esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental
### Batch Optimizations and Adsorptions Studies  
The adsorption studies for both polymers were performed at room temperature using deionized water that had previously been spiked with 5 mg L\({}^{-1}\) emtricitabine, tenofovir, naproxen, diclofenac, ibuprofen, and efavirenz. The purpose of this experiment was to investigate the impact of pH (2.5-11), MIP mass (15-55 mg), and adsorption period (10-60 min) on extraction efficiency as well as the effect of concentration range (5-50 mg L\({}^{-1}\)). During the optimization, a single parameter was altered at a time, while the others remained constant. The mixture was agitated at room temperature for 20 min before being transferred to 3 mL SPE tubes, where the liquid fraction was discarded. Fits were used below and above the polymer to prevent sorbent loss. Experimental extraction efficiency was calculated and carried out in triplicate. Using Equation (1), the extraction efficiency, or the amount of each ARV and NSAID removed by the MIP, was calculated as the difference between the spiking amount (\(C_{0}\), mg L\({}^{-1}\)) and the residual amount in solution after extraction (\(C_{f}\), mg L\({}^{-1}\)). The adsorption capacity, or the maximum amount of each ARV and NSAID absorbed by a unit mass of MIP and NIP, was calculated using Equation (2), where \(V\) is the volume (L) of the solution, and \(W\) represents the mass of the polymer in (g) [32; 33; 34].  
\[\text{Extraction efficiency}\ (\%)=\frac{(C_{0}-C_{f})}{C_{o}}\times 100 \tag{1}\]  
\[\text{Adsorption capacity}\ \left(\text{mg}\cdot\text{g}^{-1}\right)=\frac{(C_{o}-C_{f})}{ W}V \tag{2}\]  
For pseudo-first-order and pseudo-second-order kinetic models, Equations (3) and (4) were used to illustrate the adsorption method. The adsorption mechanism was describedusing the model with the highest \(R^{2}\) value, where \(Q_{e}\) and \(Q_{t}\) are the adsorption capacity parameters (mg:g\({}^{-1}\)) at equilibrium and at time t (min), respectively. \(K_{1}\) and \(K_{2}\) are the Lagergren pseudo-first-order and pseudo-second-order rate (min\({}^{-1}\)) sorption constants [30; 35].  
\[\text{Log}(Q_{e}-Q_{t})=\text{Log}Q_{e}-\frac{K_{1}t}{2.303} \tag{3}\]  
\[\frac{t}{Q_{e}}=\frac{1}{K_{2}Q_{2}}+\frac{t}{Q_{e}} \tag{4}\]  
The linearized forms of the Freundlich and Langmuir isotherms were used to describe the extent of adsorption and the isothermal analysis of the polymers, respectively, where n denotes adsorption intensity or surface heterogeneity, \(C_{e}\) the target molecules adsorption capacity (mg:g\({}^{-1}\)), \(K_{f}\) is the Freundlich constant, \(q_{e}\) is the compound uptake in at equilibrium in (mg:g\({}^{-1}\)), and \(Q_{max}\) the maximum adsorption capacity (mg:g\({}^{-1}\)), and \(K_{L}\) the Langmuir adsorption equilibrium constant. The intercepts and slopes of the linear plots of \(C_{e}/Q\) vs. \(C_{e}\) were used to calculate the constants \(K_{L}\) and \(Q_{max}\) (Equations (5) and (6)).  
\[\text{Log}q_{e}=\text{Log}K_{f}+\frac{1}{n}\text{Log}C_{e} \tag{5}\]  
\[\frac{C_{e}}{Q}=\frac{C_{e}}{Q_{max}}+\frac{1}{Q_{max}\;{}_{X}K_{L}} \tag{6}\]
## 2 Background
### Why WSN Is Important in Forest Fire Detection  
The protocol for energy-efficient clustering with weighted-based optimization in wireless sensor networks for early forest fire detection is designed to improve the energy efficiency and detection accuracy of wireless sensor networks used for forest fire monitoring. The protocol uses clustering algorithms to group sensors into clusters and assign a cluster head to each cluster. The cluster head is responsible for collecting data from its member sensors, processing the data, and sending it to the base station. The optimization of the clustering is based on weighted factors such as energy consumption, distance between sensors, and sensing range. This protocol helps to conserve energy in the network and improve the accuracy of forest fire detection. Wildfires pose a significant hazard to both human lives and natural resources. As is well known, forest fires can travel great distances  
Figure 1: Basic WSN Structure.  
