esa-sceva/satcom-chunk-collection huggingface dataset:
# Adsorption of Cadmium and Methylene Blue Using Highly Porous Carbon from Hemp Seeds
## 4 Results and Discussion
### Adsorption Data
#### 4.2.4 Temperature Effect  
Experiments were carried out at different temperatures (288-308 K) to assess the effect of temperature on the adsorption of cadmium and methylene blue by PHSs, CHS-500 and CHS-700. The results are shown in Figure 6a,b. The plots show increased cadmium and methylene blue sorption capacities at 288 to 308 K for all the adsorbents. The graphs show that the adsorption capacity increases with increasing temperature, indicating that adsorption is an endothermic process. This may be related to the enhanced mobility of the dye and cadmium ions from the bulk solution towards the adsorbent surfaces of PHSs, CHS-500 and CHS-700 with increasing temperature. At higher temperatures, many molecules may also obtain adequate energy to interact with active sites at the surface of adsorbents. A similar adsorption trend for both pollutants means that they were adsorbed on similar sites with identical hydrogen bonds, electrostatic interactions or \(\pi\)-\(\pi\) interactions between the aromatic rings and, therefore, different adsorption capacities [44].  
Figure 5: pH effect on (**a**) Cd(II) and (**b**) MB [adsorption conditions: adsorbent mass = 20 mg, the volume of solution = 50 mL, solution concentration = 100 mg/L, time = 720 min and temperature = 288 K].  
Figure 6: Temperature effect on (**a**) Cd(II) and (**b**) MB [adsorption conditions: adsorbent mass = 20 mg, the volume of solution = 50 mL, solution concentration = 100 mg/L, time = 720 min and pH = 8].
## 2 Mathematical Modeling of the Drill String System
### Mathematical Modeling of the 3D Rubbing Contact
Substituting (6) into (7) gives the expression of frictional forces in the planes \(X\) and \(Y\) as:  
\[F_{X\_rub}=-\frac{(\Delta-\delta)K_{s}}{\Delta}(X-\mu Y)\text{ and }F_{Y\_rub}=-\frac{(\Delta-\delta)K_{s}}{\Delta}(\mu X+Y) \tag{8}\]  
The angle characterizing the position of the drill string relative to the casing is such that the relative displacement of the center of the drill string of the radius is \(Z=\sqrt{S^{2}-\Delta^{2}}\) which generates a vector of nonlinear forces \(F=[F_{AX},F_{N},F_{T}]^{\text{T}}\) which are the static force loads in the radial (\(X\) and \(Y\)) and axial \(Z\) directions. In this case, with the contact force \(F_{N}\), the inviscid fluid separating the rotating shaft and the fixed casing surfaces is assumed to induce relatively low friction. Therefore, the drill string and casing interact according to  
Figure 2: (**a**) Schematic of the rubbing system. (**b**) Model of the contact force. [PERSON]'s unilateral contact law with dry friction, and the axial friction \(F_{AX}\) is modeled conventionally by [PERSON]'s law, i.e., by \(F_{AX}=\mu\,F_{N}\). A 3D rotor-casing contact friction problem is highlighted by the case study example in this section. The dissipative nonlinear force can be written in three dimensions, \(X\), \(Y\), and \(Z\), in matrix form:  
\[ \begin{cases}F_{X\_rub}\\ F_{Y\_rub}\\ F_{Z\_rub}\end{cases} =-H(\Delta+\delta_{ax}-\delta)\frac{(\Delta+\delta_{ax}- \delta)K_{s}}{\Delta} \begin{bmatrix}1&-\mu&0\\ \mu&1&0\\ 0&0&\mu\end{bmatrix}\begin{Bmatrix}X\\ Y\\ Z\end{Bmatrix}  \tag{9}\]  
where \(H\) is the Heaviside function applied along the lateral displacement (\(X\) and \(Y\)) defined as  
\[H(Z)= \begin{cases}1,\text{ if }\Delta>\delta-\delta_{ax}\\ 0,\text{ if }\Delta\leq\delta-\delta_{ax}\end{cases}  \tag{10}\]  
The term \(\delta_{ax}\) corresponds to the decrease in the radial clearance induced by a preload. For simplicity, the following set of assumptions can be made:  
\[\left\{ \begin{array}{ll}\delta_{ax}<\delta&if\text{ }the\text{ }load\text{ }is\text{ }not\text{ }sticient\text{ }to\text{ }ensure\text{ }a\text{ }rotor-\text{ }stator\text{ }contact,\\ \delta_{ax}=\delta&if\text{ }the\text{ }rotor\text{ }just\text{ }comes\text{ }into\text{ }contact\text{ }flush\text{ }with\text{ }the\text{ }stator,\\ \delta_{ax}>\delta&if\text{ }the\text{ }load\text{ }is\text{ }large\text{ }enough\text{ }to\text{ }generate\text{ }the\text{ }pretess\text{ }in\text{ }the\text{ }rotor.\end{array} \right. \tag{11}\]  
A significant point that appears for the first time in this study is the axial frictional force emanating from the axial force. It should be noted that the clearance is still well taken into account by this new formulation. Indeed, under the combined effect of this axial preload and a radial load, there may still be a loss in contact.
