esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Based on the consideration of the field environment, dry-wet cycles and NMR tests were carried out on unloaded samples. Combined with wave velocity and NMR results, the micro-pore structure evolution law of unloaded sandstone after different dry-wet cycles was studied. The main conclusions are as follows:  
1. As the dry-wet cycles increase, P-wave velocity and mass change trends both reflect the progressive deterioration process of unloaded samples. Both P-wave velocity and mass show a three-stage change trend of \"rapid decrease-slow decrease-stability\". As  
Figure 11: Microscopic deterioration model of unloaded samples at each stage under dry–wet cycles.  
the dry-wet cycle gradually intensifies, the pores of the sample continue to increase and the cumulative damage increases.
2. Analysis of T\({}_{2}\) spectrum distribution and spectrum area shows that the smaller the initial confining pressure, the more significant the confining pressure unloading effect on samples, and the higher the degree of deterioration of the sample. As the cycles increase, the damage accumulation of unloaded samples intensifies, and the overall pore diameter and number further increase. The overall trend is that smaller pores evolve towards larger pores, accompanied by the generation of new cracks.
3. The effects of unloading and dry-wet cycles cause changes in the microstructure of the specimen, resulting in significant degradation of the mechanical properties. The unloading effect causes the initial pores of the sample to increase, providing channels for the dry-wet cycle. During the dry-wet cycle, the soluble cements existing between the particles inside the sample gradually dissolve and connect, which weakens the cohesion and friction between the particles inside the sample, resulting in a gradual reduction in the internal skeleton constraints of the sample; the particle structure becomes loose, and the original microcracks continue to develop and expand.  
Data curation, L.C. and Z.L.; Investigation, S.G. and Z.S.; Methodology, X.C., L.C., S.G., Z.L. and Z.S.; Writing--original draft, L.C.; Writing--review and editing, L.C. and X.C. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (Grant No. 51979218, U1965107).  
The data used to support the findings of this study are available from the corresponding author upon request.  
We would like to thank Xi'an University of Science and Technology for providing the test equipment.  
The authors declare no competing interests.
## 1 Introduction
To solve the first problem, this paper proposes a simple and effective CAP module to partition the feature embedding modules, which performs the partitioning, as shown in Figure 1. In the CAP module, the closely correlated parts in the window are partitioned together, which preserves the structural integrity of the local feature information in the person image and maintains the consistency of the semantic information before and after the local feature segmentation. We also use two sets of parameters to learn the correlation of calculations in the window, such that the partitioning of feature embeddings in different windows is adaptive. To solve the second problem, this paper proposes an MG module, in which the feature embeddings are cut horizontally into groups and the class tokens are embedded in different groups to learn the feature information at different levels of granularity. We also design an aggregation method that is incorporated into the MG module, which aggregates the same levels of granularity in the video sequence that are present in the final feature representation. Thus, overall, this paper makes the following four main contributions:  
Figure 1: Correlation-adaptive partitioning of feature embeddings. We propose a pure transformer-based APMG network for video Re-ID tasks;
2. We propose a CAP module to adapt the partitioning of feature embeddings according to the correlation, which maintains the consistency of the overall structure and the semantic information of feature embeddings;
3. We propose an MG module that can capture feature information at different levels of granularity, thereby improving the re-identification performance of the network. 4. We perform experiments on three widely used video Re-ID datasets, which demonstrated that our APMG network achieves a better performance than other state-of-the-art models. The rest of this paper is arranged as follows: In Section 2, we review the related work on Re-ID and transformer. In Section 3, we propose an APMG network based on the pure transformer structure, and introduce two brand-new modules, CAP and MG, in detail. In Section 4, we introduce three datasets for experimental testing and make comprehensive analyses of the experimental results. In Section 5, we summarize the work content of this paper and look forward to future work.
# Analytical Study on the Load-Bearing Performance of RC Beams Subjected to ASR Expansion
## 4 Load-Carrying Capacity of RC Beams with ASR Deterioration
### Results and Discussion
#### 4.5.2 ASR-Deteriorated RC Beams with Respect to Flexural Load-Carrying Capacity  
Cracking behavior and load-displacement curves at the ultimate state of a sound and an ASR-deteriorated RC beam subjected to bending loading analysis are shown.  
