esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix B Diagnoses Examples
### Medichat  
The Medichat model provides a detailed clinical summary and treatment plan based on the patient's medical history and current condition:  
El paciente es una mujer de 69 años con antecedentes de enfermedad mixta (MM) en etapa IIIA segun el sistema de ISS. Presentan sintomas compatibles con infarto agudo de miocardio, sin elevacion del ST segmento en la ECG. La resonancia magnetica cardiaca revelo una miocardiis aguda con edema y fibrosis extensas, asi como un deterioration leve de la funcion ventricular izquierda.  
El paciente actualmente esta en tratamiento con lenalidomida, pero debido a la escasa literatura disponible sobre la cardiotoxicidad inducida por este farmaco, se decidio suspender el ciclo hasta una revision posterior en consultas de hematologia. Durante el ingreso, se inicio un tratamiento ardioprotector limitado debido a la hipotension, pero al al alta se derivo a consultas de cardio-onco-hematologia para seguimiento y titulacion del tratamiento cardioprotector.  
En el ultimo control ecocardiografico realizado un mes despues del ingreso, la funcion sistolica del ventriculo izquierdo se habia normalizado. Sin embargo, la literatura disponible sugiere que la lenalidomida puede causar cardiotoxicidad, anuque no se pudo establecer una relacion clara entre el tratamiento y el cuadro de la paciente.  
En resumen, el paciente tiene miocardiis aguda con edema y fibrosis extensas, asi como un deterioration leve de la funcion ventricular izquierda. Ademas, se encuentra en tratamiento con lenalidomida, anuque la cardiotoxicidad inducida por este farmaco no se ha establecido de manera clara en la literatura. Se recomienda una revision posterior en consultas de hematologia para evaluar el tratamiento y monitorear la funcion cardiaca de la paciente.
# Channel Model for Estimating Received Power Variations at a Mobile Terminal in a Cellular Network
## 4 Results
### Statistical Analysis of Radio-Frequency Parameter Measurements in the Coverage Area
However, the suburban zone exhibited slightly better average RSSNR values in the general coverage area and intra-handover zones despite a weaker overall signal, suggesting reduced interference. The inter-handover zone in the suburban area was an exception, where the RSSNR degraded to below that in the urban zone, indicating localized interference or challenging propagation effects. While our study focuses on the statistical behavior of signal strength and quality in intra- and inter-handover zones, it is worth noting that state-of-the-art methods--such as ray-tracing simulations, 3 GPP-based stochastic models, and machine learning approaches--can provide precise power predictions under static conditions. However, these techniques often fail to capture dynamic power changes caused by terminal mobility, which our model explicitly accounts for through directional power estimation. Additionally, advanced technologies like stacked intelligent metasurfaces for direction-of-arrival (DoA) estimation [5; 6] can help improve signal direction detection in dynamic scenarios. Moreover, the data revealed that during handover events, the average received power typically decreased by about 7 dBm, with observed variations ranging from 5 to 10 dBm. This highlights the inherent instability in signal levels during cell transitions. Consequently, while the suburban area experienced a generally weaker and less stable signal strength, it occasionally benefited from lower interference, as reflected in certain improvements in the RSSNR and RSRQ, depending on the specific handover conditions and propagation characteristics.
# A Dual-Branch Self-Boosting Network Based on Noise2 Noise for Unsupervised Image Denoising
## 3 Methodology
### Basic Idea  
As mentioned above, it is evident that Noise2 Noise exhibits its greatest advantage in not requiring clean reference images for training. This characteristic simplifies both the model design and implementation, making it highly accessible and easy to deploy. The model's ability to learn directly from pairs of noisy images enables effective noise suppression while preserving important details, enhancing its robustness in real-world scenarios. Additionally, its simplicity and straightforward implementation make it a practical and efficient solution for denoising tasks. However, despite these advantages, it is important to note that Noise2 Noise also has some limitations.  
