esa-sceva/satcom-chunk-collection huggingface dataset:
# Solid Phase-Based Microextraction Techniques in Therapeutic Drug Monitoring
## 3 Sample Pretreatment--Solid Phase-Based Microextraction Techniques
### Solid-Phase Microextraction and in-Tube Solid-Phase Microextraction
Micro-SPE is a miniaturized technique that uses a device comprising a porous membrane envelope that contains a small amount of sorbent. There are several commercially available sorbents such as activated alumina, C2, C8, C18, activated carbon (CA) that provides \(\uppi\)-\(\uppi\) and hydrophobic interactions, Haye-Sep B and A, Porapak R, carbograph (GC) and multi-walled carbon nanotubes (MWCNTs) also with \(\uppi\)-\(\uppi\) interactions; however, more recently, the most used are zeolite, silica, MIPs and metal-organic frameworks (MOFs) with electrostatic and hydrogen bonds interactions [46; 47]. This methodology aims to reduce the dimensions of the device with the use of micro or nano materials, reduce the volume of sorbents and their operating time and also the consumption of organic solvents. All this, combined with the simplicity of processing, its enrichment factor, high selectivity and sensitivity, compatibility with separation and detection systems and with headspace and immersion modes, lower time and associated costs, justify all the benefits and advantages of the technique. However, this procedure also presents disadvantages such as fragility of the fibers, restricted range of stationary phases and carry-over [46; 47; 48]. DSPE is an alternative technique applied to viscous, solid and semi-solid materials. Usually, the samples are mixed with the sorbent, and the mixture is transferred to an extraction column to be packed; subsequently, this is where the washing and elution steps will be carried out, in order to extract and isolate the analytes from the sample. This technique does not require repeated centrifugation, filtration or extraction steps; the eluents can be analyzed by GC or LC, but it cannot be completely automated. The extraction step is omitted, leading to the use of fewer organic solvents and less sample preparation time, being a more flexible and robust technique for which there is no degradation or denaturation when applied at room temperature and mild atmospheric pressure. The effectiveness and selectivity will depend on the solid support used and the elution solvent chosen. The most used materials are reversed-phases, such as C18- and C8-silica bonded phases, normal-phase materials such as alumina and florisil with adsorption/polar interactions, silica that exhibits hydrophilic interaction with the solute based on charge-based interactions, hydrogen bonding, \(\pi\)-\(\pi\) and dipole-dipole interactions, carbon-based materials and MIPs [13; 49; 50; 51]. Table 5 compiles some of the advantages and disadvantages of the aforementioned approaches [32; 47; 48; 51]. Table 6 displays the bioanalytical procedures published between 2010 and 2022 (pipette tip SPE), between 2015 and 2022 (\(\mu\)-SPE) and between 2017 and 2022 (DSPE or MSPD) concerning TDM.
## 3 Results and Discussion
### Effect of Flocculant Dosage on Harvesting Efficiency
Nevertheless, the effect of pH was also investigated, as pH is an important parameter in flocculation/coagulation [33; 34; 35], and the addition of both chemical flocculants influenced the final pH of the microalgae suspension due to their acidic nature. Therefore, a new experiment was conducted in which, instead of adding a fixed amount of flocculant, the final pH was used as the target value, i.e., the pH adjustment of the samples was performed by adding a solution of AS (pH 3.9) or IC (pH 2.6) to reach the final pH values of 4.8, 5.8, and 6.8. Since the pH of the AS solution was higher than that of the IC solution, it was always necessary to add more AS to reach a given pH value. Nevertheless, IC resulted in the highest harvesting efficiency of 99% at a pH of 5.8, while AS could not reach efficiencies higher even with a further addition to reach a final pH of 4.8 (Figure S5). Therefore, IC proved to be a better coagulant than AS in the tested conditions, with a higher maximum harvesting efficiency obtained at a lower dosage. Optimal doses for all tested flocculants were in a similar range and lower than 500 mg L\({}^{-1}\), resulting in a biomass/additive ratio between 1 and 5. Interestingly, the harvesting efficiency decreased at high doses for both chemicals. For _M. Oleifera_ seed powder, the harvesting efficiency was not altered, but more loose flocs were observed at higher dosages, resulting in higher standard deviations in the obtained results (Figure 1). This has also been observed in previous studies and is attributed to a destabilizing effect when excess flocculant molecules are present in the solution [36]. Nevertheless, _M. Oleifera_ seed powder had a good performance over a wide range of concentrations, indicating a less pronounced destabilizing effect that can come as an advantage in industrial processes, as it would result in a more robust process with a lower risk of reduced performance due to a dosage error.