# TDCMR: Triplet-Based Deep Cross-Modal Retrieval for Geo-Multimedia Data
## 4 Methodology
### The TDCMR-Quadtree-Based Index Method
#### 4.5.1 Space Layer  
TH-Quadtree is a two-tier hybrid index structure that integrates the spatial and semantic layers on the vertical dimensions. In the spatial dimension, the spatial information of spatial objects is generally represented by two-dimensional latitude and longitude coordinates, which has better pruning effect than high-dimensional semantic information. Therefore, the spatial layer index is constructed by using the spatial position relation of spatial objects in geo-multimedia dataset, as the first layer of TH-Quadtree index structure. In this paper, quadtree is used to index the spatial position information of all spatial objects, which is efficient in two-dimensional spatial information organization. First, all spatial objects are regarded as the point set in the geographical space, and then each spatial object belongs to a minimum boundary rectangle MBR, i.e., each node on the quad tree, and then all the MBR are organized into different levels of tree structure according to the spatial distribution. In general, geospatial recursion is divided into hierarchical tree-type structures. Geo-multimedia data objects are all stored on each leaf node, while the root and middle nodes do not store spatial objects.
# Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation
## 3 Model Mathematical Formulation
### Model Algorithm  
Algorithm 1 describes the healthcare scheduling model optimization process. First, in the algorithm, libraries and input data parameters are imported. Afterward, the model is generated using the input data parameters, including the number of staff members (\(N\)), time slots (\(T\)), patients (\(P\)), resources (\(R\)), tasks (\(K\)), staff work hours (\(W\)), interaction between staff members (\(I\)), patient care time (\(C\)), resource demand (\(D\)), patient quality of care (\(Q\)), overtime hours (\(O\)), patient urgency (\(U\)), staff skill level (\(S\)), task matching (\(M\)), staff hours (\(H\)), total demand (\(Dt\)), overtime budget (\(B\)), and staff familiarity (\(F\)). Decision variables, such as \(x,y,z,o\), and \(v\), reflect scheduling constraint characteristics. Moreover, the objective function minimizes _obj_. Staff hours, availability, patient slot allocation, resource slot allocation, overtime budget, patient-staff relationship, and task-staff interaction are constrained. Optimizing the model seeks the ideal solution. If an optimal solution is discovered, the algorithm outputs the respective values for the staff, patients, resources, and overtime hours.  
