esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Adsorption Performances of DE and DE-LDH
#### 3.2.10. Reusability Study  
To reduce the solid wastage after adsorption on an industrial scale, the reusability of DE-LDH was examined. The desorption was carried out using desorption media such as methanol and 0.1 M NaOH for 30 min and the dye desorbed adsorbent was washed with double-distilled water. This was repeated  
Figure 11. Effect of salt concentration on adsorption of CR onto DE-LDH ( dye concentration: 50 mg/L, dosage: 40 mg, pH: 7 and time: 1 h).  
Figure 10. The selective removal of CR dye with other organic dyes under pH 7.  
for up to five cycles of adsorption, which showed that the removal efficiency of the adsorbent severely decreased from 43.6% (first cycle) to 18% (fifth cycle) when methanol was used as the desorbing media (Figure 12). Similarly, when 0.1 M NaOH was used to desorb the CR from DE-LDH, the dye removal efficiency of this adsorbent gradually decreased from the first to the fifth cycle (from 70.6% to 24%). This study indicates that the reusability of DE-LDH is dependent on the desorption media. From the results, NaOH acts as a better desorption media compared to methanol. This decrease in removal efficiency with the increase in the cycle process is due to the presence of strongly bound CR on the adsorbent surface. This study reveals that the reusability of DE-LDH is capable for up to two to three cycles for the adsorption of CR (upon desorbing the dye either with methanol or NaOH).
## 2 Experimental Tests and Finite Element Simulations of Connector Performance
### Finite Element Modeling and Analysis  
ANSYS software is employed to create a corresponding honeycomb sandwich structure connector finite element model for the previously mentioned test specimen [29; 30]. The honeycomb sandwich structure material properties are as follows: elastic modulus \(E\) = 70.0 GPa, shear modulus \(G\) = 27.0 GPa, yield stress \(\sigma_{0.2}\) =115.0 MPa, and ultimate stress \(\sigma_{b}\) = 165.0 MPa. Figure 4 shows the simplified constitutive relationships assumed for each part of the modeled specimen. The honeycomb core is modeled with shell elements while facesheets are modeled with solid elements [31; 32; 33]; core and facesheets are connected by contact elements [34; 35]. As the model has symmetric geometrical dimensions, boundary constraint, and load, the analysis is based on a semi-structure, unidirectional, and uniform displacement load is applied to the end surface of the honeycomb sandwich structure at the slip end.  
The results of previous research on a new honeycomb sandwich structure indicate that [16; 17] the connector material accounts for more than 10% of this type of structure material. Hence, minimizing the dimensions of the connecting plate has a high economical value. However, this dimension is normally constrained by the bolt hole edge distance. In this paper, the connector model, which has dimensions that are identical to the dimensions of the specimen, is created. The bolt hole edge distances are set to 2.0, 1.5, 1.0, and 0.7\(d_{0}\) (\(d_{0}\) is the bolt hole diameter). The dimensions of the corresponding connection plate and pad are modified to perform a finite element analysis for the connector ultimate bearing capacity.
# On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN
## 3 System Design and Implementation
### System Architecture  
The overall structure is shown in Figure 2. For the issue of health hazards [35], the air quality data are logged and monitored. Moreover, the outdoor monitoring stations are charged by solar cells. There are three parts of monitoring servers in our system: the sensor data, the web platform, and the ELK Stack.
## 3 Methods  
To assess the geometric positioning accuracy of various common imaging modes of the C-SAR satellite, the methodology outlined in Figure 3 was employed. Initially, automatic CRs were placed within the calibration field. Real-time kinematics (RTK) was utilized to measure the latitude, longitude coordinates, and elevation information of the CRs. When the satellite passed overhead, calculations were made to determine the azimuth and elevation angles of the CRs, ensuring that the calibrator had a strong echo signal and imaging. Next, using the RPC parameters [17] extracted from the image file, the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Date** & **Satellite** & **Imaging Mode** & **Polarization** & **Incidence Angle/\({}^{\circ}\)** & **Orbit** & **Nominal Resolution/m** & **Imaging Swath/km** \\ \hline
14 October 2022 & C-SAR/02 & QPSI & Quad & 44.6 & D & 8 & 30 \\ \hline
15 October 2022 & C-SAR/01 & QPSI & Quad & 44.6 & D & 8 & 30 \\ \hline
16 October 2022 & C-SAR/02 & FSI & VV/VH & 26.0 & D & 5 & 50 \\ \hline
18 October 2022 & C-SAR/02 & FSI & HH/HV & 32.3 & A & 5 & 50 \\ \hline
19 October 2022 & C-SAR/01 & QPSI & Quad & 32.2 & A & 8 & 30 \\ \hline
9 November 2022 & GF-3 & PSII & HH/HV & 28.3 & D & 10 & 100 \\ \hline
12 November 2022 & C-SAR/02 & QPSII & Quad & 44.5 & D & 25 & 40 \\ \hline
14 November 2022 & C-SAR/02 & QPSI & Quad & 24.9 & D & 8 & 30 \\ \hline
20 November 2022 & C-SAR/01 & PSII & HH/HV & 35.0 & D & 10 & 100 \\ \hline
21 November 2022 & GF-3 & UFS & HH & 25.4 & D & 3 & 30 \\ \hline
22 November 2022 & C-SAR/01 & PSII & HH/HV & 23.0 & A & 10 & 100 \\ \hline \hline \end{tabular} Note: A represents ascending orbit and D represents descending orbit.  
