esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Hourglass Buoy Model
### Froude-Krylov Force  
The Froude-Krylov (FK) force, \(F_{fk}\) of Equation (3), is the integration of the pressure field forces, \(\vec{P}(t)\), over the instantaneous wetted surface of the buoy, \(S\)[27]  
\[F_{fk}=-\int\int\vec{P}(t)\cdot\hat{n}\,dS \tag{3}\]  
where \(\hat{n}\) is the unit vector pointing outwards from the buoy's surface. The pressure will be approximated using Airy's Wave Theory shown in Equation (4)  
\[P(t)=\rho g\varepsilon^{xz}A\cos\left(\omega t\right)-\rho gz \tag{4}\]  
where \(\rho\) is the density of water, \(g\) is the gravity acceleration constant, and \(\chi\) is the wave number. The two terms of \(\vec{P}(t)\), after substitution into Equation (3), give the dynamic and static FK forces used in Equation (1).  
\[F_{fk}=F_{fk,d}+F_{fk,s} \tag{5}\]  
The approach of [PERSON] [27] is used to obtain closed-form expressions for the FK forces shown in Equation (6) where cylindrical coordinates are used when integrating Equation (3).  
\[\begin{split}& F_{fk,s}=-\rho g\int_{0}^{2\pi}\int_{z_{1}}^{z_{2}} zf(z)f^{\prime}(z)dz\,d\theta\\ & F_{fk,d}=-\rho gA\cos\left(\omega t\right)\int_{0}^{2\pi}\int_{z _{1}}^{z_{2}}e^{xz}f(z)f^{\prime}(z)dz\,d\theta\end{split} \tag{6}\]  
The revolution profile for the hourglass buoy geometry of Figure 1 is \(f(z)=\overline{\alpha}(z+h)\) for the lower cone and \(f(z)=\overline{\alpha}(z-\zeta+\eta)\) for the upper cone with \(\overline{\alpha}=\tan\left(\alpha\right)\). The cylindrical coordinate rotation limits, \(\left[0,\ 2\pi\right]\), and the vertical limits, \(\left[z_{1},\ 2_{2}\right]\), are based on the instantaneous wetted surface with again with two parts: (1) the immersed portion of the lower cone with vertical limits \(\left[-h,\ 0\right]\) and the immersed part of the upper cone with limits \(\left[\zeta-\eta,\ 0\right]\). The resulting FK force terms are given in Equation (7) where the first term of \(F_{fk,s}\) is the weight of the buoy, \(F_{g}\).  
\[\begin{split}& F_{fk,s}=\frac{1}{3}\pi\rho g\overline{\alpha} ^{2}\Big{[}h^{3}+(\eta-\zeta)^{3}\Big{]}\\ & F_{fk,d}=\frac{2\pi}{\chi^{2}}\rho g\overline{\alpha}^{2}\eta \Big{[}2-e^{-\chi h}-e^{-\chi(\eta-\zeta)}-\chi(h+\eta-\zeta)\Big{]}\end{split} \tag{7}\]  
Figure 1: Hourglass buoy, notation, and coordinate system. Blue denotes the submerged volume.  
Rewriting Equation (7) for substitution into Equation (1) yields Equation (8) where the first equation is the buoyancy force and is the same as developed in [17] using a geometric approach.  
\[\begin{split} F_{b}=F_{fk,s}+F_{g}&=\frac{1}{3}\pi \rho g\overline{\alpha}^{2}(\eta-\zeta)^{3}\\ F_{fk,d}&=\frac{2\pi}{\chi^{2}}\rho g\overline{ \alpha}^{2}\eta\left[2-e^{-\chi h}-e^{-\chi(\eta-\zeta)}-\chi(h+\eta-\zeta) \right]\end{split} \tag{8}\]
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 4 Optimizing Ground Station Systems Through Numerical Design
### Numerical Calculations of Ground Station and Satellites  
In this section, the equations previously discussed will be used to calculate the required capabilities of the ground station as shown in Table 3. The results will be presented in the form of the antenna gain required (\(G_{r}\)) for each type of antenna to receive signals from different satellites. This value is crucial for designing antennas capable of receiving signals. The calculations will focus on satellites communicating in the UHF, VHF, and L-band frequencies, with the following signal reception parameters specified:  
* C/N required: The C/N (carrier-to-noise) ratio required for receiving signals from satellites such as GK-2A, FY-2H, NOAA-15, and ISS should be at least 10 dB. This value is based on experimental findings that signal reception with a C/N better than 10 dB is necessary for successful data demodulation. Except for FossaSat-2E [21], a CubeSat communicating with LoRa, demodulation at SF11 performs well even with a C/N of \(-17.5\) dB, which sets the required C/N for this satellite.