## 3 Materials and Methods  
Efficient and accurate diagnosis of an emergency event is of key importance in achieving the assumed (required) productivity and efficiency during the elimination of failures. To ensure uninterrupted production continuity, only preventive actions are no longer sufficient today, and proactive or autonomous actions, using the latest technical solutions (including programming) that enable more precise and faster diagnostics and continuous monitoring of the key (and often: critical) elements of technical systems, are becoming the standard. The selected company in the medical industry is characterized by specific requirements and expectations with regard to maintenance activities to ensure production continuity. This is due to:  
* Integrated technological process, based on production lines, when the failure of one element causes downtime of the entire technical facility;
* Short turnaround time of a production order (1-5 h), where a breakdown time of more than 2 h requires rescheduling of the production process;
* Large distances between technical facilities (up to 1000 m), which significantly increase the UR response time and in many cases delay effective fault diagnosis;
* Complexity of the technical facilities, requiring complex logistical operations related to: spare parts, maintenance operations and outsourcing;
* Provision of UR support for a multi-shift production cycle. It is the intention of the authors to expand measures to implement a maintenance strategy--predictive maintenance in a selected company by:
* Historical analysis of failures, in order to classify types of failures, their causes and maintenance activities were taken;
* Possibility of using AI to detect the causes of failures and identify the necessary maintenance actions on the basis of historical data;
* Design of an autonomous monitoring system for the technical facility that enables the automation of diagnostic processes in combination with AI.  
The starting point for the development of the activities is data from the CMMS (Computerized Maintenance Management Systems) system supporting the broadly understood Maintenance activities. The CMMS system records data: events related to all types of breakdowns, overhauls or inspections and other maintenance activities, as well as the management of maintenance personnel and service documentation.  
The selected company has had a dedicated CMMS system supporting maintenance management in place for many years, modified and adapted to new organizational and production challenges. Over recent years, the system has undergone significant transformations related to inventory management, parts and materials, procurement, breakdown handling and maintenance and repair work.
## 2 Related Work
### Safety, Reliability and Energy Efficiency  
Autonomous forklifts operate in dynamic and safety-critical environments, and scientific contributions in this area aim to improve their safety and reliability. The authors of [54] presented a detailed explanation of safety technologies. They also provided a thorough description of safety technologies and the outcomes of an experiment, assessing how workers and autonomous systems interacted. In [55], the authors showcased a system design that utilizes infrastructure sensors to mitigate human-robot collisions at blind corners, enhancing the safety of automated forklifts.  
The paper [56] focuses on discussing the key hardware and software components, detailing experiments highlighting the performance of \"the Hot Metal Carrier\" (HMC) amidst dynamic objects in a typical worksite. The focus of the paper is on developing fail-safe mechanisms, real-time monitoring systems and advanced planning algorithms that prioritize safety considerations while ensuring efficient operations.  
With a growing emphasis on sustainability, scientific contributions in this area aim to optimize the energy consumption of autonomous forklifts. Researchers and scholars are en gaged in creating energy-saving navigation algorithms, smart power management systems and the integration of renewable energy sources to diminish ecological consequences and operational expenses.  
Many researchers have made different contributions in this important domain such as [PERSON] et al. [57], whose study focuses on a systematic examination of two cargo-handling equipment types--automated guided vehicles and forklifts--within container ports. It aims to assess their economic impact, power consumption and environmental implications. Also, the study by [PERSON] in [58] emphasizes the advantages of digitizing logistics processes and investigates the sustainability implications of such digitization. It adopts a qualitative approach, employing a single case study focused on Fast-Moving Consumer Goods (FMCG) companies and their transport service providers in Turkey.  
In [59], [PERSON] illustrates a study which involved creating a robotic platform, establishing an autonomous navigation control system, and developing a verified energy consumption model. This doctoral research aimed to enhance the energy efficiency of the Ilon Mecanum wheel. The paper [60] discusses the need for improved energy efficiency in ports and terminals due to rising energy costs and environmental concerns. It also identifies research gaps and proposes future directions for research in this area.
