esa-sceva/satcom-chunk-collection huggingface dataset:
# Passive Power Line Communication Filter Design and Benchmarking Using Scattering Parameters
## 3 Filter Performance Measurement
### Impedance Measurement  
The impedance of the filter should not affect the PLC modem performance. In some cases, the PLC filter is installed very close to the SM with a PLC modem, which can severely affect the transmitted and received signal. Higher filter input impedances can be achieved using higher order filters (multi-stage) such as the filter designed by us.  
The most effective ways of measuring the impedance of the filter at PLC frequencies are:  
* Offline measurement using the RLC meter, impedance analyzer, or VNA, Figure 13a;
* Online measurement using the impedance analyzer measuring method [49], Figure 13b;
* Online measurement using the voltage ratio method [49], Figure 13c;
* Offline measurement and monitoring of the voltage and current consumption of the PLC modem while transmitting carrier frequencies, Figure 13d.  
Single port measurements with a VNA are not reliable at extreme impedance values (i.e., <1 \(\Omega\)). The single port measurement limitation (S11) comes from the fact that the VNA is designed to measure the incident wave (\(E^{+}\)) entering the DUT (device under test) and the reflected wave (\(E^{-}\)) on impedances close to 50 \(\Omega\)[50; 51; 52]. The ratio of these two waves is called the reflection coefficient (\(\Gamma\)) and it is used to compute the impedance of the DUT \(Z_{\text{L}}\). The resolution of a VNA is not high enough for single port measurements at extreme  
Figure 11: PLC signal filtering capability test setup version 1.  
Figure 12: PLC signal filtering capability test setup version 2.  
impedance values: when \(Z_{L}\) is very low, \(\Gamma\) becomes close to \(-1\), and when \(Z_{L}\) is very high, \(\Gamma\) becomes close to +1.  
\[\Gamma=\frac{E^{-}}{E^{+}}=\frac{Z_{L}-50}{Z_{L}+50} \tag{12}\]  
\[Z_{L}=50\frac{(\Gamma+1)}{(\Gamma-1)} \tag{13}\]  
In order to overcome the limitations of the single port measurements, the 2-Port VNA measurement technique can be used which is the equivalent of the 4-wire Kelvin DC technique for measuring resistance. Port 1 is used to inject to the DUT and port 2 is used to measure the voltage drop on the DUT.  
Figure 13: Impedance measurement: (**a**) offline measurement; (**b**) online measurement with an impedance analyzer; and (**c**) online measurement using the voltage ratio method and selective level meter. (**d**) Offline measurement and monitoring the voltage and current consumption of the PLC modem.
## 2 Methods
### Double Interpolation of Optimization Local Mean Decomposition (LMD)
#### 2.1.3 Limitations of Single Interpolation-Based LMD  
The envelopes constructed by different interpolation methods have unique advantages. CSI and MPCHI are two typically used interpolation envelope methods. CSI is more suitable for the construction of stationary signal envelopes due to its smoothness, and MPCHI is suitable for the construction of strong nonstationary signal envelopes owing to its flexibility. However, the actual vibration signal generally has the property of multi-component coupling, and the LMD decomposition needs to process multiple PF components with different characteristics. Applying one single interpolation method to construct the envelopes for all PF components in its iteration process is not optimal.  
Figure 1: Flowchart of interpolation-based LMD.  
It can be seen from Table 1 that the MSEs of PF components for the two methods have significant differences; the MSEs of the first and third PF components in CSILMD are lower than those for MPCHILMD, while the MSE of the second PF component in CSILMD is higher than that for MPCHILMD. We can know that applying a single interpolation to obtain all the PF components of the LMD cannot guarantee the optimal performance of each PF component, and the error of the previous-order PF component will significantly affect the decomposition accuracy of the subsequent-order PF component. To obtain a better hierarchical structure of PF components, this paper considers obtaining each PF component using two different interpolation methods and then choosing the optimal one as the final PF component.
