esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Electrospun Nanofiber Additive on Selected Mechanical Properties of Hardened Cement Paste
## 4 Conclusions  
In this study, the effectiveness of electrospun nanofibers at enhancing the mechanical as well as microstructural properties of Portland cement was estimated. The mechanical properties increased significantly depending on different types of electrospun nanofibers. For instance, the increase in compressive strength of the cement paste was observed when blending TEOS/PVP nanofibers as well as the hybrid nanofibers containing carbon nanotubes and TEOS/PVP with OPC. In addition, an increase in tensile strength was observed when incorporating nylon 66 nanofibers as well as the hybrid nanofibers containing carbon nanotubes and nylon 66 with Portland cement. Further, the toughness property also showed a significant increase when blending the electrospun nanofibers with cementitious materials. In addition, based on the observations from SEM, TEM images, and TGA-DTG results, the bridging effect as well as the filling effect were found. Therefore, the enhancing mechanism of the mechanical properties of the blended cementitious materials has been confirmed.  
Above all, this work is an initial step toward a new approach to strengthening cementitious materials. More high-strength electrospun nanofibers should be involved to estimate their effect on cement paste. For practical applications, further work should consider the setting time, the workability, as well as the adhesion with aggregate of blended cement.  
Conceptualization, J.J.K.; Data curation, T.N.M.N. and J.J.K.; Formal analysis, T.N.M.N., D.H.L. and J.J.K.; Funding acquisition, J.J.K.; Investigation, T.N.M.N. and J.J.K.; Methodology, D.H.L. and J.J.K.; Project administration, J.J.K.; Supervision, D.H.L. and J.J.K.; Validation, D.H.L. and J.J.K.; Visualization, T.N.M.N., Writing-original draft, T.N.M.N. and J.J.K.; Writing-review & editing, T.N.M.N., D.H.L. and J.J.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Research Foundation of Korea: 2020R1A2B5B01001821  
The authors declare no conflict of interest.  
Figure 8: TGA curves of the hydrated cement pastes.
# Re-Keying Scheme Revisited: Security Model and Instantiations
## 8 Conclusions  
In this paper, we reconsidered the re-keying scheme. We pointed out that the previous works lacked the security consideration on the SCA and introduced a security model of the re-keying scheme considering the SCA. We then gave concrete schemes and discussed their security and applications. In the IoT era, whereas the SCAs are a serious threat to the IoT devices, these devices are resource-constrained, and it is difficult to implement the existing countermeasures on them. In addition to the device-level approach, systematic countermeasures such as the re-keying scheme are some of the promising countermeasures.  
**Author Contributions:** conceptualization, Y.K.; methodology, Y.K. and S.H.; formal analysis, Y.K. and S.H.; writing--original draft preparation, Y.K.; writing--review and editing, Y.K. and S.H.  
**Funding:** This work was supported by JSPS KAKENHI Grant Number JP18H05289.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Problem Statement  
It is assumed that an aircraft is flying in its corridor, as shown in Figure 1. The objective is to predict the future trajectory of this aircraft for the next \(i\) steps by using a set of data at \(time=T_{n}\), composed of latitude, longitude, altitude, heading, speed, and time.  
With this aim, a deep neural network model must be trained in order to predict future trajectories in real time operations. Neural networks with deep architectures composed of multiple layers are capable of efficiently learning complex patterns. Such networks do not require complex activation functions, and they can learn from raw datasets. A prediction model using a deep neural network should be trained using a large and rich aircraft trajectories database. After training the prediction model and fine-tuning its hyper parameter, aircraft GPS datasets are applied to the trained model. The trained deep neural network is then able to predict future trajectories of the aircraft for \(T_{n+1}\) to \(T_{n+i}\). Figure 2 shows the procedure for the off-line training and then real-time testing of the Aircraft Trajectory Prediction (ATP) model.  
Figure 1: Air Corridor Containing an Aircraft Alongside its GPS Data.  
Deep neural network architectures can work efficiently if they are provided with a large and rich database. To obtain such a database, we used our UAS-S4 simulation model to generate a large number of aircraft trajectories. Figure 3 shows the Hydra Technologies UAS-S4 Ehecatl, and Table 1 gives its geometrical and flight data specifications [44].  
The aircraft trajectories' database was created using a simulator that was designed based on support vector regression flight dynamics model [45; 46] and its robust adaptive fuzzy controller [47; 48].