\end{table}
Table 2: Main parameters of C-SAR images obtained in the experiment.  
Figure 2: Acquisition footprints over the target area. (**a**) Images acquired during the first deployment scheme. (**b**) Images acquired during the second deployment scheme.  
corresponding theoretical row and column number positions of the CRs on the SLC image were derived. Then, in the SLC image, a local region encompassing the point target was identified. Subsequently, employing the Fast Fourier Transform (FFT) zero-padding interpolation [18] technique in conjunction with the sliding window method [19], the precise row-column coordinates of the point target's center position were extracted. The final step involved the evaluation and analysis of geometric positioning accuracy. This was accomplished by comparing the theoretical row-column numbers, calculated from the latitude, longitude, and RPC parameters, with the actual row-column numbers extracted from the SLC image.
## 3 Results and Discussion
### Experimental Flow Test to Determine \(d_{h}\) and \(A_{\text{eff}}\)  
Generally, experimental flow tests are conducted to determine functional parameters such as the hydraulic diameter (\(d_{h}\)) and the associated effective area (\(A_{\text{eff}}\)) of the internal channel to understand their effects on the flow functionality, i.e., the pressure drop at different mass flow rates (\(\dot{m}\)). As discussed in Section 2.3, another important parameter that potentially impacts the flow functionality through the channel is the channel surface irregularity. To investigate the impact, _msth_ determined with the aid of the fitted Gaussian was applied as the hydrodynamic sand grain roughness \(k_{s}\) in the developed mathematical model, and \(d_{h}\) was calculated at different mass flow rates based on the varying inlet pressure. Tables 3 and 4 represent a comparison between \(d_{\alpha\eta}\) and \(d_{h}\) for specimens N\({}_{1}\) and N\({}_{2}\) determined by experimental flow tests and CT analysis. From Tables 3 and 4, it is evident that \(\dot{m}\) increases with the increase in \(\Delta P\). In addition, it can be noted that, for both specimens, the flow number is constant for all \(\Delta P\) readings, thus confirming the consistency of the experimental setup and the corresponding \(\dot{m}\) measurements made. Furthermore, the \(d_{h}\) values obtained by resolving the mathematical model described in Section 2.3 are consistent for all \(\dot{m}\) for specimens N\({}_{1}\) and N\({}_{2}\), irrespective of their different nominal diameters. It can be seen that the Darcy-Weisbach friction factor decreases with the increase in \(\dot{m}\), indicating the decrease in friction due to the interaction between the fluid and the channel surface with an increase in the fluid flow velocity (\(V\)) [11,12,20]. This, in turn, proves the stability of the conducted experiment and also the developed mathematical model. Upon comparing \(d_{h}\) and the functional equivalent diameter \(d_{\alpha\eta}\), a maximum deviation of 50 \(\upmu\)m was found in the case of specimen N\({}_{1}\) and 20 \(\upmu\)m in the case of specimen N\({}_{2}\). This is observed due to the difference in the deviation between the CT analysis and the experimental flow setup. In addition, the application of _msth_ as an equivalent parameter for \(k_{s}\) is also verified due to the consistency in the determined values of \(d_{h}\) for all \(\dot{m}\) and the minimum deviation concerning \(d_{\alpha\eta}\). Therefore, this comparison helps in understanding the effectiveness of the wall thickness analysis performed in the background mode and material mode of the Sphere method to attain an equivalent parameter to \(d_{h}\) and \(k_{s}\) that could potentially reduce the number of flow tests and thereby reduce the associated evaluation time and cost. Furthermore, this analysis establishes a direct and stable correlation to
# Indoor Positioning by Double Deep Q-Network in VLC-Based Empty Office Environment
## 4 Proposed Indoor Positioning Method
### Fingerprinting  
The fingerprinting technique is a positioning method that can achieve a precise positioning performance when combined with the RSS. It involves designating specific locations, called reference points (RPs), within the indoor environment. By increasing the number of RPs and making them denser, more accurate positioning can be achieved. However, as the number of RPs increases, the processing time also increases. Hence, it is crucial to determine the optimal number of RPs, taking into account the size of the indoor environment.  
