esa-sceva/satcom-chunk-collection huggingface dataset:
# Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning
## 3 Data Acquisition and Attack Vector Generation
### Exploratory Analysis
During in-flight drone operations, situations may arise wherein communication between a radio controller and the drone is lost under certain natural weather phenomena. We argue that in the presence of a DoS attack, a similar situation may arise due to the communication between the drone and controller being lost, not as a consequence of a natural phenomena but rather through a malicious event of sabotage perpetrated by the adversary. Hence, we identify the instances of the dataset wherein communication is disrupted, to mark a potential anomaly representative of a DoS attack. It may be noted that a DoS attack even for legacy systems is hard to distinguish from flash crowds comprising legitimate network traffic, though their impact on the victim device (drones in our case) is the same [26]. For routine operations, DJI drones communicate with the radio controller using some form of wireless communication technology (i.e., cousync or other versions also use Wi-Fi). If the communication channel is under attack the drone can lose communication with the radio controller. The logs record the communication health with the radio controller using the rc-info-frameloss variable. We ignore arbitrary frame loss for the study, however, we label a continuous frame loss as being part of adversarial disruptions, that can refer to a DoS attack. In addition, we also replicate the frame loss events obtained from flight logs and increase the number of such instances wherein a frame loss has occurred, to add a range of random anomalous (DoS) events to the log file (and to obtain a good balance between normal and anomalous data samples). Similarly, instances where the connection between the radio control (RC) and the drone is lost (indicated by frame_loss) are also marked as a DoS attack, as shown in Figure 7. Variant versions of DoS attacks (including stealthy attacks) were not considered for our experimental analysis. Figure 6: Sudden drop in motor speeds observed in the drone. As shown in Figure 8, the parameter rc_connect changes the state to zero, indicating a DoS attack. In Figure 7, blue lines indicate frame-loss recorded in the flight logs and orange lines indicate the label given to these instances. Figure 8: Drone and radio control disconnected. Figure 3:0 Continuous frame loss due to DoS attacks against the drone.
# Recent Progress in Synthesis and Photonic Applications of Two-Dimensional Bismuthene
## 2 Structure and Properties of Bismuthene  
Different from its bulk, bismuthene exhibits intriguing physical properties owing to the quantum confinement effect [30; 31; 32]. The crystalline structure and fundamental properties of 2D bismuth make it attractive for the development of photonic applications. In this section, the crystal structure, electronic structure, and nonlinear optical properties of bismuthene will be outlined.
# Spatial Multiplexing Holography for Multi-User Visible Light Communication
## 3 Simulation Results  
In this section, we simulate a scenario in which five users are positioned within the forward transmission space, spanning from 0.6 m to 0.8 m, with an equal spacing of 0.05 m between them. The spatial sampling parameters for this simulation are set as follows: the light wavelength is defined as 635 nm, aligning with the experimental setup described in the subsequent section. The sampling matrix is configured to 1024 \(\times\) 1024 points, with the sampling interval varying according to the transmission distance. At the source plate, this interval is set to 8 \(\upmu\)m, consistent with the parameters of the SLM utilized in our experiments.  
For the power allocation simulation, we assign target normalized power levels to the five users as 0.2, 0.4, 0.6, 0.8, and 1, respectively. In the context of power coverage simulation, the corresponding coverage areas for these users are designated as 0.2 mm, 0.4 mm, 0.6 mm, 0.8 mm, and 1 mm, respectively. Regarding the simulation for secure communication using OAM, we encode distinct OAM modes for each user, ranging sequentially from 1 to 5. The details of these parameters and their respective values are summarized in Table 1.
