esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In power systems with less conventional generators (mainly synchronous), several functions must be added to the renewable resources to compensate for the grid requirements, such as frequency response, negative sequence, harmonics mitigation, and resynchronization and black-start ability [1].  
For adjusting the frequency of a grid during frequency deviation, there are different control strategies and approaches that have been implemented and presented in the literature. These control methods can be categorized based on duration and capability with certain features and limitations [2; 3; 4].  
The participation of wind turbines (WTs) in primary frequency control (PFC) has been discussed widely in several publications. Controllers based on rotor speed regulation or pitch angle regulation to de-load WT for PFC have been discussed in [5; 6; 7; 8; 9; 10; 11].  
In [12; 13], a coordination control was proposed between inertial, rotor speed, and pitch angle for WT, based on a doubly-fed induction generator (DFIG). This approach depends on a new classification of wind speed to select among the proposed controllers. Storing kinetic energy in the rotor shaft to be used during the PFC stage has been proposed [14; 15]. An adjustable droop controller was proposed to improve PFC in WT, based on a DFIG in [7].  
A design for a discrete linear quadratic Gaussian controller for PFC participation was introduced in [16; 17]. Controlling WT for PFC along with energy storage systems was discussed in [18; 19]. An optimization algorithm was proposed to provide a stable power reference considering wind speed prediction in [20]. This approach gives a possible range for the power reserve for a specific timeframe.  
In this paper, the aim is to de-load the turbines' power by controlling both pitch angle and rotor speed simultaneously, in order to optimize the operation of the wind turbine. The blade's pitch angle is obtained such that the rotor speed is increased to reduce the power losses (copper losses) in the stator of the machine and the conduction losses of the converter. In addition, to avoid blade fluctuation and an excessive number of pitch operations, a wind forecast will be implemented in order to predict a consistent pitch angle that is valid for longer period. This can reduce the high wear on the pitch system by reducing the number of pitch usages over time [21]. Then, the selected pitch angle and the known curtailed power are used to find the optimal rotor speed by applying a nonlinear solver. With the variation of wind speed, the control algorithm updates the pitch angle set point adaptively. Therefore, the reference of the rotor speed must follow the variation in the pitch angle and the wind speed.  
The paper is organized as follows. Section 2 presents the model of the wind turbine system and discusses the understanding of power curve for PFC. Section 3 presents the proposed de-loading approach. In Section 4, simulation and real time studies are performed to validate the proposed method. The conclusion is presented in Section 5.
## 5 Influence of Major Factors on the Performance of Building Settlement Prediction
### Hyperparameter  
The hyperparameters in sedimentation prediction will have some influence on the model prediction results. Using the proposed LSTM-RA-ANN model, the calculated results of the model evaluation indices at monitoring point F1 with different hyperparameters (the initial learning rate, maximum iteration batch, and maximum iteration period) are shown in Table 6.  
As can be seen from Table 6, the model performs best when the initial learning rate is 0.06, the maximum iteration batch is 20, and the maximum iteration period is 800 when the proposed LSTM-RA-ANN model is used for the prediction of settlement at monitoring point F1. When the initial learning rate was increased from 0.06 to 0.08, the RMSE increased by 14%, the MAE increased by 46%, and the \(R^{2}\) decreased by 1%; when the initial learning rate was increased from 0.06 to 0.10, the RMSE increased by 93%, the MAE increased by 54%, and the \(R^{2}\) decreased by 15%. With a maximum iteration batch of 15 and 25, \(R^{2}\) is only 0.65 and 0.60, respectively, which is 18% and 24% smaller than with a maximum iteration batch of 20. A maximum iteration period of 800 slightly outperforms the model at a maximum period of 400 and 600, but the difference is small. Therefore, the hyperparameters that have a greater impact on model performance are the initial learning rate and the maximum iteration batch.