The fingerprinting technique consists of two steps: offline and online. The first step is the offline phase, where the RSS database is constructed for each reference point without a specific positioning target. Typically, RP arrangement and RSS correction are performed in this step. The RSS database for the \(r\)-th reference point is stored as shown in (3):  
\[RSS_{DB}=\begin{bmatrix}h_{1}^{1}&\cdots&h_{1}^{i}&\cdots&h_{1}^{t}\\ \vdots&&\vdots&&\vdots\\ h_{r}^{1}&\cdots&h_{r}^{i}&\cdots&h_{r}^{t}\\ \vdots&&\vdots&&\vdots\\ h_{R}^{1}&\cdots&h_{R}^{i}&\cdots&h_{R}^{i}\end{bmatrix} \tag{3}\]  
where \(h_{r}^{i}\) represents the RSS between \(i\)-th AP and \(r\)-th RP. When the RSS database construction is completed, the offline step ends. Afterward, the online step is a step in which the positioning target exists in the environment and the positioning is performed by applying a matching technique. In this paper, the double deep Q-Network method is proposed as a positioning method.
# Experimental Study of the Dynamic Characteristics of PVA-Amended Expansive Soils
## 3 Analysis of Test Results
### Analysis of the Dynamic Triaxial Test Results  
#### 3.2.1 Dynamic Stress-Strain Backbone Curves  
According to the dynamic triaxial test data, the dynamic stress-strain curve for the improved soil forms an elliptical hysteresis circle during one cycle of cyclic stress loading and unloading, and the average values for the stress and strain at the apex of the hysteresis circle of 3-8 levels during the dynamic loading process are selected to obtain the trend for the dynamic strain and corresponding dynamic stress. The dynamic stress-strain curves for PVA-amended expansive soils with different circumferential pressures are plotted in Figure 5, and the dynamic stress-strain curves for soils amended with different frequencies are plotted in Figure 6.  
As shown in Figure 5, the dynamic stress-strain curves increase with increasing circumferential pressure. From the curves measured at different frequencies, the deformation under the same dynamic stress is minimized for \(\sigma_{3}=300\) kPa. The main reason for this difference is that the high-confining pressure specimen has greater compactness and less porosity than the low confining pressure specimen during the consolidation process. In addition, the contact between the PVA in the improved soil and the soil particles is more compact, so the dynamic strain under the same stress is relatively small.  
Figure 4: Relationship curve for the shear strength index with varying PVA dosage.  
Figure 6 shows that when the confining pressure and dynamic stress are the same, the higher the frequency, the lower the dynamic strain. Analysis shows that in a cycle period, the higher the frequency, the shorter the dynamic stress that acts on the specimen [27]. At this time, the deformation of the specimen occurs too late to fully develop, and the test soil exhibits a certain degree of viscosity, resulting in stiffness; that is, the higher the frequency, the lower the dynamic strain under the action of the same dynamic stress.  
By comparing the curves shown in Figures 5 and 6, the slope of the stress-strain curve is greater in the early stage of dynamic strain development (\(\epsilon_{\rm d}\leq 0.4\)), the dynamic strain of the soil responds to stress faster, and the specimen is stabilized in terms of force and deformation because elastic deformation accounts for a larger proportion of the deformation. As the dynamic strain continues to increase (\(\epsilon_{\rm d}>0.4\)), the slope of the curve decreases, and the links between the soil particles in the specimen are destroyed, resulting in a gradual increase in plastic deformation as a proportion of the deformation of the soil sample [28].
