esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Explainability-Enhancing Methods
#### 2.3.2 Natural Language Processing Models  
While FCMs offer transparency in the form of weight tables and concept interconnections, translating these numerical outputs into easily understandable language can be a formidable task for domain experts, especially in the medical domain. GPT-3.5 is a language model developed by OpenAI [40]. It is designed for natural language understanding and generation tasks. The model has been trained on a wide range of internet text to be able to provide informative and contextually relevant responses to user prompts [41]. GPT-3.5 is capable of understanding and generating human-like text, making it a versatile tool for various applications, including answering questions, assisting with writing, tutoring, and more [38; 42].  
To use GPT-3.5 for generating medical diagnoses from classification models, we integrated it into the DeepFCM framework, which incorporates both GPT-3.5 and the classification model. The process involves the following steps [38]:  
* Train the classification model: Firstly, we developed a specialized classification model specifically for medical diagnosis, as explained. This model is trained on medical data with labeled diagnoses to accurately classify patients' conditions based on their symptoms and other relevant information.
* Integrate GPT-3.5: After training the classification model, we integrated GPT-3.5 into the medical information system. OpenAI provides an API that allows developers to access GPT-3.5 programmatically and send prompts for generating responses.
* Prompt generation: When a user enters medical information, such as symptoms or test results, into the system, the classification model generates a diagnosis that involves the final classification output, the final output vector, and the final weights of the DeepFCM model. These components are used as a prompt for GPT-3.5.  
* Obtain response: GPT-3.5 will process the prompt and generate a response in natural language. This response could be a human-readable explanation of the diagnosis provided by the classification model, additional information about the condition, potential treatment options, or other relevant insights.
* Present results to users: We displayed the generated response to the user, helping healthcare professionals understand the reasoning behind the diagnosis and make informed decisions regarding patient care.
## 4 Results and Discussion
### Comparison with State of the Art Pretrain Deep Learning Models  
The key benefit of using TL classifiers and fine tuning is that it reduces overfitting problems, which frequently occurred in DL algorithms when testing with a smaller sample of training and testing images. For the detection and classification of BC, all TL models were trained and validated using the same TL parameters as those specified in Table 4. To identify BC, we used 1030 ultrasound pictures of BC. Each TL classifier produced acceptable results, as shown in Table 6, which summarizes the specific results of several TL algorithms in classifying BC images. Using assessment measures for accuracy, precision, recall, and f-measure, we examined and assessed the TL algorithms. The dataset was divided into training and testing sets for this experiment, with 70% of the data utilized for model training and 30% for testing. Alexnet, Resnet18, SqueezeNet, Shufflenetet, Resnet50, ExceptionNet, and Proposed DL-based classification techniques were used in the proposed study. The confusion matrix in Figure 4 of the Suggested model shows that the score is high in all BC classifications. It was witnessed that the DeepBreastCancerNet achieves the highest accuracy of 99.35%, Squeezenet achieves the lowest accuracy of 70.81%, while AlexNet achieves the second-lowest accuracy. ShuffleNet and ExceptionNet achieved the same accuracy of 99.03%. In addition, the findings beat those of the pre-trained models. At the same time, the categorization results of the models are demonstrated in Table 6. The DeepBreastCancerNet model outperforms all the pre-trained models concerning the accuracy, precision, recall, and F1- measure. When the Proposed model is trained and verified, the accuracy and loss values are shown in Figure 5. The graph in Figure 5 illustrates that, as indicated in Table 5, the custom model provides exceptionally high accuracy results.
