esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Characterization of DFO-loaded PLGA-PEG-PLGA NPs  
The surface morphology of DFO-loaded NPs was imaged by using an S-4800 FESEM (Hitachi, Tokyo, Japan). In short, samples of the NPs were mounted on the stub, coated with platinum, and then viewed under the FESEM at the accelerating voltage of 5.0 kV.  
It has been reported that the physicochemical features of the biological NPs play an important role in the ex vivo drug release, cellular uptake and cell toxicity, as well as their in vivo actions such as pharmacokinetics and biodistribution [38; 39]. Thus, we estimated the size and size distribution of the DFO-loaded NPs, two most representative parameters, by using dynamic light scattering (DLS) and Malvern Mastersizer 2000 (Malvern Instruments Ltd., Malvern, UK). Briefly, freshly-obtained DFO-loaded NPs were firstly diluted to a concentration of 1 mg/mL. After equilibration for 10 min at room temperature, measurements were carried out in triplicate to achieve the data.  
Next, we determined the DFO LC and EE of the NPs using high-performance liquid chromatography (HPLC, LC 1200, Agilent Technologies, Santa Clara, CA, USA) as previously reported [21]. In brief, 5 mg of DFO-loaded NPs were completely homogenized in 1 mL of dichloromethane under vortex oscillation, then the solution was transferred to 5 mL of mobile phase containing the same volume of deionized water and acetonitrile. After evaporating the dichloromethane residues via a nitrogen stream perfusion method, a clear solution suitable for HPLC analysis was prepared. HPLC analysis was performed by using a reverse-phase C18 column (150 mm \(\times\) 4.6 mm, 5 \(\upmu\)m, C18, Agilent Technologies, Palo Alto, CA, USA) with the set flow rate of mobile phase as 1 mL/min. The column effluent was assessed by applying a UV detector at \(\uplambda_{\text{max}}\) of 227 nm. Drug LC is calculated as the ratio of incorporated DFO amount to the weight of NPs, while drug EE was defined as the mass of DFO encapsulated in the NPs divided by that of DFO fed in the synthesizing process [40].
# Three-Dimensional Assessment of Upper Limb Proprioception via a Wearable Exoskeleton
## 4 Discussion
### Proprioceptive Error across Upper Limb Joints  
As previously discussed, our results highlighted a significant difference in proprioceptive acuity between the tested joints: in particular, regardless of experimental conditions (S) or MJ), the largest matching error has been found in the distal district of the arm, the elbow. Our outcomes are consistent with the hypothesis that proprioceptive signals are differently encoded if they originate from proximal or distal segments. [PERSON] and [PERSON] [47] in a study on primates, highlighted the aspect mentioned above by experimentally demonstrating that the contralateral motor cortex is responsible for mediating distal movements, while motor commands related to proximal districts involve a neural activity from both the ipsilateral and contralateral motor cortex. Different neural pathways generate diverse motor behaviours as well as sensory processing between distal and proximal limbs, resulting in a significantly different performance between joints of the same limb. There is further evidence [42] demonstrating that in a 2D proprioceptive assessment, subjects' performance was not isotropically distributed over the task workspace, but the largest errors have been found for more distal configurations of the limb.  
Other studies confirmed that proprioceptive acuity is highly influenced by the configuration of the tested limb: performance in joint position matching tasks is worse for targets located in a distal portion of the arm workspace [16; 41; 48; 49] rather than for those tested in proximal configurations [15; 50; 51].  
The current work explores a unique multi-joint (MJ) configuration. The implementation of further MJ movements would help understand the proprioceptive mechanisms involved in a 3D-workspace to be included in a proprioceptive assessment protocol. Our findings provide the first proof of concept that can be considered to develop \"evaluation protocols\" and \"ad-hoc rehabilitative interventions\" for somatosensory retraining as published in recent works [52; 53], also providing real-time feedback of the proprioceptive errors.
## 2 Artificial Orthogonal Gland (AOG) Mechanism  
This section will present a novel approach to developing artificial glands where a novel artificial orthogonal gland (AOG) mechanism will be proposed. Additionally, a strategy for building a network of AOGs within a single system will be provided. In previous attempts to develop artificial glands [14; 15; 16; 23; 24; 25; 26], the main idea was to acquire environmental stimuli, transform them into appropriate input signals and introduce them to the glands. The next part calculated hormone concentration within each gland and introduced calculated values to a neural network. Each gland was mostly independent of other involved glands, covering different environmental stimuli.  