## 2 Materials and Methods
### Land Cover Classification
#### 2.3.3 Image Fusion  
We used the variational fusion method proposed by [PERSON] et al. (2018) to fuse PlanetScope and Sentinel-2 images to improve classification accuracy. This method introduces the geometry information of the higher resolution image (PlanetScope) by aligning all edges of the higher resolution bands with each lower resolution (Sentinel-2) multispectral bands of similar spectral characteristics [59, 60]. Figure 4 highlights the spectral overlap with Sentinel-2 and PlanetScope fused bands. High-resolution PlanetScope bands 1, 2, 3, and 4, were each fused with the 10 m Sentinel-2 bands 2, 3, 4, and 8, respectively. We also fused PlanetScope's Band 4 with Sentinel's Band 8A, 11, and 12. There is no  
Figure 5: Footprint of UAV flights, GPS control points, and selected training samples for land cover classification of humid highlands and agricultural zones of the Galapagos.  
direct equivalent to fuse with Sentinel-2's Red Edge bands, so following [PERSON] et al. (2018), we generated a synthesized band (S) using Equation (1),  
\[S=\frac{B3+B4}{2} \tag{1}\]  
where \(B3\) is PlanetScope's Band 3 (Red), and \(B4\) is its Band 4 (NIR). Table 6 summarizes the band combinations that were used to obtain the fused images. Image Fusion was conducted with the open-source Orfeo ToolBox V.6.6.1.
## 4 Numerical Simulation
### Timing Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Ref.** & **Chaotic Oscillators** & **Correlation Coefficient** & **SNR in dB** \\ \hline [PERSON] and [PERSON] 2013 & [42] & circle map and logistic map & 0.0017 & \(-\)14.0065 \\ [PERSON] et al., 2017 & [43] & Henon map (2D-MH) & \(-\)0.0037 & - \\  & & and standard map & & \\ [PERSON] and & & logistic map, tent map, & 0.0119 & - \\ [PERSON] 2017 & [44] & quadratic map, and Bernoulli’s map & & \\ [PERSON] 2019 & [45] & [PERSON] map & & \(-\)0.00092 & \(-\)56.8661 \\ [PERSON] 2019 & [46] & chaotic circle map and & & \(-\)0.0011166 & \(-\)16.0483 \\ [PERSON] et al., 2022 & [47] & [PERSON]’s hyperchaotic system & & \(-\)0.0007 & - \\ Proposed System & & Unified hyperchaotic system & & 0.0004 & \(-\)71.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison results with related work for voice encryption. transmission. In terms of practical implications, our system's high performance in security metrics makes it suitable for real-time secure voice communication systems where the privacy is required. The high SNR, PSNR, and PRD values and low correlation values ensure that encrypted signals are highly resistant to attacks aimed at recovering the original signal. However, the added complexity, particularly with increasing levels (N), might require more computational resources, making the system potentially slower or more resource-intensive compared to simpler chaotic encryption methods. However, one of the limitations of our approach is the increased encryption and decryption time as the number of levels grows, which can be a drawback in real-time applications. Additionally, for N > 7, the FPGA resources were exceeded, which limits the scalability on certain hardware platforms.
## 5 Analysis of Monitoring Results
### Earth Pressure on Diaphragm Wall  
Figure 13 illustrates the earth pressure distribution at different depths of monitoring point WTL-1 along the diaphragm wall during various excavation stages, compared with the at-rest earth pressure and Rankine active earth pressure. According to Figure 13, for monitoring point WTL-1, at the \(-1.5\) m depth, the earth pressure exceeded the at-rest earth pressure throughout all excavation stages. For the \(-8\) m depth, the measured earth pressure values were sparse due to equipment issues but generally aligned with the at-rest earth pressure limit. However, for the \(-13.5\) m depth, the earth pressure was approximately distributed near the active earth pressure, with some stages slightly lower than the active earth pressure. At the \(-18.5\) m depth, the earth pressure ranged between the at-rest and active earth pressure, with some measurements slightly below the passive-active earth pressure. As excavation progressed, earth pressures at all depths initially decreased and then gradually increased. The \(-1.5\) m depth exhibited abnormally large and irregular measurements due to soil disturbance from construction. At the \(-13.5\) m depth, the earth pressure during initial excavation stages fell below the Rankine active earth pressure limit. When excavation reached \(-16.5\) m, the pressure approached the Rankine active limit but showed minimal changes with further excavation. At the \(-18.5\) m depth, the earth pressure initially aligned with the at-rest earth pressure. Upon reaching \(-6.5\) m excavation depth, it approached the Rankine active limit, subsequently decreasing before gradually converging toward this limit with continued excavation.  
Figure 13: Earth pressure distribution at WTL-1 of diaphragm wall.  