* Polarization mismatch: This occurs when the alignment of the satellite antenna and the receiver antenna does not match. This misalignment is unavoidable when the ground station is designed to receive signals from multiple satellites at different longitudes. Setting the alignment of the receiver antenna to match one satellite may result in a mismatch with others, causing loss. This loss can be severe, potentially leading to infinite loss. To address this issue, this research opts for using circular polarization exclusively, accommodating various polarizations from different satellite positions. The worst-case scenario for mismatch loss in this setup is limited to 3 dB. However, for receiving signals from NOAA-19 satellites, there is no polarization loss because NOAA-19 transmits signals as RHCP.
## 2 Materials and Methods
### Statistical Analysis  
For the statistical analysis, we first checked for the normal distribution of the CoP data using the Kolmogorov-Smirnov test with Lilliefors correction. Two-way ANOVAs for repeated measures were performed with training intervention (PRE vs. POST measures) as the within-subjects factor, and (a) sex (male vs. female) and (b) vision restriction (eyes open vs. eyes closed) as the between-subjects factors to check for possible differences in the dependent variables of CoP path length, CoP anteroposterior and CoP mediolateral sway during the single-legged and two-legged static balance assessments. In the event of a significant main or interaction effect, a Bonferroni-corrected pairwise analysis was conducted. Similarly, two-way ANOVA for repeated measures with training intervention and sex as fixed factors was performed to test for possible differences in the anthropometric and lower limb explosive performance outcome measures with post hoc Bonferroni-corrected pairwise analysis. The percentage change due to training intervention was calculated ((%\(\Delta\): (POST \(-\) PRE)/POST \(\times\) 100%), and Cohen's d with values of 0.2 \(<\) d \(\leq\) 0.5, 0.5 \(\leq\) d \(<\) 0.8 and d \(\geq\) 0.8 determining a small, medium or large effect size due to training intervention was calculated [40]. All statistical analyses were performed using SPSS IBM v.21, and the significance level was set at a = 0.05.
# Research on an Improved SOM Model for Damage Identification of Concrete Structures
## 4 Experiments and Results Analysis
### Selection of Input Samples of RPC Bending Fatigue Damage Identification Model  
There is no obvious change in the appearance of the specimen before loading in the RPC bending fatigue test. When the loading force reaches 70% to 80% of the ultimate bending strength, initial cracks appear in the mid-span accompanied by the sound of steel fibers being pulled out, and damage images are obtained during this process. It was observed that the flexural strength value did not decrease with the occurrence of mid-span cracks in the specimen until the steel fibers in the crack section were completely pulled out, and the specimen lost its bearing capacity and declared failure. With the development of the experimental phenomenon, a three-dimensional model of the concrete specimen was obtained. Figure 5 shows the entire process of acquiring images during 3D damaged specimen loading, and Figure 6 is the obtained three-dimensional model of microcrack damage.  
Figure 5: Damage 3D image acquisition system.  
Figure 6: 3D model of the model of micro-crack damage.  
In order to reduce the amount of data and improve the recognition efficiency, redundant information is removed based on a 3D point cloud projection algorithm and median filtering. Based on the GLCM, the damage model input samples are extracted. The image gray level \(g=128\) is constructed, the generation step size is \(d=1\), and the generation direction \(\theta\) takes the gray level co-occurrence matrix of \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\) and \(135^{\circ}\). The 14 feature parameters such as angular second moment and correlation are extracted. In order to improve the quality of the input samples, the \(\mathbf{P_{1}}\) (angular Second Moment), \(\mathbf{P_{2}}\) (entropy), \(\mathbf{P_{3}}\) (inertia moment), \(\mathbf{P_{4}}\) (correlation), \(\mathbf{P_{5}}\) (inverse difference moment), and \(\mathbf{P_{6}}\) (variance) are screened out as standard samples based on the DFS method. Table 1 shows the damage texture properties represented by the input sample.  