and burn for weeks or months, posing serious threats to the surrounding environment and frequently leading to air pollution in neighboring countries. The forests, however, are typically situated in inaccessible, ungoverned regions. Additionally, the forests typically include dried wood, trees, and leaves, all of which can be used as fire fuel. Both natural and human-caused climate change and human activity present risks for forest fires. As the forest fire is usually discovered after it has already spread across a broad area, putting out such blazes is an arduous task. As a result, forest fires cause substantial economic and ecological losses. Further, forest fires cause lasting harm, such as the loss of biodiversity and the acceleration of climate change. As a result, keeping an eye on forests is crucial for preventing the damage that might be caused by fires. However, due to its continuous, prolonged surveillance of the forest, the network designed to detect fires in the woods uses a great deal of energy. Thanks to its many benefits, the wireless mobile sensor network is increasingly being used for forest fire detection. These include the fact that the network's sensors are able to detect heat from fires, that it can be installed at any time and place, that it can provide access to information about the sensors' locations, that it is self-configuring, that the sensor nodes can function as routers, that it is less expensive than wired sensor networks, that it is scalable and flexible, and that it is highly reliable. However, the lifetime of the network for forest monitoring is limited by the inefficiency of the routing protocols in the field of forest fire networks with regard to data transmission and energy consumption. Energy-Efficient Clustering with Weighted-Based Optimization is a newly developed routing protocol presented in this study. For forest fire monitoring networks, the described EECWO protocol takes into account route length between nodes, energy usage, and the like, to boost network performance. Furthermore, many evaluation metrics are used to assess LARRR therapies' efficacy.
# A C-BiLSTM Approach to Classify Construction Accident Reports
## 3 C-BiLSTM-Based Classification Framework
### Data Pre-Processing  
In real applications, raw data often contain a lot of noise information which not only affects the accuracy of data mining but also influences the work efficiency. Therefore, a series of pre-processing work needs to be performed on the data before the data are used. The main tasks of text pre-processing are word segmentation and stop word removal. Word segmentation refers to the division of long text data into individual words or phrases. NLTK is a kind of natural language toolkit that is the most commonly used in the NLP field and provides a set of more professional English word segmentation tools [47]. Therefore, in this study, the tokenizer segmentation package provided by NLTK was used directly to convert the text data into a word-level dictionary. In-text datasets, the most common words may appear many times, such as \"in,\" \"the,\" and \"a,\" and they do not provide valuable information. Stop word removing means the removal of these words which can effectively reduce the size and dimension of data and will make training faster and better [48]. Other pre-processing works include the conversion of uppercase letters to lowercase letters, removal of numbers and special symbols, and lemmatization. All these can effectively clean the data to improve the accuracy and speed of data mining.
# Target Identification via Multi-View Multi-Task Joint Sparse Representation
## 5 Target Identification Method Based on Multi-View Multi-Task Sparse Learning
### Simplified MVMT Methods  
To further verify the superiority of multi-view sparse learning performance compared with single-view single-feature tracker, the single-task single-view target identification method (STMV), and single-view multi-task target identification method (MTSV) are designed, which are three special cases of multi-view multi-task (MVMT) method.  
For single-view multi-task learning algorithm (MTSV), it can be obtained from the following formula:  
\[\underset{W,P,Q}{\min}||AC-X||_{2}^{2}+\omega_{1}||P||_{1,2}+\omega_{2}||Q^{T}|| _{1,2} \tag{12}\]  
The difference between single-view multi-task learning algorithm and multi-view multi-task learning algorithm is that when constructing a dictionary, only a single-view feature is used to describe a template and train multiple particles at the same time.  
\[D=[T^{1},T^{2},\cdots,T^{n}] \tag{13}\]  
where \(T_{k}\) (\(k\) = 1, 2,..., \(n\)) is the extracted single-view template. By comparing Equations (3) and (12), it can be obtained that SVMT is a special case of MVMT.
# Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms
## 4 Proposed System
### System Components
#### 4.1.2 Server  
The server plays a central role in the system by handling user requests, verifying user credentials, and securely storing encrypted images along with metadata related to encryption processes. Furthermore, it acts as an intermediary, forwarding client requests related to cryptographic operations to the CSP, where those operations are performed. A detailed discussion of the CSP component is provided in Section 4.1.3.  
