esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Target Model and Frequency Offset Extraction Method
### Target Detection Processing Method  
For passive radar signal processing, the time-frequency cross-correlation method is often used to detect targets [17]. Generally, two signal-receiving channels are set up, namely, a reference signal channel for receiving direct waves and a monitoring channel for  
Figure 5: (**a**) Rotating target measurement scene. TX is the signal-transmitting antenna, and RX is the signal-receiving antenna. The target rotates at a constant speed of radius \(r\) under the drive of the pole. \(L_{T}\), \(L_{R}\), \(L\), \(L_{r}\), \(L_{T0}\), and \(L_{R0}\) are distances between different points; (**b**) unilateral rotating target Doppler frequency offset curve.  
receiving target echoes. Then, adaptive filtering or extended cancellation algorithms are used to filter out direct waves and multipath clutter [18; 19; 20]. This paper mainly studies methods of target detection using channel response. The CSI-RS in the 5G signal can be used to estimate the channel between the transmitting and receiving antennas and obtain the Doppler frequency shift of the rotating target based on the channel response. When the 5G signal configuration is known, the corresponding reference signal can be generated locally to obtain the transmission signal. Rotating the target causes the channel between the transmitting and receiving antennas to change. The channel response is obtained by transmitting and receiving signals, and the information about the rotating target is obtained from the channel response.
# Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance
## Appendix B  
Assuming that \(n\) motion models are set in the IMM algorithm, of which the state equation is  
\[X_{j}[k+1]=A_{j}X_{j}[k]+\omega_{j}[k]\quad j=1,2\cdots n\]  
There are four steps in a typical iteration of the IMM algorithm: (1) Model interacting, (2) parallel filtering, (3) model update, (4) estimation output.  
(1) Model interacting: Compute:  
\[\begin{array}{c}\hat{X}_{0j}(k|k)=\sum\limits_{i=1}^{r}\hat{X}_{i}(k|k)\mu_{ ij}(k|k)\\ \mu_{ij}(k|k)=p_{ij}\mu_{i}(k)/\overline{c}_{j}\\ \overline{c}_{j}=\sum\limits_{i=1}^{r}p_{ij}\mu_{i}(k),\quad j=1,\cdots,r \end{array}\]  
(2) Parallel filtering:  
In this step, an unscented Kalman filter is used in parallel filtering. \(n\) motion model-based filters work in parallel to calculate the state estimation \(\hat{X}_{j}(k+1)\) at time \(k+1\) and the covariance estimation \(P_{j}(k+1)\), mean error \(v_{j}(k+1)\) and error covariance estimation \(S_{j}(k+1)\).  
(3) Model update:  
\[\Lambda_{j}(k)=\frac{1}{\left(2n\right)^{n/2}\left[S_{j}(k)\right]^{1/2}} \exp\left(-\frac{1}{2}v_{j}^{T}S_{j}^{-1}(k)v_{j}\right)\] \[v_{j}(k+1)=Z(k+1)-H(k+1)\bar{X}_{j}(k+1|k)\] \[S_{j}(k+1)=H(k+1)P_{j}(k+1|k)H(k+1)^{T}+R(k+1)\] \[\mu_{j}(k+1)=\,\Lambda_{j}(k+1)\bar{c}_{j}/\sum\limits_{j=1}^{r} \Lambda_{j}(k+1)\bar{c}_{j}\]  
(4) Estimation output:  
\[\hat{X}(k+1|k+1)=\sum\limits_{j=1}^{r}\hat{X}_{j}(k+1|k+1)\mu_{j}( k+1)P(k+1|k+1)=\sum\limits_{j=1}^{r}\mu_{j}(k+1)\Big{\{}P_{j}(k+1|k+1)\] \[+\Big{[}\hat{X}_{j}(k+1|k+1)-\hat{X}(k+1|k+1)\Big{]}\Big{[}\hat{X }_{j}(k+1|k+1)-\hat{X}(k+1|k+1)\Big{]}^{T}\Big{\}}\]  
The IMM-UKF is applied as the sub-filters in the federated filter to improve estimation performance for nonlinear systems, and the main filter is designed as [25].
