esa-sceva/satcom-chunk-collection huggingface dataset:
# A Military Object Detection Model of UAV Reconnaissance Image and Feature Visualization
## 5 Conclusions  
In this work, we simulated Unmanned Aerial Vehicle (UAV) low-altitude reconnaissance and constructed the UAV reconnaissance image tank dataset UAVT-3. Then, we proposed UAVT-YOLOv5 from YOLOv5 by using blurred images as data augmentation, adding a large-scale feature map, optimizing the loss function, and clustering anchor boxes. The feature visualization technique Class Action Mapping (CAM) was also introduced to explore the mechanism of the proposed model. The experimental results show that mAP reaches 99.2%, an increase of 2.1% compared to YOLOv5, and the detection speed is 40 frames per second. Moreover, it is found that mAP decreases sharply when detecting fog and motion blur images. However, the fine-tuned model using the blurred images as training can improve mAP by 20.4% and 26.6% for fog and motion blur images, respectively. The CAM maps show that the discriminant region of tanks is the tur  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Schemes**} & \multicolumn{3}{c}{**AP (\%)**} & \multirow{2}{*}{**mAP (\%)**} \\ \cline{2-2} \cline{4-5}  & **Tank A** & & **Tank B** & **Tank C** \\ \hline SS1 & 14.60 & 19.40 & 45.20 & 26.40 \\ SS2 & 49.80 & 59.00 & 86.20 & 65.00 \\ RS & **57.20** & **85.70** & **93.00** & **78.63** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance on different splitting schemes. The bold part denotes the best performance.  
Figure 15: Performance of the pre-processed blurred images. (**a**) Performance of the fog-blurred images pre-processed by homomorphic filter [21] and FFA-Net [22]. (**b**) Performance of the motion-blurred images pre-processed by DeblurGAN [23].  
**Author Contributions:** [PERSON] is responsible for the formulation of the overarching research goals and aims, design of the methodology, creation of the models, and the original draft. [PERSON] is responsible for the programming and presentation of the published work. [PERSON] is responsible for project administration and funding acquisition. [PERSON] is responsible for data collection and the draft review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Special Funds for the Construction of an Innovative Province of Hunan (Grant no. 2020 GK2028), Natural Science Foundation of Hunan Province (Grant no. 2022 JJ30002), Scientific Research Project of Hunan Provincial Education Department (Grant no. 21B0833), Scientific Research Key Project of Hunan Education Department (Grant no. 21A0592), National Natural Science Foundation of China (Grant no. 61902117).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author, upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Appendix A Additional Description of Mathematical Functions in SEs  
In GPSR, some mathematical functions such as the square root, natural logarithm, logarithm with base 2 and 10, and division are differently defined to avoid generating imaginary or inf values. To avoid imaginary values, these functions have to be applied when the SEs defined with Eqs. (3)-(7) are used. The mathematical functions are defined as follows:  
* Square root: \[y_{sqrt}(x)=\sqrt{|x|},\] (1)
* Natural logarithm: \[y_{\log}(x)=\begin{cases}\log(|x|)&\text{if}|x|>0.001\\ 0&\text{if}|x|<0.001^{\prime}\end{cases},\] (2) and when natural logarithms with base 2 and 10 are used, the log in Equation (2) is replaced with \(\log_{2}\) or \(\log_{10}\), respectively.
* Division: \[y_{div}(x_{1},x_{2})=\begin{cases}\frac{x_{1}}{x_{2}}&\text{if}|x_{2}|>0.001\\ 1&\text{if}|x_{2}|<0.001\end{cases}.\] (3)  
The variables \(x\), \(x_{1}\), and \(x_{2}\) in Equations (1)-(3) are arbitrary values and have no relation with the input dataset variables.
# Coordination in Emergency Response System Design:
## 7 Case Study: Hazmat Transportation in Chengdu City  
In this section, we adopt the proposed methodology for improving the collaboration of the fire fighters and EMTs in responding to hazardous materials (hazmat) transport accidents in Chengdu, the capital of China's Sichuan province (see Figure 6). With a population of more than 10 million, it is the third most crowded city in Western China. Chengdu City has about five thousand companies carrying or stocking hazardous materials.  
We first determine the urban road segments that constitute the hazmat transport network (HMTN) of Chengdu. For the ease of exposition, we target avoiding only the  
Figure 5: The genetic algorithm solution with different user preferences when [NA,NF] = [3,4]: (**a**) minimum _RTAS_; (**b**) minimum _RTFS_; (**c**) minimum WT.  
