esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Real-World Infectious Disease Triage Scenario  
#### 3.2.1 MIMIC-III Infectious Disease Cohort: Data Extraction and Pre-Processing  
An ED infectious disease cohort was curated from the de-identified MIMIC-III v1.4 database [65]. Encounters whose primary or secondary ICD-9 codes lay within 001-139 (infectious and parasitic diseases) or the severe sepsis range 995.91-995.92 were retained. Limiting the search to ED admissions and discarding records lacking a time-stamped triage note yielded 132 unique encounters (HADM_ID = 132, SUBJECT_ID = 128) at the end of Step 2 of the pipeline.  
Table 10 summarizes the preprocessing pipeline:  
The demographic and diagnostic profile of the infectious disease cohort is summarized in Table 11.  
The complete preprocessing workflow is illustrated in Figure 5. As Figure 5 shows, mapping ICD-9 to ICD-10 concepts is the final step before model ingestion.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Step** & **Action** & **Rationale** \\ \hline
1 & Irreversibly hash patient identifiers & Maintains HIPAA/GDPR compliance \\ \hline
2 & Filter ED notes and ICD-9 codes \(\rightarrow\) 132 records remain & Focuses the dataset on acute infectious presentations \\ \hline
3 & Normalize triage text (abbreviation expansion, spell-check) & Reduces linguistic noise for the LLM \\ \hline
4 & Flag implausible vitals (\textless{}25 \({}^{\circ}\)C or \textgreater{}44 \({}^{\circ}\)C, etc.) as \({}^{\prime\prime}\)NA\({}^{\prime\prime}\) & Prevents outliers biasing inference \\ \hline
5 & Map ICD-9 codes to ICD-10 and SNOMED CT concepts & Enables ontology-level reasoning downstream \\ \hline \hline \end{tabular}
\end{table}
Table 10: Pre-processing pipeline applied to build the infectious disease cohort, detailing each step and its rationale.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Semaphore** & **Tokens/Sec** & **Time Per Task (s)** \\ \hline q4\_k\_m & 1 & 26.81 & 8.27 \\ \hline  & 2 & 20.22 & 10.55 \\ \hline  & 4 & 11.53 & 17.70 \\ \hline q8\_0 & 1 & 19.58 & 11.37 \\ \hline  & 2 & 16.92 & 12.75 \\ \hline  & 4 & 12.05 & 17.69 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Throughput and mean completion time for 4-bit (q4_k_m) and 8-bit (q8_0) Llama models in the ollama runtime under 1, 2, and 4 parallel threads.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
## 3 Results and Discussion
### Characteristics of N and P Pollution
#### 3.4.2 TN  
The average TN concentrations in the overlying water of the six rivers were very high in Figure 5b, and all exceeded the level V limit value of surface water in the national standard GB3838-2002. The mean over-standard multiples for Jingshui River, Chuan Lake, West Lake, Xinkai River, Yitong River, and Yongchun River were, separately, 5.913, 3.759, 4.815, 5.323, 4.091, and 3.719. The sampling points of the six rivers were concentrated in Kuancheng, Lvyuan, and Nanguan District of Changchun City. The distribution of NH\({}_{3}\)-N and TN had a certain similarity. Where the content of NH\({}_{3}\)-N was high, the TN also had a high content. To a certain extent, they have the same pollution sources, such as agricultural fertilizers and pesticides, animal husbandry organic waste, and domestic waste [18]. Besides these common N pollution sources, for the industrialized city of Changchun, industrial pollution is also one of the prime proveniences of water environment destruction. With social advancement, N runoff in rivers has become more and more serious. Excessive N content is the direct cause of river eutrophication, which leads to water quality degradation and endangers human and ecosystem health [19]. The TN content of these six rivers in Changchun City gravely surpassed the standard, which was a warning to the ongoing water environment treatment projects.
## 6 Conclusions
### Practical Implications  
The findings of this study provide valuable insights for practitioners involved in developing, marketing, and regulating ARSGs. For product manufacturers and developers, recognizing that privacy concerns vary greatly between cultural groups is critical. In S. Korea, where privacy concerns coexist with a pragmatic acceptance of technology, marketers and developers can emphasize practical advantages, such as increased productivity, social engagement, or collective benefits. For example, promotional strategies in Korea could highlight ARSGs applications like efficient public transportation navigation, collaborative work environments, or improved educational experiences, thus aligning with Korean users' value systems.  
