esa-sceva/satcom-chunk-collection huggingface dataset:
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
## 6 Conclusions  
In this paper, we proposed a handover approach for WBAN in heterogeneous wireless networks by integrating a CCN processing module into WiFi, UMTS, and LTE access networks. In particular, we  
Figure 6: (**a**) Comparison of the energy consumption; (**b**) Comparison of the energy efficiency for different approaches.  
proposed the effectiveness of an MADM handover approach throughan improvement of the TOPSIS algorithm to guarantee seamless connectivity for WBAN users. Based on WBAN traffic categories, our proposed approach helps to select the best connection according to the QoS requirements of the application type and user preferences. Furthermore, the integration with CCN allows WBAN users to maintain the current connection, which guarantees the QoS requirements of each application and avoids unnecessary handover. The obtained results illustrate the significant effectiveness of our proposed approach in terms of the number of handovers, enhancing the QoS, improving the packet loss and increasing the energy efficiency. As for future work, we plan to consider more attributes that are related to networks, users and devices (e.g., the velocity, security, reliability, user profile) for a more accurate selection of the candidate network method. Additionally, we plan to precisely investigate the relative importance of attribute weights and expand the calculation of the weight vectors for each popular WBAN application.  
This research is funded by the National Natural Science Foundation of China (No. 51675389 and No. 51475342).  
[PERSON] initiated the idea of the work, designed research scheme and provided the instructions during the performance. [PERSON] constructed the model and the algorithms and wrote the manuscript. [PERSON] supervised and helped with the work. All the authors have read and approved the final manuscript.  
The authors declare no conflict of interest.
# Novel Thermal Management Strategy for Improved Inverter Reliability in Electric Vehicles
## 1 Introduction
However, in many cases such derating values can be conservative, and the operation limits of the drive can be reduced unnecessarily [27]. Thus, the incorporation of active thermal management solutions is being investigated to maximize vehicle performance. Active control of power losses can be applied in different layers (drivers, modulation and control) of an electric drive [28]. Control approaches aiming at the active modification of the PWM strategy (changing between different space vector sequences, continuous and discontinuous) are presented in [29]. Simple switching frequency hysteresis regulators can also be implemented [29]. Although hysteresis regulators are mainly used by the industry due to their simplicity, their unique usage possesses some drawbacks, as they do not cancel steady state errors. Thus, more advanced approaches can be found in the literature. For example, the authors in [27; 30] propose a control algorithm for traction drives that considers electro-thermal constraints and real-time temperature estimation to impose current (torque) and switching frequency limits. By incorporating not only hysteresis but also discrete regulators, such limits are modified to precisely track a given temperature constraint. Similarly, an active thermal management algorithm is proposed in [31] based on a model predictive controller. In this work, a novel online Temperature Constraint Tracking (TCT) based switching frequency regulator is proposed to improve the reliability of automotive power converters in the whole EV operation range: (i) at low speeds and standstill, where high acceleration and regenerative braking is required under urban traffic; (ii) at high speeds, where controllability needs to be ensured by determining a minimum switching frequency value. This novel solution focuses on simplicity and low computational burden, as automotive microcontrollers have limited processing capabilities and the incorporation of an increasing number of software functionalities is demanded by the industry. The proposal is successfully incorporated in the control architecture of a 75 kW in-wheel direct drive IPMSM fed by an automotive grade Silicon Carbide (SiC) power converter, and is validated in an OPAL-RT RT-Lab OP4510 platform by means of simulation under realistic driving conditions.
## 3 Selective Mu-Calculus
### The Syntax of the Calculus  
Here, slight simplifications are made on the syntax of the selective mu-calculus to avoid useless details. Consider the set \(A\) of events. The events \(a,b\) range over \(A\), and \(S\subseteq A\) is a set of events with cardinality less than or equal to one. Moreover, \(Z\) belongs to a set of variable names. The calculus has the following syntax:  
\[\varphi\ \ ::=\ \ \mathtt{tt}\mid\mathtt{ff}\mid Z\mid\varphi_{1}\wedge \varphi_{2}\mid\varphi_{1}\vee\varphi_{2}\mid[a]_{S}\varphi\mid\langle a\rangle _{S}\varphi\mid\
u Z.\varphi\mid\mu Z.\varphi\]  
A fixed point formula has the form \(\mu Z.\varphi\) (\(\
u Z.\varphi\)), where the fixed point operator \(\mu Z\) (\(\
u Z\)) binds free occurrences of \(Z\) in \(\varphi\). An occurrence of \(Z\) is free if it is within the scope of no fixed point operator. A formula is closed if it contains no free variables. The formula \(\mu Z.\varphi\) is the least fixed point of the recursive equation \(Z=\varphi\), while \(\
u Z.\varphi\) is the greatest one. In the following, we consider only closed formulae and alternation-free mu-calculus formulae [22].  