# Attacks and Preventive Measures on Video Surveillance Systems: A Review
## 2 Attacks on VSS
### Denial of Service and Jamming Attacks  
Any action or series of actions that prevent any part of an information system from functioning is known as denial of service by jamming the communication network [35]. When monitoring important activities such as real time crimes, in many video surveillance systems, it is critically important to have an un-tampered and uninterrupted operation. DoS and jamming attacks with a very short duration of less than a few minutes can still make a VSS camera miss a fast-paced crime such as a bank robbery. A denial-of-service attack on a home surveillance camera will not have a major impact when compared to denial-of-service attacks on commercial surveillance systems, which may have a greater impact. These kinds of attacks must be taken into consideration during the early phases of the setup and testing of the surveillance system. For example, \"BrickerBot is a malware that attacks IoT devices that run a specific version of the DropBear SSH server and target Linux devices running Busy box (usually IP cameras)\".  
DoS attacks can be classified into two types: flooding and logic attacks. Flooding attacks work by overwhelming the current network with a large volume of complex data packets to deplete their resources such as memory and bandwidth. Logic attacks exploit the known vulnerabilities in the system to attack the remote servers. Out of these two types of attacks, flooding attacks are more dangerous as it is difficult (resource-intensive, time-intensive and cost-intensive) to differentiate real data packets from the flooded data.
# Telecommunication Systems for Small Satellites Operating at High Frequencies: A Review
## 3 MarCO
### Hardware Characteristics
At launch, the three reflectarray panels are folded down against the spacecraft body and against each other to form a single \(19.9\times 33.5\times 1.25\) cm\({}^{3}\) panel stack. A \(4\times 2\) element microstrip patch antenna is used as a feed for the reflectarray antenna. The feed is placed on a thin aluminum plate that has a simple spring-loaded hinge and a small rotary joint. The feed folds underneath the reflectarray. During deployment, the panel assembly rotates away from the bus on the root hinge axis and the feed automatically flips out from the bus and lock into the desired position. The measured gain of the MarCO antenna is 29.2 dBi at 8.425 GHz, and the total mass is approximately 1 Kg. The UHF antenna (Figure 8) [8] is a deployable loop fed at two locations, with each generating orthogonal linear polarization \(90^{\circ}\) out of phase with the equal amplitude, thus giving rise to circular polarization. The antenna provides a gain better than 2.5 dB over the \(\pm 30^{\circ}\) angle in the elevation and azimuthal planes which was sufficient to receive the InSight signal during the EDL phase. All the MarCO antennas (X-Band and UHF) are designed to operate without damage between \(-55\) deg C and \(+125\) deg C.
## Materials and Methods
### Data Collection  
#### Unmanned Aerial Vehicle Hyperspectral Image  
Unmanned aerial vehicle (UAV) remote sensing has rapidly developed, having the advantages of high flexibility and high image quality [34]. A UAV flight experiment can be carried out free from atmospheric interference at a relatively low price. Field measured data for this study came from a Cubert S185 airborne imaging spectrometer (Cubert GmbH, Germany) carried by an eight-claw UAV. This UAV machine has been carried out for a study to retrieve soybean leaf area index (LAI) by comparing four regression models [35]. The features of synchronous imaging for all the spectral channel of S185 are suitable for airborne mobile measurement, and the data are real and reliable without artifacts. Different from the traditional push-scan imaging method, it adopts the frame synchronous imaging technology without any moving parts. The hyperspectral images had a spectral range of 450 to 950 nm with a spectral sampling interval of 4 nm, a spectral resolution of 8 nm at 532 nm. It has 125 spectral channels in total including all the important vegetation-related bands from visible light to near infrared. The important channels for the study are concentrated around the wavelengths of 705 nm, which is the band-5 in Sentinel-2A satellite settled for vegetation red edge. For each band, the image has a 12 bit (4096 DN) dynamic range created by projecting different bands to different parts of a charged coupled device (CCD). The flight date was Sept. 28, 2017, at a height of 250 m, and the spatial resolution was approximately 6.5 cm.  
