esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study: The Tasmanian Marine Analysis Network  
The Tasmanian Marine Analysis Network (TasMAN) [20] has been developed and deployed to help manage the multiple uses of the estuaries and coastal regions of southern Tasmania, Australia (Figure 1). These regions are used by industry, shipping, aquaculture and tourism operators along with commercial and recreational boaters and fishers. The network is designed to collect real-time data that will help monitor the health of the estuaries, as well as to provide warnings to industry and the general public.  
South-eastern Tasmania is home to two estuaries with very different histories. The Derwent estuary flows through the Tasmanian capital of Hobart. While the water quality is now improving, it has been greatly impacted in the past by urban stormwater and sewerage, industry (including a zinc smelter and paper mill), dense agricultural activity in the catchment, shipping, and commercial and recreational boating and fishing. In recent years, regulations and programs have led to a dramatic increase in water quality; however the riverbed sediment remains amongst the most polluted in the developed world [21]. The neighbouring Huon estuary has its headwaters in a World Heritage Area that makes up the southwest of the state. This region is largely untouched, mainly because of its remoteness and ruggedness. While there is some agriculture in the lower reaches of the catchment, and some aquaculture (salmon farming) near the mouth of the estuary, it is in a near pristine condition.  
Figure 1: Location of node deployments and planned nodes in the TasMAN network.  
The TasMAN network consists of fixed and mobile nodes and uses mobile (3G) communications to deliver real-time data from the two estuaries, and the D'Entrecasteaux Channel which joins them, back to the CSIRO site in Hobart where they are integrated with models to produce nowcasts and forecasts of river conditions. It is also a demonstration infrastructure where sensor data are exposed for different applications and through different platforms. In these applications, data quality should be assured.
## 4 Results and Analysis
### Identification of Signal App Traffic
#### 4.2.7 Video Calls  
During the video call, similar patterns of audio calls were observed as shown in Figure 16. The only difference was found in the length of UDP packets that varies from 800 bytes up to 1250 bytes.  
Figure 16: UDP traffic patterns for video calls.  
Figure 14: The flow of UDP packets during a call.  
A summary of observed encrypted traffic patterns is shown in Table 4. As we learned the behavior of all major activities, now we can filter out the application traffic by looking at these patterns. From a generic network traffic dump, we can identify the parties without knowing anything about the parties beforehand. By looking at the patterns of protocol, forensic experts, network administrators, and security engineers can filter the Signal packets first and then further inspect them to identify the information about the user activities, type of communication, etc. to examine many cases depicting various scenarios.
## 2 The Communication Infrastructure of a Smart City  
The communication infrastructure of a smart city must be suitably designed to satisfy the specific requirements and needs of the considered environment. In particular, it has to support basic functionalities such as sensing, transmission, and control. Sensing is carried out by a large number of sensors and smart devices (even people can be sensors) that monitor the environment; this information is transferred to a control center that performs data elaboration, thus providing control instructions that are delivered to sensors/actuators. A basic characteristic of a smart city is that it is composed of multiple heterogeneous devices that are connected regardless of their locations in an autonomous and scalable way compliant with the Internet of Things (IoT) paradigm. Moreover, a smart environment usually spreads over large geographical areas, and hence requires a multilayer communication infrastructure that extends across the whole smart environment. At least three communication layers can be foreseen: \"local area networks\", \"access networks\", and \"computing/application networks\". The local area networks layer is responsible for monitoring the environment using heterogeneous devices and sensors (which can be embedded in vehicles, buildings, urban environment, and people, for example). The access layer represents the communication backbone that allows the transfer of information from the local networks to the control center. The computing layer is related to the applications that address the data collected by the sensing layer. Even if in some cases wired connections are possible, mobile and ubiquitous wireless connections are preferred for a wide class of services and applications. Mobile connections have the potential to provide remote control and monitoring without the addition of any cabling cost.  
The previously described multilayer-structure can provide efficient, reliable, and secure communications if it is designed taking into account the specific needs and challenges of a smart city. Moreover, as a final consideration, we have to note that due to the complexity of the network infrastructure and its deployment costs, a suitable planning in relation to the short, medium, and long-term demands of smart services and applications must be pursued. In particular, future cellular networks should enable to realize a truly networked society with unlimited access to information for anyone. In that way, it will be possible to support various smart infrastructures and smart cities that are green, safe, mobile, connected, and informed.
