esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Wood Densification and GFRP Reinforcement on the Embedment Strength of Poplar CLT
## 2 Materials and Methods
### Statistical Analysis  
The data was assessed statistically as a complete factorial design consisting of two factors using the SPSS 25 program. The densification ratio (in three levels: 0, 25, and 50) and two CLT loading directions (L and T directions) were the two factors that were taken into consideration for the statistical analysis. ANOVA test used to analyze those factors statistically through the main and interaction effects. Afterward, the results (means) of densified CLT samples were compared to those reinforced with GFRP. Four different sample groups, each with ten replicates, were put through their paces throughout the testing process. These sample groups included control (unreinforced) CLT specimens, CLT samples with 25% densified layers, CLT samples with 50% densified layers, and CLT samples reinforced with GFRP, all of which were loaded in both the longitudinal (L) and transverse (T) directions. The statistical differences between the means were analyzed using the multiple range test developed by [PERSON] with a confidence level of 95%.
# Using Data Mining in Educational Administration: A Case Study on Improving School Attendance
## 3 Problem Statement and the Analytical Model  
### Problem Statement  
It is well documented that providing pupils with the right incentives to attend school results in improved attendance and consequently improves pupil attainment and progress [1,4]. Given this, the problem being addressed by this study may be stated as follows: Let \(S\) be a school with all its pupils, \(U\). Let the school week, \(J\), be divided into \(m\) distinct sessions, \(J_{i}\), such that \(J_{i}\in J=\{J_{1},J_{2},\ldots,J_{m}\}\). Furthermore, let \(T\) be a database in \(S\), that contains the attendance records of all pupils across all sessions for a period, \(W\). Hence, there may exist a database, \(T_{t}\), where \(T_{t}\subseteq T\), that contains the attendance records of pupils \(U_{t}\), where \(U_{t}\subseteq U\), who have below the required attendance in at least one school session and/or the overall average attendance, but where attendance in all other sessions are above or equal to the requirement. In the UK, the required attendance target is 96% [4]. Given that the leadership and staff of the school \(S\) are intent on maximising pupil attendance (with the focus on driving up the overall average pupil attendance through incentives and interventions) while minimising effort and associated costs (largely incentives and staff costs), it becomes necessary to optimise the targeting of \(J_{i}\). Thus, this study aims to provide a framework and useful tool for schools, based on ARM and Frequent Itemset Mining (FIM), for targeting the right school session(s) with incentives and interventions that maximises the impact on improved overall school attendance.
## 2 Modeling of Skewed Integral Abutment Bridges
### Modal Analysis and Sensitivity Studies  
Model analyses were performed on 16 SIAB models, in which the skew angle varied from \(0^{\circ}\), \(15^{\circ}\), \(30^{\circ}\) to \(45^{\circ}\), and the abutment backfill varied from no-backfill (soil-free) to loosely, medium-densely and densely compacted sand. The first six mode shapes for Model 10, the prototype bridge model with a \(15^{\circ}\) skew and densely compacted backfill, are shown in Figure 4. As shown in the figure, when there is a skew angle, the major vibration modes of integral abutment bridge include transverse translation, vertical bending, in-plane rotation, longitudinal translation, rotation about the longitudinal axis (X), and rotation about the transverse axis (Y) etc., and the first vibration mode is transverse translation.  
Table 2 shows the period, mode shape and mass participation ratio in the longitudinal (X) and transverse (Y) direction for the first longitudinal mode of the 16 SIAB models.  
Figure 4: Mode shapes of Model 10.  
Note that the longitudinal mode means that the mass participation ratio in the X direction is generally higher than the Y direction. Although the longitudinal modes were not necessarily the first mode, they were chosen to be presented because backfill stiffness is one of the main parameters, and the longitudinal modes will better reflect the influence of this parameter.  
As shown in Table 2, when the skew angle increases, the mass participation ratio in the X direction decreases, and the mass participation ratio in the Y direction as well as the period of the first longitudinal mode increases. In addition, the mode shape changes from longitudinal translation (LT) to a combination of longitudinal and transverse translation (LT + TT). This is mainly because when the skew angle increases, the longitudinal lateral stiffness of the bridge provided by the abutment backfill decreases, and there is more obvious coupling between the longitudinal and transverse displacements.  