The pre-processing of the UAV hyperspectral image mainly includes the radiometric correction and accurate multi-image splicing. The radiometric calibration system is used to calibrate the hyperspectral image, converting the pixels' Digital Number (DN) into surface reflectivity. Afterwards, the structure from motion algorithm (SFM) was used to detect the feature points satisfying the image overlap degree (course overlap \(>\)70%, side overlap \(>\)30%). The matching pairs of feature points were established, and the related image orientation was corrected according to the ground control points to achieve the image re-arrangement. The dense multiview stereo algorithm (MVS) is used to reconstruct the 3D point cloud of the rearranged image, and the digital differential inverse method is used to realize the mosaic and orthophoto correction of the image. Topographical correction and noise processing were also adopted to obtain the standard reflectance hyperspectral image, eventually as shown in Figure 1.
# A Deep Temporal Neural Music Recommendation Model Utilizing Music and User Metadata
## 3 Deep Temporal Neural Music Recommendation Model
### Music Feature Component  
The music feature component extracts songs' hidden features based on song metadata. The meta fields used in the music feature component are listed in Table 1.  
There are two field types in our model. Linear fields are the fields with continuous numeric values. The values of the linear fields are scaled into [0-1]. One-Hot fields are the fields with enumeration values. We encode the field values as one-hot vectors separately. We added the one-hot vectors together in case a song has multiple values, for example, a song may have several artists. The concatenation result of all the fields is the meta vector of the corresponding song. Assuming a song \(s\)'s length is \(len(s)\), it has \(n_{g}\) genres, \(n_{a}\) artists, \(n_{c}\) composers and \(n_{l}\) languages, the meta vector of this song \(M(s)\) is calculated as Equation (1).  
\[M(s)=[len(s);(g_{1}(s)+...+g_{n_{g}}(s));(a_{1}(s)+...+a_{n_{e}}(s));(c_{1}(s) +...+c_{n_{e}}(s));(l_{1}(s)+...+l_{n_{l}}(s))] \tag{1}\]  
where \(M(s)\) is the meta vector of \(s\), \(g_{1}\) is the one hot vector of the first genre, is the concatenation operation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Type** & **Description** \\ \hline Length & Linear & length of the song \\ Genre & One-Hot & genre of the song \\ Artist & One-Hot & artists of the song \\ Composer & One-Hot & composers of the song \\ Language & One-Hot & language of the song \\ \hline \hline \end{tabular}
\end{table}
Table 1: The meta fields used in music feature component.  
Figure 1: The Deep Temporal Neural Music Recommendation Model (DTNMR).  
A Deep Neural Network (DNN) is used to extract hidden features from the meta vector \(M(s)\). We calculate the output of layer \(i\) as Equation (2).  
\[l_{i}=ReLU(W_{i}\cdot l_{i-1}+b_{i}) \tag{2}\]  
where \(l_{i}\) is the output of the \(i\)th layer, \(i=1,...N\), \(l_{0}\) is the input of the DNN, \(W_{i}\) is the weights of the neurons in \(i\)th layer and \(b_{i}\) is the bias, \(l_{N}\) is the output of the DNN and \(ReLU\) is the activation function. In DTNMR, the input vector length of the DNN is about 20,000. The DNN consists of four layers; the first layer is the input layer, the second is a hidden layer with 512 nodes, the third layer contains 64 nodes, and the output layer contains 32 output nodes. The layer sizes are chosen empirically. The output item vector is denoted as \(S(s)=l_{N}\).
# Damage Detection Method Based on Continuous Wavelet Transformation of Lamb Wave Signals
## 7 Discussion of Results
In case of a large dispersion, the wave signal will have more packets than the excitation after a certain propagation distance, and the time compensation will be larger than the half period of the excitation signal. CWT is also useful as it can suppress the dispersive nature of the Lamb waves and can also accurately process noisy signals. Furthermore, a lower frequency of the excitation signal was chosen in this method, and in the lower-frequency range of dispersion curves shown in Figure 5, the Lamb waves were less dispersive and the \(\mathsf{S}_{0}\) mode was well separated from the \(\mathsf{A}_{0}\) mode.