Figure 6: China’s Sichuan province (in red) and its capital Chengdu.  
primary schools in the urban road network. Then, we apply the genetic algorithm described in the previous section to determine the locations of the ambulance and fire stations so as to maximize the number of nodes collectively covered by these hazmat accident response resources.
# Modular Combined DC-DC Autotransformer for Offshore Wind Power Integration with DC Collection
###### Abstract  
Offshore wind farms (OWFs) integration are attractive extensively for furnishing more robust power than land wind farms. This paper introduces a modular combined DC-DC autotransformer (MCAT), which contributes to the offshore wind power integration of DC grids with different voltage levels. Traditional DC transformers contains medium- or high-frequency converter transformers, which have the disadvantages of high manufacturing difficulty and cost. These shortcomings seriously affect the progress of commercial application of DC transformers. To solve these problems, in the proposed MCAT, converter transformers are replaced with a DC-isolation capacitor and a compensation inductor in series to reduce the footprint of offshore platforms and improve economy. Theoretical analysis is carried out for the MCAT operation principle. Selection methods of main circuit parameters for the MCAT are discussed in detail. Then, corresponding control strategies of the MCAT are proposed. Finally, the effectiveness of the proposed MCAT and its control strategies are validated by time domain simulations in PSCAD/EMTDC. The time-domain simulation results show the correctness of the main circuit parameters and the rationality of the MCAT control strategies.  
offshore wind farm (OWF); DC collection grid; modular combined DC-DC autotransformer (MCAT); DC-isolation capacitor; compensation inductor +
Footnote †: journal: Applied  
doi: 10.3390/app12041810
## 5 Experimental Results: Comparison and Analysis  
### Experimental Results  
Although the K-LS and K-LC specimens were manufactured symmetrically, there was a gap between the left and right beams, which increased the torsion in the beam ends as the drift angle increased. This torsion was absent for the K-SS and K-SC specimens, as the gap for these test specimens was small. For all test specimens, formation of a plastic hinge on the beam flange was used to determine the maximum load. Table 3 shows the experimental results and Figure 5 shows the key points in the test setup.  
As shown in Figure 5a, for the K-LS specimen, excessive torsion occurred because a shim plate was not installed between the upper and lower beams of the roller end section. Thus, a loading schedule up to 0.03 rad was used for safety reasons. For the other test specimens, shim plates were installed, and the experiments were performed according to the loading schedule.  
Figure 4: Test set-up: (**a**) K-LS or K-LC; (**b**) K-SS or K-SC.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen** & **Connector Plate** & \
\begin{tabular}{c} **Positive Load** \\ **P\({}^{+}\)max (kN)** \\ \end{tabular} &
\begin{tabular}{c} **Negative Load** \\ **P\({}^{+}\)max (kN)** \\ \end{tabular} & **Failure Mode** \\ \hline K-LS & Straight & 170.3 & 84.9 & \\ K-LC & Cross & 158.4 & 106.2 & Buckling of flange in beam. \\ K-SS & Straight & 79.9 & 84.1 & Lateral-torsional buckling of beam. \\ K-SC & Cross & 81.6 & 84.2 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Observed strengths and failure mode of specimens.  
Figure 5: _Cont._  
Figure 5: Experimental photographs of specimens: (**a**) K-LS; (**b**) K-LC; (**c**) K-SS; (**d**) K-SC.
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 6 Simulation Results and Discussions
### Assessment of the Dynamic Number of Clusters  
In this section, we assess and compare the performance of the suggested clustering scheme with the previous reliability-based clustering scheme. In both schemes, the number of clusters (NoC) are assessed temporally to observe how the said protocols behave or evolve with the continuous arrival of vehicles. The temporal assessment of the connectivity and reliability-based clustering is shown in Figure 2. Both schemes are evaluated under real traces of SUMO-generated traffic. It is noticed that the number of clusters in connectivity-based as well as reliability-based schemes variate with the passage of time. Since the number of vehicles change due to continuous entry-exit of vehicles to the highway that subsequently affect the NoC. At different time stamps, the number of vehicles reduces in both schemes because of some vehicles' departure. Thus, it is determined that the proposed connectivity-based clustering scheme performs well in terms of the number of clusters. This means that the connectivity metric efficiently reflects the evolving structure of VANETs. The large number of clusters are related to the high variations of evolving VANETs.  