# Antijamming Improvement for Frequency Hopping Using Noise-Jammer Power Estimator
## 1 Introduction  
In military wireless communication systems, the bit-error rate (BER) in a receiver is degraded when a transmitted signal is jammed [1, 2]. Military wireless communications need to mitigate jammers to attain reliability. To reduce jamming, several antijam techniques, such as frequency-hopping spread spectrum (FHSS) and direct-sequence spread spectrum (DSSS), were researched [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]. When jammer power is large, the FHSS is known to be more effective [16], but BER degradation remains.  
FHSS randomly changes carrier frequency according to time within a transmission-frequency band, which reduces jammer probability for a slot. As a jammer to FHSS, a partial-band noise jammer (PBNJ) was considered in this study [17, 18]. When jamming occurs, the BER of jammed slots is so large that the receiver may not be able to decode the transmitted information. To overcome the jammer problem, a duplication method was studied where several slots with the same data are transmitted and combined to increase signal-to-jammer-plus-noise ratio (SJNR) [19]. However, when the jammer was strong, the SJNR was still small. A selective combining method was developed [19], and the detection of jammed slot methods was also researched [20, 21]. In FHSS systems, however, multipath channels cause frequency-selective fading, and nonlinear devices at the transmitter also generate an unequal frequency response. Since the unequal frequency response makes the detection of jammed slots difficult, a solution for measuring jammer power on all empty slots and detecting jammed slots was developed [21]. This full search of all empty slots requires large computational complexity.  
To overcome jammer-detection and -mitigation problems, we developed a proposal for a noise-jammer power estimator (NJPE), a jammer detector based on gamma distribution, and a slot combiner for nonjammed slots. The proposed NJPE estimates the pure power of the noise andjammer of each slot regardless of transmitted-symbol existence and unequal frequency response. The probability distribution of the NJPE output was approximated as gamma distribution on the basis of simulations, and the jammer detector based on gamma distribution was developed to precisely detect the jammer. The normalized Cramer-Rao bound (NCRB) of the proposed NJPE was also derived to theoretically prove estimation performance. Computer simulations demonstrated that the normalized mean squared error (NMSE) of the proposed NJPE was well-matched with the derived NCRB, and the detection performance of the jammer detector based on gamma distribution was better than those of conventional jammer detectors. The BER of the proposed method indicated better performance than that of conventional methods.  
This paper consists of five sections: Section 2 discusses how the proposed NJPE works; Section 3 analyzes how to assess the performance of the proposed NJPE; Section 4 compares the NCRB of the proposed scheme with NMSE and BER performance using computer simulations; and Section 5 concludes the paper.
## 5 Conclusions  
OCTA is a new imaging technique for diagnosing diseases in retina and optic nerves. OCTA has advantages over other angiographic imaging techniques, FA and ICG, of being non-invasive, rapid and reliable. OCTA also has the advantage of providing three-dimensional images, allowing visualization of retinal and choroidal vasculature in different layers. OCTA has some disadvantages such as the limited field of view (which has been partly solved in some commercially available devices), inability to identify leakage, inability to detect blood flow below a certain level, and variable image quality in a real-world, clinical setting.  
OCTA is a helpful imaging modality for diagnosing and monitoring different retinal and choroidal diseases, including age-related macular degeneration, diabetic retinopathy, retinal vein occlusion, retinal artery occlusion, ischemic optic neuropathy, and glaucomatous optic neuropathy. Although the technology is still in its infancy, further improvements in imaging quality, reduction of artifacts, improved transmission through opaque media, and increased affordability will lead likely to more widespread adoption of the technology. This, in turn, should allow for more rigorous characterizations of these disorders and others.  
Conceptualization, F.T., H.M., M.G., H.S.S. and A.E.-B.; Project administration, A.E.-B.; Supervision, H.S.S. and A.E.-B.; Writing--original draft, F.T., H.K., H.M., A.M., A.S., M.G., M.T.A., H.S.S. and A.E.-B.; Writing--review and editing, F.T., H.K., H.M., A.S., M.G., M.T.A., H.S.S. and A.E.-B. All authors have read and agreed to the published version of the manuscript.  
This work is supported, in part, by the Abu Dhabi Award for Research Excellence (AARE) 2019.  
The authors declare no conflict of interest.
