esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Gamma Radiation Absorption Method in Liquid-Gas Flow Measurements  
The technique of using gamma radiation absorption in measurements is based on the exponential attenuation of the \(\gamma\) radiation beam as a function of the geometric (thickness), physical (mass, density), and chemical (elemental composition) quantities of the absorbent [45]. This is described by the Beer-Lambert law:  
\[I=I_{0}\exp(-\eta\cdot\mu\cdot d) \tag{1}\]  
where \(I_{0}\) is the inlet to absorbent radiation intensity, \(I\) is the outlet intensity of the exit, \(d\) represent the thickness of the absorbing material, and \(\eta\) and \(\mu\) are the density and mass absorption coefficient of this material, respectively.  
If the basic Equation (1) is applied to an air-water mixture, then the final equation is as follows:  
\[I=I_{0}\exp[-(\eta_{G}\cdot\mu_{G}\cdot d_{G}+\eta_{L}\cdot\mu_{L}\cdot d_{L} +\eta_{P}\cdot\mu_{P}\cdot d_{P})] \tag{2}\]  
where the indices \(G\), \(L\), and \(P\) for \(\eta\), \(\mu\), and \(d\) denote the gas, the liquid, and the pipe, respectively.  
Changes in radiation intensity caused by flow of gas and liquid are recorded by scintillation probes and converted into electrical impulses. A typical measurement setup to test the two-phase flow by using the gamma absorption method is presented in Figure 1.  
A radioactive source, placed on one side of the pipe emits a beam of \(\gamma\) radiation (4) shaped by the collimator (1). Gamma photons penetrate the pipe (5) with the flowing liquid-gas mixture and are partially absorbed. The scintillation detector (2) with the collimator (3) are placed opposite the source on the opposite side of the pipeline. At the detector output, electrical impulses \(I_{x}(t)\) are obtained, shaped by the front-end system of the probe, and then recorded by an external data acquisition system. In the studies described in this article, a radiometric measurement system consisting of a linear Am-241 radiation source with an energy of 59.5 keV and a detector with a NaI(Tl) scintillation crystal was used.
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 4 Applications
### Disease Prediction and Prevention  
AI applications in smart healthcare play a crucial role in predicting and preventing diseases such as COVID-19, liver disease, and cancer, utilizing SVMs as an ML method and CNNs as a DL method [38]. These AI algorithms analyze EHRs and patient lifestyle data, assisting physicians in interpreting medical images such as X-rays, magnetic resonance imaging, and computed tomography scans [39]. This capability enables more efficient and accurate disease prediction, thereby facilitating effective disease prevention.  
For instance, during the COVID-19 pandemic, AI models were developed to predict outbreak hotspots by analyzing data from various sources, including social media, travel patterns, and healthcare reports. These models helped in early intervention and resource allocation, significantly impacting the management of the pandemic [40]. In liver disease prediction, AI models have been employed to analyze biomarkers and imaging data to identify early signs of liver fibrosis and cirrhosis. These models have shown higher accuracy compared to traditional diagnostic methods, enabling early treatment and better patient outcomes [41]. Similarly, in cancer detection, CNNs have been used to analyze mammograms and histopathological images, improving the accuracy of breast cancer diagnosis. AI algorithms have also been developed to predict the likelihood of cancer recurrence by analyzing patient data, which helps in tailoring personalized treatment plans and follow-up schedules [42].  
However, doctors cannot entirely rely on these applications due to certain obstacles. In the future, efforts should focus on fostering a mutually beneficial relationship between AI and clinicians [43].
