esa-sceva/satcom-chunk-collection huggingface dataset:
# Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in Complex Fire Scenarios
## 3 Methods
### Light-BiFPN Neck  
This section was inspired by the following articles: MobileNet, ShuffleNet [15; 16], EfficientDet [17], GhostNet [18], and PP-LCNet [19]. We designed a lightweight neck network that we termed Light-BiFPN (Light Bidirectional Feature Pyramid Network).  
In the practice of fire detection, where speed and accuracy are equally important factors, we observed that there was a minor difference in the accuracy between depth-wise separable convolution (DSC) and ghost convolution. DSC can reduce the number of parameters and calculations to a greater extent. However, DSC also has the disadvantage that the channel information of the input image is separated during the computing process. To solve this problem, we improved the DSC block in [19] by channel shuffling the DSC output features. We termed the improved the DSSConv module, whose structure is presented in Figure 3b, where the depth-separable convolution consists of depth and point convolutions and the input of a \(H\times W\times C\) feature map \(P\); the depth-wise convolution with one filter per input channel can be described as  
\[G_{k,J,m}=\sum_{i,j}K_{i,j,m}\cdot P_{k+i-1,J+j-1,m} \tag{8}\]  
where \(K\) is the depth-wise convolutional kernel with size \(H_{k}\times W_{k}\times C\), where the \(m_{\text{th}}\) filter \(K\) is applied to the \(m_{\text{th}}\) channel \(P\) to produce the \(m_{\text{th}}\) channel of the filtered output feature map \(G\). Then, the new features are generated by a \(1\times 1\) point convolution. The calculation process is presented in Figure 3a.  
Figure 2: The overall structure of the SepViT Block. MLP represents Multi-Layer Perceptron. Sep-Att represents Separable attention.  
We designed DSSbottleneck and DSSC3 based on the bottleneck and C3 modules of YOLOv5, whose structures are presented in Figure 4a,b.  
YOLOv5 uses PANET [20] on the neck to perform feature extraction and fusion. It uses bottom-up and top-down bidirectional fusion methods and achieves good results. However, the environment of fire detection is usually too complex, and more features need to be fused to achieve better results. BiFPN is a weighted bidirectional feature pyramid network that connects input and output nodes of the same layer across other layers to achieve higher-level fusion and shorten the information transfer path between higher and lower layers. Since weighting causes an inevitable computational increase, we removed the weighted feature fusion to create a more lightweight neck network.
## 5 Results and Discussion
### Diagnosis Inference  
In FBNs, diagnosis inference is a valuable technique for determining posterior probabilities based on new, updated information. When new evidence is available, it can be applied to the model to trigger the IEs and root causes to determine the effect on the entire network. It begins by assessing the impact of the evidence provided for the power grid failure node on its parent nodes. It is possible to propagate the impact of such evidence backward and forward to determine which parent nodes affect the likelihood of TE. In this study, the evidence has been propagated--a confirmed power grid shutdown (assuming a failure probability of 100%)--illustrating the FBN model with the failure evidence inserted. There was a significant increase in the probable occurrence of environmental factors from a baseline probability of 11% to 59%, and the significant BE fire risk increased from 3% to 35%. Similarly, the posterior probabilities of equipment conditions decreased from 36% to 22% with BE circuit breakers, generators, and transmission lines, with new probabilities of 19%, 20%, and 24%, respectively. The results from the diagnosis inference are shown in Figure 11, with a TE failure probability of 100%.  
The results indicate a significant increase in the likelihood of environmental factors and equipment conditions for the parent nodes. The baseline probabilities of 11% and 36% for these variables increased to 56% and 44%, respectively. The posterior probabilities primarily affected two parent nodes: storms and transmission lines. This highlights these nodes as the most influential factors, emphasizing the need to implement mitigation strategies to address external influences in power grid risk assessment.  
Figure 10: (**a**) Sensitivity analysis to identify the most critical variables, and (**b**) sensitivity tornado diagram.
