esa-sceva/satcom-chunk-collection huggingface dataset:
# Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples
## 2 Materials and Methods  
### Dataset Description  
The dataset for this study consisted of a set of HS images acquired from human brain histological slides, as we described earlier [10]. These biological specimens were processed and analyzed by the Pathological Anatomy Department of the University Hospital Doctor Negrin at Las Palmas of Gran Canaria (Spain). The study protocol and consent procedures were approved by the _Comite Etico de Investigacion Clinica-Comite de Etica en la Investigacion_ (CEIC/CEI) of the same hospital. After histological preparation of the tissue with H&E staining, the samples were examined by pathologists, providing a diagnosis of tissue according to the World Health Organization (WHO) classification of tumors of the nervous system [22]. After the pathologist confirmed the GB diagnosis, macroscopic annotations were made on the physical glass slides with a marker. Non-tumor areas were annotated in blue, while GB areas were annotated in red.  
The HS images were captured using a microscope optimized for HS acquisitions equipped with a push-broom camera, which performs spatial scanning for acquisition (Figure 1A). HS image acquisition was performed within the annotated areas of tumor and non-tumor in the slides (Figure 1B). The spectral range of the images was from 400 to 1000 nm with a spectral resolution of 2.8 nm, sampling 826 spectral channels. The camera captures 1004 pixels per line, and the image width was fixed to be 800 pixels. The images were captured using 20 \(\times\) magnification, producing a HS image size of \(375\times 299\)\(\upmu\)m (Figure 1C). After pre-processing, the HS cubes were formed by 275 spectral channels. Further details about the acquisition system and the data acquisition procedure can be found in [10].  
A total of 494 HS images were acquired from 13 slides from 13 different patients with GB. A brief description of the dataset can be seen in Figure 2, which demonstrates that the dataset is not balanced, having more samples from non-tumor tissue than tumor tissue. Additionally, some of the slides only contained tumor tissue, as occurs in patients P9 to P13.
###### Abstract  
An improved recommendation algorithm based on Conditional Variational Autoencoder (CVAE) and Constrained Probabilistic Matrix Factorization (CPMF) is proposed to address the issues of poor recommendation performance in traditional user-based collaborative filtering algorithms caused by data sparsity and suboptimal feature extraction. Firstly, in the data preprocessing stage, a hidden layer is added to CVAE, and random noise is introduced into the hidden layer to constrain the data features, thereby obtaining more accurate latent features and improving the model's robustness and generative capability. Secondly, the category of items is incorporated as auxiliary information in CVAE to supervise the encoding and decoding of item data. By learning the distribution characteristics of the data, missing values in the rating data can be effectively reconstructed, thereby reducing the sparsity of the rating matrix. Subsequently, the reconstructed data is processed using CPMF, which optimizes the feature extraction performance by imposing constraints on user features. Finally, the prediction rating of a user for an item can be obtained through the matrix product of user and item feature matrices. Experimental results on the MovieLens-100K and MovieLens-1M datasets demonstrate the effectiveness and superiority of the proposed algorithm over four comparative algorithms, as it exhibits significant advantages in terms of root mean square error and mean absolute error metrics.  
Keywords:collaborative filtering; conditional variational autoencoder; constrained probabilistic matrix factorization; auxiliary information; feature matrix +
Footnote †: journal:
## 1 Introduction  
One of the most common conditions which can be seen in the orthodontic clinic is the presence of a mesially tilted mandibular second molar occupying the space of the adjacent missing first molar [1]. Numerous oral health issues can result from a patient's tilted molar, particularly if prosthetic rehabilitation is intended. These include angular bone loss, inflammation, and an apparent pocket at the mesial surface of the tilted molar, all of which are symptoms of worsening periodontal health [2].  
