esa-sceva/satcom-chunk-collection huggingface dataset:
# Acquisition and Processing of UAV Fault Data Based on Time Line Modeling Method
## 4 Processing of UAV Fault Data
### Data Balance Processing Based on Failure Time Window Stretching Method  
(1) Data distribution before time window stretching  
The number of instances per file for the completed merged CSV files is too small. For example, there are only 2555 instances of GPS information, and the specific data distribution is shown in Table 7. The small size of the sample data is not conducive to learning based on artificial intelligence models. Additionally, there is an imbalance between normal and abnormal data, as shown in Figure 17; the normal data comprised 74.21%, while the remaining abnormal data accounts for only 25.79%. The imbalance between positive and negative samples results in under-learning for negative samples for the model.  
(2) Data balancing methods  
In this paper, the data is balanced by the method of Fault Time Window Stretching, and the time window of the original normal flight is defined as \(Time_{Normal}\), and the time window for the abnormal flight is \(Time_{Fault}\), \(Time_{Normal}\), \(Time_{Fault}\
eq 0\). The amount of data per unit of time is \(\beta\), \(\beta\)\(\beta\)\(\beta\)\(\beta\)\(\beta\)\(\beta  
the number of normal instances is 3609, the number of all fault type instances is 4284, and the data of faults accounts for 54.27% of all data, which increases the fault data by 28.48%. An increase in the number of abnormal samples will facilitate the learning of abnormal data by the AI-based model and improve the model's generalization.
## 6 Conclusions  
In this article, a SUWB BPF with triple-notched bands was designed and fabricated using a UTL loaded with three folded \(\lambda\)/4 short-circuited stubs for the implementation of a wideband response, and then a quad-mode stub-loaded resonator was coupled to the basic SUWB-BPF for the rejection of unwanted signals at 4.9 GHz, 8.3 GHz, and 11.5 GHz with good return loss and sharp rejection, respectively. The circuit area covered by the proposed filter is 0.52 \(\lambda_{g}\times\) 0.32 \(\lambda_{g}\). A large fractional bandwidth of 141.1% with low insertion loss of less than 0.4 dB was achieved by loading the stubs at the appropriate distance on either side of the UTL. Due to its simple planar structure and excellent performance, the proposed filter can be integrated into future UWB wireless communication system for various applications.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref. No.** & **Passband (GHz)** & **FBW (\%)** & **IL/RL (dB)** & **C.F/BW (GHz)** \\ \hline
[31] & 3.3–10.7 & 105.7 & 0.9/15 & 4.4, 5.5, 7.6 \\
[32] & 3.1–10.6 & 117.0 & 1.25/\textgreater{}16 & 3.6, 5.2, 8.4 \\
[33] & 3.3–9.7 & 104 & 0.3/29 & 7.3 \\
[34] & 3.3–10.6 & 105 & \textless{}0.5/14 & 8.95 \\
[35] & 3.1–11 & 112 & 0.66/35 & 6 \\
[36] & 3–10.9 & 110 & 0.9/\textgreater{}15 & 5.96, 8.15 \\
[37] & 3.58–10.07 & 95.1 & \textless{}1.2/\textgreater{}15 & 5.53, 8.1 \\
[38] & 3.25–10.73 & 106 & 0.52/\textgreater{}19 & 5.6, 6.4, 8.03 \\ This work & 2.9–16.85 & 141.1 & \textless{}0.4/\textgreater{}15 & 4.9, 8.3, 11.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of various notched filters.  
Figure 22: The measured and simulated results of the proposed triple-notched band filter.  
**Author Contributions:** Data curation, M.I.K. and N.S.; formal analysis, A.B.; funding acquisition, G.Z.; investigation, F.Z.; methodology, A.B.; project administration, G.Z.; resources, A.D.; software, A.D.; supervision, G.Z.; validation, M.I.K.; visualization, M.I.K.; writing--original draft, A.B.; writing--review and editing, G.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the \"Young Talent Sub-project of Ningbo Yongjiang Talent Introduction Programme\" under grant no. 20100859001.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## IGNORE for Demand Response
### Governor Design  
The governor consists of a policy server, power simulator for sensitivity analysis, and a database (see Figure 2).  