In contrast, service providers and marketers in the U.S. face a different challenge, where strong privacy concerns can inhibit behavioral adoption despite perceived utility. Here, companies should focus heavily on transparency and user-control mechanisms, clearly communicating how personal data is collected, stored, and used. For instance, ARSGs developers could integrate clear privacy consent processes or provide intuitive controls allowing users to manage data sharing proactively. Marketing campaigns targeting American consumers should explicitly address data security, emphasizing user empowerment and data protection to alleviate skepticism or resistance.  
Both Korean and U.S. marketers should design strategies that directly demonstrate the concrete value and minimal privacy risks associated with ARSGs' usage. User training programs or community engagement initiatives could further familiarize skeptical users, reducing fear of surveillance or unauthorized data sharing. Lastly, policy-makers might consider cultural variations in privacy concerns when establishing regulations for ARSGs, ensuring policy frameworks reflect and respect local norms and expectations, thereby fostering wider acceptance and adoption across diverse global markets.
_Appl. Sci._**2015**, \(5\), 1904-1921; doi:10.3390/app5041904  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Thermal Lattice Boltzmann Simulation of Entropy Generation within a Square Enclosure for Sensible and Latent Heat Transfers  
**[PERSON] 1,2 and [PERSON] 1,2,***  
* 1 Laboratoire Genie Civil et geo-Environnement (LGCgE, EA 4525), University of Artois, Bethune F-62400, France; E-Mail: [EMAIL_ADDRESS]
* 2 Laboratoire Genie Civil et geo-Environnement (LGCgE, EA 4525), Lille University Northern France, Lille F-59000, France
* * Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +33-3-2163-7141 or +33-6-1126-7983; Fax: +33-3-2163-7101.  
Academic Editor: [PERSON]  
_Received: 7 November 2015 / Accepted: 8 December 2015 / Published: 17 December 2015_  
This paper deals with the numerical simulation of heat transfer and entropy generation in a 2D square enclosure for convective melting. A thermal lattice Boltzmann method (TLBM) is used to handle the study, which has been conducted for Prandtl numbers from 0.02 to 70 at Rayleigh numbers of \(10^{4}\) and \(10^{5}\). The results are presented in terms of the total entropy generation, average Bejan number and average Nusselt number. Within the range considered for the key parameters, the entropy generation is found to be controlled by the heat transfer loss for low Prandtl numbers. However, for the large Prandtl numbers, its variation is dominated by shearing losses. Moreover, the presence of the latent heat state decreases the overall thermodynamic losses while increasing the quantity of heat transferred.  
thermal lattice Boltzmann method; entropy generation; natural convection; phase change; melting; Bejan number
## Materials and Methods
### Noise Reduction Methodology  
In order to reduce the noise content of the reconstructed depth maps, different filtering techniques are applied and the obtained results are compared. Particularly, we compared the wavelet based filtering approaches and a BM3D-based filtering approach. Our approach was to apply these filters onto individual interferograms and obtain a reconstruction from the filtered set of interferograms. To demonstrate the validity of our noise reduction method, we estimated the depth maps for a reference slide (Malvern, Cambridge, UK, PVS 5113) and reference beads (Thermo Scientific, Waltham, MA, USA, 4K100). These reference objects have pre-defined shapes and are suitable for measuring the error rates of the obtained depth maps.  
While measuring the noise content of the cell cultures, we had to follow different techniques since the ground truth depth information was not available. We conducted experiments on four different cell lines: ONCO-DG1, HCT-116, MDA-MB-231 and HUVEC. To measure the effectiveness of the noise reduction method, for each observation we capture the culture twice, once with the medium flow and once after the medium flow stopped and the remaining medium was withdrawn from the system. This allowed us to evaluate the noise introduced by the fluid flow. After the observation, the medium flow started again so as not to disturb the dynamic culture.