To prevent functional and anatomical disruptions, the loss of a permanent first molar should be addressed with a prosthetic replacement or orthodontic space closure [3]. In order to create an acceptable path of insertion for a fixed prosthesis when we have limited space for a pontic, it is necessary to over-prepare the mesial side of the tilted molar, which could result in pulp exposure and improper loading on the tooth. Hence, orthodontic molar uprighting is the treatment of choice to facilitate creating proper space for the prosthetic replacement of a missing permanent first molar while maintaining or improving the periodontal environment in that area [4].  
There are multiple appliances that can be utilized for molar uprighting [1,2,5]. One of the classical ways to upright a tilted molar is the use of a conventional molar uprighting spring, which was introduced by [PERSON] in 1966. This method is a segmental approach, where the spring is manually made by creating a loop in a heavy rectangular stainless-steel wire and hooking it on the anchorage teeth segment while engaging the tilted molar for uprighting [6,7].  
Finite element (FE) analysis has been proven as a reliable method for investigating the biomechanical effect of different orthodontic appliances on the periodontal ligament (PDL) behavior and other related structures [8]. Although FE analysis is a key numerical tool in dental biomechanical research [9, 10, 11], only a handful of studies have focused on molar uprighting springs [12, 13, 14, 15]. Furthermore, numerous FE studies have been performed to predict orthodontic tooth movement; nevertheless, only few have addressed the analysis of bone remodeling related to long-term orthodontic tooth movement [16, 17, 18, 19].  
There have been previous attempts to determine the tooth movement rate and iteration time for orthodontic applications using FE analysis [20, 21, 22]. However, the correlation and conversion of this iteration time into real clinical time in order to estimate orthodontic treatment duration remains an area to be explored [23].  
Thus, the study aims to investigate the kinetic and kinematic responses of a mesially tilted mandibular second molar and the associated anchoring segment to an activated conventional uprighting spring through a novel FE approach for estimating clinical treatment duration. The null hypothesis of this study was that the iterative finite element simulation of molar uprighting would not produce clinically realistic estimates of tooth movement, treatment duration, or stress-mediated bone remodeling behavior.
# Blockchain Interoperability for Future Telecoms
## 2 Study Background
### Blockchain Interoperability Use Cases in Telecommunications
#### 2.2.8 Integrated Sensing and Communications  
One of the key enablers of next-generation wireless networks is integrated sensing and communications (ISAC) features, which enable the seamless connection of billions of devices. ISAC is expected to support various new 6G use cases, such as autonomous driving, human activity sensing, connected autonomous systems, and unmanned aerial vehicles (UAVs), which require massive connectivity, low latency, and high reliability. According to [PERSON] et al. [35], 6G presents twelve scientific challenges that necessitate revisiting the foundations of communications theory. [PERSON] et al. have also identifiedsuper-resolution theory [36] as one of these challenges, which is crucial for addressing new use cases in 6G communications related to ISAC. As ISAC enables simultaneous sensing and communication, security has become a key issue regarding eavesdropping and the unauthorized sensing of illegal devices accessing the uplink and downlink to transmit information signals. [PERSON], [PERSON] et al. [37] present the opportunities afforded by the upper Mid-Band Spectrum for the sixth generation of wireless communications (6G). The study primarily investigates the Frequency Range 3 (FR3) spectrum, spanning from 7.125 to 24.25 GHz, as a crucial enabler for 6G communications. However, this new spectrum opportunity for integrated sensing and communication (ISAC) presents notable challenges, particularly concerning the coordination of access among a substantial number of devices operated by various network operators. The sharing of the FR3 multi-band spectrum necessitates enhanced coordination between diverse network operators and incumbent systems. Consequently, these access requirements are expected to introduce new security and privacy challenges for ISAC. In [38], a secure solution was proposed for ISAC that addresses illegal access to the transmitted signals on the up/downlink. [PERSON] et al. [39] mentioned that blockchain is a promising solution for secure ISAC in 6G, allowing for an immutable, traceable, and secure approach that provides end-to-end protection and data integrity.  
For ISAC architectures, blockchain can enhance security at the network and application layers through the introduction of decentralized identity (DID) [40; 41] and zero-knowledge proof (ZKP) mechanisms.