# Thinger.io: An Open Source Platform for Deploying Data Fusion Applications in IoT Environments
## 3 Thinger.io Overview
### Research Efforts at Thinger.io
#### 3.1.2 Real-Time Bidirectional Communication  
HTTP-based platforms can be easy to develop, deploy, and to maintain, as they operate as regular HTTP servers receiving data from sensors using well-known technology. However, such approaches cannot provide an efficient mechanism for a bidirectional communication, i.e., when it is necessary to actuate over a device, or configure it in real time. HTTP-based platforms rely on a polling mechanism where the devices check the server periodically for new commands or updates. Meanwhile, Thinger.io provides a bidirectional communication channel between the device and the cloud server, so any application can interact with the device in real time, increasing the number of use cases that can be developed with the platform.  
The other solutions based on MQTT also provide bidirectional communication between servers and devices. However, this protocol is only focused on the transport layer, allowing different endpoints to communicate, but does not define a language for modeling information like the proposed protocol in this paper; does not provide an efficient encoding schema like Protoson; or does not allow an easy interoperability with third party applications and services, i.e, over a REST API to query information in real time from the device.  
Thinger.io addresses all these issues, as the underlying protocols support bidirectional communication, with an efficient encoding schema, and moreover, it also solves the interoperability of the devices with other applications, as described in the following subsection.
# Advancements for Snowmelt Monitoring by Means of Sentinel-1 SAR
## 4 Sentinel-1 Snow Maps of Iceland and European Alps  
Figures 6 and 7 show snowmelt maps over large areas on different dates. The pixel size is 100 m, providing better spatial resolution than the snow maps derived from medium resolution optical sensors (e.g., MODIS [36]). In case of overlapping tracks, we select the pixel with the local incidence angle closest to \(\theta=45\ ^{\circ}\), in order to fetch the angle with the better separability between the snow and snow-free classes.  
Figure 6: Map of snowmelt area extent in Iceland, derived from Sentinel-1 data of 17, 19 and 22 May 2015 (Date 1) and 10, 12, and 15 June 2015 (Date 2). Light blue: decrease of snow extent between Date 1 and Date 2. Dark blue: snowmelt area on Date 2. Light grey: dry snow on ice caps; white: water surfaces; orange: mask of layover and foreshortening. Background: S1 SAR amplitude image, \(VV\) polarizations.  
For Iceland (Figure 6), snow maps posted in 100 m UTM were compiled for two epochs: (1) based on S1 IW images of 17 May (track 118), 19 May (track 147), 22 May 2015 (track 016); (2) based on S1 IW images of 10 June, 12 June and 15 June 2015 (same tracks as in May). Between the two epochs, the snowline ascended from about 600 m to 800 m in elevation. Above about 1600 m altitude, the accumulation areas on the ice caps Vatnajokull and Hofsjokull were still dry in the June images. The dry snow areas show little temporal variability in backscatter and therefore can be well identified in the ratio images.  
Snow maps over the Alps (Figure 7) are shown for three epochs based on S1 IW images of: (1) 20, 21, 22 and 23 April 2015; (2) 14, 15, 16 and 17 May 2015; (3) 1, 2, 3 and 4 July 2015. The track numbers are 66, 88, 95 and 117. The digital radar-based map, displayed in Figure 7, comprises 43 million pixels of about 100 m size in geographic map projection. The SLC database used for compiling the snow map of a single track comprises about 20 GB, for the time series including reference images about 100 GB. On date 1 (20-23 April), melting snow surfaces extended up to 3000 m altitude, and on date 2, (14-17 May), to 3500 altitude. In the combined snow map, the dry snow areas are superposed by melting snow areas of later dates.