One of the main limitations of Noise2 Noise is its focus on predicting image content while neglecting noise prediction. Incorporating noise prediction within the framework of the N2N model can lead to performance enhancement. Firstly, by leveraging the noise component, finer details in the image can be better predicted by comprehending noise characteristics. Noise often correlates with high-frequency content and fine textures in an image. Explicitly modeling and predicting noise enables the model to more effectively capture and reconstruct these subtle details, ensuring effective noise removal while preservingunderlying content, thereby enhancing image quality. Secondly, the introduction of an additional branch imposes stronger constraints during training, enhancing stability and efficacy. The noise prediction branch serves as an auxiliary task, introducing additional supervision signals to guide learning. This supplementary information aids in regularizing the model, promoting the acquisition of more robust representations. Consequently, network training becomes more stable, with the noise prediction branch contributing to a stronger regularization effect and mitigating overfitting, rendering the network more efficient for practical applications.  
In conclusion, the incorporation of the dual-branch self-boosting technique in N2N allows for the exploitation of content learning and noise learning, leading to improved denoising performance. This new approach not only enhances the stability and efficiency of generating clean images but also results in images with richer details. As a result, the utilization of the dual-branch self-boosting model can significantly enhance the effectiveness of denoising compared to the original N2N model.
## 3 Results and Discussion
### In Vivo Test  
Concerning the in vivo measurements, for all of the subjects, Figure 8 reports \(\Delta\mu_{a}\) in the \(x\)-\(y\) plane at the fixed depth of \(z=16.25\) mm, for different times, each corresponding to integration over 5 s of measurement from the beginning to the end of the acquisition. For subject 1 and subject 2, we can clearly identify that the activation appears as a dark spot only after the beginning of the task, with about a 5 s delay, and then it disappears, progressively fading up to restoring the initial background condition in about 15-20 s after the end of the task. Unfortunately, the results for subject 3 do not follow the expected time evolution: a darker variation in the absorption coefficient seems to be always present throughout the entire acquisition. The reasons are still under investigation.  
A 3D tomographic map of the changes in \(\Delta\mu_{a}\) during the activation is reported in Figure 9a for subject 1 and the time course of \(\Delta\mu_{a}\) for the three subjects is shown in Figure 9b. Three different  
Figure 8: In vivo tomographies: \(x\)-\(y\) planes reconstructions at \(z=16.25\) mm at different times from t = 1 s to t = 60 s averaged in ranges of 5 s (**rows**) for three subjects (**columns**) during a finger tapping motor task. The grey rectangle marks the task. In this case the volume used is 100 \(\times\) 100 \(\times\) 30 mm\({}^{3}\).  
regions of interest (ROIs) were identified to better estimate the spatial and temporal distribution of \(\Delta\mu_{a}\): ROI_1, a 15 mm side cube centered over the activation area, as identified in Figure 9a, with the upper surface at a depth of 5 mm; ROI_2, similar to ROI_1 but far from the center of the activation; and, ROI_3, corresponding to the whole superficial layer down to a thickness of 5 mm. In Figure 9b, we notice that the time course of \(\Delta\mu_{a}\) in ROI_3 is rather flat for all of the subjects. For subject 1 and subject 2, we notice a task related decrease of \(\Delta\mu_{a}\) in ROI_1 during the task period, consistent with the expected changes during brain cortex activation. Conversely, smaller and not task-related changes are observed in ROI_2. For subject 3, the results are very noisy in ROI_1 and ROI_2, therefore no task-related activation was detected in this case.
## 3 Theoretical Analysis and Proof
### Experiments with Different Datasets
#### 4.3.3 Zy3-02  
The third experiment was conducted based on the observation images of ZY3-02 satellite. The image has a spatial resolution of 5 m with a size of 1762 \(\times\) 1450 pixels. Unlike the previous two experiments, this sea ice image contained not only large and well-defined ice blocks, but also some fragmented ice. In addition, the uneven brightness of the image caused by external interference posed a significant challenge for CEM method (1) and made it necessary to improve the performance of these four models. Firstly, as depicted in Figure 5c-f, all four models produced good results in identifying sea ice features without external noise disturbance, which was also supported by the parameters in Table 3. As depicted in Figure 5g-j, the performance of the four models shows significant variation under random noise disturbance. This highlights the increased challenge in feature identification amidst noise. Notably, EAEND model (10) exhibits superior capability in feature identification, achieving a Kappa coefficient of 0.963492. In the task of the Arctic sea ice identification, both NRI (28) and MNI (27) models produce somewhat blurred results. The distinction in feature identification quality between these models and EAEND model (10) is particularly pronounced in regions with fragmented ice. Conversely, ETZNN model (29) displays marked misidentification issues in the Arctic sea ice features, reflected in its lower OA, AA, and PP values and Kappa coefficient compared to the other three models. Furthermore, the computational performance of each model is differently impacted by random noise.  
