esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Brassinin have been study as anti-cancer drug in cancer cells [18,20]. The most recent paper found that Brassinin may be a novel anti-colon cancer target therapy through STAT3-JAK2 pathway. However, these studies did not discuss its fundamental mechanisms, especially p53 in cancer cells. As far as we know, this is the first study to discover the Brassinin induces apoptosis by activating p53 in colon cancer cells.  
It is known that p53 acts as a tumor suppressor gene in various cancer cells.  
Once p53 is activated, it causes apoptosis, ferroptosis, senescence, and cell cycle arrest; and inhibits migration, and metastasis in cancer cells. Many of drugs have been identified about p53 responsive regulation of cancer cell growth and apoptosis [1,4,31]. Our cell-based studies showed that Brassinin activating of p53 expression dose- and time-dependent manner. Additionally, Brassinin induced apoptosis dose- and time-dependent manner as well. Consistently, Brassinin increased the sub-G\({}_{1}\) population in HCT116P\({}^{53+/+}\) and HCT116P\({}^{53-/-}\) cells.  
To check whether Brassinin induced apoptosis by activating p53, we treated Brssinin on p53 wild-type HCT116 and p53 null-type HCT116 cells. Furthermore, we treated Brassinin after treated with p53 siRNA for knockdown of p53 in p53 wild-type HCT116 cells. Interestingly, Brassinin induced apoptosis in p53 wild-type HCT116 cells more than p53 null-type HCT116 cells. Additionally, Brassinin needs L5 to cause apoptosis in cancer cells. According to previous studies, ribosomal RNA processing is critically involved in p53 activation. It is known that L5 or L11 plays a role in controlling the activity of p53 [24,25,32]. Here, our data suggested that Brassinin induced apoptosis through RPL5 in HCT116 cells.  
CNOT2, a subunit of the CCR4-NOT complex, is known to be involved in apoptosis, metastasis, angiogenesis, and autophagy in various type of cancer cells as an oncogene [6,33,34]. We found that Brassinin reduced the expression of CNOT2 dose- and time-dependent manner in HCT116 cells.  
Additionally, Brassinin enhanced the anti-cancer effect in HCT116 cells with a doxorubicin which is used to treat colon cancer drug. Brassinin induced p53 and cleaved-PARP in p53 wild-type HCT116 cells, demonstrating the combination effect of Brassinin and doxorubicin by western blotting. The results of this experiment show the possibility that Brassinin can be administered in combination with existing treatments as a new drug for colon cancer cells.  
Figure 6: Combination effect of Brassinin and doxorubicin. (**A**) HCT116P\({}^{53+/+}\) cells were treated with or without doxorubicin for 24 h. Western blotting was conducted with antibodies cleaved-PARP, p53 and \(\beta\)-actin. (**B**) A mechanistic scheme for Brassinin-induced p53 dependent apoptosis via L5.
## 2 Geological Setting  
The Yangbajing-Yangyi Basin is located in Dangxiong County, in the south-central Tibetan Plateau, north of the Yarlung Zangbo and Mantle Suture Zones, and lies within the central graben of the Yadong-Gulu Rift [40] (Figure 1a). The basin ranges in elevation from 4200 to 5800 m (Figure 1b), with topography sloping from the northwestern highlands to the southeastern lowlands. The Nyainotanglha Mountains lie to the west, and the Tangshan Range lies to the southeast [6; 41]. The basin is primarily composed of granites, metamorphic complexes, volcanic clastic rocks, and Quaternary sediments, along with localized Cenozoic igneous rocks (Figure 1c).  
The periphery of the basin is characterized by NE, near N-S, and NW trending fault systems, with the Nyainotanglha South Foreland Fault and the Tangshan North Foreland Fault acting as the primary regional structures governing basin evolution [6; 41]. These faults function as major conduits for geothermal fluid migration and magmatic activity, closely associated with deep regional thermal processes. Geophysical surveys have revealed partially molten magma chambers at depths of 5-15 km beneath the basin, which serve as the primary heat sources in the study area [42; 43].  
The Yangbajing geothermal field contains three heat reservoirs with temperatures ranging from 150 \({}^{\circ}\)C to 320 \({}^{\circ}\)C, extending to depths of up to 8 km. In contrast, the Yangyi geothermal field is characterized by shallower reservoirs, with temperatures reaching up to 100 \({}^{\circ}\)C [6; 44]. Surface thermal manifestations, including hot springs and boiling springs, are primarily distributed along fault zones. Springs in the Yangbajing and Yangyi areas reach a maximum temperature of 93 \({}^{\circ}\)C, clearly indicating the presence of deep thermal activity.  
