esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Mechanical Testing of Yeast Films  
The evaluation of mechanical properties took into consideration the simultaneous influence of two variables in yeast films, increasing concentrations of plasticizers and emulsifier and grafted bacterial cellulose (Figure 6). Increasing concentrations of glycerol as plasticizer and Tween as emulsifier in yeast film forming solutions influenced the rigidity of both functionalized yeast films with bacterial cellulose and in the control samples, without CNC (Figure 6a). In general, the higher the plasticizer concentration, the lower the rigidity, the films being more flexible. A possible explanation for this effect could be the reduced formation of intermolecular van der Waals forces and increased formation of intermolecular bond between polymeric matrix and plasticizer, as indicated by [52].  
Grafting bacterial nanocellulose into yeast film increased elastic modulus compared to the control samples without CNC (Figure 6a), which could probably be determined by an improved continuity within biopolymeric matrix, producing a mobility reduction in particles, as reported by [53] for polyvinyl alcohol/chitosan films reinforced with 4% (\(w/w\)) bacterial cellulose.  
Tensile strengths of both control and Y-CNC films presented a linear decrease with increasing concentration of the plasticizer (Figure 6b), which correlates to their role in reducing intermolecular forces, previously reported by [17; 52].  
Figure 6: Mechanical properties of yeast films (**a**). Elastic modulus, (**b**). Tensile strength.  
As SEM analysis of grafted CNC yeast film surfaces indicated, the presence of irregularities on their surface made them rougher compared to the control ones, which was indicated to be often correlated to lower tensile strengths values in films [8]. Similar tensile strengths range values were reported by [PERSON]'s research group for 7% whey protein films functionalized with oregano oil and bacterial cellulose nanowiskers (2-10% \(w/w\) based on protein amount), of approx. 1.7 \(\pm\) 0.2 MPa. Other similar results were reported by [54; 55].
# Technologies and Principles of Hot Recycling and Investigation of Preheated Reclaimed Asphalt
## 3 RAP Batching Accuracy and Precision
### Materials  
RHMA mixtures were produced according to various JMF's. To produce AC 16 PD grade mixture, imported fillers (IF), crushed gravel sifting, reclaimed dust (RD), natural sand, various fraction crushed gravel, and 100/150 grade road bitumen were used. AC 22 PS grade RHMA mixture was produced from 0/2 mm and 0/5 mm fraction natural sand, 5/8 mm, 8/11 mm and 11/22 mm fraction gravel, and 50/70 grade road bitumen. Additives were not used for RHMA mixture production. The batch masses of all materials' finite batching in one recycled HMA mix batch, the mass of which according to JMF is 3000 kg, are presented in Table 2.  
Figure 6: Distribution of RAP separate batch masses. When its job mix formula (JMF) is as follows: (a)—750 kg; (b)—1500 kg.  
To produce RHMA mixture, crushed and sieved RAP, the size of which was not larger than 32 mm, were used. RAP was prepared according to the third principle and technological scheme (Figure 3). When designing the composition of RHMA mixture and determining JMF, RAP grading and hot fraction were evaluated. Several sample units were taken from the adjacent places of the sheltered stock of RAP and prepared for use. The material of several sample units was put into a subsample. In total, five subsamples were taken this way, each of which was tested separately in a laboratory. RAP investigation data are presented in Table 3.  
The mean of five subsamples was taken as RAP average composition. RAP homogeneity was estimated by standard deviation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Particle Size, mm**} & \multicolumn{6}{c}{**Subsample Number**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{5-7}  & **I** & **II** & **III** & **IV** & **V** \\ \hline \(>\)11.2 & 6.3 & 7.5 & 8.4 & 8.7 & 7.5 & 7.7 \\ \(>\)2 & 69.4 & 73.4 & 71.2 & 68.5 & 68.7 & 70.6 \\ \(0.063\)-2 & 30.4 & 26.5 & 28.6 & 31.3 & 31.2 & 29.2 \\ \(<\)0.125 & 0.5 & 0.3 & 0.2 & 0.3 & 0.3 & 0.3 \\ \(<\)0.063 & 0.2 & 0.1 & 0.2 & 0.2 & 0.1 & 0.2 \\ Bitumen & 4.78 & 4.73 & 4.86 & 4.81 & 4.43 & 4.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RAP composition, %.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**RHMA Mixture Group**} & \multirow{2}{*}{**RAP**} & \multirow{2}{*}{**IF**} & \multirow{2}{*}{**RD**} & \multicolumn{6}{c}{**Hot Aggregate Fractions, mm**} & \multirow{2}{*}{**Virgin Bitumen**} \\ \cline{5-5} \cline{7-10}  & & & & **0/2** & **2/5** & **5/8** & **8/11** & **11/16** & **11/32** \\ \hline AC 16 PD & 750 & 99 & 99 & 612 & 219 & 411 & 384 & 306 & - & 120 \\ AC 22 PS & 1500 & - & - & 141 & 87 & 297 & 144 & - & 780 & 51 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RHMA mixture batch composition according to JMF (finite batching materials’ batch mass in kg in one RHMA mix batch of 3000 kg).  