```
1:Import required libraries
2:Set input data parameters: \(N\) (staff members), \(T\) (time slots), \(P\) (patients), \(R\) (resources), \(K\) (tasks), \(W\) (staff work hours), \(I\) (interaction between staff members), \(C\) (patient care time), \(D\) (resource demand), \(Q\) (patient quality of care), \(O\) (overtime hours), \(U\) (patient urgency), \(S\) (staff skill level), \(M\) (task matching), \(H\) (staff hours), \(Dt\) (total demand), \(B\) (overtime budget), \(F\) (staff familiarity)
3:Create the model
4:Create decision variables: \(x\) (staff assignment), \(y\) (patient assignment), \(z\) (resource allocation), \(o\) (overtime assignment), \(v\) (patient-staff assignment)
5:Create the objective function: _obj_
6:Set objective to minimize _obj_
7:Add constraints: Staff hours, Total staff, Patient slot, Resource slot, Overtime budget, Patient staff, Task staff
8:Optimize the model
9:if Optimal solution found then
10:Print the optimal solution: Staff assignment, Patient assignment, Resource allocation, Overtime Hours
11:endif
```  
**Algorithm 1** Healthcare Scheduling Optimization
## 3 Results and Discussion
### Direct and Indirect Solvent Effects  
Having obtained a rather good correlation between calculated and experimental CT bands of viologens in low and medium polar solvents, we now proceed to analyze the direct and indirect solvent effects. Direct solvent effects are the ones directly affecting the TD-DFT calculation of the absorption band, for a fixed geometry, while indirect solvent effects are those due to a different geometry (in turn due to the selection of a different solvent for the optimization). Therefore, we will see how the maximum of the CT band changes, using the methylviologen iodide as a reference system, as we change the solvent during the calculation both of the minimum energy structure as well as the absorption band.  
An interesting result that we obtain from the calculation is that direct solvent effects on the calculation of the CT energies of transition are very large, while they have a minor impact through the geometry. In other words, having optimized the geometry in a \"wrong\" solvent, that is using a different dielectric constant, does not affect too much the final result. Of course, optimizing the system in vacuum has a non-negligible effect on the final result, as we mentioned already, but the indirect solvent effect appears to levels off quite rapidly with increasing dielectric constant. Much more important is the careful selection of the solvent for the subsequent TD-DFT calculation. As we can see in Table 4, calculating the CT transition energy in vacuum leads to a huge red-shift. Moreover, the maximum of the absorption is also strongly influenced by the solvent dielectric constant, especially in passing from n-hexane to dichloromethane. The effect is less evident, but still significant, in passing from dichloromethane to acetone.
# Accelerating Electromagnetic Field Simulations Based on Memory-Optimized CPML-FDTD with OpenACC
###### Abstract
The first thing that must be taken into account to port to OpenACC is to maintain the data region persistently, avoiding unnecessary data transfer between the CPU and GPU, but in many FDTD applications are used absorbing boundary conditions to simulate an infinite domain. The energy dissipation has to be handled correctly to reduce the memory and improve the performance due to the GPU devices having a small global memory compared with the CPU. In this respect, it is shown how the absorbing boundary condition is handled, specifically the Convolutional Perfectly Match Layer (CMPL), in conjunction with the single precision to reduce to the maximum the memory used and have an excellent performance without introducing errors in long-term simulations. The study cases selected in this work have already been studied in [26], and the scenarios consist in simulating wave propagation in transverse electric mode (TE) in free space, in a parabolic reflector, and in a coplanar nanowaveguide at different frequencies based on Maxwell Curl equations solved by [PERSON]'s finite-difference algorithm [27]. Boundary conditions definition is very important for electromagnetic simulations because it allows simulating an infinite region without using infinite RAM memory, and we can find many formulations of Absorbing Boundary Conditions (ABC) for the FDTD to solve [PERSON]'s equations [28]. In this sense, ABC is essential for electromagnetic simulations to avoid spurious energy reflection, at different frequencies, within the physical domain, given the thinnest possible absorption region. There are various formulations of PML Boundary Conditions for the FDTD to solve [PERSON]'s equations [29; 30], but the Convolutional Perfectly Matched Layer (CPML) is very efficient and enough for the purposes [31; 32]. CPML plays an essential role in the code design in performance. For this reason, this work focuses on the CPMLimplementation, proving that proper management yields a reduction of memory used and execution time reduction [18]. This paper is organized as follows. Section 2 presents the governing electromagnetic equations with the FDTD method and the CPML formulation. Section 3 analyzes the algorithm considering the CPML boundary and the cycles intensities. In Section 4 is established the scenarios and carried out the simulations. Finally, the conclusion is given showing an acceleration of up to almost 13X versus the highly vectorized CPU serial version.
## DCPVP: Distributed Clustering Protocol Using Voting and Priority for Wireless Sensor Networks  
**[PERSON] and [PERSON] ***  
Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz 61639, Iran;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +98-613-333-0010 (ext. 5645); Fax: +98-613-333-0010 (ext. 5630).  