As is also shown in Table 2, when the backfill compactness increases, the mass participation ratio of the first longitudinal mode in the X direction as well as the period, decreases, since the longitudinal lateral stiffness of the bridge provided by the abutment backfill increases. When the backfill changes from soil-free to densely compacted sand, the period decreases over 50%. The backfill condition also affects the influence of the skew angle. When there is no backfill (soil-free), the mode shape is mainly the longitudinal translation with a mass participation ratio over 89% in the X direction, and the skew angle has an almost negligible influence on the period or the mass participation ratio.
# Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector
## Materials and Methods
### Methodology
#### 3.2.2 SSD Model  
The core of the SSD method is the use of a small convolution filter to predict the class score and position offset of a fixed set of default bounding boxes on the feature map and clearly separate the prediction using the aspect ratio. Figure 4 shows the network model [11].  
VGG-16 was used as the basic network to extract the feature information of the image, connecting multiple auxiliary convolution layers of decreasing size after VGG16 network. In order to obtain multi-scale detection prediction values, SSD selected six extra feature maps for detection, namely Conv4_3, Conv7, Conv8_2, Conv8_2, Conv9_2, Conv10_2, and Conv11_2. This way, we reduced the computational and memory requirements and the translation and scale invariance of the feature map on a certain scale. The specific position of the SSD design feature map is responsible for a specific area in the image and a specific object size. If we use \(m\) feature maps to make predictions, then the default box size in each feature map is calculated as follows:  
\[S_{k}=S_{min}+\frac{S_{max}-S_{min}}{m-1}(k-1)\ \ k\in[1,m], \tag{1}\]  
where \(S_{min}\) = 0.2 and \(S_{max}\) = 0.95, thereby representing the lowest and highest layer scales of 0.2 and 0.95, respectively.  
If \(\partial_{r}=\left\{1,2,3,\frac{1}{2},\frac{1}{3}\right\}\) represents the different aspect ratios of the default boxes, then the width and height of each default box are as follows:  
\[W_{k}^{a}=S_{k}\sqrt{a_{r}}, \tag{2}\]  
\[H_{k}^{a}=S_{k}/\sqrt{a_{r}}. \tag{3}\]  
The SSD network was based on the previously feed forward neural network (FFNN). It is capable of generating a set of fixed-size target location sets and target category scores for objects that exist in those target frames, and then using non-maximum suppression to produce the final results. In addition to the VGG16 base layer network, each additional feature layer used a set of convolution filters to obtain a fixed set of results. These convolution kernels either generated a score for a class or an offset from the object's default frame position coordinates.
## 4 Signal Feature Extraction Method Based on Intrinsic Mode Function Energy Permutation Entropy
### Permutation Entropy Algorithm Based on Intrinsic Mode Functions  
Since the signal after EMD can be divided into multiple IMF time series, assume that the IMF of a certain time series signal after EMD is represented by function  
Figure 7: Spectral detection of different decomposition methods: (**a**) time-domain signal after EWT decomposition; (**b**) the result of target line spectrum extraction of EWT; (**c**) time-domain signal after EMD; (**d**) the result of target line spectrum extraction of EMD; (**e**) time-domain signal after EEMD; (**f**) the result of target line spectrum extraction of EEMD; (**g**) time-domain signal after ICCEMDAN decomposition; and (**h**) the result of target line spectrum extraction of ICCEMDAN.  
\(C(t)=\{c(t_{1}),c(t_{2}),\cdots,c(t_{n})\}n=1,2,\cdots N.\) The reconstructed space of this function can be defined as  
\[\left[\begin{array}{cccc}c(1)&c(1+\tau)&\cdots&c(1+(m-1)\tau)\\ \vdots&\vdots&\vdots\\ c(j)&c(j+\tau)&\cdots&c(j+(m-1)\tau)\\ \vdots&\vdots&\vdots\\ c(K)&c(K+\tau)&\cdots&c(K+(m-1)\tau)\end{array}\right](j=1,2,\cdots,K) \tag{2}\]  
where \(\tau\) is the time delay, and \(m\) is the embedded dimension, corresponding to the number of elements in each row vector of the matrix. Each row vector in the matrix can serve as a reconstruction component, and \(K\) denotes the number of reconstructed vector subsequences. The total number of reconstructed row vectors is \(n-(m-1)\tau\) and each row vector is arranged in ascending order.  
