esa-sceva/satcom-chunk-collection huggingface dataset:
# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 6 Experimental Results
### Explainability and User Interpretability  
One of the touted benefits of our approach is improved explainability. To evaluate this, we looked at the alerts generated and whether their attached explanations were understandable. Here are a couple of examples of actual alert outputs from the system:  
Example 1: Alert: \"Fall suspected (Patient #001)\", Explanation: \"Edge Wisdom: fall risk alert triggered because fall_detected = True (from accelerometer) AND tachycardia = True (HR 142) which suggests possible syncope. Purpose: emergency_response.\"  
Example 2: Alert: \"High Heart Rate Alert (Patient #002)\", Explanation: \"Edge Wisdom: Tachycardia alert (HR 130) but delayed due to low sensitivity at night. Knowledge: persistent high HR for 10 min. Purpose: comfort (avoid waking patient). Action taken after 10 min of sustained HR.\"  
We presented a set of five such alerts (with explanations) to three individuals (two medical trainees and one layperson for perspective). They were asked if they could understand why the alert was triggered and if anything was confusing. The feedback was positive: the medical folks appreciated seeing the logic (some said it is akin to what they would think through--which builds trust), and they only suggested perhaps simplifying some wording for lay users. The layperson could follow most of it, especially when phrased in near-English (our system's messages are somewhat technical, but a real app could make it more user-friendly).  
The baseline system, if it were to explain, might just say \"threshold exceeded\" or nothing at all. So comparatively, this is a vast improvement. There is room to improve--e.g., linking to advice (\"what to do now?\") but those are application layer add-ons.
## 3 Proposed Methodology  
An underwater acoustic sensor network (UWASNs) is deployed within a 3D area comprising \(n\) sensor nodes SNs. The sensors positioned at well-known locations are tasked with monitoring events in the surrounding environment and recording signals for each event. The surface station (ship) has an AUV assistant who visits the sensor nodes, collects the sensing information from them, and then goes back to the surface station. The surface station in turn transmits the collected information to a data center via an antenna for further analysis, as shown in Figure 1. The sensor nodes can transmit acoustic signals, and the AUV can receive these signals within a specific transmission range. This paper describes a simulation of a hybrid inheritance expert system for collecting sensing event information in an AUV-based IoUT environment. The model focuses on generating a driveway that guides the AUV to move with minimum energy consumption between sensor nodes to gather information resulting from the marine environment. As a result, an efficient strategy for determining the order of nodes to visit with minimum time is developed to reduce the energy consumption of the AUV.  
As illustrated in Figure 1, each sensor node (SN) situated in 3D space possesses specific coordinates. To gather information from the nodes, an AUV must traverse a route that visits each SN sequentially without repetition, ultimately returning to the starting point. For instance, the AUV could navigate along the path \(SN_{1}\to SN_{2}\to SN_{3}\to\ldots\to SN_{1}\). The distance between any two sensor nodes can be easily calculated by using the following method [19] in Equation (1):  
\[d_{i,j}=\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}+\left( z_{i}-z_{j}\right)^{2}} \tag{1}\]  
where \(d_{i,j}\) is the distance between the \(i\)_th_ and \(j\)_th_ sensor nodes having a location represented by \(x\), \(y\), and \(z\). If \(I=j\), \(d_{i,j}=0\). Moreover, the distances between sensor nodes can be depicted as a symmetric matrix of dimensions \(n\times n\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Paper** & **Method** & **Challenges** \\ \hline
[6] & Evolutionary algorithms & High computational load in real time \\
[7] & Energy-saving strategies & Balancing energy efficiency and data accuracy \\
[8] & Full-coverage route planning & Adapting to dynamic terrain and sensor limitations \\
[9] & Dynamic detection algorithm & Underwater environmental fluctuations \\
[10] & Data collection protocols & Balancing energy efficiency and data accuracy \\
[11] & SWIPT-based path optimization & Balancing energy harvest with data transmission accuracy \\
[12] & Neural networks inspired by nature & Rapid environmental changes such as ocean currents \\
[13] & Heuristic path planning & Real-time environmental data integration \\
[14] & Evolutionary algorithms & Complexity of calculations, adaptation to real time \\
[15] & Point-based reasoning & Environmental changes such as ocean currents \\ \hline \hline \end{tabular}
\end{table}
Table 1: Methods and challenges.