First, we need to decide where to place the governor for DR functionality. Since commands are either issued by DRAS or power utility (PU), we place governors close to each DRAS and another at PU (see Figure 1). The policy server receives commands through a designated input port from the DR server. It cannot accept commands from other functionalities. It interacts with the database from where it gathers the state information about the local power system. The state information is collected from the aggregators which provide the power demand and supply information, the parent governor (PU governor shown in Figure 2), and contingency information from PU. All DRAS governors send their decision to the PU governor.  
The governor is distributed at different levels because commands are issued by different systems. The primary motive of the governor is to prevent malicious commands from reaching end systems. The PU governor prevents commands at a higher level, as it has the state information at various areas. The DRAS governor is closer to the local area and therefore it is important to evaluate commands locally. By implementing the governor for DR, we prevent all those attacks that are originated from compromised DRAS or the PU sub-system. If an adversary is able to control home devices directly (such as in [19]), the governor is unable to prevent those attacks.  
Second, the policy server verifies commands against two policies: requirement and safety. Requirement policy checks: (1) verification of whether customers to whom commands are sent have enrolled in the DR program and (2) verification of whether a set of commands are required to be implemented. The safety policy checks whether the power system will be in a safe state after implementing those commands. The safety policy makes use of a real-time power simulation tool, RTDS (real-time power system digital simulator) [45], to make decisions. RTDS is used extensively in the industry to understand a grid's behavior in the presence of contingencies. In this paper, we use the academic version of the Power World simulator to compute the safety condition for a sample grid described in Section 7.  
Third, after evaluating the commands, the governor either allows commands to go through or blocks commands since they are malicious. In the latter case, it notifies the security operations center (SOC) about the malicious commands so that power engineers start the containment procedure for the DR area to prevent an attack from propagating to other parts of the system. How SOC performs containment is out of the scope of this paper, but we discuss administrator responses in Section 8.  
Finally, PU is responsible for updating the governor's software securely. We provide details of how to protect the governor system in Section 10. In the next sub-section, we present governor rules.
# Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems
## 6 Experimental Results and Discussion
### Analysis of Bit Error Rate  
The BER performance is the second evaluation metric used to evaluate the proposed TDS-OFDM-NOMA scheme over the underwater acoustic channel. In this evaluation, the proposed TDS-OFDM-NOMA, BER performance over the underwater acoustic channel is evaluated and compared to the conventional CP-OFDM-NOMA. Thanks to the IBI-free region compressive sensing channel estimation, the TDS-OFDM-NOMA provides a BER performance better than the performance of the conventional CP-OFDM-NOMA. In conventional CP- OFDM-NOMA, we use the least square method for channel estimation. The total BER performance of the proposed TDS-OFDM-NOMA system is studied by evaluating its uncoded and coded BER performance as shown in Figures 8-10. In all of the simulation experiments, data symbols are created from random information bits which  
Figure 7: Average throughput achieved by TDS-OFDM-NOMA and CP-OFDM-NOMA under different pilots’ length for **(a)** Near user and **(b)** Far user.  
are first encoded by a rate 1/2 convolutional coder with the generator [65; 57] polynomial. For the coded case, the coded bits are interleaved by a block interleaver of depth 8 before QPSK modulation. Based on the BER metric, the proposed TDS-OFDM-NOMA scheme outperforms the conventional CP-OFDM-NOMA in total system BER (the average BER in NU and FU), the BER of the NU, and the BER of the FU. This BER improvement is due to efficient IBI removal from the TDS-OFDM data blocks thanks to the accurate channel estimation technique using the LABOMP compressive.  
Figure 8: BER-total comparison of proposed TDS-OFDM-NOMA and convention CP-OFDM-NOMA; (**a**) Uncoded system and (**b**) Coded system.  
Figure 9: BER-(far-user) comparison of proposed TDS-OFDM-NOMA and convention CP-OFDM-NOMA; (**a**) Uncoded system and (**b**) Coded system.