# Generative AI in Medicine and Healthcare: Moving Beyond the Peak of Inflated Expectations'
## 3 Applications and Implications of Generative AI and LLMs in Medicine
### Can We Trust Generative AI? Is It Clinically Safe and Reliable?
#### 3.1.2 Bias and Harm  
The use of LLMs in healthcare offers significant benefits but also raises concerns about bias and potential harm. Several studies have explored the performance and biases of LLMs compared to human clinicians.  
[PERSON] et al. compared ChatGPT-3.5 and ChatGPT-4 with primary care physicians in diagnosing and treating depression. Key findings include that ChatGPT models recommended psychotherapy for mild depression in over 95% of cases, significantly more than primary care physicians, and showed no significant gender or socioeconomic biases, unlike primary care physicians. ChatGPT favored antidepressants alone more often, while physicians preferred combining antidepressants with anxiolytics/hypnotics. This study suggested that ChatGPT aligns well with clinical guidelines and reduces biases, but further research is needed to ensure AI reliability and safety in clinical settings [57].  
While some studies, like the one above, suggest that ChatGPT may exhibit more powerful and less biased capabilities compared with humans, the potential harms of LLMs must be addressed. [PERSON] et al. examined whether four LLMs (Bard, ChatGPT, Claude, GPT-4) spread harmful race-based medical content. They found that all LLMs tested showed instances of promoting outdated race-based medical practices, and their responses varied with question phrasing. This inconsistency and propagation of debunkedracist medical ideas highlight the need for caution in their clinical use and call for further evaluation and adjustments [58].  
Advances in reducing bias are also being made. [PERSON] et al. discussed the evaluation of Med-PaLM 2, highlighting its better performance and lower risk of harm compared to its predecessor. Med-PaLM 2 was tested on adversarial datasets to identify its limitations and potential biases. Although progress has been made in reducing biases and harmful outputs, further validation and safety studies are necessary for real-world applications [59].  
[PERSON] et al. evaluated biases in responses from AI chatbots (ChatGPT-4 and Bard) and clinicians. Their study showed that both AI chatbots and clinicians displayed biases based on patient demographics, and that ChatGPT and Bard varied in treatment recommendations, sometimes aligning with or diverging from clinician biases. Specific vignettes showed discrepancies in diagnoses and treatments, indicating biases in both AI and human decision-making. They concluded that while AI chatbots can assist in medical decision-making, their responses are not bias-free, necessitating further research to prevent perpetuating health disparities [60].
# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 6 Gaps and Future Work  
As one would expect, the performance of the models varied with different percentages of labeled data. Some methods, such as SRC-MT and NoTeacher, revealed up to a 10% difference in score from 1% to 10% labeled data. GraphX\({}^{\text{NET}}\) showed an increase in more than 20%, with an AUC score of 0.53 and 0.78 for 2% and 20% labeled datasets, respectively. Other methods had smaller differences: the SSAC model's AUC score increased by 3% from 20% to 100% labeled data, and the bi-modality synthesis model increased by 4% from 1% to 10% labeled data. Further research can be conducted on accomplishing higher robustness to changes in the percentage of labeled data and achieving successful training on lower numbers of labeled data.  
As mentioned earlier, the areas of bi and multi-modality image synthesis is an areas that can be studied further. Additionally, given the promise shown by the ACPL method, it may be beneficial to explore other strategies for measuring confidence and uncertainty in order to achieve improved results with the traditional pseudo-labeling method. Another area of learning that may be worth exploring is active learning. In this method, instead of using an existing dataset, a few valuable unlabeled samples are carefully selected, and a medical expert is asked to label them. As the samples to be labeled are chosen specifically based on the information they offer, this method results in a smaller amount of labeled data needed and, therefore, less time and effort in labeling from experts.  
Lastly, more thorough experimentation is needed to gauge the performance of these models in a real-world clinical setting and with larger patient cohorts.  
Conceptualization: Z.S. and I.Z.; investigation: Z.S.; writing--original draft preparation: Z.S.; writing--review and editing: I.Z. and Z.S.; supervision: I.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Transmission Channel Analysis
### Link Budget  
The link budget equation accounts for all of the losses and gains from the transmitter to the receiver along the communication channel. Hence, the simplest link budget equation, logarithmically expressed, is a superposition of terms as  
\[P_{RX}=P_{TX}+G_{A}-L \tag{1}\]where \(P_{RX}\) and \(P_{TX}\) are in turn the received power and the transmitter power output both expressed in dBm, \(G_{A}\) represents the antennas gains on either the sides of the communication channel expressed in dBi and \(L\) stands for all the losses the channel is affected expressed in dB. Therefore, the aforesaid link margin \(L_{m}\) is promptly calculated as  
\[L_{m}=P_{RX}-S_{RX} \tag{2}\]  
where \(S_{RX}\) is the receiver sensitivity expressed in dBm.  