1. Constant expansion To investigate the relationship between the amount of aggregate expansion and load-carrying capacity, results for the cases of Constant 1 and 2 and soundness are shown in Figures 27 and 28. In case of Constant 2, cracks in the shape of a cracked shell due to ASR are conspicuous, and the expansion cracks show fracture propagation with bending load. The load-displacement curve shows that the cracks were caused by expansion cracks. The load-displacement curves for Constant 1 and 2 show that the initial stiffness of the expanded and deteriorated cases decreased compared with the healthy cases, but the maximum load increased. This is thought to be due to the internal stress generated by the expansion of the aggregate, which exerted a restraining force on the rebar, a kind of chemical prestress.
# Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models
## 3 Methodology
### The CNN-LSTM Hybrid Model
#### 3.4.1 The Convolutional Layer  
Between the CNN model, data processing happens layer by layer as the output of one layer is the input of another layer. Let us assume that  
\[\mathbf{x_{i}^{a}}=[\mathbf{x_{1}},\mathbf{x_{2}},\ldots,\mathbf{x_{N}}] \tag{1}\]  
are the input from the sensors, and \(\mathbf{a}\) represents the axis of the sensor. For the sensors used in this work, three of them, namely accelerometer, gyroscope, and magnetometer, are triaxial sensors. The other two, namely the light and microphone sensors, are single axis sensors.  
* _The Pooling layers_ We added a pooling layer to the CNN sub model to reduce the spatial size of representations, i.e., a form of nonlinear down sampling operation to help reduce the feature maps. It is a common practice to have a pooling layer between successive convolutions in a CNN [14]. In this article, we used the max pooling operation in mapping the output of the preceding layer.
* _The Flatten layer_ We included a flatten layer in the model just immediately after the pooling layer. The flatten layer, upon receiving the output of the pooling layer, converts the reduced feature maps into a single column vector.
* _The LSTM Networks_ An interesting part of the model is the introduction of the LSTM networks immediately after the flatten layer. This is where the current work differs significantly to that conducted recently by the researchers in [PERSON] et al. in [64]. According to [22], for models that need to learn the temporal patterns in input data, preceding an LSTM network with a CNN sub model always outperforms models with only one of the two. LSTM, like any other recurrent neural network, supports forward and backward propagation within its networks. Made up of several LSTM units, the LSTM networks use their memory cells to master the temporal context in input data. Each of the units has a memory cell \(\mathbf{c_{t}}\), which is readable, updatable, and erasable [15]. As mentioned earlier, LSTMs comprise the input gate \(\mathbf{i_{t}}\), forget gate \(\mathbf{f_{t}}\), and output gate \(\mathbf{o_{t}}\), which control reading, memory update, and writing operations, respectively.  
We implemented a single-layer LSTM network (Figures S3 and S4). By passing the output of the previous LSTM unit into the next, the network can begin to perform frequency and temporal modeling of the activity contexts. The input is the one-dimensional vector of features from the flatten layer of the _time-distributed_ CNN sub model. As argued by [22; 58], we fed the output of the single-layer LSTM network to a fully connected dense layer with a SoftMax activation function.
## 4 Analytical Results
### Optical Launched Power Effect  
Figure 5 presents the log(BER) as a function of launched optical power for the Fast-OFDM-CDMA system compared to AO-OFDM-CDMA and OCDMA-WDM at K = 2. As depicted in Figure 5, the IM/DD Fast-OFDM-CDMA system outperforms both the AO-OFDM-CDMA and OCDMA-WDM PON configurations. Furthermore, it is shown that at a launched optical power of 0 dBm, the AO-OFDM-OCDMA and OCDMA-WDM systems have the same Q-factor, equal to 2.5 dB. However, at 0 dBm, Fast-OFDM-CDMA improves the system performance by a Q-factor equal to 2.5 dB compared to AO-OFDM-OCDMA and OCDMA-WDM. On one hand, AO-OFDM-CDMA has a better system performance compared to OCDMA-WDM, while the launched power is less than 0 dBm. On the other hand, for a launched optical power greater than 2 dBm, the OCDMA-WDM PON configuration upgrades AO-OFDM-CDMA in terms of Q-factor. Additionally, the latter PON configurations provide a Q-factor above the Q-limit (i.e., equal to 6 dB) when the optical launched power exceeds 4 dBm.  
Figure 4: BER versus fiber length.  
Figure 5: Q-factor versus launched optical power.