In this context, communication between the server and the CSP is authenticated and conducted over a secure internal network, ensuring that only the server is exposed to the public internet. By serving as an intermediary, the server ensures the secure execution of cryptographic operations and protects sensitive data from unauthorized access.  
Finally, the server manages user sessions through the use of JWT authentication, ensuring that users remain authenticated throughout their interactions with the system. By validating the JWT token in each request, the server can authenticate users without the need for server-side session storage.
# A New Concept Compliant Platform with Spatial Mobility and Remote Actuation
## 2 Description of the New Concept Platform
### Topological Analysis  
Considering an equivalent mechanism PRBM with rigid bodies and classical U, S, and R pairs, three legs guarantee six degrees of freedom to the platform. In fact, the PRBM presents \(\ell=8\) rigid links and \(m=9\) kinematic pairs. In case Type (e) joints are replaced by U joints, the structure will be composed of three Type (h) revolute joints R with degree of constraint \(c_{i}=5\) in space, three Type (e) U joints with \(c_{i}=4\), and six S joints with \(c_{i}=3\). Therefore, according to the general topological [PERSON]'s formula, the PRBM has:  
\[F=\lambda\left(\ell-1\right)-\sum_{i=1}^{m}c_{i}=6\left(8-1\right)-3\cdot 5-3 \cdot 3-3\cdot 4=6 \tag{1}\]  
overall degrees of freedom (DoF), where \(\lambda=6\) is the mobility number for general spatial motion.  
As mentioned above, torsion cannot always be excluded on U elastic Type (e) joints, depending on the minimum diameter of the normal cross-section, which would make them practically equivalent to S joints. In this case, [PERSON]'s formula would yield \(F=9\) DoFs for the platform, but it must be remarked that three of such DoFs would be uniquely dedicated to providing idle rotations to the upper Type (d) links. In this case, any Link (d) would be connected to the remaining parts of the structure by means of two S type pairs, and so, rotations about an axis passing through the centers of the two spherical joints would be possible. These rotations are naturally counterbalanced by internal elastic reactions that would drive the system back toward the minimum of the potential elastic energy. Alternatively, they could make it easier to achieve an optimal attitude of Type (f) cables that pull down Type (d) links. For these reasons, a different manipulator could be obtained, as an alternative, where Type (e) links are replaced by Type (c) links, with no problems for mobility.  
According to the result obtained by using Equation (1) and considering that each leg is supposed to be controlled by two cables, a three-legged platform will be the one that has a number of independent actuators that is equal to the number of DoF and therefore will be considered as an optimal choice for the compliant structure. In fact, according to the principle of selective compliance, any elastic element where compliance is concentrated presents a peculiar geometry that is intended for a specific deformation, for example a prescribed flexion or torsion axis, and all the deformations different from the prescribed ones are considered to be as parasitic deformations, because they do not contribute to the desired motion, but only increase the stress level in the elastic material. For this reason, a satisfying geometry of a compliant mechanism is the one that minimizes the parasitic deformations.
## 3 Results and Discussions
### Optimizing Printing Parameters with MOPSO  
Based on the rank from the sensitivity analysis, we selected four highly sensitive parameters that affect surface roughness the most: PI and PA concentrations, layer thickness (LT), curing time (CT). These parameters are used as control parameters in MOPSO algorithm. Since our goal is to determine process parameters to produce high surface quality part as fast as possible, the objectives for MOPSO are RMSE of surface profile of a printed part and printing time. Time required to print a structure (\(\text{t}_{\text{total}}\)) is given by  
\[\text{t}_{\text{total}}=\text{ t}_{\text{layer}}*\text{number of layers}, \tag{15}\]  
\[\text{t}_{\text{layer}}=\text{ t}_{\text{exposure}}+\text{t}_{\text{stage}}, \tag{16}\]  
where \(\text{t}_{\text{layer}}\) is the time required to complete one cycle for a layer and \(\text{t}_{\text{exposure}}\) is the curing time that the UV light is exposed on resin surface. \(\text{t}_{\text{stage}}\) is the time required for the linear stage to move sample holder in each process cycle for a layer (measured to be 5 s in experiment). The number of layers is determined by the overall height of structure divided by the layer thickness. In this study, the height that we want to print was set to be 1 mm.