However, note that the syntax of the modal operators can be easily extended as follows (and so their meaning) to manage a set of events without affecting the remainder of the paper.  
\[[\{\alpha_{1},\ldots,\alpha_{n}\}]_{\{\beta_{1},\ldots,\beta_{m}\} }\varphi=\] \[\big{(}[\alpha_{1}]_{\{\beta_{1}\}}\varphi\vee\cdots\vee[\alpha_ {1}]_{\{\beta_{m}\}}\varphi\big{)}\wedge\cdots\wedge\big{(}[\alpha_{n}]_{\{ \beta_{1}\}}\varphi\vee\cdots\vee[\alpha_{n}]_{\{\beta_{m}\}}\varphi\big{)}\] \[\big{(}\langle\alpha_{1}\rangle_{\{\beta_{1}\}}\varphi\wedge \cdots\wedge\langle\alpha_{1}\rangle_{\{\beta_{m}\}}\varphi\big{)}\vee\cdots \vee\big{(}\langle\alpha_{n}\rangle_{\{\beta_{1}\}}\varphi\wedge\cdots\wedge \langle\alpha_{n}\rangle_{\{\beta_{m}\}}\varphi\big{)}\]  
**Example 4**.: _Some examples of formulae are shown in the following._  
\(\varphi_{1}=\
u Z.\langle a\rangle_{\emptyset}Z\)_: \"there exists a run in which the event \(a\), preceded by any event, can always occur\"_._  
\(\varphi_{2}=[c]_{\{a\}}\langle a\rangle_{\emptyset}\mathtt{tt}\)_: \"in any run where an event \(c\), not preceded by the event \(a\), occurs, the event \(a\) must always follow._
# A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators
## 2 Scalable Multicore KNN-Based Demodulator  
The KNN algorithm is a machine learning technique that consists of the comparison of the input data against data previously labeled with a specific class, called training data. This comparison is made by measuring the distance between the input data and the trained data. The training data with the lowest distance values are known as the nearest neighbors. The label that repeats the most among the \(K\) nearest neighbors is assigned to the input data. Algorithm 1 shows the execution of the KNN algorithm in a sequential computation. The algorithm starts with the loading of the training data with a length of \(L\). The input of the KNN algorithm are symbols that come from a channel with m-QAM modulation, so each symbol is represented by a pair \((I,Q)\). Each data point contains three values: In-phase \((I)\), quadrature \((Q)\), and the \(label\), which identifies that data point in a cluster of the m-QAM constellation. Also, the number of nearest neighbors used in the algorithm is chosen and stored in the variable \(K\).  
The Euclidean distance between the input symbol and all training data is calculated using the following expression: \(d(i)=\sqrt{(I-I_{t}(i))^{2}+(Q-Q_{t}(i))^{2}}\), where the subindex \(t\) indicates that it belongs to the training data, and \(i\) is the array index taking values in the range 1 to \(L\). Please note that the distance calculation must be done \(L\) times. In a hardware implementation, the square root is not calculated to reduce operations. The distances should be sorted in an ascending order to select the smallest distances. The \(labels\) should be sorted simultaneously to get the \(K\) labels for the nearest neighbors. The execution time of the sorting algorithms depends on the distribution of data, but time complexity is well known. In this work, we use the Merge Sort algorithm, whose time complexity is \(\mathcal{O}(n\log n)\). The distance array and the label array to be sorted are of length \(L\).  