# 5G V2X Performance Comparison for Different Channel Coding Schemes and Propagation Models
###### Abstract  
Channel coding is a fundamental procedure in wireless telecommunication systems and has a strong impact on the data transmission quality. This effect becomes more important when the transmission must be characterised by low latency and low bit error rate, as in the case of vehicle-to-everything (V2X) services. Thus, V2X services must use powerful and efficient coding schemes. In this paper, we thoroughly examine the performance of the most important channel coding schemes in V2X services. More specifically, the impact of use of 4 th-Generation Long-Term Evolution (4G-LTE) turbo codes, 5 th-Generation New Radio (5G-NR) polar codes and low-density parity-check codes (LDPC) in V2X communication systems is researched. For this purpose, we employ stochastic propagation models that simulate the cases of line of sight (LOS), non-line of sight (NLOS) and line of sight with vehicle blockage (NLOSv) communication. Different communication scenarios are investigated in urban and highway environments using the 3 rd-Generation Partnership Project (3 GPP) parameters for the stochastic models. Based on these propagation models, we investigate the performance of the communication channels in terms of bit error rate (BER) and frame error rate (FER) performance for different levels of signal to noise ratio (SNR) for all the aforementioned coding schemes and three small V2X-compatible data frames. Our analysis shows that turbo-based coding schemes have superior BER and FER performance than 5G coding schemes for the vast majority of the considered simulation scenarios. This fact, combined with the low-complexity requirements of turbo schemes for small data frames, makes them more suitable for small-frame 5G V2X services.  
5G-NR; 4G-LTE; V2X; turbo codes; LDPC; polar codes; stochastic V2X model; implementation scenarios +
Footnote †: journal: _Sensors_**2023**, 23, 2436. [[https://doi.org/10.3390/s23052436](https://doi.org/10.3390/s23052436)]([https://doi.org/10.3390/s23052436](https://doi.org/10.3390/s23052436))
# RSTSRN: Recursive Swin Transformer Super-Resolution Network for Mars Images
## 2 Related Works
### Single-Image SR Algorithms
#### 2.2.1 Shallow Learning-Based SR Algorithms  
Classic shallow learning-based algorithms include example-based algorithms [11; 25] and sparse-representation-based algorithms [38].  
In the reconstruction stage, the example-based algorithm uses the low-frequency information block of the LR image to be reconstructed as an index to search for similar samples in the sample library, and then it uses the high-frequency information corresponding to the matching samples to perform SR reconstruction. This type of algorithm needs to establish a huge sample library, and each LR block needs to search in the sample library, resulting in very high computational complexity. Moreover, due to the mismatching problem of the searched sample blocks, artifacts will be generated at the edges of the reconstructed image blocks, which will seriously affect the reconstruction quality. In order to eliminate artifacts, the reconstruction is performed by overlapping blocks. Averaging high-frequency information of overlapping regions leads to over-smoothed edges in the reconstructed image.  
The sparse-representation-based algorithms perform sparse representation on the sample library composed of LR and HR sample image blocks. The sparse coefficients obtained by the corresponding LR block and HR block through their respective dictionaries are relatively similar, thus establishing the connection between LR and HR. This type of algorithm obtains better reconstruction quality, but the sparse coding and reconstruction process require multiple iterations, resulting in high algorithm complexity.  
Shallow learning-based algorithms are mainly divided into three stages: feature extraction, learning, and reconstruction. Each stage is independently designed and optimized, and the feature extraction and expressive capabilities of the learning model are limited.
## 5 Comparison and Discussion
### BOSMs Requirements  
Currently, the main blockchains used in social applications are both social blockchain (Steem and Hive), and general blockchain, such as Ethereum and EOSIO. It is clear that in that specific scenario, scalability is very important, as are the transaction fees. Nowadays, Ethereum is the most used, but this is probably related to the fact that Ethereum, as well as Bitcoin, is the one of the most well-known platforms, and it provided the smart contracts to be able to provide DApps. As described before, in a real scenario, such as Facebook, we could have a huge amount of social activity. This requires two important features in a BOSMs: scalability and no transaction fees. In particular, without scalability, a structure might fail to hold a massive amount of accounts. The characteristics of Ethereum are not well suited for a social environment, due to the block production time and the transactions fee. It is worthy to notice that new solutions were proposed and are currently included in Ethereum to reduce these two issues. For example, Arbitrum is a Layer 2 scaling solution that promises to handle many more transactions than Ethereum at lower costs. It processes transactions on a sidechain that uses optimistic rollups technology and then regularly settles them in batches to the main Ethereum blockchain. However, the technology, as is, is far from being a good solution for a social application. It is clear that a social blockchain, such as Steem or Hive, represents today the best solution, even if the current consensus protocol used by them, which is the DPoS, has problems concerning the control over the blockchain, as happened to Steem.  