## 3 Results  
### Percolation Threshold and Sensor Sensitivity Characteristics  
We varied the percolation threshold to find an appropriate piezoresistivity boundary and to check the uniformity of the samples fabricated in each batch. We fabricated the samples at five different MWCNT weight fractions and measured their resistances, as shown in Figure 3a. The fabrication procedure was repeated two times to obtain reliable data. The resistances changed rapidly below 0.35 wt.%, and we decided on which piezoresistive design to use based on these results.  
When the samples were fabricated using a different process with the same recipe, their electrical properties were not identical under 0.35 wt.%, as shown in Figure 3a. This may be due to the relationship between the electrical conducting path and the filler-loading fraction in composites. In the case of higher filler content, we might be able to obtain similar electrical conductivity from the samples due to their high electrical filler density, as shown for 0.5 wt.% in Table 1. However, in the case of lower filler content, the electrical conducting path may vary widely due to their lower filler-loading fraction, as shown for 0.35 wt.% in Table 1. We also fabricated sensor samples with different lengths for cases with both 0.35 and 0.5 wt.% filler to test the piezoresistivity in terms of the geometrical factors of the sensors. We prepared samples with lengths of 30, 40, and 50 mm and fixed their  
Figure 3: **(a)** Percolation threshold of the samples and (**b**) sensitivity characteristics (according to wt.%) of the NCSS electrodes.  
Figure 2: Experimental setup for the electrical and sensitivity characteristics of the nanocarbon composite strain sensor (NCSS).  
width (10 mm) and thickness (1.2 mm), as shown in Table 1. The resistance change ratio (normalized resistance, Rn) of the sensors were also increased according to their length, as shown in Figure 3b. This indicates that the sensor length may correlate with sensitivity, and the length should therefore be one of the factors considered in sensor design.
## 5 Evaluation and Discussion  
In this Section, we evaluate the differences and similarities between the related query management systems, as discussed in Sections 2.1 and 4. For this purpose, we are considering Data Release 1 dataset for implementation. The query management system can be evaluated by various criteria such as data support, query support function, architecture flexibility, and users. The query management systems focus on providing a uniform/single query language from multiple heterogeneous data sources which have some differences and similarities.
# Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm
## 4 ACA Integrated with the APF Method  
### Improvement of HF Based on the Attraction of APF  
The HF has a crucial role in the ACA, as its selection directly influences the ants' path selection behavior, and it affects the algorithm's convergence efficiency and the quality of the final solution. In conventional ACAs, the HF is solely related to the Euclidean distance \(d_{ij}(t)\) between the current node \(i\) and the available node \(j\). Initially, the differences among \(d_{ij}(t)\) are small, and the initial PC is uniformly distributed, leading to a high degree of blindness in the algorithm's initial search and deficiencies in its optimization ability and convergence. Therefore, this paper improves the traditional HF \(\eta_{ij}(t)\). Considering that in a grid environment, obstacles can be avoided through grid attributes, and when the target attraction and obstacle repulsion act together, problems such as \"unreachable target\" and \"local extremum trap\" may occur, this paper eliminates the repulsive effect of obstacles and only considers the target attraction to create a potential field HF \(\eta_{\rm{F}}(t)\), using the attraction in the APF to guide ants towards the TP. At the same time, the original distance HF formula is improved, replacing \(d_{\bar{y}}(t)\) with the Euclidean distance \(d_{\bar{z}}(t)\) from the current node \(i\) to the target node \(E\), thereby enhancing the guidance of the target node to the current node and enabling the algorithm to find the optimal path more quickly. The improved way of expression is like the following:  
\[\eta^{\prime}_{ij}(t)=\eta_{F}(t)\cdot\eta_{d}(t) \tag{11}\]  
\[\eta_{F}(t)=a^{F} \tag{12}\]  
\[U=\frac{1}{2}kd_{iE}^{2} \tag{13}\]  
\[F=-grad(U)=-kd_{iE} \tag{14}\]  
\[\eta_{d}(t)=\frac{1}{d_{\bar{z}}(t)} \tag{15}\]  
In Equations (11)-(15), \(\eta_{\bar{y}}\) represents the improved HF; \(\eta_{F}(t)\) is the attraction potential field HF; \(\eta_{d}(t)\) is the distance HF; \(F\) is the APF attraction; \(a\) is the influence factor of the APF attraction; \(U\) is the gravitational potential field function; \(k\) is the gravitational gain coefficient; and \(d_{\bar{z}}(t)\) is the distance between the current node \(i\) and the target node \(E\) at time \(t\).