Algorithm 1 describes the pseudocode for the sequential implementation of KNN-based m-QAM demodulators. However, a hardware-based approach is necessary for accelerating the algorithm in a parallel architecture, enhancing the particularities of the m-QAM modulations and the independence in the operations of the KNN algorithm. For this, we define custom processing units.
## 2 Materials and Methods
### Technical Approach
#### 2.2.2 Bead Perception  
We first apply the histogram equalization method (on either smudge-free or original images) to more equally distribute the intensity values across the images based on the intensities frequencies. This equalization improves the contrast of the locally low contrast regions of the image. The outcome of this method on \(I_{jr}^{inv}(x,y)\), \(I_{jr}^{inv}(x,y)\) and \(I_{jr}^{sm}(x,y)\) to yield images \(I_{jh}^{inv}(x,y)\), \(I_{jh}^{inv}(x,y)\) and \(I_{jh}^{sm}(x,y)\), respectively, is shown in Figure 4. Following this step, to reduce the noise without adversely affecting the contrast of the object edges, the median blurring method is applied. This blurring is done by replacing individual pixel intensities with the median intensity of the neighboring pixels that fall inside a user-specified kernel.  
Observing that the beads possess similar properties such as area and intensity in all the the image sequences \(I_{jh}^{inv}(x,y)\), \(I_{r}^{inv}(x,y)\) and \(I_{r}^{sm}(x,y)\), we next use the blob detection method to identify the beads.  
Figure 4: Histogram equalized bright field images of beads and cells: (**a**) \(I_{jh}^{inv}(x,y)\); (**b**) \(I_{jh}^{inv}(x,y)\); (**c**) \(I_{jh}^{sm}(x,y)\).  
Figure 3: Smudge free image of amoeba cell and silica beads in water.  
In this method, the source image is first converted to multiple binary images by applying a binary thresholding for a set of evenly spaced thresholds. Contours of connected components are then found in each of these binary images using a topological border following algorithm [41]. The centers of the connected components are computed, and they are grouped together on the basis of their closeness (parametric distance) to each other. Every such group is designated as a blob, and the overall blob centers and radii are recorded. One challenge here is that this method compares the properties of the blobs with those of their surroundings for detection. As the image boundary objects do not have surrounding pixels on all the sides for comparison purposes, white boundaries are added to the median blurred images. The spherical cells are not identified as they do not qualify as blobs owing to irregularities in their intensities. The detected blobs are drawn on a copy of each image, and are represented as \(l_{b}^{inv}(x,y)\), \(l_{b}^{inv}(x,y)\) and \(l_{b}^{sm}(x,y)\). Example images with identified beads are shown in Figure 5.
## 3 Results and Discussion
However, once these further models have been developed using the methodology proposed in this paper, they will be more robust, cost-effective and accurate enough for repetitive sampling. Figure 7: Overall artificial neural network regression model showing the observed (x-axis) and predicted (y-axis) values of the peak area of the volatile aromatic compounds (Model 2). Abbreviations: R: correlation coefficient, T: targets. Among the potential applications of this method are that it would allow coffee producers and brewers to assess their products in a more efficient and affordable way and associate results with coffee quality based on its aroma, which may be measured in every batch of coffee. This would also be useful for manufacturers to identify the most abundant aromas that may be reported in their labels, which would be a more objective, less time-consuming, and rapid method rather than using trained sensory panels. Further studies should focus on the development of ML models to detect and identify faults.
# Augmented Reality in Precision Farming: Concepts and Applications
## 3 Deployment Models
### Existing Hardware Frameworks  
Three main traditional AR interaction models (Monitor-based AR, Optical see-through, Video see-through), have been well documented for over 20 years, for example in [34, 35, 36]. Figure 2 displays an adapted version of each inspired by the aforementioned articles [34, 35, 36]. Of the three models, optical and video see-through can be considered the most dominant at present within the AR precision farming applications discussed in Section 2.1, largely due to the use of AR on smart phones. Video see-through, for example, would be the equivalent of using a mobile phone for the AR view but directly looking at the screen. Whereas, optical see-through would be a projection in front of the user's eyes combining video captured in  
Figure 1: Types of AR deployment within crop and livestock management, adapted from the model in [32].  
real-time and coupled with the AR input. For example the OGD hardware used in [15] is poised around the head-mounted display optical see-through approach.  
