esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Efficiency Closed-Loop Control of a Robotic Fish via Virtual Musculoskeletal Methodology
## 2 Modeling of Two-Joint Robotic Fish  
### The Kinematic Model  
Figure 1a,b shows the prototype of a two-joint robotic fish, which consists of three parts: the head, body, and tail. The length of the robotic fish is 0.6 m and the front 1/3 part is the head, whose length \(L_{\rm h}\) is 0.2 m. \(L_{1}\) and \(L_{2}\) are the length of the body and the tail, respectively. They are greater than that of the driving motor. Let \(L_{\rm motor}\) be 0.1 m, \(\min(L_{1},L_{2})>L_{\rm motor}\). In Figure 1c, \(O_{1}\) is the gravity center of the body and \(O_{2}\) is the gravity center of the tail. \(F_{0}\) is the resistance force of the flow to the fish. \(F_{1}\) is the hydrodynamic force to the body and \(F_{2}\) is the hydrodynamic force to the tail. \(D_{1}\) is the point of the action of \(F_{1}\) while \(D_{2}\) is the point of the action of \(F_{2}\). The solid straight line stands for the midline of robotic fish, while the dashed curve represents the midline of the fish modeled by [PERSON] [24]. The \(f(x)\) is the position of the robotic fish to the axis and \(g(x)\) is the position of the live fish to the axis.  
\[g(x,t)=(c_{1}x+c_{2}x^{2})\sin(\frac{2\pi}{\lambda}x+\omega t) \tag{1}\]  
where \(c_{1}\) and \(c_{2}\) stand for the first and the second coefficient of wave amplitude envelope, respectively; \(\lambda\) denotes the wavelength of the body; \(x\) is the body axis; and \(\omega\) is the body wave frequency.  
Moreover, \(S_{y}\) is the area between \(f(x)\) and \(g(x)\). We set \(L_{1}=0.24\) m and \(L_{2}=0.16\) m. Then, \(S_{y}\) obtains its minimum, which indicates that the swing curve of the robotic fish is close to the fish in nature to most. Thus, the motion functions of the two joints are as follows:  
\[\left\{\begin{array}{l}\theta_{10}(t)=\theta_{10A}\times\sin(2\pi ft)\\ \theta_{21}(t)=\theta_{21A}\times\sin(2\pi ft+\phi)\end{array}\right. \tag{2}\]  
where \(\theta_{10}(t)\) is the angle between the body and \(x\)-axis; \(\theta_{10A}\) is the amplitude of \(\theta_{10}(t)\); \(\theta_{21}(t)\) is the angle between the tail and the body, while \(\theta_{21A}\) is the amplitude of \(\theta_{21}(t)\); and \(\phi\) is the phase difference between the two joints.  
Figure 1: Model of the two-joint robotic fish: (**a**) the prototype of the robotic fish, (**b**) the top view of the robotic fish, and (**c**) the geometry parameters of the robotic fish.
###### Abstract  
With the advancement of information technology, artificial intelligence (AI) has demonstrated significant potential in clinical prediction, helping to improve the level of intelligent medical care. Current clinical practice primarily relies on patients' time series data and clinical notes to predict health status and makes predictions by simply concatenating cross-modal features. However, they not only ignore the inherent correlation between cross-modal features, but also fail to analyze the collaborative representation of multi-granularity features from diverse perspectives. To address these challenges, we propose a deep dynamic memory-driven cross-modal feature representation network for clinical outcome prediction. Specifically, we use a Bi-directional Gated Recurrent Unit (BiGRU) network to capture dynamic features in time series data and a dual-view feature encoding model with sentence-aware and entity-aware capabilities to extract clinical text features from global semantic and local concept perspectives, respectively. Furthermore, we introduce a memory-driven cross-modal attention mechanism, which dynamically establishes deep correlations between clinical text and time series features through learnable memory matrices. In addition, we also introduce a memory-aware constrained layer normalization to alleviate the challenges of multi-modal feature heterogeneity. Besides, we use gating mechanisms and dynamic memory components to enable the model to learn feature information of different historical-current patterns, further improving the model's performance. Lastly, we combine the integrated gradients for feature attribution analysis to enhance the model's interpretability. Finally, we evaluate the model's performance on the MIMIC-III dataset, and the experimental results demonstrate that the model outperforms current advanced baselines in clinical outcome prediction tasks. Notably, our model maintains high predictive accuracy and robustness even when faced with imbalanced data. It can also provide a new perspective for researchers in the field of AI medicine.  
