esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete
###### Abstract  
Predicting the properties of concrete before its design and application process allows for refining and optimizing its composition. However, the properties of lightweight concrete are much harder to predict than those of normal weight concrete, especially if the forecast concerns the insulating properties of concrete with artificial lightweight aggregate (LWA). It is possible to use porous aggregates and precisely modify the composition of lightweight concrete (LWC) with specific insulating properties. In this case, it is advisable to determine the parameters of the components and perform preliminary laboratory tests, and then use theoretical methods (e.g., artificial neural networks (ANNs) to predict not only the mechanical properties of lightweight concrete, but also its thermal insulation properties. Fifteen types of lightweight concrete, differing in light filler, were tested. Lightweight aggregates with different grain diameters and lightweight aggregate grains with different porosity were used. For the tests, expanded glass was applied as a filler with very good thermal insulation properties and granulated sintered fly ash, characterized by a relatively low density and high crushing strength in the group of LWAs. The aim of the work is to demonstrate the usefulness of an ANN for the determination of the relationship between the selection of the type and quantity of LWA and porosity, density, compressive strength, and thermal conductivity (TC) of the LWC.  
artificial neural networks; thermal conductivity; lightweight concrete; lightweight aggregate; predicting properties +
Footnote †: journal: Applied Sciences  
1 Faculty of Civil and Environmental Engineering, Gdansk University of Technology, ul. Narutowicza 11/12, 80-233 Gdansk, Poland 1  
[PERSON], [PERSON]; [PERSON], [PERSON].
## 3 Materials and Methods
### Traffic Direction
#### 3.3.2 Azimuth Calculation  
In this study, we calculated the heading angles of ships in a given sea area to identify the direction of traffic. Using the Haversine formula (with adjustment for \(-180^{\circ}\) to \(+180^{\circ}\)) in Equation (1), we calculated the azimuth between two points \(A\) and \(B\) with latitude and longitude. For simplicity, the azimuth is initially returned in radians and subsequently converted to degrees.  
\[\theta=atan2(\cos{(\phi_{2})}\times\sin(\lambda_{2}-\lambda_{1}),\cos(\phi_{1 })\times\sin(\phi_{2})-\sin(\phi_{1})\times\cos(\lambda_{2}-\lambda_{1}) \tag{1}\]  
\[\phi_{1},\lambda_{1}(\textit{latitude and longitude of point }A)\]  
\[\phi_{2},\lambda_{2}(\textit{latitude and longitude of point }B)\]
# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 6 Internet-of-Things Architectures for Modeling Trust
### Distributed Architectures  
In [68], authors introduced a distributed IoT architecture for modeling trust. In this architecture, trust management is performed on every IoT object. In [69], the same authors improved their architecture presented in [68] by making each object stores and updates only trust values of other objects of interest in order to minimize the computation and the storage cost.  
A distributed architecture [70] for modelling trust is proposed to utilize a decentralized approach that consists of objects providing services and feedback trust values. A decentralized bulletin board holds encrypted feedback and the reported non-interactive zero-knowledge proof by interacting machines. The reputation scores for machines are computed in a distributed manner and feedback values reported to the decentralized public bulletin board.  
Authors in [7] proposed their blockchain-based architecture for modeling trust to enable IoT nodes to store tamper-proof trust information. In this paradigm, each node stores the full data of the entire system creating unnecessary redundancy. Furthermore, trust values must be validated by miners to ensure a consensus of data. Also, blockchain offers a tamper-proof data structure and not an efficient structure with respect to storage footprint and lookup time [71; 72; 73; 74].
# A New Intra-Cluster Scheduling Scheme for Real-Time Flows in Wireless Sensor Networks
## 3 Performance Evaluation
### Impact of Flows  
For the impact of flow analysis, the number of clusters is fixed to 6, while the number of flows ranges from 4 to 8. As shown in Figure 5a-e in contrast to the previous schemes--the proposed scheme shows a high acceptance rate. The acceptance rate is high in the proposed scheme because the clusters utilized slots of both IntraSend and IntraRecv flows. For instance, if there are no incoming flows in the cluster then IntraSend flows can utilize the IntraRecv slots. Similarly, if there are no outgoing flows in the cluster then the IntraRecv scheduling can utilized the IntraSend time slots. While in the previous scheme for clusters that have incoming or outgoing flows then the IntraSend and IntraRecv flow cannot use one another empty time slots.  
