esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Ti65 Powder Spreading Behavior in Multi-Layer Laser Powder Bed Fusion
## 3 Simulation Conditions and Model Validation
### Simulation Setup  
The schematic of the multi-layer powder spreading process is presented in Figure 3. During spreading, powder particles are driven forward by the recoating blade at a defined velocity (\(V\)), depositing a layer with a set thickness (\(H_{\text{set}}\)) onto either the substrate or the previously spread/solidified powder layer. The computational domain for the powder spreading process is set to 10,000 \(\upmu\)m \(\times\) 1000 \(\upmu\)m \(\times\) 3000 \(\upmu\)m (X-Y-Z), with periodic boundary conditions applied in the Y direction. Notably, the spreading thickness is fixed at \(H_{\text{set}}\) = 60 \(\upmu\)m, based on the experimental median particle diameter (\(D_{50}\approx\) 38.7 \(\upmu\)m).  
Following powder deposition, the melting process is simulated within a computational domain of 1000 \(\upmu\)m \(\times\) 1000 \(\upmu\)m \(\times\) 400 \(\upmu\)m (X-Y-Z), as illustrated in Figure 4. The laser melting area covers 800 \(\upmu\)m \(\times\) 800 \(\upmu\)m in the X-Y plane. The laser processing parameters are set to a laser power of 250 W, scanning speed of 1.4 m/s, and hatch spacing of 100 \(\upmu\)m [37]. The initial temperature of both the powder bed and substrate is 300 K. To simulate the actual manufacturing process, a 67\({}^{\circ}\) rotation of the scanning direction is applied between successive layers, with the printing strategies for different layers shown in Figure 4c.  
Figure 3: Schematic illustration of the multi-layer powder spreading process, where \(W\) is the width of the powder layer; \(H\) represents the height between the blade and substrate; and \(V\) is the spreading velocity, with its base value being 0.100 m/s.  
Figure 2: (**a**) Experimental (**left**) and numerical (**right**) setups; (**b**) comparison between numerically and experimentally measured AOR and packing density.
## 3 Bond Behavior and Bond Strength  
A comprehensive understanding of the bond characteristics between FRP bars and FRCC is crucial for the successful integration of this emerging technology within the construction industry. In contrast to the research focused on the bond behavior between FRCC and traditional steel reinforcement, there exists a notable lack of studies addressing the bond performance of FRCC in conjunction with FRP bars. Additionally, the different surface properties of FRP bars make their bond behavior much more complicated than steel bars. The mechanisms that lead to bond failure at the interface between FRP bars and FRCC are still under investigation, and important factors such as the type of bar, its diameter, and surface design have not been adequately studied. Furthermore, the absence of a reliable model for the bond strength between FRP bars and FRCC complicates the design and evaluation of FRCC-FRP bar systems, limiting the practical applications of both materials in engineering [20]. Several researchers conduct experimental, numerical, and analytical investigations to study these bond characteristics [20; 21; 22; 23; 24; 25; 26; 27; 28; 29].  
[PERSON] et al. [21] carried out a theoretical analysis of the debonding mechanism between FRP and ECC using a thick-walled cylinder model. The proposed model aimed to enhance the understanding of the bond behavior between the materials by examining the bond stress-slip relationship. The validity of the model is confirmed through comparison with an experimental pullout test. As shown in Figure 5, the bond stress (bond shear stress)-slip relationship was categorized into three distinct stages: an elastic stage, a softening stage, and a frictional stage. The solution to the second-order differential equation governing the bond behavior of FRP bars along the bond length was derived by incorporating the three-stage linear bond stress-slip relationship. The influence of the nonlocal parameter resulted in an increase in slip during the elastic stage and a decrease during the softening stage, while the slip remained unchanged in the frictional stage. The model's predictions for the pullout force aligned well with the experimental data. However, it was noted that the nonlocal parameters vary across different types of FRP bars.  
Figure 4: Sources of the selected publications.
