esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The aim of this work was the development of fouling-resistant UF membranes for Cd remediation via blending to synergically exploit the flexibility of CA with the complex properties of PAA. The strong hydrogen interactions between the two polymers observed with FT-IR and the presence of a single peak in DSC results confirmed the homogeneity of the blending of CA and PAA into the membranes prepared via NIPS. The membranes presented asymmetric and highly porous structures with morphologies affected by the PAA concentration. In fact, the porosity enhanced from 44.5% to 75.6% by raising the concentration of the PAA from 0 wt. % to 15 wt. %. Measurements of contact angle and the pure water flux evidenced the improvement of the hydrophilicity of the membrane by introducing PAA in the CA structure. The ultimate result is that the membrane prepared  
Figure 10: Effect of pH variation on percentage removal of cadmium for the prepared membranes.  
with a higher content of PAA (M15) showed superior properties in both permeability and HA rejection. Benefits of blending CA with PAA were observed also in terms of antifouling properties, allowing for effective flux recovery of membrane fouled by treating HA solutions. In fact, the total fouling ratio (Rt) was remarkably reduced owing to the repulsive interactions between the foulants (HA) and the surface of the membranes made of CA blended with PAA rich of carboxylic groups. Concisely, a remarkable mitigation of total fouling ratio from 31.5% for CA membrane (M0) to 22% for M15 was observed. Moreover, PAA provided functional properties to the membrane, enabling their employment in the efficient remediation of heavy metal-contaminated aqueous solution. In fact, the Cd rejection raised with the concentration of PAA into the membrane hitting for M15 values above the 80%, whereas the neat CA (M0) was inefficient in the Cd removal. Overall, the blending of CA with PAA was found to be beneficial in the development of antifouling UF membranes useful in the remediation of heavy metal contaminated waterbodies  
I.O.: Investigation; Writing; Formal Analysis; Data Curatio; Y.G.: Writing--Original Draft Preparation; C.U.: Formal Analysis; Data Curatio; Writing--Original Draft Preparation; Writing--Review and Editing; S.S.: Writing--Original Draft Preparation; Writing--Review & Editing; S.M.: Formal Analysis; A.H.: Writing--Review and Editing; E.F.: Supervision; A.F.: Supervision; Writing--Original Draft Preparation; Writing--Review and Editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData will be made available on request.  
The authors wish to thank the Tunisian Ministry of Higher Education and Scientific Research for the financial support of this work and the Institute on Membrane Technology (ITM-CNR), Italy, for providing some characterization tools.  
The authors declare no conflict of interest.
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 5 Results
### Evaluation Outcomes  
* The YOLOv11 pose estimation was highly stable under normal lighting and frontal view;
* Borderline motions (like abrupt sitting or crawling) were often correctly classified, likely due to pose stability and consistent spatial features;
* The voice assistant successfully reduced false alarms;
* Pose estimation inference time averaged 34 ms/frame on an RTX 4070 and 0.5 ms postprocess per image. Total time for detection, response generation, and alerting was ~1.1 s.
# Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond
## 3 Results and Discussion
### Reported Application of AI to RT
#### 3.2.6 Patient Positioning and Monitoring and Adaptive Planning  
To guarantee consistency in online imaging, in the treatment phase it is essential that the patient is in the same position as established in the simulation phase. The CBCT is the most used 3D online imaging technique to verify patient positioning, despite its much lower quality than for the planning CT images. AI allows the improvement of the CBCT image quality using DL-based methods [91; 92], thereby enabling more accurate patient positioning. Similar approaches can be used for patient monitoring using onboard MRI, ultrasonography, or optical surface imaging [93]. In addition, DL-based methods were used to automatically detect gold fiducial markers before treatment [94].  
During the RT delivery, patient or internal organ motion can increase the dose delivered to non-target tissues if motion management methods are not adopted. AI can be used to generate patient-specific dynamic motion management models, which can improve tumor tracking and interrupt irradiation in inadequate target positions. These algorithms could automatically adjust for complex breathing patterns in real-time to accurately track and predict the tumor position in advance [95]. AI models were used to predict geometric changes occurring in head and neck cancer patients and to identify the most appropriate treatment week for acquiring images for planning adaptations [29; 96].  
DL methods were also implemented to develop a complete adaptive RT strategy [29; 54], reducing the time required for other tasks [97].