Academic Editor: [PERSON]  
_Received: 16 December 2014 / Accepted: 27 February 2015 / Published: 10 March 2015_  
This paper presents a new clustering protocol for designing energy-efficient hierarchical wireless sensor networks (WSNs) by dividing the distributed sensor network into virtual sensor groups to satisfy the scalability and prolong the network lifetime in large-scale applications. The proposed approach is a distributed clustering protocol called DCPVP, which is based on voting and priority ideas. In the DCPVP protocol, the size of clusters is based on the distance of nodes from the data link such as base station (BS) and the local node density. The cluster heads are elected based on the mean distance from neighbors, remaining energy and the times of being elected as cluster head. The performance of the DCPVP protocol is compared with some well-known clustering protocols in literature such as the LEACH, HEED, WCA, GCMRA and TCAC protocols. The simulation results confirm that the prioritizing- and voting-based election ideas decrease the construction time and the energy consumption of clustering progress in sensor networks and consequently improve the lifetime of networks with limited resources and battery powered nodes in harsh and inaccessible environments.  
**Keywords:** wireless sensor network; clustering; routing; voting; prioritize; energy efficiency
## 3 Multiple-Access Reservation Receiver Initiation MAC (MAR-RiMAC)  
In this paper, we suggest an enhancement that allows multiple devices to inform their intended receiver (or receivers) of their intent to transmit to it (to them) simultaneously. The suggested enhancement can be applied to various protocols. However, since we want to present a full protocol and since the suggested collision avoidance enhancement is highly suited for receiver-initiated protocols, and particularly the RI-MAC protocol, we exploit RI-MAC and integrate our enhancement into it. Accordingly, we start with a brief overview of the RI-MAC protocol. The full description of RI-MAC can be found in [9].
# Dynamic Performance of Soil-Tunnel System under Transverse Sinusoidal Excitations
## 5 Responses of Segmental Tunnel
### Acceleration Responses of Model Tunnel  
Time histories of acceleration responses at the top and bottom of the tunnel are shown in Figure 19.  
In the case of the Sin-2, acceleration responses at the top and bottom of the tunnel are basically consistent, and the waveforms of acceleration responses are approximately sine function. In the case of the Sin-3, obvious differences between the top and the bottom of the tunnel are found in peak acceleration. In the case of the Sin-6, the phase difference is showing in acceleration responses between the top and the bottom of the tunnel. Compared with the input motions, the peak acceleration at the bottom of the tunnel decrease significantly. In the cases of the Sin-9, Sin-11, Sin-14, and Sin-17, the waveforms of acceleration responses are irregular, the phase difference also exists in acceleration responses between the top and the bottom of the tunnel. The peak acceleration at the bottom of the tunnel is greater than that at the top of the tunnel.  
Figure 17: Acceleration responses with depths in ST model. (**a**) Sin-2 (**b**) Sin-3. (**c**) Sin-6 (**d**) Sin-9. (**e**) Sin-11 (**f**) Sin-14. (**g**) Sin-17.  
Figure 18: Difference between FF model and ST model.  
Figure 19: Acceleration responses of segmental tunnel. (**a**) Sin-2. (**b**) Sin-3. (**c**) Sin-6. (**d**) Sin-9. (**e**) Sin-11. (**f**) Sin-14. (**g**) Sin-17.  
As shown in Table 9, phase difference increases first and then decreases with the input frequency increasing. Phase difference reaches a maximum of 1\(\pi\) with the input frequency of 9 Hz. However, the difference of peak acceleration reaches a maximum of 69.41% with the input frequency of 17 Hz.  
From the respect of waveform, phase, and peak, these results indicate that the variation trend of acceleration at the top and bottom of the tunnel are basically similar with the acceleration distribution in model soil at the near depth, as can be drawn from Figures 13 and 14. This is due to the fact that the response of underground structures is dominated by the seismic response of surrounding soil [27]. On the other hand, the model tunnel is placed in the middle soil layer. The variations of phase differences also reflect the propagation law of wave in the middle soil layer under a different frequency of excitation.
## 3 System Overall Process
### Decoder  
#### 3.3.1 Classification Decoder  
In this experiment, the fully connected (FC) part of ResNet50 is used as the classification feature decoder. The task of this decoder is to further classify the features extracted from ResNet50, determining whether each region in the target image contains defects and further refining the type of defect. Specifically, the classification task divides the images into three categories: complete defects, no defects, and partial defects. If no defects are detected during the classification stage, the system halts further processing to improve  
Figure 6: Framework of feature extraction and dual decoders.  