## 3 Background of SDN  
SDN has recently received much attention to address some of the enduring networking challenges. SDN's concept is based on ideas of generalization network hardware and  
Figure 1: Proposed research flow for Internet of Things (IoT) leveraging software-defined networking (SDN).  
decoupling the network controls software from the implementation devices [15]. SDN is a new intelligent architecture for network programmability. SDN's main idea is to separate the control plane from the network devices, enabling the data control from a central and external software entity called an SDN controller. The Open Networking Foundation (ONF) [16] is a non-profit consortium dedicated to the development, standardization, and commercialization of SDN for the transport and IP network layer. Figure 2 presents SDN layered architecture, consisting of [17] four planes: data plane, management plane, control plane and application plane. The Data plane presents a forwarding table that forwards the incoming packets to a network device. Management plane provides intelligent provisioning and orchestration systems for entire network management. The control plane is also termed as the brain of the networks that control different types of data planes using SBIs and protocols. The application plane defines the layer on different northbound applications that exist, which can help out SDN perform and solve future challenges of 5G, as this plane brings innovation, openness, and flexibility for the network vendors. SDN architecture provides dynamicity, network flexibility, and management capabilities. Research studies suggest SDN is the most reliable and promising for separating strategic network computation and data forwarding.  
Several northbound interfaces (NBIs) between the control plane and applications were introduced to provide high-level abstractions to the applications that reside on the control plane and in the form of various network-level services. OpenFlow [18] is a significant addition to the southbound interfaces (SBIs) that enable the network to be managed efficiently. Nevertheless, SDN can not be limited to OpenFlow as other less conventional protocols exist such as ForCES [19], NETCONF [20], OVSDB [21], Pollex [22], LISP [23], and OpenState [24]. OpenFlow departs all the intelligence to a centralized entity called SDN controller, which enables the separation of the control plane from the forwarding plane. There are three SDN functions in the panorama of OpenFlow, status reporting for each device connection, slicing, and flow-based forwarding. OpenFlow-based interconnection device matches the packets against a flow table inside the forwarding plane. FlowVisor controller is responsible for handling the flows, decisions, and publishing of the policies.  
Figure 2: SDN Architecture.
## 3 Results and Discussion  
### Performance of BDS-3 SAIM Based on On-Orbit Data  
With the actual SAIM telemetry data of the BDS-3 on-orbit satellites, taking the data of the C30 and C34 satellites for the full year 2021 as examples, the variation in the characteristics and the stability of various SAIM monitoring data were analyzed and evaluated. Telemetry data may contain abnormal data such as single burr point mutation due to on-orbit single particle events or satellite-to-ground link communication transmission. Generally, such data are invalid and should be eliminated in the data preprocessing process. The simulated data in this paper have been preprocessed and will not be described separately in the following.