# Fibers by Electrospinning and Their Emerging Applications in Bone Tissue Engineering
## 3 Mechanical Support
Inspired by the complex structure of natural bone tissue, previous works reported their novel approaches to designing hierarchical scaffolds. [PERSON] et al. [16] prepared coaxial electrospun fibrous layers with different core size by adjusting the ratio of core/sheath solution flow rates, and then the strain gradient scaffold was obtained. In vitro study revealed that multiphenotypic differentiation of hMSCs depended on the local compressive strains. The prepared scaffolds provided a novel gradient platform for osteochondral tissue regeneration. To mimic the complex fiber orientation in native cartilage, [PERSON] et al. [68] prepared electrospun fibers with random orientation and alignment as well as cryo-printed helix to fabricate a multilayer scaffold. The prepared scaffolds possessed compressive properties for matching natural cartilage. Moreover, crosslinking agents have been employed for improvement of mechanical properties of electrospun polymeric fibers by enhancing links among polymer chains. For example, genipin is a biocompatible crosslinking agent and has been widely used to provide chemical crosslinking in BTE scaffolds [148]. [PERSON] et al. [108] employed genipin as a crosslinking agent for fabricating electrospun PLA/gelatin nanofibers. UV light has also been used for crosslinking network formation. [PERSON] et al. [149] prepared electrospun biomimetic periosteum incorporating methacylated gelatin (GelMA), l-arginine-based unsaturated poly (ester amide) (Arg-UPEA), and methacylated hydroxyapatite nanoparticles (nHAMA). Exposed under UV light, the organic-inorganic network inside the fibers was formed and the mechanical strength was enhanced.
## 3 Observer Design
### Observer Design for the Interconnected System
#### 3.1.2 State Estimator Design for the New Process Subsystem
\right] \tag{14}\]  
The state estimation error is expressed as  
\[\mathrm{e}_{\mathsf{p}}(\mathsf{t})=\dot{\xi}_{1}(\mathsf{t})-\dot{\xi}_{1}( \mathsf{t}) \tag{15}\]Then, by subtracting the corresponding Equations (10) and (11), the following error dynamics can be obtained:  
\[\dot{\mathrm{e}}_{\mathrm{p}}=\mathrm{G}\big{(}\dot{\xi}_{11}\big{)}\dot{\xi}_{1}+ \mathrm{F}\big{(}\dot{\xi}_{11}\big{)}+\overline{\varepsilon}\big{(}\mathrm{u}, \dot{\mathrm{u}},\dot{\xi}_{2}\big{)}+\mathrm{H}\big{(}\dot{\xi}_{1}\big{)} \big{(}\mathrm{C}\dot{\xi}_{1}-\mathrm{y}\big{)}-\mathrm{G}(\xi_{11})\dot{\xi} _{1}-\mathrm{F}(\xi_{11})-\overline{\varepsilon}\big{(}\mathrm{u},\dot{ \mathrm{u}},\xi_{2}\big{)} \tag{16}\]  
**Assumption 5**. : _Is that for any \(u\in\mathfrak{U}\big{(}t,e_{p}\big{)}\in\mathcal{AC}(\Re^{+},\Re)\), there exists a continuously differentiable function \(V_{p}\), and the positive constants \(\alpha_{1},\beta_{1},\gamma_{11},\gamma_{12}\) satisfy the following:_  
\[\left\{ \begin{array}{l}(a)\gamma_{11}\parallel e_{p}\parallel^{2}\leq V_{p} \big{(}t,e_{p}\big{)}\leq\gamma_{12}\parallel e_{p}\parallel^{2}\\ (b)\frac{\partial V_{p}}{\partial t}\big{(}t,e_{p}\big{)}+\frac{\partial V_{p }}{\partial e_{p}}\big{(}t,\,e_{p}\big{)}\dot{e}_{p}\leq-\alpha_{1}e_{p}{}^{2 }\\ (c)\parallel\frac{\partial V_{p}}{\partial e_{p}}\big{(}t,e_{p}\big{)} \parallel\leq\beta_{1}\parallel e_{p}\parallel\end{array} \right.. \tag{17}\]  
**Theorem 1**. : _It states that if Assumption 1 and Assumption 5 are satisfied by properly choosing a relatively high-gain tuner parameter \(\theta\) such that the following conditions are met: (1) if \(\parallel\xi_{2}-\dot{\xi}_{2}\parallel\)converges to 0, then make \(\theta>\frac{\eta_{1}}{\alpha_{1}}\), and (2) if \(\parallel\xi_{2}-\dot{\xi}_{2}\parallel\)is bounded by \(\widetilde{e}_{p}\), then choose a value for \(\theta\) such that \(\big{(}\theta^{*}\alpha_{1}-\eta_{1}-\frac{\eta_{2}}{\theta^{*}}\big{)}\geq 0\)._  
Then, the system in Equation (11) becomes a converging observer for the system described in Equation (10), which is a transformed form of the process subsystem described in Equation (1). The proof is given in Appendix A.