# Wavelength Selection for Detection of Slight Bruises on Pears Based on Hyperspectral Imaging
## 2 Materials and Methods
### Data Processing and Analysis
#### 2.4.1 Principal Component Analysis  
PCA is an effective method for dimension reduction and has been widely used in the field of spectral analysis [17]. On the basis of preserving the original information as far as possible, the samples in high dimensional space can be mapped to the lower dimension principal component space. The basic idea of PCA is to use an optimization method to concentrate measured data (Y), the data matrix is simplified to reduce the number of principal component for a few linear combinations of the original variables, in order to reveal the characteristics of Y data structure and extract basic features. The main components after the dimension reduction of PCA can be expressed in formula (2).  
\[PC_{s}=\sum_{i=1}^{m}\beta_{i}^{s}\lambda_{i} \tag{2}\]  
where \(PC_{s}\) is the \(s\)th principal component, \(\lambda_{i}\) is the \(i\)th wavelength (A total of \(m\) wavelengths), \(\beta_{i}^{s}\) is the load factor of the \(s\)th principal component on the \(i\)th wavelength. The bigger the load factor is, the more effective the principle component is.  
Choosing some representative characteristic wavelengths is very important because the data processing and modeling will be influenced by the abundant spectral data and strong inter band redundancy [18]. This study firstly chose five characteristic wavelengths according to the load factor.
## 3 Dynamic Resource Allocation and ACB Scheme for Delay-Sensitive Devices  
In this section, we address the implementations of our scheme, as depicted in Figure 2. Specifically, our scheme is composed of two parts. Firstly, on the basis of delay requirements, we consider that MTC devices are classified into two clusters. Secondly, the most critical design is achieved by dynamically adjusting the value of ACB factor and the number of preambles allocated to the two clusters.  
Figure 1: Random access procedure through ACB mechanism.
# A Basic Experimental Study on Analysis of Leak Signal and Monitoring Method for Water Supply Pipe
## 4 Simulated Leak Piping System  
### Introduction of Simulated Leak Piping System  
To evaluate the leak detection applicability of the smart bolt, a simulated leak piping system was configured, as in Figure 5. The acceleration sensor for detecting leakage signals and the strain sensor for tensile testing used a Radian Co. Ltd. SDL 600R datalogger that can get 1024 Hz sampling data to acquire each measurement raw data. In general, the frequency range of the leak detection equipment using the accelerometer sensor is from 300 Hz to 5000 Hz. Since the smart bolt is in the joint part of the valve, it is expected that damping will occur due to the rubber packing. Therefore, it was set to a lower frequency band than the general frequency range. The simulated leak piping system was composed of a 200-mm main cast iron pipe (total length of 3.2 m) and an 80-mm branch pipe. Both ends of the simulated pipe were put on a jig with free ends. The simulated leak was made to flow through the branch pipe as per normal (height 10 m). The water pressure in supply pipes is generally 0.1-0.5 MPa, and we confirmed that the water pressure in the simulated pipe was 0.18 MPa through a water pressure gauge. For the leak measurement experiments and tensile tests, the smart bolt was installed on the valve joint.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Specimen No.** & **\#1** & **\#2** & **\#3** & **\#4** & **Avg.** \\ \hline Tensile velocity (mm/min) & 1 & 1 & 2 & 2 & \\  & & & & 170.56 kN \\ \hline Max. load (kN) & 177.73 & 168.95 & 170.15 & 165.43 & \\ \hline \end{tabular}
\end{table}
Table 2: Results of tensile tests.  
Figure 4: Results of tensile tests.