ETZNN model (29), with an RE of 10\({}^{7}\), struggles notably in noise suppression, aligning with the visual outcomes shown in Figure 5j.
# Routing Design for Pipe System Considering Natural Frequency
## 2 Related Works
### Obstacle Identification for Pipe Routing in Aero Engine  
Most scholars studying pipe routing algorithms assume that the routing space is already known. This is true for some simple ship and aero-engine models. Because simple geometric models can be concatenated with a small number of containment blocks, obstacle recognition can be easily and quickly achieved. However, for geometric models with complex components, it is very difficult and time-consuming to obtain the routing space. Therefore, scholars adopt various schemes to describe the obstacle. There are primarily two types of obstacle description methods: cell decomposition (Figure 1a) and hierarchical decomposition (Figure 1b). Cell decomposition is the process of decomposing a 2D or 3D model into cells of the same size and shape. By determining the presence of obstacles in each cell, it identifies whether the pipeline can pass through. This method is widely used in traditional routing algorithms [5]. Cell decomposition demands an adequately fine grid during grid scanning to accurately depict the routing space. However, using fine-grained grids can raise concerns related to storage and computational time. For instance, the aero-engine model with the size of \(400\;*\;600\;*\;800\;\text{mm}^{3}\) presented in Figure 2. Figure 2 is partitioned into grids based on various sizes, as depicted in Table 1.  
Figure 1: Two types of decomposition method for routing space.  
Figure 2: The cell decomposition for aero engines.  
Article [6] further developed the cell decomposition method, which assigns lower weights to the edges of obstacles and walls to achieve the goal of the pipeline sticking to obstacles and walls. In order to reduce the processing time during each installation of cell decomposition, article [3] proposed a hierarchical decomposition for obstacle identification. Hierarchical decomposition mainly divides the routing space into several layers along a specific direction and only identifies obstacles in the layers required for the following routing pipe (Figure 3). The hierarchical decomposition method reduces the obstacle identification time of a single pipe. However, it does not completely eliminate the need to model the entire routing space when pipes are dispersed across it. As a result, the hierarchical decomposition method has not fundamentally resolved the issue of time consumption for obstacle identification. Therefore, research into a new obstacle definition method is a prerequisite for applying pipe routing algorithms to practical aero engines.
# BDS-3 Integrity Risk Modeling and Probability Evaluation
## 2 BDS-3 Integrity Monitoring Architecture
### BDS-3 Integrity Monitoring Method
#### 2.2.1 Ground Integrity Monitoring  
The ground integrity monitoring system consists of one main control station, some ground monitoring stations and uplink injection stations. Multiple ground monitoring stations form a ground monitoring network through transmission links. The ground monitoring network transmits the observation data sampled in one second to the main control station in real time. First, the main control station performs data cleaning, including gross error identification and elimination, pseudorange data phase smoothing, system error calculation and correction. Then, the satellite orbit calculation, satellite clock error calculation, ephemeris fitting parameter calculation, orbit and time synchronization processing are carried out by using the \"clean\" data. This work can realize SISRE monitoring, which can identify the satellite ephemeris and clock error or ionospheric anomalies. The SISREs of different satellites can be expressed in the form of grade values (SISMAIs). Users can weigh different satellites according to their service requirements. However, SISRE monitoring is limited by regional monitoring networks, which can monitor satellites with fourfold coverage in the service area.  
Figure 1: BDS-3 integrity monitoring process.  