## 7 Results and Discussion
#### Array Structure  
The unit cell does not function satisfactorily as an independent unit. Hence, an array of unit cells is the best technique to obtain the expected EM properties from a metamaterial. The 2 \(\times\) 1 and 2 \(\times\) 2 arrays of the proposed unit cell with the same substrate material were simulated by CST (Figure 18a,b). A parametric study was performed using 2 \(\times\) 1 and 2 \(\times\) 2 arrays to investigate the coupling effect between the designed unit cells for different combinations of the array recorded nearly identical S21 responses to the unit cell. Therefore, the efficiency of the arrays was manifested. Figure 19 demonstrates the graphical presentation of S11 and S22 comparison by CST for array combination and the proposed unit cell. Although the resonance frequencies were a little distorted in the new array metamaterial, the triple band resonance properties were still functioning based on the simulated results of the array presented in Figure 19. The authentication between the previous configurations and proposed configuration values are listed in Table 5.  
Figure 19: Comparison of S11 and S21 between the unit cell and array combinations.  
Figure 18: (a) 2 \(\times\) 1 array, and (b) 2 \(\times\) 2 array.  
On the other hand, the symmetric pattern of the used SSR and hexagonal metal strip were important in achieving the abovementioned performance. This symmetric pattern of the unit cell helped to decrease the induction effects of the magnetic field. For this reason, the transmission coefficient (S21) is not affected by the harmonic and shows almost the same scattering behaviour and EM properties in the full frequency spectrum.
## 4 Matrix Converters in the Connection of Renewable Energies to the Grid
### Matrix Converter Application for the Connection of Offshore Wind Farms to the Grid  
Offshore wind farms will constitute a significant part of future renewable power plants. Bigger and more powerful WECS are required to reduce the life cycle cost of these technologies due to the higher cost of installation, operation and maintenance because of challenging weather conditions, operator transportation and material shipment from the mainland to offshore wind parks [78].  
A fully modular configuration based on the M3C topology, as depicted in Figure 16a, is analyzed in [89], featuring high scalability potential and easy redundancy integration. The proposed control approach is detailed and the WECS operation under different scenarios including operation under FRT is provided.  
Figure 15: IMC based topologies with voltage boosting capability. (**a**) Switched-inductor Z-source matrix converter. (**b**) Quasi-Z-source matrix converter. (**c**) Zero-link impedance direct drive wind turbine topology proposed by [88].  
The transmission distance from offshore wind farms to mainland brought new ideas on how the traditional windfarm can be redesigned. An HVDC connection from the coast to the offshore windfarm where the WECS are all connected in series, as shown in Figure 16b, is proposed in [90]. A study of six different topologies featuring a Medium Frequency Transformer (MFT) is presented comparing three B2B converter based topologies and three matrix converter based topologies (DMC, IMC, CSI). Although the analysis was carried out for an ac-link frequency in the range of 500 Hz to 20 kHz, considering only one MFT core material, not allowing generalization for other magnetic materials, the CSI topology was clearly the preferred choice due to its lower volume and higher efficiency for ac-link frequencies above 3 kHz.  
A technology that has gained some attention for possible application on offshore windfarms transmission lines is the low-frequency alternating current (LFAC). By reducing the frequency bellow 50 Hz on the transmission line, its capability of transmitting power without increasing the voltage level is improved. A study of a M3C converter-based LFAC transmission system and its control strategy is presented in [91].
## 2 System Model
If CSI and DI could be perfectly fed back, this interference term would be zero. With the inherent quantization error, the interference term decreases the achievable rate. In the following sections, we will discuss the relationship among the achievable rate, quantization bits and quantization error under different quantization strategies.
## 2 Modeling of Distribution System Components
### Model of the Squirrel-Cage Induction Generator (SCIG)-Equipped Variable-Speed Wind Turbine
#### 2.1.3 Sclg Model  
The voltage equations of the stator and rotor of the SCIG in the revolving \(d\)-\(q\) reference frame are as follows [20]:  
\[\begin{cases}v_{ds}=R_{s}i_{ds}+\frac{dv_{ds}}{dt}-\omega_{s}\psi_{qs}\\ v_{qs}=R_{s}i_{qs}+\frac{dv_{qs}^{\prime}}{dt}+\omega_{s}\psi_{ds}\end{cases} \tag{5}\]  
Figure 2: Wind turbine with the SCIG system.  