# PolySorbate-Based Drug Formulations for Brain-Targeted Drug Delivery and Anticancer Therapy
## 2 Basic Characteristics of PSs  
### Chemical Structures and Physicochemical Properties of PSs  
The common backbone structure of PSs is a sorbitan ring (sugar alcohol) with poly (ethylene oxide) (PEO) conjugated to hydroxyl groups. The different numbers of ethylene oxide subunits are conjugated to the four hydroxyl groups of the sorbitan ring, but the overall number of repeating units of ethylene oxide is approximately 20. The chemical preparation of PSs involves a two-step process: sorbitan is ethoxylated, followed by esterification with fatty acids (e.g., lauric acid, palmitic acid, stearic acid, and oleic acid). The synthesis scheme of PSs is shown in Figure 1.  
The number following the \"polysorbate\" refers to the type of fatty acid ester associated with the polyoxyethylene sorbitan portion of the molecule. Fatty acid ester moieties of PS 20, PS 40, PS 60, and PS 80 are laurate, palmitate, stearate, and oleate, respectively. Figure 2 illustrates the chemical structures of various PSs (i.e., PS 20, PS 40, PS 60, and PS 80). The PEO segments in PS are hydrophilic, and the fatty acid segments are hydrophobic. Therefore, amphiphilic PSs can serve as effective surfactants. The amphiphilic nature of PSs allows them to self-assemble into micelles in aqueous solutions after reaching critical micellar concentrations. PS-based micelles have been widely employed for drug delivery because of their nanoscale size and high drug-loading capacity [16; 17; 18]. PSs have been used as emulsifiers and solubilizers for hydrophobic drugs and as protein-stabilizing agents [19]. For example, PS 80 was used as a stabilizer in the Janssen COVID-19 vaccine formulation [20]. Emulsion-based formulations containing PSs as emulsifiers have been intensively developed and have shown potential as effective drug carriers for both hydrophobic and hydrophilic drugs [21; 22; 23]. The behavior of protein aggregation in the absence or presence of PS 80 as an excipient was evaluated [24]. The addition of PS 80 substantially inhibited protein aggregation during agitation [24]. PS 80 and PS 20 have been used in the formulation of therapeutic monoclonal antibodies owing to their biocompatibility, low toxicity, and good protein-stabilizing properties [25]. PSs have been utilized to increase the permeability of various drugs in vivo and in vitro [26; 27; 28; 29]. Additionally, PSs exhibit high biocompatibility and low toxicity and are thus advantageous for use as drug carriers.  
Figure 1: Schematic illustration for the synthesis of polysorbates from sorbitan.  
In particular, nonionic surfactants are less toxic and more biocompatible and have lower hemolytic potential than charged surfactants (cationic, anionic, or amphoteric) [30].
# Power Control Techniques for Interference Management-
## 4 Power Control Technique Evaluation
### Power Control Techniques: Case Studies
#### 4.2.3 Case Study 3: Interference Management Strategy for 5G Femtocell Clusters  
This review paper examines the concerns of interferences discovered and researched in various network topologies and techniques. It also pays attention to recent advances in its management, such as advanced receiver, joint scheduling, and network information theory, among others. Potential advantages of each interference management technique are illustrated, and it is proven that if 5G cellular networks use intricate joint scheduling, the advantages of sophisticated receivers can be effectively utilized.  
This study explores interference management strategies for femtocell clusters, addressing inter-cell interference and power control challenges.  
Details of the case study are found in [13].
## 5 Integrating Language Modeling with STEP
### N-gram Modeling  
Latent Dirichlet Allocation is based on the \"bag-of-words\" assumption, in that the order of words in a document does not matter. However, _word order_ and _phrases_ are often critical to capturing the meaning of text. N-gram models can be imagined as placing a small window over a sentence or text, in which only \(n\) words are visible at the same time. We have therefore performed experiments using the N-gram modeling technique, where every sequence of two adjacent entities (_bi-gram model_) [29] in micro-contributions for an expert is identified and annotated with concepts from domain ontologies.  
It is important to note that while we are still using the NCBO Annotator for annotating terms that result from topic and n-gram modeling, the way in which these terms are identified is independent of the annotation process, differs from the method used by the annotator for identifying terms and is in fact, domain-agnostic. Furthermore, missing or inaccurate annotations are not the result of the \"annotation\" process, but stem from the method by which terms are identified in the context of the given micro-contributions. Therefore, our proposed mechanisms aim at _complementing term/topic extraction_ given the _context of micro-contributions_. This process is independent of and a pre-requisite to the annotation process.  
