esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area Overview  
Dashiling Gully is situated in the northwest region of Bulaotun Town, Myun District, Beijing, at longitude \(116^{\circ}56^{\prime}48.66652^{\prime\prime}\) E and latitude \(40^{\circ}40^{\prime}20.18105^{\prime\prime}\) N (Figure 1). The gully is located upstream of Dashiling Village, with a steep slope. Dashiling Gully is located upstream of Dashiling Village and is characterized by a steep slope and significant terrain undulation. The catchment area of Dashiling Gully spans approximately 0.427 km\({}^{2}\), featuring one main gully and five branch gullies arranged in a dendritic pattern. The main gully extends for about 1312.47 m, with its lowest elevation being 429.59 m and its highest elevation being 605.12 m, resulting in a relative elevation difference of 175.53 m and an average longitudinal gradient of 133.74%. The steep topography, substantial longitudinal gradient, and prevalent weathering, denudation, and collapse phenomena within the main and branch gullies provide favorable conditions for debris flow formation and movement. Additionally, the constricted nature of the gullies facilitates the rapid accumulation of surface water, mobilizing the loose sediment and generating mudslides with significant kinetic energy.  
Based on regional geological data and field surveys, the exposed strata in the Dashiling area primarily consist of Cretaceous southern porphyry coarse-grained biotite grandodiorite  
Figure 1: Geographic location of the Dashiling Gully study area within Myun District, Beijing (**a–c**).  
genesis (K\({}_{1}\)N) and Pleistocene alluvial and residual slope deposits. The latter is mainly composed of basalt and volcanic rocks. The Pleistocene alluvial succession largely comprises gravel beds interspersed with large boulders, sand, and other residual slope components. K\({}_{1}\)N and Qp\({}^{2}\) represent the typical deposits of the Cretaceous (southern porphyry coarse-grained biotite granodiorite gneiss) and Pleistocene (alluvial and residual slope deposits) epochs in the Miyun District, respectively.  
The Dashiling Gully study area experiences a warm temperate monsoon continental semi-arid climate. The average annual temperature is 10 \({}^{\circ}\)C, with an extreme minimum temperature of \(-\)27.3 \({}^{\circ}\)C. The long-term average annual rainfall is 600 mm, exhibiting a decreasing trend from southwest to northeast.
# Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet
## 4 Problem Formulation and System Model
### IoT System Modeling  
This section introduces the IoT model used to produce the dataset for the ML training. We built the IoT system, using the AnyLogic simulator [9; 10].  
Figure 3 depicts the structure of an IoT system for creating traffic data and modeling a set of IoT tools. The model contains an IoT traffic system that models the process of an IoT device or group of IoT devices, a traffic source for conventional communication facilities, and TI traffic referred to as H2H + TI (H2H--human to human, TI--tactile internet). The accomplished entry traffic moves to the connection node, and the architecture is introduced as a queuing system _G_/_G_/_I_/_k_ with the service disciplines (with delay and failure basis system). The arrival time is denoted by \(\overline{t}\).  
The number of arrivals per time of IoT traffic is defined by \(\lambda_{IoT}\), H2H traffic \(\lambda_{H2H}\), total flow \(\lambda=\lambda_{H2H}+\lambda_{IoT}\). Assume that with probability \(p\), a message reaches the model entry in which all locations in the queue are busy, and an attack (such as DoS) occurs. The entire traffic flow at the outcome of the system is defined by \(\lambda\). The characteristics of the two flows determine the whole traffic flow at the system's entry; therefore, it is generally different from the characteristics of normal traffic and IoT traffic.  
The above queuing system (QS) can be described as a _G_/_G_/_I_/_k_ system. However, this system does not have accurate computational modeling to evaluate the probability of the packet loss and delayed arrival. Therefore, in [34; 35], the diffusion approximation is used to assess the probability of packet loss, given the aware allocation criterion that describes the incoming and packet processing.