Platforms such as Facebook or YouTube are also easy to use. Instead, several DApps require knowledge about the blockchain as well as wallets in order to use them. The complexity of blockchain technology might pose a serious barrier to the adoption of BOSMs. In this direction, Steem and Hive are more user friendly than DApps built on top of Ethereum, which require installing plugins to interact with the wallet.  
Another important goal to reach is to provide consensus protocols that can be feasible in a social scenario. Several consensus protocols have been proposed [25]. However, the analysis proposed in previous works described that DPoS, the most used consensus protocol used by social DApps, may not be a desirable approach for establishing highly decentralized social media platforms [12].  
Finally, another important point is the reward strategy used to reward users in social media. The current reward strategies do not provide a good way to reward the user. Indeed, the economic impact affects the reward strategies, as described in [8; 9; 26], and they incentivize bot activity [27].
## 5 Experimental Verification
### Measurement of the 4 \(\times\) 4 MIMO Uplink Channel Capacity
On the other hand, in the range of 65\({}^{\circ}\)-90\({}^{\circ}\), the channel capacity is high because the BS correlation for the circular array is low. Considering the BS correlation at \(\phi=65^{\circ}\), there are three correlation values of approximately 0.93, two of approximately 0.76, and one of approximately 0.53 for both the circular and linear arrays. Therefore, the channel capacity is the same regardless of the arrangement of the BS antenna. As described above, the fluctuations in the channel capacity characteristics with regard to the incoming wave angle, shown in Figure 16, can be considered via association with the BS correlation characteristics, shown in Figure 15. Thus, it is possible to use a bidirectional fading emulator to conduct an OTA evaluation of the uplink from a MIMO terminal antenna that considers the BS correlation characteristics.
# Output Properties of Transparent Submount Packaged FlipChip Light-Emitting Diode Modules
## 4 Conclusions  
In conclusion, the optical properties of the FCLEDs mounted on different submounts are investigated. FCLEDs on transparent sapphire or glass submounts have a better light extraction ratio. As compared to the non-transparent counterpart Si submount, FCLEDs mounted on transparent sapphire or glass showed an 80% increase in light output power, which makes them promising candidates for future solid-state light applications. However, there is a heat dissipation issue that needs to be solved. All these electrical and optical observations are verified by the thermal image results as well as the ANSYS simulation data. Among these three submounts, sapphire came out to be the best option for FCLED fabrication, as it adopted both a good transparency and a good thermal conductivity. If the heat problem of the glass submount can be solved in the future, we believe there will be a huge demand for transparent glass-mounted LEDs due to the high light extraction ratio when compared to the traditional mounting method.  
This work was supported by the Ministry of Science and Technology of Taiwan, Project No. 104-2221-E-182-038.  
[PERSON] organized and designed the experiment procedures; [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON] and [PERSON] contributed the measurement tools; [PERSON] and [PERSON] supported the measurement results and wrote this paper; All authors read and approved the final version of the manuscript to be submitted.  
The authors declare that there is no conflict of interests regarding the publication of this paper.  
Figure 6: Average surface temperature _vs._ operation current for different submounts.
## 4 Lifetime Improvement Mechanisms in Routing
### Multi-Path Mechanism
#### 4.3.1 Braided/Meshed Multipath  
One of the protocols that uses position information of the nodes to make braided multi-path and location-based routing is the _Mesh Multi-Path Routing (M-MPR)_ algorithm [41] with two functional modes. The first one is M-MPR with Selective Forwarding (_M-MPR-SF_) and the other one is M-MPR with Packet Replication (_M-MPR-PR_). The M-MPR protocol is designed based on multi-path searching and multi-path routing phases. In the first phase, each node firstly broadcasts some information such as its ID, location information and residual energy to the neighboring nodes. Next step is _Route discovery_ in which each of the sensor nodes tries to implement a meshed multipath towards the sink. As shown in Figure 5, an intermediate node is only allowed to accept a maximum of _two_ copies of a discovery packet in order to reduce the power consumption and the receiver complexity. Meanwhile, a maximum of _two_ downstream neighbors will be chosen by the intermediate node to forward the _first_ arrival copy of discovery packet. This can minimize the control overhead for alternate routes towards the sink.  