By analyzing Table 1, the damage texture features represented by each input sample can be clearly grasped
###### Abstract  
Febrile diseases such as malaria, typhoid fever, tuberculosis, and HIV/AIDS pose significant diagnostic challenges in Low- and Middle-Income Countries (LMICs). Misdiagnosis leads to delayed treatment, increased healthcare costs, and higher mortality rates. This study presents a prototype diagnostic framework integrating machine learning (ML) and explainable artificial intelligence (XAI) to enhance diagnostic performance, interpretability, and usability in resource-constrained settings. A dataset of 3914 patient records from secondary and tertiary healthcare facilities was used to train and validate predictive models, employing Random Forest, Extreme Gradient Boost, and Multi-Layer Perceptron with optimized hyperparameters. To ensure transparency, XAI techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Large Language Models (LLMs) were integrated, enabling clinicians to understand model predictions. A prototype mobile-based diagnostic system was developed to explore its feasibility for real-time decision-making. The system features an intuitive interface, patient record management, and AI-driven diagnostic insights with visual and textual explanations. While usability testing with simulated case studies demonstrated its potential, real-world deployment and large-scale clinical validation are yet to be conducted. The system is designed with scalability in mind, allowing for future adaptation to different LMC settings. However, limitations such as dataset imbalance and exclusion of pediatric data remain. Future research will focus on refining the model, expanding the dataset, and conducting extensive clinical validation before real-world implementation. This study serves as a foundational step toward AI-driven diagnostic tools in resource-limited healthcare environments.  
data-driven; intelligent diagnostics; explainable AI; febrile diseases; machine learning; ChatGPT-3.2020
## 7 Discussion
### Benefits  
* Personalization of Therapy: The implementation of AI can enable the customization of the rehabilitation process according to the individual needs of each patient. Based on a wide range of data, the intensity and type of exercises could be tailored to the patient'sspecific characteristics and capabilities, which would enhance the effectiveness of the therapy.
* Monitoring Progress: The ability to automatically monitor patient progress would facilitate dynamic adjustment of the therapy plan. This would allow for quick responses to changes in the patient's condition, resulting in more effective management of the rehabilitation process.
* Greater efficiency: Based on the patient's data--such as age, gender, depression level, and assessed ability for independent functioning and mobility (parameters RMA and ADL) before rehabilitation--the therapist can utilize the developed model. Depending on the input data and recommended rehabilitation program, the model will determine if the chosen program will yield the expected benefits. If not, the model (in an extended version) will suggest a new rehabilitation program that offers a higher chance of recovery. The developed model can also be used by doctors to select the appropriate rehabilitation program after evaluating the patient's motor skills. This approach will help reduce costs associated with ineffective rehabilitation treatments and prevent unnecessary extension of the rehabilitation process.
# OTFS-IM Modulation Based on Four-Dimensional Spherical Code in Air-to-Ground Communication
## 3 Performance Analysis
### Papr  
The transmitted signal \(\mathbf{s}\) can be also represented as \(s[u,v],0\leq u\leq M-1,0\leq v\leq N-1\)[29]:  
\[s(u+vM)=M\sum_{n=0}^{N-1}\widetilde{x}_{u}[n]g_{tx}([u+vM-nM]_{MN}) \tag{20}\]  
where \(\widetilde{x}_{u}[n]=\sum_{n=0}^{N-1}x[k,u]e^{j2\pi\frac{\delta u}{N}}\) is the \(N\)-point IFFT of the DD domain signal \(x[k,u]\) along the Doppler axis. According to the system model, \(\mathbf{X}_{\text{DD}}\) contains \(N/n\) sub-blocks in each row. Therefore, there are \(kN/n\) active symbols from \(\mathcal{A}\) or zero otherwise in each row of \(\mathbf{X}_{\text{DD}}\). For the \(u\)-th row of \(\mathbf{X}_{\text{DD}}\), we denote \(E\{|x[k,u]|_{2}\}=\sigma_{x}^{2}\) and \(E\{|\widetilde{x}_{u}[n]|_{2}\}=N\sigma_{x}^{2}\).  