SISMAI is updated and broadcast every 30 s. To compensate for the lack of real-time alarm ability, AIF is used. If SISMAI indicates the SISRE correctly, the corresponding AIF parameter is assigned a value of \"0\", otherwise, AIF is assigned a value of \"1\" to send an alarm to the users.  
SISREs, or message anomalies greater than the alarm limit, can be monitored in real time. If the anomaly is monitored, DIF can be switched from \"0\" to \"1\" to give a warning information and injected into satellites within 1 s. Then, the integrity monitoring information is arranged to the navigation message according to the navigation message broadcast period and broadcast by each satellite. The integrity messages broadcast period is 18 s and 3 s for B1C and B2a signal, respectively.
###### Abstract  
Handling faults in a running cellular network can impair the performance and dissatisfy the end users. It is important to design an automatic self-healing procedure to not only detect the active faults, but also to diagnosis them automatically. Although fault detection has been well studied in the literature, fewer studies have targeted the more complicated task of diagnosing. Our presented method aims to tackle fault detection and diagnosis using two sets of data collected by the network: performance support system data and drive test data. Although performance support system data is collected automatically by the network, drive test data are collected manually in three mode call scenarios: short, long and idle. The short call can identify faults in a call setup, the long call is designed to identify handover failures and call interruption, and, finally, the idle mode is designed to understand the characteristics of the standard signal in the network. We have applied unsupervised learning, along with various classified algorithms, on performance support system data. Congestion and failures in TCH assignments are a few examples of the detected and diagnosed faults with our method. In addition, we present a framework to identify the need for handovers. The Silhouette coefficient is used to evaluate the quality of the unsupervised learning approach. We achieved an accuracy of 96.86% with the dynamic neural network method.  
cellular network management; network optimization; cellular network troubleshooting; data mining; unsupervised learning; supervised learning; self-healing networks Article
## 4 Use of Spectroscopic Techniques for Monitoring Changes in Muscle Foods during Processing
### Meat and Poultry Products
#### 4.2.1 Thermal Treatments
A PCA model on the relaxation times allowed recognizing samples spread along PC1 according to the cooking temperature; the authors concluded that water mobility and distribution are affected by this factor, while cooking time seems to have limited consequence on the dispersion of water in the meat. In a similar context lays the study by [PERSON] and others who used NMR to monitor the variations in terms of water migration and protein structure during heat-related gelation in meat batters [219]. The authors observed that the development of gel is induced by the production of \(\beta\)-sheets (by means of transformation of \(\alpha\)-helix) and by water transfer outside meat. In particular, different processes take place at diverse temperatures. In a first stage, between 39 and 50 \({}^{\circ}\)C, proteins start denaturating and an increasing amount of bound-water is made free. Then, between 50 and 74 \({}^{\circ}\)C, proteins agglomerate and \(\beta\)-sheets amount increase.
## 3 Network Model
### Network Deployment Scenarios  
In the network deployment scenario proposed, referred to as Flex-O, the aggregation of wavelengths by means of OADM devices is allowed at the intermediate nodes traversed by the TPs, as shown in Figure 2a. Moreover, the type of an OADM is selected flexibly according to the number of wavelength connections requested in the intermediate node at which the traffic aggregation is realized. Namely, \(o(d)\) represents the OADM of the lowest number of added/dropped wavelengths that can support \(W(d)\) wavelengths, i.e., \(o(d)=\arg\min_{o\in\mathcal{O}}W(o)\) such that \(W(o)\geq W(d)\).  
In the evaluation of the Flex-O approach, we consider the following reference scenarios:  
* Fix-O--Similarly as in Flex-O, the aggregation of wavelengths is allowed; however, the OADMs have a fixed capacity corresponding to the capacity of the WDM transmission systems. This scenario was considered in our previous work [19] and is shown in Figure 2b.
* Flex-D--Here, the end nodes (i.e., a remote site and the hub) are connected using direct TPs and the aggregation of wavelengths is realized only at the end nodes by means of MUXs; the MUXs have the capacity selected flexibly according to the number of aggregated wavelengths, as shown in Figure 2c.