_Route reply_ is the last step of the multi-path searching phase. As soon as the sink receives the discovery packets from its neighbors, it chooses the first two neighboring nodes and forwards the route reply messages on the links in the reverse direction. Each intermediate node relays this message until it reaches the source node. Now, the meshed multipath routes are ready to carry the data packets. In the multipath routing phase, the protocol can use either selective forwarding (SF) or packet replication (PR). In SF mode, the source should analyze each packet individually and send it through a high quality link while in PR the source follows a method based on dissemination several copies of the same packet sent on different paths simultaneously. Furthermore, both the SF and PR algorithms employ an end-to-end forward error correction (FEC) coding for successful message routing by avoiding acknowledgment-based retransmission.  
Although RP could increase the throughput, it imposes a large amount of overhead on the network, especially when it uses the FEC mechanism [41]. Comparatively, energy consumption in M-MPR-PR is about 2.8 times more than in M-MPR-SF. This excessive energy wastage is because of forwarding redundant packets on multiple paths [41].
## 5 Security Analysis, Performance Evaluation, and Discussion
### Key Strength Analysis of Proposed Key/Subkey Generation Technique
#### 5.1.2 Bit Independence Tests (BITs)  
Using this test, we can find out how secret key and subkeys are related to each other, and how a single bit change in a key affects the subkeys. We define a function around which the tests are carried out as  
\[\text{f: (GF(2))^{n}}\rightarrow\text{(GF(2))^{m}}\]  
where n indicates input bits and m indicates output bits.  
1. Completeness (d\({}_{c}\)): a function f is said to be complete if each output bit depends upon all input bits.
2. Avalanche effect (d\({}_{a}\)): a function f has the avalanche effect if a one bit change in input affects more than half of the output bits.
3. Strict avalanche criteria (SAC-d\({}_{sa}\)): a function f satisfies the SAC if the complement of a single bit in input affects more than half of the output bits.  
Figure 5: Frequency test for the proposed KSA.  
The ideal values of d\({}_{\rm c}\), d\({}_{\rm a}\), and d\({}_{\rm sa}\) should be 1.  
Results Discussion:  
From the results tabulated in Table 3, we see that the proposed method has a good degree of completeness, avalanche effect, and the strict avalanche effect as d\({}_{\rm c}\), d\({}_{\rm a}\), are equal to 1, and d\({}_{\rm sa}\) is close to 1.
# Chemical Composition and Antimicrobial Properties of Honey Bee Venom
## 2 Results and Discussion
### Chemical Composition of Volatile Compounds
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Groups of Compounds**} & \multicolumn{2}{c}{**Dried Venom**} & \multicolumn{2}{c}{**Fresh Venom**} \\ \cline{2-5}  & **Dv-1** & **Dv-2** & **Fv-1** & **Fv-2** \\ \hline \multicolumn{5}{c}{Aromatics, including:} & 18.34 & 1.46 & 9.45 & 5.51 \\ \hline - toluene & 3.40 & 1.15 & 1.93 & 1.42 \\ \hline - \(p\)-cymene & trace & trace & 0.64 & 0.28 \\ \hline - benzaldehyde & 2.62 & - & 0.15 & 0.11 \\ \hline - acetophenone & 0.33 & trace & 0.43 & 0.58 \\ \hline - 1-phenyl ethanol & - & - & 0.12 & 0.21 \\ \hline - 2-phenyl ethanol & trace & trace & 1.39 & 0.82 \\ \hline - cresol & - & - & 1.22 & 1.29 \\ \hline - \(p\)-ethylguaiacol & - & - & 0.37 & 0.37 \\ \hline - methyl benzoate & 1.88 & - & - & - \\ \hline -methyl salicate & 9.52 & - & - & - \\ \hline \multicolumn{5}{c}{Terpenoids, including:} & 3.91 & 0.56 & 12.60 & 12.48 \\ \hline - \(\alpha\)-pinene & 1.78 & trace & 2.06 & 2.39 \\ \hline - \(\beta\)-pinene & 0.54 & - & 