esa-sceva/satcom-chunk-collection huggingface dataset:
# Shedding Light on FIRE Syndrome: An Overview of a Novel Condition in Eosinophilic Esophagitis
## 5 Diagnostic Criteria  
The limited data available and the lack of clarity involving the pathophysiological mechanisms involved in FIRE syndrome make it difficult to formulate diagnostic criteria. Drawing from extensive clinical experience of European and United States experts, coupled with thorough examination of patient-reported outcomes, [PERSON] et al. [16] were able to identify the most important clinical characteristics, which can now inform a preliminary diagnostic algorithm. While objective biomarkers for FIRE syndrome are still lacking, the pattern of clinical presentation, particularly in the context of EoE, provides the basis for a stepwise clinical diagnostic framework formulation (see Figure 2) [16; 17; 18; 19].  
Firstly, the primary complaint is defined by intense retrosteral discomfort or pain, typically appearing within a timeframe of 5 min, lasting for about 30 min, strictly linked to the ingestion of a specific food or beverage. The symptoms manifest as angina-like or burning discomfort [16].  
Secondly, this intense sensation is reproducible after re-exposure to the same trigger and appears with high intensity, often leading to avoidance behavior and food-related anxiety. Approximately 30-40% of patients report coexisting symptoms, such as throat discomfort, burning sensation, or panic-like symptoms [16].  
Thirdly, these patients appear to have a particular EoE phenotype, having a younger age at diagnosis and a high rate of comorbid atopic disease. Male patients with a history of food bolus impaction necessitating endoscopic intervention for removal tend to display a higher prevalence of FIRE syndrome [16].  
Additionally, an optional diagnostic step could include a temporary elimination diet followed by a clinician-supervised re-challenge, which may be taken into consideration in order to confirm the reproducibility of symptoms and avoid misdiagnosis. This approach must follow a shared decision with the informed patient and should be conducted with caution, particularly in patients with a history of severe symptoms.  
Figure 2 represents a proposed diagnostic algorithm synthesizing these features into a structured potential clinical decision tool.
## 6 Evaluation
### Performance Evaluation
#### 6.1.1 Cost Evaluation  
Our c cost evaluation considers the amount of _gas_ required for transactions. The functions are the same as discussed in Table 1. The functions that involve a write operation require a _gas_ fee, while a read operation does not (as a private blockchain is used in our implementation). The two most commonly used inputs required for these functions are either _userID_ or _product_id_. Table 2 provides a summary of the functions and their respective _gas_ fees. As outlined in Table 2, the operations incur a cost of 0.11363819 DOT for the transactions performed on the Polkadot platform used in the framework.  
When incorporating a product onto the blockchain, what's actually recorded is the MP reference, a 46-character IPFS string, preserved as a hash datatype in _inkl_. During the process of changing ownership of a product, the transaction details (including block and transaction hashes) are documented. Unlike in a single-layer blockchain, where the details can be accessed using just the transaction hash, Polkadot necessitates both [33]. Embedding transaction details within the smart contract allows network participants to backtrack ownership information independently.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Functions** & **Executable** & **Operation** & **Purpose** \\ \hline grant\_roles & Admin-specific & Write & Used by the admins to create and assign roles to the stakeholders \\ \hline revoke\_roles & Admin-specific & Write & Used to revoke the current role of a member \\ \hline check\_roles & Admin-specific & Read & Used to find the current role of any member \\ \hline get\_participants & Admin-specific & Read & Used to obtain a list of all participating members \\ \hline add\_product & Manufacturer-specific & Write & Used by the manufacturer to add the product details to the chain \\ \hline transfer\_product & Any member & Write & Only the current owner is authorised to transfer ownership \\ \hline get\_MP & Any member & Read & Used to retrieve the MP reference stored on-chain \\ \hline get\_owner & Any member & Read & Used to find the current owner of a product \\ \hline get\_pro\_details & Any member & Read & Used by the members to access the details of the product \\ \hline get\_pro\_history & Any member & Read & Used to obtain previous ownership details \\ \hline \hline \end{tabular}
\end{table}
Table 1: Smart contract functions.
