esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Loss Function of AEUNet++  
As described above, our proposed AEUNet++ comprises an edge branch and segmentation branch, and we define the multi-task loss as follows:  
\[L=\lambda_{1}L_{seg}+\lambda_{2}L_{DC} \tag{6}\]  
where \(L_{seg}\) is the semantic segmentation task loss function and \(L_{DC}\) is the distance class map prediction task loss function, and they are weighted by \(\lambda_{1}\) and \(\lambda_{2}\), respectively. Usually, the weighting terms \(\lambda_{i}\) are set equal or found through an expensive grid-search. Here, the uncertainty-based multi-task loss [35] is introduced to determine the weighting terms \(\lambda_{i}\) by using the uncertainty in the model's prediction for each task, in which, depending on the confidence of the individual task prediction, a relative task weight is learned. Equation (6) can be rewritten as follows:  
\[L(x;\theta,\sigma_{DC},\sigma_{seg})=L_{seg}(x;\theta,\sigma_{seg})+L_{DC}(x; \theta,\sigma_{DC}) \tag{7}\]  
where \(\theta\) denotes the network parameters, \(x\) is the trained images, and \(\sigma_{DC},\sigma_{seg}\) are the corresponding task weights for \(\lambda_{i}\), respectively.  
If the likelihood of the model for each classification task is represented by the model output \(f(x)\) with the uncertainty through a SoftMax function:  
\[P(C=1|x,\theta,\sigma_{t})=\frac{\exp[\frac{1}{\sigma_{t}^{2}}f_{c}(x)]}{\sum_ {\sigma=1}\exp[\frac{1}{\sigma_{t}^{2}}f_{\sigma^{\prime}}(x)]} \tag{8}\]  
where \(P\) is the multi-task estimation, \(f_{c}(x)\) is the desired output, \(f_{\sigma^{\prime}}(x)\) is the original actual input, and \(\sigma_{t}\) denotes the scaling factor.  
Using the negative log likelihood for Equation (4), and expressing the classification loss with uncertainty as follows:  
\[\begin{array}{l}L(x,\theta,\sigma_{t})=\sum\limits_{c=1}^{C}-C_{c}\log P(C_ {c}=1|x,\theta,\sigma_{t})\\ =\sum\limits_{c=1}^{C}-C_{c}\log\{\exp[\frac{1}{\sigma_{t}^{2}}f_{c}(x)]\}+\log \sum\limits_{c\
eq t=1}^{C}\exp[\frac{1}{\sigma_{t}^{2}}f_{\sigma}(x)]\end{array} \tag{9}\]  
Figure 5: Extracted building representation. (**a**) Original image. (**b**) Building mask. (**c**) Boundary map. (**d**) Distance class map (different numbers stand for different nearest distances from pixels to the boundaries).  
Assuming the multi outputs consist of continuous and discrete outputs, they are modeled by Gaussian likelihood and SoftMax, respectively. Then, the loss function can be simplified as follows:  
\[\frac{1}{\sigma_{t}^{2}}\sum_{c^{\prime}}\exp[\frac{1}{\sigma_{t}^{2}}f_{c^{ \prime}}(x)]\approx\{\sum_{c^{\prime}}\exp[f_{c^{\prime}}(x)]\}^{\frac{1}{ \sigma_{t}^{2}}} \tag{10}\]  
Combining Equations (9) and (10), the loss function of the network is described as follows:  
\[L(x,\theta,\sigma_{t})\approx\frac{1}{\sigma_{t}^{2}}\sum_{c=1}^{C}-C_{c}\log P (C_{c}=1|x,\theta)+\log(\sigma_{t}^{2}) \tag{11}\]
## 5 Discussion and Limitations  
The results demonstrate that our MedMixtral 8x7B, fine-tuned on the HealthCareMagic-100k dataset, excels in answering medical questions.  
The robust performance of the MedMixtral 8x7B model is largely attributed to the extensive medical Q&A data provided by the HealthCareMagic-100k fine-tuning dataset. However, the model's remarkable performance cannot be solely attributed to its comprehensive learning of the dataset. The superior performance of MedMixtral 8x7B may also stem from the unique advantages of the MoE architecture in addressing medical Q&A issues. During the learning process, MedMixtral 8x7B, with its multiple experts, allows different experts to acquire diverse aspects of medical knowledge. When answering medical questions, the gate evaluates the input query and selects two suitable experts to respond. This method, which involves choosing a few experts rather than combining the opinions of all experts, reduces the model's bias and enhances accuracy.  