Next, each reconstructed subsequence is sorted in ascending order, i.e.,  
\[c(i+(j_{1}-1)\tau)\leq c(i+(j_{2}-1)\tau)\leq\cdots\leq c(i+(j_{m}-1)\tau) \tag{3}\]  
If two subsequences in the vector are identical:  
\[c(i+(j_{1}-1)\tau)=c(i+(j_{2}-1)\tau) \tag{4}\]  
The original sequence can be sorted accordingly:  
\[c(i+(j_{1}-1)\tau)\leq c(i+(j_{2}-1)\tau)\quad(j_{1}\leq j_{2}) \tag{5}\]  
In conclusion, any time series signal \(C(t)\), it can be mapped to a symbol sequence:  
\[S(g)=(j_{1},j_{2},\cdots,j_{m}) \tag{6}\]  
where \(g=1,2,\cdots,l\) and \(l\leq m!\) are one of the permutations among \(j_{1}\) to \(j_{m}\) in \(m!\) arrangements, and each subsequence \(c(t)\) of dimension \(m\) can be mapped to the symbol function \(S(g)\). If the probability distribution of all symbols is denoted as \(p_{1},p_{2},\cdots p_{l}\), the probability entropy (PE) of the symbol sequence of the time series can be represented as follows:  
\[H_{p}(m)=-\sum_{j=1}^{l}p_{j}\ln p_{j} \tag{7}\]  
When \(P_{j}=1/m!\) occurs, \(H_{p}(m)\) reaches its maximum value \(\ln m!\), and it is typically normalized as follows:  
\[0\leq H_{p}(m)/\ln m!\leq 1 \tag{8}\]  
where the value of \(H_{p}(m)\) represents the time series randomness. A lower value of \(H_{p}\) indicates a more regular time series, while a higher value suggests a more irregular time series, containing a greater amount of information.
# Numerical Analyses of Ultrasonic Atomization Utilizing Acoustic Effects of a Beam Diaphragm
## 5 Conclusions  
Numerical analyses of an oscillating body in previously conducted experiments of atomization were conducted for a better understanding of the mechanisms of atomization. In the experiment, an ultrasonic transducer indirectly excited a beam diaphragm through water. A water film formed on the surface of the diaphragm and oscillated together with the diaphragm, and atomization only occurred in the experiment with the hydrophilic diaphragm. Numerical models using the finite element method, considering the interaction between the diaphragm and water film, were developed. From the comparison of the results of the experiments and numerical analyses, the following conclusions were obtained.  
* The onset of atomization was indicated as a result of the superposed acceleration of the primary wave from the ultrasonic transducer and extra acceleration due to the interaction between the resonating diaphragm and the water film.
* Only the higher range of the resonance frequencies of the diaphragm (more than 10.0 kHz in the present case) contributed to the atomization onset.
* Atomization required the hydrophilic surface of the diaphragm to provide sufficient acceleration to the surface of the water film. The hydrophobic diaphragm was not suitable for atomization.  
In future studies, the amplitude of the diaphragm will be experimentally measured for a more precise evaluation of the superposed acceleration of the surface wave and oscillating diaphragm. Methodology to quantitatively estimate energy dissipation due to interaction between the diaphragm and water film is being actively sought.  
Conceptualization, A.K. and H.M.; methodology, A.K.; writing--original draft preparation, A.K.; writing--review and editing, H.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in [26].  
The numerical analyses in the present study were conducted on computer resources offered by the Institute for Information Management and Communication, Kyoto University.  
The authors declare no conflicts of interest.
## 3 Results
### Electro-Mechanical Impedance  
Figure 4 shows the change of EMI signal of PZT sensor embedded in the mortar samples according to material age. The EMI resonance peak amplitude continued to decrease over time without significant change in the EMI resonance peak frequency until about 6 h after casting. After that, the EMI resonant peak frequency moved to the high frequency region. Afterwards, the EMI resonant peak disappeared at a specific time point.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Initial Setting Time** & **Final Setting Time** & **Time Gap between the Initial** \\  & **(min)** & **(min)** & **Set to the Final Set (min)** \\ \hline Plain & 371 & 573 & 202 \\ F-0.5 & 249 & 440 & 191 \\ F-1.0 & 213 & 406 & 193 \\ F-2.0 & 172 & 385 & 213 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Setting times of fiber-reinforced mortar determined by ASTM C 403.  