## 5 Discussion  
The performance of our in-training explainability frameworks compared to post hoc methods aligns with recent criticisms of surrogate-based approaches [1; 21]. Our fidelity improvements of up to 10.9% support the argument that integrating interpretability during training produces more faithful explanations than post hoc approximation [24]. The computational efficiency gains (30\(\times\) faster than LIME) address a critical limitation identified by [17], noting that post hoc methods often introduce prohibitive computational overhead in production systems. Our approach makes real-time explainability feasible for large-scale deployments, similar to the goals outlined in [52] but with significantly lower computational requirements. The stability improvements observed in our experiments (78% reduction in total variation) directly address the consistency concerns raised by [1] regarding LIME's sensitivity to sampling variations. This stability is crucial for building practitioner trust, as emphasized by [5].  
Our finding that explainability constraints can improve model performance confirms recent work on beneficial regularization effects [38], though our approach differs by using an interpretable model as the regularizer rather than explanation-based constraints. Our frameworks address several fundamental challenges identified in the literature on explainability. By integrating the explainer during training rather than approximating it post hoc, we mitigate the surrogate model faithfulness problem that methods like LIME and SHAP face. The black-box model learns to align with interpretable patterns from the start, resulting in explanations that more accurately reflect the model's actual decision-making  
Figure 5: HELOC Dataset: Local explanation for a test instance, showing the top 10 feature importance scores. Red color means negative contribution, green color means positive contribution on features.  
process. This approach also eliminates the need for potentially problematic perturbation strategies that can alter feature semantics or produce out-of-distribution samples.  
The success of our in-training approach contributes to a paradigm shift in explainable AI, moving from post hoc rationalization to explainability by design. This shift has implications for machine learning workflows and tool development, suggesting that explainability should be considered an objective alongside accuracy during model development. For practitioners in regulated industries or high-stakes applications, our frameworks provide a path to achieving both transparency and performance without the computational overhead associated with post hoc methods. A comprehensive discussion of limitations and future research directions is provided in the following section, Limitations and Future Directions.
## 3 Experimental Results
### Effect of the Volume of Firestop Material
These results further suggest that reducing the firestop volume is feasible for smaller penetrations, as peak temperatures remain constant or even decrease when the firestop-to-opening ratio drops from approximately 95% to 91%. This trend may be related to more effective expansion of the intumescent material in narrower annular gaps, or to a reduction in the available heat-transfer path through the penetration as the opening size decreases. These potential mechanisms require further verification through additional experiments. These observations suggest that more flexible volume-based criteria could be considered for small openings.
