esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Furthermore, in the presence of packet losses, one successfully received probe per end-to-end path is sufficient (there is no need for extensive packet loss statistics), leading to rapid topology discovery. Compared with the traditional methods that are based on measuring monotonic properties of shared paths that do not decrease as the number of traversed links increases (e.g., loss rates, mean delay, delay covariance, link utilization, etc. ), the number of required probes under network coding is significantly smaller. Additionally, the complexity of the local additions and multiplications at the node level is not particularly high. _Compressed sensing_ (CS) is a signal-processing technique for efficiently acquiring and reconstructing a signal, which has demonstrated a wide applicability in communications systems [5]. More precisely, it is a method to obtain a unique solution from an under-determined linear system (when the number of measurements is less than that of unknowns, the system is under determined and its solution is no longer unique) by taking advantage of the prior knowledge that the true solution is sparse [6]. A signal is said to be sparse (or exactly sparse), when most of its components are exactly equal to zero. It is said to be \(k\)-sparse, when it has at most \(k\) nonzero elements. The signals that can be well approximated by sparse signals are called compressible (approximately sparse or relatively sparse). The basic principle of compressed sensing is that, through \(\ell_{1}\)-optimization (i.e., minimization of the \(\ell_{1}\)-norm), the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Nyquist-Shannon sampling theorem [7]. The goal is to reconstruct a finite-dimensional sparse vector based on its linear measurements of dimension smaller than the size of the unknown sparse vector. The application of compressed sensing in network tomography stems from the observation that, usually in typical networks, only a limited number of links exhibit large delays or high loss rates. If the focus is on the identification of those bottleneck links, one can reasonably assume that the vector of the unknown parameters of interest will be sparse (or in other words, compressible). In this article, we explore in more detail network coding and compressed sensing as enabling technologies that can be applied in tomographic approaches from the whole range of network tomography taxonomy, with the goal of improving the estimation accuracy, the time and computational complexity, the probing and operational cost, as well as facilitating link identifiability. The rest of this review is organized as follows. Section 2 presents the fundamental concepts of network tomography and some interesting classifications of relevant approaches. Section 3 presents approaches in the class of network coding, while Section 4 presents works belonging in the class of compressed sensing. Section 5 provides some overall qualitative discussion on the presented techniques, and finally, Section 6 concludes the paper.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 6 Network Data Analytics Function (NWDAF)  
The Network Data Analytics Function (NWDAF) was introduced in 3 GPP _Rel-15_[22] as a new network function of the 5G core architecture. It was subsequently enhanced and detailed in 3 GPP _Rel-16_ and _Rel-17_, including some early use cases. Although the idea to ingest analytics to the mobile core network is not new, it lacked standardization that would have enabled better adoption. It was previously done through a custom approach driven by specific vendor implementations. The 3 GPP NWDAF is an attempt to standardize the way to do analytics in the 5G core network as well as leverage machine learning operations (MLOps) best practices. The NWDAF can interface with various control and data plane network functions to collect events that are of interest for analysis to monitor, for example, compliance with relevant service-level agreements (SLAs), or to intelligently optimize the network resources. The NWDAF includes one or more of the following functionalities:  
* Support data collection from network functions (NFs) and analytical functions (AFs),
* Support analytics information provisioning to NFs and AFs,
* Support machine learning (ML) model training and provisioning to NWDAFs (containing analytics logical function).  
The architecture of NWDAF is defined in the technical document (_TS 23.288_) [23] and the detailed specification with APIs, etc., is defined in the technical document (_TS 29.520_) [24], whereas the NWDAF use case is defined in the technical document (_TS 23.791_) [25]. The NWDAF is expected to have a distributed architecture providing analytics at the edge in real-time and a central function for analytics that need central aggregation (e.g., service experience). Therefore, the implementation of NWDAF should happen in the SA-based 5G with its service-based architecture. The NWDAF collects data and provides analytics services using a request or subscription model. It means, any consumer NF, such as PCF, AMF, or NSSF, should first subscribe to the analytics service with agreed data inputs from the consumer and return analytics or ML model inference results. The types of observed events by NWDAF include:  
* Network slice instance load level information,
* Service experience,
* Network Function (NF) load,
* Network performance,
* Abnormal behavior,
* UE mobility and UE communication,
* User data congestion, and
* QoS sustainability.  