In summary, the earth pressures at different depths of the diaphragm wall generally followed a pattern of transitioning from at-rest earth pressure toward Rankine active earth pressure during early excavation stages. However, the high-pressure jet grouting pile reinforcement at the pit bottom significantly enhanced the embedment effect of the lower diaphragm wall. This restraint limited wall deformation and redistributed external earth pressures, leading to temporarily lower-than-active earth pressure values during intermediate excavation phases. As excavation advanced, progressive wall deformation allowed earth pressures to gradually realign with the Rankine active earth pressure.
## 3 System Design
### Variable Spray System Design  
The overall structure of the plant protection drone variable spray system is illustrated in Figure 6. The self-developed subsystems of prescription graphic translation and spray controller were installed in the M23 UAV (Shenzhen Hi-tech New Agriculture Technologies Co, Ltd., Shenzhen, China). Along with the medicine box, a miniature diaphragm pump (PLD-2201, Shijiazhuang Prandi Co, Ltd., Shijiazhuang, China) and a pressure nozzle (110-015 types, LECHLER Company, Germany) were used. Among these, the STM32 chip (STM32F103 ZET6, Langyi Electronic Technology Co, Ltd., Shanghai, China) was the spray controller core. The Hall flow sensor (MJ-HZ06K, Mocho Technology Co, Ltd., Shenzhen, China) was used to measure the flow of the system, and the measuring range of the hall flow sensor was 0.1-1 L/min.  
The micro-diaphrag pump was controlled by the signal sent by the interpretation system. The liquid in the medicine box was transported to the nozzles and fractured into tiny droplets under pressure. The Hall flow sensor was used to measure the flow inside the system, and the flow rate information was fed back to the spray controller. Subsequently, the PWM was adjusted according to the deviation between the actual flow and the target flow. The schematic diagram of hardware representing the variable spray controller is shown in Figure 7.  
Figure 6: Physical diagram of the variable spraying system. **Note:** 1. Prescription map interpretation system and spray controller; 2. medicine case; 3. Hall flow sensor; 4. miniature diaphragm pump; 5. pressure nozzle.  
Figure 7: Schematic of hardware representing the variable spray controller.
## 5 Results and Discussion
### Effect of Cable Segmentation on \(\rm R_{AC}/R_{DC}\) Ratio  
Segmentation plays a pivotal role in managing the skin and proximity effects within power cables. Figure 7 depicts the \(\rm R_{AC}/R_{DC}\) ratios for single-segment, three-segment, and five-segment cables at 50 Hz.  
The data reveal that increasing the number of segments from one to five results in a slight decrease in the \(\rm R_{AC}/R_{DC}\) ratio. Specifically, at 50 Hz, the single-segment model exhibits a ratio of 1.85, which decreases to 1.74 in the five-segment model. This reduction suggests that segmentation distributes the current more evenly across the conductors, thereby mitigating the proximity effect and slightly reducing AC resistance. This presents a potential avenue for further study to explore innovative approaches for reducing skin and proximity effect losses.
