the yellow boxes of Figure 5a,b, where (a) only identifies a cold front, and (b) correctly distinguishes both a cold front and a warm front.
## 4 Conclusions  
In conclusion, the phytochemical investigation on the methanolic fraction of olibanum provided a novel prenylmaaliane-type diterpenoid (**1**), three new prenylmaadendrane-type diterpenoids (**2-4**), five new highly oxidized cembrane-type diterpenoids (**5-9**), and three known diterpenoids (**10-12**). Compound **1** possesses an unprecedented diterpenoid skeleton with a 6/6/3 tricyclic system and a prenyl moiety at C-15. Based on the TGF-\(\beta\) inhibition screening, compounds **1**, **5**, **9**, and **11** showed TGF-\(\beta\) inhibition activities. Furthermore, compounds **1** and **5** dose-dependently suppressed TGF-\(\beta\)-induced Smad3 phosphorylation and the upregulation of N-cad and FN in LX-2 cells, which indicated that highly oxidated prenylmaaliane-type and cembrane-type diterpenoids could have stronger TGF-\(\beta\) inhibitory activity. These findings give a new insight into the chemical constituents of the polar fraction of olibanum and their inhibitory activities on the TGF-\(\beta\)/Smad signaling pathway.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/plants13081074/s1](https://www.mdpi.com/article/10.3390/plants13081074/s1)]([https://www.mdpi.com/article/10.3390/plants13081074/s1](https://www.mdpi.com/article/10.3390/plants13081074/s1)); Figures S1-S90: HR-ESI-MS spectra, IR, ECD, UV-Vis, and 1D and 2D NMR spectra of compounds **1-9**, TGF-\(\beta\) inhibition screening and DFT computational optimized conformation of compound **1**; Tables S1 and S2: Detailed quantum chemical ECD computational data of compound **1**.  
Conceptualization, G.-Y.Z.; methodology, G.-Y.Z. and Z.-R.L.; formal analysis, D.C., G.-Y.Z. and Z.-R.L.; investigation, Z.-R.L., M.-Y.B. and H.-M.X.; resources, Z.-R.L., H.-M.X. and C.-Y.C.; data curation, H.-M.X., G.-Y.Z. and Z.-R.L.; writing--original draft preparation, Z.-R.L. and M.-Y.B.; writing--review and editing, G.-Y.Z., L.-P.B. and W.Z.; supervision, Z.-H.J. and G.-Y.Z.; funding acquisition, C.-Y.C., Z.-H.J. and G.-Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by grants from the Macau Science and Technology Development Fund (0019/2022/AGJ and 006/2023/SKL) and GDST-FDCT Projects (2023A0505020004).  
Data Availability Statement:Data on the compounds are available in the Supplementary Materials.  
The authors acknowledge [PERSON] (State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology) for his technical support in NMR and HRMS.