###### Abstract  
The Fisher-Snedecor (F-S) \(\mathcal{F}\) distribution has recently been introduced as a tractable turbulence-induced (TI) fading model that fits well with the experimental data. This paper provides a performance evaluation of a free-space optical (FSO) re-configurable intelligent surface (RIS)-assisted communications (ACs) link over the F-S \(\mathcal{F}\) TI fading channels, assuming the intensity modulation-direct detection (IM-DD) technique. In particular, novel and closed-form (C-F) analytical expressions for the probability density function (PDF) and cumulative distribution function (CDF) of the end-to-end signal-to-noise ratio (SNR) in terms of Gaussian hyper-geometric functions are efficiently derived. Capitalizing on the obtained results, novel C-F analytical expressions for the moment generating function (\(\mathcal{M}_{MGF}\)), outage probability (OP), average bit error rate (BER) and ergodic channel capacity (\(\mathbb{C}_{\gamma}\)) of the FSO RIS-ACs system over the F-S \(\mathcal{F}\) TI fading channels are provided and numerically evaluated under the various TI fading severity conditions. Furthermore, the second-order (S-O) statistical expressions for the level crossing rate (LCR) and average fade duration (AFD) are obtained and thoroughly examined for various FSO RIS-ACs system model parameters.  
6G; beyond 5G; Fisher-Snedecor \(\mathcal{F}\) distribution; FSO communications; outage probability; re-configurable intelligent surface (RIS)
## 6 Conclusions  
We designed, developed, and tested an application-aware gateway framework for constrained devices--the LDAF. It facilitates flexible implementation proxying services to reduce the communication loads by efficiently encoding binary message content and adapting application protocol operation to affect the number of exchanged messages; it further reduces the traffic for the end device. The implementation proved to be robust, able to run several concurrent services and serving various clients.  
Currently we are implementing LDAF into a smart city ecosystem (EkoSMART). A part of this project is an integration platform, which supports a variety of smart city applications, including e-health, well-being, and smart mobility. These subsystems contribute IoT measurements into the integration platform and share it to various cross-domain applications. LDAF will enable the connection of communication-constrained devices and API integration.  
Our further research is focused on implementing an LDAF server on a LoRaWAN network server for application services that are aware of the network status.  
The next foreseen use cases are university e-learning and study management platforms. These platforms are independent, yet they must be synchronized. The available REST APIs, however, are not flexible enough to reduce the amount of traffic required to synchronize the two. This is not an IoT case and the traffic volumes are expected to be substantially higher. At the moment, ~15 MB of data is transferred for periodic syncing, although only ~1 MB is actually required. With LDAF we expect to lower the communication and processing during one synchronization, and thus enable more frequent synchronizations.  
Conceptualization, M.P. and A.K.; Methodology, M.P. and D.D.; Software, D.D. and M.P.; Validation, A.K., M.P. and D.D.; Investigation, M.P. and A.K.; Resources, M.P.; Writing - Original Draft Preparation, M.P., D.D.; Writing - Review & Editing, M.P. and A.K.  
The authors acknowledge the financial support from the Slovenian Research Agency (research core funding \"Algorithms and Optimization Procedures in Telecommunications\", No. P2-0246-1538-04).  
Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 1 Introduction
As the analysis of RSSI is straightforward, technology independent and less demanding in terms of system requirements, it is more suitable for achieving technology recognition on constrained devices. Note that we consider constrained devices to be the equipment with no more than 50 kB RAM and 250 kB nonvolatile memory (e.g., flash), and has limitations on energy consumption [14]. The remainder of this paper is organized as follows: Section 2 discusses the related work; Section 3 explains how the properties of signals affect the characteristics of the RSSI distribution; next, the RSSI of representative technologies (Wi-Fi, LTE, DVB-T and Bluetooth) are explored in Section 4, followed by a thorough study of the appropriate feature space; then, the usage of novel features from the RSSI distribution is illustrated with a sample algorithm and experimentally validated in Section 5; finally, Section 6 concludes this paper and proposes directions for future work.
## 4 Experiment
### Influence of residual connection depth on classification:  
We conducted comparative experiments on two datasets by setting six groups of residual connection depths, with depth increasing by increments of two layers. The experimental results are shown in Figure 7.  
