esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Multimodal AI to Aid Scene Perception for the Visually Impaired
## 1 Introduction  
The development of assistive mobility devices for the visually impaired requires an interdisciplinary research approach that integrates advances in artificial intelligence, robotics, and human-computer interaction. One of the key strategies in assistive vision systems is sensory substitution, which enhances spatial awareness by converting visual information, most commonly, into auditory or tactile cues [1, 2, 3].  
To address these challenges, numerous solutions have been proposed using various sensors and feedback methods. These include depth cameras, ultrasonic and infrared sensors, RFID tags, and smartphone cameras to detect obstacles and gather spatial data. Feedback is typically delivered via audio, tactile, or vibrational signals, aiding users in safe navigation [4].  
However, real-world implementation of these technologies remains challenging due to issues related to system ergonomics, real-time processing, and adaptability to diverse environments.  
The cited literature reviews show that stereovision and RGB-D sensors are commonly used to obtain depth information, enabling the identification of obstacles in a scene. Several assistive vision prototypes employ head-mounted sensors of this type, with sensory feedback provided through auditory or haptic signals [5, 6, 7, 8, 9]. Stereovision is a passive technique that reconstructs 3D space from images captured at different viewpoints, operating effectively under natural lighting conditions. Its advantages include the use of low-cost cameras and well-established algorithms for depth computation. However, challenges such as high computational demands, sensitivity to lighting variations, and occlusion issues limit itsreliability. RGB-D sensors, on the other hand, integrate active depth measurement methods, such as infrared projection, improving performance in low-light conditions. Nevertheless, structural lighting is ineffective for depth estimation in strong illumination (e.g., sunlight) or when dealing with reflective surfaces, such as glass or mirrors.  
This work proposes a novel approach to assist the visually impaired in scene perception. It presents an empirical analysis of the feasibility of using generative multimodal models for image analysis. This involves selecting parameters for 3D scene segmentation algorithms for presenting non-visual scenes to blind users.
# Sphere Fitting with Applications to Machine Tracking
### Our Main Contribution  
We suggest an algorithm that computes an \(\varepsilon\)-coreset for the family of spheres in \(\mathbb{R}^{d}\) for the \(\ell_{1}\)-norm of distances. The cardinality of the coreset is \(O(d^{2}\log^{2}n/\varepsilon^{2})\). See Theorem 2 for details.  
Using existing algorithms, we can then extract a \((1+\varepsilon)\)-approximation to the optimal sphere of the original input in additional \((d\log n)^{O(1)}\) time. That is, the algorithm outputs \((1+\varepsilon)\)-approximation for (1), i.e., a pair \(x\in\mathbb{R}^{d}\) and \(r\geq 0\) such that  
\[\sum_{p\in P}\mid\|p-x\|-r\mid\leq(1+\varepsilon)\sum_{p\in P}\mid\|p-x^{*}\| -r^{*}\mid.\]  
We highlight the strengths of the technique by implementing and testing it for different values of number \(n\) of points in the input set and for distorted and noisy input points. Our solution holds for any dimension, and provides several examples for the case of points in two and three dimensional space.  
In theory, the price for using our approach is that we get only an approximated solution to the desired shape in the worst case by running the optimal solution on the coreset. In practice, we do not compute the optimal solution also on the original data, but run heuristics that search for a local minimum instead. In these cases, we see that the quality of the results on running the heuristics on the coreset is better than running them on the full large set. Intuitively, the coreset \"cleans\" the data, removes noise and reduces the number of bad local minima. Hence, in practice the resulting fitting error using the coreset is smaller. For example, by running the common \"RANSAC\" heuristic on our coreset, our experimental results show that the results are more accurate and faster; See Figure 4 in Section 3.4.  
Moreover, while most of the existing solutions aim to detect a circle in two-dimensional space, our algorithm returns provable approximation for every \(d\geq 2\).  
As an example of a robotics application, we demonstrate our algorithms on a mechanical pressure control system which requires an efficient and extremely accurate tracking of the dynamics of a falling ball using a laser profiler, see Section 4.