0.47 & trace \\ \hline - 3-carene & 0.36 & trace & 0.28 & 0.32 \\ \hline - limonene & 0.56 & 0.35 & 2.90 & 3.12 \\ \hline - dihydroremol & - & - & 2.37 & 3.08 \\ \hline - camphor & - & - & 0.28 & 0.33 \\ \hline - borneol & - & - & 0.32 & 0.37 \\ \hline - bornyl acetate & - & - & 0.64 & 0.38 \\ \hline - \(\beta\)-caryophyllene & trace & 0.21 & 0.06 & 0.17 \\ \hline - \(\gamma\)-cadinene & - & - & 0.07 & 0.11 \\ \hline \multicolumn{5}{c}{Alkanes and alkenes, including:} & 2.95 & 19.66 & 14.66 & 8.45 \\ \hline - \(n\)-hexane & 2.62 & - & - & - \\ \hline - \(n\)-decane & - & trace & - & 0.48 \\ \hline - \(n\)-dodecane & - & trace & 2.00 & 0.26 \\ \hline - \(n\)-tridecane & 0.33 & 0.19 & 0.16 & 0.28 \\ \hline - \(n\)-tetradecane & - & - & 0.17 & 0.11 \\ \hline - \(n\)-pentadecane & - & 0.49 & 1.44 & 1.36 \\ \hline - \(n\)-heptadecane & - & 0.18 & 1.47 & 1.95 \\ \hline - 1-pentadecene & - & 16.99 & 0.17 & 0.10 \\ \hline - nonadecene & - & 0.45 & 5.51 & 2.87 \\ \hline \multicolumn{5}{c}{Lactones, including:} & 0.30 & trace & 5.53 & 5.68 \\ \hline - valerolactone & trace & - & 0.16 & - \\ \hline - \(\gamma\)-caprolactone & trace & - & 1.71 & 2.56 \\ \hline - \(\gamma\)-heptalactone & - & trace & 2.05 & 2.05 \\ \hline - \(\gamma\)-octalactone & 0.30 & trace & 1.05 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 1 Introduction
### Ground Penetrating Radar (GPR)
The GPR records the strength and time of the return of any reflected signals after sending the energy pulses into the ground. This series of pulses spanning over a single area is called a scan. The signals are reflected when the energy pulses transmitted through various materials hit the object buried underground. This, thus, changes the velocity of signals originated due to contrasts in dielectric properties that are reflected back. The changes in the velocity depend basically on two primary electrical properties of the subsurface: electrical conductivity (\(\sigma\)) and relative permittivity (\(\epsilon\)) [24] According to [PERSON]'s equation, the water content (\(\Theta\)) of the soil impacts the minor magnetic permeability of the soil, as shown in Equation (1) [25]. \[\epsilon_{eff}=3.03+9.3\Theta+146\Theta^{2}-76.7\Theta^{3}, \tag{1}\]  
Hence, it is expected that the change in the moisture content due to the leak will affect the dielectric constant and would, in turn, impact the velocity of signals, thereby, detecting the leak.
# An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation
###### Abstract  
Due to fluctuating characteristics of loads, dynamic reactive power optimization over a certain time period is essential to provide effective strategies to maintain the security and economic operation of distribution systems. In operation, reactive power compensation devices cannot be adjusted too frequently due to their lifetime constraints. Thus, in this paper, an online reactive power optimization strategy based on the segmentation of multiple predicted load curves is proposed to address this issue, aiming to minimize network losses and at the same time to minimize reactive power-compensation device adjustment times. Based on forecasted time series of loads, the strategy first segments each load curve into several sections by means of thresholding a filtered signal, and then optimizes reactive power dispatch based on average load in each section. Through case studies using a modified IEEE 34-bus system and field measurement of loads, the merits of the proposed strategy is verified in terms of both optimization performance and computational efficiency compared with state-of-the-art methods.  
online reactive power optimization; load curve; time series segmentation +
Footnote †: journal: Applied Sciences