esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Blind Source Separation and Fault Feature Extraction Method for Mining Machine Diagnosis
## 1 Introduction
[19] employed a single channel method to separate nonlinear dynamic sources based on an instantaneous model. [PERSON] et al. [20] introduced a separator for marine gearbox fault source extraction based on the Chaos algorithm. However, the main limitation of nonlinear blind source separation (BSS) methods is that their solution is not unique [15]. In view of the fact that the neural networks can effectively identify the uncertainty of a nonlinear system, it is possible to combine the neural network and ICA to separate the nonlinear mixtures. [PERSON] et al. [21] used a self-organizing neural network (SOM) to extract the independent components of nonlinear mixed signals. [PERSON] et al. [22] proposed a nonlinear blind separation model based on the probabilistic neural network (PNN). [PERSON] et al. [14] introduced a post-linear blind separation model by combining radial basis function (RBF) network and ICA to eliminate the uncertainty of a nonlinear mixture. However, most of the existing nonlinear separation models have not considered additive noise in the mixture, which is not realistic, because in practical applications the mixture is always subject to various noise sources. For instance, the vibration signal collected by an accelerator is inevitably mixed with additive noise due to sensor noise. If the additive noise is neglected in the BSS processing, the separation performance may decrease. To our best knowledge, the nonlinear BSS considering additive noise has not been found in the fault detection of mining machines. In order to bridge the aforementioned research gap in the fault detection of mining machines, this paper proposes a novel wavelet-based nonlinear ICA separator for fault vibration source extraction from mining machine vibration signals. The contribution of this work is a novel wavelet-nonlinear ICA model with consideration of additive noise. Experimental tests were carried out to verify the effectiveness of the proposed method. The analysis results demonstrate that the fault source can be separated from the nonlinear mixture using the proposed method with the unwanted source removed. Hence, the fault feature extraction efficiency is improved. The structure of this paper is organized as follows. Section 1 introduces the background and surveys the literature. Section 2 presents the proposed nonlinear BSS method. Section 3 presents the experimental results. The conclusions of this paper are drawn in Section 4.
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## 2 Preliminaries
### Large Language Models  
Large language models are a class of artificial intelligence models designed to understand and generate human language. These models are trained on vast amounts of text data and can perform a wide range of natural language processing tasks, such as text generation, translation, summarization, and question answering [17; 18; 19; 20]. LLMs leverage deep learning techniques, particularly transformer architectures [4], to capture complex linguistic patterns and contextual information.  
Key characteristics of LLMs include scalability, versatility, and the ability to improve performance through prompt engineering. Scalability allows the LLMs to handle large volumes of text data and generate coherent and contextually relevant responses. Versatility enables their application across various domains, including healthcare, finance, and customer service, to automate and enhance text-based tasks. Prompt engineering involves crafting specific input prompts to guide the model's output, significantly improving performance. Advanced LLMs are designed to be computationally efficient, allowing them to handle complex tasks with high performance and minimal resource usage.  
Mixture of experts (MoE) is an advanced technique that is used to enhance the capabilities of LLMs by leveraging a combination of specialized sub-models, or \"experts,\" each trained to handle specific aspects of a task. In an MoE architecture, the model dynamically selects the most appropriate experts to process different parts of the input, allowing for more efficient and effective handling of complex tasks. This approach has been shown to improve the performance and scalability of LLMs, especially in scenarios requiring specialized knowledge or fine-grained processing [21]. The MoE approach allows for the integration of a large number of parameters, making it well suited for handling complex and diverse datasets.  
Finetuning is a process that is used to adapt a pre-trained LLM to specific domains or tasks by further training the model on domain-specific data. This approach allows the model to learn the unique linguistic patterns, terminology, and contextual nuances of the target domain, enhancing its performance and accuracy. Finetuning has been successfully applied in various domains, including healthcare, where models are finetuned using specialized clinical data to improve their understanding and generation of medical information [22; 23]. By adapting the model to the unique characteristics of the target domain, finetuning can significantly improve its accuracy and reliability. Finetuned models are better equipped to understand and generate contextually relevant responses, making them more effective in specialized applications.  