artificial intelligence; electronic health records; clinical outcome prediction; cross-modal attention +
Footnote †: journal: Applied Sciences
## 3 Field Monitoring
### Analysis of Monitoring Results
#### 3.2.2 Horizontal Displacement of the Pile Top  
As shown in Figure 7, ZQS-7, which is the horizontal displacement of the pile top in the middle of the long side AB, is the largest (16.18 mm) and the pile top displacement decreases gradually from the middle to the sides of the foundation pit. ZQS-34 and ZQS-14, which are the horizontal displacements of the pile top of the pit corner, are small--1.03 mm and 1.11 mm, respectively. In addition, the horizontal displacements of ZQS-15 and ZQS-33 on the short side are 8.16 mm and 7.97 mm, which are both smaller than the value at ZQS-7 and larger than ZQS-34 and ZQS-14. The distribution of horizontal displacement of the pile top presents an obvious spatial effect.
# New Insights into Cement-Soil Mixtures with the Addition of Fluidized Bed Furnace Bottom Ashes
## 4 Discussion
The study determined that utilizing 10% wollastonite and 10% bottom ash as partial substitutes for cement and fine aggregate produces sustainable concrete with superior compressive and flexural strength compared to the control mixture. Making reference to [47], the replacement of cement by wollastonite and 10% of fine by bottom ash increases with increasing compressive strength and flexural strength due to an increased C-S-H gel formation. Incorporating an adequate percentage of wollastonite and bottom ash into modified concrete increased its strength and resistance to water penetration and decreased waste and CO\({}_{2}\) emissions in the environment. Some researchers adopt a different approach to managing bottom ash, considering it as a substitute for aggregates [47; 48]. However, it is crucial to note the binding properties of bottom ash. Furthermore, the altered approach presented in this paper is supported by the grinding of the ash to sizes characteristic of cement. In the studies [48], it was demonstrated that due to high water absorption, mixtures with the addition of bottom ash result in a loss of workability when used in cementitious materials, occasionally leading to zero slump. Additionally, these mixtures reduce compressive, tensile, and flexural strength, aligning with the results of studies conducted by the authors of this article.
# FBG-Based Monitoring of Geohazards: Current Status and Trends
## 6 Conclusions  
This paper presents an up-to-date review of research and development activities in geohazard monitoring using FBG sensing technologies. The principle of the FBG sensing technology has been introduced briefly, and a comparison of features, including measurement accuracy and distance resolution, is given. Due to the inherent advantageous properties of FBG, such as immunity to EMI, being waterproof, and resistance to corrosion, FBG sensing systems have emerged as a suitable solution in monitoring and early warning of geohazards. As FBG enables quasi-distributed, automated, and long-distance geotechnical monitoring, it has great potential to replace point-mode monitoring techniques. At the same time, the FBG systems can greatly improve monitoring precision and substantially improve geohazard mitigation levels.  
Currently, the application of FBG systems for long-term monitoring and early warning of geohazards is still in its infancy and has not been widely recognized. Past experiences, including those gained in this review, tell us that close collaboration of a group of geotechnical practitioners and optoelectronic engineers plays a vital role in the successful implementation of fiber optic geohazard monitoring.  
The authors gratefully acknowledge the financial supports provided by the National Natural Science Foundation of China (Grant Nos. 41672277, 41427801 and 41230636) and the Natural Science Foundation of Jiangsu Province (Grant No. BK20161238). Special thanks are given to [PERSON] for making corrections to this paper in English.  
The authors declare no conflict of interest.
