esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Cooperative Low-Power Wide-Area Network for Energy-Efficient B5G Systems
###### Abstract  
Low-power wide-area networks (LPWANs) have received extensive attention from both academia and industry, since they can efficiently provide massive connectivity to internet of things (IoT) devices in wide geographical areas with low cost and low power consumption. Recently, it was shown that macro-diversity among multiple gateways significantly improves the performance of uplink LPWANs by coherently combining multiple received signals at gateways. We call such networks _cooperative_ LPWANs. In this paper, the error performance of an uplink cooperative LPWAN is mathematically analyzed in terms of outage probability, bit error rate (BER), and diversity order. It is assumed that there exist multiple (two or more) gateways that have multiple antennas and are located at arbitrary positions in the LPWAN area. Each gateway exploits the optimal maximum-ratio combining (MRC) technique to decode the received signal, and then the signals after MRC are delivered to the cloud fusion center for coherent combining in the cooperative LPWAN. The main results, the closed-form expressions of outage probability and BER, were derived by utilizing the _hyper-Erlang_ distribution. Furthermore, the macro-diversity order was mathematically derived. The mathematical analysis was validated through extensive computer simulations. It worth noting that the mathematical analysis of the error performance of cooperative LPWANs is the first theoretical result in the literature to the best of our knowledge.  
low-power wide-area network (LPWAN); cooperative communication; macro-diversity; bit-error-rate (BER); outage probability; diversity order +
Footnote †: journal: Electronics Electronics
# Sonoporation, a Novel Frontier for Cancer Treatment: A Review of the Literature
## 3 Results
### Analysis of Results by Body District
#### 3.2.4 Breast  
[PERSON] et al. [20] (2012) investigated the enhancement of chemotherapeutic-loaded liposomes through acoustic cavitation. They found that doxorubicin-loaded liposomes (DOX liposomes) combined with acoustic cavitation effectively inhibited tumor growth in both in vitro and in vivo experiments. Acoustic cavitation improved the absorption efficiency of DOX liposomes, offering the potential to reduce adverse effects in clinical chemotherapy. The results demonstrated that low-frequency insonation of microbubbles enhanced the uptake of larger molecules while minimizing cancer cell viability. In [21] (2020), [PERSON] et al. investigated the therapeutic efficacy of ACT\({}^{\circled{\otimes}}\) (as shown in Figure 7), as in [18; 19], with liposomal doxorubicin for the treatment of triple negative breast cancer using orthotopic human tumor xenografts (MDA-MB-231-H.luc) in athymic mice (ICR  
Figure 5: Photographs of experimental setup. Mouse undergoing ACT\({}^{\circled{\otimes}}\) injection (**A**) and subsequent treatment using the 300 kHz ultrasound probe (**B**). Images from [18].  
Figure 6: Experimental setup from [19] shows a similar approach to [11] with a bag made of plastic material to adapt to the curves of the mouse body. Images from [19].  
NCr-Foxn1 nu). The results showed that combining ACT(r) with liposomal doxorubicin improved the absorption of the drug and, consequently, a remission of the mass.  
More recently, in 2022, [PERSON] et al. [22] explored the use of low-frequency (250 kHz) microbubble-mediated sonoporation for the efficient delivery of molecules with varying sizes in 4T1-type cells that were murine tumor-derived. They optimized the ultrasound parameters and found that an optimal peak negative pressure (PNP) of 500 kPa was crucial for maximizing cell uptake of different-sized molecules, ranging from 1.2 kDa to 70 kDa.
## 3 Power Loss Analysis of the Cell Balancing System
Moreover, these two effects are also influenced by the thickness, stacking, and coupling coefficient of the wire. While the transformer DC impedance \(R_{L(DC)}\) is relatively easy to measure, to simplify the calculation of \(R_{L(AC)}\), using the transformer AC impedance factor \(\lambda\) represents the relationship between \(R_{L(AC)}\) and \(R_{L(DC)}\)[28], as shown in Equation (10), and the copper loss is calculated as in Equation (11). The transformer iron loss calculation needs to query the corresponding transformer material, frequency, temperature, excitation current, etc. In the research, it is hard to obtain relevant parameters of the transformer used in the system, so the research takes the material of PC40 as an example, the iron loss formula provided by the manufacturer is as Equation (12) shown where \(\Delta B\) is the magnetic induction intensity variation of the transformer, and the \(V_{e}\) is the effective volume of the transformer core. Figure 3:5  
Figure 3:6  
Figure 3:7  
The total loss of the system in a cycle can be obtained by summing up all the losses parts estimated above as  
Figure 3:8  
The loss model of the proposed circuit operating in boost mode is similar to the proposed operating in buck mode. The only difference is the current direction and parameters. Therefore, the loss model of the proposed circuit operating in boost mode will not be discussed in this research.
