esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Improving the Distribution Uniformity of the Droplets by ITS  
It is found that in the plateau region of \(D_{{}_{p}}\) in Figure 4, the polydispersity of the TiO NCs/monomer droplets is still rather high. For example, in the case of \(C_{\rm SDS}=0.36\) wt%, from the DLS measurements, \(D_{p}=170\) nm, and the polydispersity index, PDI = 0.22. To improve the uniformity of the droplet dispersion, we have applied the probe sonicator for another 10 min but at a power (280 W) substantially larger than that used for the experiments in Figure 4. The values of \(D_{p}\) and PDI reduce to 133 nm and 0.18, respectively. In order to further improve the uniformity, we have applied the ITS technique which, as mentioned above, is more efficient to break up agglomerates, compared to sonication.  
Figure 5 shows the evolution of the \(D_{{}_{p}}\) and PDI values of the emulsion droplets with the number of passes through the MC at three shear rates, \(\dot{y}=2.9\times 10^{6}\) s\({}^{-1}\), \(3.4\times 10^{6}\) s\({}^{-1}\) and \(3.9\times 10^{6}\) s\({}^{-1}\),  
Figure 4: The hydrodynamic diameter and \(\zeta\)-potential of mini-emulsion droplets, at the TiO\({}_{2}\)/monomer mass ratio, 1:2, as a function of the added amount of SDS, in the presence of Tween 20, \(C_{\rm T20}=1.0\) wt%.  
respectively. At the shear rate, \(\dot{\gamma}=2.9\times 10^{6}\) s\({}^{-1}\), for the first three passes, both the \(D_{p}\) and PDI values decrease with the pass number, and no significant change was observed with further passes. The reached minimum values of \(D_{p}\) and PDI are \(\sim\)115 nm and \(\sim\)0.12, respectively. At the two larger shear rates, \(\dot{\gamma}=3.4\times 10^{6}\) s\({}^{-1}\) and \(3.9\times 10^{6}\) s\({}^{-1}\), for the first three passes, the trends are very similar to that at \(\dot{\gamma}=2.9\times 10^{6}\) s\({}^{-1}\), and the minimum values for \(D_{p}\) and PDI are also equivalent. However, as the pass number increases, the PDI value tends to increase significantly, while the droplet diameter decreases. This becomes more evident in the case of Figure 5c of \(\dot{\gamma}=3.9\times 10^{6}\) s\({}^{-1}\). A possible explanation of this behavior is, that at very high shear rates, the TiO\({}_{2}\) NCs captured inside each droplet were restructured to become more compact [49], and some extra monomer at the periphery of the droplet were peeled off through shear erasion, forming very small (new) droplets without the TiO\({}_{2}\) NCs. It follows that the polydispersity increases and the average droplet size decreases. Based on the above results, it can be concluded that in the explored range of the shear rate, \(\dot{\gamma}\in[2.9\times 10^{6},3.9\times 10^{6}]\) s\({}^{-1}\), in order to improve the distribution uniformity of the droplets, three to four passes through MC is adequate to guarantee sufficient breakage and homogeneous dispersion.
###### Abstract  
In seismic refraction exploration, travel time tomography is the most widely used method in engineering and environmental geophysical exploration. In this paper, we mainly optimize the travel time tomography of refraction. First, with respect to the forward algorithm, we introduce a new travel time calculation method to improve the accuracy and efficiency of forward calculation. Based on the fast marching method (FMM), we introduce an improved forward calculation method called the multi-stencil fast marching method (MSFM). In the process of inversion, we propose a dynamic prior model composite constraint (DPMCC) method based on the T0 difference method from the idea of multi-scale inversion. Meanwhile, we use the prior information to improve the accuracy of inversion. Furthermore, we use the dynamic regularization factor selection method to make the inversion solution more stable and reliable. Finally, we test and analyze the synthetic data and the measured data to verify the effectiveness of the optimized travel time tomography algorithm.  
Keywords:refraction travel time tomography; T0 differential method; MSFM (multi-stencil fast marching method); DPMCC (dynamic prior model composite constraint); dynamic regularization factor +
Footnote †: journal: Applied Physics
# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
## 5 Discussion
### Future Research Directions  
Following the insights gained from this research, some elements in deep learning frameworks for EEG-based ADHD diagnostics can be explored further to improve their clinical applicability, scalability, and scientific rigor.  