## 2 Materials and Methods
### Collection of Multispectral Images  
The jujube fruit multispectral image collection took place from 13:18 to 13:28 on 6 October 2022. For the experiment, 100 jujube fruits were selected as test samples. The jujube fruits were chosen based on their similar sizes and absence of surface damage. Before the experiment, all jujube fruit samples were numbered and placed on the outdoor ground, with a diffuse reflection whiteboard positioned on one side for flat field correction. The jujube fruit is oval as a whole, and the long axis of the jujube fruit was placed in the north-south direction. The experiment commenced once the sample temperature was consistent with the ambient temperature. During the experiment, the UAV gimbal angle (\(\theta_{r}\)) was set to 45\({}^{\circ}\), with the UAV positioned approximately 150 cm above the ground. The UAV rotated counterclockwise around the center of the sample circle, which had a radius of 150 cm. The azimuth angle \(\varphi\) ranged from 0\({}^{\circ}\) to 360\({}^{\circ}\). Multispectral images were collected at intervals of 45\({}^{\circ}\), resulting in a total of 8 azimuth angles (0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\), 180\({}^{\circ}\), 225\({}^{\circ}\), 270\({}^{\circ}\), 315\({}^{\circ}\)). Through the above method, the spectral data of 100 jujube fruits were finally obtained (Figure 3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Spectral Band** & **Center Wavelength/nm** & **Bandwidth/nm** \\ \hline Blue light & 450 & 16 \\ Green light & 560 & 16 \\ Red light & 650 & 16 \\ Red edge & 730 & 16 \\ Near-infrared & 840 & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: DJI Phantom 4 UAV multispectral camera parameters.  
Figure 3: Collection of multispectral images.
## 5 Experiment and Discussion
### Experiment Result and Discussion
#### 5.3.4 Impact of Time Accumulation  
In order to verify the impact of time accumulation, we record the DBLP data set as Y4, Y8, Y12, Y16, Y20, Y24, Y28, Y32, Y36, and Y40 according to the time sequence that every four years as a time stamp. For each time stamp, we use MRDE to learn the node embed-ding for the downstream node classification task, the results are shown in Figure 4. From the figure we can find that the accuracy of node classification is gradually improved with the continuous accumulation of time, which means that with the accumulation of time, the effective information on the network is gradually increasing, which is conducive for better node embedding. It also means that DHIN embedding method can capture the time consistency information in the network  
Figure 3: The attention weight distribution of different meta-paths on the IMDB dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & **DBLP** & **IMDB** \\ \cline{2-3}  & **ARI** & **ARI** \\ \hline Raw & 0.369 & 0.355 \\ GCN & 0.625 & 0.682 \\ GAT & 0.718 & 0.733 \\ M2V & 0.785 & 0.775 \\ JUST & 0.725 & 0.737 \\ CTDNE & 0.805 & 0.827 \\ C2V & 0.798 & 0.905 \\
**MRDE** & **0.815** & **0.929** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of node clustering task.
Multi-Area State Estimation: A Distributed Quasi-Static Innovation-Based Model with an Alternative Direction Method of Multipliers  
[PERSON]  
1 Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611-6200, USA; [EMAIL_ADDRESS] (N.A.); [EMAIL_ADDRESS] (T.Z.)  