From the Figure 2, it is noted that connectivity plays a vital role in optimally dividing the highly dynamic VANET such as link reliability [3]. We also noted that the number of clusters in connectivity-based clustering is slightly higher than reliability-based clustering. Additionally, it is also determined that the heuristic Eigen gap performs well when selecting an optimal number of clusters based on the variation in vehicles temporally. Initially, the number of clusters are very few due to the smaller number of vehicles but, later, increases gradually with the temporal increase in continuous arrival of traffic. See that, at scale 800 to 900 in Figure 2, the number of clusters significantly increases.
# DEEDP: Document-Level Event Extraction Model Incorporating Dependency Paths
## 5 Experiments and Analysis
### Effect of Different Transformer-M Layers  
In this section, we describe experiments performed to verify the importance of the feature integration encoder by setting different layers of _TF-M_ encoder. To be specific, we set the layers of the _TF-M_ encoder to {0, 1, 2, 4}, where 0 represents that the _TF-M_ encoder is to be removed. The effect of different _TF-M_ encoder layers is depicted in Figure 6, and we can observe that the best average F1-score is achieved for our method when we set the number of layers to 4. We conjecture that stacking more layers of the _TF-M_ encoder facilitates better aggregation of sentence-aware and document-aware information.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**EF**} & \multicolumn{2}{c}{**ER**} & \multicolumn{2}{c}{**EU**} & \multicolumn{2}{c}{**EO**} & \multicolumn{2}{c}{**EP**} & \multicolumn{2}{c}{**AVG**} \\ \cline{2-13}  & **S. (\%)** & **M. (\%)** & **S. (\%)** & **M. (\%)** & **S. (\%)** & **M. (\%)** & **S. (\%)** & **M. (\%)** & **S. (\%)** & **M. (\%)** & **S. (\%)** & **M. (\%)** & **S. (\%)** \\ \hline DCFE-O & 56.0 & 46.5 & 86.7 & 54.1 & 48.5 & 41.2 & 47.7 & 45.2 & 68.4 & 61.1 & 61.5 & 49.6 & 58.0 \\ DCFE-M & 48.4 & 43.1 & 83.8 & 53.4 & 48.1 & 39.6 & 47.1 & 42.0 & 67.0 & 60.0 & 58.9 & 47.7 & 55.7 \\ GreedyDec & 75.9 & 40.8 & 81.7 & 49.8 & 62.2 & 34.6 & 65.7 & 29.4 & 88.5 & 42.3 & 74.8 & 39.4 & 60.5 \\ Doc2 EDAG & 80.0 & 61.3 & 89.4 & 68.4 & 77.4 & 64.6 & 79.4 & 69.5 & 85.5 & 72.5 & 82.3 & 67.3 & 76.3 \\ DE-PTN & 82.4 & 46.3 & 78.3 & 53.9 & 82.2 & 45.6 & 78.1 & 39.3 & 82.8 & 38.5 & 80.7 & 44.7 & 66.2 \\ DE-PPN & 82.1 & 63.5 & 89.1 & 70.5 & 79.7 & 66.7 & 80.6 & 69.6 & 88.0 & 73.2 & 83.9 & 68.7 & 77.9 \\ MMR-1 & 79.2 & 52.1 & 88.2 & 49.3 & 70.3 & 49.4 & 74.2 & 44.7 & 87.4 & 45.2 & 79.8 & 48.1 & 63.4 \\ MMR & 81.2 & 61.8 & 89.8 & 70.1 & 77.9 & 65.4 & 80.8 & 71.7 & 86.2 & 72.6 & 83.2 & 68.3 & 77.4 \\ \hline DEEDP-O & 80.2 & 52.3 & 79.4 & 51.3 & 78.6 & 48.6 & 76.4 & 45.5 & 86.9 & 47.9 & 80.3 & 49.1 & 67.8 \\ DEEDP & **82.8** & **66.4** & **93.6** & **73.7** & **82.4** & **68.4** & **81.7** & **71.9** & **87.5** & **74.1** & **85.6** & **70.9** & **79.5** \\ \hline \hline \end{tabular}
\end{table}
Table 4: F1 scores for each pre-defined event type and the averaged ones (Avg.) on single-event (S.) and multi-event (M.) sets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **EF (\%)** & **ER (\%)** & **EU (\%)** & **EO (\%)** & **EP (\%)** & **AVG. (\%)** \\ \hline DEEDP & 73.9 & 91.1 & 76.6 & 77.2 & 78.9 & 79.5 \\ w/o RicFea & \(-0.9\) & \(-0.7\) & \(-1.1\) & \(-0.4\) & \(-0.3\) & \(-0.7\) \\ w/o DocEnc & \(-2.3\) & \(-1.2\) & \(-1.9\) & \(-0.6\) & \(-0.8\) & \(-1.4\) \\ w/o SF-ATT & \(-3.1\) & \(-2.7\) & \(-3.8\) & \(-1.1\) & \(-1.4\) & \(-2.4\) \\ w/o TF-M & \(-2.9\) & \(-3.1\) & \(-4.2\) & \(-1.5\) & \(-1.7\) & \(-2.7\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: F1 scores of ablation tests on DEEDP variants for all event types and the averaged ones (Avg.).