Our model, fine-tuned on the HealthCareMagic-100k dataset, surpasses traditional models such as ChatGPT and Llama3 in terms of precision, recall, and F1 score. Moreover, MedMixtral 8x7B is compatible with our proposed efficient inference offloading architecture. Our efficient inference offloading algorithm dynamically adjusts the number of experts per layer based on available VRAM, thereby reducing resource consumption. By employing the LRU cache strategy and introducing model weight offloading techniques, MedMixtral 8x7B exhibits lower VRAM usage during deployment, making it a potential candidate for IoMT devices. Compared to other large LLMs, MedMixtral 8x7B offers superior or comparable medical Q&A capabilities with reduced resource consumption--utilizing VRAM typically required by models under 10B in size. Compared to other small LLMs deployable on IoMT devices, MedMixtral 8x7B provides a significant performance advantage in medical Q&As. Additionally, deploying MedMixtral 8x7B locally can effectively protect user privacy and mitigate network security risks.  
In addition to addressing resource requirements, we examined the impact of latency on LLM inference speed. Through investigating local and interconnection communication models, we identified key factors affecting latency, including device interconnection methods, offloading locations, and disk speeds. We propose strategies to reduce latency, such as enhancing disk speed and utilizing wireless communication instead of SSH connections.  
Our study highlights the potential of MedMixtral 8x7B in answering medical questions. Below, we discuss the limitations and outline directions for future research.
## 3 Proposed Methods
### Superimposed-LFM-Based Preamble Signal  
If the transmitter only transmits one LFM signal, the receiver cannot estimate the Doppler correctly, since the offset is unknown. In order to obtain the Doppler factor, two LFM signals should be included in a data frame. Moreover, to achieve real-time Doppler estimation while reducing the length of the preamble signal, a superimposed-LFM-based preamble signal is designed. In addition, the designed preamble signal can also be used as the training sequence for the channel estimation.  
Figure 2 illustrates the structure of the communication frame which consists of a designed preamble signal and the data signal. The designed superimposed-LFM-based preamble signal is composed of a down-sweeping LFM signal and an up-sweeping LFM signal, where LFM\({}^{-}\) and LFM\({}^{+}\) denote down-sweeping and up-sweeping LFM signals, respectively. Although the up-sweeping and down-sweeping LFM signals are superimposed in the time domain, they can still be identified in the frequency domain. A guard interval is added between the preamble signal and the data signal.  
The designed preamble signal, \(s_{\text{P}}(t)\), can be denoted as  
\[s_{\text{P}}(t)=\frac{s_{\text{d}}(t)+s_{\text{u}}(t)}{2},0\leq t\leq T_{\text {P}}, \tag{15}\]  
\[s_{\text{d}}(t)=\cos(2\pi f_{\text{H}}t-\pi kt^{2}), \tag{16}\]  
\[s_{\text{u}}(t)=\cos(2\pi f_{\text{L}}t+\pi kt^{2}), \tag{17}\]  
\[k=\frac{f_{\text{H}}-f_{\text{L}}}{T_{\text{P}}}, \tag{18}\]  
where \(s_{\text{d}}(t)\) and \(s_{\text{u}}(t)\) are LFM\({}^{-}\) and LFM\({}^{+}\), respectively; \(f_{\text{L}}\) is the lower bound of the frequency range; \(f_{\text{H}}\) is the upper bound of the frequency range; and \(T_{\text{P}}\) is the time duration of the preamble signal.
# Resilience of LTE-A/5G-NR Links Against Transient Electromagnetic Interference
## 5 Conclusions and Future Scope  
This study presents an experimental evaluation of communication link susceptibility using software-defined radio platforms. The results indicate that 5G-NR links experience greater degradation in capacity and the bit error rate (BER) under unintentional interference. Previous studies have reported disruptions near hospitals and universities, particularly in areas with tram routes. The findings further support the claim that LTE systems exhibit greater resilience to high-frequency interference.  
The primary objective of this work was to analyse performance degradation caused by this type of interference. Our future research will focus on assessing its impact when combined with a narrowband smart jammer, with the aim of developing mitigation strategies to address this issue.  
**Author Contributions:** Sections 1-5, S.S.; supervision and project administration, M.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are unavailable to due privacy and ethical restrictions.  