## 1 Introduction
For multirotors, reducing frame resonance and motor-induced vibrations is as important as minimizing weight. Topology optimization [18; 19] refines arm and hub structures to achieve an optimal stiffness-to-weight ratio. Fixed-wing UAVs prioritize reducing aeroelastic effects and engine-induced vibrations by optimizing wing and fuselage structure. Finite element analysis (FEA) and CFD provide accurate vibration predictions but require significant computational resources. This paper builds upon the work presented in [20; 21], which introduced a stability-preserving interpolation method for single-input single-output (SISO) linear time-invariant (LTI) systems with a scalar parameter. In the case of a scalar parameter, the poles of the parametric LTI system move along a root locus or trajectory. However, for sampled systems, only discrete points along these loci are known. The key idea was to replace the unknown continuous trajectories with artificial ones between the sampled points, enabling interpolation along these constructed paths. It was shown that using hyperbolic lines as interpolating paths is particularly advantageous, as they preserve stability when the system is stable for all parameter values. Moreover, the interpolated system remains close to the original samples in the \(H_{\infty}\) norm and, in certain cases, retains the minimum-phase property. The problem with this approach is that one must connect the corresponding poles across different samples. However, in one sample, we have a distribution of poles, and in another sample, we have a different set of poles, with no apparent correspondence between them. To address this issue, the previous work solved an optimal matching problem based on the hyperbolic distance between the poles. Other limitations of the previous work include that it only guarantees deviation bounds for SISO systems and restricts the pole trajectories to hyperbolic lines.
# Grapevine Phenology in Four Portuguese Wine Regions: Modeling and Predictions
## 2 Material and Methods
### Modeling Software and Performance Parameters  
In this study, the PMP (Phenology Modeling Platform) version 5.5 [16] was used to test and calibrate the SM. PMP is a user-friendly platform that allows applying and testing different phenological models for different purposes. On this platform, meteorological and phenological data are used to calibrate and validate models. The PMP estimates parameters based on the Metropolis annealing algorithm [36]. For each specific location, the input data are daily minimum, maximum and minimum two meter air temperatures, latitude, and the three observed phenological stages (in days of the year). The model performance is assessed by two metrics: the root-mean-squared-error (RMSE, Equation (2)) and the efficiency coefficient (EF, Equation (3)). Their respective formulations are as follows:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(O_{i}-P_{i})^{2}}{n}} \tag{2}\]  
\[EF=1-\frac{\sum_{i=1}^{n}(O_{i}-P_{i})^{2}}{\sum_{i=1}^{n}(O_{i}-\overline{O_{i }})^{2}} \tag{3}\]  
where \(O_{i}\) means the observed phenological date, and \(\overline{O_{i}}\) denotes the average of the observed value over the full record period. \(P_{i}\) is the simulated phenological date, \(n\) is the sample size (number of years), and \(k\) is the number of model parameters (two in the case of SM). The efficiency coefficient (EF) varies between \(-\infty\) and \(+1\). A \(+1\) score corresponds to a perfect fit, zero when the model has the same performance as the observed average and less than zero when the adjustment has a lower performance than the observed average. In order to avoid model overfitting, model calibration should be followed by model verification, preferentially using additional validation periods, which should also be randomly selected from the original time series to discard serial auto-correlation. Nonetheless, owing to the short sample sizes available for the present study, this methodology cannot be robustly applied. Therefore, a leave-one-out cross-validation was used for model validation. Equation (4) defines the adapted RMSE of prediction (RMSEP):  
\[RMSEP=\sqrt{\frac{\sum_{i=1}^{n}\left(O_{i}-\hat{P}_{i}\right)^{2}}{n}} \tag{4}\]  
where \(\hat{P}_{i}\) is the predicted value by cross-validation [31].
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 6 ML for Resource Management  
Network slicing will provide flexibility, heterogeneity, and scalability; on the other hand, network resources need to be highly manageable. Resource management in network slicing can be an issue and it can be addressed by applying the ML techniques. Network slicing can improve the resource efficiency by separating the infrastructure resources and network functions. In the past, network operators manages the network services by themselves but now due to network slicing they can lease them to multiple tenants. Tenants can deliver the network services to the end customers without the need of having their own infrastructure. In Figure 7, difference between existing unsliced and sliced network is shown. This new model has opened the new business opportunities, this service is called as \"slice-as-a-service\" (SlaaS) [40].  
Figure 6: Discrete-time Markov Chain with 2 states (i.e., DBSs), only states 1, 2 are shown for readability.  