## 3 Controller Design  
### Control System Architecture  
As shown in Figure 3, the USV motion control system consists of three parts: the controllers, the controlled objects, and the state estimator. Among them, the state estimator fuses the measured information from sensors and outputs the estimated values of the USV's motion status. The controller provides three control modes that combine speed control: yaw rate mode, heading mode, and path-following mode. The speed controller receives the commanded surge velocity \(u_{\text{c}}\) and estimated surge velocity \(\dot{u}\), and outputs the commanded thrust in surge \(T_{\text{xc}}\). In the yaw rate mode, the yaw rate controller receives the commandedyaw rate \(r_{\rm c}\) and estimated yaw rate \(\hat{r}\), and outputs the commanded azimuth angle \(\delta_{\rm c}\). In the heading mode, the heading controller receives a commanded yaw angle \(\psi_{\rm c}\) and estimated yaw angle \(\hat{\psi}\), and outputs \(\delta_{\rm c}\). In the path-following mode, the guidance module receives the planned waypoints and estimated vessel position \((\hat{x},\hat{y})\), calculates the commanded yaw angle \(\psi_{\rm c}\), and outputs it to the heading controller, and then the heading controller outputs \(\delta_{\rm c}\). According to \(T_{\rm xc}\) and \(\delta_{\rm c}\), the commanded thrust \(T_{\rm c}\) is calculated from Equation (8). The two padded thrusters receive \(T_{\rm c}\) and \(\delta_{\rm c}\), and the thrust vector \(\tau\) containing the thrust and yaw moment is output, thereby achieving motion control of the USV. For the sake of concise writing, in the formulas and simulation curves in the subsequent sections, the symbols \(\hat{\eta}=[\hat{x},\hat{y},\hat{y}]^{\rm T}\), and \(\hat{v}=[\hat{u},\hat{v},\hat{r}]^{\rm T}\) for the estimated values of the motion parameters will be represented as \(\eta=[x,y,\psi]^{\rm T}\) and \(v=[u,v,r]^{\rm T}\), respectively.
# CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications
## 1 Introduction
Therefore, in both cases, timely congestion detection and taking an appropriate step for avoiding its consequences is important, which is the main focus of this paper. There are two general approaches to solve the congestion problem: resource control and traffic control [6]. In the resource control strategy, selection of an alternative non-congested or less-congested path towards the destination is employed. Unlike the resource control method, traffic control implies solving congestion by adjusting the traffic rate at source nodes or intermediate nodes. However, for an industrial monitoring system, reducing the sending rate of nodes is not acceptable in an emergency scenario IoT application. Therefore, considering these facts, the main objective of this paper is to design congestion-aware RPL (CoAR) that is suitable for dynamic and high traffic load. To achieve this, we mainly focus on three aspects that are the resource-control based congestion alleviation method, adaptive congestion detection mechanism and multi-criteria decision-making (MCDM) technique for parent selection method. The main contributions of this paper can be summarized as follows:  
* This paper presents a congestion-aware routing protocol named CoAR, that addresses the congestion problem for IoT applications in LLN. It adopts the resource control strategy to fulfill the application requirements in emergency scenarios. * This paper suggests a new objective function, known as a congestion-aware objective function (CoA-OF). CoAR implements it as the parent selection framework. It solves the parent selection problem within the congestion by implementing MCDM technique, which utilizes the multiple routing criteria. * We propose a simple yet effective congestion detection mechanism based on an adaptive threshold. It allows the affected nodes to avoid transient congestion situation, and then to eliminate the persistent congestion situation, it triggers the parent change procedure to create the alternative path towards the root. * We implement and analyze the proposed routing protocol, and conduct extensive simulations to validate the overall performance through Cooja simulator. The rest of the paper is organized as follows. Section 2 presents a review of the related works. Section 3 explains the RPL and formulates the problem. Section 4 describes the proposed protocol in detail. Section 5 evaluates the performance of the protocol in the different scenarios and presents the results with a comparison. Section 6 discusses the key points of the work and some design limitations. Finally, Section 7 concludes the paper.
## 3 Path Generation and Optimization Scheme  
After obtaining the lane-changing position information, we perform path planning starting from this position. Subsequently, we construct the solution space for paths utilizing the B-spline curve. On this basis, a cost function that takes both the path length and the average curvature into consideration is designed. Then, we employ PSO to optimize the path and generate a set of optimal path candidates.