The novel approach proposes a gland structure based on some previously established gland features in combination with new in-gland mechanisms. Further networking of single gland units will enable mutual interactions between different hormones and an additional level of adaptability of connected neural networks. In many previous attempts [14; 15; 16; 23; 24; 25; 26], each environmental stimulus was assumed to be completely inde pendent of other stimuli. However, in practice and real-world industrial applications, one environmental condition could affect other related dependencies and volatility of numerous factors. For example, a strong wind signal can significantly affect a control system's performance, if mounted outside. Additionally, the wind can directly influence decreasing the system's temperature, increasing mechanical parts' vibrations and noise level, etc. From this example, it is evident that one environmental stimulus may affect the values of other stimuli. For that reason, a network of hormones can better represent similar situations, simulate mutual correlations, and put the importance and influence of all included stimuli on a system of interest in proper order. In order to propose upgraded functionalities to an artificial gland, the novel gland architecture is proposed in Figure 1.  
The gland mechanism from Figure 1 is designed in such a manner to accept two inputs: control input (\(CI\)) and signal input (\(SI\)). The primary purpose of \(CI\) is to control hormone production, and it is not related to any calculation of hormone stimulations. \(CI\) enables optional interactions between different glands and allows hormones to connect and make a network of hormones. The second input to the gland mechanism, \(SI\), determines the hormone stimulation level within a gland and defines the gland's influence on the neural network. The AOG mechanism includes four main sub mechanisms: control mechanism, signal mechanism, activation mechanism and hormonal mechanism. All these sub-mechanisms will be presented next.
# The Linac Coherent Light Source: Recent Developments and Future Plans
## 2 LCLS Recent Representative Highlights
### XFEL Physics  
The development of powerful new XFEL capabilities has been a hallmark of LCLS since its inception. Prominent examples include ultrashort X-ray pulse generation [20; 21], self-seeding in both the hard X-ray [22] and soft X-ray ranges [23], and novel approaches for characterizing the X-ray pulse duration [24]. These developments are driven primarily by scientific need and potential impact, and are facilitated by close interaction between X-ray scientists and XFEL physicists at LCLS. At the same time, new XFEL capabilities trigger the development of creative new experimental approaches that enhance the scientific impact of the facility.  
Recently, LCLS has developed a novel fresh-slice technique for multicolor XFEL pulse production (Figure 7), wherein different temporal slices of an electron bunch lase to saturation in separate undulator sections [25]. This method combines electron bunch tailoring from a passive wakefield device with trajectory control to provide multicolor pulses. The fresh-slice scheme outperforms existing techniques at soft X-ray wavelengths. It produces femtosecond pulses with a power of tens of gigawatts and flexible color separation. The pulse delay can be varied from temporal overlap to almost one picosecond. We have further demonstrated the first three-color XFEL and variably polarized two-color pulses.
# RIS-Assisted High-Speed Communications with Time-Varying Distance-Dependent Rician Channels
## 2 System Model with Time-Varying Distance-Dependent Rician Channels  
We assume a downlink single-input single-output RIS-assisted HSC system, as shown in Figure 1. There are one roadside BS, one train-side moving mobile relay (MR) with speed \(v\), and one RIS with \(M\) elements near the BS. Both the BS and the RIS are surrounded by certain obstacles, such as buildings and trees [20,21]. The single antennas of the transceiver and the \(M\) elements of the RIS are all isotropic. The origin point of the Cartesian coordinate system is \(D_{0}=[0,0,0]^{\rm T}\). The positions of the BS, the MR, and the \(mn\)-th element of the RIS are denoted as \(D_{\rm BS}=[x_{\rm BS},y_{\rm BS},z_{\rm BS}]^{\rm T}\), \(D_{\rm MR}(t)=[x_{0}+v\cdot t,y_{\rm MR},z_{\rm MR}]^{\rm T}\) and \(D_{mn}\), respectively. Besides, we assume the channel state information can be obtained in advance using the predictive information [10,11].  
Figure 1: An RIS-assisted HSC system. Note that the obstacles cause NLoS components for direct and cascaded links.  