We integrated _lemmatization_ followed by _topic_ and _n-gram_ modeling with the _concept extraction_ module of the STEP methodology, in order to improve the accuracy of resulting profiles and facilitate a _domain-independent_ method of _identifying entities in micro-contributions_. Topic modeling and n-gram modeling techniques were implemented on our datasets as two separate experiments. Concepts resulting from each experiment were then used to create short-term and long-term expertise profiles. The following section outlines our datasets, experiments and evaluations.
# Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments
## 4 Realization of ANMO-SDR in SDR-Based CRNs
### Performance Objectives and Metrics  
In order to evaluate the proposed approach and quantify its performance in real SDR equipment, the following metrics can be derived (which are being measured in the following section):  
* _Reconfiguration Flexibility:_ The proposed approach should present fast convergence in different types of topologies, taking into consideration the number of MNs and the number of channels used in the experimental scenarios. This can be indicated by the number of sweeps required for convergence in the Gibbs sampling phase of the MRF RCA approach (as analyzed above).
* _Spectrum Utilization_: To successfully address the problem of spectrum under-utilization, the proposed resource allocation algorithm should employ all the available channels sufficiently, without overloading particular frequencies. This can be quantified by the number of times each channel is utilized for SU transmissions.
* _Fair Spectrum Sharing among SUs_: Demonstrates that no node should be repeatedly favored by getting assigned the abundant resources. This can be quantified by the average number of assigned channels per node.
* _Transparency with Regard to the Primary Network Operation_: The operation of the SUs should be transparent to the PUs. Whenever a channel is occupied with active PU transmissions, all SUs must immediately evacuate it. To evaluate this feature the number of collisions between Primary and Secondary users should be examined.
* _Collision Avoidance:_ An important aspect of any channel allocation algorithm is to allocate channels in a way that minimizes the collisions between users. Hence, the number of collisions between SUs should be employed during evaluation.
## 2 Related Work
In another study [27], the authors have reported the effects of several parameters, e.g., source directivity, transmitted optical power, position of on-body sensors, and the number of PDs that were installed on the ceiling. However, in all of these OBAN related studies, mainly the UL connection has been investigated and the impacts of fixed transmitter and receiver locations on the DL and UL communication, respectively, have been ignored. Accordingly, this study emphasizes investigating the effects of LED arrangements while taking into account the body losses (BL) as well as the patient's posture to estimate and ensure QoS availability for DL and UL connections.
# Two-Dimensional Beam Steering Based on Compact Programmable Coding Metasurface
## 1 Introduction  
Metasurfaces, as a two-dimensional form of metamaterial, have gained considerable interest for their high flexibility and accuracy in the manipulation of electromagnetic (EM) waves [1, 2, 3]. Inspired by the generalized Snell's law, metasurfaces with phase discontinuities imparted can be used to manipulate the wavefront, and the anomalous reflection and refraction can be observed [4, 5, 6]. In the past decade, many passive metasurfaces have been designed, fabricated, and implemented in many applications, such as polarization conversions, beam forming, optical focus, and holograms [7, 8, 9, 10, 11]. Although these passive metasurfaces offer extremely strong EM manipulation capabilities, real-time control of EM waves has been in high demand for many practical applications such as high-speed communications, remote sensing, imaging, and radar [12, 13, 14].  
In the past decade, the concept of digital coding and programmable metasurfaces was proposed and developed, which bridges the physical and digital worlds [15, 16]. By encoding different states of the meta-atoms, the function of a metasurface can be represented by a string of binary codes [17]. Furthermore, these binary codes bring us an information science perspective on metasurfaces, and provide a new wireless communication architecture [18, 19]. For the 1-bit digital coding metasurface, two states with a 180 deg phase difference are required, and the two states can be encoded as digital codes \"0\" and\"1,\" respectively. By changing the digital coding sequence, the phase distribution of the metasurface is changed, and the function of the metasurface is also determined. To date, digital metasurfaces have extensively achieved different functions, such as beam steering, orbital angular momentum (OAM) beams, holograms, and so on [10; 20; 21].  