## 1 Introduction
### Operating Temperatures in AD
Anaerobic microorganisms can adapt to different temperatures, but the specific methanogenic activity, and thus the CH\({}_{4}\) production, decreases proportionally to process temperature [25; 31; 52]. Other studies [30; 53] complement this correlation. It was \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Reference**} & \multicolumn{1}{c}{**Year**} &
\begin{tabular}{c} **Psychrophilic** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} & **Mesophilic (\({}^{\circ}\)C)** & **Thermophilic (\({}^{\circ}\)C)** \\ \hline Z\&[PERSON] et al. [29] & 2000 & - & 30–40 & 50–70 \\ [PERSON] et al. [30] & 2001 & - & - & 55–70 \\ [PERSON] et al. [31] & 2003 & \textless{}20 & 32–38 & 50–55 \\ [PERSON] et al. [32] & 2006 & \textless{}20 & 25–45 & 45–65 \\ [PERSON] [10] & 2006 & & \textless{}50 & \textgreater{}50 \\ [PERSON] [33] & 2007 & \textless{}20 & 30–42 & 48–55 \\ [PERSON] et al. [34] & 2008 & \textless{}25 & 32–42 & 50–57 \\ [PERSON] et al. [27] & 2009 & 12–16 & 35–37 & 55–60 \\ [PERSON] et al. [35] & 2013 & \textless{}25 & 37–42 & 50–60 \\ [PERSON] et al. [36] & 2013 & 15–25 & 35–37 & 50–55 \\ [PERSON] et al. [26], [PERSON] et al. [37] & 2015, 2013 & - & 35 & 55 \\ [PERSON] et al. [38] & 2016 & - & 35–37 & - \\ [PERSON] et al. [22] & 2016 & \textless{}25 & 35–42 & 50–55 \\ [PERSON] et al. [39] & 2016 & - & 25–37 & 55–65 \\ [PERSON] et al. [40] & 2019 & \textless{}20 & 20–45 & 45–60 \\ [PERSON] et al. [15] & 2019 & - & 35–40 & 55–60 \\ [PERSON] and [PERSON] [41] & 2020 & \textasci{}20 & -35 & -55 \\ [PERSON] et al. [9] & 2020 & - & 35–40 & 50–57 \\ [PERSON] et al. [42] & 2020 & \textless{}20 & 20–45 & - \\ [PERSON] et al. [43] & 2020 & - & 35–40 & 55–70 \\ [PERSON] et al. [44] & 2021 & 9–25 & 25–35 & 35–70 1 \\ \hline \hline \end{tabular}  
* \({}^{1}\) subdivided into thermophilic (35–55 \({}^{\circ}\)C) and extreme-thermophilic (55–70 \({}^{\circ}\)C).  
\end{table} 
Table 1: Temperature ranges and optima in technical anaerobic digestion processes according to different studies. found that an increased process temperature (up to 55 \({}^{\circ}\)C) leads to an increased CH\({}_{4}\) yield in the AD of cattle manure. However, further increased AD temperatures (65 \({}^{\circ}\)C) lead to a reduction in CH\({}_{4}\) yields. This can be explained by findings of other studies [53; 54] in which it was found that the diversity of microorganisms decreases with increasing AD temperatures. Therefore, CH\({}_{4}\) yield increases through heightened process temperatures are limited (e.g., the CH\({}_{4}\) yield of waterweed was halved at 65 \({}^{\circ}\)C, compared to 55 \({}^{\circ}\)C). The diversity of methanogenic organisms also decreases at low AD temperatures, e.g., when reducing the process temperature from 37 to 15 \({}^{\circ}\)C [55].
# Effect of Environmental Temperature on the Insulating Performance of Epoxy/MgO Nanocomposites
## 3 Results and Discussion
### Thermal Properties  
As shown in Figure 2, the thermal conductivity of the composites increased with an increasing MgO nanofiller content [31]. This was attributed to the high thermal conductivity of the nano-MgO. When the content of MgO nanoparticles was 7%, the thermal conductivity of the composites increased by 75% compared with the pure epoxy resin. The thermal conductivity of the composites increased  
Figure 1: SEM images of cross-sections of MgO/epoxy composites. (**a**) 0.5 wt%; (**b**) 1 wt%; (**c**) 3 wt%; (**d**) 5 wt%; (**e**) 7 wt%.  
slowly at low nano-MgO doping ratios, which may be due to the fact that the nanoparticles were independent of each other, and a thermal conductive chain was not yet formed. The internal thermal conductivity of the epoxy composites consisted of a tandem structure, which increased with the increase in MgO nanoparticle content. The nanoparticles and polymer matrix interacted with each other at higher MgO nanoparticle contents in the matrix to form a single thermal conductive chain which reduced the thermal resistance of the channel and formed parallel structures, leading to the emergence of a thermal conductive network.
# Polling Mechanisms for Industrial IoT Applications in Long-Range Wide-Area Networks
## 4 Results and Discussion
### Energy Consumption Analysis  
Based on the capacity of the selected 3000 mAh battery, Figure 14 shows the expected operating lifetime of a node, depending on the class and SF used. On the one hand, as expected, Class C polling requires much more energy because the nodes' radio is constantly active, which also means that the use of a different SF or \(n_{ch}\) is not relevant. This results in duration of around 2 days, considering only radio and CPU consumption (real duration will depend on usage of sensors, screen, buttons, or other added components to the device).  