## 3 Results and Discussion
### Radiation Pattern Performance
#### 3.2.3 Beam Squint and Beamwidth Performance
Figure 9: Beam Squint Performance of (**a**) Element and 1 \(\times\) 2 Regular Palm Tree (RPT-CVA), 1 \(\times\) 2 Front Cut Palm Tree (FCPT-CVA), 1 \(\times\) 2 Middle Cut Palm Tree (MCPT-CVA), 1 \(\times\) 2 Back Cut Palm Tree (BCPT-CVA), 1 \(\times\) 2 Complete Cut Palm Tree (CCPT-CVA), 1 \(\times\) 2 Left Tilt Palm Tree (LTPT-CVA), Right Tilt Palm Tree (RTPT-CVA), 1 \(\times\) 2 Hilbert Fractal Structure Palm Tree (HFSPT-CVA), and (**b**). Element and 1 \(\times\) 2 Regular Palm Tree (RPT-CVA), 1 \(\times\) 2 Koch Fractal Structure Palm Tree (KFSPT-CVA), 1 \(\times\) 2 Exponential Slot Edge Palm Tree (ESEPT-CVA), 1 \(\times\) 2 Vertical Wave Structure Palm Tree (VWSPT-CVA), 1 \(\times\) 2 Horizontal Wave Structure Palm Tree (HWSPT-CVA), 1 \(\times\) 2 Regular Lens Palm Tree (RLPT-CVA), 1 \(\times\) 2 Elips Lens Palm Tree (ELPT-CVA), and 1 \(\times\) 2 Metamaterial Lens Palm Tree (MLPT-CVA).
# Privacy and Security in Federated Learning: A Survey
## 2 An Overview of Federated Learning
### From Centralized to Federated Learning  
In a traditional centralized approach, the aim is to learn a single model \(f(\theta_{C})\) from a large data collection built from the aggregation of data coming from a variety of sources. Despite its impressive success, several issues must be highlighted. Indeed, when dealing with very large data collections and complex models, server-level data and communication costs must be reduced to efficiently distribute the data across computing devices [11]. Moreover, advanced machine learning techniques require large computing resources, which induce costs that may not be sustainable depending on the application economic model and may create dependencies on such computation power in the long term.  
To overcome traditional machine learning weaknesses, federated learning was introduced in 2016 by [PERSON] et al. [3] as a learning technique that allows users to collectively reap the benefits of shared models trained from their data, without the need to centrally store them. Federated learning aims at identifying a general model \(f(\theta_{FL})\) by aggregating local ones \(f(\theta_{i})\) trained by a set of participating clients that keep their data, but occasionally share their parameters. Indeed, FL introduces a new paradigm that reduces communication costs and pushes forward data privacy approaches in several application domains [12].
# Knowledge Graph Alignment Network with Node-Level Strong Fusion
## 5 Experiments
### Experimental Results
#### 5.4.2 Ablation Studies  
Ablation studies are conducted on the proposed SFEA model and the results are shown in Table 4. Specifically, (1) (w/o SF) denotes information from different aspects is aggregated through the connection operation. (2) (w/o UE) denotes the entity representations are not updated by sub-alignment tasks. (3) (w/o IS) denotes the SFEA model without iteration strategy. From the experimental results, it can be observed that (w/o SF) has the largest performance drop, which confirms that the strong fusion strategy can obtain more accurate and realistic entity embedding representations. In addition, the sub-alignment tasks also play a role in updating the entity representations. Iterative strategy also improves model performance.
# VHF Omnidirectional Range (VOR) Experimental Positioning for Stratospheric Vehicles
## 2 Materials and Methods  
### VHF Omnidirectional Range (VOR)  
VOR is a radio-navigational aid used for civil aviation since the late 1940s [12]. The system is based on a network of transmitting ground stations and on receiving-only airborne receivers. The aim of VOR is to provide the user with the radial information, which is the angle between the Magnetic North and the line between the airborne receiver and the ground station. When VOR was used as primary navigation system for the civil aviation, the pilots could divide their optimal (orthodromic) route into straight segments between different VOR stations [18], maintaining the radial value constant to fly towards the destination. For achieving the radial calculation, the receiver carries out several signal processing tasks that are described as follows:  
1. At signal reception, the receiver discriminates two different sub-modulated signals, namely, an AM sub-modulated reference sine wave and a FM sub-modulated directional sine-wave;
2. The two sub-modulated sine waves are independently de-modulated and brought back to base band, at 30 Hz;
3. The phase of the two signals is then compared and the obtained value is re-scaled as an angle (with values between 0 and 360 degrees);
4. The radial information is the obtained value.  
As visible, VOR replicates in VHF the old concept of lighthouse navigation, where the omnidirectional signal substitutes the lighthouse white light and the directional sine wave substitutes the lighthouse rotating green light. The role of the receiver is to passively decode the signal and calculate the phase shift between the signals, which will be, as an example, superposed when the radial is 0 degrees and opposed when the radial is 180 degrees [17; 18].