For the sake of completeness, it has to be also underlined that there exists another crucial requirement that has to be usually guaranteed in order to fulfill a satisfactory transmission: the SNR limit. In general, apart from achieving enough link margin, a signal whose power is bigger than the one of the noise that was added on it during its transmission has to be received. Such a condition entails a positive SNR provided that it is logarithmically expressed or, alternatively, greater than 1 if the SNR is linearly expressed. However, LoRa modulation enables receivers to demodulate signals below the noise floor power level due to its robustness.  
Coming back to link budget, a more detailed equation, though still simplified, can be derived by making reference to Figure 1 which displays a block diagram of an example of a communication channel:  
\[P_{RX}=P_{TX}+G_{TX}-L_{TX}-L_{FS}-L_{M}+G_{RX}-L_{RX}. \tag{3}\]  
In Equation (3), \(P_{TX}\) and \(P_{RX}\) are the same of Equation (1), \(G_{TX}\) and \(G_{RX}\) respectively are the gains of the transmitting and receiving antennas expressed in dBi, while \(L_{TX}\) and \(L_{RX}\) are the losses due to cables and connectors severally at transmitter and receiver sides. \(L_{FS}\) is the path loss and it is normally named as free space loss. Finally, \(L_{M}\) represents miscellaneous losses due to manifold potential causes (e.g., antennas polarization mismatch, obstructions within the first Fresnel zone and so forth). Both the latter two losses are expressed in dB.  
\(L_{FS}\) in LoS propagation is the attenuation between the two antennas (i.e., the transmitting and the receiving ones) due to the distance between the extrema of the transmission link, considering that such a gap, better if the entire first Fresnel zone taken into account is free from obstacles. Such a loss can be expressed via different equations in function of the units of measure in which its composing terms are specified. A commonly adopted version is  
\[L_{FS}=32.45+20 log_{10}(D)+20 log_{10}(f) \tag{4}\]  
where \(D\) is the distance between the transmitter and the receiver expressed in km and \(f\) is the transmission carrier frequency expressed in MHz.
## 3 Experimental Details and Data Acquisition  
Figure 1 shows the schematic description for the process module of 300 mm dry asher (SUPRA, PSK). The process module consisted of the plasma sources, a chamber, and a vacuum exhauster. The PSK dry ashing tool contained two plasma sources in one chamber called the peanut chamber, which shared a supply of gas and a vacuum exhaust line for these two sources. Thus, each wafer was processed under the same conditions. The plasma source used was ferrite core inductively coupled plasma (FCIP R3, MKS). The chamber body was made of aluminum. The maximum temperature set point for the aluminum heater chucks was 350 \({}^{\circ}\)C. The chamber pressure was monitored using a capacitance gauge (MKS). The chamber pressure was controlled using a butterfly-type automatic pressure  
control valve (MKS), the open ratio range of which lies between 0 (closed) and 100,000 (fully open) steps.  
The chamber body temperature was monitored using a screw-type thermal sensor (Figure 2, PSK). In order to minimize the influence of outside air, PSK developed a screw type TC (thermocouple) sensor which uses K-type thermocouples. The temperature measurement point was about 1.5 cm from the chamber body surface. Its temperature measuring range was from \(-200\)\({}^{\circ}\)C to 1372 \({}^{\circ}\)C. The test error was \(\pm 1\)\({}^{\circ}\)C in \(-100\)-1372 \({}^{\circ}\)C, without considering the error of the thermocouple. In order to produce a controlled artificial vacuum leak, a micro vacuum leak control valve was installed on the gas inlet (vacuum leak valve series 590, MKS). The control vacuum leak rate was from 10 mT/m to 1000 mT/m. During the process, the plasma source power was fixed at 4400 W and the pressure to 1.4 Torr. The total gas (O\({}_{2}\), N\({}_{2}\)) flow was 14,000 sccm. The chuck temperature was set to 250 \({}^{\circ}\)C.  