The experimental results indicate that the chosen residual connection depth performs best across samples in two different languages and styles. In Figure 7a, the performance at depths 12 and 16 significantly outperforms other residual connection depths. Moreover, in the comparison between these two depths, the validation accuracy remains consistent between 16 layers and 12 layers. However, the model with 12 layers demonstrates advantages in terms of test set accuracy and F1 score over the larger-parameter model with 16 layers. Additionally, the RQ-OSPTrans with 16 layers occupies 67 GB of GPU memory. Meanwhile, the model with 12 layers requires only 51 GB of GPU memory under the same batch size conditions. Overall, the Transformer encoder with 12 layers of residual connections can sufficiently learn classification clues in repeated questioning and achieve satisfactory performance without the need for further parameter expansion to learn long-range semantics.  
In Figure 7b, increasing the depth of residual connections is no longer an improvement method. The RQ-OSPTrans with 16 layers exhibits significant performance degradation compared with the model with 12 layers, indicating that a depth of 12 layers can balance the model parameters and classification performance. Considering all depth selections  
Figure 7: Residual connection depth and its influence on classification performance. (**a**) Study on the THUCNews dataset. The performance metrics include accuracy on the test and validation sets (**b**) Study on the arXiv-10 dataset. The performance metrics include accuracy on the test set.  
across the two datasets, a depth of 12 layers can balance accuracy, model parameter size, F1 score, and other metrics. This condition enables the model to grasp long-range semantic correlations and classification clues in 12 repetitions of questioning, proving that our choice is the optimal strategy.
# A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
## 2 Methodology
### Dynamic Bayesian Network Analysis
#### 2.3.3 Probability Adaptation  
Probability adaptation allows the BN model to dynamically adjust the probabilities of REs and IEs when new safety-related observations, such as near-misses or incidents, are recorded. Near-misses, which are defined as events that came close to causing accidents but revealed critical vulnerabilities in the system, are especially valuable for refining risk assessments. When a near-miss occurs, it offers indirect evidence of potential system weaknesses. For instance, a near-miss resulting from a faulty valve in a GDS may not lead to an accident due to timely intervention, but it indicates a higher probability of valve failure.  
To incorporate near-misses into the framework, we utilize abductive reasoning (as shown in Equation (17)) to update the BN. The observed near-miss is regarded as soft evidence (e.g., via likelihood weighting or virtual evidence) to adjust the probabilities of associated REs and intermediate events. For example, if a near-miss is associated with a specific RE (e.g., manual shutdown valve failure), the conditional probability tables (CPTs) of the corresponding nodes are modified to reflect the increased risk. This adjustment is proportional to the severity and frequency of the near-miss, as determined by expert judgment or historical data. After this adjustment, the diagnostic analysis (Section 2.3.4) is repeated to re-evaluate the criticality of REs and prioritize risk-mitigation strategies.  
\[P(X_{i}=x_{i}|UE=1)=\frac{P(X_{i}=x_{i})\times P(UE=1|X_{i}=x_{i})}{P(UE=1)},i=1,2,\cdots,n \tag{17}\]  
where \(P(X_{i}=x_{i}\,|\,UE=1)\) denotes the self-adapted probability of root event \(X_{i}\); \(X_{i}\) is more likely to become the direct cause of a UE when \(P(X_{i}=x_{i}\,|\,UE=1)\) is close to 1.
## Evaluating Parameter Adjustment in the MODIS  
Gross Primary Production Algorithm Based on  
Eddy Covariance Tower Measurements  
[PERSON] 1, [PERSON] 1,2,*, [PERSON] 3, [PERSON] 1,2 and [PERSON] 1,*  
State Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; E-Mails: [EMAIL_ADDRESS] (J.C.); [EMAIL_ADDRESS] (M.C.)  
University of Chinese Academy of Sciences, Beijing 100049, China  
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; E-Mail: [EMAIL_ADDRESS]  
Authors to whom correspondence should be addressed; E-Mails: [PERSON]S] (B.C.); [EMAIL_ADDRESS] (H.Z.); Tel./Fax: +86-10-6488-9574.  