1 Department of Electrical & Computer Engineering, University of Sao Paulo, Sao Carlos 13566-590, Brazil; [EMAIL_ADDRESS]  
2  
[PERSON]  
1 Department of Electrical & Computer Engineering, University of Florida, Gainesville,One main characteristic of Distributed State Estimation (DSE) is enabling regional control centers to perform their own SE process with enhanced robustness. Considering DSE, the state-of-the-art literature can be divided into two categories: hierarchical and neighbor-to-neighbor SE. In the hierarchical approach, each local control center performs their own SE, and upon convergence, estimated states are communicated with a central control for coordination. The works in [4; 5; 6; 7; 8; 9; 10] have explored this approach. The main shortcoming of this method is that there is still a need for a central controller to consent the global estimation of the states by matching with the boundary bus measurements. The neighbor-to-neighbor SE approach, on the other hand, eliminates the need for the central processor. In this approach, the communication happens between neighboring control centers. The authors in [11; 12; 13; 14; 15] have proposed different techniques for designing a fully DSE. In [11], a relaxed semidefinite programming non-linear SE is used to achieve near-optimal solution. However, the method may fail in the absence of voltage magnitude meters at all buses. In [12], a distributed non-linear SE is presented. However, the main drawback is the need for estimating the global state vector for each local area. In [14], a decomposition method is proposed. However, local observability is required and convergence is not always guaranteed. In [15], the authors developed a DSE based on Alternating Direction Method of Multiplier (ADMM). However, the method considers only a linear measurement model, known as DC SE. In the DC SE model, measurements are assumed to be linearly related with the system states. DC SE model is an approximation model that might not be suitable for applications where accurate representation of the underlying physical system is needed. In fact, the relationships between recorded measurements and state variables are non-linear [16; 17]. A review on the concept of multi-area SE can be found in [18].  
This paper presents a distributed non-linear multi-area state estimator which is based on the Gauss solution. The specific contributions of this work towards the state-of-the-art are two-fold:  
* A distributed measurement model for non-linear multi-area state estimation which utilizes ADMM;
* Applying the Innovation Concept, which takes into account the masked error component in the Jacobian range space.
## 1 Introduction
Most LSTM-based fall detection systems applied raw data to LSTM, not feature parameters, and further research concerning, e.g., z-score normalization following a Gaussian distribution, and regularizations to prevent overfitting and sampling methods of rescaling data for the best fall detection has not been investigated [26, 27, 28, 29, 30, 31]. Power efficiency is very important for wearable devices, which are embedded devices with limited resources. To increase the power efficiency of wearable devices to which a fall detection system using deep neural networks is applied, a fall detection algorithm with good power efficiency is required. A study has been published to increase power efficiency by combining a fall detection method using a simple threshold and HMM, one of machine learning (STM-HMM) [17]. HMM predicts the next event with respect to the previous event, therefore it may be unsuitable for multiple continuous data such as fall data [24]. In this STM-HMM, even changing to deep neural networks instead of HMM may improve both fall detection accuracy and power efficiency. Accordingly, it is necessary to study a fall detection system that applies LSTM, deep learning only the data exceeding the threshold of the 3-axis acceleration sensor data or its pre-processing data. Because the training data and the verification data were not tested separately in the fall detection using the existing HMM [17], it was not confirmed whether the training data were overfitted. In a fall detection system using STM-HMM, it is necessary to divide training data into training and validation data to determine overfitting and prevent it. When applying LSTM to a fall system, it is also necessary to investigate the optimal conditions to increase the accuracy. In this study, a fall detection system combining the STM with LSTM (STM-LSTM) is proposed. The 3-axis accelerometer data are calculated with several parameters, and then the STM is processed. For this fall detection system, the LSTM is applied only when the parameter exceeds the threshold to detect the fall events. Section 2 describes the materials used in this study. Section 3 explains the proposed STM-LSTM for fall detection. In Section 4, the performance of fall detection for the SMM-LSTM is compared against that for the STM-HMM, and in Section 5, optimal parameters in the proposed STM-LSTM are investigated. Finally, we present the conclusion of this study.
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 6 Test Campaigns and Results
### DGNSS-Based Cooperative Positioning
#### 6.3.2 Service Availability  
The availability of such a near-real-time service was measured as the ratio between the number of actual cooperations and total number of positioning solutions. Such a metric was strictly related to the congestion status of the network. Non-ideal links indeed can limit upload/download channel capacity of the raw GNSS measurements to/from the CPS database, thus making their combination unfeasible.  