As shown in Figure 5a-e, as the number flows increases the acceptance rate decreases. Since the number of flows is increasing then the interference is also increasing. An increase in the interference means the acceptance rate will be decreasing. However, the acceptance rate of the proposed scheme is higher than the previous scheme.  
Because in the proposed scheme when the flows have interfered in the IntraSend time slots then the interference flows can be scheduled in the IntraRecv time slots. Whereas in the previous scheme if the flows are interfering in the IntraSend time slots then the flows cannot be scheduled in the IntraRecv time slots, hence increasing the scheduling. Therefore, fewer flows are scheduled in the given deadline and hence the acceptance rate is decreasing.  
Figure 4: Simulation results for w.r.t deadline.
# Personalized Context-Aware Authentication Protocols in IoT
## 3 Results
### Distributed Challenge-Response Algorithm with Masking--Distributed CPRA  
As can be seen, for the algorithm described in Section 3.1 to be used successfully, a relatively large number of nodes in the LAAP have to be available. Using only one or two data points will negate the algorithm's ability to choose a meaningful contextual set and might not provide enough data to ensure uniqueness, thus reducing the system to using only the most basic and unvaried methods of authentication without context or added security. On the other hand, it does not take advantage of one of the most prominent IoT features--interconnectivity. In an environment with a large number of interconnected devices belonging to one IoT gateway, a cloud-like solution can be implemented as a distributed CPRA. In such a case, a gateway can store any connected device's LAAP profile, treating it as a set, and the sum of all of the LAAP sets available at a given moment defines the combined authentication ability profile (CAAP) that can be used with all the devices and sensors working simultaneously. Thus, as the authentication begins for any of the networked devices, an attempt is made to obtain the CAAP. If no connection or CAAP is available, the algorithm can only downgrade to use LAAP (see Section 3.1 above). With a successful handshake, however, the device might use CAAP as the basis for the algorithm, indexing the data streams and sensors it does not physically own and does not have the access/permissions to use. When the protocol is negotiated and the challenge determined, the authenticating device determines its own sensor input and pattern matching and sends a request to the gateway to fill in for the lacking challenges (Figure 2). The networked devices are then polled by the gateway as needed (according to their advertised LAAPs) to perform their own authentication procedures on the subject (again, using a subset of the available methods chosen by the original device and provided by the gateway) and then return the result. In this manner, the original device does not have access to any external sensors or data it might not be authorized to access (which minimizes the risk of an accidental compromise to an infected device) and only receives partial reading results to use in its own final verdict of passing or failing the login attempt. It has to be noted that, although the individual communication and results from intermediate devices might be subject to spoofing attacks, the algorithm's unpredictability in choosing the challenge scales with network size, as with an abundance of similar sensors, a random suitable device can be chosen, and the same challenge can be issued to be processed by more than one device (which might then take the form of either a majority or a unanimous vote on the tested characteristics between the obtained results) (Figure 3).  
Figure 3: Distributed authentication response.  
Figure 2: Distributed authentication challenge.