In this paper, a 1-bit programmable reflective metasurface is designed, fabricated, and tested. The metasurface can realize two-dimensional dynamic beam steering with loaded PIN diodes. The millimeter wave metasurface possesses 16 \(\times\) 16 meta-atoms using the double-patch metal pattern. With the careful design of the digital control system and the biasing network, the phase of the 256 meta-atoms can be modulated independently. Simulation results show that the metasurface can achieve flexible two-dimensional beam steering in the upper half-plane with a maximum steering angle of 60\({}^{\circ}\). In addition, the compact metasurface is only 6.4 cm \(\times\) 6.4 cm in size. For experimental verification, we processed a prototype and tested it in a standard microwave anechoic chamber. Experimental results show that the designed metasurface can achieve a maximum steering angle of 45\({}^{\circ}\) in the upper half-plane. Programmable metasurfaces which combine the performance of arrays and aperture antennas can provide an efficient and low-cost platform for high-gain adaptive beam steering. The proposed metasurface with its excellent beam-steering capability has great application in many fields such as radar, communication, and remote sensing.
# Design and Implementation of Inverse Kinematics and Motion Monitoring System for 6 DoF Platform
## 2 Inverse Kinematics Design
### Proposed Inverse Kinematics
 \tag{13}\]  
Equations (4)\(-\)(9) are substituted into (13) to derive  
\[d_{i}^{2}+a^{2}-s^{2}+2a\cdot\sin(\theta_{i})\left(z_{pi}-z_{b\_Mi}\right)-2 a\cdot\cos(\theta_{i})\cdot\left\{\cos(\phi_{i})\left(x_{pi}-x_{b\_Mi} \right)\right.+\sin(\phi_{i})\left(y_{pi}-y_{b\_Mi}\right)\right\}=0 \tag{14}\]  
Figure 3: The relationship of axis i (i = 1–6) and upper platform coordinate. To facilitate the calculation, the equation is rewritten as follows:  
\[H=E\cdot\sin(\theta_{i})+K\cdot\cos(\theta_{i}) \tag{15}\]  
where  
\[H=d_{i}^{2}+a^{2}-s^{2} \tag{16}\]  
\[E=-2a\cdot\left(z_{pi}-z_{b\_Mi}\right) \tag{17}\]  
\[K=2a\cdot\cos(\theta_{i})\{\cos(\phi_{i})\left(x_{pi}-x_{b\_Mi}\right)+\sin( \phi_{i})\left(y_{pi}-y_{b\_Mi}\right)\} \tag{18}\]  
Equation (15) belongs to the trigonometric identity, and \(\theta_{i}\) can be written as  
\[\theta_{i}=\sin^{-1}\left(H/\sqrt{(E^{2}+K^{2})}\right)-\tan^{-1}(K/E) \tag{19}\]  
The aforementioned equation demonstrates that the actual rotation angles (19) of the six motors can be directly derived using the known conditions--s, a, bi = [x\({}_{\text{b\_Mi}}\)\(y\)\({}_{\text{b\_Mi}}\)\(z\)\({}_{\text{b\_Mi}}\)], pi = [x\({}_{\text{pi}}\)\(y\)\({}_{\text{pi}}\)\(z\)\({}_{\text{pi}}\)], \(\phi_{i}\), and the distance (di) from the upper platform coordinate to the motor coordinate.
# Stability of Single Electron Devices: Charge Offset Drift
## 2 Experimental Observations of Charge Offset Drift
### Summary of Experimental Observations  
Experimentally, it is clear that metal-based devices exhibit a large amount of charge offset drift \(\Delta Q_{0}\geq 1e\)), that silicon-only devices show very little (\(\Delta Q_{0}\leq 0.01e\)), and that devices made in silicon with metallic gates show an intermediate level of drift (\(\Delta Q_{0}\approx 0.15e\)).  