\[\begin{cases}0=R_{r}i_{dr}+\frac{d\psi_{qr}}{dt}-(\omega_{s}-\omega_{r})\psi_{qr} \\ 0=R_{r}i_{qr}+\frac{d\psi_{qr}}{dt}+(\omega_{s}-\omega_{r})\psi_{qr}\end{cases} \tag{6}\]  
where the stator voltages are \(v_{ds}\) and \(v_{qs}\) on the \(dq\) frame, respectively; the stator currents are \(i_{ds}\) and \(i_{qs}\), respectively; the rotor currents are \(i_{dr}\) and \(i_{qr}\), respectively; the stator flux components are \(\psi_{ds}\) and \(\psi_{qs}\), respectively; and the rotor flux components are \(\psi_{dr}\) and \(\psi_{qr}\), respectively. Moreover, \(\omega_{s}\) is the synchronous speed; \(\omega_{r}\) is the rotor's angular speed; and \(R_{s}\) and \(R_{r}\) denote the stator and rotor resistances, respectively.  
The following equations express the relations between fluxes and currents:  
\[\begin{cases}\psi_{ds}=L_{s}i_{ds}+L_{m}i_{dr}\\ \psi_{qs}=L_{s}i_{qs}+L_{m}i_{qr}\end{cases} \tag{7}\]  
\[\begin{cases}\psi_{dr}=L_{r}i_{dr}+L_{m}i_{ds}\\ \psi_{qr}=L_{r}i_{qr}+L_{m}i_{qs}\end{cases} \tag{8}\]  
where \(L_{s}\), \(L_{r}\), and \(L_{m}\) indicate the stator, rotor, and magnetizing inductances, respectively.  
The equation of the electromagnetic torque of the SCIG is as follows:  
\[T_{em}=\frac{3}{2}p\big{(}\psi_{ds}i_{qs}-\psi_{qs}i_{ds}\big{)} \tag{9}\]  
where \(p\) denotes the machine pole pair number.  
Furthermore, the following formula is used to compute the active and reactive stator power output values:  
\[\begin{cases}P_{s}=\frac{3}{2}\big{(}v_{ds}i_{ds}+v_{qs}i_{qs}\big{)}\\ Q_{s}=\frac{3}{2}\big{(}v_{ds}i_{qs}-v_{qs}i_{ds}\big{)}\end{cases} \tag{10}\]
## 2 Materials and Methods
### Nanogel Preparation and Analyses  
The nanogels are synthesized as described in the literature [34]. In a 30 mL screw-top jar, 16.5 mL n-dodecane containing 125 mg Span 80 is ultrasonicated (Branson Digital Sonifier W-450 D; 40% amplitude; 1% inch tip; titanium horn) under ice cooling while 250 uL BSA solution (200 mg mL\({}^{-1}\) in MES buffer (100 mM, pH 5)) containing the desired amount of plasmid DNA and 250 uL EDC:HCI solution (100 mg mL\({}^{-1}\) in MES buffer) are mixed, added to the continuous oil phase immediately, and sonicated for another 30 s. The emulsion is then left at room temperature for 12 h. To change the continuous phase to a more polar solvent, the nanogel dispersion was transferred in a 50 mL conical centrifuge tube and centrifuged at 13,400\(\times\)\(g\) for various indicated time intervals. DLS measurements were performed in relevant pure solvents to surveil the size of the nanogels and ensure successful redispersion. The supernatant was discarded and the nanogels were redispersed in the new solvent by vigorous shaking and ultrasonication (Elma TI-H-5; 25 kHz). After n-dodecane (5 min), the continuous phase is changed to cyclohexane (5 mL, 5 min), cyclohexane/ethanol (3.5 mL each, 4:20 min), ethanol (5 mL, 5 min), and ethanol/water (2.5 mL each, 20 min) and then washed twice with water (5 mL, 20 min) before the final uptake in TRIS-HCl buffer (10 mM, pH 7.5) as a ready-to-use product. The experiments were conducted in triplicate (\(n=3\)) and plotted as mean values (\(\overline{x}=\frac{1}{n}\sum_{m}^{x=1}x_{i}\)) with error bars representing the standard deviations (SD = \(\sqrt{\frac{\sum(x-\overline{x})^{2}}{n}}\)).