The maximum peak-to-average-power ratio (PAPR) of the transmitted signal \(\mathbf{s}\) is:  
\[\text{PAPR}=\frac{\max\limits_{u,v}\{|s(u+vM)|_{2}\}}{P_{avg}} \tag{21}\]  
where \(P_{avg}\) is the average energy of \(\mathbf{s}\) and can be expressed as:  
\[\begin{split} P_{avg}&=\frac{1}{MN}\sum_{v=0}^{N-1 }\sum_{u=0}^{M-1}E\{|s(u+vM)|_{2}\}\\ &=M\sigma_{x}^{2}\sum_{v=0}^{N-1}\sum_{u=0}^{M-1}\sum_{n=0}^{N-1 }E\{|g_{tx}([u+vM-nM]_{MN}|_{2}\}\end{split} \tag{22}\]  
The numerator of Equation (20) is simplified by the Cauchy-Schwartz inequality:  
\[\begin{split}\max\limits_{u,v}\{|s(u+vM)|_{2}\}\leq& M^{2}\max\limits_{u}\sum_{n=0}^{N-1}|\widetilde{x}_{u}[n]|^{2}.\\ &\max\limits_{u,v}\sum_{n=0}^{N-1}|g_{tx}([u+vM-nM]_{MN}|^{2} \end{split} \tag{23}\]  
For a certain \(u\), according to discrete [PERSON]'s theorem, we have:  
\[\begin{split}\max\limits_{u}\sum_{n=0}^{N-1}|\widetilde{x}_{u}[n ]|^{2}&=N\max\limits_{u}\sum_{k=0}^{N-1}|x[k,u]|^{2}\\ &\leq\frac{k}{n}N^{2}\max\limits_{k,l}\left|x[k,l]\right|^{2} \end{split} \tag{24}\]  
Therefore, Equation (23) is simplified as follows:  
\[\begin{split}\max\limits_{u,v}\{|s(u+vM)|_{2}\}\leq& M^{2}N^{2}\frac{k}{n}\max\limits_{k,l}\left|x[k,l]\right|_{2}.\\ &\max\limits_{u,v}\sum_{n=0}^{N-1}|g_{tx}([u+vM-nM]_{MN}|^{2} \end{split} \tag{25}\]For the rectangular pulse shaping function, the upper bound of PAPR is:  
\[\text{PAPR}\leq\frac{\frac{\sum\limits_{n}N}{n}\underset{k,l}{\max}\left|x[k,l] \right|^{2}}{\sigma_{x}^{2}} \tag{26}\]  
The PAPR of the OTFS-IM system increases linearly with the number of active symbols in each row of \(\mathbf{X}_{\text{DD}}\) rather than \(N\), which indicates that the PAPR of the OTFS-IM system is lower than that of the OTFS system. Under the same sub-block parameters, a four-dimensional symbol is carried by two two-dimensional symbols. These two two-dimensional symbols are not of uniform amplitude, but they are uniform in two-dimensional modulation. Therefore, the PAPR of four-dimensional modulation is slightly different from that of the two-dimensional modulation, which we will verify by simulation in Section 4.
# A Random PRIM Based Algorithm for Interpretable Classification and Advanced Subgroup Discovery
## 3 Proposed Methodology
### Illustrative Example
The final model metrics: accuracy, precision, recall, and F1-score _Accuracy = 96.7%; precision = 95%; recall = 100%; F1-score = 97.4%_ As illustrated by this simple example, R-PRIM-Cl offers at each step the possibility to evaluate the boxes and reconsider them depending on the experts and the domain knowledge. The metarule (R4, R5, R8) obtained means that the rules R4, R5, and R8 cover the same data in the feature space and hence describe the containment between the three rules. The final rules retained by the classifier to predict if a flower is a \"setosa\" (class = 1) are: - If (4.3 < sepal length (cm) < 5.95 AND 0.1 < petal width (cm) < 0.8 AND 1.0 < petal length (cm) < 3.75) THEN flower ='setosa' - If (1.0 < petal length (cm) < 1.79) THEN flower ='setosa' - If (4.3 < sepal length (cm) < 5.35 AND 2.95 < sepal width (cm) < 4.4) THEN flower ='setosa' The other rules have smaller coverage and support and can be given as a description of smaller groups in the dataset, as shown by the rule R7 (IF 3.25 < sepal width (cm) < 4.4 AND 1.0 < petal length (cm) < 6.05 THEN flower ='setosa'), which has a support of 7% and a coverage of 5%. These rules are more specific to the dataset and are presented to the experts as pattern discovery. According to different domains, the experts can explore these subgroups. By having perfect recall and high precision, in particular, the model's metrics show that it is operating at a high level and making accurate and comprehensive predictions. The model shows promise for applications needing both sensitivity and accuracy because of its balance between recall and precision, as evidenced by its high F1-score.