As a primary step, further research should focus on multicenter EEG datasets with diverse demographics, expanding age ranges, comorbid conditions such as ASD and anxiety, and including different task paradigms.  
Second, in order to tackle the noted instability of transformer-based models on unseen test splits, future research can combine domain generalization and transfer learning approaches. Models fine-tuned across standalone datasets or investigating domain-invariant feature learning can potentially reduce sensitivity to data distribution shifts.  
Third, there should be attempts to build explainability into the model architecture itself rather than relying on post hoc analysis. Methods such as attention visualization, concept bottleneck models, or modular interpretable networks can potentially offer real-time insight into decision processes, which would enhance clinician trust and model transparency.  
Fourth, because of the computational expense of hybrid architectures, model compression methods--pruning, quantization, knowledge distillation, or lightweight neural design--should be investigated for implementation on mobile EEG devices or embedded clinical devices. This would particularly be valuable in low-resource or point-of-care settings. Finally, generalizing the diagnostic framework to multimodal data fusion, i.e., combining EEG with eye-tracking, behavioral measurements, or genetic markers, can render more specific information regarding neurodevelopmental trajectories and further enhance classification performance and interpretability.
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 4 City-Wide Deployments
### Infrastructure Deployment in the City of Bristol
#### 4.2.2 Edge/MEC Tier  
In Bristol, the edge/MEC nodes are deployed in two locations: the _We-The-Curious_ (WTC-DC) and the _MShed Museum_ (M-DC). In both locations, standard rack servers and edge-format servers are installed, which are configured to act as edge NFVI. Both locations are interconnected with the core DC and corresponding RAN devices.
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 2 System Model
### Propagation Model  
The value of signal-to-interference plus noise ratio (SINR) for the communicating vehicles located at the distance \(d\) in the mmWave/sub-THz bands can be generally written as [25]  
\[S(d)=P_{T}G_{A}G_{U}\Big{[}\frac{d^{-\gamma}}{(N_{0}+I)L_{A}(f,d)L_{B}}\Big{]}, \tag{1}\]  
where \(\gamma\) is the path loss exponent, \(I\) is the interference, \(N_{0}\) is the thermal noise, \(P_{T}\) is emitted power, \(G_{A}\) and \(G_{U}\) are the transmit and receive side gains, \(L_{A}(f,d)\) is the absorption losses, and \(L_{B}\) represents impairments caused by bypassing conditions.  
The presence of molecular absorption loss \(L_{A}\) due to internally vibrating molecules is a unique feature of the sub-THz and THz bands. The molecular absorption of electromagnetic waves is the effect occurring when the frequency of the wave is close to the resonant frequency for internal vibrational modes of a molecule. By following [25; 26], the absorption loss is characterized by  
\[L_{A}(f,d)=\frac{1}{\tau(f,d)}, \tag{2}\]  
where \(f\) is the operating frequency, \(d\) is the separation distance between the transmitter and the receiver, while \(\tau\) is the transmittance of the medium that can be well approximated by the Beer-Lambert law \(\tau(f,d)\approx e^{-K(f)d}\), where \(K\) is the overall absorption coefficient. The procedure to calculate \(K(f)\) is detailed in [26; 27]. In this paper, we use the coefficient for the standard air having 78.1% of nitrogen and 20.9% oxygen, at the standard altitude with a water vapor fraction of 2%, which can be retrieved from the HITRAN database [28]. In our study, we adopt the bypassing path losses obtained in [23] through an extensive measurement campaign that provides the comprehensive and accurate results summarized in Table 1.
## 4 Micro-CT Bone Structure Analysis in OP-Related Studies  
As previously introduced, micro-CT instruments are mainly used for the assessment of bone architectural features of human biopsies or of small animals. The investigation of bone architecture is possible thanks to the differential X-ray absorption of bone compared to the soft tissues and the bone marrow, leading to the generation of images with different shades of grey.  