## 4 Simulation Experiments: Security Analysis and Comparison
### Ciphertext-Only Attack  
A ciphertext-only attack [50] arises when a cryptographer obtains certain ciphertexts and endeavors to acquire the confidential keys and eventually the plaintext. However, without the secret keys of the proposed scheme, the cryptanalyst cannot reveal the plaintext. Moreover, as explained earlier, the key space of the proposed scheme is at least \(2^{100}\), making it extremely time consuming to conduct a brute-force attack to decrypt the ciphertext without the keys. Thus, the proposed scheme is considered secure against ciphertext-only attacks.
# MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design
## 4 Results and Discussion
### Antenna Position Optimization
#### 4.1.1 Design 1: Peak Side Lobe Level Minimization of a 10-Element LAA  
The MAOA algorithm is applied to estimate the position of all the antenna elements from the origin. The fitness function for this design is provided in Equation (27), where \(\mathrm{\Theta}\) represents the region of the side lobes. In this equation, \(\mathrm{max}(20\mathrm{log}|\mathrm{AF}(\mathrm{\Theta})|)\) provides the maximum SLL called PSLL, and \(\mathrm{AF}(\mathrm{\Theta})\) denotes the array factor given in Equation (3). The antenna position has been optimized for minimizing the PSLL in the region \(\mathrm{\theta}=[0^{\circ},74^{\circ}]\) and \(\mathrm{\theta}=[106^{\circ},180^{\circ}]\).  
\[\mathrm{fitness\ function}=\mathrm{min}\bigg{[}\mathrm{max}\bigg{(}\frac{20 \mathrm{log}|\mathrm{AF}(\mathrm{\Theta})|}{\mathrm{max}|\mathrm{AF}(\mathrm{ \Theta})|}\bigg{)}\bigg{]} \tag{27}\]  
The number of antenna elements is even; hence, to make a symmetrical layout of the array, no elements are placed at the origin. The results are shown in Table 1, highlighting PSLL and the optimized positions of the right-sided five array elements from the origin expressed as a multiple of \(\lambda\). The left-sided elements have the same position from the origin on the opposite side. The mentioned PSLL indicates that MAOA provides considerable PSLL minimization from \(-12.97\) to \(-21.82\) dB, i.e., a reduction of \(8.85\) dB from conventional ULA. Similarly, a reduction of \(2.1\) dB from PSO, \(0.51\) dB from GOA, and \(1.57\) dB from SMO-optimized antenna array is observed. The array pattern obtained using the optimized position is illustrated in Figure 6.
## 2 Materials and Methods
### Evaluation of Temperature Fields  
The focus of this section is solving the transient temperature distribution in a 1-D composite medium with finite thickness; the overall thickness of the present model is \(L_{1}+L_{2}+L_{3}\) (see Figure 1). Mathematically, the conduction of heat into a composite medium with internal heat generation within each layer has to obey  
\[\frac{\partial T_{j}(x,\,t)}{\partial t}=\alpha_{j}\frac{\partial^{2}T_{j}(x, \,t)}{\partial x^{2}}+\frac{\alpha_{j}}{K_{j}}q_{j}(x,\,t),\,\,j=1,\,2,\,3, \tag{5}\]  
where \(\alpha_{j}=K_{j}/(\rho\ c_{j})\) is the thermal diffusivity, \(c_{j}\) is the specific heat capacity, \(T_{j}\) is the temperature, \(t\) is the time measured in seconds, \(K_{j}\) is the thermal conductivity, and \(q_{j}\) is the volumetric rate of heat generation.  
The molecular vibration in the tissue due to ultrasound will result in temperature change and achieve the therapeutic effect. The volumetric heat generation rate, \(q_{j}\), which results from the propagation of ultrasound can be calculated from the computed pressure by [26]:  
\[q_{j}=a_{j}\frac{P_{j}P_{j}^{*}}{R_{j}},\,\,j=1,\,2,\,3, \tag{6}\]  
where \(a_{j}\) stands for the attenuation coefficient, and \(P_{j}\) is the pressure resulted from the ultrasound diathermy (see Equation (3)). The superscript \({}^{*}\) represents the conjugate of complex numbers.  