# Why Do Tethered-Bilayer Lipid Membranes Suit for Functional Membrane Protein Reincorporation?
## 1 Introduction
Ideally, MPs should be reconstituted in natural lipid extracts as it is now well-known that lipids in the immediate vicinity of membrane proteins influence their activity [13; 15; 17]. As a result, there is a great need to develop biomimetic membrane platforms, in which, not only one but several purified membrane lipid components can be used for in vitro reconstitution, and in which reincorporated membrane proteins can retain their structural integrity and functional activity. Different types of models have been developed through the years to mimic cell membranes as well as possible and reproduce the basic functions of cell membranes. These models are solid-supported lipid membranes [18; 19], polymer-cushioned membranes [20; 21; 22], hybrid lipid bilayers [23; 24; 25], free-standing lipid layers or suspended-lipid bilayers [26; 27; 28] and tethered-bilayer lipid membranes or tBLMs [29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39]. All these models are suitable for systematic studies of different types of membrane-related processes and provide the lipid environment required for the study of membrane-associated proteins. They correspond to models of planar membranes confined to a solid support and localized at the bulk interface, allowing the application of a manifold of surface-sensitive techniques for their own characterization [40] or biosensing applications [41; 42; 43]. Besides all these advantages, tBLMs appear as very attractive platforms for the reinsertion of transmembrane proteins. Because they are lifted from the support, they best mimic the cellular environment, and transmembrane proteins with protrudant domains extending outside the membrane can \"comfortably\" reinsert into the bilayer without steric hindrance or loss of mobility due to a close contact of the membrane with the support [44]. The current review focuses on the design of tBLMs suitable for reinsertion and characterization of MPs. After a brief overview of all the techniques of characterization, we will describe the different models of tBLMs including polymer-tBLMs, anchorlipid-tBLMs, peptides-tBLMs, and other modes of attachment like avidin-biotin systems. Then, we will focus on the attached-protein-tBLMs, which allow the protein to be oriented in a lipid bilayer, to finally finish on the advantages of cell-free expression for reinsertion of membrane proteins before or after formation of tBLMs.
## 1 Introduction  
The theoretical basis for determining non-conducting bodies in seawater is the problem of diffraction of the field of a grounded cable on a conductive elongated spheroid [1]. The elongated spheroid is a model of a swimmer in a light diving suit. The excitation of the primary electromagnetic field in seawater is carried out by an electrode antenna (EA) in the form of a grounded cable, the electric field of which is determined by free charges on metal electrodes, compared to which the field from the connecting cable can be neglected [2]. The field of a grounded cable excites a secondary field on a dielectric spheroid, which can be represented by the fields of two equivalent dipoles, one of which is oriented along the large semi-axis of the spheroid, and the second along the small semi-axis of the spheroid [1].  
In shallow water conditions, seawater forms a conductive layer bound by the seabed and atmosphere. The problem of a horizontal electric dipole placed over a semi-conducting space in the air has been solved [3, 4]. In our case, the field in the conducting layer is excited by an electrode antenna located inside the conducting layer. The problem of the diffraction of an electromagnetic field on an elongated dielectric spheroid, in this case, should take into account the distortion of the primary field of the EA and the secondary field of the spheroid by a conductive layer. There is no ready-made solution for such a case.  
The purpose of this article is to solve the diffraction problem for an elongated non-conducting spheroid in the field of a grounded cable in a conductive layer, with an accuracy level acceptable for practice. This is achieved by strictly solving the problem of diffraction of a homogeneous field at a direct current on an elongated dielectric spheroid in free space, decomposing it into a Taylor series and representing the first dipole term of the expansion at alternating currents, taking into account the influence of the conducting layer.  
The authors were able to calculate the flow characteristics of an elongated dielectric spheroid in a conductive layer using real data and comparing it with the results of an experiment on a mock-up of a device protected by a patent [5].
# Optimization Techniques for a Distributed In-Memory Computing Platform by Leveraging SSD
## 4 Experimental Results and Analysis
### TC Experiment Analysis  
Figure 7 shows the results of TC (transitive closure) experiments that use the input data involving 50,000 edges and 25,000 vertexes generated at random. The iteration number is 10. Through the iterations, the number of tasks are doubled at each iteration, and therefore, the size of the RDD increases and the amounts of shuffle read and write also increase. In the last iteration, the number of tasks becomes 4096. As there are more iteration stages, there is a larger effect on the total job execution time, and the last iteration stage is the biggest stage, consisting of many tasks that can lower the overall performance.  