## 6 Factors Affecting the CTE of Concrete
### Relative Humidity and Curing Conditions  
[PERSON] et al. [79] found that CTE values peak between 70% and 80% RH due to maximal capillary pressure, with a 3% difference at 100% RH, while [75] observed similar results with the highest CTE at 85% RH and the lowest at 100% RH and below 50%; other studies also report CTE being low at low RH, peaking around 70%, and decreasing at higher RH, reaching a minimum when water-saturated [80,81]. Therefore, we can conclude that CTE values peak between 70% and 80% RH and decrease at both higher and lower RH levels. Rapid temperature spikes from cement hydration, followed by uneven cooling, make early-age concrete particularly vulnerable to thermal cracking. The chance of thermal cracking can be decreased by managing the CTE. As the temperature rises, the gel water expands, increasing the meniscus' radius, and according to the Kelvin equation, a larger meniscus results in less surface tension and more relative humidity.  
CTE can be decreased by keeping the RH in pores near 100%. Internal curing techniques employing saturated lightweight aggregates or superabsorbent polymers can accomplish this. The benefit of internal curing is that it helps reduce autogenous shrinkage and CTE [82]. Therefore, while choosing a suitable CTE for design, the study's findings highlight the necessity of establishing a standard curing time [83].
# Low-Latency QC-LDPC Encoder Design for 5G NR
## 3 Design of 5G QC-LDPC Encoder
### Execution Pipeline Scheduling  
When the maximum code length is not over the length defined by base graph, the parallel operation process of the encoder is shown in Figure 6, which can be divided into five calculation parts and three steps. The first clock pipeline is for the generation of coding parameters and CRC calculation of TB, the second pipeline is the parallel calculation of \(AS^{T}\), \(P_{1}\) and \(CS^{T}\), and the third pipeline is the calculation of \(DP_{1}^{T}\). The latency of the whole encoding process includes CRC calculation and Part II calculation, and the clock cost of LDPC encoding is determined by Part II.  
Figure 7 is the flow chart of 8424 bits data encoding operation under BG1. Firstly, the parameters required for encoding are obtained from the transport block data in the encoding parameter module of the encoder. The bit stream is transmitted to the CRC calculation module through the FIFO module, and the CRC is attached to the TB to form the TBS. Then, the elements of the matrix \(A\) and matrix \(C\), \(D\) are read to complete the encoding calculation in Part I and Part II respectively. After obtaining two groups of parity code blocks, the filler bits of codeword are processed in the codeword output processing module of the encoder; the invalid bits will be removed and the first two columns of information bits and parity bits are punctured. Finally, the information code blocks and the parity check code blocks are connected as the encoder output.  
The control circuit of the encoder issues the overall control of the operation of the encoder, so that each calculation module can orderly perform their own tasks. In this design, the state machine and control signal are used to control the operation of the encoder. It mainly realizes the control signal in sequence to all modules. Figure 8 is the pipeline diagram of the hardware structure of the encoder.  
Figure 6: Parallel operation process of encoder.  
Figure 7: Encoder operation flow chart.  
After the parameters are calculated and information bits are stored in SRAM, finite state machine (FSM) issues CRC calculate enable signal. When CRC calculation is completed, the encoder is enabled, the parity check matrix is read in, and the information encoding is carried out. After completing the Part I operation, the calculation of \(P_{1}\) starts. After obtaining the first set of parity codes, the hardware waits for the completion of the first round running of Part II, and the input information is switched to \(P_{1}\) to calculate the second set of parity codes. When all parity codes are obtained, the encoding end signal is enabled to reset the FSM. The encoder returns to the initial state to perform the encoding of the next frame of information data.
###### Abstract  
Dive computers have an important potential for citizen science projects where recreational SCUBA divers can upload the depth temperature profile and the geolocation of the dive to a central database which may provide useful information about the subsurface temperature of the oceans. However, their accuracy may not be adequate and needs to be evaluated. The aim of this study is to assess the accuracy and precision of dive computers and provide guidelines in order to enable their contribution to citizen science projects. Twenty-two dive computers were evaluated during real ocean dives for consistency and scatter in the first phase. In the second phase, the dive computers were immersed in sufficient depth to initiate the dive record inside a precisely controlled sea aquarium while using a calibrated device as a reference. Results indicate that the dive computers do not have the accuracy required for monitoring temperature changes in the oceans, however, they can be used to detect thermoclines if the users follow a specific protocol with specific dive computers. This study enabled the authors to define this protocol based on the results of immersion in two different sea aquarium tanks set to two different temperatures in order to simulate the conditions of a thermocline.  