###### Abstract
However, if not carefully controlled, its application can result in untreated chlorite or excessive chlorine levels in treated water, leading to the formation of chlorate or chlorinated disinfection byproducts. Studies on chlorine dioxide have also reported issues with taste and odor [3]. Several previous studies have conducted experiments to inactivate chironomid larvae using a combination of coagulation with other oxidizing agents. [PERSON] et al. [3] revealed that the coagulation process can inactivate larvae with an efficiency rate of 75.5%. Substituting chlorine with chlorine dioxide can significantly inactivate chironomid larvae at a dose of 24.8 mg min/L. [PERSON] and [PERSON] [7] experimented with inactivating chironomid larvae, which showed that exposure to 8 mg/L of chlorine could inactivate larvae for 30 min. Another method used to inactivate larvae is _Bacillus thuringiensis subsp. Israelensis_ (Bti), where 100% inactivation of the first instar larvae of chironomid was obtained. The required Bti concentration was 0.5 mg/L, while 1.0 mg/L was required due to the high resistance of the fourth instar larvae of chironomid [8]. Although these studies successfully demonstrated their ability to inactivate larvae, most water treatment plants still use chlorine as an oxidant to kill bacteria and other microorganisms. However, there has been no research conducted on the relationship between the process of inactivating larvae and their behavioral response. Understanding the behavioral response of chironomid larvae during the inactivation process is essential for developing effective strategies to ensure the production of clean tap water. Therefore, the purpose of this study is to evaluate and compare the efficacy of coagulation and chlorination in inactivating chironomid larvae in water treatment plants. Additionally, this study seeks to investigate the behavior of chironomid larvae when exposed to commonly used coagulants and chlorine. The results of this study are expected to provide valuable information regarding the optimization of coagulation and chlorination processes for ensuring the quality and quantity of clean water that meets consumer needs.
# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 6 Conclusions  
The latest literature in the field of \"diabetic retinopathy segmentation and lesion detection from fundus images using deep learning\" was surveyed. The studies in this field can be grouped as blood vessel segmentation based studies, lesion detection based studies, and OD segmentation-based studies. Lesion detection-based studies can further be classified into MA detection, EX (soft and hard) detection, and HM detection.  
We found that almost all recent DL networks/architectures have been utilized effectively for DR segmentation and lesion detection in the studies that were reviewed. It was also observed that there is a spike in the number of these kinds of studies recently. We also created a table of the generally utilized retinal fundus image datasets for DR retinal blood vessel segmentation and lesion detection. Finally, we performed a comparison based on the performance of studies from each type of DR study reviewed here. As future work, we may do a systematic literature review in this field.  
Conceptualization, A.S., O.E. and S.A.-M.; data curation, A.S.; formal analysis, A.S.; methodology, A.S., O.E. and S.A.-M.; project administration, S.A.-M. and N.A.; supervision, S.A.-M. and N.A.; validation, A.S., O.E., S.A.-M. and N.A.; visualization, A.S. and O.E.; writing--original draft, A.S.; writing--review and editing, A.S., O.E., S.A.-M. and N.A. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Performance comparison of deep-learning based diabetic retinopathy with hard exudate and microaneurysm detection studies which employed the IDRiD dataset [22; 28; 31; 33; 38; 40; 41; 50; 53; 54].  
**Funding:** Open Access funding provided by the Qatar National Library.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This publication was supported by Qatar University Internal Grant QUHICENG22/23-548. The findings achieved herein are solely the responsibility of the authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Industrial Applications
### Electronics and Communication  
Over the past years, metal AM has received attention from researchers and industries to make significant inroads in the design and fabrication of novel electronic and communication components such as resistors, circuit boards, antennas, waveguides, and couplers [90; 91; 92; 93]. These electronic and communication parts are made of functional materials such as metal, semi-conductor, and polymer using printing and/or patterning technologies. Among these materials, metal/inorganics are commonly used in this industry sector as they offer good conductivity and durability [90; 93]. For example, antennas that are made of conductive materials, such as ferrite, are essential components in wireless  
Figure 7: Building components [88]: (**a**) Arup original node, (**b**) Arup topology optimized node, (**c**) Nematox facade node, and (**d**) Max 3D bridge [9].  
communications [94]. These products require smaller structures to provide a high level of carrier frequency of antennas [92]. Many researchers have explored AM techniques to develop uniquely shaped antennas. For instance, [PERSON] et al. [95] employed a Fujifilm Dimatix DMP-2800 inkjet printer to fabricate patch antennas for low-power wireless applications.  
The cartridge includes 1.5 mL of ink with silver nanowires. The Robert Hofmann company developed a lightweight RF filter for communication satellites [5]. Figure 8 shows an AM RF antenna manufactured by Optisys LLC. This company manufactured a metallic micro-antenna with Concept Laser's PBF technology, and this AM product is employed in aerospace and defence applications. Compared to an equivalent conventionally manufactured antenna, the AM antenna provides benefits such as 95% weight saving, 20% production cost reduction, and lead time reduction from eleven months to two months [96]. Another example of the metal AM application is developing high efficiency metal waveguides. [PERSON] et al. [97] developed a W-band component, which includes a 10 cm straight waveguide piece and a 20 dB coupler. This component was manufactured with DMLS technologies in Managing Steel (MS), which provides structural integrity and reasonable conductivity.