The composite system is initially set at a uniform temperature, hence, the initial conditions are  
\[T_{j}(x,\,t=0)=T_{init},\,\,j=1,\,2,\,3, \tag{7}\]  
Without losing generality, the front surface (at \(x=0\)) of the composite system is exposed to a change in the temperature up until \(T_{end}\) after heating [27]. In addition, the rear surface is set as adiabatic, resulting in the corresponding boundary conditions,  
\[T_{1}(x=0,\,\,t=t_{end})=T_{end}, \tag{8a}\]\[\frac{\partial T_{3}(x,\ t)}{\partial x}=0,\ \text{at}\ x=L_{1}+L_{2}+L_{3},\ t>0, \tag{8b}\]  
Moreover, the temperature matching condition across the interface between adjacent layers is given by  
\[T_{j}(x_{j},\ t)=T_{j+1}(x_{j},\ t),\ x_{j}=\sum_{r=1}^{r+j-1}L_{r},\ j=1,\ 2,\ t>0, \tag{9}\]  
and the heat-flux matching condition that assures perfect thermal contact is  
\[K_{j}\frac{\partial T_{j}(x_{j},\ t)}{\partial x}=K_{j+1}\frac{\partial T_{j+ 1}(x_{j},\ t)}{\partial x},\ x_{j}=\sum_{r=1}^{r+j-1}L_{r},\ j=1,\ 2,\ t>0, \tag{10}\]  
The analytical solution for the one-dimensional transient heat conduction problem with non-homogeneous boundary conditions can be obtained with the aid of the orthogonal expression technique and a Green's function approach [28]. More details of the derivation are listed in the Appendix A.
# Robust Static Structural System Identification Using Rotations
## 4 Using Redundant Rotations for Parameter Estimation  
When essential measurement sets are used, the target parameters are observable. However, due to the ill-conditioned inverse problem, the estimations using essential sets are far from satisfactory. A reasonable solution to alleviate the adverse effect of measurement errors is to include redundant measurements. Four strategies to use redundant rotations are presented in Section 4.1. The effectiveness of these strategies was investigated in two structures regarding the estimation accuracy and dispersion. The first structure (Section 4.2) was a simply supported bridge (Figure 3) and the second (Section 4.3) was a 13-story frame structure.
# An Experimental Study on Ice Accretion under Bridge Cable in Different Conditions
## 4 Conclusions  
The test was carried out under specific low-temperature laboratory conditions, and the obtained icing results may also be affected by the high-density polyethylene (HDPE) material on the cable surface.  
(1) The growth of the ice rib is mainly affected by temperature, and the width of the ice ribs increases continuously as the temperature decreases. However, the width of the ice rib is less affected by the inclination angle and diameter of the stay cable.  
(2) The change in inclination angle has a particularly pronounced effect on the lower part of the cable. With the increase of inclination angle, the maximum length and number of icicles at the bottom of the stay cable will decrease. Therefore, the bottom area of the stay cable with inclination of \(\alpha\) < 60\({}^{\circ}\) is the main area of anti-icing and deicing.  
(3) The amount of ice accumulation in the lower part of the cable decreases with increasing diameter of the cable, and the increase in the diameter can effectively reduce the formation of icicles in the lower leg. Therefore, smaller-diameter cables are more prone to ice accumulation at the bottom.  
(4) Ambient temperature is an essential factor influencing ice accumulation in the lower part of the cable. The maximum length of icicles in the lower part of the cable increases linearly with decreasing temperature. Lower temperatures create more icicles at the bottom of the stay cables.  
Figure 11: (**a–c**) Influence of cable inclination angle on the number of icicles (per meter).  
In future work, we plan to expand the icing test and add more environmental factors to include a wider range of icing conditions and icing shape changes. In addition, by changing the surface material and surface roughness of the stay cable, the icing results obtained can be promoted to a greater extent. We also plan to study the deicing facilities based on the obtained test results to provide assistance for cable deicing.  
Writing--original draft preparation, Z.G.; writing--review and editing, W.L.; project administration, H.X.; software, Y.P.; data curation, K.Y. All authors have read and agreed to the published version of the manuscript.  