Keywords:diver; underwater; SCUBA; temperature measurement +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Comprehensive Scoring Criterion Using EWM  
To validate the correctness of this objective evaluation method, the EWM was employed to objectively consolidate the scoring criteria of ten parameters into a comprehensive score. The EWM is an effective approach for calculating comparative scores as it utilizes entropy values to assign weights to various indicators. This objective weighting technique minimizes the influence of subjective judgments, thereby more fairly reflecting the importance of each evaluation criterion and ensuring a scientifically grounded weight distribution [39; 40]. The method for obtaining the comprehensive score using the EWM is as follows:  
Firstly, the scores for each influencing factor were first normalized to eliminate the impact of varying units. The normalized score \(s_{i}(j)\) is calculated as follows:  
\[s_{i}(j)=\frac{S_{i}(j)-\min S_{i}}{\max S_{i}-\min S_{i}}, \tag{13}\]  
where \(S_{i}(j)\) is the original score of the \(j\)-th participants on the \(i\)-th score, and \(\min S_{i}\) and \(\max S_{i}\) are the minimum and maximum scores of the \(i\)-th score, respectively. Here, \(i\) ranges from 1 to 10, which is from \(S_{1}\) to \(S_{10}\). \(j\) also ranges from 1 to 10, which is from participant 1 to participant 10.  
Figure 4: Illustration of the tolerance threshold for handwriting trajectory quality during the drawing tasks. The section enclosed within the dashed frame is magnified tenfold and shown on the right. The tolerance threshold is the distance from the border to the dotted line, measuring 10 pixels (0.96 mm) on the tablet. The distance from the \(p\)-th sampled point on the trajectory to the dotted line is denoted as \(d(p)\). The scores for evaluating handwriting quality are given in Equations (9) and (10).  
Secondly, the entropy \(E_{i}\) for each score is calculated with the formula, using the normalized data:  
\[E_{i}=-k\sum_{i=1}^{m}p_{i}(j)\ln{(p_{i}(j))}, \tag{14}\]  
where \(p_{i}(j)=\frac{s_{i}(j)}{\sum_{i=1}^{m}s_{i}(j)}\), \(k=\frac{1}{\ln{n}}\), and \(n\) is the number of participants (\(n=10\)). Lower entropy values indicate greater variability and richer information content of the criterion.  
Next, the weight \(w_{i}\) for each score is determined based on its entropy value using the formula:  
\[w_{i}=\frac{1-E_{i}}{\sum_{i=1}^{m}(1-E_{i})}, \tag{15}\]  
where \(m\) is the total number of score criteria, \(m=10\).  
Finally, the comprehensive scoring criterion \(S(j)\) is calculated using the determined weights and the original scoring data, as follows:  
\[S(j)=\sum_{i=1}^{m}w_{i}S_{i}(j). \tag{16}\]  
This comprehensive score \(S(j)\) obtained using EWM was then compared with the comprehensive score from the subjective evaluation in Section 2.3.3 to assess the consistency and validity of the objective method. All scoring criteria of the objective method in this study are shown in Table 3.