Figure 7: Distribution histograms, theoretical curves of normal distribution and statistical parameters of RAP batch masses in HMA batches of mixtures: (**a**)—AC 16 PD (750 kg); (**b**)—AC 22 PS (1500 kg).
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 2 Materials and Methods
### Method
#### 2.2.2 The Second Phase: The Implementation of Locating Algorithms
If the found point is not one of the route points or not valid for people to pass in the environment, then according to a table information application called Maps, the nearest equivalent to that point replaces the result of previous filters and then is sent to the customer. 2.2.3.The Third Phase: Combining Algorithms, Locating and Checking for Errors  
In this section, 4 final methods for locating through various implemented algorithms are selected to obtain the final results of the work. These 4 methods are listed as follows:  
1. Locating with WCL algorithm;
2. Locating with PWCL algorithm;
3. Locating with HYBRID;
4. Locating with HYBRID-MAPPED. The first two methods use only the stated algorithms to find the target point, but for the last two methods a combination of filters is selected. 2.2.3.1.HYBRID  
In this method, the outlier detection filter is first applied to the training data. Then, with more filters, the frequency of the end point is obtained. But if it is not found in this shared filter, location is performed through the PWCL method. 2.2.3.2.HYBRID-MAPPED  
In this method, in addition to applying all the filters of the previous method, the mapping filter is also applied on the passable points on the final result. 2.2.4. The Fourth Phase: Measurement of Locating Error  
In the operational phase of user location, at each point the user sends the target point and the signals received from the transmitters to the server; the output of each of the 4 methods of location is stored within a table called trace_logs with the measured error. The measured error is actually the distance from the target point to the point found by the algorithms, which is obtained by the Euclidean distance method on the Cartesian coordinate axis:  
(X\({}_{t}\), Y\({}_{t}\)) is the target point and (X\({}_{i}\), Y\({}_{i}\)) is the point found by the location algorithms. \[\mathbf{R}=\sqrt{\left(\mathbf{X}\mathbf{t}-\mathbf{X}\mathbf{p}\right)^{2}+\left(\mathbf{Y}\mathbf{t}- \mathbf{Y}\mathbf{p}\right)^{2}} \tag{4}\]  
Equation (4)--Euclidean distance measurement. Then, considering that each unit of Cartesian coordinates is equal to 40 cm, the Euclidean distance is multiplied by 0.4 to convert the error scale into meters. Finally, this process is executed for each point in all 4 algorithms and stored in the table.
## 5 Experiments
### Comparative Experiments on Bone Stick Preprocessing
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & \begin{tabular}{c} **Rank-** \\ **1/\%** \\ \end{tabular} & \begin{tabular}{c} **Rank-** \\ **5/\%** \\ \end{tabular} & \begin{tabular}{c} **Rank-** \\ **10/\%** \\ \end{tabular} & \begin{tabular}{c} **Rank-** \\ **15/\%** \\ \end{tabular} & \begin{tabular}{c} **Average/\%** \\ \end{tabular} & \
\begin{tabular}{c} **Params/M** \\ \end{tabular} &
\begin{tabular}{c} **TD/S** \\ \end{tabular} \\ \hline (1) ViLT-Large & 32.03 & 49.62 & 66.71 & 72.34 & 55.17 & 347.17 & 89.12 \\ (2) VLMo-base & 35.07 & 55.72 & 75.84 & 83.01 & 62.41 & 425.63 & 94.01 \\ (3) VR-L and R6 & 38.04 & 58.89 & 79.84 & 86.78 & 65.89 & 339.47 & 61.93 \\ (4) ViLT-Large and Bb & 36.62 & 57.60 & 78.20 & 85.36 & 64.45 & 454.52 & 94.29 \\ (5) VLMo-base and Bb & & & & & & & \\ (_normal contact_) & 37.70 & 58.48 & 79.17 & 86.02 & 65.34 & 532.98 & 100.13 \\ Test Set 1 & (6) VR-L and R6 and Bb & & & & & & \\ (_normal contact_) & 40.01 & 61.38 & 82.97 & 90.36 & 68.68 & 446.82 & 67.11 \\ (7) ViLT-Large and Bb & & & & & & & \\ (_Dynamic Cross-Feature Fusion_) & 39.02 & 60.13 & 81.40 & 88.57 & 67.28 & 475.46 & 97.46 \\ (8) VLMo-base and Bb & & & & & & & \\ (_Dynamic Cross-Feature Fusion_) & 41.72 & 62.83 & 84.10 & 91.27 & 69.98 & 553.92 & 112.67 \\ (9) VR-L and R6 and Bb & & & & & & & \\ (_Dynamic Cross-Feature Fusion_) & 46.71 & 67.84 & 87.26 & 94.73 & 74.13 & 467.76 & 67.54 \\ -_ours_ & & & & & & & \\ \hline (1) ViLT-Large & 26.12 & 44.31 & 61.70 & 65.79 & 49.48 & 347.17 & 481.25 \\ (2) VLMo-base & 28.24 & 47.83 & 66.95 & 73.64 & 54.16 & 425.63 & 535.86 \\ (3) VR-L and R6 & 33.34 & 54.66 & 75.84 & 83.25 & 61.77 & 339.47 & 357.19 \\ (4) ViLT-Large and Bb & & & & & & & \\ (_normal contact_) & 32.56 & 53.79 & 74.41 & 82.07 & 60.71 & 454.52 & 603.46 \\ (5) VLMo-base and Bb & & & & & & & \\ (_normal contact_) & 33.73 & 55.07 & 76.56 & 83.63 & 62.25 & 532.98 & 624.81 \\ Test Set 2 & (6) VR-L and R6 and Bb & & & & & & \\ (_normal contact_) & 35.71 & 57.51 & 79.31 & 85.13 & 64.42 & 446.82 & 390.02 \\ (7) ViLT-Large and Bb & & & & & & & \\ (_Dynamic Cross-Feature Fusion_) & 33.73 & 55.07 & 76.56 & 83.63 & 62.24 & 475.46 & 662.79 \\ (8) VLMo-base and Bb & & & & & & & \\ (_Dynamic Cross-Feature Fusion_) & 34.91 & 56.50 & 78.30 & 84.58 & 63.57 & 553.92 & 766.16 \\ (9) VR-L and R6 and Bb & & & & & & & \\ (_Dynamic Cross-Feature Fusion_) & 42.83 & 62.16 & 83.21 & 87.33 & 68.88 & 467.76 & 391.40 \\ -_ours_ & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between the proposed method and other pretrained large-scale models. “Average” denotes the mean matching accuracy of the model across Rank-1 to Rank-15 (expressed as a percentage). “Params/M” refers to the total number of model parameters, measured in millions. “TD/S” indicates the inference time (in seconds) required by the model on the designated test set.
# An IoT System for Remote Monitoring of Patients at Home
## 3 Structure of the Patient at Home Monitoring IoT System
In this study, the Protocol Converter module is added to the MessageHandler module for protocol conversion between ISO/IEEE 11073 messages and oneM2M messages. The proposed protocol conversion mechanism will be discussed in Section 5. The Resource Manager module consists of ResourceManager and FileHandler. The ResourceManager module performs operations (Create, Retrieve, Update, Delete) on resource trees, based on the received \begin{table}
\begin{tabular}{c c} \hline
**oneM2M Primitive Request Message** & **HTTP Message** \\ \hline Operation & Method in Header \\ To & Request-Target in Header \\ ResourceType & Content-Type in Header \\ RequestID & X-M2M-RI in Header \\ Content & Body \\ \hline \end{tabular}
\end{table} 
Table 1: Mapping between oneM2M primitive Request messages and HTTP messages. \begin{table}
\begin{tabular}{c c} \hline
**oneM2M Primitive Response Message** & **HTTP Message** \\ \hline responseCode & Status Code \\ RequestID & X-M2M-RI in Header \\ Content & Body \\ \hline \end{tabular}
\end{table} 
Table 2: Mapping between oneM2M primitive Response messages and HTTP messages. Figure 2: Internal structure of the oneM2M (Machine-to-Machine) IoT system. oneM2M primitive Request messages. It also stores the results of the operations in oneM2M primitive Response messages. The FileHandler module manages resource trees stored in physical data stores. There are two different communication protocols used in the proposed system. The ISO/IEEE 11073 communication protocol is used between PHDs (ADN-AE) and gateways (MN-CSE) for PHD communication, and the oneM2M communication protocol is used between the patient monitoring IoT server (IN-CSE) and gateways for IoT networks. It is natural for the ISO/IEEE 11073 communication protocol to be used for PHD-gateway communication in this paper because the protocol is a standard communication protocol for PHD communication. Similarly, the oneM2M communication protocol is a standard communication protocol for IoT systems, and it is also reasonable that the oneM2M communication protocol is used for the IoT server-gateway communication in this paper. Otherwise, ordinary IoT servers that naturally support the oneM2M standard communication protocol would not understand the message sent from PHDs. As you know, standard communication protocols are used to enable interoperability between different machines or equipment. Therefore, one of the gateway's responsibilities is to convert the ISO/IEEE 11073 protocol into the oneM2M protocol and vice versa to seamlessly transmit a patient's biomedical data from a PHD to the IoT server.