## 5 Results and Discussion
### Algorithm Performance Evaluation
#### 5.1.1 Time Complexity Analysis  
With a population size of \(N\), problem dimension \(d\), and a maximum number of iterations \(T\), the time complexity of the algorithm primarily stems from population initialization, dynamic search, and elite retention. During the initialization phase, \(N\) individuals are generated using Tent chaotic mapping, each with \(d\) dimensions, yielding a complexity of \(O(N\cdot d)\). In the dynamic search phase, both GWO and HHO algorithms update individual positions per iteration, with a computational complexity of \(O(N\cdot d)\). The elite retention and perturbation mechanism involves sorting and perturbing elite individuals, resulting in an approximate complexity of \(O(N\cdot\log N+P\cdot N\cdot d)\).  
Thus, the total time complexity per iteration is \(O(N\cdot d+N\cdot\log N+P\cdot N\cdot d)\). Given that \(P\) and \(\log N\) are relatively small, their impact can be neglected. Therefore, the overall time complexity of the algorithm across \(T\) iterations is \(O(T\cdot N\cdot d)\). Compared with the standard GWO and HHO algorithms, the IGWOHHO algorithm introduces multiple improvement strategies without significantly increasing computational cost. It effectively enhances global search ability and convergence performance, making it suitable for high-dimensional and complex optimization problems.
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## 2 Background and Related Work
### Map-Reduce Communication-Cost Model  
In general, there may be several performance measures for evaluating the performance of an algorithm in the Map-Reduce model (see below). In this study, we follow the communication-cost model as described in [30]: a task is a single map or reduce process, executed by a single computer in the network. The _communication cost of a task_ is the size of the input to this task. Note that the initial input to a map task (the input that resides in a file) is also counted as an input. Also note that we do not distinguish between map tasks and reduce tasks for this matter. The _total communication cost_ is the sum of the communications costs of all the tasks in the Map-Reduce job.  
Other proposed cost models, which we will not focus on, are _total response_ time and the _Amazon total cost_. Total response time refers to the elapsed time from the start of the Map-Reduce job to the end of it, without considering the number of nodes that have participated in the job, the nodes' specifications or the number of messages passed from node to node during the computation process. This model is practical because the most significant drive for the development of the Map-Reduce framework is the need to finish big tasks fast. However, it is difficult under these uncertain conditions to compare performance between different algorithms.  
The Amazon total cost is the cost in dollars of the execution of this job over the Amazon Elastic Map-Reduce [31; 32] service. This model considers all factors participating in the job: combined running time of all nodes that participated in the job weighted by the specifications of each node (a single time unit of a node with a fast CPU costs more than a single time unit of a node with a slow CPU), size of data communicated and the use of storage during the execution. This may be the most effective cost model, but it is bound to Amazon.
## 2 Materials and Methods
### Data Analysis
#### 2.3.2 Thematic Analysis  
Thematic analysis is a method that focuses on identifying, examining, and interpreting patterns (themes) within qualitative data [79]. The method is widely recognized for its flexibility, making it suitable for analyzing diverse forms of qualitative data across various research contexts such as interview transcripts and focus group discussions [80]. Thematic analysis enables researchers to explore and understand participants' perspectives and experiences in-depth, offering valuable and comprehensive insights [79; 81; 82].  
The data collected from the interviews were analyzed using thematic analysis. The six phases of thematic analysis outlined by [PERSON] and [PERSON] [81] were systematically followed in this study: (1) familiarization with data; (2) generating initial codes; (3) searching for themes; (4) reviewing themes; (5) defining and naming themes; and (6) producing the report. Audio recordings were carefully transcribed into Word documents, enabling a detailed review of the participants' responses. Repeated listening the recordings ensured transcription accuracy and helped uncover additional themes that might have been initially overlooked.
# Graphene-Based Materials for the Separator Functionalization of Lithium-Ion/Metal/Sulfur Batteries
## 2 Preparation Methods of Graphene-Based Materials  
Graphene, a two-dimensional carbon material arranged in a honeycomb structure by hybrid sp2 C atoms, has attracted significant attention from researchers for its unique properties. Since the proposal of micromechanical cleavage in 2004 [12], a multitude of methods for graphene preparation have been reported, the most common being methods such as CVD, pyrolysis, exfoliation, etc. Here, we will focus on some representative strategies that have been widely applied in laboratory research and industrial production.
# Comparison of Thermal Response for RF Exposure in Human and Rat Models
## 3 Computational Results
### Thermal Time Constant in Rat and Human Head Models  
Figure 6 shows typical examples of the time evolution of temperature elevation in the rat and human models. The frequency was chosen as 10 GHz, and TARO was used for the human model. As shown in Figure 6a, the heat conduction time (\(\tau_{D}\) in Equation (9)) was not necessary to describe the temperature elevation in the skin of rat or human. The time evolution of temperature in the skin was expressed as a primary delay function. As shown in Figure 6b, the heat conduction time in the rat brain was marginal, whereas that in the human brain was significant. The thermal time constant in the human brain was expressed as \(\tau+\tau_{D}\) for comparison with the rat in the following discussion.  