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## Materials  
### Study Sites  
In this study, three different irrigation sites are examined. The first site is located in Montpellier, southeast France (Occitanie region), the second is located in the Catalonia region of northeast Spain and the third in Tarbes of southwest France (Occitanie region) (Figure 1). It is important to mention that both the Montpellier and Catalonia sites are nearly similar in terms of climatic conditions given that both zones are typically Mediterranean. The average annual precipitation in Montpellier is 629 mm where that of Catalonia is 500 mm. However, the summer season in Catalonia is drier than that of Montpellier. On the other hand, the climate in Tarbes is humid to oceanic with an average annual precipitation of 1200 mm. The summer season in Tarbes is more humid with an average precipitation of 300 mm in this season. However, in the three regions, irrigation mainly occurs in the summer season between May and October of each year.
###### Abstract  
In this paper, an ultra-wideband (UWB) power amplifier (PA) on a 0.25 \(\upmu\)m gallium-nitride (GaN) on silicon carbide (SiC) high-electron-mobility transistor (HEMT) process, operating in Kuband, is presented. The broadband PA design is based on the four-stage non-uniform distributed amplifier structure. In order to improve the efficiency of the PA, a harmonic suppression network is added at the output of the drain artificial transmission line. At the same time, a capacitor is connected in series at the input of the gate, which is used to compensate for the phase offset of the gate and increase the cut-off frequency of the PA. The final gate width of the first stage is 0.56 \(\upmu\)m, and the other three-stage gate widths are all 0.32 \(\upmu\)m. Over the frequency range of 2-16 GHz, the simulated results of this NDPA exhibit a power-added efficiency (PAE) of 16.6-27%, a saturated continuous wave (CW) output power of 35-37 dBm, a small signal gain of 9.1-11.6 dB, and output return losses of 5-15 dB.  
GaN-On-SiC HEMT; non-uniform distributed; power amplifier; ultra-wideband; harmonic suppression +
Footnote †: journal: Applied Physics Letters  
1
Footnote 1: email: lengyongqing@ime.  
excellent performance and a novel structure have been published [17; 21; 22; 23; 24; 25; 26]. A new biasing technique was proposed to prevent bandwidth degradation caused by cascading multiple amplifiers [8]. This design comes with a power added efficiency (PAE) of 18.4-36.5% and a large signal gain of 15.8-17.5 dB in the 2-20 GHz frequency range. A novel trifilar transformer and space-saving architecture was proposed using the WIN NP15 process [2]. In the 4-16 GHz frequency range, the highest PAE is up to 34%, the large signal gain is 8.7-14.6 dB, and the output power is 37.8-40.2 dBm. The authors of [3] designed a 6-18 GHz NPDA using a 0.25 um gallium-nitride (GaN) high-electron mobility transistor (HEMT). In order to increase the output power, it halves the characteristic impedance by doubling the phase of the drain line. The PAE is 13.6-33.8%, the large signal gain is 8-13 dB, and the output power is 6.8-14.5 W. A harmonic suppression method was proposed by [27]. This design, with the presence of the balun, achieved high-power-level even-order harmonic suppression properties. Moreover, the simulated results show a minimum linear gain of 10.8 dB, with the saturated output power of 44.6 dBm in the 2-6 GHz band. The author of [9] states that for NDPA to achieve a higher efficiency, the load impedance must be reduced and the total FET periphery must be increased. However, the current technology cannot maintain bandwidth and improve efficiency at the same time.  
In this work, the factors affecting the efficiency of the NDPA are analyzed in detail, and a harmonic suppression network at the output of the drain ATLAS is proposed. Section 2 introduces device models used in the design of circuits. Section 3 starts from the transmission line theory, explains the basic principle of the DA, as well as the design method and the final design schematic diagram. Section 4 shows the simulation results in detail and the conclusion is in Section 5.