**Acknowledgments:** The authors express their gratitude to the University of Bristol for providing the support and resources that made this research possible. During the preparation of this manuscript/study, the authors used [GPT-4-turbo] for the purposes of generating Figure 2. The authors reviewed and edited the output and take full responsibility for the content of this publication.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# ECC: Passenger Counting in the Elevator Using Commodity WiFi
## 3 Materials and Methods
### Multi-Dimensional Feature Extraction
#### 3.5.2 Spectral Feature  
As shown in (7), the energy of CSI attenuation is gradually becoming stronger with the growing number of people. Hence, a new feature of CSI called TVS is proposed, which can be calculated through the following steps:  
(1) Frequency feature extraction: For the challenge of few data brought by the short running time of the elevator, the short-time Fourier transform (STFT) method [30; 35] is used to transform the signal into the time-frequency domain. STFT adds a sliding time window to the signal and obtains the time-varying frequency spectrum in a few fragments of the signal. The major challenge here is that the frequency resolution is inversely proportional to the time resolution, which requires us to find an optimal window size to obtain satisfying time and frequency resolutions. This paper opts for an FFT window size of 256 samples at a sample rate of 1000 pkts/s and chooses the overlap size of two windows to be 128 samples for two reasons: (a) Excessive window size results in poor time resolution. (b) Higher value increases the computational efforts as it introduces high interpolation. We calculate the frequency resolution of the \(\frac{sample\cdot rate}{FT\cdot size}\approx 5\) Hz and a time resolution of \(\frac{window-overlap}{sample}\approx 0.1\) s. The spectrograms obtained through this process are illustrated in Figure 7. The results show that the high-frequency information gradually reduces, and the CSI attenuation strengthens as the crowd number grows. It illustrates that the frequency/energy variation of CSI attenuation can characterize the variation of CSI.  
(2) Energy statistics of CSI attenuation: The power burst curve (PBC) is adopted to calculate the energy information, adapted from Doppler radar fall detection techniques [30; 36]. Doppler radar associates the negative frequencies because it needs to consider the motion direction. Unlike Doppler radar, our spectrum \(U(g,f)\) does not consider the motion direction.  
Figure 7: Comparison of spectrograms for different numbers of passengers.  
tion, this enables us to define the PBC only for positive frequencies, so our TVS feature can be expressed as:  
\[TVS=\sum_{g=1}^{N}PBC(g)=\sum_{g=1}^{N}\sum_{f=f_{l}}^{f_{u}}|U(g,f)| \tag{13}\]  
where \(f\) is the frequency range, \(f_{l}\) is the lower frequency bound, and \(f_{u}\) is the upper frequency bound. We perform this operation on AOA and sum the CSI spectral in the whole frequency range. The average TVS values of each Rx with 10 experiments are also shown in Figure 6. The TVS curve can effectively reflect the monotonous relationship between the energy variation of CSI attenuation and the crowd number.
## 3 Methodology
### Integration  
Relying on factors such as engineering, geology, meteorology, and hydrology, traditional susceptibility assessment models can only provide a macroscopic reflection of the susceptibility in a region due to data classification limitations. With the rapid development and application of InSAR technology, integrating InSAR techniques with susceptibility models has become an important approach for fine-scale susceptibility mapping. Many scholars have successfully employed this approach.  
Establishing decision matrices and utilizing weighted overlays are the two most common methods in integrating models. Many researchers [44] have successfully improved susceptibility mapping by combining susceptibility mapping results with InSAR using decision matrices. Therefore, in this study, we will construct decision matrices to refine susceptibility mapping. The specific decision matrices are as follows:
## 2 Related Work
The proposed system, DeepFi-LoRaIn, integrates RSSI fingerprints with deep learning techniques to enhance localization in dynamic environments, accounting for interference and changing environmental conditions. While robust against interference, this model requires frequent retraining to maintain accuracy in varying environments, which can become impractical in deployments where continuous updates are challenging. In [42], the authors presented the Extreme RSS (ERSS) method to stabilize the fingerprint database and formulated boundary autocorrelation to significantly reduce search complexity while improving localization accuracy. Although computationally efficient, this approach requires extensive reference point setup, making it costly and challenging to implement across environments where regular adjustments are needed. Our approach addresses the limitations identified in these studies, which rely on static data collection methods, fail to account for dynamic environmental changes during data acquisition, and lack validation on unknown test data, making them less adaptable and effective in practical applications.
## 3 Results
### Dependence on the Grooves' Depth  
A series of samples was prepared on the wafer B (two QWs) with the grating period of 8 \(\upmu\)m, the same width of etched grooves equal to 3.2 \(\upmu\)m and the grooves' depth equal to 20 nm, 40 nm, 100 nm, 190 nm and 300 nm. Let us recall that the QWs in the wafer B are positioned at 45 nm and 100 nm below the surface so etching to the depth of 100 nm cuts the upper QW and most probably strongly influences the lower one, and etching to 190 nm and 300 nm cuts both of them forming a structure similar to a grating composed of conducting stripes.  