## 4 Conclusions  
In this study, the synthesis of the [WO\({}_{2}\)(C\({}_{5}\)H\({}_{7}\)O\({}_{2}\))\({}_{2}\)] complex was investigated. Its efficiency as a precursor in the hydrothermal synthesis of WO\({}_{3}\) film was shown, and microstructural features and electrochromic properties of such a film were evaluated. Thus, the behavior of the synthesized complex in n-butanol medium under hydrothermal conditions in the temperature range of 120-220 \({}^{\circ}\)C was studied. It was found that the chelate ligand undergoes partial destructive substitution for the alkoxyl fragment upon an increase in temperature, and the process is significantly intensified at a processing temperature above 140 \({}^{\circ}\)C. Using these data, the formation of WO\({}_{3}\) films on glass substrate and glass/ITO substrate under hydrothermal conditions using an alcohol solution [WO\({}_{2}\)(C\({}_{5}\)H\({}_{7}\)O\({}_{2}\))\({}_{2}\)] was studied. It was found that the oxide films on different substrates exhibit different microstructures: the WO\({}_{3}\) film on a glass/ITO substrate consists of nanorods 350 \(\pm\) 25 nm long and 110 \(\pm\) 25 nm wide, while the film on a glass substrate is formed by a mixture of submicron spheres (\(\sim\)500 nm diameter), ridges of submicron and micron sizes, and individual nanoparticles 60-160 nm in size. According to KPFM data, the WO\({}_{3}\) film on a glass/ITO substrate is characterized by an electron work function of 4.77 eV. This film also showed sufficient transparency for use as a transparent component of optical devices, in particular, electrochromic ones. The optical contrast for the studied material was about 17.5% at 600 nm, as well as in the near-infrared range, which indicates the prospects for using the proposed approach to the growth of electrochromic hierarchically organized WO\({}_{3}\) films in hydrothermal conditions using the [WO\({}_{2}\)(C\({}_{5}\)H\({}_{7}\)O\({}_{2}\))\({}_{2}\)] complex as a precursor. It was also found in the course of CV recording that the coloration process for the obtained WO\({}_{3}\) film at varying potentials can proceed in two steps after the stabilization of the electrochemical behavior of the system, which provides additional information on the mechanism of the processes taking place. The obtained WO\({}_{3}\) film showed good stability of electrochromic properties after 100 cycles of coloration and bleaching, practically not losing any contrast, and a coloration efficiency of 15.5 cm\({}^{2}\)/C in the tested two-electrode cell with polymeric electrolyte.  
Conceptualization, N.P.S. and P.Y.G.; investigation, P.Y.G., M.K.G., T.L.S. and N.P.S.; writing--original draft preparation, P.Y.G. and N.P.S.; writing--review and editing, N.P.S.; visualization, P.Y.G. and M.K.G.; supervision, N.P.S., E.P.S. and N.T.K. All authors have read and agreed to the published version of the manuscript.  
The SEM analysis and XRD measurements were performed using shared experimental facilities supported by IGIC RAS state assignment.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Gut Microbiota-Targeted Interventions in the Prevention and Progression of Atherosclerosis
### Prebiotics  
Currently, a prebiotic is defined as a substrate that is selectively utilised by host microorganisms conferring a health benefit [220,221].  
This definition encompasses not only nondigestible carbohydrate-based compounds, such as fructooligosaccharides (FOS) and galactooligosaccharides (GOS), but also other compounds like certain polyphenols and polyunsaturated fatty acids, provided that there is robust evidence of their selective microbial utilisation and associated health benefits. Furthermore, prebiotic effects are no longer confined to the gut, but may also occur at other body locals colonised by microorganisms [220].  
The key concept of prebiotics is selectivity, as they specifically promote the stimulation, growth, and proliferation of beneficial microorganisms, including, but not limited to, _Lactobacillus_ spp. and _Bifidobacterium_ spp., which inhibit the proliferation of pathogenic bacteria by reducing intestinal pH [222,223]. Additionally, prebiotics stimulate changes in bacterial metabolism, such as increased production of SCFAs, namely butyric and propionic acids, which, among the other benefits already discussed, improve epithelial integrity and exert anti-inflammatory effects [224]. Moreover, prebiotics may improve lipid metabolism through microbiota-mediated mechanisms, including bile acid metabolism and SCFA production. Taken together, and as previously discussed, prebiotics exert positive effects on the prevention and development of atherosclerosis [203].  