Figure 7 displays a comparison of the thermal time constant of the skin and brain in the rat and human models. The thermal time constant was computed at the positions where the peak temperature elevation appeared. The thermal time constant nonlinearly decreased with increasing exposure intensity. As shown in Figure 7a-c, the thermal time constant of the skin decreased with increasing frequency in both rat and human models. The thermal time constant also increased with increasing distance between the antenna and the skin surface. The thermal time constant in the human skin was 1.5 times higher than that in the rat skin at \(d=5\) mm.  
The thermal time constants of skin in the rat and human models were 3 and 6 min, respectively. The time required to reach the thermal steady state was estimated as 6 and 12 min in rat and human models, respectively (2\(\tau\) in Equation (9), which is the time to reach 86% of steady-state temperature).  
As shown in Figure 7d-f, the thermal time constant in the rat brain had similar tendencies to rat skin. One noteworthy point is that the time constant in the human brain was almost frequency-independent. The thermal time constant in the brain of the human was approximately two to four times larger than that in the rat brain at the same antenna-head distance. The maximum thermal time constant of rat brain tissue was 3.5 min at 3 GHz, and that of the human brain was 10 min at 10 GHz for \(d=25\) mm. The time required to reach the thermal steady state was 7 min and 20 min (or more) in rat and human models, respectively.  
Figure 6: Time evolution of temperature elevation in the (**a**) rat and (**b**) TARO models at 10 GHz. The SAR averaged over 10-g tissue was adjusted to 2 W/kg. The distances from the antenna to the head were (**a**) 5 mm and (**b**) 25 mm. The dotted line in (**b**) shows the regression curve from Equation (9).
## 2 State of the Art
### Prior Comparisons of FDM and SLA
#### 2.1.2 Mechanical Properties  
In terms of tensile strength, flexural properties, and elongation at break, FDM-printed parts frequently exhibit anisotropic behaviors due to layer interfaces [4; 15]. The vertical build direction can be particularly prone to delamination if process parameters or infill strategies are not optimized [6]. SLA parts are often more isotropic but can be brittle under impact loads, especially when resins are not formulated for toughness [16]. Although many works address FDM or SLA mechanics separately, side-by-side comparisons under uniform geometry and testing protocols are still relatively scarce.  
Additionally, AM technologies influence tribological properties, such as surface friction and wear resistance. The inherent roughness and porosity of FDM prints lead to higher friction coefficients, whereas SLA parts exhibit lower wear rates due to their smoother finish and superior layer bonding. Recent research highlights the role of surface wetting properties in determining lubricity, with SLA showing more hydrophobic behavior, leading to better fluid retention and reduced friction under lubricated conditions [17]. These differences should be considered for applications requiring low friction or wear resistance, such as biomedical implants or sliding components.
# Quantitative Assessment Methods of Early Enamel Caries with Optical Coherence Tomography: A Review
## 4 Quantitative Assessment Methods of Early Enamel Caries with OCT
### Quantitative Assessment Based on Degree of Polarization  
Demineralized enamel results in rapid depolarization of polarized light in the NIR due to increased scattering [72], which has been confirmed by PS-OCT measurements [73]. Polarization imaging can provide higher contrast images of early enamel caries. Since OCT is an interferometric imaging method, only the contribution of fully polarized light can be measured. Thus, the degree of polarization within a single speckle is always equal to 1. However, when depolarized, the polarization state of the adjacent speckles is uncorrelated. Therefore, the degree of polarization uniformity (DOPU) has been proposed to assess various lesions [74]. DOPU can be derived by an averaging of Stokes vectors over adjacent speckles, as follows:  
\[\text{DOPU}=\sqrt{Q_{mem}^{2}+U_{mem}^{2}+V_{mem}^{2}} \tag{3}\]  
where \(Q_{mem}\), \(U_{mem}\) and \(V_{mem}\) are the mean values of the Stokes vector elements within a certain evaluation kernel. It can be seen that the value of DOPU depends on the number of speckles in the chosen kernel.  
The combination of the DOPU algorithm and PS-OCT was first applied to detect carious lesions by [PERSON] et al. [74], and has been used for ophthalmologic research [75]. They measured three tooth samples with different proximal lesions, and the significant DOPU contrast provided better identification of lesions in comparison with reflectivity images. Furthermore, the effect of different DOPU evaluation kernel sizes on the resulting contrast was investigated. In a following study, they improved the DOPU algorithm by noise-immune processing, and adopted this approach to examine two tooth samples with stains and occlusal lesions [76]. Then, the research group measured the DOPU of bovine enamel at different stages of demineralization by using PS-OCT, and compared it with lesion depth obtained from PLM measurements [77]. The results showed that there was no depolarization in sound enamel, but an increased depolarization after 15 days of demineralization, corresponding to a decrease in DOPU. There was a high linear correlation (\(R^{2}=0.7118\)) between the DOPU and measured lesion depth with PLM, as shown in Figure 2. The summary of the above research results is shown in Table 4.  