## 1 Introduction
[22] proposed a three domains fuzzy wavelet neural network (TDFWNN) algorithm without prior knowledge of the biomechanical model to estimate force through sEMG. In [23], [PERSON] et al. presented a method for estimating the applied forces that are based on using fuzzy theory and deep learning. However, parameter optimization is a difficult and important task for machine learning methods. Furthermore, the crucial problem is that the gradient of the error function vanishes easily after back-propagation. It can be concluded from the above analysis that the means of obtaining timely and accurate interaction force estimation in advance is an enormous challenge. This study is aimed at resolving this challenging problem. That is to say, it proposes a timely and accurate estimation of human interaction force for a musculature-driven human-machine system. A novel deep learning framework is presented to accurately estimate the elbow interaction force. We selected ResNet, which is the latest CNN to automatically extract the valid feature of sEMG, instead of the manual way because it provides outstanding performance in terms of feature extraction. Subsequently, the BiLSTM network with attention mechanism was selected to map the nonlinear relationship between sEMG features and interaction force. To verify the estimation results, the experimental condition was selected as the elbow flexion in the horizontal plane during isometric contraction. The outline of our approach is illustrated in Figure 1. The purpose of this study is to demonstrate the feasibility of estimating elbow interaction force during isometric contraction based on a deep learning algorithm. To highlight the advantages of muscle force estimation which we proposed, we compared our algorithm with other mainstream force estimation algorithms in the experiment. The main contributions are summarized as follows:  
1. Our framework can automatically extract the features of the sEMG signal and capture the local dependence between the data. 2. By introducing the attention mechanism, we can capture the salient structures of input data and explore the correlations among multiple dimensions of data, improving the learning performance of the model. Our framework overall outperforms the non-ensemble methods in accuracy. In addition, the training time and testing time of the network are the shortest among other reported methods.
## 2 Materials and Methods
### Heat Treatments
#### 2.2.2 Ohmic Heating Treatment  
A pilot-scale OH plant provided by the German Institute of Food Technology--DIL e.V. was employed for OH treatments. The OH plant comprises a function generator and an OH chamber equipped with an OH cell. The function generator applies an alternating current through rectangular bipolar direct-current pulses, regulated by the pulse height and controlled via a proportional integral derivative controller. The alternating-current flow occurs at a frequency of 10 kHz. The cylindrical OH cell, as depicted in Figure 1, was constructed from 5 mm thick polymethylacrylate, with an internal diameter of 11 mm for the cell and electrodes. Electrodes, fashioned from 2 mm thick stainless steel, were securely inserted into the sides of the cell. Temperature measurements are facilitated by PT-100 thermocouples (type HET/E, FuehlerSysteme eNet International GmbH, Nurnberg, Germany) positioned at three distinct measuring points, namely core, halfway, and outer temperatures, as shown in Figure 2d.  
For heating, 2.3 kg of meat batter was placed in the cell, with electrodes maintained at a fixed position during heating. Heating was initiated at a constant power of 2.3 kW until  
Figure 2: (**a**) Time–temperature profile of the core temperature ohmic and conventionally heated (OH and COV, respectively) Bologna-style sausages, (**b**) temperature distribution of OH-treated Bologna-style sausages. Outer temperature (T\({}_{\text{O}}\)), halfway temperature (T\({}_{\text{H}}\)) and core temperature (T\({}_{\text{C}}\)) are located as shown in (**d**). (**c**) Electrical conductivity of Bologna-style sausages during the OH process.  
reaching a final core temperature of 76 \({}^{\circ}\)C. A holding time of 5 min at the final temperature was achieved by reducing the applied power to 0.1 kW.  
According to [PERSON] et al. [6], the electrical conductivity of the Bologna-style sausages was calculated using Equation (1):  
\[\sigma=\frac{I*L}{V*A} \tag{1}\]  
where \(\sigma\) is the electrical conductivity (S/m), \(I\) is the electrical current (A), \(L\) is the distance between the electrodes (m), \(V\) is the voltage (V) and \(A\) is cross-section surface area of the electrodes (m\({}^{2}\)).
# CubeSat Imaging Payload Design for Environmental Monitoring of Greenland
## 4 Results
### Design Development  
The USB switch communicates with the SOMs so that both are connected using a single USB port. The selection of each SOM comes from the PDU, which has two independent voltage and signaling channels. This ensures that under no circumstances do both SOMs operate simultaneously. In addition, the USB switch has data flow control diodes to avoid collisions in the messages. After the SOM is selected, the USB switch has an internal two- to four-channel mux-demux so that the SOM can communicate with each camera. Its selection comes from a decoder of digital inputs to the output of the mux-demux so that only one camera can establish communication with the SOM. The decoder inputs are also multiplexed for the two SOMs using GPIO digitally. In addition, the cameras are powered by a voltage line from the PDU independently of the SOMs. However, this device is not commercially available, and it is necessary to develop it from scratch.  