## 4 Proposed Approach to Assure Anonymity and Privacy in E-Voting Using Blockchain Technology
This is an analogy of envelopes, where the inner envelope carries the ballot but no information about the voter, whereas the outer envelope contains voter data but no ballot data. * Step 7. After the operation in step 6, the encrypted votes will be stored in the EVB. Because the so-called outer wrapper, which was the voter's signature, is removed in this step, only the encrypted votes remain as presented in Figure 4. According to the envelope analogy, in this case, it is only the inner envelopes that do not contain any information about the outer envelope (voter data). * Step 8. The voter's signature is removed from the encrypted ballot, assuring that the vote is not linked to the voter. According to the envelope analogy in this case it is only the outer envelopes, that do not contain any information about the inner envelope (vote data). The DKB stores voter signatures as well as other voter information. Both voters and authorities can verify that a voter has voted by storing the voter's signature and other voter data in the DKB. * Step 9. The Encrypted Votes Blockchain stores the encrypted votes and the hash of the voter's private key throughout the voting session. * Saving the votes in the EVB without the voter's signature guarantees anonymity and privacy, whereas saving the voter's signature at the DKB prevents double voting. With  
Figure 4: Hash and encrypted vote + nonce. Figure 5: Signature of hash and encrypted vote + nonce. Figure 3: Encrypted vote + nonce. an Encryppted Votes Blockchain, the vote cannot be associated with the voter, but even the Distributed Key Blockchain can never associate the signature (voter) with the vote, thus meeting the two main preconditions of voting. Smart Contracts can manage voting time in both DKB and EVB. When the voting time is over, the generation of keys will not be allowed, and consequently, neither will the voting. Next, the counting begins, and if the Distributed Key Blockchain and Encrypted Votes Blockchain have agreed to this, the Encrypted Votes Blockchain signs the dataset of all encrypted votes with its private key and sends this dataset to the Distributed Key Blockchain, as presented in Figure 6. The dataset, in this sense, represents a ballot, a list of votes without voter information, thus assuring voter anonymity and privacy. The Distributed Key Blockchain validates the signing of encrypted ballot data sent by the Encrypted Votes Blockchain using EVB's public key; if it is valid, it decrypts the encrypted votes. The private key of the Distributed Key Blockchain is used to decrypt the votes. The Distributed Key Blockchain verifies that the number of voter signatures equals the number of votes received by the Blockchain Encrypted Votes prior to decryption, proving that there are no more votes than voters or vice versa. After decrypting the votes, the Distributed Key Blockchain calculates the votes and announces the results based on the legally defined criteria.
# A Graph-Based Approach to Recognizing Complex Human Object Interactions in Sequential Data
## 5 Discussion  
In the proposed HOIR framework, all input images were pre-processed using sigmoid stretching and Gaussian filtering techniques. Raw input images produced relatively poor results; hence, their quality was improved using these pre-processing techniques. Although context and value of each key body part is computed scene information can provide additional cues, it is also important to observe the humans and objects in isolation. Therefore, the human-object pairs in the pre-processed images were localized, and the backgrounds were removed. Using the detected human silhouettes, 12 key body parts were identified. Then, dense trajectories and LIOP features were extracted from the full-body silhouettes. Similarly, kinematic posture and LOP features were mined from the key body parts. Lastly, SPM and GIST feature descriptors were obtained from the entire images. Experiments showed that each one of the six of feature descriptors contributed toward the system's overall efficiency. Removing any of these features in the final feature vector showed a negative effect on the system's performance. The three different types of features were merged together to form a large feature vector, which was then reduced using ISOMAP. The optimized feature vector was finally sent to a fine-tuned GCN architecture for the classification of interactions.  
Although the proposed system classifies complex human-object interactions with high accuracy rates, it is not without limitations. It struggles to identify the correct interaction when two interactions are of a similar nature: for instance, opening and closing the same object in the same scene. One example of this would be the opening and closing of the microwave in the kitchen in the D3D HOI dataset. Although the system uses a time sequence to distinguish between the two interactions, it often misclassifies them. Similarly, when the size of the object is too small, the accuracy drops. This was evident during the testing of some examples in the MSR Daily Activity 3D dataset; for example, playing a game on a phone and reading a book were misclassified because the subject has both objects in their hands in a similar fashion and the sizes of the objects are too small to form a clear distinction.  
The results and analysis of the proposed system are as follows. The mean interaction recognition accuracy of the proposed system over Olympics Sports dataset is 94.1%. For the MSR Daily Activity 3D dataset, the accuracy is 93.2% and 89.6% accurate results are achieved with the D3D HOI dataset. These results prove that the proposed HOIR system outperforms the available state-of-the-art techniques.
## 5 Conclusions  
Although the European Commission clearly identified IVD as a relevant element in radiotherapy QA programs, only a few Institutions use it routinely for different reasons. First of all, many of the commercially available systems, although exhibiting good performances, do not respond in real time, often having very long data processing times. Additionally, many devices require dedicated staff, because of their complicated method to enter the data for processing. Finally, often a single system is not able to discriminate between all treatment errors, as demonstrated in this work. In this study we have presented a pilot study showing a new strategy to perform fast and reliable QA tests with a low impact on the workflow of the institution and patient waiting list. The combined use of IQM and SoftDiso devices was demonstrated to be able to efficiently detect small delivery and setup errors in 3 DCRT breast irradiation. We have shown that the two devices provide complementary information to detect in almost real time all types of errors. The method would represent an important step forward in the clinical routine to increase the quality of external breast irradiation. Our study concentrated on breast irradiation, but the method could be easily extended to other anatomical districts or treatment modalities, especially when advanced techniques are used. The method proposed helps to comply with the European Commission requirements [24], and represents a new strategy in QA programs, thereby overcoming the drawbacks of conventional pre-treatment verification, which are very time consuming and ineffective to detect in-treatment errors.  