SlaaS can be categorized as application-as-a-service (AaaS), platform-as-a-service (PaaS) and infrastructure-as-a-service (IaaS). AaaS allows delivering the software-based services according to the end-customers demand. PaaS enables the cloud services to provide the application data. IaaS allows delivering the required network functions to manage the infrastructure resources. ML techniques can be applied to provide all these services. In [41], RL algorithm is proposed in order to enhance the network slicing policy. In [42], the authors presented the ANN-based network slicing resource management prediction model and grouped the model into three layers in order to estimate the requirements of service capacity.
## 2 Method
### Instruments  
The Activ5 instrument (Figure 1) is a digital grip dynamometer that measures generated grip strength and sends the results to a smartphone via Bluetooth technology to an application (Activ5 application) for Android/iOS devices. The instrument has dimensions (length \(\times\) width \(\times\) height) [mm] of 95 \(\times\) 78 \(\times\) 33, a sampling frequency of 10 Hz, a maximum load design/production at full scale (FS) of 90 kg, and an accuracy of (0.635 kg + 5% of applied force) [37]. Meanwhile, the Jamar dynamometer is an analog instrument that is easily calibrated with a specific weight. This dynamometer has been widely used in various research studies due to the availability of normative data [26; 27].  
Activ5 is designed for measuring compressive forces up to 90 kg/200 lbs by applying pressure to Activ5's exterior gray surfaces when placed between two body parts, such as your palms. Pressing on or near the orange belt will result in an inaccurate force  
Figure 1: Activ5 force dynamometer. Source: [[https://activ5.com/](https://activ5.com/)]([https://activ5.com/](https://activ5.com/)).  
measurement. The grip strength test with the Activ5 instrument was performed with the participant seated, feet flat on the floor, the unsupported arm in a neutral position, the shoulder in adduction, the elbow flexed at 90\({}^{\circ}\), and the wrist and forearm in a neutral position, with the wrist supported between 0 and 30\({}^{\circ}\) of dorsiflexion and 0 to 10\({}^{\circ}\) of ulnar deviation; this guideline was developed in other studies to ensure the quality of the assessment [16]. Additionally, for accurate evaluation, protocols involving three repetitions (trials) with a 15 s rest between each were suggested.
## 4 Discussion
Instead of performing image modifications on a CPU, SMAs can be implemented using different camera sensors or image signal processors (ISPs). Camera sensors vary in sensitivity, shutter speed, exposure time, and type, while modern ISPs aim to optimize image quality. Adjusting ISP settings to enhance image processing for the model can be beneficial. Given the constraints of edge devices, using large models may be impractical due to their high computational and memory requirements. Offloading some generalization tasks onto ISPs and sensors can enable the use of smaller models with improved performance. Additionally, a fully parallel structure allows for multiprocessing until the final fusion stage, improving the inference time. In this context, the tuning criteria for various fusion strategies and the balance between accuracy and resource efficiency in real-world scenarios are critical for practical applications. These aspects will be further investigated in future research, particularly with consideration of the hardware-specific constraints of EC/EI devices. The experimental results highlight the potential of multi-backbone SMA architectures to enhance performance. However, the practical application of EC/EI requires careful consideration of many aforementioned aspects. This is why future research should focus on optimizing multi-backbone models specifically for EC/EI contexts, including the following aspects:  
* Verifying other data augmentation techniques (like scalings, rotations, distortions, etc. );
* Estimating the impact of other real-world parameters, like different hardware setups (sensor settings, camera sensor types, etc.) and environmental conditions (varying light, focus, transparency, color, and other conditions);
* Validating models on larger datasets, including the above-mentioned data augmentation techniques and real-world parameters. Figure 14: Photos from CityScape dataset [40] in different light conditions: (**a**) original photo; (**b**) brightness distribution; (**c**) applying “contrast” (0,40) transformation—image modified by applying applying “contrast” function with cutoff parameters (0,40); (**d**) applying “contrast” (40,0) transformation—image modified by applying applying “contrast” function with cutoff parameters (40,0).
# Free Space Optics Communication for Ultra-High-Speed Train Running in Evacuated Tube
## 2 System Model
### Data Rate  
To estimate the data rate for FSO, \(P_{tx}\) the transmitted power, \(\theta\) divergence angle, receiver area \(A\), and \(\tau_{opt}\) optical efficiency for the transmitter and receiver must be determined; thus, R, the data rate is achieved by:  
\[R=\frac{P_{tx}\ \tau_{opt}\tau_{ATM}A}{\pi{\left(\frac{\theta}{2}\right)}^{2}L^{2 }E_{p}N_{p}} \tag{12}\]  
where \(E_{p}=\frac{hc}{A}\) is the photon energy, and \(N_{p}\) is the receiver sensitivity in \(\frac{photons}{Rt}\).  