## 5 Conclusions  
In this paper, we provided baseline payload information entropy metrics across a broad range of common network services by analysing several widely used datasets in cybersecurity research. To the best of our knowledge, these data have not been published previously--at least not comprehensively. From our analysis, mean information entropy values for packet payload are generally consistent across a range of packet capture environments and illustrate the varying degrees of data protection provided by internet and enterprise services, with subtle differences in inbound and outbound directions. These metrics may be used to approximate ground truth for efficiently characterising encapsulated content, from which it should be feasible to help identify certain types of anomalous behaviour. While payload information entropy alone is insufficient to detect broader classes of suspicious behaviour, it can be useful to help identify unusual network behaviour, particularly when correlated with other features, such as flow direction, source and destination network addresses, destination port, timing, state flags, and complementary volumetric features, such as payload size and transfer rate.
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
Footnote †: This paper proposes a secure routing protocol based on an ad hoc on-demand distance vector to simultaneously achieve communication efficiency and security. Many studies have discussed secure protocols. However, conventional protocols tend to exhibit low communication efficiencies owing to the long packets required by digital signatures, specifically in large-scale networks. Hence, our proposed method aims to allow the intermediate node to initiate a route reply (RREP), which is prohibited in conventional protocols because of digital signature restrictions. Based on an ID-based signature, the proposed protocol allows each intermediate node to hold a packet received from a specific node in the past. Each node then appends it to the route request of another node and generates its own signed RREP. This procedure guarantees that a third party holds the route to the destination. Theoretical evaluations demonstrate that the proposed method outperforms the communication efficiency of conventional secure protocols. We measured the time required for routing (i.e., the sum of communication and cryptographic calculation times) using a Raspberry Pi with C language. We show that the proposed protocol can improve the average routing time by more than 3\(\times\) compared with conventional methods when 30 relay nodes are randomly distributed in a 300-square meter area.  
[PERSON], [PERSON], [PERSON], K. A Communication-Efficient Secure Routing Protocol for IoT Networks. Sensors2022227503. [[https://doi.org/10.3390/s22197503](https://doi.org/10.3390/s22197503)]([https://doi.org/10.3390/s22197503](https://doi.org/10.3390/s22197503))
# On the Use of Transformer-Based Models for Intent Detection Using Clustering Algorithms
## 3 Materials and Methods
### Ensemble Models  
An ensemble clustering approach involves the use of several base algorithms, either multiples of the same or different ones, to reach superior performance. The main goal of ensemble clustering is to obtain more stable results, and it has been successfully applied to intent clustering [12]. Another advantage of ensemble clustering is the potential to obtain various perceptions from the data.  
Multiple ensemble configurations can be considered. Hence, there is a need to focus only on suitable solutions. Initially, a homogeneous ensemble composed of several K-Means clustering algorithms was employed to determine the results' stability by continuously increasing the number of clustering algorithms used. A combination of different clustering algorithms was also evaluated since the quantity of algorithms does not necessarily imply quality. Additionally, multiple embeddings can be used in conjunction or individually. Combining embeddings from different models with dissimilar interpretations and knowledge can yield better performance due to their combined understanding. In this scenario, the consensus function used was standard plurality voting, where the most agreed-upon cluster determines the class of a point.
## 4 Performance with Phase Noise
### Test Setup  
The general model used for the performance investigation in the presence of phase noise is shown in Figure 5, yet the following modifications have been introduced:  
* The multipath fading simulator is replaced by a short connection.