Each SOM is connected to the other modules on the satellite, including the OBC via CANBUS, and using the CSP protocol for communication. In order not to block the control channel during bulk image data transfer, each SOM must also be connected to a high-speed RS-422 bus to send images directly to the RF module. Ethernet is included on the SOM and was also considered for the data bus, but excluded for power consumption and complexity. RS-422 has sufficient data transfer capabilities compared to the radio downlink, and is simple, noise-tolerant and robust. Finally, a 3.3-volt converter is needed to power the USB switch and GNSS module. The GNSS communicates with the SOM using the SPI protocol. The electronic diagram payload is presented in Figure 3.  
Figure 2: Imaging payload electronic design proposal.
# Urban Seismic Networks: A Worldwide Review
## 2 USN Catalogue
### Geographical Distribution
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**ID** & **Country** & **City-Met. Area** & **Objective 1** & **Objective 2** & **Objective 3** & **Source (s)** \\ \hline
1 & Turkey & Bursa & Site effect research & & [13] \\
2 & Greece & Thessaloniki & Site effect research & & [14] \\
3 & USA & Anchorage & Earthquake monitoring & Site effect research & [15,16] \\
4 & Colombia & Medellin & Earthquake monitoring & Early warning & [17] \\
5 & Netherlands & Groningen & Earthquake monitoring & Other & [18, 19, 20, 21] \\
6 & Japan & Yokohama & Seismic intensity mapping & Earthquake monitoring & Post Event damage assessment & [22, 23, 24] \\
7 & USA & Oakland & Seismic intensity mapping & & [25] \\
8 & Colombia & Bogota & Site effect research & Post Event damage assessment & Seismic intensity mapping & [26, 27] \\
9 & Colombia & Armenia & Earthquake monitoring & & [28, 29] \\
10 & Turkey & Izmiti & Earthquake monitoring & & [30] \\
11 & Greece & Athens & Earthquake monitoring & & [31, 32] \\
12 & Kazakhstan & Almaty & Earthquake monitoring & & [33] \\
13 & Germany & Cologne & Site effect research & & [34] \\
14 & Usa & Spokaine & Earthquake monitoring & & [35] \\
15 & Turkey & Istanbul & Post Event damage assessment & Early warning & Seismic intensity mapping & [36, 37] \\
16 & Canada & Ottawa & Site effect research & Earthquake monitoring & [38] \\
17 & Romania & Bucharest & Seismic intensity mapping & Earthquake monitoring & [39] \\
18 & Turkey & Bursa & Earthquake monitoring & & [40] \\
19 & Romania & Bucharest & Earthquake monitoring & Site effect research & [41] \\
20 & Canada & Vancouver & Seismic intensity mapping & Site effect research & [42] \\
21 & Japan & Tokyo & Other & Seismic intensity mapping & Site effect research & [43, 44, 45] \\
22 & USA & Anchorage & structural monitoring & Site effect research & [46, 47, 48, 49] \\
23 & Iran & Barn & Site effect research & & [50] \\
24 & Italy & Potenza & Site effect research & & [51] \\
25 & Iran & Tabriz & Site effect research & & [52] \\
26 & Iran & Tehran & Earthquake monitoring & & [53] \\
27 & USA & Portland & Site effect research & & [54] \\
28 & Switzerland & Basel & Earthquake monitoring & Other & Site effect research & [55, 56, 57, 58] \\
29 & Mexico & Mexico City & Earthquake monitoring & Site effect research & [59] \\
30 & Colombia & Bogota & Earthquake monitoring & & [29] \\
31 & Indonesia & Yogyakarta & Site effect research & & [60] \\
32 & Lebanon & Beirut & Site effect research & & [61] \\
33 & India & Delhi & Site effect research & & [62, 63, 64, 65] \\
34 & Iceland & Heveragerdi & Earthquake monitoring & Site effect research & [66] \\
35 & Taiwan & Taipei & Site effect research & Earthquake monitoring & [67] \\ \hline \end{tabular}
\end{table} 
Table 1: General information of the collected USNs. Figure 2: Global seismic hazard map from [12] and locations of urban seismic networks (USNs). The histogram indicates the frequency of expected PGA (m/s\({}^{2}\)) corresponding to the USN locations.