## 4 Discussion
### NSRME vs. SARME
However, [PERSON] and [PERSON] found that SARME resulted in greater skeletal expansion through palatal width increase than NSRME, and thus, suggesting a higher ratio of skeletal to dental expansion [41]. No direct comparison between the amount of expansion gained through each modality was possible since the transverse expansion needs vary between patients and the type of intervention (SARME or NSRME) was not subject of randomization. Additionally, insufficient evidence was available to correlate the amount of expansion to the magnitude of adverse periodontal effects. In the present review, multiple studies showed varied rates of successful sutural separation potentially affected by patient age and type of intervention (e.g., SARME). [PERSON] and colleagues found that diastema formation between the central incisors rarely occurred among an all-adult cohort (mean age: 29.9 years) treated with NSRME and tissue-tooth-borne expanders [29]. On the other hand, [PERSON] and associates observed a successful suture opening in every patient of an all-female population (average age: 17.4-18.1 years) treated with NSRME, tooth-borne and tissue-bone-borne expanders [31]. Similarly, a study by [PERSON] and colleagues found that NSRME and tissue-tooth-borne expanders in adults resulted in successful diastema formation in 81.5% of patients [53]. Given the available evidence, it can be presumed that the surgical release of the circummaximillary sutures gives the greatest likelihood of achieving a sutural separation in skeletally mature individuals.
# Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality
## 2 Materials and Methods
### Noise
#### 2.3.5 Noise Tolerance of the Features  
The generated datasets were used to test the tolerance of the features to different types of noise at different levels. The noise tolerance of the statistical features--namely, ApEn, SampEn, ShEn, mean, and STD--in detecting the rotor tip was assessed against the different types of noise. The results were compared in relation to the location of the rotor tip obtained from noise-free signals. Accuracy was evaluated in terms of Euclidean distance, and visual sharpness was assessed by a user. Following the procedure presented in [4], the ability to discriminate between CFAE and non-CFAE signals was analyzed using a Mann-Whitney U test with \(p\)-values of [0.001, 0.05, 0.01] applied on EGMs. The Euclidean distance was calculated to determine the location of the rotor tip. Similar procedures were applied to the remaining level 1 metrics presented in Table 2.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Level** & **Criteria** & **Metric** & **Equation** \\ \hline \multirow{4}{*}{0} & \multirow{4}{*}{Accuracy} & Number of spikes (\(S_{n}\)), power relation between a component of 60 Hz and the total power of the signal. & \(1-S_{n}=\sum_{l=1}^{m}\sum\frac{\mathit{Spikes}}{\mathit{iszamples}}\), where \(m\) is the number of the signals. \\  & & & \(PLI=P_{60 Hz}/P_{total}\), where \(P_{60 Hz}\) is the power of the component of 60 Hz, and \(P_{total}\) is the total power of all EGM signals. \\ \hline
0 & Precision & Sample rate (\(T_{s}\)) & \(T_{s}=t_{n+1}-t_{n}\) \\ \hline
0 & Data amount & Resolution (R) & \(R=\frac{\mathit{nb}_{y}\mathit{nls}}{\mathit{SizeMap}}\), where \(\mathit{nb}_{y}\) is the number of virtual electrodes by row and \(\mathit{nb}_{x}\) is the number of virtual electrodes by column. \\ \hline \multirow{4}{*}{0} & \multirow{4}{*}{Completeness} & Number of lost samples per signal (\(N_{LS}\)); number of lost signals (\(N_{LS}\)); number of lost signals (\(N_{LSSignals}\)) & \(N_{LSS
# Advanced Techniques for Geospatial Referencing in Online Media Repositories
## 3 Methodology  
The primary objective of this study is to extract and geocode location references from video files using a combination of different methods. This multimodal methodology involves several stages: acquiring data and extracting location data from the visible image, from text in the image, and from audio and subtitles.  
As a reference or comparison data set, the location references of the videos for the automated analysis are first manually extracted from the image, audio, and subtitle sources and provided with time stamps. The hits, misses, and false positives of the automated methods to be subsequently evaluated are determined based on this reference data set.  