The above events give an idea of how NWDAF can be powerful to advance the network automation that is needed for complex use cases such as autonomous driving. An accurate prediction of the network slice can be translated into a proactive adjustment for the network slice and optimized resource allocation.
# Enhanced LDM for Next-Generation Digital Broadcasting Transmission
## 3 Proposed En-LDM Scheme
### Channel Capacity Analysis
#### 3.2.2 Analysis for En-LDM  
In the En-LDM system, mobile services and fixed services removed from puncturing are combined based on frequency division multiplexing and transmitted through the CL of LDM, where the capacity allocation of each service is linearly proportional to the spectrum allocation [29]. The EL of LDM only transmits the remaining fixed service of puncturing. Therefore, the channel capacity for the transmission of mobile services and fixed services can be calculated as  
\[\begin{array}{l}C_{M}^{En-LDM}=C_{U}\cdot\frac{B_{m}}{B_{t}}\\ C_{F}^{En-LDM}=C_{L}+C_{U}\cdot\frac{B_{f}}{B_{t}}\end{array} \tag{13}\]where \(B_{t}\) is the bandwidth occupied by a frame of signal, \(B_{m}\) and \(B_{f}\) are the bandwidth of CL to transmit mobile services and fixed services removed by puncturing, respectively, and \(B_{t}=B_{m}+B_{f}\).  
Therefore, by substituting Equations (10) and (11) into Equation (13), the Shannon limit for the two services of the En-LDM system are calculated as  
\[\begin{split} C_{M}^{En-LDM}&=\log_{2}\Big{(}1+ \frac{10^{\alpha/10}}{1+N\cdot(1+10^{\alpha/10})}\Big{)}\cdot\frac{B_{m}}{B_{t }}\\ C_{F}^{En-LDM}&=\log_{2}\Big{(}1+\frac{1}{N\cdot(1+ 10^{\alpha/10})}\Big{)}+\log_{2}\Big{(}1+\frac{10^{\alpha/10}}{1+N\cdot(1+10^ {\alpha/10})}\Big{)}\cdot\frac{B_{f}}{B_{t}}\end{split} \tag{14}\]  
Equation (14) shows that the distribution of each service capacity in the En-LDM system is only controlled by the signal injection level and the spectrum allocation ratio. Since the power distribution calculation involves the logarithmic calculation of Equation (11), the capacity is allocated for the mobile service and fixed service in a non-linear manner.
# Modeling Actual Evapotranspirationiration with MSI-Sentinel Images and Machine Learning Algorithms
## 2 Materials and Methods
### Training and Statistical Evaluation of Models  
#### 2.5.1 Production and Selection of Predictor Variables  
By aiming to increase the number of predictor variables, the NRPB (normalized ratio procedure between bands) technique was applied to the primary predictor variables in approaches 1 and 2. The NRPB normalized all primary variables, therefore increasing the explanatory variable number for the models (Equation (8)). Hence, the number of variables produced on each approach was higher than the primaries, especially in approach 2, which has the biggest variable number.  
\[\text{NRPB}=\frac{\rho_{\text{x}}-\rho_{\text{y}}}{\rho_{\text{x}}+\rho_{\text {y}}} \tag{8}\]  
where \(\rho_{\text{x}}\) and \(\rho_{\text{y}}\) correspond to the surface reflectance of the wavelengths of the MSI sensor.  
The NRPB process was performed using the band ratio function of the labgeo package [29] for the R language, successfully applied by [PERSON] [14; 30].  