In the context of healthcare, LLMs have shown promise in tasks such as clinical note summarization, medical information extraction, and diagnostic support [23; 24; 25]. However, their effectiveness in converting unstructured clinical data into structured formats, such as FHIR, remains an active area of research.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### AI-Enabled Interference Mitigation
#### 4.4.3 Network Slicing-Aware IM  
To support diverse service types (e.g., eMBB, URLLC), 3 GPP encourages AI-assisted network slicing [197]. AI models ensure interference-aware resource isolation between slices, reducing cross-interference. For example, AI can dynamically allocate spectrum to URLLC slices while ensuring latency and reliability targets are met, even under high-load conditions [198].
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 5 Simulation Analysis
### Coordinated Control of Simulation Conditions
#### 5.3.1 Condition 1  
Under Condition 1, when unknown obstacles appear in the first half of the road, verification of obstacle avoidance replanning and trajectory tracking control of driverless ferry vehicles under double-shifted line conditions under coordinated control of horizontal and longitudinal controllers take place when the road adhesion coefficient is 0.8. Figure 24 illustrates the control algorithm's simulation effect.  
The horizontal motion and the longitudinal motion work separately; that is, there is no coordinated control. From Figure 24a, it can be seen that when the obstacle is in the first half of the path and there is no coordinated control in the horizontal and longitudinal directions, the driverless ferry vehicle is about to encounter obstacles and bends. Although it can avoid obstacles, the tracking accuracy of the reference trajectory is poor. When coordinated control is carried out horizontally and vertically, the tracking accuracy of the ferry vehicle relative to the reference trajectory is significantly improved. From Figure 24b,c, we can see that, when there is no coordinated control in the horizontal and longitudinal directions, the ferry vehicle will wobble frequently when it encounters obstacles. When horizontal and longitudinal coordinated control is carried out, the swing amplitude and frequency of the driverless ferry vehicle are greatly reduced, the output front wheel angle is more stable, and the tracking stability of the expected yaw angle is significantly improved. Figure 24d shows that, in the absence of coordinated control in the horizontal and vertical directions, the maximum lateral position error has exceeded 1.5 m. However, when coordinating control horizontally and vertically, the maximum horizontal position error is reduced to less than 0.7 m, and the tracking error is reduced by 53.33% compared to uncoordinated control in the horizontal and vertical directions. Through the above analysis, we can see that when there are obstacles in the front half of the road, the horizontal and longitudinal motion coordination control strategy designed shows a more outstanding effect of obstacle avoidance replanning and trajectory tracking control.
# The Impact of Spoofing Attacks in Connected Autonomous Vehicles under Traffic Congestion Conditions
The main contributions of this paper are:  
* We investigate the effects of a basic spoofing attack on a range of CAVs, that is generated in an ad-hoc fashion on the road and has some minimal security guarantees (i.e., minimum safety gap distance of 2.5 m and any member of the platoon is running a highly protected piece of code, whereby if it stops for a reasonable 10 s threshold, it alerts the following vehicles to change routes to reach their destination). * We estimate the statistical measure of CoV and we prove that its absolute value increases under spoofing attack conditions. Moreover, without any cyber-attack, CoV remains at high levels, at about 23 for the speed parameter, under realistic traffic conditions for an urban environment, in contrast to theoretical studies, which indicate a value close to zero [8]. * Finally, we investigate how cross-layer parameters, such as PDR, impact the attack's detection in a future detection system, and we observed how the attack causes a high volume of traffic in the wireless V2X network. This indicates higher vulnerability, a fact to be easily detected. The rest of this paper is organized as follows: In Section 2, we present related work, and Section 3 presents the system model analysis and the spoofing attack model. InSection 4, we give the experimental evaluation of the proposed spoofing attack scenarios and provide a comparison using cross-layer parameters, such as speed, acceleration, and PDR. Finally, Section 5 concludes the paper and gives some future work directions.