_Received: 4 January 2014; in revised form: 24 March 2014 / Accepted: 31 March 2014 / Published: 14 April 2014_  
How well parameterization will improve gross primary production (GPP) estimation using the MODerate-resolution Imaging Spectroradiometer (MODIS) algorithm has been rarely investigated. We adjusted the parameters in the algorithm for 21 selected eddy-covariance flux towers which represented nine typical plant functional types (PFTs). We then compared these estimates of the MOD17A2 product, by the MODIS algorithm with default parameters in the Biome Property Look-Up Table, and by a two-leaf Farquhar model. The results indicate that optimizing the maximum light use efficiency (\(\varepsilon_{max}\)) in the algorithm would improve GPP estimation, especially for deciduous vegetation, though it could not compensate the underestimation during summer caused by the one-leaf upscaling strategy. Adding the soil water factor to the algorithm would not significantly affect performance, but it could make the adjusted \(\varepsilon_{max}\) more robust for sites with the same PFT and among different PFTs. Even with adjusted parameters, both one-leaf and two-leaf models would not capture seasonally photosynthetic dynamics, thereby we suggest that further improvement in GPP estimaiton is required by taking into consideration seasonal variations of the key parameters and variables.  
**Keywords**: gross primary production; MODIS; parameter adjustment; model structure; light use efficiency; eddy covariance
# Geometric Morphometric Shape Analysis of Mandibular Post-Canine Dentition
###### Abstract  
**Background**: Genetic and epigenetic alterations have significant impacts on the morphology of permanent mandibular premolars and molars. Geometric morphometry is a powerful technique, which can be utilized to identify specific landmarks that exhibit variation and that are associated with ancestry and dimorphism. **Methods**: The geometric and anatomic landmarks of mandibular premolars and molars were extracted from 3D digital replicas of diagnostic dental casts prepared for model analysis (n = 160). Tooth shape analysis was conducted using various techniques, including Procrustes superimposition, Procrustes ANOVA, discriminant function analysis, and the regression of shape over the centroid size. **Results**: Procrustes ANOVA showed that centroid size was not significantly different between the two sexes, but shape was significantly different in the two-cusp-type second premolars (p = 0.0035) and in the first/second molars (p < 0.001). The three-cusp type of the second premolars showed the highest degree of allometry, with 3.35%, followed by the mandibular second molars, with 3%, indicating that distal class types have a tendency to exhibit allometry. The distal and lingual components of the tooth showed more variability, and females tended to have sharper cusp configurations. **Conclusions**: This study shows how landmarks vary in permanent human post-canine dentin, a crucial finding for anatomic reconstruction and restorative dentistry. In particular, the molars and premolars of the mandible post-canine teeth are critical for achieving optimal mastactory efficiency and overall health. Additionally, a higher degree of allometry and the later formation of cusps correlate with greater shape variation, particularly on the distal and lingual sides. For precise restorative procedures, a thorough understanding of the anatomy of these teeth is required.  
geometric morphometry; sexual dimorphism; landmark; Procrustes ANOVA; 3D; human variation; enamel thickness; dentistry; tooth +
Footnote †: journal: Applied Sciences