The individual processing steps, especially the computationally intensive ones, were implemented side by side and are thus scalable. This ensures that our methods are efficient and capable of processing large amounts of data, which is crucial for the potential application of our methodology in real large-scale video media libraries.
# Intrusion Detection and Prevention in CoAP Wireless Sensor Networks Using Anomaly Detection
## 3 Anomaly-Based Intrusion Detection in Internet-Integrated CoAP WSN  
We now present the system architecture in the context of which we implement intrusion detection, in particular for the detection of DoS attacks and attacks at the application-layer against the CoAP Protocol.
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 3 Results
### Accuracy Results  
The results from the accuracy assessments can be found in Table 1. Overall, both classifications had a high total accuracy of >0.995. As the focus of this work is specifically mapping banana plantations which only represent 2.3% of the total area of the Tully River catchment, Table 1 shows the user's and producer's accuracy for this class. We found the QLUMP banana plantation classification had a user's and producer's accuracy of 0.862 and 0.921 respectively and the U-Net banana plantation classification had a user's and producer's accuracy of 0.983 and 0.959 respectively (Table 1). The Jaccard Index for the QLUMP classification was 0.341 and U-Net classification was 0.943.  
As Table 1 indicates that both classifications are more likely to miss areas of banana plantations (false negatives) than classify other land use features as banana plantations (false positives). It must be reiterated that the QLUMP data were compiled using lower resolution imagery and mapped land use features at a scale of 1:50,000. As a result, small-area and narrow land uses (e.g., farm sheds and roads) were aggregated into surrounding land uses. Also, banana plantations were mapped as bananas, regardless of them being active or fallow in the imagery. The scale of QLUMP data and fallow plantations affected the accuracy of the mapping, when compared with the U-Net classification. When specifically analyzing the location and extent of banana plantations, the accuracy assessment results suggest the U-Net classification is more accurate than the QLUMP data.  
The quality of the imagery for U-Net is restricted to data processed by a commercial third-party. Inconsistencies in the geometric and radiometric corrections and post-processing operations (such as tile mosaicing and color balancing) have affected the output classification.
## 3 Resilience
### Disturbance Event Phases
In 2003, An initial framework was proposed to include the seismic resilience of society [37] by presenting the theory of _Resilience Loss_, later referred to as the resilience triangle. Figure 1 plots an example of the concept of resilience phases and also shows the resilience triangle, where the figure determines the property or functionality and the overall infrastructure performance after around a half function loss. The resilience triangle depicted in the figure represents the loss of functionality due to a disruptive event and the process of restoration and recovery over time. The fundamental objective of resilience-enhancing metrics is to shrink the area of the resilience triangle. This is achieved by implementing various strategies to boost the functionality and performance of the infrastructure (represented by the vertical axis in the figure), thereby reducing the recovery time. For instance, mitigation measures can be employed to augment infrastructure performance and trim down recovery time. Furthermore, the time needed for recovery can be expedited by enhancing measures for the restoration and replacement of impaired infrastructure.
## 5 Proposed Neural Network Solution  
### Network Architecture  
The objective is to train a neural network to extract the identifying characteristics inherent in time-domain OFDM signals processed by conventional CF-based solutions. A feed-forward multi-layer perceptron (FFMLP) neural network is pretrained offline before it is deployed for the real-time processing of original OFDM signals prior to transmission. As shown in Figure 3a, the general structure of FFMLP consists of three main sections: an input layer defining the input format (type, dimensionality, etc.,) followed by a set of hidden layers, each including a configurable number of neurons with a unified activation function for all neurons of each layer. The last layer is the output layer, which is a convergence point collecting the data traversing the network from the input layer through the hidden layers. The data are weighted and summed by the output layer's neurons and passed through an activation function to produce the final output. The input to the FFMLP is \(M\)-dimensional. There is a total of \(V\) hidden layers. The \(i\)-th hidden layer contains \(h_{i}\) processing neurons. The final output of the FFMLP network module is \(P\)-dimensional. The response of the \(j\)-th neuron in the \(i\)-th hidden layer is:  
\[s_{j}^{(i)}=f_{i}\Bigg{(}\sum_{k=1}^{h_{i}-1}w_{jk}^{(i)}s_{k}^{(i-1)}+b_{j}^{ (i)}\Bigg{)}\,, \tag{8}\]  
Figure 2: Top-level view of SCF.  