# On the Virtual Cell Transmission in Ultra Dense Networks
## 1 Introduction
### Related Works
#### 1.1.1 BS-Centric Transmission  
In the traditional BS-centric transmission approach, a UDN is deployed in cellular systems and users are served by the RBAs in the cell in the form of a distributed antenna system (DAS) [8]. Nevertheless, it is pointed out in [9] that there are always cell edge users who suffer from severe inter-cell interference (ICI) if the cellular structure is adopted and users are served in a BS-centric manner.
## Materials, Methods and Results
### Methods  
FBB was first produced using each of the granular materials. Subsequently, the Weipa and Stonemaster granular materials were improved by blending with the Bluerock material, which had its own gradation issues but had sound material properties. These improved granular materials, referred to as Weipa I and Stonemaster I, were then used to produce otherwise identical FBB and all materials were subsequently prepared, cured and tested following the same standard protocols.  
In all cases, the granular (or improved granular) material was first conditioned to 80% of its optimum moisture content (OMC) and the hydrated lime was added and mixed for two minutes in a Wirtgen WLM 30 laboratory bitumen foaming and mixing device. After 45 min of resting, the bitumen was foamed and added to the granular material, prior to an additional two minutes of mixing.  
FBB samples were sub-sampled using a riffle box and eighteen specimens of each were compacted using 50 blows of a Marshall hammer, as required by the local specification (MRTS 07-C). Samples were then cured in a 40 \({}^{\circ}\)C oven until removed for resilient modulus testing. Half the specimens were tested at their cured moisture content, after conditioning to 25 \({}^{\circ}\)C. The other specimens were saturated by immersion in deionised water for 10 min at 13 kPa absolute pressure and then surface dried and conditioned to 25 \({}^{\circ}\)C prior to testing. The unsaturated test is designated as the 'cured' condition and the post-saturation test is designated as the'saturated' condition.  
Triplicates specimens of each material/condition/age were tested, as in normal practice in Australia for measuring the resilient modulus of FBB, as well as for hot and warm mixed asphalt. Modulus testing was performed after \(3,7\) and 14 days of curing, following common practice in Australia [9]. The resilient modulus test was performed at 25 \({}^{\circ}\)C according to the local method (Q139), which is a repeated, strain controlled (50 \(\mu\)\(\epsilon\)) haversine loading with a rise time (10-90% of maximum) of 0.04 s and a period (peak-to-peak) of 3 s.
## 4 Conclusions
### Weaknesses of the Proposed Methodology  
According to the proposal, the possible limitation, if any, lies in the initial Phase (1) of the definition of reference condition, since the second Phase (2) has already been validated. The limitations are mainly focused on the quality and quantity of data that can be obtained throughout a building thermal system, which are essential for modelling the reference facility and its operation. The higher the number of sensors, the higher the accuracy in modelling and diagnosis. Moreover, implementing neural networks in this context requires prior knowledge in computer science and machine learning, which could represent an additional challenge.  
Unfortunately, at present, thermal facilities in buildings have a limited number of sensors, which are restricted to monitoring specific variables, dictated by current regulations. Common examples include boiler flow temperature and flue gas concentration (to ensure proper performance), temperature in domestic hot water storage tanks (to prevent Legionella proliferation), and heating flow and return temperatures (to account for energy costs), among others. This shortage of sensors limits the full application of PINNs in today's thermal systems.  
However, sensor technology is advancing at an accelerated pace, improving accuracy and reducing cost, which makes its integration in thermal facilities increasingly viable.  
In addition, the rise of IoT technology is significantly facilitating the expansion of sensors in this type of systems, which opens the door to the application of the proposed methodology in the facilities of the future.
# Enhancing Density Prediction of Agricultural Land Soil through Void Area Curve Analysis
###### Abstract  
Thresholding is a digital image analysis method used to distinguish objects from the background in images and it is mainly used for void and density analysis in soil. It is important to select an appropriate thresholding method because the accuracy of void analysis can vary significantly depending on the threshold value; however, there is currently no standard for soil density analysis. Therefore, this study proposes an image analysis method for soil density prediction. The experimental process involved collecting soil samples from agricultural lands, encompassing various soil textures including sandy loam, loam, silt loam, and silty clay loam. These samples were then meticulously prepared under controlled conditions, ensuring a comprehensive range of dry densities and water content levels. Digital images of the soil samples were acquired using a Canon EOS100d camera, employing a high-resolution setup that provided precise imaging capabilities. The porosity of the soil image is calculated by various thresholding methods. Based on the analysis results, we propose a void area curve, a new approach that can be applied to the soil density prediction. The void area curve is the relationship curve between the threshold value and porosity of the soil image. The standard deviation of the void area curve showed a high correlation with the dry density of the soil. The standard deviation of the void area curve was used to predict the dry density under various soil texture and water content conditions, and the RMSE was 0.037 t/m\({}^{3}\). The method of estimating soil density with the standard deviation of the void area curve can be used more generally than the existing analysis method because there is no need to select a specific threshold value.  