Furthermore, power at the receiver \(P_{REQ}\) (watts) to achieve a given data rate, \(R\) (bits/sec), is given by P\({}_{\text{REQ}}\)[21]:  
\[P_{REQ}=N_{p}Rhv=\frac{N_{p}Rhc}{A} \tag{13}\]  
where \(v\) is the frequency of the laser light (\(h\) = [PERSON]'s constant, c is the velocity of light). However, In Si and InGaAs avalanche photodetectors, the usual receiver sensitivity for transmitting at 1.25 Gbps with ON-OFF keying modulation is \(-36\) dBm, as it was calculated by previous researchers in real-world scenarios [23], and this paper uses the same metrics and equations for calculated the received power (dBm), and it took as a reference the works in [10; 26]. Therefore, the work in this paper and all assumptions focus on this sensitivity, considering the different received power calculated for several divergence angles and distances.
# Neuroimaging of Pediatric Cerebellum in Inherited Neurodegenerative Diseases
## 1 Introduction
### Autosomal Receive Spastic Ataxia of Charlevoix-Saguenay (ARSACS)  
The autosomal recessive spastic ataxia of Charlevoix- Saguenay (ARSACS) is an early-onset neurodegenerative disease characterized by cerebellar ataxia, pyramidal tract involvement, and peripheral neuropathy 4. In late-onset cases [12], patients missing pyramidal tract involvement [13] and occasionally present with hearing loss [14]. Retinal fibers hypermyelination observed at funduscopic examination is considered a pathogenomic hallmark [15].  
The responsible gene (_SACS_) is located on chromosome 13q. It encodes for the Sacsin protein, which is widely expressed throughout the brain with a predilection for pyramidal neurons, Purkinje cells, thalamic and pontine nuclei, and reticular formation [16].  
MRI studies show characteristic features of the disease: atrophy of the superior cerebellar vermis and cervical spinal cord with bulky pons and increased thickness of the middle cerebellar peduncles. On T2w-FLAIR images, the pons shows a subtle hyperintensity (mainly in the raphe and lateral aspects) and exhibit hypointense stripes, acknowledged as the corticospinal tract (CST). Similar hypointensity is also observed in the medial lemnisci [4]. Thinning of the corpus callosum and a rim of T2-hyperintensity around the thalami are also frequent findings (Figure 4).  
Figure 3: INAD. Brain MRI scan of a 6-year-old girl. MR images demonstrate global cerebellar atrophy and cerebellar cortex hyperintensity, very thin optic chiasm, and “clava hypertrophy” due to enlargement of gracile and cuneate nuclei by axonal swellings and spheroids.  
Analyses of diffusion tensor tractography (DTI) [4,17] revealed an over-representation of transverse pontine fibers (TPF), causing a bulky shape of the pons and thick middle cerebellar pedunces (MCP) and probably preventing the reconstruction of the CST at this level. Significant changes consistent with microstructural alterations were found in the supratentorial white matter, forceps, cingulum, and superior longitudinal fasciculus 4. Pathological correlations are still lacking.
# A 2-V 1.4-dB NF GaAs MMIC LNA for K-Band Applications
## 5 Conclusions  
This paper presents a four-stage LNA using a 100 nm GaAs pHEMT process that covers the 5G New Release n258 frequency band (24.25-27.58 GHz). The proposed LNA achieves a maximum gain of 34 dB, a minimum NF as low as 1.3 dB, an input return loss better than \(-10\) dB from 23 to 29 GHz, a \(P_{\rm 1 dB}\) of \(-18\) dBm, and an OIP3 of 24.5 dBm. The LNA draws a total current od 59.1 mA from a 2V DC supply, resulting in a chip size of 3300 \(\times\) 1800 \(\upmu\)m including pads. Electromagnetic simulations as well as a Monte Carlo analysis results at room temperature demonstrate a final gain of 33 dB, a 1.4 dB NF, an \(|S_{11}|\) better than 10 dB, an \(|S_{22}|\) better than 5 dB, a \(P_{\rm 1 dB}\) of \(-18\) dBm and an OIP3 of 24.5 dBm in the band of interest. The paper also presents a design methodology focused on the selection of the active device size and DC bias conditions to obtain the lowest NF when source degeneration is applied. The design procedure ensures a minimum NF design by selecting a device that facilitates a simple input matching network implementation and obtains a reasonable input return loss thanks to the application of source degeneration. This approach minimizes the number of elements in the input matching network. Comparisons with similar works demonstrate the developed circuit is competitive with state-of-the-art solutions.  