## 2 Principle
### Improved-MIMO-Algorithm-Based LS  
In the aforementioned channel estimation method, the influence of noise is not taken into account. When the optical signal-to-noise ratio (OSNR) is relatively low, the accuracy of the channel estimation is compromised. Here, we employ an ISFA method to process the estimated channel. This method performs a weighted summation of each subchannel and its adjacent channels to derive new channel information. This process is equivalent to applying a windowed smoothing operation to the channel estimation results. The definition of the window is as follows:  
\[w(m)=\begin{cases}I_{0}(m),\;1\leq m\leq 2n+1\\ 0,\;others\end{cases} \tag{6}\]  
Here, \(I_{0}(\bullet)\) represents a Gaussian function. In this paper, we utilize a Gaussian function with a mean of 3 and a variance of 3. After applying the ISFA method, the channel matrix is given by:  
\[\begin{bmatrix}H_{xx}^{\prime}(q)&H_{xy}^{\prime}(q)\\ H_{yx}^{\prime}(q)&H_{yy}^{\prime}(q)\end{bmatrix}=\sum_{k=q-n}^{q+n}\begin{bmatrix} H_{xx}&H_{xy}\\ H_{yx}&H_{yy}\end{bmatrix}\bullet w(k-q+n+1)/\sum_{m=1}^{2n+1}w(m) \tag{7}\]  
In the summation on the right-hand side of the above equation, if the subcarriers exceed the valid range, their corresponding channel matrix elements are set to zero. Similarly, the window function weights at those positions are also set to zero. This ensures that only valid subcarriers contribute to the final result, maintaining the integrity and accuracy of the  
Figure 2: The training sequences design.  
channel estimation. The averaging after summation utilizes the sum of the weights of the window function at each point. This ensures that the smoothed channel matrix maintains an amplitude comparable to that before smoothing, preserving the overall energy and characteristics of the channel while reducing noise and fluctuations.
## 1 Introduction
### Contribution  
The present work describes an algorithm called _Masketeer_ that de-identifies (more precisely, pseudonymizes) unstructured clinical notes, addressing challenges that have not yet been fully described in the relevant literature. The _Masketeer_ program removes references to identifiable data from free text and uses the HIPAA Safe Harbor Method as a general guideline. These six main features are to be highlighted:  
1. **Regular expressions** to reduce formulaic references (e.g., phone numbers, addresses).
2. **Dictionaries** from both private (i.e., internal databases) and public sources (e.g., public lists of doctors) to remove names and locations. The algorithm checks for spelling variations as well as hyphenated (i.e., double) names.
3. **Common salutations** to remove names that do not occur in any dictionaries but follow a common structure for the occurrence of names.
4. **Support for manual corrections** to correct any specific occurrences that are not addressable by any of the rules above (e.g., abbreviations, nicknames).
5. **Entity recognition** to retain a degree of semantic context within the notes.
6. **Corpus-wide** pseudonymization of all entities to further retain context for readers and models.  
This study aimed to develop a solid tool that de-identifies German medical notes to improve a telehealth program for patients with chronic heart failure and to evaluate its effectiveness and runtime performance with an increasing dataset size. Lastly, since studies investigating German text are sparse compared to those with English texts, the documenting of our findings is aimed to hopefully fill knowledge and understanding gaps in the intricacies of different languages.
# Intelligent Reflective Surfaces for Wireless Networks:
## 3 IRS-Assisted Wireless Networks
### Signal Model  
In this subsection we introduce the main elements of the IRS-aided systems. Different setups can be considered, including single-IRS-assisted systems, schemes with non line-of-sight (NLOS) between both ends, wideband transmissions, etc. In the following, we present the signal model of two multiuser multiple-input multiple-output (MIMO) IRS-assisted exemplary schemes.