In the proposed HSC scenario, large signal penetration losses can be avoided by establishing the MR on the top of the vehicle [5; 6]. Besides, the channel model contains two main links. The first one is the direct link \(h_{d}(t)\), which represents the channel between the BS and the MR. The second one is the cascaded RIS link, which means the channels from the BS and the RIS \(g_{mn}(t)\), and from the RIS to the MR \(g_{mn}(t)\). Note that both \(g_{mn}(t)\) and \(g_{mn}(t)\) include LoS and NLoS components. Moreover, in this paper, we assume that the proposed channel experiences block fading [7; 14; 15; 19].
# Rotate Vector Reducer Fault Diagnosis Model Based on EEMD-MPA-KELM
## 2 Industrial Robot RV Reducer  
_RV Reducer Structure of Industrial Robots_  
As shown in Figure 1[19], the RV reducer uses the solar wheel as the input shaft, and the solar wheel with fewer teeth drives the planetary wheel with more teeth to rotate, so as to carry out first-stage deceleration. The large planetary wheel drives the crank shaft to rotate, and the crank shaft drives two RV wheels to rotate, the two RV wheels rotate for one circle, and the pinion gear rotates for one tooth, so as to carry out second-stage deceleration.  
Since the center of gravity of the crank shaft is not on the shaft, its torque will change periodically with rotation. The calculation formula is shown in Equation (1):  
\[M_{x}=\overline{TF}(W_{x}-B)-M_{c}sin(\theta_{x}+\gamma) \tag{1}\]  
In the formula, \(M_{x}\)--Crank shaft torque, kNm; \(\overline{TF}\)--Torque factor, m; \(W_{x}\)--Suspension instantaneous load, kN; \(B\)--Crank structure unbalanced weight, kN; \(M_{c}\)--Maximum crank balance torque, \(\theta_{x}\)--Crank Angle; \(\gamma\)--Phase Angle.  
\[M_{c}=W_{p}R_{p}+W_{b}^{\prime}R_{b} \tag{2}\]  
_Wp_ is the weight of the crank balance block, _Rp_ is the distance between the crank shaft and the center of gravity of the balance block, \(W_{b}^{\prime}\) is the weight of the crank, and \(R_{b}\) is the distance between the crank shaft and the center of gravity.  
Since the crank shaft of RV reducer does not need a crank balance block, the formula for RV reducer can be expressed as:  
\[M_{c}=W_{b}^{\prime}R_{b} \tag{3}\]  
The torque factor can also be expressed as follows:  
\[\overline{TF}=\frac{A}{B}d\frac{sin\alpha}{sin\beta} \tag{4}\]  
Figure 1: Structure diagram of RV reducer of industrial robot.  
In formula, \(A\) is the length of the forearm of the traveling beam, \(B\) is the length of the rear arm of the traveling beam, \(d\) is the distance of the central crank shaft of the cycloidal wheel, \(\alpha\) and \(\beta\) are the Angle between the connecting rod and the crank. Because does not have the RV reducer, connecting rod, the Angle \(\alpha\) is zero, so the torque factor \(\overline{TF}\) also is zero, so the type (1) can be simplified as:  
\[M_{x}=-W_{b}^{\prime}R_{b}sin(\theta_{x}+\gamma) \tag{5}\]  
While the center of the cycloidal wheel is not in line with the axis, assuming that the difference is \(s\) and the radius of the cycloidal wheel is \(r\), the formula of the cycloidal wheel is:  
\[M_{r}=\frac{M_{x}[r+s\cdot sin(\theta+\beta)]}{r+s} \tag{6}\]  
When cycloidal wheel and pinion gear transfer torque, the torque needs to add a mesh coefficient \(C_{1}\).  
Then its final output torque is:  
\[M_{\rm output}=c_{1}M_{r}=c_{1}\frac{M_{x}[r+s\cdot sin(\theta+\beta)]}{r+s} \tag{7}\]  
Since the efficiency formula is:  
\[\eta=\frac{M_{\rm output}}{nM_{\rm input}}=\frac{c_{1}M_{x}[r+s\cdot sin( \theta+\beta)]}{nM_{\rm input}(r+s)} \tag{8}\]  
In formula, \(n\) is the deceleration ratio.  
From Formula (8), it can be concluded that the efficiency of RV reducer in normal operation is periodic.