While the above results indicate the feasibility of assessing the demineralization stage by DOPU, there is a need to investigate the validity of DOPU at various polarization changes and the correlation between DOPU and mineral loss measured by  
Figure 2: Correlation between the calculated mean DOP by PS-OCT and determined lesion depths by PLM. This figure was adapted from [77].
# Blockchain and Self Sovereign Identity to Support Quality in the Food Supply Chain
## 3 The Proposed SSI System
### On-Chain Subsystem
The solidity code implemented for the _CertificationVerifier.sol_ smart contract to verify the validity of a certification is shown in Figure 4.  
Figure 3: Solidity smart contract CertificationRegistry. contractCertificationVerifier{ //Claimholder_identityistheaddresscertificationRegistryContract functioncheckCertification(CertificationRegistry_registry,string memory_IPFShash) publicview returns(boolclaimValid) { addressholder; addressinsuer; bytesmemorysignature; stringmemorydataState; stringmemorycertificationName; if(certificationIsValid(_registry,_IPFShash)) //Fetchcertificationfromregistry (holder,issuer,signature,dataState,certificationName)= -registry.getcertification(_IPFShash); if(kececak256(abi.encodePacked(dataState))==kececak256(abi. encodePacked(\"Active\")) returntrue; }else(returnfalse;) } functioncertificationIsValid(CertificationRegistry_registry,string memory_IPFShash) internalview returns(boolcertificationValid) { addressholder; addressissuer; bytesmemorysignature; stringmemorydataState; stringmemorycertificationName; //Fetchcertificationfromregistry (holder,issuer,signature,dataState,certificationName)= _registry.getCertification(_IPFShash); bytes32 dataHash=kececak256(abi.encodePacked(_IPFShash,dataState, certificationName)); bytes32 prefixDHash=kecak256(abi.encodePacked(\")x19 Ethereum SigedMessage:n32\", dataHash)); //Recoverdnessofdatalsigner addressrecovered=getRecoveredAddress(signature,prefixedHash); if(recovered==issuer) returntrue; }else(returnfalse;) } functiongetRecoveredAddress(bytesmemorysig,bytes32 dataHash) internalpure returns(addressaddr) { bytes32 ra; uint8v; //Checkthesignaturelength if(sig_length!=66) { returnaddress(0); } //Dividethesignatureinr,sandvvariables assembly{ ra:=mload(add(sig,32)) sa:=mload(add(sig,64)) va:=byte(0,mload(add(sig,96))) } if(va<27) { va+=27; } addressrecoveredAddress=ecroever(dataHash,va,ra,sa); return(recoveredAddress); } } ```  
Figure 4: Solidity smart contract CertificationVerifier. Valid() and getRecoveredAddress() functions implement the logic to retrieve the issuer's address used to sign a given certification.
# Swin Transformer Assisted Prior Attention Network for Medical Image Segmentation
## 2 State of the Art and Related Work
### Transformer Complements CNNs  
In the last few years, researchers have attempted to apply self-attention mechanism to complete CNN in order to further improve the generalization ability of the network [24]. [PERSON] et al. [23] proposed to append the skip connections with an additive attention gate to perform accurate segmentation of medical images. Specially, PANet [4] reaches competitive segmentation performance with significantly lower additional computational cost by combining the two phases of a cascaded network via a spatial attention mechanism, enabling it much easier to train and apply to production applications. It also introduces a spatial attention-based strategy that integrates the attention of lesion sites with the features, allowing for effective synchronous training of multiple modules in the network, and incorporates intermediate supervision strategy into the proposed network to improve the convergence and performance of network. Compared with the cascaded U-Net, the proposed Prior Attention Network (PANet) achieves competitive results with substantially lower computational cost for 2D segmentation. Nevertheless, it is essentially still based on the backbone of convolution neural network that belongs to CNN-based method. Currently, TransUNet is proposed to utilize an integrated CNN-Transformer architecture to take advantage of rich spatial information from CNN and global semantic information encoded by Transformers. Similar to TransUNet [2], Transfuse [33], MedT [34], Swin-Unet [3], and UTnet [35] use the complementarity of CNN and Transformer to improve the feature extraction and generalization ability of the model. The results show that this hybrid architecture has superior performance. Various combinations of CNN and Transformer are applied in 3D medical image segmentation such as multi-modal brain tumor segmentation [36].