All generated sensor data were recorded by the central controller. The APC position value range was from 0 to 100,000 and the sampling time was 100 ms. The chamber body temperature data were recorded to one decimal place and the sampling time interval was set to 1 s. The minimum sampling time of the temperature sensor controller was 1 s. High performance controllers were not applied because it would take more than 10 s for the chamber temperature to change by 0.1 \({}^{\circ}\)C.  
Figure 1: Schematic of the process chamber module.
## 5 Assessment and Deployment  
### Assessment  
Phase 5 of the methodology measures the models evaluated in the previous sections and the accuracy of modeling. Using training and testing sets is an important step to evaluate the decision tree accuracy. The higher the accuracy of the model means that its performance is better. So, the data were divided into two groups (training and testing set). 70% of the records were taken as the training set and the remaining 30% as the testing set. In addition to accuracy, the lift criterion, which indicates the degree of correlation, was calculated for the C5.0 tree branches. If its value is greater than 1, this means a positive correlation. For all the branches presented in Tables 2 and 4, the lift value was more than one, hence indicating a positive correlation.  
As stated, the purpose of the proposed approach is to achieve results with more precision and details. Therefore, the accuracy of the models of Sections 3 and 4 should be compared in order to assess the efficiency and effectiveness of this approach. In Table 6 the accuracy of the C5.0 tree in all the data and in each cluster is provided. As can be seen, the accuracy of modeling using the proposed approach (modeling in each cluster separately) is greater than analyzing all the data together. This means that the presented approach exposed unknown information more accurately and its tendency to a majority of records has declined.  
Since this classification is of a multi-class type, the target feature (energy efficiency group) has six different modes ((A and B), C, D, E, F and G), the percentages obtained being acceptable. If the accuracy were by chance, as the target field has six different states, the accuracy of the tree would be about 17% (1/6). But Table 4 offers percentages other than this. The percentages indicate that the accuracy of the algorithm in the analysis of each cluster separately is greater than the total analysis of the data. This approach is capable of providing knowledge and discovering unknown information more accurately.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input Data** & **The C5.0 Accuracy** \\ \hline All the data & 54.4\% \\ Cluster 1 (medium-consumption) & 51.5\% \\ Cluster 2 (high-consumption) & 57.83\% \\ Cluster 3 (low-consumption) & 69.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy of the C5.0 Algorithm in the entire data and also in each cluster.  
Data clustering is also evaluated through the silhouette index (Table 3). Based on the values of this index in different scenarios and the analysis of characteristics in different values of k, 3 clusters were selected as the most suitable number of clusters (mentioned in Section 4.1)
# Optimization of Multi-Day Flexible EMU Routing Plan for High-Speed Rail Networks
## 2 Model Description
### Problem Description
#### 2.1.2 Basic Assumptions  
To support the formulation of the EMU routing optimization model, the following assumptions are made:  
1. The multi-day train timetable is known and fixed, including all train numbers, service schedules, and formation types. The model performs routing optimization based on this predetermined timetable without altering train paths or schedules.
2. No additional empty train movements are introduced. Optimization is limited to the given timetable and the deadhead trips implied by it.
3. Only first-level maintenance is considered. Each EMU is required to return to a depot for overnight inspection upon completing its daily assignments.
4. EMUs of the same type are allowed to couple and decouple at designated stations.
5. Coupling and decoupling capacity constraints at stations are not explicitly modeled.
# Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles
###### Abstract  
This paper considers nonlinear model predictive control for simultaneous path-following and collision avoidance of connected autonomous vehicles. For each agent, a nonlinear bicycle model is used to predict a sequence of the states and then optimize them with respect to a sequence of control inputs. The objective function of the optimal control problem is to follow the planned path which is represented by a Bezier curve. In order to achieve collision avoidance among the networked vehicles, a geometric shape must be selected to represent the vehicle geometry. In this paper, an elliptic disk is selected for that as it represents the geometry of the vehicle better than the traditional circular disk. A separation condition between each pair of elliptic disks is formulated as time-varying state constraints for the optimization problem. Driving corridors are assumed to be also Bezier curves, which could be obtained from digital maps, and are reformulated to suit the controller algorithm. The algorithm is validated using MATLAB simulation with the aid of ACADO toolkit.  
Keywords:nonlinear model predictive control; autonomous driving; path following; optimization +
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