## 3. Methodology
### Model Performance Assessment and Comparison
#### 3.4.2 Receiver Operating Characteristic  
The receiver operating characteristic (ROC) curve has been commonly used to validate the quality of a probabilistic model. The ROC curve is plotted by statistical index value pairs, with the false positive rate (sensitivity) on the \(x\)-axis and the \"100\(-\)false negative rate\" (100\(-\)specificity) on the \(y\)-axis. The ROC curve can be classified as a success rate curve or prediction rate curve, depending on the dataset used. The success rate curve, calculated using the training dataset, represents how well the LSMs fit the data. The prediction rate curve, calculated using the validation dataset, represents how well the model and landslide explanatory variables predict a landslide [11]. The ROC curve can be verified quantitatively when the area under the ROC curve (AUC) is calculated. AUC values range from 0.5 to 1.0. AUC values closer to \(1\) indicate a more accurate model.
# Beyond Prompt Chaining: The TB-CSPN Architecture for
## 4 TB-CSPN: A Hybrid Architecture for Semantic and Structural Coordination
### Distinguishing TB-CSPN from Classical Colored Petri Nets  
TB-CSPN avoids the rigidity and scaling limitations of classical formal systems by decoupling semantic processing from coordination logic. The framework supports interpretability, adaptability, and real-time reconfiguration through the architectural separation of LLM-mediated topic spaces and Colored Petri Net structures. Table 2 summarizes the core differences between TB-CSPN and classical CPN architectures, reinforcing the conceptual and functional departure. The following sections demonstrate how this semantic-coordination duality translates into practical, real-world agentic collaboration.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dimension** & **Classical CPN** & **TB-CSPN** \\ \hline Coordination Logic & Fully encoded in net topology & Confined to net structure; \\ Semantic Processing & Typically external or & separate from semantics \\  & reduced to token types & Managed via LLM-based \\  & & reduced to token types & topic modeling \\ Token Semantics & Static, syntactic color sets & Dynamic topic vectors with \\  & & contextual relevance \\ Scalability & State-space explosion with & Sub-linear scaling via \\  & complexity & shared semantic layer \\ Agent Behavior & Often hard-coded & Emergent via topic \\  & or external & thresholds and \\ Adaptivity & Requires structural & Driven by changes in \\  & changes to evolve & semantic topic spaces \\ Verification Focus & Entire system logic & Coordination verifiable \\  & embedded in net & independent of semantics \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key differences between TB-CSPN and classical Colored Petri Nets.
# Channel Characterization and SC-FDM Modulation for PLC in High-Voltage Power Lines
## 2 HV-PLC Channel
### HF Signal Propagation Along Power Lines
Matrices \(\mathbf{A}_{1}\), \(\mathbf{A}_{2}\), \(\mathbf{B}_{1}\) and \(\mathbf{B}_{2}\) result from the cascading process [14].
# Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance
## Materials and Methods
### Ionospheric Correction Models
#### Bim  
The Klobuchar model corrects the ionospheric delay by broadcast ephemeris, which has the advantages of convenient calculation and simple structure [3]. The model considers the periodic and amplitude variations of the ionosphere on a daily scale when setting the parameters, and reflects the characteristic variations of the ionosphere to ensure the reliability of large-scale ionospheric forecasts. The drawbacks of the model are that it is only suitable for the mid-latitude area and it has limited accuracy on the ionospheric delay correction. The input parameters of the Klobuchar model are the eight model coefficients, geodetic latitude and longitude of the GNSS antenna, GPS time, as well as the azimuth and the elevation of the observed satellite.  
The improved Klobuchar model is slightly different from the GPS Klobuchar model as it is especially used for Beidou to adapt to the Chinese region. The broadcast parameters of the Beidou Klobuchar are derived based on the monitoring stations in China. The method of Beidou Klobuchar can be found in the Beidou navigation satellite system signal in space interface control document [16].  
The NeQuick model is a time dependent ionospheric electron density and three-dimensional model and is applicable for real-time single-frequency correction for the Galileo satellite navigation system. It provides the electron concentration in the ionosphere and total electronic content (TEC) along the ground to satellite ray-path by means of numerical integration [17]. The input parameters of the NeQuick model include the geodetic position of the GNSS antenna, GPS observing time and effective ionospheric level factor AZ determined by three coefficients broadcasted by Galileo ephemeris. The description of the Galileo NeQuick model is available at [[https://www.gsc-europa.eu/system/files/galileo_documents/Galileo_Ionospheric_Model.pdf](https://www.gsc-europa.eu/system/files/galileo_documents/Galileo_Ionospheric_Model.pdf)]([https://www.gsc-europa.eu/system/files/galileo_documents/Galileo_Ionospheric_Model.pdf](https://www.gsc-europa.eu/system/files/galileo_documents/Galileo_Ionospheric_Model.pdf)).  