## 1 Introduction
By using a partial parallel architecture, the proposed decoder allows column processing to commence after three row processing calculations instead of waiting for all row processing to complete, resulting in a one-third reduction in decoding time. In addition, a shift-register-based memory strategy has been employed to reduce the read/write latency. The choice of decoding algorithm is a crucial factor affecting the performance of LDPC decoders. The Min-Sum algorithm (MSA) simplifies the multiplication operation in the decoding process to an addition operation, and approximates the summation process to finding the minimum value, thereby significantly reducing the algorithm's complexity [21]. Building on this, the Modified 2-bit MSA optimizes the storage structure and utilizes two bits to represent the information in the operation, which further reduces the resource overhead [22]. In this paper, in response to the inability to perform deep overlap decoding for CCSDS (8176, 7154) LDPC codes, we propose a method for designing a QC-LDPC that is suitable for overlap decoding, while maintaining an equivalent submatrix structure and code length. We design a specialized partially parallel decoder based on the Modified 2-bit MSA for our proposed LDPC code, which exhibits a substantial coding gain of 5 dB at a BER of 10\({}^{-6}\) compared to uncoded BPSK. Furthermore, when compared to the original CCSDS (8176, 7154) LDPC code with Modified 2-bit MSA decoding, our proposed LDPC maintains high performance with a marginal coding gain loss of less than 0.5 dB, while improving throughput and HUE. This paper is organized as follows. In Section 2, QC-LDPC codes and overlapped decoding scheme are introduced, and the construction of our proposed QC-LDPC code optimized for overlap is discussed in detail. In Section 3, the Min-Sum Algorithm and Modified 2-bit MSA are reviewed. The simplification of combinatorial logic to realize the algorithm is illustrated. In Section 4, the system architecture is described, and two computation units design for decoder are presented in Gate-level netlist. In Section 5, the data stream and scheduling structure are described. The shift-register-based memory strategy and overlap controller are introduced. Section 6 analyzes the simulation results and experimental results of our decoder. Section 7 concludes this paper.
## 3 Optimization Methods and Results
### Hardware System Optimization
#### 3.2.4. Memory Access Optimization Results  
We implement the CNN accelerator on Xilinx FPGA. The device part is Xilinx xc7a100 tfgg484-2 (Xilinx, San Jose, CA, USA), and the synthesis tool is Vivado 2018.03 (Xilinx, San Jose, CA, USA). The comparison of the unoptimized solution (marked as Solution 1) and three other optimized solutions is shown in Table 2, which lists each layer's time consumption. Here the solutions described in Section 3.2.1, Section 3.2.2, and Section 3.2.3 are marked as Solution 2, Solution 3, and Solution 4. As shown in Table 2, the intervals of random read operations take plenty of time, reducing the throughput of memory access. Moreover, the system architecture, which uses the ping-pong structure has higher throughput. The result shows that the time consumed by Solution 4 is significantly reduced by up to 96.8% compared with the unoptimized solution.  
The comparison in resource consumption and performance of four solutions on FPGA is shown in Table 3. The result shows that Solution 4, which consumes fewer resources but performs better than Solution 3. Moreover, the power consumed is only 0.714 W under a working frequency of 100 MHz,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Solution 1/Cycles** & **Solution 2/Cycles** & **Solution 3/Cycles** & **Solution 4/Cycles** \\ \hline CONV1 & 33,908 & 10,320 & 4027 & 3411 \\ CONV2 & 77,650 & 14,309 & 6069 & 4710 \\ CONV3 & 136,134 & 21,171 & 9884 & 6648 \\ CONV4 & 267,820 & 35,892 & 19,037 & 11,292 \\ CONV5 & 535,148 & 67,012 & 37,794 & 20,868 \\ CONV6 & 533,049 & 60,047 & 30,894 & 20,484 \\ CONV7 & 298,832 & 30,694 & 14,344 & 10,812 \\ FC1 & 76,335 & 9981 & 4614 & 4254 \\ FC2 & 4184 & 530 & 303 & 238 \\ Total & 1,963,060 & 249,956 & 126,966 & 82,717 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of unoptimized and optimized memory access solutions.  