In more detail, the X-ray radiation transmitted by the sample is revealed by a scintillator detector that converts it into visible light with an intensity proportional to the radiation energy. Successively, the light signal is revealed by a photodetector generating a 2D greyscale digital image, called a shadow image. Each pixel of the shadow image contains the information of the sample adsorption in that point along the considered irradiating direction [59]. The 2D shadow images collected along the different directions are successively combined according to appropriate mathematical algorithms in order to reconstruct the 3D volume of the scanned bone sample. In particular, the adsorption information contained in the different shadow images is integrated and combined in order to generate a series of 2D grey-shaded images representing the transversal sections of the analysed sample [60]. Usually, the reconstructed images have a bitmap format of 8 or 16 bits, allowing the conversion of the X-ray absorption level of the sample into 256 or 65,536 shades of grey, respectively. Thanks to this conversion, the \"raw\" adsorption information of the sample is bypassed, and the eventual post-elaboration operations are performed referring to the image grey levels and, consequently, allowing a faster and more reproducible image processing. Obviously, the 16-bit format allows for distinguishing finer variations of the X-ray absorption thanks to the higher number of grey shades available, but it leads to heavier files requiring more powerful instruments for their elaboration.  
A schematic representation of the conversion of the X-ray absorption information into the sample transversal sections performed during a micro-CT analysis is shown in Figure 11.  
The greyscale-reconstructed images can be used for several applications, such as the digital 3D rendering of the bone sample analysed, the execution of 3D morphometric analyses to investigate bone structure organisation, the measurement of bone mineral density or the implementation of finite element method (FEM) models for the computational simulation of bone response when subjected to different loads. The various applications of micro-CT images will be described in more detail in the following sections, focusing on their benefits in the investigation of OP tissues.  
Figure 11: Schematic representation of the conversion of X-ray adsorption information into the transversal slices of the sample.
## 1 Introduction
### Organization of the Paper  
The rest of the paper is organized as follows. A brief review of the related work is provided in Section 2. In Section 3, we provide our methodology, describe our data set in detail, introduce the performance metrics, and then, show the selection procedure. The experimental results and performance evaluations are presented in Section 4. Finally, we conclude the paper in Section 5 and outline future research directions.
# Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules
## 3 Proposed Self-Evolving FLC Design Method
### Online Stage
#### 3.3.2. Variable Selection  
The first step of the proposed self-evolving controller design is the selection of the candidate input variable \(x_{j^{*}}\) where a new fuzzy control rule should be added.  
The criterion to select the candidate input variable \(x_{j^{*}}\) is based on the estimated controller error obtained by the function approximation of the plant's inverse function (Section 3.1). The estimation control error \(e_{c}(k)\) is obtained by:  
\[e_{c}(k)=\sum_{m=1}^{T_{M}}(u(m)-\hat{u}(m))^{2}, \tag{13}\]  
where \(u(m)\) represents the applied control signal (real controller), which is stored in the sliding window \(\mathbf{M}\) (10) at the window's sample time \(m\), and \(\hat{u}(m)\) is the control signal for sample time \(m\) given by the current FLC designed at time instant \(k\), \((u(\tilde{\mathbf{x}}(m)))\), using the vector \(\tilde{\mathbf{x}}(m)\) instead of \(\mathbf{x}(m)\). In this way, the estimation control error of \(x_{j}\) at instant \(k\) is obtained by:  
\[e_{c_{j}}(k)=\sum_{m=1}^{T_{M}}(\boldsymbol{\psi}_{j}(\mathbf{x}(m))\mathbf{u }_{j}^{T}(m)-\boldsymbol{\psi}_{j}(\tilde{\mathbf{x}}(m))\boldsymbol{\Theta} _{j})^{2}, \tag{14}\]  
where \(\boldsymbol{\psi}_{j}(\mathbf{x}(m))\) is defined in (4), and each element of \(\mathbf{u}_{j}(m)=u(m)[1,\ldots,1]_{1\times N_{j}}\) is the real controller signal \(u(m)\) stored in the sliding window \(\mathbf{M}\) at sample \(m\) of the window.  
The candidate input variable \(x_{j^{*}}\) to which can be added a fuzzy control rule is given by the one that has the largest estimation control error:  
\[j^{*}=\operatorname*{arg\,max}_{j\in J}(e_{c_{j}}(k)), \tag{15}\]  
where \(J=\{1,\ldots,n\}\) is the set of indices of the input variables.