## 3 Results
### Variations in the V2 Segment  
In the lower cervical spine region, UE-V2 and MM variations were observed. Among all variations, the incidence of UE-V2 was relatively high, with a frequency of 10.44% (121/1158). The incidence of UE-V2 in males was 10.35%, while in females it was 10.58%. The incidence of UE-V2 variation on the right and left sides was 5.44% and 5.01%, respectively. The incidence of UE-V2 at different vertebral levels was observed in the sequence of C5 > C4 > C7 > C3 with a frequency of 71% (86 out of 121), 20.7% (25 out of 121), 7.43% (nine out of 121), and 0.8% (one out of 121), respectively. Figure 5 shows UE-V2 at the third cervical vertebra, which is a rarely reported anomaly.  
Figure 4: (**A**) A coronal CT angiography image using the bone window shows left paracondyl process with the left vertebral artery (arrow) within a canal formed by an enlarged lateral mass of C1 (dashed arrow) merging with the occipital condyle. (**B**) A paracondyl process is seen in another patient in the axial plane with an enlarged left lateral mass of C1 (dashed arrow) articulating with the left occipital condyle (arrowhead). (**C**) 3D reconstructed image shows the paracondyl process on the left side (arrow).  
There were no statistical differences between the occurrence of UE-V2 incidence and sex (\(p\) = 0.979) or side (\(p\) = 0.701) (Table 1). The other variation of the V2 segment was MM, which refers to a VA closer to the midline of the cervical spine. The overall incidence of MM variation was 1.29% (15 out of 1158) of courses. It was observed in 1.18% and 1.45% of courses in males and females, respectively (Table 1). This variant was observed predominantly in older subjects. Except for one patient who was twenty-two years old, the average age of the patients with MM was 63.3 years with an age range of 46-87 years. The incidence of MM variation was highest on the left side with 0.95% (Table 1). There were no significant differences in MM incidence between sex (Table 1, \(p\) = 0.892) or side (Table 1, \(p\) = 0.119). The average distance of MM variation on the right side was 6.25 mm (range: 3.3-8.7) and 7.8 mm (range: 6.4-9.6) on the left side.
## 3 Stress Integration Algorithms of the e-HAH Model for Elastic-Plastic FE Simulations
### Stress Update Algorithms for Elastic-Plasticity  
In the implementation of small strain elastic-plasticity, the total strain increment, \(\Delta\varepsilon\), is additively decomposed into its elastic component, \(\Delta\varepsilon^{\text{e}}\), and plastic component, \(\Delta\varepsilon^{\text{P}}\).  
\[\Delta\varepsilon=\Delta\varepsilon^{\text{e}}+\Delta\varepsilon^{\text{P}}. \tag{15}\]  
If the associated flow rule is applied, \(\Delta\varepsilon^{\text{P}}\) is calculated using the gradient of the yield function,\(\Phi\), and a scalar multiplier, \(\Delta\gamma\).  
\[\Delta\varepsilon^{\text{P}}=\Delta\gamma\,\frac{\partial\Phi}{\partial \sigma}. \tag{16}\]  
The stress increment, \(\Delta\sigma\), can be expressed as for the isotropic linear elastic metals:  
\[\Delta\sigma=\mathbf{C}\,:(\Delta\varepsilon-\Delta\varepsilon^{\text{P}})= \mathbf{C}\,:\left(\Delta\varepsilon-\Delta\gamma\,\frac{\partial\Phi}{ \partial\sigma}\right), \tag{17}\]  
where \(\mathbf{C}\) is the fourth-order elastic stiffness matrix. The relation between the plastic multiplier, \(\Delta\gamma\), and the equivalent plastic strain increment, \(\Delta\overline{\varepsilon}\), is obtained from the 1 st order homogeneous function and plastic work equivalence principle.  
\[\Delta\overline{\varepsilon}=\frac{\boldsymbol{\sigma}:\Delta\varepsilon^{ \text{P}}}{\Phi}=\frac{\boldsymbol{\sigma}:\Delta\gamma\frac{\partial\Phi}{ \partial\sigma}}{\Phi}\,=\,\Delta\gamma. \tag{18}\]  
Therefore, it is noted that the stress integration in Equation (17) is used to update the equivalent plastic strain increment in Equation (18). Depending on the algorithmic treatment in the above general procedure, various algorithmic procedures have been proposed for numerically accurate and efficient integration methods.  