void area curve; digital image processing; image thresholding; dry density; agricultural land +
Footnote †: journal: Applied Sciences
###### Abstract  
This study proposed a developed horizontal-connection and energy-dissipation structure (HES), which could be employed for horizontal connection of prefabricated shear wall structural system. The HES consists of an external replaceable energy dissipation (ED) zone mainly for energy dissipation and an internal stiffness lifting (SL) zone for enhancing the load-bearing capacity. By the predicted displacement threshold control device, the ED zone made in bolted low-yielding steel plates could firstly dissipate the energy and can be replaced after damage, the SL zone could delay the load-bearing and the load-displacement curves of the HES would exhibit \"double-step\" characteristics. Detailed finite element models are established and validated in software ABAQUS. parametric analysis including aspect ratio, the shape of the steel plate in the ED zone and the displacement threshold in the SL zone, is conducted. It is found that the HES depicts high energy dissipation ability and its bearing capacity could be obtained again after the yielding of the ED zone. The optimized X-shaped steel plate in the ED zone exhibit better performance. The \"double-step\" design of the HES is a potential way of improving the seismic and anti-collapsing performance of prefabricated shear wall structures against large and super-large earthquakes.  
energy dissipation; \"double-step\" characteristics; stiffness lifting; seismic performance; horizontal connection; prefabricated shear wall structural systems +
Footnote †: journal: Applied Soft. 2020, 10, 1240
## 3 Results and Discussion  
### Metagenomic Sequencing Statistics and Quality Control  
A total of 39 samples were sequenced by the Illumina NovaSeq 6000 sequencing systems, and 1708.39 \(\times\) 10\({}^{6}\) raw reads, about 257.97 GB, with an average of 6.61 GB of each sample were generated. After the quality processing and eliminating the sample host gene, the sequence utilization of each sample was more than 98.48, including 787.85 \(\times\) 10\({}^{6}\) clean reads in the red radish samples and 776.19 \(\times\) 10\({}^{6}\) clean reads in the cabbage samples. A total of 966,355 contigs were assembled with Megahit, and the single sample's contigs were between 9431 to 47,483. N50 and N90 were two indices for the distribution of contig lengths within a draft assembly, and the longer the assembly of samples the better. In all samples, the minimum value of N50 reached 1677 bp, the average value was 6680 bp, the N90 minimum was 421 bp, and the average value was 528 bp. Furthermore, a total of 1,778,731 genes were obtained by ORF prediction of the contigs, and the sequence length was 1,150,522,526 bp (Table S1). All predicted genes were clustered using CD-HIT (parameters: identity, 95; coverage, 90), resulting in a total of 122,012 nonredundant gene set. Figure 1A shows the sequence length distribution of nonredundant gene sets, mostly concentrated between 201-600 bp, with an average sequence length of 557.88 bp, and the information was used for subsequent species and functional annotations to reveal microbial community of samples.