In the BIM of this study, the GPS and GLONASS satellite ionospheric delay is corrected by the Klobuchar model, the Beidou satellite ionospheric delay is corrected by the improved Klobuchar model, and the NeQuick model is used for Galileo ionospheric delay correction.
## 3 Finite Element Modeling and Damage Criteria
### Damage Model for CARALL  
#### 3.2.1 Damage Model for Aluminum Sheets  
The 6061-T6 aluminum alloy adopted an elastoplastic model. The linear elastic phase of aluminum alloy was modeled as an isotropic condition, defined by its elastic modulus and Poisson's ratio. Furthermore, the aluminum alloy utilized a ductility failure criterion [28] to judge its initial failure. This criterion suggest that plastic deformation occurs during the loading process of the material, leading to initiation, expansion, and aggregation of internal micropores and microcracks, which reduces the stiffness of the material and degrades its performance. The damage is assumed to occur when the equivalent plastic strain epsilon is a function of a three-axis stress and strain rate.  
\[w_{D}=\int\frac{de^{pl}}{\epsilon_{D}^{pl}(\eta,\epsilon^{pl})}=1 \tag{4}\]  
where \(\eta=-p/q\), \(p\) is the compressive stress, \(q\) is the Von Mises equivalent stress, and \(w_{D}\) is the state variable that increases monotonically with the plastic deformation. Based on the continuum damage mechanics theory, the damage evolution process is as follows [29]:  
\[\sigma=D^{el}:\left(\epsilon-\overline{e}^{pl}\right) \tag{5}\]  
\[D^{el}=(1-d)D_{0}^{el} \tag{6}\]  
where \(D_{0}^{el}\) is the initial elastic stiffness, \(D^{el}\) is the stiffness after performance degradation, and \(\epsilon^{pl}\) is the equivalent plastic strain at any time. The stiffness damage variable \(d\) changes from 0 to 1, representing the damage start to complete fracture.
## 4 Evaluation  
To assess the effectiveness of the proposed adaptive user interface system, an evaluation was conducted with 100 postgraduate students who used the system as part of a structured Java programming course. The participants, aged between 23 and 50 years old, represented a diverse population with varying levels of prior programming experience, ranging from limited exposure to programming to intermediate proficiency in other languages before learning Java. The primary goal of this evaluation was to measure the impact of dynamically adjusted fuzzy weights and reinforcement learning-based optimization on student learning performance, engagement, usability, and overall effectiveness of the adaptive UI.  
This study lasted six weeks, during which students completed programming exercises in an environment where UI changes (hints, difficulty adjustments, step-by-step guidance) were controlled by the Fuzzy Inference Module and continuously optimized by reinforcement learning. The evaluation aimed to determine whether the system effectively adapted to different learning speeds, providing meaningful interventions without disrupting the learning process.  
Figure 2: Flow diagram of the system’s functionality.  
The evaluation was conducted across multiple dimensions:  
* Performance-Based Evaluation (measuring completion time, error rates, and success rates).
* Engagement and Interaction Tracking (analyzing UI interactions and hint requests).
* Usability Study (surveying students on system adaptability and UI effectiveness).
* Subjective User Feedback (gathering qualitative insights from participants).  
These analyses provided a comprehensive understanding of how reinforcement learning and fuzzy logic interact to enhance learning experiences.
# Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array
## 2 System Requirement and Coding Theory
### Spike-Encoding Methods
#### 2.4.3 Encoding Method Selection  
In accelerated mode, every pixel of the sensor area is output. Therefore, the design seeks to reduce the number of pulses as much as possible and minimize the burden of information transmission. In accurate mode, only a small fraction of the sensor's pixels are activated, allowing more pulses to deliver more precise information. It can be seen from the aforementioned encoding theory that the TTFS scheme in temporal encoding only generates one pulse, which is in line with the design pursuit in accelerated mode. A pulse interval is the reciprocal of the frequency for a fixed period of time (or counting period); therefore, the count frequency code can be obtained by repeating TTFS steps to generate a pulse sequence. To ensure circuit design reusability, this paper chose TTFS coding as the accelerated coding method and counting frequency coding as the accurate coding method.