Figure 7: Feature extraction attention map.  
detection efficiency; if complete defects are detected, the system moves on to the localization decoder to further determine the specific location of the defect; for partial defects, the system judges based on preset defect size standards, triggering the localization decoder if the defect size exceeds the threshold, otherwise marking it as a minor defect that does not require localization. This design effectively balances detection efficiency and processing complexity.
# Development and Evaluation of a LiFi-Transceiver Module for TMTC Intra-Satellite Communication +
## 4 Evaluation  
### Test Setup  
The evaluation is carried out with two LiFi-transceivers aligned in front of each other. To avoid unwanted external influences, these are placed in a closed box (see Figure 4(b)). The two LiFi-transceivers are set up at different distances from each other and are controlled by a host PC via the serial interface (as depicted in Figure 4(a)).  
The host PC configures the settings such as the baud rate and code. The test data are written into the buffer of the sender and the receiver is configured with the expected datalength. The test data consists of \(3\times 500=1500\) bytes \(=12\),000 bits. Since the receiver knows the expected number of bytes, it can send a signal to the host PC when these have arrived after successful transmission. Otherwise, there is an additional timeout that can be used if the data are lost during transmission. The host PC then starts the transmission. Following the data transmission, the host PC reads the received data from the receiver and calculates the Bit Error Ratio (BER).
## 5 Performance Evaluation
### Estimation Model Results Analysis
#### 5.2.4 Performance of the Number of Past Moments \(n\)  
LSTM needs to consider information from past moments. Considering too few moments may result in too much focus on current information while ignoring historical information, whereas considering information for excessively long times will introduce more noise. Therefore, we tested the estimation accuracy of the network according to variations in \(n\). The results are shown in Table 6.  
When \(n=1\), the LSTM degenerates to a single memory cell with a large MAE. When \(n=2\), the LSTM considers the information of the most recent past moments and therefore has the highest accuracy. However, because the network only considers information from very few moments in the past, it is overly reliant on this information and tends to perform poorly in real scenarios with high variability. When \(n=3\), the accuracy decreases again, indicating the possibility of overfitting the network at this point, which confirms our conclusion above. As \(n\) continues to increase, the MAE also slowly increases. We finally take \(n=5\), which not only takes into account the past information more fully but also does not introduce too much noise.
###### Abstract  
The generation of digital aerial photogrammetry products using unmanned aerial vehicle-digital aerial photogrammetry (UAV-DAP) has become an essential task due to the increasing use of UAVs in the world of geomatics, thanks to their low cost and spatial resolution. Therefore, it is relevant to explore the performance of new digital cameras equipped in UAVs using electronic rolling shutters instead of ideal mechanical or global shutter cameras to achieve accurate and reliable photogrammetric products, if possible, while minimizing workload, especially for their application in projects that require a high level of detail. In this paper, we analyse performance using oblique images along the perimeter (3D perimeter) on a flat area, i.e., with slopes of less than 3%. The area was photogrammetrically surveyed with a DJI (Da-Jiang Innovations) Inspire 2 multirotor UAV equipped with a Zenmuse X5S rolling shutter camera. The photogrammetric survey was accompanied by a Global Navigation Satellite System (GNSS) survey, in which dual frequency receivers were used to determine the ground control points (GCPs) and checkpoints (CPs). The study analysed different scenarios, including the combination of forward and transversal strips and oblique images. After examining the ideal scenario with the least root mean square error (RMSE), six different combinations were analysed to find the best location for the GCPs. The most significant results indicate that the optimal calibration of the camera is obtained in scenarios including oblique images, which outperform the rest of the scenarios for achieving the lowest RMSE (2.5x the GSD in Z and 3.0x the GSD in XYZ) with optimum GCPs layout; with non-ideal GCPs layout, unacceptable errors can be achieved (11.4x the GSD in XYZ), even with ideal block geometry. The UAV-DAP rolling shutter effect can only be minimised in the scenario that uses oblique images and GCPs at the edges of the overlapping zones and the perimeter.  
U