## 5 System Synthesis and Example
The two power flows are transferred by the CVU and gearset G\({}_{1}\) simultaneously and then merged before entering gearest G\({}_{2}\) and locked-up planetary gearset PG\({}_{2}\). The ratio of the torque entering the CVU, T\({}_{\rm r1}\), and the torque entering gearest G\({}_{1}\), T\({}_{\rm s1}\), can be determined by the ratio of planetary gearset PG\({}_{1}\). \[\left\{ \begin{array}{l}\rm T_{r1}\ =\ \frac{T_{i}}{T_{i+{\rm r}1}}\ =\ 0.74T_{i}\\ \rm T_{s1}\ =\ \frac{T_{i{\rm r}1}}{T_{i+{\rm r}1}}\ =\ 0.26T_{i}\end{array} \right.. \tag{1}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline r\({}_{1}\) & Ratio of gearest G\({}_{1}\) & 0.5 \\ \hline r\({}_{2}\) & Ratio of gearest G\({}_{2}\) & 2.0 \\ \hline r\({}_{\rm p1}\) & Ratio of planetary gearest PG\({}_{1}\) & 0.35 \\ \hline r\({}_{\rm p2}\) & Ratio of planetary gearest PG\({}_{2}\) & 0.35 \\ \hline r\({}_{\rm cv\_max}\) & Maximum ratio of CVU & 2.0 \\ \hline r\({}_{\rm cv\_min}\) & Minimum ratio of CVU & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Parameters of the synthesized system. Figure 16: FPG of the synthesized system developed by a BPD. Figure 17: Relation of torque and speed under the electric mode. Therefore, only 74% of the torque load is carried by the CVU regardless of the ratio of the CVU. The fraction of input power passing through the CVU is expressed as follows:  
\[\frac{\mathrm{P_{CVU}}}{\mathrm{P_{input}}}\ =\ \frac{\mathrm{T_{r1}\cdot\omega_{r1}}}{ \mathrm{T_{s1}\cdot\omega_{s1}\ +\ T_{r1}\cdot\omega_{r1}}}=\ \frac{\frac{\mathrm{T_{i}}}{\mathrm{1}+\mathrm{r_{p1}}} \frac{\omega_{w}\mathrm{r_{2}}}{\mathrm{r_{c}}}}{\frac{\mathrm{T_{i}r_{p1}}} \cdot\omega_{o}\mathrm{r_{1}r_{2}}\ +\ \frac{\mathrm{T_{i}}}{\mathrm{1}+\mathrm{r_{p1}}} \frac{\omega_{o}\mathrm{r_{2}}}{\mathrm{r_{c}}}}=\ \frac{1}{\mathrm{1}\ +\ \mathrm{r_{c}}\mathrm{r_{1}r_{p1}}}. \tag{2}\]  
The system ratio under the electric mode is expressed as follows:  
\[\mathrm{r_{sys}}\ =\ \frac{\omega_{i}}{\omega_{o}}\ =\ \frac{\mathrm{r_{2}/ \mathrm{r_{c}}\ +\ r_{1}r_{2}r_{p1}}}{\mathrm{1}\ +\ \mathrm{r_{p1}}}. \tag{3}\]  
In the engine mode, the input power of the ICE is split into two after gearest \(\mathrm{G_{1}}\). The two power flows are transferred by the CVU and gearest \(\mathrm{G_{2}}\) and then merged together on planetary gearest \(\mathrm{PG_{2}}\). The ratio of the torque passing through the CVU, \(\mathrm{T_{r2}}\), and the torque passing through gearest \(\mathrm{G_{2}}\), \(\mathrm{T_{s2}}\), can be determined by the ratio of planetary gearest \(\mathrm{PG_{2}}\). \[\left\{\begin{array}{rcl}\mathrm{T_{r2}}\ =\ \frac{\mathrm{T_{o}}}{\mathrm{1}+ \mathrm{r_{p2}}}&=\ 0.74\mathrm{T_{o}}\\ \mathrm{T_{s2}}\ =\ \frac{\mathrm
# A Novel Wavelet-Based Algorithm for Detection of QRS Complex
## 3 Methodology
### Calculation of the Positions of the Candidate QRS Peaks in the Time-Domain  
According to the candidate extremum pairs in d3' or d4', we define the following equation to calculate the positions of the corresponding candidate QRS peaks in the original ECG signal:  
\[x_{p}=x_{1}\frac{\left|y_{2}\right|}{\left|y_{1}\right|+\left|y_{2}\right|}+ x_{2}\frac{\left|y_{1}\right|}{\left|y_{1}\right|+\left|y_{2}\right|}-\beta \tag{8}\]  
where \(\beta\) denotes the time delay produced by the filtering of the wavelet filter banks. The values of \(\beta\) in d3' and d4' are 9 and 12 samples, respectively. If \(\left|y_{1}\right|=\left|y_{2}\right|\), \(x_{p}\) is located in the middle of \(x_{1}\) and \(x_{2}\), then subtract \(\beta\). If \(x_{p}\) is closer to \(x_{2}\), then subtract \(\beta\). If \(\left|y_{1}\right|<\left|y_{2}\right|\), \(x_{p}\) is closer to \(x_{1}\), then subtract \(\beta\).