## 5 Result and Analysis
### Comparison with Advanced Systems  
Table 3 presents the DID performance of some advanced systems and our proposed system on the ADI17 dataset. Results from different systems in the table show that the length of the speech has a significant impact on the test results, with longer speech typically resulting in better performance. The official best baseline is an end-to-end system that uses softmax layer output as the posterior probability of each dialect, which achieves an average cost performance \(\text{C}_{avg}\) of 13.7% and an accuracy of 82% on the test set. Theoretically, transformer models can capture long-term information, which is beneficial for DID performance, but experimental results show that the improvement is very limited, indicating that overly long linguistic information cannot have a significant positive impact on Arabic DID  
Figure 5: Dialect confusion matrix (speed perturbation).  
Figure 6: Dialect confusion matrix (balanced fine-tuning).  
tasks. DKU and Uken are two teams that participated in and won awards in the ADI17 competition. The DKU team that won first place achieved an accuracy of 93.8% on the test set using a single network model, while the system that combines multiple network models has an average cost performance \(\text{C}_{avg}\) of 4.3% and an accuracy of 94.9%. The Uken system that won second place had an average cost performance \(\text{C}_{avg}\) of 6.2% and an accuracy of 91.1% on the test set. Based on the characteristics of spectrogram information analysis and the shortcomings of ECAPA-TDNN in DID tasks, we propose a competitive MSCA-TDNN model. The model achieves an average cost performance \(\text{C}_{avg}\) of 4.19% and an accuracy of 94.28% on the test set. Experimental results show that the multi-scale channel adaptive module is effective for dialect identification. The average cost performance \(\text{C}_{avg}\) of our MSCA-TDNN system is the best result reported in the literature to date.
## 3 Influence of Structural Parameters of the Magnetic Circuit on Magnetic Induction Intensity
### Effect of Core Radius on Magnetic Induction Strength  
The core radius was set to range from 6 to 15 mm, with an interval of 2 mm. The other structural parameters remained unchanged, and the simulation results are shown in Figures 13 and 14.  
As depicted in Figures 13 and 14, the magnetic induction strength at the core increases with the expansion of the core radius and extends toward the wing edges. At approximately  
Figure 14: Plot of magnetic induction intensity clouds corresponding to different lengths of  
an 11 mm core length, the magnetic induction intensity within the gap channel reaches a saturation point of about 0.5 T, as illustrated in the cloud diagram. With a continued increase in the core radius value, there is a heightened risk of magnetic flux oversaturation. Therefore, it is deemed more appropriate to opt for a core radius between 9 and 11 mm.
# Analysis of the Aggregate Effect on the Compressive Strength of Concrete Using Dune Sand
###### Abstract  
In this study, the compressive strengths of concrete were investigated based on water content and aggregate volume fractions, comprising dune sand (DS), crushed sand (CS), and coarse aggregate (CA), for different ages. Experimental data were used to analyze the effects of the volume fraction changes of aggregates on the compressive strength. The compressive strength of concrete increases until the volumetric DS to fine aggregate (FA) ratio (DS/FA ratio) reaches 20%, after which it decreases. The relationship between changes in compressive strength and aggregate volume fractions was analyzed considering the effect factor of each aggregate on the compressive strength and at 2 conditions: (1) 0 \(<\) DS \(<\) CS \(<\) CA and (2) 0 \(<\) CA \(<\) CS \(<\) DS. For condition (1), when the effect factor of CA = 1, those of DS and CS were within 0.04-0.83 and 0.72-0.92, respectively, for all mixtures. For condition (2), when the effect factor of DS = 1, those of CS and CA were within 0.68-0.80 and 0.02-0.79, respectively.  