As observed in the test campaigns, a considerable availability of the service (higher than 75%) was experienced in the morning test sessions. Differently, late morning experiments showed a progressively decreasing availability. By comparing the results with the preliminary test campaign (TRN-DUB), it can be observed that the service availability of the experiments performed during the UAB test campaign (UAB-UAB) is lower on average. Sm01 and Sm02 tested in Turin showed an average availability of 86.96% and 81.23% respectively, while these values were dramatically reduced in the UAB test campaign. By considering only the set of best-case experiments, we observed an average availability of 74.28% (sm01) and 78.35% (sm02), which is still lower than the aforementioned values observed in TRN-DUB, but still acceptable and similarly promising.
## 8 Conclusions  
The present work presents a new cooling design concept using TEC technology. This experimental investigation had two main objectives: (i) to cool down the air in an air duct channel for cooling purposes and (ii) then use the heat from the liquid coming out from the cold plate for heating purposes. Therefore, various parameters such as input current and water flow rates were varied to examine the effect on system behavior. Moreover, the aim was to cool down the air in the duck channel as low as possible and heating the insulated room as high as possible. Therefore, the cooling temperature was measured at various input currents 2, 4, 6 and 8 A at different inlet air temperatures and water flow rates inside the cold plate.  
In the first set of experiments, it is observed that the cooling effect is increased upon increasing the current supplied to the TEC module. An approximately 40% cooling effect increase was seen while the applied current increased. Moreover, the temperature inside the closed room also shows a significant increment in its temperature. In the second set of experiments, the cooling effect decreases as the air inlet speed increases at various air inlet speeds. As the inlet airspeed was increased from 2.35 to 3.55 m/s, there was a rise in temperature of 58%.  
Furthermore, there is no significant effect of inlet airflow rate for the bulk mean temperature. It is in the range of 21-22 for all different fan speeds. Besides, the cooling effect increases with a decrease in the water flow rate. It is noticed that the 15% cooling effect increased with a decrement of water flow rate from 3.04 to 1.80 L/min. Future work could be focused on using different liquids which have faster heat releasing and absorbing capacity than water  
Conceptualization, Y.P. and S.M.; methodology, Y.P. and A.R.M.S.; software, Y.P., A.R.M.S. and M.R.M.; validation, Y.P., A.R.M.S. and M.R.M.; formal analysis, S.M. and S.H.T.; investigation, Y.P.; resources, Y.P. and S.M.; data curation, Y.P., A.R.M.S. and M.R.M.; writing--original draft preparation, Y.P.; writing--review and editing, A.R.M.S. and M.R.M.; visualization, Y.P. and A.R.M.S.; supervision, S.M.; and S.H.T.; project administration, S.M.; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Cost (CAD)** \\ \hline TEC module (4 Pcs) & 15 \\ \hline Aluminum plate type heat sink (2 Pcs) & 36 \\ \hline Inlet air flow pump & 14 \\ \hline Cold plate heat exchanger (2 Pcs) & 130 \\ \hline Copper tube heat exchanger & 50 \\ \hline Water circulating pump & 85 \\ \hline Additional cost & 20 \\ \hline Total cost of the system & 350 \\ \hline Available system in the market & 600–700 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Cost analysis of the proposed TEC-based cooling system.
## 4 Conclusions  
To assess the effect of nodal deviations on structural reliability performance during shape optimization, this study performs shape optimization of a free-form surface of the rectangular plane with the objective of minimizing strain energy. The influence of nodal deviations was analyzed through the PDF of structural responses. The PDF of the maximum nodal displacement responses under a vertical uniformly distributed load was calculated for representative iteration steps during the optimization process. Through comparative analysis of the PDF results derived from two sets of random variables (with and without nodal deviations), the convergence of two PDF curves and reduction in discrepancies of displacement expectations were observed. Compared with the initial structure, the PDF of the maximum nodal response of the optimized structure shows a higher degree of coincidence, including smaller displacement expectations (from \(4.05\%\) to \(0.11\%\)) and smaller variances (from \(78.69\%\) to \(11.76\%\)), demonstrating that adjusting control point coordinates during strain energy minimization inherently suppresses the influence of nodal deviations.  