The study area demonstrates significant potential for geothermal development. The Yangbajing Geothermal Power Station (established in 1977) and the Yangyi Geothermal Power Station (completed in 2018) are China's earliest and only commercially operational geothermal power plants. Together, they have a combined installed capacity of 25.15 MW and a cumulative power generation exceeding 500 million kWh, further underscoring the substantial geothermal potential of the study area.
# Antitumor Effects of _Cannabis sativa_ Bioactive Compounds on Colorectal Carcinogenesis
## 2 Bioactive Compounds of _Cannabis sativa_
### Terpenes
Studies have indicated that, in addition to cannabinoids, terpenes possess multiple biological potentialities such as antifungal, antiviral, anticancer, anti-inflammatory, antihyperglycemic, antiparasitic, antioxidant, and antimicrobial effects (Table 2). \begin{table}
\begin{tabular}{l l l} \hline \hline
**Terpenes/Terpenoids** & **Pharmacological Effects** & **Refs.** \\ \hline  & Anti-pain, anti-inflammatory, hepatoprotective, & \\ \(\beta\)-myrcene & analgesic, antioxidant, neuroprotective, & [32; 33; 34; 43; 44] \\  & gastoprotective, antiociceptive, & [32; 33; 34; 44] \\  & anticancer, antidiabetic & \\ \(\beta\)/\(\alpha\)-caryophyllene & Antibacterial, antifungal, antioxidant, & \\  & anti-inflammatory, anticancer, anxiolytic & [40; 43] \\ \(\alpha\)/\(\beta\)-pinene & Antimetastatic, anti-inflammatory, antibacterial, & [33; 34; 40] \\  & antidepressant, anticancer & \\ D-limonene & Anolytic, immunostimulatory, anticancer & [32; 34] \\  &linalool & Sedative, antiepileptic & [34] \\  & Antincociceptive, antifungal, & [43] \\  & anti-inflammatory, antidiarrhoeal & [43] \\  & Bisabolol & Anticancer & [33] \\  & \(\beta\)-ocimene & Anticonvulsant, anticancer, antifungal & [26] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pharmacological effects of the major terpen/terpenoids identified in _C. sativa_. Figure 2: Biosynthetic route of terpenes. CBGA, cannabinoid acid; DMAPP, dimethylallyl pyrophosphate; FPP, famesyl pyrophosphate; G3P, glyceraldehyde 3-phosphate; GCPP, geranylgeranyl diphosphate; GPP, geranyl pyrophosphate; IPP, isopentenyl pyrophosphate; MEP, methylerythnitol phosphate; MEV, mevalonic acid; TPC, terpene synthase.
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 2 System Model
### Targeted Problems
#### 2.2.2 The Search of Knowledge Graph for Unknown Jamming Patterns  
There are no direct corresponding anti-jamming strategies for the unknown jamming patterns, and the factors determining the corresponding anti-jamming strategy are different with the difference of the unknown jamming patterns. How to use the anti-jamming knowledge graph to make anti-jamming decision for the unknown jamming patterns is an important problem. The processing method of unknown patterns in this paper is as follows: firstly, process the jamming parameters, and extract the time-frequency domain features of the jamming patterns; then, search the time-frequency domain features and match the corresponding anti-jamming strategies. The relationship from parameters to features can also be regarded as a triplet relationship, in which nodes are parameters and features, respectively, and edges are calculation rules. Since the Neo4j graph database cannot store calculation rules, the step from parameters to features needs to be realized through time-frequency domain feature extraction. The time-frequency domain feature extraction method will be studied in detail in Section 3.
# Angular Circle Array Multiple Input Multiple Output Underwater Optical Wireless Communications +
## 4 Analysis and Discussion of Data Results
### Outage Probability  
We assume that a link outage occurs when the channel capacity per unit bandwidth is less than one. Corresponding to the graph where the path loss is less than the green line, the communication system is considered to operate normally. From this, the system outage probability can be calculated. In Figure 20a, the outage probabilities of the planar circle array system and the angular circle array system are 75% and 89%, respectively. In Figure 20b, the outage probabilities are 82% and 100%, respectively. In Figure 20c, the outage probabilities are 69% and 76%, respectively. The data show that the proposed system is superior to the planar circle array communication system in different mobile scenarios. Moreover, when only the angle of the transceiver changes, the performance optimization of this system is more evident.  