Inulin or GOS supplementation has been associated with modest reductions in IL-6, TNF-\(\alpha\), high-sensitivity C-reactive protein (CRP), and circulating lipopolysaccharideaccharides in patients with coronary artery disease or elevated metabolic risk, according to recent clinical research. In addition, improvements in lipid profiles and endothelial function have also been reported. However, evidence for direct effects on atherosclerotic burden remains scarce, and no trials to date have demonstrated the clear regression of vascular lesions [225,226]. While preclinical findings in animal models have provided mechanistic support, the translation of these effects to clinical outcomes in humans remains to be firmly established [227].
# Discerning Reality through Haze: An Image Dehazing Network Based on Multi-Feature Fusion
## 2 Related Work  
### Atmospheric Scattering Model  
The atmospheric scattering model (ASM) is used to explain the imaging process of devices capturing images under atmospheric environments such as fog and haze. This model simulates the propagation of light through the atmosphere, calculating the extent to which the brightness value of each pixel in the image is affected by atmospheric scattering, thus providing a robust theoretical basis for the task of image dehazing. The model was originally proposed by [PERSON] et al. [1], and it has been continuously improved in subsequent research [2; 31]. It can be expressed as follows:  
\[I(z)=J(z)t(z)+A(1-t(z)) \tag{1}\]  
where \(z\) is the pixel coordinate values of the image, \(I(z)\) is the single foggy image, \(J(z)\) is the haze-free image, \(A\) is the global atmosphere light, and \(t(z)\) is the transmission rate, which can be described as follows when light propagates through a homogeneous medium:  
\[t(z)=e^{-\beta d(z)} \tag{2}\]  
where \(\beta\) is the atmospheric scattering coefficient and \(d(z)\) is the distance between the object and the imaging equipment. In the task of image dehazing, \(t(z)\) and \(A\) are estimated from the foggy image using prior knowledge or other methods; by substituting it into Equation (1), the dehazed image can be restored.  
\[J(z)=\frac{A\Big{(}1-e^{-\beta d(z)}\Big{)}-I(z)}{e^{-\beta d(z)}} \tag{3}\]
# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 3 Uncertainty Estimation in Neural Networks -- Mathematics and Algorithm
### Uncertainty Estimation for Embedding Vectors  
The basis for our architecture to estimate the three types of uncertainty is the Uncertainty Aware Learning (UAL) approach [25]. The structure of our architecture is given in Figure 2 (detail) and Figure 3 (overview).  
As shown in Figure 3, the basic feature extraction is performed by the backbone \(f\), which processes the input \(x\in\mathbb{R}^{3\times 256\times 128}\) and extracts a feature tensor \(F\in\mathbb{R}^{d\times 16\times 8}\) (Equation (3)). Therefore, as a result of the downsampling operations in the backbone, the height and width of the feature map are reduced to \(\frac{1}{16}\) of the height and width of the input image (see Figure 3).  
\[F=f(x) \tag{3}\]  
The backbone is a ResNet-50 based on the BOT approach [66], which is pre-trained on ImageNet. In this case, the number of channels in the feature tensor \(F\) is \(d=2048\). The feature tensor \(F\) is then processed by dedicated modules--the Bayesian module and the embedding heads--to estimate the three types of uncertainty in addition to the feature vector.  
The Bayesian module enables the estimation of _model uncertainty_. The embedding heads realize a probabilistic embedding, which enables the estimation of _data uncertainty_. The combination of the Bayesian module and the variance head enables the estimation of _distributional uncertainty_. All modules for uncertainty estimation are initialized randomly. Their structure is detailed in Figure 2, and details about their realization are presented below.  
Figure 2: Structure of (**a**) the Bayesian module, (**b**) the mean head, and (**c**) the variance head (top row) and of their building blocks (bottom row). Underneath the diagrams in (**a–c**), the number of channels at each stage is indicated. Abbreviations: B—Bayes convolution, C—standard convolution, N—batch normalization, R—ReLU.
## 3 Results and Discussion
### Compressive Strength  
The compressive strength determination results for HC after 28 days of curing are presented in Figure 6.  