On the other hand, the possible configurations on Class B can be translated into battery durations from 15 to 34 years, values that are not realistic due the energy consumed by other device components or even to the natural degradation of battery materials. For reference, various device manufacturers specify battery durations of up to 2 or 3 years when network traffic is around 24 uplink packets per day in Class A.  
In summary, the relevant result is the ability to extend the operation time of a node drastically by using Class B polling configurations, which can ensure several months of unattended service.
# Antimicrobial Efficacy of Edible Mushroom Extracts: Assessment of Fungal Resistance
## 3 Results and Discussion
### Antifungal Activity of T. camphoratus Methanol Extract (TcM)
#### 3.2.1 Antifungal Activity Test of TcM on PDA (Defined Medium)  
We also determined the antifungal activity of TcM against foodborne fungal pathogens/contaminants by monitoring their radial growth on PDA (defined medium) containing 0.8 or 1.6% (\(w/v\)) of TcM. As determined in _A. fumigatus_ (Section 3.1), there were incremental increases in antifungal activity with concentrations as shown by a reduction in the radial growth (Table 2). The only exceptions were _N. fischeri_ and _P. expansum_ W2, which showed a similar level of susceptibility to either 0.8% or 1.6% of TcM.  
Of note, two antioxidant mutants of _P. expansum_, FR2 and FR3, showed a 34% to 46% higher growth rate (tolerance) to TcM when compared to the respective wild type strains, W1 and W2, except for FR3 at 1.6% of TcM, which showed TcM susceptibility similar to that of W2. _P. expansum_ FR2 and FR3 have a mutation in their antioxidant system, possibly in the mitogen-activated protein kinase (MAPK) pathway involved in oxidative stress signaling [38; 48]. As determined in the _A. fumigatus_ MAPK mutants [41], FR2 and FR3 mutants were tolerant to the phenylpyrrole fungicide, FLU [38; 48]. The antifungal action of certain antifungal agents such as FLU, fenpicloni, aromatic hydrocarbons, etc. is mediated through the normal antioxidant signaling system, especially the intact MAPK pathway [49; 50]. Therefore, it is speculated that the antifungal activity of TcM could also be mediated through the intact antioxidant signaling system in fungi, while fungi with mutations in the system escape the toxicity triggered by the mushroom extracts (Table 2) (see also Section 3.3 below).
# Numerical Design Method for CVT Supported in Standard Variable Speed Rubber V-Belts
## 2 Numerical Continuously Variable Transmission (CVT) Design Method
### Force Balance  
The difference in clamping forces in the driver and driven pulleys for CVT systems leads to a change in the ratio \(R_{ext}\). When the lateral force of the driver pulley exceeds the driven one, a lateral closing displacement in the driver pulley occurs and the belt occupies a greater radius. Given that the length of the belt is fixed, the driven pulley will open where the belt movers to a lower radius. This change in radius modifies the ratio between them, increasing the speed of the driven pulley.  
In this case, there is a set of centrifugal elements to develop these forces in the driver pulley. These involve a ramp which reorients part of the radial force (\(F_{cen}\)) in an axial or lateral direction to the pulley.  
Figure 6: Rubber V-belt system forces.  
For the driven pulley, this force is derived from a spring. The increase or decrease in this force is carried out by compressing the spring with a cam. To transmit more torque the cam compresses the spring which is actuated by the torque on the output shaft.  
These lateral forces have an additional function, they produce a lateral compression on the belt called clamping force (\(F_{cl}\)), which prevents the belt from slipping and losing efficiency. Figure 7 presents four possibilities for this balance of forces, which determines the dynamics of the mechanism. The numbers correspond to the positions of the torque and angular speed graph in Figure 8.  
Before calculating the parameters of the centrifugal actuator, certain data must be defined, which then results in a graph as presented in Figure 8. A blue curve is drawn on the motor torque graph, which proportionally corresponds to the lateral force required to transmit the torque without slippage. Additionally, the centrifugal force is shown in red; the desired component of the centrifugal force in the axial or lateral direction in orange; and the required lateral force on the driven pulley spring in green. Finally, in Figure 8 the ratio change zone is highlighted in a grey section. The designer chooses two engine speeds: the rpm \(\omega_{CUT\_ini}\) where the CVT should start changing ratio, usually close to the maximum torque, and a second speed \(\omega_{CUT\_end}\) where the speed ratio change should end, usually near maximum engine efficiency.