## 2 Permeability Calculation Model  
The permeability coefficient is an index that comprehensively reflects the permeability of surrounding rock, and the correct determination of its value is of great significance to the calculation of water gushing. The literature [21; 22] shows that, under the influence of in situ stress, the pore structure of surrounding rock continuously decreases with the increase of in situ stress, and the permeability coefficient of surrounding rock generally decays in a negative exponential curve with burial depth. At the same time, surrounding rock is damaged by excavation and produces a disturbance zone. The literature [23; 24; 25; 26] found that the permeability of surrounding rock in the disturbance zone will increase by orders of magnitude after a tunnel is excavated via field measurement of the permeability of the surrounding rock of a tunnel. According to the authors of [27], it is assumed that the permeability coefficient of surrounding rock in a disturbance zone changes linearly and gradually with the radial distance from the tunnel. The spatial expression of the permeability coefficient of the surrounding rock on one side of a tunnel is:  
\[k=\left\{\begin{array}{cc}me^{ny}&x>l+t;l<x<l+t,y>t\\ (m_{0}+ax)e^{ny}&l<x<l+t,0<y<t\end{array}\right. \tag{1}\]  
\(m_{0}\) is the equivalent permeability coefficient of the tunnel axis position affected by the excavation disturbance; \(n\) reflects the variability of the permeability coefficient of surrounding rock under the influence of ground stress; \(a\) reflects the variability of the permeability coefficient of the surrounding rock under the influence of excavation disturbance.  
Based on this, considering the coupling effects of ground stress and excavation disturbance, a simplified spatial model of the permeability coefficient of the surrounding rock is established, as shown in Figure 1.  
In Figure 1, the length of the disturbance zone is AB, the width is AD, and the radial distance between the edge of the disturbance zone and the tunnel is \(l\).  
It is assumed that the buried depth of the tunnel is 100 m; the equivalent permeability coefficient of the tunnel axis position affected by excavation disturbance is \(5\times 10^{-8}\)m/s; the permeability coefficient of the undisturbed zone is \(10^{-8}\)m/s; the coefficient of variation of the permeability coefficient of the surrounding rock under the influence of in situ stress is 0.01. The distribution of permeability coefficient of surrounding rock within 200 m horizontal distance from the tunnel is shown in Figure 2.
# User Experience of Older People While Using Digital Health Technologies: A Systematic Review
## 1 Introduction  
The healthcare landscape has undergone a profound transformation with the advent of digital technologies. These advancements encompass a wide spectrum of innovations, including biosensors, wearables [1; 2], digital healthcare applications such as health apps [3; 4; 5; 6] and chatbots [7; 8], remote clinical management tools [9; 10; 11; 12; 13], integrated machine learning algorithms aiding decision-making [14; 15], and immersive experiences such as virtual reality [16; 17; 18; 19] and augmented reality [20; 21; 22]. Moreover, electronic medical records [23; 24] and visual analytics/dashboards [25; 26; 27; 28] have become integral components of modern healthcare, forming a digital ecosystem that presents unprecedented opportunities to enhance patient care and monitoring [22]. As far as we know, there is no general system for digital health technology, but it includes the components outlined in Table 1 from our previous research. Amidst the challenges posed by global aging, these digital health solutions hold promise in addressing issues associated with reduced physical and cognitive function, multiple chronic conditions, and shifts in social networks [29]. Theapplication of digital technologies has the potential to not only improve quality of life and well-being, but also foster independent living among older people. In light of advancements in digital health technologies and demographic shifts, it has become imperative to comprehensively investigate the user experience (UX) of these technological innovations. UX is defined as a \"person's perception and responses resulting from the use and/or anticipated use of a product, system, or service\" [30]. UX includes all of the user's emotions, beliefs, preferences, perceptions, physical and psychological responses, behaviors, and accomplishments that occur before, during, and after use. Usability, when interpreted from the perspective of the user's personal goals, can include the kind of perceptual and emotional aspects typically associated with UX [30]. The UX investigation of digital health technologies is a crucial step toward understanding specific needs, such as those of older people, and achieving optimal healthcare experiences. Various methods have been used to investigate the UX of digital health technologies, encompassing questionnaires, interviews, and observations [31]. The focus of this systematic review is on questionnaires as a method for investigating the UX of digital health technologies among older people. Questionnaires provide structured and quantifiable data which can help gather a comprehensive overview of the experiences of older people with digital health technologies. Moreover, validity and reliability are crucial factors in ensuring the scientific rigor of such investigations. Original questionnaires are vital when evaluating specific aspects of digital health technologies [32]. Consequently, both standardized and custom-designed questionnaires tailored to the nuances of digital health technologies play a crucial role in uncovering the UX of older people.