The lowest compressive strength has been determined for the concrete HC0 containing only 20% of the binder in its volume. Such a low strength was not only an effect of the very low amount of binder but also a result of the lack of pressure compaction of the concrete mix during its preparation and the consequence of the hemp absorbing a part of the water needed for cement hydration. The concrete containing a lime binder, HC1, has achieved more than 12 times higher strength than the reference concrete. However, the average strength still needs to be higher for accepted concrete as a structural material. The cement-lime concrete, HC2, has achieved the highest average strength. The average compressive strength was 9.56 MPa, and the minimal strength was 9.15 MPa. EN-206 allows the concrete HC2 to be categorised as structural concrete of the class LC8/9 class. The cement concrete, HC3, had slightly lower (by 9%) strength than the concrete HC2. The lower strength of HC3 can be explained by the fact that according to the studies [27; 29], the hemp shives contain pectins that hinder cement hydration. Therefore, the pectins' action should be neutralised by mineralisers. The compressive strength of HC with a lime binder can help extend the curing time of wet conditions. Therefore, in the case of HC on lime binders, the mechanical properties are most often tested after 56 days from moulding [8; 21]. As has been found in the biological tests described below, the water glass appeared to be ineffective in that range.
###### Abstract
In order to automatically determine the value of each parameter used in STMCF, a parameter optimization method based on differential evolutionary (DE) algorithm is designed and a training strategy is explored in this method. 3. A series of comparative experimental results indicate that our scheme can achieve accurate and high-quality 3D reconstruction efficiently and the proposed STMCF can provide better performance on the aspects of accuracy, computation time and reconstruction quality than the state-of-the-art method based on spatiotemporal correlation, which is widely used in random speckle 3D reconstruction. The remainder of this paper is organized as follows. The related work is discussed in Section 2. Section 3 describes the STMCF. Disparity computation and subpixel refinement are given in Section 4. The parameter optimization method based on DE algorithm is presented in Section 5. Experiments and discussion are provided in Section 6 and finally, Section 7 concludes this paper.
## 3 Results and Discussion
### Weight, Size, and Waste Reduction Analysis  
The footprints and weight of the system as a function of the feed inlet temperature have been shown in Figure 11. The figure shows that the footprints of the MD/MCr unit reduce by almost 2.3 times by increasing the feed inlet temperature from 38 \({}^{\circ}\)C to 56 \({}^{\circ}\)C. Similarly, the weight of the unit drops down by more than 2.5 times by increasing the feed inlet temperature from 38 \({}^{\circ}\)C to 56 \({}^{\circ}\)C. These observations are due to increased flux at a high-feed inlet temperature (Figure 3a). High flux decreases the membrane area that is required to treat a particular volume of the wastewater; thus, the associated weight and footprint of the membrane unit reduce. Figure 11 reveals the same trend, where the P/W and P/S ratios increase with the feed inlet temperature due to the corresponding increase of productivity, which is defined as the product of flux and the corresponding membrane area of a given MD/MCr unit with the feed temperature. As shown in Figure 11, the waste intensity approaches zero at the final freshwater recovery factor obtained in this study (approximately 90%), indicating that almost all of the wastewater has been converted into freshwater and some salt crystals, thus minimizing the wastewater volume to be disposed.  
Figure 11: _Cont._  
Figure 10: Normalized flux for virgin and used (before and after cleaning) membrane.
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
## 2 Internet Latency
### Experimental Setup  
We measured RTTs for UDP traffic between two parties, \(\mathcal{S}\) the Sender and \(\mathcal{R}\) the Receiver, sometimes relayed by \(\mathcal{A}\) the Attacker. We define below the key points of our experiments.  
- USA, Oregon
* _Hijacked traffic._ We ran our experiments on the Internet, hence we had no control over the route between \(\mathcal{S}\) and \(\mathcal{R}\). For this reason, we simulated the presence of a relay by sending directly the packets from \(\mathcal{S}\) to \(\mathcal{A}\) and then from \(\mathcal{A}\) to \(\mathcal{R}\).
* _Packet size._ The impact of packet length on RTT is very weak for realistic variations [27]. Hence, we arbitrarily chose to use 512-bytes packets across all our experiments.