# Design and Analysis of Joint Source-Channel Code System with Fixed-Length Code
## 6 Conclusions  
This paper presents a novel, low-complexity joint source-channel code system consisting of a fixed-length source block code and an irregular channel code. Block codes with the minimum Hamming distance \(d_{H,\min}\geq 2\) are designed for guaranteeing the convergence of ISCD and reducing the SER of the source symbol decision. In addition, a well-matched irregular channel code is employed to further improve the waterfall performance of ISCD. The irregular channel code is constituted by unity-rate RNSC codes with small constraint length, and, therefore, it has a low decoding complexity. EXIT charts are used for the analysis of the convergence behavior of ISCD. The proposed system realizes a near-capacity performance by exploiting both the source statistic redundancy and the artificially introduced redundancy in the source code. As the simulation results of the Gaussian distribution sources show, the proposed system outperforms the SSCC system by more than 1 dB gains. Compared to the JSCC system based on LBC, the optimized block code has better error-correction property. Furthermore, in comparison with the JSCC system based on VLC, the proposed scheme achieves about 0.5 dB gains and also reduces the decoding complexity. Our future work will be the further extension of the JSCC system over other nonstandard coding channels and the investigation of the employment of the JSCC system in multimedia applications.  
Conceptualization, methodology, and writing: all authors; software and data curation: H.B.; supervision and project administration: C.Z. and S.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers No. 61571416 and No. 61271282, and the Award Foundation of the Chinese Academy of Sciences, grant number No. 2017-6-17.  
Not applicable.  
The authors declare no conflict of interest.
# Practical Estimation of Machine Tool Spindle Dynamics for Maintenance Decision Making
## 2 Materials and Methods
### Mechanical Impulse Generator  
The design of the mechanical IG, shown in Figure 1, was based upon an automatic center punch. The IG consisted of four components: (1) housing; (2) impacting mass; (3) tapered pin; and (4) impact tip. Two springs were used to drive the impact and to automatically reset the device between cycles. During operation of the IG, the linear motion of the CNC table is used to activate the impact. The tool is positioned to press against the tip, and the preload springs are compressed, as seen in Figure 1b. At maximum compression, the tapered pin slips into a matching hole in the impact mass, and the mass begins to rapidly accelerate under the force of the preload spring. The impact of the mass imparts a pulse through to the tip which is translated to the tool and excites the spindle system, as seen in Figure 1c. The IG automatically resets itself after an impact is generated to achieve repeated tests, as seen in Figure 1a.  
The design was selected to address the practical challenges with continual spindle FRF monitoring, which were identified during the literature review. In contrast to many of the other solutions proposed in literature, the presented design is low cost, can be completely actuated by the CNC controller, requires minimal space on the CNC table, does not require power, and is not sensitive to the coolant and chips present in the manufacturing environment. Most notably, the proposed design is robust because of the simplicity of the mechanical components used.  
The forces from the IG were compared against a conventional PCB Piezotronics 086C01 impulse hammer (Depew, NY, USA). Spindle vibrations were measured with accelerometers at three locations on static locations of the spindle housing. Two Kistler 8702B K-Shear-type single axis accelerometers (Mississauga, ON, Canada) were mounted at the static part of the spindle housing to record accelerations in the machine +x and +y directions. A redundant third PCB Piezotronics 352A21 (Depew, NY, USA) single-axis accelerometer was also included to measure accelerations in the machine -x direction for verification and error checking. Data acquisition was performed by a National Instruments NI-9234 data acquisition card at a sampling frequency of 51,200 Hz. Finally, the saved time-series data was exported to MATLAB R2023b for experimental modal analysis.
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 5 Results  
This section evaluates simulation results from four controllers: Computed Torque PD, Computed Torque PID, PD-PI fuzzy, and MPC, using these three performance indicators. Additionally, the analysis encompasses PID and PID with Gain Scheduling controllers, along with their performance in obstacle avoidance trajectories.  
The transient response in the Cartesian space of all controllers has improved with the proposed trajectory design, bringing the desired trajectory points closer to the Cartesian position given by the initial conditions in the 3D plots.