# An Improved Galerkin Framework for Solving Unsteady High-Reynolds Navier-Stokes Equations
## 3 Numerical Methods
### Variational Multiscale Stabilization
\tag{7}\]  
Assuming that the magnitude of the small-scale \(\mathbf{u}^{\prime}\) is much smaller than the large-scale \(\mathbf{u}_{h}\), neglecting higher-order small terms \(\mathbf{u}^{\prime}\cdot\
abla\mathbf{u}^{\prime}\), and retaining the first-order cross terms leads to  
\[((\mathbf{u}_{h}+\mathbf{u}^{\prime})\cdot\
abla)(\mathbf{u}_{h}+\mathbf{u}^{ \prime})\approx\mathbf{u}_{h}\cdot\
abla\mathbf{u}_{h}+\mathbf{u}_{h}\cdot \
abla\mathbf{u}^{\prime}+\mathbf{u}^{\prime}\cdot\
abla\mathbf{u}_{h}.\]  
Next, using the [PERSON]'s formula, neglecting higher-order small terms \(\mathbf{v}_{h}\cdot\Delta\mathbf{u}^{\prime}\) and substituting the previous equations and (6) into (7) yields the VMS form including the stabilization term  
\[\left\{ \begin{aligned} &\int_{\Omega}\frac{\partial\mathbf{u}_{h}}{ \partial t}\cdot\mathbf{v}_{h}\,d\Omega+\int_{\Omega}(\mathbf{u}_{h}\cdot \
abla\mathbf{u}_{h})\cdot\mathbf{v}_{h}\,d\Omega+\
u\int_{\Omega}\
abla \mathbf{u}_{h}\cdot\
abla\mathbf{v}_{h}\,d\Omega-\int_{\Omega}p_{h}\
abla \cdot\mathbf{v}_{h}\,d\Omega\\ &+\int_{\Omega}\frac{\partial\mathbf{u}^{\prime}}{\partial t} \cdot\mathbf{v}_{h}\,d\Omega+\int_{\Omega}\mathbf{u}^{\prime}\cdot\mathbf{u} _{h}\cdot\
abla\mathbf{v}_{h}\,d\Omega+\int_{\Omega}p^{\prime}\
abla\cdot \mathbf{v}_{h}\,d\Omega=\int_{\Omega}\mathbf{f}\cdot\mathbf{v}_{h}\,d\Omega,\\ &\int_{\Omega}\mathbf{q}_{h}\
abla\cdot\mathbf{u}_{h}\,d\Omega- \int_{\Omega}\mathbf{u}^{\prime}\cdot\
abla\mathbf{q}_{h}\,d\Omega=0.\end{aligned} \right. \tag{8}\]  
If the VMS form (8) is written in matrix form (ignoring higher-order small terms), then the following matrix form is obtained as  
\[\begin{aligned} &\left[\frac{1}{\Delta t}\mathbf{M}+ \theta(\mathbf{A}+\mathbf{C}(\mathbf{u}_{h}^{n+1})+\mathbf{S}_{K})\quad \theta\mathbf{B}^{T}\right]\left[\mathbf{u}^{n+1}\right]\\ &\theta\mathbf{B}-\mathbf{S}_{\mathrm{C}}\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\  
Substituting the error (10) into the weak form (2), the error equation [20] can be obtained as  
\[a(\mathbf{e_{u}},\mathbf{v})+b(\mathbf{v},\mathbf{e_{p}})+b(\mathbf{e_{u}}, \mathbf{q})\approx\int_{K}\mathbf{R}_{M}\cdot\mathbf{v}\,dK+\int_{K}\mathbf{R}_ {C}\cdot\mathbf{q}\,dK+\int_{\partial K}[\
u\partial_{n}\mathbf{u}_{h}-p_{h} \mathbf{n}]\cdot\mathbf{v}\,dS,\]  
among which the terms \(\mathbf{R}_{M}\) and \(\mathbf{R}_{C}\) represent the element-wise residuals for the momentum and continuity equations, respectively. Physically, \(\mathbf{R}_{M}\) measures the local imbalance of momentum for the computed solution (i.e., how well it satisfies the momentum conservation law within each element), while \(\mathbf{R}_{C}\) measures the local mass imbalance (i.e., the divergence error). The jump term \([\
u\partial_{n}\mathbf{u}_{h}-p_{h}\mathbf{n}]\cdot\mathbf{v}\) across element boundaries accounts for the non-conformity of the numerical fluxes between adjacent elements.