## 1 Introduction  
Urine is formed in the kidneys, and it flows through the renal pelvis and the ureter to the bladder with the help of hydrodynamic pressure and peristalsis [1; 2]. If a focal or segmental lesion develops in the ureter, a urinary disturbance can occur. This disturbance can be relieved through a urinary diversion, such as with a percutaneous nephrostomy using a drainage tube [3]. Another way to solve the problem is to use ureter stents. Currently, plastic stents made of polyurethane are commonly used along with metallic stents [4; 5; 6; 7; 8]. A double-J stent is a plastic stent and it consists of a shaft with coils on both ends, with multiple side holes [9; 10]. It is used in the upper urinary system in cases of ureteral stenosis or occlusion caused by a ureter stone or extrinsic tumor. The placement of a double-J stent in the extrinsic tumor involving the ureter does not guarantee effective urine flow as the tumor compression of the stent often makes fluid drainage outside the stent impossible, and the use of a double-J stent in the tumor can result in stent failure in many cases [11]. In contrast, the placement of a double-J stent in the case of a ureter stone is effective in maintaining urine flow as urine flow is allowed through and around the stent.  
In a clinical setting, the 5-, 6-, 7-, and 8-Fr double-J stents are used for ureter stones before and after lithotripsy. The selection of size depends on the clinician's experience based on the possible urine flow rate through the stent. [PERSON] et al. [12] reported that luminal flow is directly proportional to the internal diameter of the stent, and extraluminal flow does not correlate with the external diameter of the stent once a 7-Fr size is reached although it is proportional to the external diameter. In this study, we investigated which size of double-J stent is better at achieving an acceptable urine flow through theented ureter using computational fluid dynamics (CFD) to determine the total flow, the sum of luminal and extraluminal flow. We developed a curved ureter model based on human anatomy [13]. Sixty-four CFD models of the combinations of two different gauge ureters (4.57-mm diameter and 5.39-mm diameter) with four levels of ureteral stenosis (0%, 33%, 52%, and 74%) and different sizes of double-J stents with different numbers of side holes were used for the numerical analysis of urine flow in stenotic and stented ureters.
## 5 Conclusions  
Based on Vis/NIR reflectance data of fresh kiwifruit, the kiwifruit sweetness was estimated across four harvest periods by employing SG, MSC, and SNV transformation methods, SPA and CARS feature selection techniques, and MLR, PLS, LSSVM, and BP modeling approaches. SNV and MSC algorithms could remove the scatter information from the Vis/NIR data due to the uneven surface distribution of kiwifruit, which significantly enhanced the relationship between the spectral reflectance and sweetness. SPA and CARS methods for feature extraction reduced the number of spectral variables from 2101 to a range of 8-20, which significantly diminished data redundancy and made a simple equation for sweetness estimation. The accuracy of the sweetness estimation model in the whole harvest stage was the best. The optimal model combination was MSC-SPA-LSSVM with R\({}^{2}\) of 0.79 and RMSE of 1.32. This combination effectively reduced the original 2101 spectra to just 8, achieving substantial data reduction while rapid, non-destructive, and accurate predicting of kiwifruit sweetness levels. The sensitive wavelength selected in this study will provide a theoretical basis for the potential application of Vis/NIR technology in the sweetness prediction of different fruits. The procedures for sweetness estimation would partially provide a theoretical and technical basis for the development of portable non-destructive monitoring equipment for kiwifruit sweetness. However, it remains a particular challenge to test the model performance of more kiwifruit cultivars.  
Conceptualization, C.W.; methodology, C.W.; software, C.W.; validation, C.W. and F.L.; formal analysis, C.W.; investigation, C.W., X.C., R.Y., K.F. and Z.L.; resources, F.L.; data curation, C.W., X.C., R.Y., K.F. and Z.L.; writing--original draft preparation, C.W.; writing--review and editing, C.W.,R.Y. and F.L.; visualization, C.W.; supervision, F.L.; project administration, F.L.; funding acquisition, F.L. All authors have read and agreed to the published version of the manuscript.  
Funding: This research was funded by the National Natural Science Foundation of China (41701398). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data is contained within the article. Conflicts of Interest: The authors declare no conflict of interest. Abbreviations The following abbreviations were used in this manuscript: SG Savitzky-Golay Smoothing SNV Standard Normal Variate MSC Multiplicative Scatter Correction SPA Successive Projections Algorithm CARS Competitive Adaptive Reweighted Sampling LSSVM Least Squares Support Vector Machine BP Back Propagation Neural Network PLS Partial Least Squares MLR Multiple Linear Regression