Figure 3: Penetration resistance as a function of the age: (**a**) Plain; (**b**) F-0.5; (**c**) F-1.0; (**d**) F-2.0.  
Figure 4: EMI change of PZT sensor according to the material age: (**a**) Plain; (**b**) F-0.5; (**c**) F-1.0; (**d**) F-2.0.  
Figure 5 shows the change in EMI resonant peak frequency with time. The EMI resonance peak frequency was not changed significantly at the early stage of hydration of the mortar but started to increase from a specific time point, and then the resonance peak disappeared. In general, when the stiffness of material contacted on PZT sensor increases, the EMI resonant peak frequency moves to a higher frequency range [9; 10; 11]. Cement paste exhibits rapid stiffness increase after the initial set during the hydration process. Therefore, the time point at which the EMI resonance peak frequency starts to increase was considered as the initial setting time. A total of 10 min before the resonance peak disappeared was considered to be the final setting time.  
Table 3 shows the initial and final setting times of the mortar samples. The initial setting times of the SFRM did not show a significant difference compared to that of the plain mortar (less than 20 min). The final setting times of the mortars with fibers neither show significant difference compared to that of the plain mortar (less than 20 min). The time gaps between the initial setting time and the final setting time of all the mortar samples were almost identical.  
Figure 5: EMI resonance peak frequency as a function of the material age: (**a**) Plain; (**b**) F-0.5; (**c**) F-1.0; (**d**) F-2.0.
## 5 Conclusions  
The proposed D*-KDDPG method effectively improves DDPG by integrating kinematic analysis and the D* algorithm, significantly enhancing the model's path planning quality and safety while avoiding local optima issues. The proposed method significantly improves path planning efficiency in training and experiments. Training experiments indicate that D*-KDDPG achieves convergence with only 26.7% of the training steps required by DDPG and a higher navigation success rate. For experiments, D*-KDDPG outperforms DDPG and KDDPG with a higher success rate, faster planning speed, and more secure paths for static and dynamic scenarios. In static scenarios, D*-KDDPG shows only a marginal increase in path length and navigation time (less than 3.98%) compared to D*-DWA while increasing the safety distance by 67.3%. Also, D*-KDDPG demonstrates higher navigation efficiency than D*-DWA in dynamic scenarios since it achieves a 16.2% reduction in navigation time, improved curvature smoothness, and a 72.1% increase in safety distance. Future research will focus on improving the robustness of the algorithm and the fusion of sensor data to optimize the robot's path-planning performance.  
W.L.: conceptualization; writing--original draft. C.L.: methodology, validation. D.Z.: software. X.S.: writing--review and editing and formal analysis. Y.H.: supervision. X.M.: project administration. X.Y.: funding acquisition. X.W.: computational analysis. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science Foundation of China, grant number 52105574, and Major Science and Technology Projects of Longmen Laboratory, grant number 231100220500.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Towards an Optimized Blockchain-Based Secure Medical Prescription-Management System
## 6 Conclusions  
In conclusion, this paper introduces a distributed application tailored for the pharmaceutical domain, revolutionizing medical prescription management through the integration of a blockchain-based web application. The framework incorporates an IoT system adept at QR code reading, enhancing security and transparency in the pharmaceutical landscape. By meticulously assessing the energy consumption, our study highlights the advantages of utilizing the Quorum blockchain platform, employing its QBFT consensus mechanism. Notably, this approach demonstrates a 5% increase in efficiency compared to Ethereum's PoW protocol.  