# Assessment of Water Consumption Behavior in Single Households Using Smart Water Meters
## 5 Conclusions  
This paper presents an advanced methodology that uses data from smart water meters to calculate water consumption patterns, enabling the identification of the number and types of households. Smart water meter data enabled the assessment of hourly water consumption for all 1871 households, providing insights into demand patterns in the Chibata district of Kosai city. The results revealed that on weekdays in single-worker households, consumption peaks occurred in the morning hours at 8:00, with an average rate of 29 L/h. In contrast, single-nonworker households peaked 1 h later at 9:00 with 20 L/h. Consumption in worker households decreased until the late afternoon because residents were absent from the home for work. During this same period, nonworker households continued to consume water consistently. The peaks of worker households continued in the evening at 20:00-21:00, with an average water consumption of 32 L/h corresponding to evening activities in households. The evening peaks were higher than the morning peaks, which might be due to the Japanese evening bathing culture in single-worker households. In contrast, single-nonworker households had peaks at 19:00-20:00 with an average of 19 L/h. However, worker household daily water consumption peaked at 8:00 and 20:00 on weekdays, but on weekends, it peaked at 9:00 and 19:00. Moreover, compared with worker and nonworker household water consumption throughout the day, the changes in water consumption were gradual. Compared with that on weekdays, worker household water consumption on weekends increased by 10%, with an average of 262% from 13:00 to 16:00. These disparities indicate how household composition can influence water consumption patterns on different days of the week, with this information being obtained via smart water meters. Moreover, these findings may be helpful for water supply management and scheduling distribution planning more effectively.  
Conceptualization, T.I. and S.O.; methodology, T.I. and S.O.; validation, S.O. and K.H.; formal analysis, S.O. and K.H.; investigation, T.I., K.Y. and N.M.N.; resources, T.I., K.Y. and N.M.N.; data curation, S.O. and K.H.; writing--original draft preparation, S.O.; writing--review and editing, S.O. and N.M.N.; visualization, S.O.; supervision, T.I., K.Y. and N.M.N.; project administration, T.I.; funding acquisition, T.I. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Kosai City for a Joint Research project titled \"Application of water smart meter to analyze water supply pipelines, and residual chlorine concentration changes in distribution network\" between Kosai City and Toyohashi University of Technology.  
Not applicable.  
The original contributions presented in this study are included in this article; further inquiries can be directed to the corresponding author.  
The authors extend their sincere gratitude to the Kosai City Water Supply Division for providing the smart water meter data.  
The authors have no conflicts of interest to declare.