In general, for most constitutive laws and their corresponding numerical implementations, the following predictor-corrector algorithm is employed:  
\[\boldsymbol{\sigma}_{\text{n}+1}^{\text{Trial}}=\boldsymbol{\sigma}_{\text{n} }+\mathbf{C}\,:\Delta\varepsilon_{\text{n}+1}, \tag{19}\]  
\[\mathbf{R}=\Phi\Big{(}\boldsymbol{\sigma}_{\text{n}+1}^{\text{Trial}},\, \mathbf{g}_{\text{i},\boldsymbol{n}},\,\mathbf{\hat{R}}_{\text{n}}^{\text{s} }\Big{)}-\overline{\sigma}(\overline{\varepsilon}_{\text{n}})<0\,,\,(\text{ i}=1\sim 4,\,\text{L},\,\text{S},\,\mathbf{R}), \tag{20}\]  
where \(\Delta\varepsilon_{\text{n}+1}\) is the total strain increment at the current time step \(\text{n}+1\), and \(\boldsymbol{\sigma}_{\text{n}+1}^{\text{Trial}}\) is the trial stress as a predictor. The numerical algorithm starts by checking the condition in Equation (20) as an elastic process, and an iterative process is followed if the condition in Equation (20) is not satisfied. Note that Equation (20) is simplified to represent the yield function \(\Phi\) as a function of the state variables in the e-HAH model.  
In the next sub-sections, existing numerical algorithms for the stress integration of the e-HAH model are firstly introduced, followed by the algorithmic development of the proposed modeling.
# Video Multi-Scale-Based End-to-End Rate Control in Deep Contextual Video Compression
## 3 The Proposed Algorithm  
### Framework  
The proposed algorithm has three main steps. For the first step, the original video is dealt with using multi-scale bicubic downsampling to reduce the data. The interpolated pixel \(I(i+u,j+v)\) can be expressed as  
\[I(i+u,j+v)=A\cdot B\cdot C \tag{8}\]  
where \(i\) and \(j\) are integer values of the interpolated pixel position in the horizontal and vertical coordinate axes, respectively. \(u\) and \(v\) are decimal values of the interpolated pixel position in the horizontal and vertical coordinate axes, respectively. Arrays A, B, and C are expressed as  
\[A=\left[S(1+u)S(u)S(1-u)S(2-u)\right] \tag{9}\]  
\[B=\left[\begin{array}{cccc}I(i-1,j-2)&I(i,j-2)&I(i+1,j-2)&I(i+2,j-2)\\ I(i-1,j-1)&I(i,j-1)&I(i+1,j-1)&I(i+2,j-1)\\ I(i-1,j)&I(i,j)&I(i+1,j)&I(i+2,j)\\ I(i-1,j+1)&I(i,j+1)&I(i+1,j+1)&I(i+2,j+1)\end{array}\right] \tag{10}\]  
\[C=\left[S(1+v)S(u)S(1-v)S(2-v)\right]^{T} \tag{11}\]  
where array B is the 16-integer pixel in the original image. \(S(\cdot)\) in array A and C is the interpolated function, which is defined as  
\[S(n)=\left\{\begin{array}{cc}1-2|n|_{2}+|n|^{3}&|n|<1\\ 4-8|n|+5|n|_{2}-|n|^{3}&1\leq|n|<2\\ 0&|n|\geq 2\end{array}\right. \tag{12}\]  
Therefore, according to Equation (8), the frames in the original video can be multi-scale downsampled. In this paper, \(\times 2\), \(\times 3\), and \(\times 4\) downsampling methods are mainly used.  
For the second step, the low-resolution frame is placed in the end-to-end rate control model for coding. For end-to-end rate control, a two-branch residual-based network is designed to obtain the optimal bit rate ratio for every frame by considering the temporal coding feature. With the optimal frame bit rate ratio, the bit rate can be controlled accurately and allocated reasonably to every frame. A two-branch regression-based network is used to predict the optimal \(\lambda\) to control bit streams by balancing the bit rate and distortion. Then, \(\lambda\) is input into the encoder of DCVC to generate the bit stream. The bit cost will be stored in a bit buffer to adjust the bit allocation of the next frame. Finally, the decoder will reconstruct the low-resolution frame from the bit stream. Therefore, with end-to-end rate control, the low-resolution frame can be encoded under a limited bit rate and reconstructed from the bit stream.  