* Fix-D--As in Flex-D, the end nodes are connected using direct TPs (i.e., without wavelength aggregation at intermediate nodes) and the MUX capacities reflect the WDM system capacity, which is illustrated in Figure 2d.
# Pollen-Food Allergy Syndrome: Allergens, Clinical Insights, Diagnostic and Therapeutic Challenges
## 7 PFAS Diagnosis
### Skin Prick Testing (SPT)
The interpretation of these results requires careful consideration of the clinical history, patient-specific factors, and the broader diagnostic context. Clinicians should use diagnostic thresholds as flexible tools rather than definitive answers, ensuring that allergy evaluations remain patient-centered and adaptable to individual needs [98]. The results of SPT play a critical role in guiding dietary recommendations and allergen avoidance strategies for PFAS patients. For example, individuals with confirmed birch pollen sensitivity and positive SPT to apple, hazelnut, and carrot may be advised to avoid these foods in raw form but tolerate them when cooked, as heat denatures PR-10 \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**Category** & **Description** & **Details/Insights** \\ \hline
**Limitations** & & \\ \hline _False negatives_ & Heat-labile proteins in PFAS, such as PR-10 proteins and profiles, may degrade in commercial extracts, leading to false negatives. & Fresh food testing is essential to overcome this limitation. \\ \hline _Non-specific reactivity_ & Non-specific skin reactions or demographism can complicate interpretation, necessitating careful control comparisons. & Negative and positive controls are critical for accurate interpretation. \\ \hline _Requires expertise_ & Accurate interpretation of SPT results demands expertise to distinguish between primary senstizations and cross-reactive alleges. & Specialized training is needed to correlate results with clinical history and dietary triggers. \\ \hline _Limited specificity_ & Positive SPT results indicate sensitization but do not always confirm clinical allergy, as some sensitized individuals may remain asymptomatic. & Additional diagnostics, such as oral food challenges, may be needed to confirm clinical relevance. \\ \hline _Risk of systemic reactions_ & Rarely, SPT may trigger systematic allergic reactions, especially in highly sensitized individuals. & Emergency medications (e.g., epinephrine) should be readily available during testing. \\ \hline _Geographic variability_ & Allergen extracts may not represent local or regional allergen sources accurately, leading to incomplete diagnostic coverage. & Custom extracts may be needed for certain endemic allergens. \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._proteins [98,188]. Patients with profilin sensitivity may need broader dietary modifications due to profilins' widespread presence in plants. Additionally, SPT results can inform decisions regarding allergen immunotherapy (AIT), targeting the primary pollen allergen to reduce cross-reactive food symptoms.
## 3 Observation of Scintillation in OEM7 Measurements
### Amplitude scintillation  
C/N\({}_{0}\) is a measure of the signal strength relative to the noise level and is often used as a signal quality indicator. During scintillation, it has been proven [5] that C/N\({}_{0}\) measurements experience considerable fluctuation due to the received signal's amplitude attenuation. In severe scintillation conditions, C/N\({}_{0}\) values may drop to levels where the GNSS receiver struggles to maintain signal lock or tracking.  
Figure 5 shows C/N\({}_{0}\) variations during a scintillation period for GPS PRN 23 at L1 and L5 bands. GPS PRN 2 is also overlaid for comparison. As shown, GPS PRN 23, which undergoes significant fading in a short amount of time, experiences challenges in maintaining signal lock, resulting in multiple instances of a loss of lock, which can be seen in Figure 5b. For example, in Figure 5a, at around 407,456, the GPS PRN 23 L5 signal has a 20 dB fade in just over 4 s and experiences a loss of lock. Deep fades and difficulty in tracking are also discussed in [6]. Concurrently, other satellites, such as GPS PRN 2, do not experience any scintillation. In general, signals of lower frequency are more affected by scintillation than signals of higher frequency [7]. This can be observed in Figure 5, where the C/N\({}_{0}\) of GPS PRN 23 L1 CA has lower variation and is more consistent in maintaining lock, whereas the variation in GPS PRN 23 L5 is higher and it experiences deeper fades.