Emerging immersive approaches, such as the Aryzon headset [4] extend these models further by adopting a mirror-based approach for the optical merging, as displayed in Figure 3. This takes the output from monoscopic to stereoscopic. Where an affinity with the real world environment is created, meaning the user is able to view as a volumetric output without looking directly at the device monitor, transforming the approach from video see-through to optical (however, given its infancy, at the time of writing this article, the related work search found that this technology is yet to be employed in an agricultural setting).  
Advancing the AR hardware framework further, approaches include coupling the headset with hand-held controllers, for instance with embedded QR code markers on the controller shaft. This provides real-time detection of the users' hand movements. In this instance, the AR hardware framework can be extended to include motion input, as in Figure 4. Humans naturally prefer a multimodal approach for a higher level of interaction [33], rather than being passive observers. The integration of controllers into the mix provides natural extension of the AR interaction processes for a greater level of control.  
Figure 3: (a) Mirror-based AR, (b) Aryzon Headset.  
Figure 2: (a) Monitor-based AR, (b) Optical see-through, (c) Video see-through.
# Nanocarriers-Assisted Nose-to-Brain Delivery of Levodopa: Current Progress and Prospects
## 6 Nose-to-Brain Delivery of L-DOPA
### Hybrid Nanoparticles for Nose-to-Brain Delivery of L-DOPA  
[PERSON] et al. implemented the concept of covalently binding L-DOPA to lecithin by formulating the drug into a novel vesicular type of carrier, the so-called pharmacosomes [188]. When chitosan was additionally added to the optimized formulation as a potential permeation enhancer, the authors obtained a mean vesicle size of 125.5 \(\pm\) 5.2 nm, zeta potential of +33.7 \(\pm\) 0.5 mV, drug content of 96.5%, and entrapment efficiency of 99.98%. The ex vivo permeation analysis showed a 3.12-times enhanced steady-state flux achieved with the pharmacosomal formulation in comparison to the standard drug solution. The in vivo tests on Wistar rats showed significantly better recovery of locomotor activity and neuronal tissue damage, as well as a significantly greater decrease in the level of lipid peroxidation in the pharmacosomes-treated group as compared to the control groups treated with standard L-DOPA solution nasally and orally, respectively.  
All reports found of lipid-based and hybrid nanocarriers of L-DOPA for nose-to-brain delivery are summarized in Table 4.
## 4 SEA Modeling  
In the low-frequency range, the FEM-BEM modeling described previously proven to be relevant to predict vibroacoustic responses. At high frequencies, this ability is limited by the modal truncation. As modal density increases at high frequencies, a modal truncation at a significantly higher frequency (\(\gg\)2500 Hz) would greatly increase the computational cost of the method. At these frequencies, the main expected engineering result is only the overall root mean square acceleration level.  
Thus, a complementary method was used to evaluate the vibroacoustic response levels until 16 kHz with a low computational cost: Statistical Energy Analysis (SEA). The theory of the method is quickly introduced in the next section. The setup of the SEA model is then described, including dedicated experiments for the identification of key parameters. Finally, the simulation results are compared with diffuse acoustic field measurements.
# Unmanned Aerial Vehicle Routing Problems: A Literature Review
## 2 UAV Routing with an Emphasis on the Problem Type, Transportation Mode, and Degree of Automation
### Significance of UAVRP  
UAV routing problems involve a huge amount of stochastic information in contrast to VRPs in general, as UAVs should be able to change, adapt, modify, and optimize their routes in real-time. In contrast to general routing problems, several individual objective functions can be used in UAV routing such as reducing individual UAV costs, enhancing its profit, increasing safety in operations, reducing lead time, and increasing the load capacity of the entire system [64; 65].  
Influencing parameters for UAV routing includes numerous parameters and constraints in contrast to traditional VRP problems. UAVs' nature is routing and scheduling in 3D environment [66], whereas land- and maritime-based transportation are 2D [67] and in UAV routing, changing weather conditions (wind speed, wind direction, air density) should be considered in solutions. Moreover, UAVs specifications, energy consumption affected by weather conditions, carrying payload of UAVs, and collision avoidance with respect to moving/fixed objects adds more complexity in finding solutions in the domain of UAV routing. All these elements emphasize the significance of UAV routing as it is challenging to develop models considering all the influencing aspects together.  