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 5 Results and Discussion
At other higher considered frequencies, the outage ratio increases with the increase in BD-reader distance. However, it is fascinating to see that even at 900 MHz frequency, both LoRa and NB-IoT offers zero outage ratio for BD-reader distance until 35 m, it means that LoRA backscatter and NB-IoT can be effectively used for those outdoor AmBC applications that do not require placing the reader much further away e.g., less than 35 m from the BD. However, Figure 9c shows that in an outdoor environment the BLE 5 is only effective for very short distance between the BD and the reader i.e., up to 10 m at 200 MHz, otherwise the outage ratio is found to be quite high for BLE 5 at higher frequencies. Whereas, for indoor BDs, the BLE 5 is found to be highly ineffective due to a significantly higher outage ratio, as shown in Figure 9f. LoRa and NB-IoT show impressive outage results, even for indoor BDs operating at 200 MHz frequency, and a zero outage is ensured with LoRa for BD-reader distance of up to 60 m, whereas the NB-IoT shows the outage ratio of only 1.4% at 60 m BD-reader distance. LoRa still shows promising outage results at 500 MHz in a low-loss building type. Clearly, for NB-IoT, the 700 MHz and 900 MHz bands are not suitable for the cellular AmBC system with indoor BD and reader; however, LoRa backscatter can be used for short range up to 20 m BD-reader distance with a low outage ratio. Figure 9: _Cont._
## 1 Introduction  
Field Programmable Gate Arrays (FPGAs) integrated circuits (ICs) are a crucial key electronic component embedded in today's sophisticated electronic systems, primarily due to their excellent programmable and reconfigurable capabilities combined with relatively low non-recurring engineering cost (NRE) and short design cycle. FPGAs' advantages quickly supersede that of application-specific of IC (ASIC) [1, 2]. Additionally, the inclusion of specialized hardware blocks, such as hardcore, and the ability to implement softcore in the FPGA makes the FPGA more commonly adopted in many critical application domains, such as industrial, spacecraft, automotive, networking, and prototyping. However, as with many other semiconductor devices and integrated circuits, FPGAs also suffer from induced errors and reliability issues, which may jeopardize confidential and sensitive information. Therefore, it is paramount to guarantee the electronic system's security by ensuring the FPGA device is reliable against the variation of operating parameters, namely temperature and static signal probability.  
Physically unclonable function (PUF) is a fundamentally new hardware security system that promises a model shift in numerous security applications; its relatively simple architecture can avoid various data breaches. Some security mechanisms have currently used the concept of PUF on deep-submicron silicon materials due to the presence of intrinsically unique [3]. The main principle of PUF is to take advantage of its self-existing variations in integrated circuit production processes. The difference, such as the delay of each transistor, is random and unpredictable. Therefore, there are no identical chips in nanoscale technology. Due to this, PUF is suitable to be the digital identity of an integrated circuit or device [3].  
Various types of PUF have been developed, though detailed analyses of stability and reliability are substantially lacking. Among others, PUF can be classified as follows: memory-based PUF; PUF based on delay [4]; and physical PUF. However, the grouping of the PUFs is not finalized, as researchers are continuously looking for new and better PUF models. The following are some of the PUF models in which operations are based on the principle of memory; SRAM-PUF [5], latch PUF [6], butterfly PUF [7], and flip flop PUF [8; 9; 10]. Meanwhile, PUF, the operation of which is based on delay, is the ring oscillator PUF (RO-PUF) [11; 12] and arbiter PUF [13]. The RO-PUF improvement research continues due to its simple structure, ease of generating responses, and its promises of primitive security.
## 2 Materials and Methods  
### Reagents  
Simazine (SIM), cyanazine (CYA), atrazine (ATZ), proparzine (PPZ) and terbutylazine (TER) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Stock standard solutions (1 g L\({}^{-1}\)) were prepared in acetonitrile and stored at \(-\)2 \({}^{\circ}\)C. HPLC-grade toluene, acetonitrile (ACN), methanol (MeOH), and acetone were purchased from Scharlab (Barcelona, Spain). Methacrylic acid (MAA), divinylbenzene-80 (DVB-80) and 2,2\({}^{\prime}\)-azo-bis-isobutyronitrile (AIBN) were purchased from Sigma-Aldrich (St. Louis, MO, USA). MAA was freed from stabilizers by distillation under reduced pressure, and DVB-80 was purified with a neutral alumina column (Aldrich, St. Louis, MO, USA). AIBN was recrystallized within MeOH prior to use. All other chemicals were used as received. Purified water was obtained from a Milli-Q purification system (Millipore, Molsheim, France). Acetic acid (HOAc) was purchased from Panreac (Barcelona, Spain). The Q3/2 polypropylene hollow fibre used to support the organic phase was purchased from Membrana (Wuppertal, Germany) with an internal diameter of 600 \(\upmu\)m, 200 \(\upmu\)m of wall thickness and 0.2 \(\upmu\)m pores. Sample stirring was performed in a Vibramax 100 agitator (Heidolph, Kelheim, Germany). The ultrasonic water bath (290 W, 50/60 Hz, S 40 H Elmasonic) was supplied by Elma (Singem, Germany).