Transmission spectra for these samples are shown in Figure 6 and can be compared with transmission obtained on unprocessed sample, showing a spectrum of the CR. A shift due to plasmonic excitation is apparent for samples with etched gratings except for the deepest etching when there are no signatures of the resonant signal at all. This indicates that \"conducting stripes\" which are left after etching are practically depopulated of electrons. This gives us an important information concerning processing of CdTe-based structures that etching carried out in this study leads to depopulation of QWs of 2 DEG. We propose that positively charged surface states are generated during etching and then neutralized with electrons from the QWs.  
Figure 5: Transmission spectra through samples from wafer B with Au gratings of the period equal to 8 \(\upmu\)m, 4 \(\upmu\)m and 2 \(\upmu\)m (bottom to top) and \(\alpha\approx 0.5\). A reference unprocessed sample shows the position of the CR (indicated with a vertical bar). The wavelength of radiation was 118.8 \(\upmu\)m.  
Figure 6: Influence of depth of grooves on transmission. The wavelenght of radiation was 118.8 \(\upmu\)m. Position of the CR in unprocessed sample is indicated with a vertical bar.  
The curves for grooves of 20 nm, 40 nm and 100 nm show a minimum at the same magnetic field 9.12 T which we interpret as the signature of a plasmonic resonance which occurs in the lower QW. This interpretation is based on the fact that this value of \(B\) quite well corresponds to estimates based on Equations (1), (A1) and (A2) with material parameters of CdTe/CdMgTe QW [24] and \(n_{s}\) given by magnetotransport measurements. However, with the shallowest depth of grooves equal to 20 nm, one clearly gets a resonance also at 8.68 T. At the moment, the origin of this resonance is not clear to us. It cannot be the second mode of the plasmon observed at 9.12 T because then it should have appeared at about 8.87 T. Clearly, at the moment there is not enough data to give a reliable interpretation of this resonance.
## 2 Quality Assessment of COSMIC-2 GPS Observations
### Utilization of GPS Observations  
Data utilization refers to the ratio of the actual epochs of data to the theoretical epochs, which is defined as follows:  
\[\eta=\frac{n_{h}}{n_{e}} \tag{1}\]  
where \(n_{h}\) is the actual number of epochs, \(n_{e}\) is the number of theoretical epochs. This indicator can reflect the utilization of observation data and the observation environment around the receiver [25]. Table 2 shows the utilization of the observed data of six COSMIC-2 satellites of DOY 138-142.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**DOY** & **Sat-1** & **Sat-2** & **Sat-3** & **Sat-4** & **Sat-5** & **Sat-6** & **Mean** \\ \hline
138 & 27 & 33 & 28 & 28 & 27 & 28 & 29 \\
139 & 26 & 35 & 30 & 27 & 30 & 28 & 29 \\
140 & 31 & 32 & 30 & 37 & 27 & 27 & 31 \\
141 & 43 & 30 & 34 & 38 & 33 & 28 & 34 \\
142 & 28 & 31 & 29 & 34 & 30 & 27 & 30 \\ Mean & 31 & 32 & 30 & 33 & 29 & 28 & — \\ \hline \end{tabular}
\end{table}
Table 2: Utilization of GPS observations of COSMIC-2, DOY 138–142, 2021 (%).  
Figure 2: Numbers of visible GPS satellites of six COSMIC-2 satellites, DOY 138, 2021 (**a–f**) are visibility of satellite 1–6 respectively).  
From Table 2, it can be seen that the highest average observation efficiency is in DOY 141, and the observation efficiency of Sat-4 is relatively higher than that of other COSMIC-2 satellites in DOY 138-142. It can be seen that the effectiveness rate of COSMIC-2 space-borne observation data is lower than 40%, which indicates that the quality of the observation environment around the receiver is not well.
## 3 Results  
The experimental section is organized into two parts, each utilizing datasets from different satellites. Given an interferometric pair of SLC images in their native slant-range geometry, the two images are separately whitened and used to calculate an interferogram, from which the modulus, phase, and coherence are extracted. The goal is to demonstrate that the benefits of the whitening of SLC data are not related to a particular satellite system or a specific processing of the interferograms. In principle, the accuracy of the phase is better if the slave image of the interferometric pair is whitened after it is resampled over the master image, In practice, the two procedures are equivalent.