For the third step, HEDS-Net is designed to restore the high-resolution frame from the reconstructed low-resolution frame, which mainly consists of the multi-branch distillation network, the lightweight attention LCA block, and the upsampling network. The multi-branch distillation network aims to extract deep features. The lightweight attention LCA block aims to enhance learning and expression abilities to rebuild detail features. The upsampling network aims to enhance the resolution and rebuild low-frequency features. The framework of the proposed algorithm is shown in Figure 2.
## 4 Results on a Real Case: Proof-of-Concept Validation
### Results of the Path Planner System
#### 4.2.2 Determining the Reference Points  
Algorithm 8 introduced how to determine the reference points that could be used as intermediate points in the path planner. Figures 7 and 8 show a graphical example of these reference points. The former set of images shows how the tree row vertexes are computed, whereas the latter includes the process to extract the middle point of each path row.  
Figure 6: Detecting tree rows with Hough. (**a**) current parcel view with the first detected row, (**b**) current parcel view with the first and second detected rows, (**c**) current parcel view with all detected rows.  
Figure 7: Detecting tree rows with Hough: (**a**) the extended satellite image related to the parcel with the detected lines and (**b**) the extended parcel with detected vertexes.
## 2 Key Segment Closing Method
### Thermal Prestressing Method  
In bridge design, the thermal effect has been considered previously as merely a source of undesirable forces. However, there have been recent studies on the thermal prestressing method (TPSM) indicating constructive uses of the thermal effect [7; 8]. The basic concept of the thermal prestressing method is to convert the elastic deformation energy accumulated from temporary thermal deflection into a prestressing force for a structural system. Thermal deflection is induced by a specially designed heating system for heating certain portions of steel members. Like any other prestressing method, the thermal prestressing method can effectively reduce member stresses due to design loads.  
The thermal prestressing method provides artificial heat to the pre-selected structural members, and can be categorized into three types according to the heating method as illustrated in Figure 2[6]. If the whole cross section of a member is subjected to a uniform temperature distribution by heating, the member is elongated in one direction without producing eccentricity (Figure 2a). After member elongation reaches a pre-calculated length, the connection is fixed to unify the structural member and heating is then removed. When heating is removed, the member in the heated region (shadow area) tends to shrink. However, the connection restrains the contraction, thereby producing initial tensile force throughout the member.  
Another type of thermal prestressing can be observed in Figure 2b. If a member is subjected to temperature gradient, it undergoes bending deformation (dashed line). When the temperature gradient is removed, the ends are fixed after the bending deformation and the contracting force of the heated part induces initial stresses in the member. The last type of thermal prestressing uses a separate prestressing member (Figure 2c). A heated cover-plate is attached to the member in need of prestressing. The contracting force of the cover-plate then induces prestressing in the member.
###### Abstract
Meanwhile, according to the feedback mechanism [18], the scheme creatively proposes that messages will be sent to the future two-hop relay nodes, which have a relatively higher transmission preference value than message carriers. In conclusion, the FCNS is a novel routing-forwarding method, which considers the multiple and comprehensive similarity factors of nodes--especially mobile similarity. The contributions of this paper are listed as follows:  
* By effectively collecting and updating user information, message applications are able to accurately assess the degree of comprehensive similarity between users (the social and mobile similarities) and determine the special transmission relationships between them. * According to the information entropy and fuzzy inference system, we synthetically consider the social and mobile similarities of nodes and obtain the level of transmission preferences of each node. Additionally, message carriers can select suitable relay nodes by collecting and comparing the transmission preference values of nodes in opportunistic social networks. * To achieve an efficient and reliable data transmission process, this scheme uses the feedback mechanism to ensure that the future two-hop relay nodes have a higher transmission preference value than message carriers, which makes the routing and forwarding of messages more continuous and stable. * In accordance with the simulation results in the Opportunistic Networking Environment (ONE), this novel algorithm (FCNS) enhances performances on the delivery ratio, network overhead, and end-to-end data transmission delay. The rest of the paper is organized as follows: In Section 2, we will give a brief introduction to the related works; the FCNS routing-forwarding algorithm will be proposed and analyzed in Section 3; in Section 4, the simulation results and performance analysis are provided; the discussion of the paper will be shown in Section 5, and; finally, the conclusion of the paper is shown in the last section.