## 4 Attack Resistance
### Identity Attacks
It is proved, as shown in previous sections, that the system adapts to individual attacks. If the node's behavior is consistent with the other nodes, the attack is undetectable, but the information obtained is not significant. In the clone attack the system can detect that the same identity is being used in two different locations, so the server would reduce the reputation of both nodes. On the other hand, the sybil attack can be dangerous to the system because it reduces the effect of the system's redundancy. Our architecture solves the sybil attack problem by reducing its attack rate. When an aggregation agent receives information from an unknown node, the trust level default value is zero. This is enough to send data from this node to the servers to collect behavior history, but not enough to be taken into account in any decision or aggregation. If the node behaves correctly, its reputation will grow eventually, but always at a controlled rate. If many sensors are appearing in a short time in the same area, the required time to have positive reputation will increase.
## 5 Discussion  
### Relationships between NPP and Meteorological Factors  
The relationships among NPP, temperature and precipitation have been previously reported. Results showed that NPP was correlated with temperature (R\({}^{2}\) = 0.924, Figure 10A) and precipitation (R\({}^{2}\) = 0.571, Figure 10B) at every month interval. The monthly change in NPP was similar to that in precipitation, especially from January to June and from August to December. NPP continued to increase in July, although precipitation was lower than that in July (Figure 10B), whereas NPP and temperature had the same trend (Figure 10A). The highest temperature occurred in July, during which NPP also peaked. The probable reason for this situation was that water-absorbing processes caused a lag in vegetation growth in the study area (Figure 11). In this paper, we analyzed the relationship between NPP and meteorological factors without considering vegetation cover. We can find that NPP was consistent with the change of temperature and was not affected by precipitation fluctuation, which indicated that the value of NPP was mainly affected by temperature. NPP sharply decreased from July to August. The spatial distribution of NPP could also be explained by the relationship to temperature/precipitation via the spatial distribution of heat and water in the Chongqing area (Figure 11), and the special mountainous terrain conditions have a significant effect on the distribution of NPP.  
Figure 10: Relationships between estimated NPP and precipitation and temperature (**A**,**B**).  
Figure 9: Comparison of NPP values among the estimated model, the CASA model (A) and MOD17 (B).  
FPAR was another factor that influenced spatial and temporal variation in NPP. Different land cover types varied in FPAR patterns, thereby generating a variation in PAR absorption and consequently changing NPP. Moreover, seasonal changes in FPAR in the IMAR caused temporal NPP changes. In this study, FPAR inversion in particular was based on the satellite remote sensing data. Thus, the relationship between FPAR and atmospheric parameters and vegetation mechanism can be seen more easily when using MODIS than when only meteorological station statistical data are used.
## 5 Experimental Results and Discussion
### Experiment and Analysis
The pixel accuracy, relative limit measurement accuracy, and mean intersection over the union of the extraction results of the proposed algorithm, N-cuts algorithm, SAS algorithm, and HCS algorithm extraction results are shown in Figure 12.  
Analysis of the image algorithms corresponding to the data in Figure 12 led to four main findings: (1) Compared with the N-cuts, SAS, and HCS algorithm, the relative limit measurement accuracy _RLMA_ of the proposed algorithm was the lowest, which indicates that the foreground region extracted by the proposed algorithm had the least deviation from the reference's true value; (2) an accurate extraction method produces a _PA_ value that is close to 100%. Thus, our proposed method yields better extraction than the other methods for all the images except Figure 11(5). Although the HCS algorithm produced the best extraction in Figure 11(5), the foreground and background images could not be effectively extracted; (3) the mean intersection over union _MloU_ of the proposed algorithm was remarkably higher than those of the SAS and HCS algorithms, and slightly higher than that of the N-cuts algorithm, which indicates that the proposed algorithm has the best accuracy and completeness; (4) the mean intersection over union _MloU_ of the proposed algorithm was more than 70, which proves that the proposed algorithm has a high applicability in wildlife monitoring images. The above results show that our algorithm is more suitable for the extraction of wildlife images captured by WMSNs. In order to further verify the performance of the proposed algorithm, this experiment randomly selected 120 images from the wildlife monitoring images and calculated the performance evaluation index values of the different algorithms. As shown in Figure 13, the proposed algorithm improved the pixel accuracy compared with the N-cuts, SAS, and HCS algorithm. By comparing the mean of the experimental data, the pixel accuracy of the proposed algorithm increased by 11.25%, 5.46%, and 10.39%, respectively; the relative limit measurement accuracy improved by 1.83%, 5.28%, and 12.05%, respectively; and the average mean intersection over the union increased by 7.09%, 14.96%, and 19.14%, respectively. The above results show that the proposed algorithm consistently outperforms other algorithms with respect to both pixel accuracy and average mean intersection over the union. Figure 12: Comparative results of each algorithm. PA: Pixel accuracy; RLMA: Relative limit measurement accuracy; MIoU: Mean intersection over the union.