Figure 19: Channel capacity of different communication system settings under different SNR levels when the transmitter is deflected: (**a**) 5 dB and (**b**) 40 dB.  
Figure 18: Channel capacity of different communication system settings under different SNR levels at the LOS link: (**a**) 5 dB and (**b**) 40 dB.
## 4 PMT&T Board Measurements
### CITIROC's Preamplifiers and Shapers Calibration  
As mentioned before, the CITIROC's preamplifiers and Shapers can be configured with different values for the amplification and the shaping time.  
To select the optimal combination of these parameters, a series of measurements have been conducted using square pulses with different amplitudes and durations.  
Three classes of signals were used, considering the FWHM of the typical signal produced by the PMTs for different types of scintillators: 5 ns wide pulses for T1 scintillators, 10 ns for the calorimeter and 50 ns for the LYSO crystals.  
Figure 7 shows, as an example, the mean of ADC counts  
At first, an amplification of 75 for high-gain preamplifiers and 7.5 for low gain was used, but after the detector integration, crosstalk between channels was observed for particles that produced high-amplitude signals in a large number of scintillators.  
This effect was less evident for lower amplifications and led to a change in the selected parameters for the T1 and calorimeter preamplifiers.  
The final values for preamplifiers gain are shown in Table 3.
## 3 The Proposed SensorAnt Protocol
### SensorAnt Description
#### 3.6.2. Pheromone Tables  
In our protocol, each sensor node \(x\) maintains a pheromone table (routing table) \(R_{x}\). Each entry in the table \(R\overset{z}{{}_{xy}}\) includes the information of the path from sensor \(x\) to the base station (sink) \(z\) through its neighbor \(y\). The value of both pheromones are referred to as real and non-real, whereby the real pheromone indicates the path quality from sensor \(x\) to the base station (sink) \(z\) through its neighbor \(y\), gathered by Ant-Forward (Ant-F) and updated by Ant-Backward (Ant-B). The non-real pheromone which indicates the alternative path quality from sensor \(x\) to the base station (sink) \(z\) through its neighbor, will be obtained during path recovery stage by using the reinforcement learning mechanism.  
Figure 4: Messages scheme (**a**) request Ant-Forward; (**b**) reply Ant-Backward.  
Figure 3: The framework of the proposed protocol.  
Therefore, this table keeps the data routing up-to-date using the pheromone trail collected by Ant-F and reinforced by Ant-B, which tracks back the same route constructed by Ant-F.
## 3 Results and Analysis
### Statistical Analysis of Results  
The results obtained in the measurement and in the simulations with the ODEON and CATT-Acoustic software were analyzed by means of a statistical study of the correlations between the different elements and a global reliability analysis.  
This reliability analysis is based on the (Pearson) correlation, the Cronbach's alpha coefficient, and the intraclass correlation index between acoustic parameters obtained for measurements and for simulations.  
The Cronbach's alpha (\(\alpha\)) is a coefficient used to measure the reliability of a measurement scale, but as there is no p-value associated to allow hypothesis rejection of scale reliability, we cannot use it as a rule of thumb. However, the closer it is to its maximum value, which is 1, the greater the reliability of the scale [42]. In addition, the intraclass correlation index (ICC) is a descriptive statistic that can be used when quantitative measurements are made on units that are organized into groups. It describes how strongly units in the same group resemble each other. Sometimes, it is perceived as a type of correlation, but unlike most of the other correlation measures, it operates on data structured as groups, rather than data structured as paired observations [42].  
Below is the comparative analysis performed for time parameters T30 and EDT, and energy parameters C80 and C50.  
Figure 8: (**a**) Just noticeable difference of C80 (CATT-Acoustic simulation). (**b**) Just noticeable difference of C50 (CATT-Acoustic simulation).  