The authors appreciate the financial support from Joint Funds of the Natural Science Foundation of Hubei Province (No. 2022 CFD130), the Key Research and Development Program of Hubei Province (No. 2021 BGD015), and the Knowledge Innovation Project of Wuhan (No. 2022010801010259).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Removal of Cochineal Dye Color through Atmospheric Pressure Plasma Discharge Jet
## 3 Results and Discussion
### Effects of Plasma Treatment on pH and Electrical Conductivity during Dye Degradation  
The blue curve in Figure 2 shows that the pH of the dye solution exponentially decreased with the interaction time between the dye and the reactive species (RONS) generated by the APPJ discharge. A significant decrease in pH was observed during the first two min, while it tended to stabilize between 2 and 10 min. The pH value change from 5.4 to 2.7, indicating a transformation from an acid dye to an extremely acidic state. These findings are consistent with those reported by [PERSON] et al. [42], where a decrease in pH (<5) was associated with the following chemical reactions:  
\[\mathrm{NO_{2}+NO_{3}+HO_{2}\to NO_{2}^{-}+NO_{3}^{-}+2H^{+}}\]  
\[\mathrm{NO+NO_{2}+HO_{2}\to 2 NO_{2}^{-}+2H^{+}}\]  
\[\mathrm{HO_{2}+e^{-}\to HO_{2}+2e^{-}}\]  
\[\mathrm{O+HO_{2}\to H_{2}O_{2}}\]  
\[\bullet\mathrm{OH+\bullet OH\to H_{2}O_{2}}\]  
The results obtained from the conductivity measurements (red curve in Figure 2) indicate a variation between 10 and 650 \(\mu\)S cm\({}^{-1}\) during the first 10 min of irradiation with APPJ plasma. Similar findings were reported by [PERSON] et al. [42], who showed a substantial increase in the electrical conductivity of water from 24 to 724 \(\mu\)S cm\({}^{-1}\) after 30 min of plasma irradiation. This significant conductivity variation is associated with the formation of free radicals, such as O\({}_{3}\), H\({}_{2}\)O\({}_{2}\), NO\({}_{3}\)\({}^{-}\), and NO\({}_{2}\)\({}^{-}\), which contribute to the formation of the acid dye and induce changes in the conductivity [16].  
Figure 2: Changes in pH and dye conductivity owing to APPJ exposure.  
Plasma treatment has been found to effectively reduce the pH of the dye solution and increase its electrical conductivity during dye removal. These changes are a result of chemical reactions involving reactive species generated by plasma discharge. However, further research is required to understand the mechanisms involved in dye degradation. Understanding the effects of APPJ plasma parameters on dye degradation can be used to tailor the degradation process for specific applications. These findings have important implications for the future research and development of plasma-based solutions for wastewater treatment and environmental remediation [13].
# COVID-19 Detection Model with Acoustic Features from Cough Sound and Its Application
## 2 Related Work
### Coswara  
A project named Coswara [25] was carried out in India to develop a tool for diagnosing COVID-19 using audio recordings such as speech, breathing, and coughing. Worldwide data were gathered with an easy-to-use, interactive user interface. Participants recorded samples using a device microphone, such as a laptop or mobile phone, and provided metadata using a web browser application. During recording, participants were instructed to keep a distance of 10 cm between their mouth and the device. Nine audio samples were recorded per person: cough sounds (deep and shallow), breathing sounds (fast and slow), sustained vowel ('eu', 'I', 'u') pronunciation sounds, and counting sounds (slow and fast). The participant's age, gender, area, history of illness, and current state of health with respect to COVID-19 were all included in the metadata. The sampling frequency of audio samples was 48 kHz. Data from 2747 individuals were made public as of 24 February 2022. Of these, 681 individuals had COVID-19-positive test results.  
[PERSON] et al. [21] used Viruty [26], a dataset collected under the supervision of medical professionals in hospitals, along with the Coswara dataset, to study a COVID-19 diagnostic model. In the Coswara dataset, only shallow cough recordings were used, and 200 samples of healthy people's data were randomly selected and used to balance the training data set. The Virufty dataset consists of 16 recordings: 7 recordings from people diagnosed with COVID-19 and 9 recordings from healthy people. The Coswara dataset was used for training and the Virufty dataset was used for testing. First, silent recordings and noise/speaking recordings were distinguished through the SVM model trained with the energy features of each audio recording, and only the parts containing sound were extracted. Features of the audio signal were then extracted: centroid, energy, entropy of energy, spectral flux, MFCCs, spectral spread, and zero-crossing rate. A k-nearest neighbors (KNN) model was used to distinguish conversational sounds from cough sounds in a recording, and a new recording was created by connecting the cough sounds detected within one recording. The next step was to train four models--the KNN, SVM, random forest, and recurrent neural network (RNN)--to classify coughs in COVID-19-positive participants and healthy individuals. As a result of testing with the RNN model, which had the best results during the training process, the accuracy was 0.81 and the AUC was 0.79.