Figure 6: The normalized PageRank job execution time for 1 GB dataset.  
As we can see from Figure 7, the performance improves in the order of \(3\), \(2\), and \(1\) on the M, M&S, and S options, which means that obtaining a sufficient shuffle memory of the JVM heap is helpful. With the M option, the performance of option _1 is 18% faster than option _3, whereas in the M&S option, the performance of option _1 is 3% faster than _3. In the S option, the performance of _1 is 2% faster than _3._  
When we focus on changing the RDD caching option, the performance of option S_1 is 42% faster than N_1, and it is also 31% faster than M_1. The reason for the performance gain of job execution time is dependent on the last iteration stage. The key factor affecting the last iteration stage is the shuffle read blocked time. Shuffle read blocked time occurs when the RDD executed in the previous stage is read from another worker node through the network because of the lack of executor memory. Even if each task has a performance gain of about 1-2 s through solving the shuffle read blocked time, we can achieve a significant performance gain because in the last state, the number of tasks is quite large (i.e., 4096). In addition, one of the main factors affecting job execution time is the count stage, which counts how many edges the TC matrix has at the last job. With option N, because there are no RDDs cached in the count stage, Spark reads the shuffle data executed from previous stage, which takes 60 s. In addition, in option M, the RDD is not cached on memory because of the lack of the executor memory. As a result, it also takes 60 s. However, in the M&S and S options, the RDD can be cached on the memory and SSD, so that it takes only 2 s in the count stage.
# Self-Organized PT-Symmetry of Exciton-Polariton Condensate in a Double-Well Potential
## 2 The Exciton-Polariton System
In the upper panels of Figure 2, we show the polariton populations \(|\psi_{L,R}|^{2}\) for different pumping rates and initial conditions, with and without non-linear interaction, as obtained from the numerical solution to Equations (1) and (2). The insets show the evolution of the exciton populations \(n_{L}\) and \(n_{R}\) and their sum \(n_{L}+n_{R}\). For pumping below threshold, we observe a decay in the initial (seed) polariton populations with rate \(\gamma_{L}+\gamma_{R}\simeq\frac{R}{\Gamma}(P_{L}+P_{R})-\kappa<0\), accompanied by Rabi-like oscillations, while the exciton populations settle to \(n_{L,R}=P_{L,R}/\Gamma\). For pumping above threshold, the polariton populations grow until they reach certain values \(|\psi_{L,R}|^{2}\) at which \(n_{L}+n_{R}\simeq\frac{2\kappa}{R}\), while the Rabi-like oscillations persist or are eventually damped, depending on the initial conditions or presence of non-linear interaction, as discussed below. Remarkably, the polariton and exciton populations increase and decrease, respectively, reaching the same stationary values which satisfy the PT-symmetry conditions, independently of the pumping rates, as long as pumping is retained above threshold.
# Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition
## 2 Machine Learning Techniques for Time Series Analysis
### Transformer Models  
Though initially designed for natural language processing tasks, Transformers have demonstrated remarkable effectiveness in time series analysis. These models utilize self-attention mechanisms to capture relationships across temporal data points, providing significant advantages over traditional recurrent neural networks (RNNs) in scenarios requiring the modeling of long-range dependencies. Recent research by [PERSON] [26] documented the superior performance of Transformer architectures in traffic flow forecasting applications. In the financial sector, [PERSON] (2025) [27] has adapted these models to capture complex temporal patterns in stock price movements, further validating their versatility across domains. The key innovation behind Transformers' success lies in their self-attention mechanism, which allows the model to assign variable importance to different time steps when generating predictions. This capability proves particularly valuable for time series data where relevant information may be distributed across distant temporal points. Unlike RNNs, which process sequential information step by step, Transformers can directly model relationships between any points in a sequence, regardless of their distance from each other. This architectural advantage enables Transformers to identify subtle patterns and correlations within time series data that might otherwise remain undetected, making them increasingly valuable tools for researchers and practitioners working with temporal datasets across various scientific and industrial applications.