Figure 9: (**a**) Comparison of C80 in measurement and simulations with ODEON and CATT-Acoustic in source A. (**b**) Comparison of C50 in measurement and simulations with ODEON and CATT-Acoustic in source A.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 4 Research Challenges  
UAVs-based networks face numerous challenges. It is difficult to establish reliable, low-latency UAV control communication in a cellular network. The availability of infrastructure is a major goal for improving terrestrial communication services. Terrains with limited terrestrial BS coverage may be unable to provide connectivity services to  
Figure 8: HO for UAVs as normal users in a future ultra–dense heterogenous network.  
cellular-connected UAVs, necessitating a solution for effective deployment of the technology. Numerous studies and research projects have demonstrated that low-altitude UAVs can be used in conjunction with cellular networks. With low-latency, high-throughput cellular network spectrums, UAV integration will be possible. According to 3 GPP, aerial UAVs have lower SINR than terrestrial UEs. Excessive HO failures and HOs are among the issues. These concerns must be addressed because the consequences could seriously impact network stability in the future. Consider environmental implications, routing protocols, channel effects, antenna designs, and HO management to maximize UAV benefits. These issues must be addressed to properly connect UAVs. A UAV-based cellular network faces challenges in terms of 3D coverage area and ground channel [36; 64; 65]. These challenges are discussed in this section and summarized in Table 2 so that the reader can get a complete picture of them.
## 2 Background
### Steganography  
Steganography allows a sender to send a seemingly innocuous cover message to a receiver that conceals some critical message. In this way, the critical message can be delivered to the receiver without the adversary being aware by staying in the middle. Compared to cryptography, which protects the content of a critical message (e.g., encryption transform the message to some random format based on secret keys so that the plaintext of the message can be concealed), steganography is more advantageous as it not only hides the content of the message but also essentially hides the fact that the message exists. Our design will leverage the steganography technique to hide the communication between the user-level manager running in the TrustZone secure world and the malware detector running in the FTL.
## 3 Data and Methods  
### SAR Data  
Two independent sets of stripmap SAR images, acquired from different frequency bands, were selected to carry out a comprehensive analysis of the deformation phenomena occurring over the area of Lingang New City (Shanghai). The first dataset is composed of 35 SAR images, collected by the C-band ASAR/ENVISAT sensor [49], from 26 February 2007 to 13 September 2010 (ascending passes, VV polarization, Track 497, Frame 616, swath IS2, with a sensor side-looking and a satellite heading angle of about 23\({}^{\circ}\) and 12\({}^{\circ}\), respectively). The second dataset consists of 61 SAR scenes acquired by the SAR sensors of the X-band COSMO-SkyMed (CSK) satellites constellation [50], from 7 December 2013 to 18 March 2016 (descending passes, HH polarization, with a side-looking angle of about 29\({}^{\circ}\) and a satellite heading angle of about 8\({}^{\circ}\)). As is evident, the two available SAR datasets are not time-overlapping, and there is a long temporal gap (of about three years) between the two relevant groups of images. The distribution of the available SAR images in the temporal/perpendicular baseline plane is also pictorially depicted in Figure 2; the full list of the used SAR data is included in the Supplemental Material.  
Figure 2: Distribution of the available ASAR/ENVISAT **(a)**; and COSMO-SkyMed CSK **(b)** Synthetic Aperture Radar SAR data in the temporal/perpendicular baseline domain; SAR data are represented by **red (a)**; and **black (b)** diamonds, whereas connecting arcs are representative of the selected, small baseline interferometric data pairs.
# Compact Four-Port Circularly Polarized MIMO X-Band DRA
## 4 Four-Port CP MIMO DRA
Figure 15 shows the simulated RHCP and left-hand (LH) CP field pattern at the frequency where the dip of AR was obtained. The antenna provided the dominant LHCP by around 18 dB over the cross-polarized component in the direction of radiation, which remained broadside. Gain (simulated and measured) and efficiency (simulated) results are shown in Figure 16. The antenna provided a gain of more than 4.5 dBic over the passband with each port. The radiation efficiency of the antenna also remained at more than 80% in the operating passband. The simulated and measured radiation pattern results of the suggested antenna are illustrated in Figure 17. Far-field measurement was conducted using the Starlab system. The envelope of the simulated far-field response matches well with the measured one. Figure 8:0 (**a**) S\({}_{11}\) parameter, (**b**) AR response, and (**c**–**e**) isolation between ports for 4-port MIMO configuration. Figure 8:1 Measured and simulated (**a**) reflection and (**b**) isolation response for 4-port MIMO configuration. Figure 8:2 Simulated 3D realized gain of four-port DRA at **(a)** 9 GHz and **(b)** 11 GHz. Figure 8:3 AR response of four-port CP MIMO DRA (simulated and measured). Figure 8:4 Simulated field pattern of the four ports DRA at 9.77 GH in **(a)** \(\varphi=0^{\circ}\) and **(b)** \(\varphi=90^{\circ}\) plane (with the applied input at Port 1 and keeping the other terminated with the matched load).