In the pursuit of in-depth insights, a comprehensive performance evaluation was conducted on the Quorum blockchain with the QBFT consensus mechanism. Utilizing network configurations encompassing 5, 10, and 20 nodes, alongside varying send rates, our analysis delved into the nuanced interplay between network size, transaction velocity, and system performance. Through the interpretation of the throughput and latency results, we shed light on the scalability and responsiveness of the Quorum blockchain in managing medical prescriptions, thereby providing valuable guidance for the integration of blockchain technology within the pharmaceutical sector.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **VigilRx**[22] & **Safe Prescription [1]** & **[13]** & **[14]** & **Proposed Solution** \\ \hline Decentralization & YES & YES & NO & NO & YES \\ \hline BC platform & Ethereum & Ethereum & — & — & Quorum \\ \hline Mode of Operation & public & private & — & — & private \\ \hline Transparency & YES & YES & NO & NO & YES \\ \hline Traceability & YES & YES & NO & NO & YES \\ \hline Privacy & YES & YES & NO & NO & YES \\ \hline Throughput & NO & NO & NO & NO & YES \\ \hline IoT integration & NO & NO & NO & YES & YES \\ \hline Energy consumption & \multirow{2}{*}{NO} & \multirow{2}{*}{NO} & \multirow{2}{*}{NO} & \multirow{2}{*}{NO} & \multirow{2}{*}{YES} \\ measurement & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between our proposed solution and existing solutions.  
Figure 12: Throughput and latency of the function getPresc.  
**Author Contributions:** Methodology, M.T. and M.B.; Software, I.A. and M.T.; Validation, M.B., B.D. and A.A.; Formal analysis, I.A. and M.T.; Investigation, M.T.; Resources, I.A. and A.A.; Data curation, I.A.; Writing--original draft, I.A. and M.T.; Writing--review & editing, M.B. and B.D.; Visualization, B.D. and A.A.; Supervision, M.B.; Project administration, M.B.; Funding acquisition, B.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research project was funded by the Deanship of Scientific Research, Princess Nourah bint Abdulrahman University, through the Program of Research Project Funding After Publication, grant No. (44- PRFA-P- 137)  
**Data Availability Statement:** The data presented in this study are available in this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Low-Temperature and Low-Pressure Distillation Plant for Dairy Wastewater
## 1 Introduction
The following attributes of vacuum distillation of whey wastewater have not yet been quantified:  
* The degree of purification achievable on the polluting load;
* The possible recovery of valuable products (as whey proteins have excellent functional properties, they are widely utilized in the food industry);
* The overall energetic cost of the treatment per unit of raw, processed product. Moreover, the small dairy industry is continuously searching for inexpensive treatment for purification and recovery of by-products that could be economically utilized to increase its overall income. As recovery plants can become expensive when installed in a small cheese-making factory, all the alternative treatments, competitive in comparison with other available systems, are greatly appreciated. This work investigates the existing gap in the literature related to raw whey wastewater treatment using a system of vacuum distillation that also uses a low treatment temperature. This method could make the processes of purifying the polluting load and the recovery of whey protein more economically profitable. Therefore, in the present paper, an alternative treatment technology, based on a \"low-temperature and low-pressure vapor distillation\" plant has been proposed and applied to the cheese-making industry's wastewater effluent. The experimental results obtained in the treatment process demonstrate the process's suitability to purify the effluent wastewater and to recover whey products; indeed, this purification technology can recover high quality products (e.g., whey protein concentrate; lactose) thanks to its low thermal damage on the treated product and to its relatively low energy cost.
# Research on Wi-Fi Fingerprint Database Construction Method Based on Environmental Feature Awareness
## 1 Introduction
This research not only fosters the development of indoor localization and enhances the user experience, but also supports large-scale deployment in scenarios requiring extensive coverage, such as airports, shopping centers, and hospitals. It provides valuable insights and references for future research and practical applications, contributing to the advancement of an intelligent society. The structure and main contents of this paper are outlined as follows:  
Section 2 provides a review of the relevant background and related work on Wi-Fi fingerprint localization, with a detailed analysis of the challenges and limitations in current fingerprint database construction methods. Section 3 introduces the environment feature-aware model proposed in this paper, covering the model's design concept, feature selection, and data processing workflow. Section 4 presents the experimental design, data collection process, and result analysis, focusing on the model's performance evaluation and comparison of experimental outcomes. Finally, Section 5 summarizes the key contributions of this paper and offers prospects for the technique's application in the field of indoor localization.