# The Future for End-Stage Kidney Disease Treatment: Implantable Bioartificial Kidney Challenge
## 3 Summary  
The kidney comprises numerous complex functions that, until now, have been replaced by extracorporeal purification techniques aimed at removing a narrow spectrum of molecules through diffusion and convection. Metabolic (reabsorption and secretion of specific molecules) and endocrine functions have not yet been specifically and completely reproduced by the purification techniques available for clinical use. The feasibility of implantable bioreactors for renal cell therapy opens the challenge of developing a completely implantable bio-artificial kidney (iBAK) based on silicon nanopore membranes that ensure immunological isolation, cell viability, and the possibility of maintaining a blood substrate for metabolic activities. The development of specific technologies for keeping renal tubular cells alive for long periods in complex devices is of particular interest, as it forms the basis for achieving the metabolic and catabolic functions of artificial kidneys. The possibility of having separate modules for the management of individual kidney functions (filtration, molecules diffusion, reabsorption of water and solutes, endocrine and catabolic activity of drugs, and immunomododulation) will make it possible in the future to build complex, implantable devices, the lifetime of which will allow patient treatment without the need for continuous invasive maintenance. The challenge for the future is to miniaturize the individual modules and integrate them into complex devices that can be implanted in patients with ESKD while ensuring acceptable lifetime.  
The complexity of iBAKs requires a multidisciplinary effort with the support of regulatory agencies such as the FDA, the EMA, and EU-notified bodies, as well as standards-issuing organizations, to promote innovation and expedite access to the new technology in this field.  
Conceptualization, F.N. and F.G.; methodology, F.N., F.G., L.C., E.B., M.C., L.F.S. and C.S.; validation, F.N., L.F. and L.A.C.; formal analysis, F.N., F.G. and L.F.; writing--original draft preparation, F.N., F.G., L.C., E.B., M.C., L.F.S. and C.S.; writing--review and editing F.N., F.G., L.F. and L.A.C.; supervision F.N. and L.A.C.; F.N. and F.G. contributed equally. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Software-Defined Networking: Categories, Analysis, and Future Directions
## 4 Network Security and Management
The proposed parallel online deep learning (PODL) algorithm envisions the two important aspects and adjusts the weight for queuing the controller packets (packet-in) and flow rule installation capacity in a forwarding device. In [140], SDN-based load-balanced opportunistic routing is proposed for duty-cycled WSNs. In this study, the controller computes and controls the candidates. It prioritizes the candidates by considering the average of three distributions, that is, transmission distance, expected number of hops, and residual energy. It helps to guide the network in such a way that more traffic can flow through the nodes with higher priority. The results show that the proposed approach improves network lifetime, routing efficiency, energy consumption, sender waiting time, and duplicate packets compared with existing approaches. The study in [141] explores the prospects of offloading the 3 GPP radio access network (RAN) traffic through WiFi access networks with the help of wireless mesh networks (WMNs). This study reveals an IP wireless mesh network using an SDN-based NFV controller to control and manage the network from the central controller, which results in configuring network devices and services deployment in a fast and effective manner. The study in [142] offers an edge-cloud framework for electric vehicles (EVs). It presents an intelligent network for collaboration between cloud and edge devices to make intelligent decisions regarding charging and discharging of EVs and anticipated demand-supply balance. The proposed solution based on opportunistic SDN (Opp-SDN) exploits the use of EVs in two ways: as energy reservoirs for instantaneous DRM and as forwarding nodes in Opp-SDN.
# A Dynamic Restructuring Algorithm Based on Flexible PON Slices
## 6 Conclusions  
In this paper, a dynamic PON slice restructuring algorithm based on flexible PON architecture is proposed. The proposed algorithm avoids the influence of previous steps on subsequent steps by directly dividing the graph into subgraphs of the target number and regrouping them at the global level. In addition, since the decision condition is that the exchange gain of any two nodes in all subgraphs is less than 0, DRA is less affected by the randomness of initialization and has more stable performance. The simulation results show that compared with DGA and DONUSA, the proposed DRA has better performance and stability. Specifically, the performance of the proposed algorithm is about 10\(\sim\)30% higher than that of DGA and DONUSA in the case of 8 OLIs. Furthermore, in the case of 16 OLIs, the performance of the proposed algorithm is about 30% higher than that of DGA and 10% higher than that of DONUSA.  
**Author Contributions:** Conceptualization, Q.T. and S.L.; methodology, F.W. and S.L.; software, S.L. and Q.T.; validation, S.L., X.T. and D.S.; formal analysis, H.Y., X.X. and X.T.; investigation, Q.T., S.L. and F.T.; resources, Q.Z. and X.X.; data curation, Q.T., S.L. and F.T.; writing--original draft preparation, S.L.; writing--review and editing, Q.T.; visualization, S.L.; supervision, Q.Z. and X.X.; project administration, Q.T. and D.S.; funding acquisition, D.S. and X.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Key R&D Program of China under Grant 2021 YFB2900905, in part by the National Natural Science Foundation of China under Grant 62021005.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.