## 3 Experimental Setup
### Implementation Details  
The proposed model and the comparison methods were all implemented and run on an NVIDIA GeForce RTX 4090 GPU environment, using Python 3.9 and PyTorch 1.8. The proposed model utilizes ResNet-18, pre-trained on ImageNet, as its backbone network. The training framework was set with an input size of 512 \(\times\) 512 pixels, a batch size of 8, and an initial learning rate of \(1\times 10^{-4}\), with AdamW used for parameter optimization. The training process was conducted for 100 epochs, and general data augmentation techniques were applied to the input dual-temporal images. Data augmentation included random rotations, vertical flips, and horizontal flips.
# Path Optimization Using Metaheuristic Techniques for a Surveillance Robot
## 4 Optimization Methodology  
The objective of this study is to find the optimal trajectory in a defined space so that an automatic surveillance robot can work as efficiently as possible. For this purpose, a series of waypoints must be defined to establish a surveillance route. The robot works in the following way: it first recognizes a fixed set of points as coordinates and passes through them sequentially [32]. The robot follows the movement given by the state machine, shown in Figure 6, that has been coded. The right side of the figure shows the states reached by the machine along a trajectory.  
Each point consists of three reference parameters: coordinates \((x,\ y)\) and angle \(\theta\), that is, \((x_{r},y_{r},\theta_{r})\), where the sub index \(r\) indicates that the values are reference points for the robot. In order not to make the program execution time too long, the angles have been fixed. These do not affect the trajectory since the sensing area is circular, so they can only influence the computational time. With the angles fixed, the variables that control the algorithms are reduced to the position coordinates \((x_{r},y_{r})\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Hardware** \\ \hline Linear speed & \(V_{ref}\) & 0.5 \(\frac{\text{rad}}{\text{m}}\) & Robot \\ Angular speed & \(W_{ref}\) & 0.5 \(\frac{\text{rad}}{\text{m}}\) & Robot \\ Linear acceleration & \(\alpha_{v}\) & 0.5 \(\frac{\text{rad}}{\text{m}}\) & Robot \\ Angular acceleration & \(\alpha_{w}\) & 0.5 \(\frac{\text{rad}}{\text{m}^{2}}\) & Robot \\ Sensor distance range & \(r_{max}\) & 4 m & Sensor \\ Sensor angular resolution & \(\alpha_{r}\) & 1\({}^{\circ}\) & Sensor \\ Minimum sensing angle & \(\alpha_{min}\) & 360\({}^{\circ}\) & Sensor \\ Maximum sensing angle & \(\alpha_{max}\) & 360\({}^{\circ}\) & Sensor \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the main parameters of the robot and sensor.  
Figure 6: State diagram.
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 6 Experimentation and Performance Analysis
### Computational Time Performance Analysis
We also measure the computation time required for shared-key generation on the same device (computer) at both the UE and CN sides. Our results demonstrate that Kyber-512 exhibits less computational time at the UE side compared to the CN side, attributable to differing functions performed on the UE and CN, as shown in Figure 3d. Regarding the share key generation for encapsulation/encryption at the UE side, using Kyber-512 utilizes 13.47 us, which utilizes 59.13% and 85.12% less time than X25519 (32.93 us) and secp256r1 (90.64 us), respectively. The shared-key generation time at the CN for decapsulation/decryption is illustrated in Figure 3d. Kyber-512 consumes 19.16 us, that is, 41.97% and 79.52% less time than X25519 (32.0 us) and secp256r1 (93.42 us), respectively. **Summary:** The above results compare the computational time measurements of public-key generation and shared-key generation for encapsulation/decapsulation operations in the SUCI calculation process as described in Section 5. Regarding the post-quantum KEM, Kyber-512 demonstrates shorter processing times for key generation compared to pre-quantum ciphers X25519 and secp256r1, both at UE and CN sides, across different platforms, including the computer, Mini-PCs, and Raspberry Pi, which can improve the overall performance and responsiveness of mobile devices, crucial for tasks requiring secure communication and data transmission.