## 5 Conclusions  
In this paper, an additional variable, a linearized change function based on the high correlation between the vehicle body and water, is introduced, and a multi-modal HAUV motion model is integrated into a relatively accurate, continuous dynamic model. The model can be used in air flight, during underwater motion, and during trans-medium processes, and system instability caused by model mutation is avoided. To solve the HAUV's trajectory tracking problem due to ocean disturbances and time-varying model parameters, a novel ANFTSMC technique is used to avoid the singularity problem of traditional TSMC. Based on the Lyapunov theory, the finite-time stability of the NFTSMC controller demonstrates the controllability and stability of the system, and the finite-time fast convergence of all state variables is realized. To reduce the adverse effect of buffeting on actuators in traditional SMC, an adaptive law is designed to estimate the upper bound of lumped uncertainty online in real time. The simulation results show that the proposed controller is feasible. The proposed method has good tracking ability, and the control errors converge to zero in a finite time.  
In future work, the design and manufacture of the prototype will be further investigated, and the influence of real, random wind gusts and waves will be considered in combination with experimental methods to make the modeling more accurate. It is worth noting that the transient control performance at the water-air interface during the trans-medium process should also be seriously considered. The knock of high-speed rotating propellers on the water surface is very likely to cause takeoff failure. Therefore, a preset performance control algorithm will be developed in subsequent work. We will also attempt to use an interference observer or RBFNN technology to compensate for lumped uncertainty.  
Conceptualization, M.L. and B.X.; methodology, M.L. and F.L.; software, M.L. and B.X.; validation, F.L.; formal analysis, F.L.; writing--review and editing, M.L. and B.X.; writing--review and editing, F.L. and W.W.; visualization, B.X.; supervision, Y.S. and Z.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Feng Lei Youth Innovation Fund of CARDC, grant/award numbers FL018070012 and ZZZH2000907098, and the National Defense Key Laboratory Fund, grant/award number (614222003061709).  
Not applicable.  
The author of this article is very grateful for the support of the Cross-media Vehicle Research Center of the China Aerodynamics Research and Development Center and the Science and Technology on Underwater Vehicle Laboratory of Harbin Engineering University.  
The authors declare no conflict of interest.  
Figure 20: Estimated values of the adaptive parameters of the attitude loop.
## 4 Case Study
### SRRI Calculation
#### 4.2.3 SRRI Results  
Figures 8 and 9 show the SRRI results for the two cases and the five considered scenarios, with the following hypotheses: (1) The original functionality (at the time of occurrence of the earthquake) for both the infrastructures was 100%. (2) The applied repair functions were considered linear trends, as commonly assumed in the event of insufficient  
Figure 6: Case 1 (\(n_{1}=3\), \(n_{2}=6\)).  
Figure 7: Case 2 (\(n_{1}=n_{2}=3\)).  
information. (3) The parameters c and p (Equations (5) and (6), respectively) were both considered to be 1, in order to consider both the prolongation of travel and losses connected with connection loss at the same level. These assumptions may be developed by improving the data from the characteristics of the road infrastructures. Figures 8 and 9 show that for intensities between 0.21 g and 0.68 g, resilience depends on the different scenarios. The most resilient is Scenario 5, where the infrastructures are both opened (50% of Infrastructure 1 and 100% of Infrastructure 2). SRRI is shown to be minimal for Scenario 3, at all intensities.  
Overall, the results demonstrate the validity of resilience as a parameter to define the state of road infrastructures after earthquakes. In particular, achieving a balance between resilient infrastructures and services is a duty for civil engineers in order to maintain post-earthquake functionality of communities. In this regard, a critical interpretation of  
Figure 8: SRRI: case 1 (\(n_{1}=3\), \(n_{2}=6\)).  
Figure 9: SRRI: case 2 (\(n_{1}=n_{2}=3\)).  
SSRI results is fundamental for decision-makers in order to guarantee a minimal level of services for the infrastructures, adequate services and business for the communities, and rapid recoveries following seismic events.