# Software-Defined Visible Light Communication for Internet of Things: A Low-Complexity Approach
###### Abstract  
This work presents a software-defined visible light communication (SD-VLC) system that integrates carrierless amplitude and phase (CAP) modulation with an adaptive sign-data least mean squares (SDLMS) equalizer. The proposed solution is designed to address key challenges in VLC systems, such as LED bandwidth constraints, inter-symbol interference, and nonlinear distortions, and leverages the PYNQ platform to offer a flexible, reconfigurable, and cost-effective communication architecture tailored for IoT applications. Simulation results demonstrate that CAP modulation not only delivers high spectral efficiency but also inherently mitigates issues such as IQ imbalance and phase noise, thereby reducing hardware complexity. Moreover, the adaptive SDLMS equalizer significantly improves performance in multipath fading environments and reduces the bit error rate by approximately two orders of magnitude. These results underscore the potential of the proposed SD-VLC system to achieve low-cost and highly flexible wireless communication.  
VLC; adaptive; nonlinear compensation; CAP; LMS; BER Article
## 2 Materials and Methods
### Color Measurement  
The chromatic characteristics of edible fixed oil samples were defined by the colorimetric coordinates L* (lightness), a* (redness/greenness), b* (yellowness/blueness), and h* (hue angle in degrees). The above values were measured using a tristimulus chromatometer (model CR-400, Minolta, Tokyo, Japan) calibrated with a standard white plate (L*: 97.83, a*: \(-\)0.45, b*: +1.88). Three random readings per sample were taken and averaged. Additionally, the color of oil samples was measured in glass cells using a Lovibond(r) Model Rx spectrocolorimeter (The Tintometer Limited, Lovibond House, Sun Rise Way, Amesbury, UK), providing color data according to the Lovibond(r) RYBN, chlorophyll and b-carotene color scales, with color units expressed as neutral (N), red (R), yellow (Y), and blue (B), according to the AOCS Official Method Cc 13e-92 [12]. The Lovibond scale is based on the color absorbance of different glasses, where the Lovibond degrees are determined by comparing the color of light transmitted through an optical path of the sample with the color of light that exits from the same source after passing through a series of standard colored glass filters. The combination of three primary colors (red, yellow, and blue) and neutral filters that matches the sample absorption defines the RYBN Lovibond color.
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 3 System Model and Problem Formulation
### Channel Modeling for UAV-Assisted Non-Terrestrial Networks
#### 3.3.2 Reflected Path  
The reflected path consists of two segments: one from the NTN node to the RIS and the other from the RIS to the user. Let \(\mathbf{h}\in\mathbb{C}^{M}\) denote the channel vector from the NTN node to the RIS, and \(\mathbf{g}\in\mathbb{C}^{M}\) represent the channel vector from the RIS to the user, where \(M\) is the number of RIS elements. Both links are subject to path loss, large-scale shadowing, and small-scale fading.  
The channel vector \(\mathbf{h}\) from the NTN node to the RIS is modeled as  
\[\mathbf{h}=\sqrt{\text{PL}_{h}}\cdot\sqrt{10^{\text{Sh}_{h}/10}}\cdot\mathbf{ \hat{h}}, \tag{3}\]  
where \(\text{PL}_{h}=\left(\frac{\lambda}{4\pi d_{h}}\right)^{2}\) denotes the path loss component, with \(\lambda\) being the carrier wavelength and \(d_{h}\) the distance between the NTN node and the RIS. The term Sh\({}_{h}\) represents the shadowing effect, modeled as a log-normal random variable, and \(\mathbf{\hat{h}}\sim\mathcal{CN}(0,\text{I}_{M})\) accounts for small-scale fading, modeled as a complex Gaussian vector with zero mean and an identity covariance matrix.  
Similarly, the channel vector \(\mathbf{g}\) from the RIS to the user is given by  
\[\mathbf{g}=\sqrt{\text{PL}_{g}}\cdot\sqrt{10^{\text{Sh}_{g}/10}}\cdot\mathbf{ \hat{g}}, \tag{4}\]  
where \(\text{PL}_{g}=\left(\frac{\lambda}{4\pi d_{g}}\right)^{2}\) is the path loss for the RIS-user link, with \(d_{g}\) being the corresponding distance. The shadowing effect for this segment is represented by Sh\({}_{g}\), and the small-scale fading is captured by \(\mathbf{\hat{g}}\sim\mathcal{CN}(0,\mathbf{I}_{M})\), consistent with the fading model used for the NTN-RIS link.  