###### Abstract  
Food products that are ready-to-eat have become increasingly popular in recent years due to their efficiency, affordability, and convenience. However, there are concerns about public health because certain products, particularly animal products, may contain antibiotic-resistant bacteria. This study aimed to quickly and accurately identify foodborne pathogens, such as _Escherichia coli_ (_E. coli_) and _Staphylococcus aureus_ (_S. aureus_), in samples of shawarma and chicken burgers using peptide mass fingerprinting (PMF) technology. Additionally, the prevalence and levels of antibiotic resistance in the pathogens were determined. The study utilized 300 samples obtained from fast food restaurants in Al Qassim, Saudi Arabia. A variety of methods were used to identify foodborne pathogens, including culture on specific media, bacterial counts by numerical dilutions of homogenized samples, and proteome identification by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The Kirby-Bauer method was applied to detect the susceptibility and resistance of the bacteria to various antibiotics. PCR was utilized to identify antimicrobial resistance genes such as _blatTEM_, _tet(A)_, _blaz_, and _mCA in S. aureus_ and _E. coli_ isolates. The percentage of _E. coli_, _S. aureus_, _Salmonella_, _Listeria monocytogenes_ (_L. monocytogenes_), _Acitetobacter baumannii_ (_A. humannii_), and _Hafnia alcevi_ (_H. alcevi_) was 34%, 31%, 10.67%, 7.33%, 6.67%, and 4%, respectively. Shawarma samples were found to contain the highest levels of pathogens, compared with chicken burger samples. According to the MBT Compass Flex Series Version 1.3 software, all isolates were identified with 100% accuracy. The log score for MBT identification ranged from 2.00 to 2.56. Among _E. coli_ isolates, ampicillin, and penicillin had the highest resistance rate (100%), followed by tetracycline (35.29%). A number of antibiotics were reported to be resistant to _S. aureus_, includingalidic acid (100%), followed by penicillin (96.77%), piperacillin (45.16%), and nonfloxacin (32.26%). Some _E. coli_ isolates were susceptible to tetracycline (49.02%), nalidic acid (47.0%), and piperacillin (43.14%), whereas amikacin was the only drug that was effective against 32.72% of _S. aureus_ isolates. The proportions of the _blatTEM_ and _tet(A)_ genes in _E. coli_ isolates were 55.89% and 45.1%, respectively, whereas _S. aureus_ strains did not posses either of these genes. However, 21.5% and 47.31% of _blaz_ and _mCA_ genes were present among various isolates of _S. aureus_, respectively. In contrast, _E. coli_ strains did not possess either of these genes. In conclusion, the fast identification and antimicrobial profiles of the foodborne pathogens were useful in identifying which restaurants and fast food outlets may need to improve their food safety practices. Ultimately, our results will be used to devise targeted strategies to control foodborne pathogens.  
Keywords:foodborne pathogens; frequency; identification; antibiotic resistance genes; public health
## 2 Materials and Methods
### Justification of the Model Choice  
Several advanced machine learning and simulation-based approaches exist for spatiotemporal disaster modeling, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and agent-based models (ABMs). While these methods have demonstrated effectiveness in prior disaster response studies [22]. Given the need for a transparent, interpretable model to inform real-time decision-making, we opted for Generalized Additive Models (GAMs). GAMs provide flexibility to modeling the complex spatial and temporal interactions in evacuation behaviors, while providing interpretability, making them suitable for impact analysis in policymaking applications. Table 2 summarizes a comparative evaluation of these modeling approaches.  
GAMs provide an optimal balance between model complexity, interpretability, and computational efficiency, making them particularly suitable for policy-driven evacuation modeling where transparency is crucial. However, future research could explore hybrid models that combine GAMs with deep learning approaches.
# A Novel Dual Authenticated Encryption Scheme Suitable for Social Networking Services
## 5 Efficiency  
To ensure the practical benefits, we evaluate the efficiency of our DAE scheme in terms of computational efforts in the three-party communication environment. For convenience, some time-consuming computation and their approximate running time experimented by [37] are first defined as Table 1. It is believed that the bilinear pairing operation is the most complicated operation in a pairing-based system. We show detailed evaluation with some related protocols including [PERSON] et al.'s (LCL for short) [38], [PERSON] (HL for short) [22], [PERSON] (IB for short) [39] and [PERSON] et al.'s (CZXY for short) [40] schemes in Table 2. The comparisons of communication overheads in terms of ciphertext length are also demonstrated in Table 3 utilizing a super singular elliptic curve \(E/F_{p}\): \(y^{2}=x^{3}+x\) with a 160-bit prime \(q\) and a 512-bit prime \(p\). It is obvious to see that the proposed DAE scheme is more efficient from either the computational perspective of sender or that of recipient. However, it should be noted that more than 100 ms total time would be unacceptable for a real-time synchronous voice or video communication. As for the text messaging, we claim that the transmission latency is noticeable, but acceptable with the tradeoff of higher security level. Figure 1 further demonstrates the difference of computational efforts among different quantities of ciphertext.  
\begin{table}
\begin{tabular}{c c c} \hline
**Symbol** & **Description** & **Approximate Running Time** \\ \hline \(T_{B}\) & the computational time of a bilinear pairing operation & 20.01 ms \\ \(T_{E}\) & the computational time of an exponentiation in \(G_{2}\) & 11.20 ms \\ \(T_{M}\) & the computational time of a pairing-based scalar multiplication & 6.38 ms \\ \hline \end{tabular}
\end{table}
Table 1: Definition of utilized notations.