A two-step selection was applied to the NPRB variables to select the most important model variables. The first step removed correlated variables. Explanatory variables with a correlation above 95% were removed to decrease information redundancy. The second step removed the least important variables. Therefore, the recursive feature elimination algorithm (RFE) was applied through the caret package [31] in the R language. This algorithm removes the least important predictor variables from the model by building a base model with all predictors. Then, the importance of each predictor for the base model was calculated, and subsequently, the algorithm classifies these predictors and removes the least important ones, leaving a minor number of variables for the training steps. This is a key process, as it reduces unnecessary variables, aiding both training steps and future applications.
# Nanomechanical Characterization of Bacterial Polyhydroxyalkanoates Using Atomic Force Microscopy
## 2 Materials and Methods
### Methods
#### 2.2.4 Gel Permeation Chromatography (GPC)  
Molecular weight measurements on extracted and fractionated PHA samples were performed using a Waters 510 Gel Permeation Chromatography (GPC) apparatus working in tetrahydrofuran (THF) at 40 \({}^{\circ}\)C, equipped with a set of Ultrastyragel(r) columns and a Waters 410 differential diffractometer as a detector. The calibration was performed with monodisperse fractions of polystyrene.
## 5 Conclusions  
This paper mainly focused on the permeability evaluation of clay-quartz mixtures based on NMR tests and fractal analysis. The main conclusions that can be drawn are as follows:  
1. The \(T_{2}\) spectra of M-Q mixtures and K-Q mixtures are quite different. The \(T_{2p}\) of M-Q mixtures and K-Q mixtures ranged from 1.20 to 2.25 ms and 7.84 to 11.89 ms, respectively. The \(T_{2p}\) can be used to determine the kind of clay mineral in the clay-quartz mixture;
2. During evaporation, all the \(T_{2}\) spectra of clay-quartz mixtures shifted to the left. This indicated that shrinkage of the mixture volume led to a decrease of the total pore radius;
3. According to the decay rate of the \(T_{2}\) total signal amplitude, the evaporation process can be divided into two stages: the constant rate stage and the falling rate stage. The \(T_{2}\) cut-offs can be determined from the \(T_{2}\) spectra of the mixtures at the initial saturated state and the beginning of the falling rate stage;
4. A prediction model for permeability coefficients of clay-quartz mixtures based on the T-C model was established. The relationship between \(T_{2}\) cut-offs and fractal dimensions of \(T_{2}\) spectra of saturated mixtures was also proposed. Based on these, a simplified method for predicting permeability coefficients of clay-quartz mixtures by using only the \(T_{2}\) spectrum of saturated samples without centrifugal and evaporation tests was established. Compared with the traditional method, which is effort- and time-consuming, the new prediction method proposed in this study can determine the permeability coefficients of clay-quartz mixtures rapidly, nondestructively, and accurately.  
Conceptualization, Z.L., A.S., and W.W.; methodology, Z.L., A.S., and W.W.; software, Z.L., A.S., and W.W.; validation, Z.L. and W.W.; formal analysis, Z.L.; investigation, Z.L.; resources, Z.L. and A.S.; data curation, Z.L.; writing--original draft preparation, Z.L.; writing--review and editing, A.S. and W.W.; visualization, Z.L. and W.W.; supervision, A.S.; project administration, A.S.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2018 YFB1600200.  