* The LO exploited for the demodulation is modified to introduce adjustable level narrowband noise to the oscillator controlling signal, which yields the following expression for its output signal: \[s_{LO}(t)=\sin(2\pi f_{0}t+\varphi_{noise}),\] (3) where \(f_{0}\)--frequency of the LO (140 MHz); \(\varphi_{noise}\)--phase noise.  
The study assumes the generation of baseband noise with a power spectral density relative to the carrier's power (dBc), which turns to a dual sideband with the same power levels when shifted at the carrier using (3). The measured phase noise of the SMC100 signal generator using an internal oscillator from the datasheet [58] in Figure 15a is used to generate baseband noise with the power spectral density presented in Figure 15b. This approach allows adjusting the levels of the phase noise concerning the actual performance of the generator. While the curves in Figure 15b sweep across frequencies and show different power levels, the power level at 1 Hz is used to adjust the power spectral density level. The value at 1 Hz also refers to the noise curve used for the experiments.  
The study investigates the performance of the systems in the AWGN with several power levels of the phase noise signal. This study demonstrates the combined influence of the two noises on the systems' performance.
## 2 Material and Methods  
### Aquaponic Systems Description  
Nine autonomous aquaponics systems with a total volume of 630 L per system were used. Each system consisted of (i) a fish tank with a 400 L water volume, (ii) a 50 L hydroponic cultivation tank (112 \(\times\) 73 \(\times\) 20 cm) filled with clay pebble (8-16 mm) substrate, (iii) a biological sump filter of 180 L total volume, and (iv) a mechanical filter of 60 L. The sump filter was filled with 10 L of porous cylindrical substrate K1 (11 mm diameter each), which provided a large specific surface area (SSA) for nitrifying bacteria to colonize. A pump (HAILEA-hx-8830, 45 W, 2900 L/h, h\({}_{\text{max}}\) 2.3 m) was placed in the last part of the filter to supply the aquaponic system with water through the filter (Q = 5.10 L/min). The mechanical filter consisted of three layers of fiberglass material, each layer 10 cm thick, in order to retain the solid residues from the fish tank (uneaten food and feces). Clay pebble substrate of the hydroponic tank also provided enough biofiltration, increasing the efficiency of the system. An air-lift pump (HAILEA, ACO-328, 70 L/min) was used to diffuse oxygen to the aquaponics system. Oxygen was equally distributed in the fish tank and the biological filter. A two-month period was required for the setup of the biological filter setup. According to [PERSON] [24], 40-60 days are necessary for the establishment of bacteria and the efficient oxidation of ammonia to nitrate ions.
## 2 Materials and Methods
### Data Processing and Post Processing  
The fluxes of energy and CO2 were calculated using the LoggerNet software (Campbell Scientific, Inc., Logan, UT, USA) by converting the high frequency data into the binary format (TOB1) with a 30 min timestep. The high frequency data were processed using the EdiRe software, developed by [PERSON] and [PERSON] of the University of Edinburgh. The EdiRe algorithm transforms high frequency data in half-hourly averages. Data processing includes corrections such as: the detection of spikes, delay correction of H2O/CO2 in relation to the wind vertical component, coordinates rotation correction (2D rotation) using the planar fit method, correction of spectral loss, sonic virtual temperature correction, corrections for flux density fluctuation: WPL-correction [40], as well as the incorporated frequency response correction derived from the following studies [41; 42].  
For the detection of spurious data (spikes) we used a method described in the literature [43]. Gap filling due to data inconsistency and the rejection of spurious values was carried out by using the method described as proposed in the literature [44], which takes into consideration the covariance between fluxes and meteorological variables and also the temporal self-correlation of fluxes. In this algorithm, actions are taken considering the following conditions: (i) if there are missing flux data, but meteorological data (incoming solar radiation--Rg, Ta and vapor pressure deficit--VPD) are available, then the gap is filled with the mean value considering similar meteorological conditions in a 7-day window; (ii) if only incoming solar radiation data are available, the gap is filled with the mean value considering similar meteorological conditions in a 7-day window; (iii) if no meteorological data are available, the gap is filled with the mean value in the last hour, thus considering the diurnal variation of each variable. If data gaps still exist after applying the algorithm, the same procedures will be carried out but considering larger time windows. The gap filling method was carried out by using an online tool by the Max Planck Institute. Further details about data processing and post processing were presented in the literature [15].