The small-scale fading components \(\tilde{h}_{d}\), \(\tilde{h}\), and \(\hat{g}\) are modeled as circularly symmetric complex Gaussian random variables, i.e., \(\tilde{h}_{d},\tilde{h},\tilde{g}\sim\mathcal{CN}(0,1)\), corresponding to a Rayleigh fading environment. This assumption is appropriate for NLoS propagation scenarios, which are common in urban and indoor UAV deployments due to dense building obstructions and multipath reflections. However, in scenarios where a dominant LoS component is present, such as in rural or high-altitude NTN environments, a Rician fading model is more accurate. In such cases, the channel coefficients can be modeled as  
\[\tilde{h}_{d}=\sqrt{\frac{K}{K+1}}h_{\text{LoS}}+\sqrt{\frac{1}{K+1}}h_{\text{ NLoS}}, \tag{5}\]  
where \(K\) is the Rician K-factor, and \(h_{\text{LoS}}\), \(h_{\text{NLoS}}\sim\mathcal{CN}(0,1)\). For simulation simplicity and consistency across diverse scenarios, we use the Rayleigh model as a baseline, whileacknowledging that environment-aware selection of fading models is critical for real-world implementation.
## 3 Computed Amplification Factors: Main Statistical Characteristics  
### Validation of Adopted Methodology for Computation of AFs  
Earlier studies confirmed that the equivalent linear method combined with the wave propagation solution in the frequency domain, as adopted in this study, instead of the nonlinear time history analysis demanding much more computational effort, typically yields satisfactory results for engineering applications [47]. Many examples of the validation of this approach are documented in DEEPSOIL [70]. Furthermore, using the DEEPSOIL 7.0 software, we carry out a nonlinear time history analysis on a specific site among the soil profiles used in this study (a deposit of 5 m in depth with a shear wave velocity of 300 m/s on a bedrock with a shear velocity of 800/s), submitted to the Kobe (1995) record (_PGA_ = 0.82 g). The results are compared to those obtained using DEEPSOIL with the equivalent linear model, and practically identical free surface response time histories and free surface response acceleration spectra are obtained. A difference of 0.005, less than 0.2%, is observed in the amplification factor.  
For more flexibility, ease of automation, and to efficiently carry out the parametric study, the above procedure is implemented in Matlab R2023a(r) and used to compute the ground time-history responses. To validate the developed code, the results are carefully and successfully checked for many validation examples against those provided by other codes, namely, DEEPSOIL [70] and EERA [71]. A relative difference of less than 0.3% is obtained in terms of the maximum acceleration at the free surface.
## 3 Application and Results  
### Loads and Loading-Rate  
Figure 4 plots the typical loading response (\(P\)) for both BTC and BTD tests under a quasi-static condition. Clearly, the load-time curves increased linearly from the initial time for the BTC. However, for the BTD tests, the load of \(P\) firstly grew at a slow loading rate and then went up rapidly after the densification of the contacting interface. Since the later stage of the load curve increased almost linearly with the increase of time in the BTC, the increasing slope of the linear curve could be regarded as a constant loading rate. Therefore, the loading rate \(\dot{\gamma}\) could be defined as:  
\[\dot{\gamma}=\frac{dP}{dt} \tag{5}\]  
Thus, as shown in Figure 4, the average loading rate \(\dot{\gamma}\) for the BTD and BTC was calculated as about 0.512 kN/s and 0.598 kN/s, respectively. Although the results of \(\dot{\gamma}\) of the BTD tests was larger than that of the BTC, the loading rates were approximately the  
Figure 3: Setups designed for the concentrated and distributed boundary conditions which are loaded by steel plates (**a**) and steel-arc jaws (**b**), respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Setting** \\ \hline Subset & 15 \(\times\) 15 pixel\({}^{2}\) \\ Step & 10 pixel \\ Magnification factor & 55 mm/pixel \\ Strain filter size & Gaussian (5) \\ Matching criterion & Normalized squared differences \\ Interpolation & Optimized 8-tap interpolation \\ Shape function & Affine \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters of the analysis windows in this study.  
same. Hence, both BTC and BTD were considered to have the same loading rate. According to Figure 4, the maximum loads of the BTD tests were apparently larger than those of the BTC even though the loading rate of the BTC was slightly lower than that of the BTD. It could also be observed that three load-time curves of the BTD did not coincide perfectly before the peak force, but the maximum loads were approximately the same (a maximum value of 17.15 kN and a minimum value of 16.13 kN). However, a larger difference in maximum loads, which were 16.90 kN and 14.53 kN, was observed in the results of the BTC. Thus, the tensile strength of the Brazilian test could be expected to be dependent on the boundary conditions.