Metal-based devices also show the phenomenon of transient relaxation that is correlated with the time from fabrication. More drift is also associated with the amount of amorphous insulator in or near the device. The frequency dependence of the charge offset drift noise in metal devices appears to be either \(1/f^{2}\) in some devices or \(1/f\) in others. The former is correlated with observed steps or clusters of steps in the charge offset drift or current measured as a function of time, while the latter is correlated with a more continuous variation. This invites interpretation in terms of TLSs in the amorphous AlO\({}_{x}\). These defects affect the device's stability when they are found anywhere nearby as well as in the tunnel barriers of metal devices through their electrostatic coupling. Finally, while many experimental knobs have been turned to investigate correlations of charge offset drift with various fabrication and measurement parameters, they have revealed no correlations.  
Silicon devices show very little charge offset drift even after thermal cycling, voltage pulses, and long periods of time between measurements. The relative lack of drift in silicon devices, combined with the observation of TLSs in certain energy ranges (temperature or gate voltage ranges), intimates that the crucial difference with metallic devices lies in the lack of interactions between TLSs in SiO\({}_{2}\), not their absence.  
Measurements on devices which combine the above material systems where the dot is formed in the silicon below a SiO\({}_{2}\) gate dielectric with aluminum gates exhibit \(\Delta Q_{0}\approx 0.15e\). That these results occupy a middle ground between metallic and all-silicon devices, in combination with the observation of a non-deliberate AlO\({}_{x}\) layer below the gates, is consistent with the idea that the effect of TLSs reduces with distance from the dot.
## 2 Materials and Methods
### Image Registration  
Image alignment aims to overlay two or more images using spatial transformations such as translations, rotations, and cropping [38]. Typically, images are aligned by selecting one image as the reference image and aligning it with the others (referred to as the moving images).  
The evaluation of the geometric transformations (offsets) required for the alignment of the multispectral (MS) bands was carried out according to the experimental setup described in previous work [24].  
In summary, a checkerboard pattern was captured at distances ranging from 500 mm to 1500 mm from the sensor. For each distance, five different image positions were captured to enable the calculation of geometric offsets between the bands from more viewpoints. Once again, the R band was chosen as the reference.  
Three methods were selected for image alignment: the CB (checkerboard) and FT (Fourier transform) methods, as described in [24], combined with a conventional method. The conventional method consisted of the RG (rigid transformation), which involved a rigid affine transformation based on the similarity of homologous points between images. This method is available in the _Image Processing Toolbox_TM in MATLAB (2023b), MathWorks.  
Briefly, while the CB and RG methods used affine transformations to align images while maintaining geometry and proportions, the FT method used a transformation in frequency domain, identifying a principal point to correct misalignment. The inclusion of the RG (rigid transformation) method as a conventional approach allowed us to verify the performance of the previously used CB (checkerboard) and FT (Fourier transform) methods under comparable conditions.  
Due to the effect of the sensor acquisition distance on the geometric offsets required to align the bands, an interpolation model of the experimental data was established to fit the alignment.  
The proposed interpolation model follows an inverse linear behavior, as described by the equation \(y=a+b/x\). In this equation, \(y\) represents the offset, \(x\) is the distance, and \(a\) and \(b\) are model parameters estimated through the analysis of experimental data.  
From a physical perspective, the value of the constant \(a\) can be interpreted as the minimum geometric offset for alignment, while \(b\) represents a weighting factor between the offset (\(y\)) and the distance (\(x\)).  
As the distance increases (\(x\rightarrow\infty\)), the term \(b/x\) approaches zero, and therefore, \(y\) approaches the value of \(a\). Under these conditions, the geometric offset is minimally affected by the distance, and the model simplifies to \(y\approx a\).  
On the contrary, as the distance decreases (\(x\to 0\)), the term \(b/x\) increases, thereby significantly affecting the offset (\(y\)).  
The relationship expressed by the model is particularly useful in contexts where the geometric offset is strongly dependent on the distance between the sensor and the measurement object, such as in close-range applications.