## 4 Experiment and Results  
### Experiment Settings  
We conducted extensive experiments on five datasets: CelebA [33], Helen [34], LFW [35], Widerface [36], and WebFace provided by [37]. Among them, Widerface and Webface are real  
Figure 5: The architecture of Feature Enhancement Module.  
world datasets that exhibit unknown complex degradation processes. CelebA serves as the training set, consisting of 202,599 face images belonging to 10,177 individuals with 40 attribute classes. Similar to [16], we performed preprocessing on the face images. Specifically, we selected 158,026 face images from CelebA, ensuring a balanced distribution across different ages and genders, as the training set. To maximize the utilization of training data, we applied data augmentation techniques such as random scaling, mirroring, horizontal flipping, and random rotation (\(90^{\circ}\), \(180^{\circ}\), or 270\({}^{\circ}\)) to enhance the training samples. For the testing phase, we used CelebA, Helen, LFW, Widerface, and WebFace datasets, ensuring that the face images in the training and test sets are mutually exclusive. Following DIC [15], we employed MTCNN [38] for face detection, and the face regions were cropped from the center without pre-alignment to obtain HR images of size \(128\times 128\) through Bicubic interpolation. Subsequently, these HR images were downsampled to obtain LR images of size \(16\times 16\).  
All experiments were conducted in a virtual environment using Python 3.7, CUDA 11.1, and PyTorch 1.10 on a RTX 3070 GPU. The batch size was set to 16, with the momentum coefficient \(\beta_{1}\) set to 0.9 and the squared momentum coefficient \(\beta_{2}\) set to 0.99. We employed the Adam optimizer with a fixed learning rate of \(10^{-5}\) and a scale factor of \(\times 8\).
# GNSS Accuracy Under White Gaussian Noise Jamming +
## 3 GNSS Jamming Experiment
### Software-Defined GNSS Receiver  
The receiver used in the experiments was the open-source software-defined GNSS receiver GNSS-SDR [4]. A software-defined receiver was chosen because it allows one to exactly define the tracking parameters that do influence the accuracy of the receiver. With commercially available receivers, the user usually has no control over these parameters, which complicates the theoretical prediction of their accuracy. The signals were captured using a USRP B210 front end, which was directly connected to the simulator output using a coax cable, so no signals were transmitted over the air. The signals were captured at a sample frequency of 16 MHz as 16-bit IQ data at a central frequency of 1575.42 MHz.  
For GPS C/A code tracking, an early-minus-late discriminator was used, with an early-late spacing of 0.4 chips, a DLL loop bandwidth of 0.25 Hz, and an integration time of 10 ms. For Galileo E1 tracking, the GNSS-SDR very-early-minus-very-late discriminator was used, with an early-late spacing of 0.2 chips. The very-early-late spacing was set to be equal to the early-late spacing such that the very-early-minus-very-late discriminator  
Figure 2: (**a**) The normalized power spectral density of BPSK, BOC(1, 1), and CBOC modulations. (**b**) The normalized received power for the BPSK, BOC(1, 1), and CBOC modulations as a function of the bandwidth of the front end of a receiver.  
effectively became an early-minus-late discriminator. This setup might result in ambiguity problems due to the additional correlation peaks of the CBOC modulation. This problem, however, was not observed in these experiments. The loop bandwidth for E1 was set to 0.25 Hz, and the integration time was set to 8 ms. The output of the receiver was configured as RINEX files, which contain the pseudo-range, carrier phase, and \(C/N_{0}\) measurements of the tracked satellites, with a rate of 10 measurements per second.