Methodology, D.G.-S., S.L.K. and J.d.P.; Investigation, D.G.-S., S.L.K. and J.d.P.; Supervision, S.L.K. and J.d.P.; Writing--original draft, D.G.-S.; Writing--review and editing, S.L.K. and J.d.P.; Visualization, D.G.-S., S.L.K. and J.d.P.; Project administration, D.G.-S., S.L.K. and J.d.P.; Funding acquisition, D.G.-S., S.L.K. and J.d.P. All authors have read and agreed to the published version of the manuscript. This work has been partially supported by Grant PID2021-127712 OB-C21 funded by MCIN/AEI/10.13039/501100011033, by \"ERDF a way of making Europe\", and by the Canarian Agency for Research, Innovation, and Information Society (ACIISI) of the Canary Islands Government by Grant TESIS2019010100. The authors declare no conflict of interest.
## 2 Related Work
### Accessibility to Auditory Information  
Many studies have improved information accessibility via audio media. For example, Text-to-Speech (TTS) [1] increases the accessibility to textual content in smart devices. Screen readers convert digital text on the screen into spoken text, enabling users to navigate the web interface and access the text in it, such as documents, menus, icons, and web pages without sight [1,7,24]. BlindReader [25] is designed to help a visually-impaired reader understand the materials effectively by using haptic feedback that provides a sense of touch.  
In addition, there has been paid much attention to the accessibility of printed textual materials like books or newspapers. Many studies have proposed user interfaces for delivering printed information to visually-impaired people. For example, finger-worn designs for the blinds have been proposed to control reading in comfortable ways [8,26,27]. FingerReader [8], a wearable device with a small finger-worn form, helps blind users read the printed texts by scanning a single line and reading out the words as synthesized speech, along with the finger.  
Furthermore, there have been studies to interpret other types of visual materials (e.g., graph, map, and picture). OrCam [28] is a voice-activated device that attaches to virtually any glasses. It helps blind users live a more independent life by processing a book text, smartphone screen, and recognize faces. Access Lens [29] harnesses computer vision technologies to enable users to use accessible gestures on paper documents and other physical objects, such as product packages. Given the printed image material, Access Lens locates the text and reads specific content where the fingertips touch.
## 3 Materials and Methods
### New Upsampling Method  
When using the deep convolution neural network to classify and predict each pixel in an image, it is necessary to keep the resolution of the output predicted image consistent with the input image, so it is necessary to upsample the feature map. The YOLO network uses nearest neighbor interpolation to upsample the feature maps, which leads to ambiguous region semantics, insensitivity to details, and easy loss of objects. Although deconvolution cannot restore the feature map, it has similar effects and can effectively recover a small part of the lost information to the maximum. However, it brings higher parameter quantity to the network model than upsampling using nearest neighbor interpolation.  
[PERSON] et al. [28] first introduced deep separable convolution, which decomposes the standard convolution into deep convolution and point by point convolution, reducing the number of parameters. Because deep separable convolution can extract the channel information more carefully and deeply, the network accuracy is further improved.  
In this paper, we propose an upsampling method using depth-separable deconvolution. It can reduce the number of model parameters and slightly increase the model accuracy through deep separable deconvolution while upsampling. With this special way of expanding the feature map, the pixel values around the central pixel can be diversified when the resolution is expanded, so that the obtained feature map is closer to the original map, thereby improving the accuracy of the network model.  
In order to further reduce the improvement of parameter quantity caused by deconvolution, we apply group convolution to our deep separable deconvolution and adjust the convolution kernel size of deconvolution. Finally, our method adjusts the size of the feature map and improves the accuracy of the network model on the premise of adding a few parameters. Figure 1 shows the principal diagram of deep separable deconvolution. In the model, we adjust the number of corresponding deconvolution groups according to the number of channels in the network.