## 4 Results  
### Results for Aeronautical Mobile Service  
#### 4.1.1 Results for AMS with Omnidirectional Antenna  
Interference analysis for the system with the omnidirectional receiver for different distances showed that I/N protection criteria is met 97% of the time for the worst-case scenario and has more than 14 dB margin for other distances. Figure 10 show distribution functions of I/N when interfering from IMT to the System 1 AMS receiver.
## 5 SSFXG Architecture and Basic Interactions
### Bidding-Based Service Provisioning  
The preceding discussion assumes that NW subscribers request an NW service from the first available nearby NW provider. However, the SSFXG model can support other service provisioning scenarios. For instance, NW providers serving the same geographical location can compete to attract NW subscribers by offering competitive costs and special service quality. After completing the synchronization and identity verification phases with the NW subscribers-owned SC, the SC will search for available NW providers within the same geographic area. This is illustrated in Figure 4.  
This diagram outlines a decentralized service provisioning strategy where an SC deploys a bidding-oriented smart contract to facilitate fair competition among NW providers for delivering services to a subscriber. The process begins with the SC, representing the NW subscriber, initiating the smart contract deployment. The contract includes specific details about the subscriber's communication service needs, such as coverage requirements, bandwidth demands, and other preferences.  
Once the smart contract is deployed, nearby NW providers are notified of the opportunity to bid for the subscriber's service request. These providers submit their bids, specifying their capabilities, service levels, and pricing to match the subscriber's requirements. The SC acts as an intermediary, collecting and evaluating the bids based on the criteria outlined in the smart contract.  
After receiving and reviewing the bids, the SC selects the NW provider that offers the best match for the subscriber's needs. Factors such as service quality, coverage, cost, and reputation may influence the selection process. Once a provider is chosen, the subscriber is seamlessly transferred to the selected NW provider. The smart contract ensures the agreed-upon terms are upheld, and billing settlement is managed transparently based on the winning bid.  
The use of a bidding-oriented smart contract introduces a fair, transparent, and competitive mechanism for service provisioning. It ensures that NW providers compete based on service quality and pricing, creating an efficient marketplace. The smart contract enforces the terms of the agreement, applying penalties automatically in case of any violations, such as service interruptions or failure to meet promised service levels. This enhances accountability and trust in the system.  
This decentralized strategy benefits both NW subscribers and providers. Subscribers gain access to a wide range of service options, allowing them to choose the best solution based on their preferences and needs. Simultaneously, NW providers can improve their services and attract more subscribers by offering competitive bids.
## 2 Materials and Methods
### Methodology
#### 2.2.1 Heliosat-2
As for \(\rho_{\mathrm{min}}(t)\), denoting ground albedo from a clear-sky pixel, we blend methods, leaning towards the rationale described in the study of [PERSON] et al. We opt for the second minimum albedo within a \(d_{\mathrm{max}}\) time frame to mitigate potential image defects, ensuring reliability. The \(d_{\mathrm{max}}\) was fixed to a rolling window of 60 days in the current study. * Step 4: Calculate Cloudiness Index (\(n\)-index). This step computes the cloudiness index, or \(n\)-index, or \(n(t)\), for which a higher value indicates a greater presence of clouds in the observed area. \[n(t)=\frac{\rho_{sat}(t)-\rho_{min}(t)}{\rho_{max}(t)-\rho_{min}(t)}\] (5)
* Step 5. Calculate K-Multiplier or Clearness Value. Here the K-multiplier, representing the clearness of the sky, and based on the calculated cloudiness index, is filtered out from an empirical set of values, as given below. \[K(t)=\left\{ \begin{array}{lr}1.2&n(t)<-0.1\\ 1-n(t)&-0.1<n(t)<0.8\\ 2.55-3.725(n(t))+1.667(n(t))^{2}&0.8<n(t)<1.05\\ 0.15&n(t)>1.05\end{array} \right.\] (6)
* Step 6. Calculate GHI. This step utilizes the previously determined clear-sky GHI, along with the K-multiplier, to calculate the GHI for the given timestamp (t). \[GHI(t)=GHI_{CS}(t)\times K(t)\] (7)