Figure 1: Transitioning from teams of operators managing a single unmanned aerial vehicle (UAV) to a single operator managing multiple UAVs [14].  
Unlike the traditional routing problems, the UAV routing should address different decision layers in the system architecture (Figure 2), which includes the fleet level where the fleet is managed to provide delivery services using the UAV fleet and the platform level where it focuses on the individual functioning of the UAVs [13; 14]. The current state of research is fragmented as shown in the layers illustrated in Figure 1 and neglects that different types of decisions are addressed at different abstraction levels [14].
# Effects of Wave-Induced Processes in a Coupled Wave-Ocean Model on Particle Transport Simulations
## 2 Methods
### Observational Data
#### 2.2.2 HF Radar Data  
HFR surface current data were acquired by three radar stations in the German Bight [50; 51]. The radar systems are based on linear antenna arrays installed near the shoreline. More details on the required processing can be found in [52]. For the considered system, the spatial radar resolution is 1.5 km in range. The radar system in the German Bight can reach out to about 120 km off the coast in favorable conditions and provides measurements with a 9 min averaging window every 20 min. The observations are available as interpolated to a 2 km Cartesian grid. Through a combination of the radial components from the different antenna stations, meridional and zonal current components can be derived; however, the original radial components were used for the subsequent data assimilation procedure. Further details of the system can be found in [51].
# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 4 Experiment Implementation
### Deep Learning Model  
We received four types of data for analysis: original fundus images, cup-disk images, vascular images, and pseudo images. Each type of data was classified using a deep learning model, and the results from each input were combined using an ensemble approach to determine the final classification. For the initial values of our network, we used models trained on the ImageNet dataset. We constructed a network model that analyzes each of the four input data types and ensembles their respective results.  
The training model was based on a modified version of EfficientNetV2 [31]. Instead of focusing solely on deepening the layers, as seen in earlier models, such as VggNet, ResNet, and Inception-ResNet, our model also incorporated approaches from models that emphasize cardinality--applying various sizes of convolution filters between layers, such as ResNext and MobileNet. Additionally, we considered models that have evolved based on the resolution of the input images.  
EfficientNet V2 is an improved model focusing on speed and efficiency. In Figure 6, you can see this structure. It utilizes Fused-MBConv layers, which combine the traditional \(1\times 1\) and \(3\times 3\) convolutions into a single \(3\times 3\) convolution. This adaptation accelerates the training process right from the initial stages of input.  
Figure 5: The overall framework.  
The vision transformer (ViT) [32] model is currently receiving significant attention across various fields due to its exceptional performance. Unlike traditional CNNs, the ViT processes information based on similarity calculations using the query, key, and value components. However, ViT requires a substantial amount of data, and in datasets where data are limited, CNNs may still yield better results. This is particularly true in the medical field, where data are often scarce. The use of CNN-based models is more common in such scenarios due to their adaptability to smaller datasets and the challenges associated with data augmentation, given the constraints of medical data. Thus, CNNs might be a more suitable choice under these conditions.
# Diagnosis and Treatment of MODY: An Updated Mini Review
## 4 MODY Subtypes and Their Treatments
### Neuroid-mody (mody 6)  
Neurogenic differentiation 1 (NEUROD1), a transcription factor with a basic-loop-helix structure that is expressed in neuronal and pancreatic cells, is essential for pancreatic and neuronal development, influencing pancreatic morphology and neuronal differentiation [65; 73]. NEUROD1 plays a role in insulin transcription activation by binding to and activating the promoters of sulfonyl-urea receptor 1 (_SURI_), _GCK_, and _PAX6_ (glucose-6-phosphatase catalytic subunit-related protein) [34; 74]. Mutations in the _NEUROD1_ gene result in MODY 6 [40]. Heterozygous mutations in the _NEUROD1_ gene result in beta cell dysfunction [73]. Insulin therapy is a common treatment option, but MODY 6 patients present with diabetes with incomplete penetrance; thus, half of MODY 6 patients appear to benefit from OADs and diet management [75].