## 4 Results: State-of-the-Art Approaches
### Cybersecurity in IoUT
Thus, organizations protect their real systems from external threats, by attracting adversaries and determining their strategy [91]. Honeypots are usually placed near the assets they are attempting to mimic. An interesting survey for honeypots in IoT is presented in [92], presenting a basic honeypot architecture, known as honeynet. Honeypots and honeynets can be classified in various categories based on their purpose, role, level of interaction, scalability, etc. This method is applicable in IoT and can be integrated by IoUT in several ways. [PERSON] et al. [91] present an interesting approach of an adaptive honeypot concept which, through a Q-learning method, supports the functionality of firewalls, and other security mechanisms for severity analysis. Furthermore, in [93], dynamic interaction of a honeynet through reinforcement learning is proposed, resulting in risk analysis and adaptive security policies for effective automated decisions related to risk, cost, and time factors. Even though security mechanisms become constantly more sophisticated, the main challenge of cyber warfare remains, i.e., the precedence of cyber adversaries. Every protective asset is vulnerable to be compromised or avoided. Therefore, the probability factor should be a principal component in defending strategies. Issue of uncertainty in both domains, cybersecurity and IoUT, can be taken as an advantage, by incorporating semantic knowledge and reasoners. Strong interoperability between underwater assets, such as UUVs and nodes, in combination with a robust security system can give early alerts about potential exploitation.
## 2 Materials and Methods
### Procedures  
The study consisted of a clinical evaluation (at the Geriatric Unit of AOU Parma) in which all participants were screened for the presence/absence of visual or hearing impairment, health equipment use, drugs use, fall history; then, patients completed, as the clinical pathway requested, tests and a questionnaires battery composed of Mini Mental State Examination (MMSE), Geriatric Depression Scale (GDS), Short Physical Performance Battery (SPPB), Hand Grip Test, Physical Activity Scale for the Elderly (PASE), Fall Efficacy Scale (FES), Dual-TASK Gait performance, Mini Nutritional Assessment (MNA-SF).  
On a different day, gait and balance performance were evaluated at the Movement Analysis Laboratory (Parma). Also performed was a bioelectrical impedance analysis (BIA) to obtain the body mass index (BMI), percentage of fat mass and fat free mass.  
The static condition was evaluated with a stabilometric platform (PoDataTM 2.0). Participants stood on the platform barefoot, arm at their side, for about 30 s, with open eyes (OE), followed by the same analysis with closed eyes (CE). During the analyses, the Global Postural System (GPS) recorded the movement of the centre of pressure (CoP) and the related parameters: path length (mm), sway area (mm\({}^{2}\)), and maximum path velocity (mm/s).  
Gait performance was then evaluated with the G-Sensor (G-Sensor(r), BTS Bioengineering s.p.a, Milan, Italy). Subjects wearing the wireless device at L5-S1 were asked to walk on a ten-metre-long path at a self-selected speed while performing the 10 m walking test (10 MWT), six-minute walking test (6 MWT), and the timed up and go (TUG) test [52; 53]. During the tests the Ground Reaction Force (GRF) was recorded while participants walked on the force platforms (BTS-P6000). A rest period of 10 min among tests was considered. Data collected were analysed with the BTS G-Studio software (BTS Bioengineering G-Studio(r), Milan, Italy).  
After 12 months, participants were interviewed by phone in order to check for fall events (number of falls with fractures or other consequences, hospitalization). Thus, patients were classified into 4 groups: the Fall group included subjects who experienced at least one fall event in the last year, in contrast with the No Fall group in which subjects did not experience fall events in the past twelve months; frail people were included in the Frail group for subjects who met the criteria to identify frailty according to [PERSON] et al. [22]--at least three criteria satisfied among involuntary weight loss in the last year, muscular strength decrease (evaluated with the hand grip test), weakness, physical inactivity (low PASE score)--in contrast with the Able group that identified participants with higher scores and an overall better performance.