# Session-Based Recommendations for e-Commerce with Graph-Based Data Modeling
## 3 Recommendation Methods
### The Graph Models  
In both proposed methods, the graphs consist of two types of nodes which represent sessions and items. In the HSP graph, the connections between the nodes represented relationships as follows:  
* Item \(o\) appearing in session \(s\) is connected with \(s\) via the _ItemInSession_ relationship;
* An item \(o_{1}\) appearing in session step \(t\) and an item \(o_{2}\) appearing in the same session in step \(t+1\) are connected with the relationship _Next_.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Approach** & **Citation** \\ \hline Probabilistic Matrix Factorization (PMF) & Matrix Factorization & [13] \\ SLIS, SLIT, SLIST & Matrix Factorization, Linear item–item recommendation & [3] \\ Matrix Factorization & Item-based Collaborative filtering & [15] \\ MDP-Based Recommender Model & Markov models & [16] \\ FPMC & Markov chain, Matrix Factorization & [17] \\ Recommendation of Influenced Products & Neo4j, Association rules & [20] \\ Using Association Rule Mining & Neo4j, Knowledge Graph database & [21] \\ Goods Recommendation & Neo4j, Collaborative filtering & [6] \\ Fair Popularity & Neo4j, Collaborative filtering & [23] \\ GRU4 REC & RNN & [24] \\ HRNN & RNN & [25] \\ M[1,2,3,4](GRU Size) & RNN, Data augmentation & [26] \\ NARM & GRU RNN, Attention mechanism & [27] \\ WH (KNN, GRU) & GRU RNN, k-NN & [28] \\ PSRN & GRU RNN, Mixture-channel Purpose Routing Networks (MCPRNs) & [29] \\ HATE & Attention mechanism, Item embeddings & [31] \\ HMLG & Gated Graph neural network, Path-based matrix factorization model & [7] \\ SR-GNN & Graph neural network, Attention mechanism, Item embeddings & [7] \\ GCE-GNN & Graph Neural Network, Session-aware attention mechanism, & [8] \\ Neighbors’ embeddings & & [9] \\ IC-GAR & Graph neural network, Session co-occurrence patterns & [9] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of reviewed methods.  
Thus, the graph data provide sequence information about items in sessions. Figure 1a shows part of the data graph including the _ItemInSession_ and the _Next_ relationships between the items and the sessions.  
In the RIC method, similar to the HSP, the graph has two types of nodes, for the items and sessions representations and also two types of following connections:  
* The _ItemInSession_ relationship as in the HSP method, and
* The _InNameSession_ relation connects the items' co-occurrences in the same session independently to the sequence they appeared in. This is an undirected relationship.  
In this case, the graph data model does not provide the sequence information about items in sessions. Figure 1b shows part of the data graph including the _ItemInSession_ and the _InNameSession_ relationships between the items and the sessions.
# Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification
## 2 Related Work
### Development of Color QR Code for Visual Beautification  
[PERSON] et al. [24] developed halftone QR codes, which improved the quality and appearance of QR codes by replacing each QR code module with smaller, 3 \(\times\) 3 submodules. The submodules were rearranged through a module subdivision process to create a new, smaller pattern that could be easily manipulated to achieve a desired shape. [PERSON] et al. [25] later enhanced this technique by embedding halftone patterns as QR masks into color images. While this technique improved the visibility of the background color, the area of interest in the background image was still covered by a halftone mask, and high-resolution QR codes were needed to minimize readability issues.  
[PERSON] et al. [26] used image-saliency-detection technology to compute the saliency value of the background image and generate a saliency map, which is an image that highlights the region on which the eye focuses first. This technique, which is based on human perception, allows some areas to be free of QR codewords while maintaining the redundancies of the Reed-Solomon (RS) error-correction blocks. In 2017 and 2018, [PERSON] et al. [27] improved upon this machine-defined saliency map by introducing an interactive framework in which the area of interest could be user-defined by creating strokes on the target area. Another improvement was the implementation of the Adaptive Contrast Enhancement (ACE) [28] algorithm, which enhances image contrast to improve visual quality and increase the number of modules that can be erased with minimal impact on readability.  
In 2019, [PERSON] and [PERSON] [29] introduced a new method for embedding QR codes. Unlike traditional QR codes, which are embedded in the spatial domain, this method involves storing both the halftone masks and the background image in a discrete wavelet transform (DWT) domain, taking into account the Human Visual System (HVS). This allows the background host image to be considered as part of the scannable region.  
[PERSON] and [PERSON] [30] updated the previous work by adding a deep learning solution, Mask R-CNN, which generates an area of interest (Auto Mask) similar to [26], but with the added feature of object detection for the background image. This automated method can expand the area of interest or remove codewords from the furthest area. This latest study on color QR code generation is the primary source for our color QR code dataset. The source images can be seen in Figure 3.
# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 2 Wastewater Treatment Processes in an Industrial Wastewater Treatment Plant
### Performance Indicators Used in Wastewater Treatment
Studies have shown that the optimization of a plant treatment processleads to a reduction of plant emissions by up to 18% [51]. The reduction of greenhouse gas emissions is achieved through the real-time control of the operational parameters (such as chemical reactant dosage, aeration rates, etc.) that are dynamically adjusted to comply with the requirements of the treatment processes, thus avoiding unnecessary tasks and consumption of resources [51]. The AI models' robustness, adaptability to different input conditions, and their ability to maintain high performance for various scenarios are other important KPI indicators [27]. Another important indicator is the fact that AI models can handle the complex and highly nonlinear processes from a WWTP, with real benefits to the prediction accuracy, the process control, and the plant's human operator decision-making process [39]. All these KPI indicators are useful tools for evaluating the performance of AI-based systems used in a WWTP, ensuring that they adhere to the imposed normative standards in the domain and contribute to environmental protection.
## 4 Results and Discussion
### Interpretative Map Analysis  
The apparent iso-resistivity maps show the dispersion and lateral variation of the apparent resistivity (\(\rho\)a) in the study area for different slices of the ground. Two iso-resistivity maps have been developed (\(\rho\)a\({}_{\text{AB-80 m}}\) and \(\rho\)a\({}_{\text{AB-200 m}}\)). The analysis of the apparent resistivity map \(\rho\)a\({}_{\text{AB-80 m}}\) presented in Figure 10a for a line length of AB = 80 m, corresponding to an investigation depth of about 15 m, allowed us to distinguish the following areas: (i) highly resistive areas (\(\rho\)a >200 \(\Omega\)m), corresponding to zones where the basalts are quite thick, notably in the SW of the study area; (ii) quite conductive areas (\(\rho\)a \(\leq\) 100 \(\Omega\)m), located in the east and north-east of the study area. This decrease in apparent resistivity (\(\rho\)a) is most probably linked to the absence of basalts on the surface; (iii) the rest of the map is occupied by intermediate resistivity (100 \(\leq\)\(\rho\)a \(\leq\) 200 \(\Omega\)m), occupying large areas and corresponding to zones where basalts are present with small thicknesses, or they are more altered. For a deeper slice, the map \(\rho\)a\({}_{\text{AB-200 m}}\) (Figure 10b) corresponds to a depth of investigation of about 30 m, and this map roughly follows the pattern of the previous map \(\rho\)a\({}_{\text{AB-80 m}}\) and shows a very resistant zone (\(\rho\)a \(\geq\) 500 \(\Omega\)m), located SE of the study area. This increase in \(\rho\)a reflects the presence of very resistant terrain corresponding to basalts. A relatively conductive zone (\(\rho\)a \(\leq\) 100 \(\Omega\)m) is located to the north-east of the surveyed area. This decrease in electrical resistivity is due to the presence of Pliocene marl and marl-limestone soils from the surface. The zones of intermediate resistivity (100 \(\leq\)\(\rho\)a \(\leq\) 500 \(\Omega\)m) correspond to areas where the basalts are of low thickness above the Pliocene marl-limestone bedrock.  
The isobynse map of the marl-limestone bedrock (Figure 10c) shows that the roof altitude varies between 1800 m, recorded at the upstream end of the plain, and 1460 m, the minimum value recorded downstream. It shows, in a general way, the behavior of the roof of the marly-limestone substratum, which gradually plunges from the south-west to the north-east in accordance with the flow of Oued Guigou and the water table. The isopach map represents the thickness distribution of the basaltic aquifer. The examination of this map (Figure 10d) shows that the maximum values are located to the south-west of the study area, mainly at the level of the electrical boreholes of profile 1 where the thickness values are close to 150 m. Overall, the thickness of the basaltic formations decreases progressively from the south-west to the north-east, with a slight increase in profiles 5 and 6.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 3 Methodology
### Step 4: Calculate Resilience
#### 3.4.3 Step 4.3: Calculate Mission Impact Across All Threats  
Next, mission impact across all threats is calculated using the EEDMI metric developed in Equation (2). This involves using the probability of occurrence for each threat developed in Step 3.2 and the results from Step 4.1. The EEDMI for each potential nanogrid architecture can be compared against the baseline microgrid EEDMI score to determine if specific architectures are better or worse than the baseline microgrid which is performed in the next step. EEDMI is a measure of resilience of electrical energy to threats so that critical loads at a military base can perform important national security missions.