The authors declare no conflicts of interest. There are no personal circumstances or interests that may be perceived as inappropriately influencing the representation or interpretation of the reported research results. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Discussion
### 3D Photogrammetry  
Photogrammetry is widely employed for its didactic purpose, especially in geological, geomorphological, or mineralogical studies [41] and further for the three-dimensional construction of aerial maps of extended areas, like archaeological sites [42]. Photogrammetry applied to cultural heritage has been limited due to the technical requirements because the process needs the use of professional photographic equipment and specific software for graphic elaboration. The acquisition of such equipment and software often requires significant financial efforts. The application of 3D photogrammetry using the image-based methodology enables the reproduction of all the samples with an acceptable accuracy due to the high quality of the photos captured. Nevertheless, it is recognised that photogrammetry based on laser scanning technology guarantees a higher spatial resolution and accuracy of 3D results than the image-based method [58]. 3D models may be utilised not only for the optical examination of the samples but also for their valorisation. Using Sketchfab--one of the most known and easy-to-use platforms designed for the download and interactive visualisation of 3D models [11]--researchers and archaeologists manage to study and share artefacts wherever they are working with their computers. Studying artefacts with digital platforms also prevents all the possible exogenous factors that may cause damage due to the manipulation and transport of archaeological findings [64]. Moreover, archaeological heritage valorisation may be carried out by the introduction of digital reproductions into the museum collections through the most common sharing technology, like QRcodes, or digital devices, such as tablets and monitors. On these supports, 3D models can be observed and interactively moved by the users which enhances the quality of the experience offered to visitors by museums [10; 12]. In addition, digital reproductions enable the display of artefacts that might be locked in a museum's deposit due to limited exhibition space, the artefact's fragility, and specific requirements for conservation [64]. In this way, 3D models serve also as a useful tool for cultural heritage conservation as they are the digital record of the condition of the object at a specific moment [64].  
Three-dimensional models of R1, R2, R3, R4, and R5 obtained in the present study faithfully reproduce the shapes and superficial details of the samples, as demonstrated by the observation of engravings on the BTV surface, which has not been individuated through mesoscopic analysis with the naked eye (see Figure 7b). However, the 3D model colour output usually does not maintain the original sample tone. For this reason, the colour correction of photos through the ColorChecker reference is considered an effective solution, as evidenced by the BTV example (see Figure 7b).
## 3 Methodology  
### Overview  
Missing information and under-reporting of crashes constitute key challenges in crash datasets across the globe, thus posing major limitations in traditional statistical analyses for accurately predicting crash injury severities [34; 35]. To overcome these hurdles, the decision tree approach can manage missing data, providing a foundation for modeling complex relationships within the dataset [36]. In this study, the decision tree CART model is suitable for capturing non-linear relationships between variables, making it adept at uncovering intricate patterns in crash severity data. Its adaptability in handling both numerical and categorical features aligns well with the variables present in the dataset that can serve as potential determinants of crash severity for young drivers [37]. To better handle the imbalance (in terms of injury severity) that is typically observed in crash data, the RUMC method is coupled with the CART model. The interpretability of decision trees plays a key role in understanding the nuanced factors contributing to crash severity within the demographic of young drivers. Figure 1 illustrates how the RUMC undersampling method operates by selectively picking instances from the overrepresented class and leaving them out of the training data to ensure an even distribution of classes. This approach is implemented to address the issue of data imbalance, which occurs when the distribution of classes in a dataset is skewed, leading to models that are biased toward the majority class. Undersampling is a technique used to balance the dataset by reducing the size of the majority class, ensuring that both classes have equal representation in the training data [38]. Figure 2 provides a flowchart outlining the steps followed in this study, detailing how both CART and RUMC-CART models were developed and implemented for the data analysis.  
Figure 1: Random undersampling of the majority class (RUMC).