Figure 11: Optimized system architecture. Two blocks of BRAM which form the ping-pong structure simplify the memory access process and reduce unneecessary latency.  
which is far lower than the system architecture with DDR as cache memory. Moreover, when the system works under a working frequency of 170 MHz, the power consumption is only about 1.089 W, and the latency is 0.487 ms, which also satisfies the real-time requirement.
## 2 Results and Discussion
### Application of MMIPs to Biomatrix Samples  
A series of experiments were carried out to evaluate the proposed method. The linear range, correlation coefficient (R\({}^{2}\)), limit of detection (LOD), and limit of quantitation (LOQ) were determined and used to validate the analytical methodology in this work. Calibration curves were built using standard solutions of OFL enantiomers in the concentration range of 0.25-5 ug\(\cdot\)mL\({}^{-1}\). The regression equations were y = 108.25x \(-\) 4.183 (R\({}^{2}\) = 0.9996) and y = 118.16x \(-\) 7.482 (R\({}^{2}\) = 0.9998) for \(S\)-(\(-\))-OFL and \(R\)-(+)-OFL, respectively. The LOD and LOQ values, defined as 3 and 10 times the signal-to-noise ratio, were 0.018 and 0.059 ug\(\cdot\)mL\({}^{-1}\), and 0.020 and 0.067 ug\(\cdot\)mL\({}^{-1}\) for \(S\)-(\(-\))-OFL and \(R\)-(+)-OFL, respectively.  
Under the optimized conditions, the MMIPs were applied to the analysis of OFL in fish samples. Fish samples spiked with 0.5, 2.5, and 5.0 ug\(\cdot\)g\({}^{-1}\) of OFL were used to evaluate the repeatability, accuracy, and recovery of the MMIPs as a sorbent for the extraction process (Table 4). The intra-day precision was evaluated by measuring the recovery of OFL six times in one day, whereas the inter-day precision was investigated by analyzing the recovery of OFL on six consecutive days. The recoveries of the spiked fish samples for \(S\)-(\(-\))-OFL and \(R\)-(+)-OFL ranged from 79.3% to 84.1% and 79.2% to 84.4%, respectively. Moreover, for \(S\)-(\(-\))-OFL, the relative standard deviations (RSDs) of the intra- and inter-day recoveries ranged from 2.9% to 6.0% (\(n=6\)), whereas for \(R\)-(+)-OFL, the RSDs of the intra- and inter-day recoveries ranged from 3.2% to 5.6% (\(n=6\)). Figure 9 shows the chromatogram of the solution eluted from OFL-loaded MMIPs with 10% acetic acid-methanol. These results revealed that the use of the MMIPs as a MSPE sorbent coupled with chiral HPLC can be applied to the selective adsorption and determination of OFL enantiomers in fish samples.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analytes** & **q\({}_{MIP}\) (\(\mu\)g\(\cdot\)g\({}^{-1}\))** & **q\({}_{NIP}\) (\(\mu\)g\(\cdot\)g\({}^{-1}\))** & **K\({}_{MIP}\) (mL\(\cdot\)g\({}^{-1}\))** & **K\({}_{NIP}\)** & \(\alpha\) \\  & & & & (mL\(\cdot\)g\({}^{-1}\)) & \\ \hline \(S\)-(\(-\))-OFL & 1455.83 & 821.90 & 69.72 & 24.50 & 2.85 \\ \(R\)-(+)-OFL & 1449.15 & 824.00 & 68.95 & 24.59 & 2.80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Adsorption capacities, partition coefficients, and imprinting factors of OFL on MMIPs and MNIPs.  
Figure 9: Chromatogram of a solution eluted from MMIPs with 10% acetic acid–methanol (\(v/v\)).