Methodology, formal analysis and Writing-original draft preparation: C.A.; Funding acquisition: F.F. and S.P.; Investigation: Y.W. and C.G.; Software: M.G. and A.P.; Validation: L.M.; Visualization: S.C. and I.D.; Conceptualization, Supervision, Writing-review and editing: C.T. All authors have read and agreed to the published version of the manuscript. This work was supported by Italian MIUR (the Italian Ministry of Education, University and Research) under Grant ex60% to the Department of Experimental and Clinical Biomedical Sciences \"[PERSON]\", University of Florence. The authors declare no conflict of interest.
## 2 Experiments
### Ship-to-Infrastructure Wireless Channel Measurement  
The temporary S2I mobile communication network was built on the basis of a high-precision signal generation system, including a time-division multiplexing single-input single-output (SISO) channel sounder provided by Super Radio AS in Norway and Norwegian University of Science and Technology (NTNU) [20], transmitter (Tx) and receiver (Rx) antennas, and high-resolution global positioning system (GPS) receivers. The S2I wireless channel measurement had been conducted at 5.9 GHz band with a bandwidth of 100 MHz and a transmit power of 16 dBm. Chirp signal with 10 ns delay resolution was emitted from transmitter part. The receiver part would receive 1933 chirp signals in 1 s. An omni-directional antenna with the gain of 10 dBi and a directional antenna with 16 dBi gain and 3 dB beam-width of 90\({}^{\circ}\) (H-plane) and 8\({}^{\circ}\) (E-plane) were used as Rx and Tx antennas, respectively. The heights of the Tx and Rx antennas were 3.13 m and 3.12 m above the water surface. Figure 1 presents the diagrams of the S2I and S2S measurement campaigns.  
During the measurement, the Tx boat anchored on the pontoon over the sea and another boat carrying the Rx part sailed away from the pontoon with an average speed of 8.37 knots (about 15.5 km/h) in the Jiaozhou Bay. The maximum distance between Tx and Rx parts was 1.35 nautical miles (about 2.5 km).  
Figure 1: Diagrams of the S2I and S2S measurement campaigns. (**a**) is for S2I and (**b**) is for S2S.
# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 8 Simulation
### Simulation Results
#### 8.3.4 Inter-Cluster Communication Assessment  
Number of packets exchanged among clusters to build a network is shown in Figure 12b. The number of rounds is shown on the X-axis, packets delivered are represented by the Y-axis and the colored curves shows different protocols. The lower the curve the better the system is. It is observed that about 140 k packets are exchanged among cluster members and cluster heads in 1000 rounds just only to keep network live and updated in the form of periodic updates, clustering information and CH notifications. In the proposed system, only 20 k messages are exchanged, which is a great factor that improves the system performance a lot.  
Figure 12: Comparison of existing routing protocols with the proposed system (**a**) Number of rounds vs. dead nodes; (**b**) comparison of the amount of inter-cluster communication in different routing algorithms that is required to formulate a cluster.
## 4 Evaluation Methodology
### Experiment Result of Feature Selection with Permutation Decoding
#### 4.4.3 Result of ROC Curve  
Performance evaluation using the ROC curve and AUC was performed as the next evaluation method. Figure 9 shows ROC curves for the proposed algorithm, wrapper-DR, and PCA, with the results of AUC shown on the lower right. Visual analysis confirmed that the proposed method and wrapper-DR had excellent performances, with graph curves approaching 0 on the \(x\)-axis and 1 on the \(y\)-axis. PCA, on the other hand, showed an ROC curve close to a line graph \(y=x\), implying that the performance is poor. The AUC results show that the proposed algorithm performed better by about 0.0005, with our algorithm at 0.9899 and wrapper-DR at 0.9894. PCA was excluded from the performance evaluation since it performed poorly, at close to 0.5. Although large differences were not seen in the ROC curves, the proposed method achieved the best numerical performance, confirming that it is an excellent algorithm.  
Figure 8: Feature selection vs. all-feature.  
Figure 9: Result of ROC curve and AUC.