Following shape optimization targeting strain energy minimization, the structure not only achieved performance enhancement but also showed significantly reduced sensitivity to nodal deviations. This finding holds significant implications for the practical application of shape optimization in engineering, and other types of loads may be studied in the future.  
Conceptualization, D.L. and B.J.; methodology, D.L.; software, D.L.; validation, D.L.; formal analysis, D.L.; investigation, D.L.; resources, B.J.; data curation, D.L.; writing--original draft preparation, D.L.; writing--review and editing, B.J.; visualization, D.L.; supervision,B.J.; project administration, B.J.; funding acquisition, B.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (52468020).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
PDF Probability Density Function
## 1 Introduction
The pressure transducers measure the differential pressure in the capillary. The higher the differential pressure, the higher the viscosity. Although systems of this type are an excellent tool for controlling viscosity changes, however, taking into account that viscosity measurement for viscoelastic liquids requires the use of numerous conversion calculations, the so-called rheological corrections, measurements during the actual production process do not give such accuracy as the results from the capillary rheometer. In the case of polymer flow measurements, several basic conversion factors are used: Weissenberg-Rabinowitsch correction for non-parabolic velocity profile [2; 27; 28]; Bagley Correction--for inlet and outlet pressure loss [12; 29; 30; 31]; Mooney correction--for wall slip in the capillary [32; 33]. The problem with using this type of calculation is the need to perform several preliminary measurements at different shear rates, often also for different capillary geometries. In the case of in-line measurements, such tests are impossible to perform because the geometry of the measuring channel is constant, and the measurement itself is usually carried out at a constant shear rate. The present study discusses the results of industrial tests of the in-line rheological measurement system. The properties of materials obtained during the technological testshave been subjected to mechanical tests, transparency analysis (haze test), and differential scanning calorimetry (DSC) thermal analysis.
# Fault Diagnosis Method Based on Time Series in Autonomous Unmanned System
## 4 Fault Diagnosis Method Using Improved Clustering Algorithm
### Fault Diagnosis Method Based on Clustering  
There are three types of anomaly detection methods based on clustering:  
* The first type of method is based on the assumption that each piece of normal data belongs to a cluster, while the abnormal data do not belong to any cluster. Generally, this type of method does not force every instance to belong to a cluster.
* The second type of method is based on the following assumption: normal data distribution is close to the center of the cluster, while abnormal data distribution is far away from the cluster's center. In this method, the data are firstly clustered, then the anomaly is evaluated by calculating the distance from each point to the center of the corresponding cluster.
* The third method is based on the following assumptions: the samples of the cluster where the normal data are located are relatively dense, while the samples of the cluster where the abnormal data are located are relatively sparse.  
Considering that the abnormal data of autonomous unmanned systems belong to the small sample data, the fault diagnosis can choose the first and third types of clustering algorithms. The first type of method mainly uses DBSCAN clustering method based on density, while the third type is based on k-means based on partition and hierarchical clustering. In this paper, we adopt the third clustering method for fault detection.
## 4 Case Studies
### Natural Gas Supply Chain Minimum Cut Set  
Considering the correlation between the reliability of the power system and the natural gas system, it is necessary to calculate the final gas supply to the power system by considering the natural gas delivered from the gas source through the pipeline and the gas-processing station, which determines the final output of the gas-fired power units. To obtain the final gas supply, it is necessary to calculate the possible gas delivery paths, also known as the minimum cut set, from the gas source to the gas-fired power units. Based on Section 2.1, the concatenated set matrix \(\mathbf{T}\) of Figure 5 is obtained as:  
\[\mathbf{T}=\begin{bmatrix}1&0&0&0&0\\ 0&1&0&1&0\\ 0&1&0&0&1\\ 0&0&1&1&0\\ 0&0&1&0&1\end{bmatrix}\]  
where the columns are numbered \(A\), \(B\), \(C\), \(D\), and \(E\). It is known that the system consists of two third-order minimal cut sets, (\(A\), \(B\), \(C\)) and (\(A\), \(D\), \(E\)), and the system can be further represented as a series of minimal cut sets.