## 4 Discussion and Conclusions  
To improve the reliability and stability of the proposed image-reject downconverter in future applications, a possible system with an auto-control function can be fabricated, as demonstrated in Figure 9. First, since all the sub-MZMs work at special bias points, they can be controlled via a bias controller (YY-Lab) to mitigate the effects of bias drift. The output image-reject signals--after hybrid coupling--are digitalized through an ADC, and the digital IF signal is processed in the DSP with matched filtering and clock recovery. In addition, in our scheme, a three-paddle PC is used to manually adjust the polarization state, which may influence the long-term stability of the system. To solve this problem, the three-paddle PC can be replaced with a commercial electrical polarization tracker (EPT) in practical applications. The polarization state can be automatically controlled in real-time by a commercial EPT, such as the General Photonics POS-002-E [35].  
In this paper, an image-free microwave photonic sub-harmonic downconverter based on a DP-QPSK modulator is theoretically analyzed and experimentally demonstrated. Thanks to the special SH-SSB modulation of the LO signal and the CS-SSB modulation of the RF signal, the modulated signal is immune to the chromatic dispersion-introduced power fading effect. By building two photonic microwave phase shifters using PCs and PBSs, I/Q mixing is realized. Furthermore, by constructing an equivalent Hilbert architecture based on the photonic phase shifters, the image-induced IF signal can be successfully rejected. Experimental results show that the IRR can reach more than 55 dB when using back-to-back (BTB) transmission. When transmitting through 25 km SMF, the IRR can reach more than 50 dB without periodic power fading. The image rejection performance of the 16 QAM vector signal with a symbol rate of 50 MBaud is also demonstrated. The measured EVMs of the useful IF signal and the image-induced IF signal are 4.6% and 31.57%, respectively, indicating a good image rejection effect for the broadband signal. Compared with other image-reject downconverters, our proposed scheme shows superior performance, as can be seen in Table 1. Moreover, due to the absence of optical filters and the doubling operation of the LO signal, the system offers good frequency tunability and a large working frequency range. However, the SBS-based image-reject methods are restricted above the Brillouin frequency shift. Our proposed image-free microwave photonic sub-harmonic downconverter can potentially be applied in RoF systems, broadband RF front ends, and satellite receiving systems.  
Figure 9: The architecture of automatic polarization control for our proposed link.
# Mitigating Urban Congestion: A Cooperative Reservation Framework for Automated Vehicles
## 7 Experimentation and Results  
The software ecosystem explained in the previous sections was fully implemented in ROS2 [26] and tested on Carla Simulator [27]. The operations between both containers presented are based on ROS2 communication standard with Topics, Services and Actions, and the communication between components for the collaboration uses its Data Distribution Service (DDS) for real-time communication, in which some Quality of Service (QoS) configurable parameters can be adjusted if needed. DDS exhibits inherent compatibility for integration with vehicular communication standards, including ITS-G5 [53], C-V2X [54], and 5G-V2X [55]. Interfacing ROS2 with ITS-G5 necessitates a bridge node (or nodes) for bidirectional translation between ROS2 DDS middleware and the ITS-G5 communication interface. This involves mapping ROS2 messages to ITS-G5 formats and vice versa, publishing parsed ITS-G5 data as ROS2 topics. For specific ITS-G5 hardware, a dedicated ROS2 driver is required to manage low-level hardware communication, exposing data as ROS2 topics/services for higher-level processing and message interpretation.  
Furthermore, in order to test the solution proposed, two traffic scenarios are analyzed. On the one hand, the first suggested scenario represents a 4-way intersection in a realistic simulated world where four Cooperative Connected Automated Vehicles converge at similar times at the intersection managed by the infrastructure container previously explained. On the other hand, in the second scenario, a Cooperative Connected Automated Vehicle approaches a roundabout generated in a custom Carla world and lets the intelligent system deployed at its center arbitrate its entrance based on detected threats inside the _Survey Clusters_.  
Figure 7: Parametrization of the _Tracking Node_ for time optimization.
## 4 Experimental Verification  
To verify the feasibility of the proposed method, a rotating parts fault diagnosis bench was designed based on two groups of tests. The first group classified the test data according to different parts, while the second group classified the test data on the basis of different fault locations.