where \(f_{i}(.)\) is the activation function of the \(i\)-th hidden layer and \(s_{k}^{(0)}\triangleq l_{k}\) is the \(k\)-th entry in the input layer. \(w_{j,k}^{(i)}\) is the learnable weight connecting the \(k\)-th neuron in the \((i-1)\)-th layer to the \(j\)-th neuron in the \(i\)-th layer. \(b_{j}^{(i)}\) is the learnable bias of the \(j\)-th neuron in the \(i\)-th layer. Similarly, the response of the \(j\)-th neuron in the output layer is:  
\[s_{j}^{(o)}=f_{o}\left(\sum_{k=1}^{h_{V}}w_{j,k}^{(o)}s_{k}^{(V)}+b_{j}^{(o)} \right). \tag{9}\]  
Figure 2(b) illustrates the application of an FFMLP module to process raw OFDM input signals. The input layer extracts the in-phase and quadrature components of the input OFDM signal. The two parts are then simultaneously processed by the subsequent core of the pretrained neural network module to generate the corresponding in-phase and quadrature components of a CF-like output signal with the desired characteristics.
## 3 Materials and Methods
### Evaluation Metrics
#### 3.1.2 Evaluation of Deraining  
In order to compare deraining performance, two main assessment indices are used based on reference rain-free images: peak-signal-to-noise ratio and structural similarity  
Figure 1: Proposed deraining framework.  
index (SSIM) [59]. PSNR is the ratio between the maximum power of a signal and the power of the noise distorting this signal. In order to calculate the PSNR of a test image \(g\) and a reference image \(f\). Both images are of size M \(\times\) N, the PSNR between \(f\) and \(g\) is defined by Equation (5).  
\[PSNR(f,g)=10\ \log_{10}(25^{2}\big{/}MSE(f,g)), \tag{5}\]  
where  
\[MSE(f,g)=\frac{1}{MN}\sum_{i=1}^{M}\ \sum_{j=1}^{N}\big{(}f_{ij}-g_{ij}\big{)}^{2}, \tag{6}\]  
As the mean square error (MSE) approaches zero, the PSNR value approaches infinity; therefore, high PSNR value corresponds to a better image quality, on the other hand, a small PSNR value implies big numerical differences between images.  
Another well-known evaluation metric that is used to measure the similarity between two images is the SSIM. It was developed by [PERSON] et al. [60], It is correlated with the quality perception of the human visual system (HVS). SSIM models image distortion in terms of three factors: loss of correlation, luminance distortion, and contrast distortion. It is defined in Equation (7)  
\[SSIM(f,g)=l(f,g)c(f,g)s(f,g), \tag{7}\]  
where \(l(f,g)\) corresponds to the luminance comparison, \(c(f,g)\) corresponds to the contrast comparison, and finally \(s(f,g)\) corresponds to the structure comparison.  
However, in order to evaluate the performance of the deraining process on real rainy images, where their corresponding clear images are not available, we used two other different evaluation metrics: the naturalness image quality evaluator (NIQE) [61] and spatial spectral entropy-based quality (SSEQ) [62].
## 2 Data Analysis and Features Extraction
### Health Features Extraction
#### 2.2.4 Second-Order Health Features Extraction  
The second-order processing curve of the battery is derived by differentiating the monitoring data recorded by the BMS. The dV/dt curve during the constant current charging phase, as shown in Figure 3, provides a more intuitive visualization of the rate of voltage change per unit of time during the charging process [31]. From the perspective of the battery's internal mechanisms, as SOH declines, both the positive and negative electrode active materials and lithium-ions are progressively lost, leading to reduced charge capacity. During charging, this manifests as a decrease in the voltage rise rate and a shortening of the stable charging time. Therefore, the maximum value of the voltage change rate during charging, as shown in Figure 3 (maximum value of dV/dt, CVTMAX), is chosen as health feature 7 (HF7), and the duration of the stable voltage change rate during the constant voltage charging phase (Stable Time of dV/dt, CVTST) is chosen as health feature 8 (HF8).