## 5 Post-Processing EMG Signals: Proposed Supervised Learning Model
### Data Assessment  
The post-processing phase of signal analysis involved examining biopotential signals through sEMG on 200 subjects, 110 females and 90 males, aged between 20 and 40 years. These subjects were divided in two classes: healthy and potential neuropathy. The signals were obtained by placing six electrodes on the bicep muscle of each subject, assessing voltage values over time during MVC conditions and during \"task\" operation for 10 s (Figure 5).  
The signals are normalized by making the ratio between the signal acquired during the task and that derived from the MCV. The EMG data collected represent tension over time measured across the six electrodes.  
The voltage profile on healthy subjects is homogeneous, and there are no sudden changes in the trend of the curves (Figure 6).  
Figure 5: (a) sEMG acquisition system; (b) setting of electrodes on bicep muscle.  
Patients that have abnormal voltage values on the EMG have conditions potentially attributable to a neuropathy.  
In particular, the EMG graph shows spikes that at their highest or lowest points are more than 30% away from the mean trend line (Figure 7).
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 4 Hybrid Algorithm for UAV Path Planning
### Network Model  
The proposed hybrid path planning (HPP) algorithm comprises classical PRM and ABC algorithms. The PRM calculates the shortest path in collision-free environments but cannot adapt according to network constraints. Therefore, we considered a modified ABC that obtains optimal solutions even in harsh environments using optimization methods. The path coordinates from the hybrid solution should be free from static and dynamic obstacles. It should not pass through interdicted areas such as military offices and airports, avoid all possible threats such as radar networks, and ensure that the UAV encompasses the WSN collection zones well. Minimization of flight time and energy is necessary for UWSN-based emergency environments. In our study, the SNs are static and deployed randomly. Moreover, the SNs are clustered, and UAV and SNs are equipped with GPS for achieving higher efficiency of location and timing information. We assume a quadcopter UAV for our work so that the movement and speed of the UAV can be optimized concerning the environmental factors. As a result, the UAV is capable of hovering in the target area.  
The UAV incorporates two transceivers, one of which is used to broadcast beacon signals and receive registration packets. In contrast, another is used to transmit scheduling information and receive data packets. The UAV is equipped with a directional antenna with flare angle \(\varphi\) = 60\({}^{\circ}\) because directional communication offers many benefits such as extended transmission, minimized delay, and spatial reuse. The sensors lying on the coverage area and receiving beacon signals from the UAV become active. If a sensor does not receive signals or the received signal strength is weak, it goes to sleep mode to save energy. In this way, the directional antenna limits the UAV coverage and overcomes the directional deafness problem. The UAV moves in a 3D environment with an absolute velocity that optimized the UAV flight time reduction. Different constraints of dynamic and complex environments considered for optimizing the proposed path are discussed in the following subsection.
## 4 Discussion
[12] only achieved better results against a study from [PERSON] et al., in 2018, which compared several combinations of seven IMU placements and achieved 89.1% accuracy when using a shank-based sensor and decision-tree-based model [10]. Another study that used only two shank-mounted accelerometer achieved its best result with 93.9% accuracy of classifying multi-class gait patterns (healthy, peripheral neuropathy, post-stroke, and Parkinson's disease) [13]. To summarize, our study outperformed the other studies which used wearable devices to classify different types of gait patterns; even more important, our study achieved the result by using only a single IMU sensor placed on the lower back (Table 4). However, there are still limitations in our study: first, the gait-event definition that still used data from a motion-capture system. Further investigation is required to replace the motion-analysis system to classify the walking cycle, using IMU sensor data, so that the amount of equipment required can be reduced and simplified. Second, the analysis and data processing was not being done in real-time manners. In the future, an additional mobile-computing device may be considered to be used, in order to create real-time application for clinical setup/hospital. In terms of future application, this study opened up the possibility for building an in-home healthcare monitoring system, together with other wearable sensor algorithms for gait event detection, gait analysis, and physical activity classification. On those systems, wearable devices will take part in monitoring the user's gait pattern in home; thus, early detection of gait alteration could be known before the user gets a medical diagnosis from a hospital. Furthermore, for elderly people, the application will not be limited to monitoring the gait pattern but also to monitor their activity intensity and fall detection. All of those applications for in-home healthcare monitoring will become valuable data sources for clinicians in hospitals, in order to create more precise treatments for each patient.