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 5 Experiment Evaluation
### UE-UAV Bootstrapping and Mutual Authentication  
We next establish the communication set up between the UE and its supporting UAV. we made some changes to the messages' data structures in the srsRAN source code of the UE to make it able to handle the newly introduced connection modes while maintaining the ability to connect in normal mode. We added a new attribute to specify the connection mode whether it is normal mode or delegation mode, and an identity attribute to the _rrc_conn_setup_s_ data structures in _dl_ccch_msg_ module. Additionally, adding an identity attribute to the _rrc_conn_setup_complete_r0_ies_s_ data structures in the _ul_dcch_msg_ module.  
The RRC connection establishment between the UE and the UAV is as follows:  
1. The UE initiates a session to the UAV using a random RA-RNTI.
2. The UAV, functioning as a flying base station, assigns a C-RNTI and sends timing adjustment to the UE.
3. The UE then adjusts the timing and sends a normal RRC Connection Request since the UE does not recognize whether it is connecting to a normal eNB or the UAV.
4. When the UAV receives a connection request from a valid subscriber, the UAV replies with an RRC Connection Setup message to the UE specifying that the connection is in _Delegation mode_ and includes the UAV identity encrypted by the core network's private key. This identity is pre-loaded to the UAV as aforementioned in Section 4.1.
5. When the UE receives a Connection Setup message with the mode parameter set to _Delegation mode_, it parses the UAV's identity after validating the proxy signature message. Once verified, the UE sends an RRC Connection Complete message, including an ACK of the UAV's warrant and the UE CID, while connected to the home core network, this CID is updated and sent periodically to the UE; therefore, the UE can use it in emergency cases.
6. The UAV parses and verifies that the received CID is registered for the inter-operator service agility feature before starting the mutual authentication process with the UE.
## 4 Results and Discussions
### Implementation Challenges
#### 4.4.2 Cybersecurity, Privacy, and Data Management
Constant monitoring of workers can also induce psychosocial stress, particularly when employees feel pressured to share personal information or adapt to unfamiliar digital environments [53]. Thus, the confidentiality and appropriate use of the collected health data raise important concerns. In short, data security poses a significant challenge and is a major obstacle due to perceived vulnerability to hacker attacks and challenges in managing data consistency and integrity. Ensuring respect for fundamental workers' rights, including privacy, independence, and human dignity, is of significant importance. Therefore, robust security and privacy protocols, encompassing encryption and strict access control, are not only essential for maintaining employee trust and legal compliance but also for fostering a safe and secure production environment that prioritizes worker well-being, aligning with the principles of Industry 5.0.
## 4 Quality Assessment Methodology
### Validation Methods  
The validation methodology follows the CEOS LPV good practice protocol for the validation of satellite-derived albedo products [21], and the validation metrics are presented in Section 4.2.1. The different strategies for product intercomparison and direct validation are described in Sections 4.2.2 and 4.2.3, respectively.  
Figure 4 shows the global distribution of the sampling used in this study. The product intercomparison is evaluated over a 725-site land validation network (LANDVAL) of sites [67], which is designed to globally represent the variability of land surface types, and was used as the spatial sampling to evaluate these criteria. This network also includes 20 well-known desert calibration sites [26] for the precision evaluation, due to their high temporal stability. For the direct validation, the 20 selected GBOV homogeneous stations with available ground data (see Table 5) were used.  
Figure 4: Global distribution of the 725 LANDVAL (including 20 desert calibration sites) and 20 GBOV sites.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Optimal** & **Target** & **Threshold** \\ \hline
**Surface Albedo** & Max [5\%, 0.0025] & Max [10\%, 0.01] & Max [20\%, 0.02] \\
**Uncertainty Requirements** & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Predefined uncertainty requirement levels used for SA validation.  
Figure 3: Uncertainty levels as a function of SA values.