# Enhancing the SRAM PUF with an XOR Gate
## 2 Background
### Our Contributions  
Despite their simplicity, ease of use, and high reliability, SRAM PUFs suffer a major drawback due to a limited number of available CRPs. The limited number of linearly scaled CRPs renders them relatively weak PUFs [4; 28], as they scale linearly. As a result, they are susceptible to several forms of invasive and non-invasive attacks [29; 30]. Even though SRAM PUFs are considered weak, they are still being adopted to secure Internet of Things (IoT) devices because they are incredibly simple and easy to implement [31].  
This work proposes a novel method for expanding the number of CRPs available for an SRAM PUF while maintaining a high level of reliability without complicating the simplicity of the SRAM design, thanks to using a single XOR gate. While this design does not counter every method of attack, it makes it more difficult to extract the CRPs  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PUF Metrics** & \begin{tabular}{c} **Microcontroller** \\ **SRAM PUF**[22] \\ \end{tabular} & \
\begin{tabular}{c} **FPGA SRAM** \\ **PUF**[23] \\ \end{tabular} &
\begin{tabular}{c} **Reconfigurable** \\ **SRAM PUF**[26] \\ \end{tabular} \\ \hline Number of CRPs & \(n\) & \(n\) & \(n\) \\ BER or \(HD_{intra}\)1  & \(6.85\times 10^{-7}\)* & 0.02712 & 0.009 \\ \(HD_{inter}\) & 0.4954 & 0.4806 & 0.499 \\ Entropy H(X) & 0.9974 & 0.9979 & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 1: PUF metrics of previously implemented SRAM PUFs. \(n\) is the number of SRAM cells in the device. Work in [22; 26] used fuzzy extractors, a mix of filtering fuzzy cells and error correction codes, to improve reliability.  
through non-invasive measures. Moreover, this design was implemented on low-power client devices using physical SRAM devices.
## 3 A Methodology to Differentiate Dissolution from Gas Losses in Gas-Metal Systems  
### Modelling Approach and General Assumptions  
Figure 3 shows the proposed model to simulate experiments of gas dissolution in metals. The top of the image shows how a metallic barrier can be represented by a 1D diffusion channel. Any given metallic barrier can be represented by a single channel, that computes the average diffusion process through its total surface. The channel is sub-divided into nodes, which provide a mesh to apply a numerical method. In this work, the resulting system of equations was obtained with the inlet Differences method, as usually presented in related literature (e.g., [39]). A Backwards Euler (implicit in time) 2 nd-order (in space) scheme was applied to ensure model stability and limited truncation error without much computational cost [40; 41].  
The bottom of Figure 3 shows shows a diagram of the expected behavior of the concentration profile of a gas, and its evolution in time, inside a double metallic barrier. In this particular example, the diagram represents metals with similar Sieverts' constants, but significantly different diffusivities. Any discontinuous variation of medium properties requires the diffusion channel to be split into two sub-channels, solved in succession.  
Below follows a list of additional general assumptions applied in the proposed model:  
* Diffusion is not hindered by surface oxidation, chemical bonds or micro-structural defects (in case of solid medium) [14; 33].  
Figure 3: Representation of a generic double metallic barrier being simulated with the proposed model. The nodal division of the barrier provides a mesh along the channel length to apply a numerical method (**top**). In this example, the **left** side of the barrier experiences a finite amount of gas and the **right** side is ideally ventilated. The spatial concentration profiles in each metal evolve in time (**bottom**, in _blue_, from light to dark). Furthermore, in this example Sieverts’ constants for both metals are assumed equal, so no profile discontinuity is seen at the interface. Three possible types of boundary conditions (BCs): Coupled BCs (in _red_), Co-Dependent BCs (in _orange_) and Fixed BCs (in _green_). When BC can also evolve in time, this is indicated by an arrow.  
* Low pressure regimes imply that Sieverts' Law may be applied. Subsequently, in these conditions the gas concentration in a metal is low enough to allow one to approximate chemical-activity to concentration, with the purpose of computing the diffusion process in a single medium [7].
* Concentration gradients only exist in the direction of the diffusion channels, and temperature gradients along them are negligible. This way the diffusivity and Sieverts' constant do not vary along the diffusion channel. This can be assumed if the width of the metal barriers, along the dimension of the diffusion process, is considerably smaller than its other dimensions [14].