# A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time
## 4 Implementation
### Data Collection
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Country** & **Data Type** & **Total** & **Total** & **Min. Object** & **Max. Object** & **Avg. Object Size** & **Min. Ambulance** & **Max. Ambulance** & **Avg. Ambulance** \\  & & **Images** & **Labels** & **Size (px)** & **Size (px)** & **(px)** & **Count** & **Count** & **Count** & **Count** \\ \hline All data & Training & 200 & 2206 & 247.86 & 15,538,778.40 & 109,254.97 & 1 & 8 & 1.15 \\ All data & Validation & 1000 & 1132 & 470.20 & 10,116,762.21 & 191,851.62 & 1 & 6 & 1.13 \\ Ambulance–Germany & Training & 200 & 220 & 1007.49 & 1,136,804.65 & 517,278.0 & 1 & 8 & 1.10 \\ Ambulance–Germany & Validation & 100 & 112 & 1491.36 & 3,201,299.98 & 275,850.71 & 1 & 4 & 1.12 \\ Ambulance–Italy & Training & 200 & 203 & 1376.61 & 3,937,977.63 & 151,716.60 & 1 & 2 & 1.01 \\ Ambulance–Italy & Validation & 100 & 111 & 483.85 & 887,854.47 & 122,364.63 & 1 & 4 & 1.11 \\ Ambulance–Japan & Training & 200 & 211 & 1187.14 & 15,538,778.40 & 165,862.70 & 1 & 2 & 1.06 \\ Ambulance–Japan & Validation & 100 & 113 & 923.05 & 4,434,590.25 & 214,963.03 & 1 & 5 & 1.13 \\ Ambulance–Norway & Training & 200 & 218 & 812.92 & 8,467,327.29 & 155,564.99 & 1 & 2 & 1.10 \\ Ambulance–Norway & Validation & 100 & 108 & 601.87 & 1,917,667.76 & 37,890.46 & 1 & 5 & 1.08 \\ Ambulance–Russia & Training & 200 & 253 & 666.66 & 4,447,929.09 & 917,301.16 & 1 & 4 & 1.26 \\ Ambulance–Russia & Validation & 100 & 116 & 1033.73 & 1,401,825.30 & 92,035.72 & 1 & 3 & 1.16 \\ Ambulance–Said Arabia & Training & 200 & 215 & 247.86 & 313,263.73 & 243,212.19 & 1 & 4 & 1.08 \\ Ambulance–Said Arabia & Validation & 100 & 104 & 470.20 & 926,91.14 & 138,453.92 & 1 & 2 & 1.04 \\ Ambulance–Spain & Training & 200 & 233 & 1165.99 & 9,633,192.61 & 324,979.75 & 1 & 5 & 1.17 \\ Ambulance–Spain & Validation & 100 & 114 & 2008.01 & 9,597,687.84 & 345,183.35 & 1 & 3 & 1.14 \\ Ambulance–Sweden & Training & 200 & 216 & 993.95 & 50,081.73 & 25,0450.00 & 1 & 4 & 1.08 \\ Ambulance– Sweden & Validation & 100 & 112 & 1908.89 & 2,348,605.69 & 138,267.40 & 1 & 5 & 1.12 \\ Ambulance–Tarkey & Training & 200 & 250 & 398.81 & 5,857,812.51 & 71,457.92 & 1 & 6 & 1.25 \\ Ambulance–Tarkey & Validation & 100 & 111 & 1540.82 & 94,586.69 & 44,950.89 & 1 & 3 & 1.11 \\ Ambulance–United Kingdom & Training & 200 & 286 & 481.81 & 5,063,227.92 & 46,313.38 & 1 & 6 & 1.43 \\ Ambulance–United Kingdom & Validation & 100 & 131 & 781.50 & 10,116,762.21 & 419,075.79 & 1 & 6 & 1.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset Specification
## 1 Introduction
The system employs a dual closed-loop structure: the outer loop uses a biologically inspired neural dynamics-based backstepping method for position control, while the inner loop applies a biologically inspired neural dynamics-based sliding mode control for attitude control. The proposed method not only avoids the control signal jump problem in traditional backstepping control, but also effectively suppresses the control signal chattering problem in traditional sliding mode control. The paper is organized as follows. Section 2 introduces the kinematic and dynamic models of the quadrotor unmanned aerial vehicle (UAV). In Section 3, a bioinspired neural dynamics model is presented as the foundation for the development of the bioinspired backstepping position controller and the bioinspired sliding mode attitude controller for the quadrotor UAV. The stability analysis of the proposed control system is conducted in Section 3. Section 4 provides the numerical simulation results, which are compared with other control systems to showcase their advantages and effectiveness. Finally, Section 5 summarizes the development philosophy and justifies the superiority of the proposed hybrid control strategy for quadrotor UAVs. The conclusion also includes suggestions for areas of improvement.