# Multilevel Bipolar Electroforming-Free Resistive Switching Memory Based on Silicon Oxynitride
## 3 Results and Discussion
### Effect of Write-Current Level and Silicon Oxynitride Thickness
the SiO\({}_{\rm x}\)N\({}_{\rm y}\) layer is required for the charge carriers to travel by hopping trap-to-trap from the bottom electrode to the interface of the Ni and SiO\({}_{\rm x}\)N\({}_{\rm y}\) layers. If the thickness of the SiO\({}_{\rm x}\)N\({}_{\rm y}\) layer is less than the required thickness, the charge carriers can easily move to the interface by tunneling with the small amount of positive bias voltage. Furthermore, the filament formation mechanism takes place which causes the device changing from the HRS to LRS, eliminating the IRS state. As a result, the IRS state is not observed in the 7-nm-thick SiO\({}_{\rm x}\)N\({}_{\rm y}\)-based device. It shows a direct change from the HRS to LRS at lower bias voltage than that of the 10-nm-thick SiO\({}_{\rm x}\)N\({}_{\rm y}\)-based device. The thickness-dependent resistance switching analysis shows that a certain thickness (10 nm) of the SiO\({}_{\rm x}\)N\({}_{\rm y}\) layer is essential to form the IRS state as well as to obtain three resistance states when the device is fabricated using the process mentioned in this study because the amount of traps and oxygen in the SiO\({}_{\rm x}\)N\({}_{\rm y}\) depends on its thickness. The thickness-dependent resistive switching was also reported by other researchers [57; 58].
# Detection of Periapical Lesions on Panoramic Radiographs Using Deep Learning
## 2 Materials and Methods
### Proposed Method
#### 2.2.1 Periapical Root Area (PRA) Detection Model  
We employed the Faster R-CNN object detection model using the Detectron2 detection platform for localizing the region of interest (ROI), which is the PRA on a panoramic radiograph. Detectron2 provides state-of-the-art detection algorithms, such as object detection, semantic, instance, and panoptic segmentation. We tested Detectron2 implementation of Faster R-CNN using different backbone models and configurations. The results showed that Faster R-CNN ResNet X101 base model achieved the highest detection rates on panoramic radiographs.  
We performed training, validation and testing experiments with NVIDIA Tesla P100 and T4 on GoogleColab notebook. We used Detectron2's default data preprocessing settings  
Figure 1: Proposed System Architecture and Workflow of the AI developed in this study. Input: Panoramic radiograph. The system first performs ROI extraction on the input images using the “Faster RCNN” algorithm. Then, the system classifies the extracted PRAs into two possible categories: apices with periapical lesion (PL) or healthy apices (H); this was done using the “Inception v3” algorithm. The final output: the detected periapical lesions depicted by red bounding boxes with the system confidence score depicted on them.  
for image flipping and resizing (to 1333/800 pixels). Aspect ratios were maintained while resizing. We used 513 panoramic radiographs for training, 57 images for validation, and 143 images for testing. We selected a batch size of 4 due to the high-resolution images. We initialized the Faster R-CNN base model from the ResNet101 pretrained ImageNet classifier. We initially used a learning rate of 0.001, but we decreased the learning by half at epoch 1000 because learning stagnated. We trained until convergence. The model achieved the best performance on iteration 1500.  
During the inference time, we fed the entire panoramic radiograph, with 2250 \(\times\) 1000 resolution, as input to our detection model. The detector then localized all PRAs on a given panoramic radiograph by drawing a bounding box on each detected PRA. We used Detectron2 default Intersection over Union (IoU) and Non-Maximum Suppression (NMS) thresholds, which by default set to 0.5. On average, it took 0.57 s per image using NVIDIA Tesla T4 to detect all PRAs in a given panoramic image. We developed a custom function that returned the detected PRA coordinates, crops, and processes each detected PRA to prepare it for the lesion classification task.
## 3 Development and Results
### Test Phase  
We proceeded to test the system using metrics that were obtained using a test set of 2723 images. Firstly, EER was obtained to find the detection sensitivity to which the system provided the least amount of errors, this value was obtained through the FAR and FRR values. The crossing between FAR and FRR provides the EER value, which corresponded to 0.09, with a sensitivity of 0.52. This is shown in Figure 9.  