## 4 Experimentation  
The validation of the work carried out was conducted through experiments in which the implemented approach can be quantified, starting from the formulated hypothesis and all the elements implemented for its identification. In order to assess the effectiveness of the model and algorithm, the experiment was executed conducting the following steps:  
Goal: measure the evaluation metrics of the information retrieval system to determine the model's effectiveness and verify whether quality has a positive impact in the information retrieval process in terms of the evaluation metrics. Evaluation metrics: below, the common evaluation metrics of precision, recall, and the F1-score for information retrieval processes are specified. \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FP}}\]  
The formula to calculate F1 combines the precision and recall metrics into a single value. \[\text{F1}=\frac{2*\text{Precision}*\text{Recall}}{\text{Precision}+\text{ Recall}}\]  
Where:  
True positive (TP) = retrieved relevant documents  
False Positive (FP) = retrieved not relevant documents  
False negative (FN) = did not retrieve relevant documents  
Dataset selection: For this experiment, a balanced sample of 40 scientific documents was taken, 20 with high quality and 20 with low quality, from different information sources (see Table 7). In this case, the scientific documents were gathered from databases such as IEEE Explore, Springer, and Elsevier. Furthermore, twenty-five potential user queries (see Table 8) were designed and used for the two groups. Execution of the experiment: Table 9 shows the queries performed in the information retrieval system created and the values obtained for each of the precision, recall, and F1 metrics used for their evaluation. Figure 3: Classification of documents by groups according to the quality value processable machine. Analysis of results: The results of the 25 queries are presented, and divided into two groups: the first group corresponds to the documents that have obtained a low evaluation; therefore, the precision was 42%, the recall was 17%, and the F1 was 20%, which is in the range of the expected result. In the second group, made up of the documents that obtained a high evaluation, hence a pre-accuracy of 74%, recall of 71%, and F1 of 66%, this result is within the expected range, achieving, in general, excellent accuracy and a good recall. In this sense, it can be observed that the metrics of the second group are higher than those of the first group (Figures 4-6), thus evidencing that retrieval with high-quality documents shows better results. In addition, a two-sample Student's T-test was performed, assuming that the variances are different to compare the processes in each of the metrics, using the F-test with an alpha of 0.05 to affirm or reject the hypothesis: the quality of scientific documentation impacts information retrieval. The results are shown in Tables 10-12, respectively.
# Study on Path Planning Method for Imitating the Lane-Changing Operation of Excellent Drivers
###### Abstract  
Lane-changing is an important operation of an autonomous vehicle driving on the road. Safety and comfort are fully considered by excellent drivers in lane-changing operation. However, only the kinematic and dynamic constraints are taken into account in the traditional path planning methods, and the path generated by the traditional methods is very different from the actual trajectory of the vehicle driven by the excellent driver. In this paper, a path planning method for imitating the lane-changing operation of excellent drivers is proposed. Five experienced drivers are invited to do the lane-changing test, and the lane-changing trajectories data under different conditions are recorded. The excellent driver lane-changing model is established based on the genetic algorithm (GA) and back propagation (BP) neural network trained by the data of the lane-changing tests. The proposed approach can plan out an optimized lane change path according to the vehicle condition by learning the excellent drivers' driving routes. The results of simulations verify that the path generated by the proposed algorithm is basically same as the track selected by the excellent drivers under same conditions, which can reflect the characteristics of the operations of the excellent driver. While applying safe lane-changing to autonomous vehicle, it can improve the ride comfort of the vehicle and therefore reduce the probability of motion sickness of the passengers caused by improper operation during lane change.  
path planning; lane change; excellent driver model; neural networks; autonomous vehicle +
Footnote †: journal: Applied Sciences