## 4 Discussion  
The failure of available theory to predict \(R_{2}\) and \(R_{2}\)* values for large particle volume fraction (Figures 6-9) is not surprising since their derivation relied on the assumption of low particle volume fraction (See e.g., [23] or [26]). For low particle volume fraction, the magnetization of each particle (and hence its magnetic field) was assumed to be independent of the magnetic field of other particles i.e., only depends on \(H_{0}\) through \(M\sim\chi H_{0}\). This assumption is of course violated for the large particle volume fraction when the inter-particle spacing becomes very small and hence the magnetization of the particle becomes affected by the magnetic field of the adjacent particles. This situation is more relevant for smaller applied field (B\({}_{0}\)--i.e., the when the magnetic moment of the MNP has not reached saturation) and for large susceptibility (\(\chi\)). The second important factor influencing relaxation is the fact that increasing the particle volume fraction causes saturation of the magnetic field gradients. For low and high particle volume fraction within MAR the particle size arrangement is important since particles are still small and hence their overlapping magnetic fields are not fully saturated, while it is not for the large particles within SDR. For the second particle arrangement where particles are few and large (MAR--Figures 6B and 7B) the efficiency of averaging the magnetic fields improves with echo-time. Furthermore, increasing the volume fraction of particles seems to reduce \(R_{2}\)* (Figure 6B) and have smaller reduction on \(R_{2}\) for the same echo-time (Figure 7B). This may suggest that the distribution of the magnetic field gradient values has reached the saturation level. This point needs further investigation since there are now two competing factors: the efficiency of diffusion to average outfield inhomogeneities and field gradient distribution width. Similar analysis applies for \(R_{2}\)* SDR data shown in Figure 8B, however, \(R_{2}\)* values are almost double those of MAR data.  
For the \(R_{2}\) and \(R_{2}\)* SDR data (Figures 8 and 9) particle size arrangement is irrelevant since particle size is already large and the extent of its associated magnetic field is large for all sizes included in the range of 1040-3000 nm. The \(R_{2}\)* values (Figure 8A,B) show similar dependence on the echo-time as that for the fixed size MAR particles (Figure 6B) reaching saturation (for the two larger echo-times) at a smaller volume fraction value \(\sim\)0.05 compared to \(\sim\)0.1. Relaxation rates seem to reach the same values for echo-times greater than 8 ms.  
This analysis is important in order to understand relaxation mechanisms and predict relaxation rates under conditions present when nanoparticles cluster inside tissues or being trapped inside cells leading to large agglomerated particles and larger volume fraction [28; 29; 30; 31].
# Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features
## 3 Model Architecture
### Omni-Dimensional Dynamic Convolution Module  
For feature extraction of small-target, weak-feature, and multi-angle data, static convolution employs an invariant convolutional kernel to process all inputs, limiting its ability to adapt to variations in target size, orientation, or viewpoint. This may result in inadequate  
Figure 2: Image augmentation example.  
capture of fine local details. In few-shot learning scenarios, traditional CNNs struggle to learn effective features, leading to overfitting, underfitting, and poor generalization.  
Dynamic convolution addresses this by adjusting kernel parameters based on input characteristics, enabling adaptive processing of diverse target features. This adaptability is particularly valuable for small targets and weak features, allowing kernels to refine their focus. Thus, this study integrated a dynamic convolution module into the feature extraction network.  
Recent advances in dynamic convolution include Conditionally Parameterized Convolutions (CondConv) [37] and Dynamic Convolution (DyConv) [38], which primarily adjust the number of kernels. [PERSON] et al. [39] proposed Omni-Dimensional Dynamic Convolution (ODConv), which dynamically adapts spatial dimensions, input channels, output channels, and kernel counts, enhancing flexibility and expressiveness. The implementation is illustrated in Figure 3.  
The input data undergoes a global average pooling (GAP), which is followed by a fully connected (FC) layer and ReLU activation function for feature transformation [39]. A four-branch module then computes four types of attention \(\alpha_{si}\), \(\alpha_{ci}\), \(\alpha_{fi}\), and \(\alpha_{ui}\) in the kernel space \(W_{i}\). ODConv is defined as follows:  
\[y=\left(\alpha_{w1}\odot\alpha_{f1}\odot\alpha_{c1}\odot\alpha_{s1}\odot W_{1 }+\cdots+\alpha_{wn}\odot\alpha_{fn}\odot\alpha_{cn}\odot\alpha_{sn}\odot W_{ n}\right)*x. \tag{4}\]  
where \(\alpha_{si}\), \(\alpha_{ci}\), \(\alpha_{fi}\), and \(\alpha_{ui}\) represent the attention weights for spatial, input channel, output channel, and kernel space, respectively. The symbol \(\odot\) denotes multiplicative operations along kernel space, and * denotes convolution.