dune sand; crushed sand; effect factor; compressive strength; concrete
# A High-Resolution Ultrasonic Ranging System Using Laser Sensing and a Cross-Correlation Method
## 5 Results and Discussion
### Uncertainty Analysis  
The distance calculated by pulse-echo system is calculated by the following:  
\[s=\frac{1}{2}vt. \tag{11}\]  
Therefore, the measurement uncertainty of the distance of the targets in this experiment can be calculated as follows:  
\[u^{2}(s_{m})=[\frac{\partial s}{\partial v}u(v_{measured})]^{2}+[\frac{ \partial s}{\partial t}u(t_{measured})]^{2}+[u(s_{expansion})]^{2}+[u(s_{ measured})]^{2}. \tag{12}\]  
The uncertainty of sound velocity is \(u(v_{measured})\), which is a combination of three uncertainty sources. The accuracy of sound velocity measured by the mini SVP, \(v_{accuracy}\), is 0.02 m/s. The repeatability error of sound velocity, \(v_{repeatability}\), is 0.003 m/s. The uniformity of sound velocity in the water tank is \(5\times 10^{-4}\) m/s. Therefore, the \(u(v_{measured})\) is 2.35 \(\times\)\(10^{-2}\) m/s.  
The uncertainty of TOF is \(u(t_{measured})\). The main component is the repeatability error of TOF, \(t_{repeatability}\), which is 4.0 ns.  
The uncertainty of the measured value of the distance is \(u(s_{measured})\). It is the maximum of standard deviation compared to the reference distance, which is 0.199 mm. Uncertainty of the thermal expansion coefficient, \(u(s_{expansion})\), is 1.18 \(\times\)\(10^{-6}\)\(\times\)\(T\)\(\times\)\(s_{m}\). The temperature variation T is 0.05 K. However, if the distance is not too long, it can be neglected.  
The uncertainty sources are shown in Table 4. We use the distance of target 1-2 as an example. In summary, the combined uncertainty of the proposed ranging system can be estimated as follows:  
\[u^{2}(s_{m})=(v_{measured}\cdot\text{4.0 ns})^{2}+(t_{measured}\cdot\text{11.75 mm/s})^{2}+(1.9\textThe proposed system shows potential for combining the laser sensing system with a conventional ultrasound detection method. In our future work, we will take advantage of the laser sensing system for potential applications in underwater ultrasonic ranging and underwater imaging.  
[PERSON] and [PERSON] designed the experiment. [PERSON] and [PERSON] performed the experiment. [PERSON] and [PERSON] analyzed the data. [PERSON] wrote the original manuscript. [PERSON] and [PERSON] did the review and editing.  
This research was funded by the Major Scientific Instrument and Equipment Development Project in National Key Research and Development Program of China, grant numbers 2016 YFC1401203 and 2016 YFF0101802; the National Natural Science Foundation of China, grant number 61505140; the Natural Science Foundation of Tianjin, grant number 18 JCYBJC17100; and Research projects of Tianjin Municipal Education Commission, grant number JWK1616.  
We thank [PERSON] for distance calibration of targets by the coordinate measuring machine. We thank [PERSON] for his help in sound velocity measurement using mini SVP.  
The authors declare no conflict of interest.
## 3 Results  
### Quorum Sensing (QS)  
To determine if AHLs were being produced by the various species of _Marinobacter_, we used two different bioreporter strains, CV026 and A136, which both failed to show evidence for the production of either long or short chain homoserine lactones. In addition, all attempts to isolate and characterize any AHLs from cultures of 40+ species of _Marinobacter_ using standard extraction and LC-MS methods failed to yield any evidence for their existence. Spiking _Marinobacter_ media with small quantities of known AHLs followed by extraction clearly revealed the presence of the AHL by LC-MS, indicating that the failure to identify any AHL in those _Marinobacter_ species tested did not arise from an inadequacy in the extraction and characterization methodology but rather from the actual lack of AHL production. To further support the absence of an AHL based QS system, we searched for putative AHL biosynthetic genes in several of the available _Marinobacter_ genomes. The typical Gram-negative QS system contains LuxR proteins, which upon binding to associated AHL autoinducer molecules induce transcription of target genes [30]. LuxR proteins function in response to LuxI proteins, which synthesize AHLs from S-adenosylmethionine substrate [30; 32]. However, consistent with our bioreporter and AHL isolation results, we could find no homologous LuxI genes within the _Marinobacter_ genomes, reinforcing the idea that an AHL-based QS system was not present.