The confusion matrix and the recall and precision values were obtained for the test set; these are shown in Tables 6 and 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Images** & \multicolumn{2}{c}{**Predictions**} \\ \cline{3-5}  & & **With Firearms** & **Without Firearms** \\ \hline \multirow{2}{*}{Classes} & With Firearm & 1361 & 1175 & 186 \\  & Without Firearm & 1362 & 192 & 1170 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Results** \\ \hline Recall & 0.86 \\ Precision & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Recall and precision values.  
Figure 9: Equal error rate (EER). FAR: false positive rate; FRR: false negative rate.
# Defining Blood Plasma and Serum Metabolome by GC-MS
## 4 Approaches of Metabolome Exploration
### Nmr  
The NMR method is based on the magnetic properties of the \({}^{1}\)H and \({}^{13}\)C nuclei. Suppose a molecule containing such nuclei is placed in a magnetic field and irradiated with a radio-frequency pulse. In that case, the atomic nuclei will go into an excited state, and the researcher will be able to register the signal of subsequent relaxation. This signal depends on the amount of the irradiated substance and ultimately contains information about the environment of the nucleus. Thus, the NMR spectrum of a substance is a superposition of signals from all resonating nuclei.  
Figure 1: Diversity of chemical substances comprising human blood metabolome based on HMDB 5.0.  
With over 20 years of extensive usage in metabolomics and over 1.5 thousand scientific papers (Figure 2), this method has proven to be a robust and reliable technique with exceptional reproducibility [58; 59].  
NMR requires relatively simple sample preparation [60]. However, moderate resolution and sensitivity of NMR impede the determination of low-abundant metabolites [61]. Another significant drawback of NMR is the small number of determined metabolites in complex mixtures (from 20 to 200 unique substances, depending on the resolution of NMR) in comparison with MS (potentially more than 500 identified substances). This advantage makes mass spectrometry dominant for exploring a wide range of metabolites [62].
## 4 Result and Discussion
### Comparison of LSGB and BRFFNN Based on UAV Movement Pattern  
The QoS performance in terms of the PDR, throughput, and delay for UAV-GS communication using bandwidth allocation based on LSGB and the BRFFNN with two different UAV movement patterns is compared in this section.
# MCST Scheme for UAV Systems over LoRa Networks
## 3 System Model
### Interference Model  
The interference model determines the interfering nodes from other concurrent transmissions and interference power on an ongoing transmission. In this paper, we assume the signal-to-noise ratio (SNR)-based interference model to define the interfering nodes set, i.e., \(\mathcal{I}\), of the ongoing transmission as in [26]. As illustrated in Figure 3, the interfering nodes are decided according to the SNR of the ongoing transmission from node \(i\) to node \(j\). Specifically, transmitting node \(k\) is defined as interfering node and \(k\in\mathcal{I}\) if and only if \(SNR_{ki}<SNR_{ij}\) and/or \(SNR_{kj}<SNR_{ij}\). Then, the signal-to-interference-plus-noise ratio (SINR) at node \(j\) is given as:  
\[SINR_{ij}=\frac{G_{ij}\cdot P_{i}}{\eta_{j}\cdot B+\sum_{k\in\mathcal{I},k\
eq i }G_{kj}\cdot P_{k}} \tag{3}\]  
where \(P_{i}\) is the transmit power of node \(i\), \(\eta_{j}\) is the noise level of node \(j\), \(B\) is the channel bandwidth and \(k\) represents the interfering node to the ongoing transmission.
# A Novel Self-Forming Virtual Sub-Nets Based Cross-Layer MAC Protocol for Multihop Tactical Network
## 3 Proposed VSCL-MAC Architecture
### Time Slots Allocation for Data Transmission  
The proposed VSCL-MAC design integrates an intelligent dynamic slot allocation algorithm that allows multihop data transmission. Each SDR can allocate multiple time slots on the channel depending on the type of data SDR wants to transmit, i.e., text or multimedia messages. The algorithm runs at each SDR and calculates the time slot vector (TSV) for all frequency sub-bands selected by each of the sub-nets using the same  
Figure 5: Sub-nets formation and frequency selection for data transmission.  
TRANS_table. The algorithm allocates time slots starting from the lowest node ID of the sub-net and ensures the source node of multihop transmissions gets the slot before the relay node; this is discussed in the next section. Rather than allocate multiple slots to a transmitter, there is a possibility of making the size of data slots variable for different data requirements, but this will create a time synchronization problem among radios.