esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Related Work
### Overview of Related Studies  
The 3 GPP TR 38.874 [7] identified a number of challenges that need to be investigated:  
* _Topology management:_ Emphasis is placed on designing the network protocols and architecture, as well as the description of traffic control procedures.
* _Route selection:_ When adopting multi-hop access technology, the classic problem of traffic routing and resource allocation in the RAN is complicated by the half-duplex constraint and the need to take into account the spectral efficiency due to wireless backhauling.
* _Resource allocation between backhaul and access:_ Channel multiplexing and cross-link interference are proposed for study. Resource management in the context of interference avoidance is required because 5G NR can operate at relatively low frequencies, for which antenna arrays do not have such a high directivity as for mmWave. In the case of mmWave, the problem is largely solved by spacing the IAB-node antennas at a short distance away from each other [10].
* _Spectral efficiency:_ The need for improving the existing mechanisms, such as modulation and coding schemes, is expressed. However, the report also emphasizes the importance of reusing standard NR solutions as much as possible.  
Figure 2: IAB link types (adapted from [7]).  
As can be seen in Table 1, the above-mentioned challenges have been addressed in the recent literature; however, the way to tackle any of them highly depends on whether half-duplex or full-duplex transmission is assumed. We note that the classification in Table 1 is somewhat subjective because the challenges are often related to each other. For example, it is hardly possible to study route selection and resource allocation separately, so the studies are classified according to the problem that received more emphasis. In the remainder of the section, we briefly review the literature dealing with the identified problems.
## 3 QSAR Today
### Model Validation: Internal Using the Training Dataset and External Using the Test Sataset
For prediction, the same descriptors used during QSAR model training are calculated for new chemical compounds. These values are then input into the model to obtain a predicted response. When used this way, the QSAR model functions like a \"black box\", where its internal processes remain hidden or not easily understood. As a result, the model generates predictions without explaining why specific results are produced. This approach makes QSAR models useful for virtual screening, enabling the evaluation of large virtual libraries to identify new compounds with desirable properties. A key benefit of this method is that the required computations are relatively straightforward and cost-efficient. Another important application of QSAR models is identifying which chemical properties of studied compounds contribute most significantly to their biological effects. The interpretability of a QSAR model depends on both the selected descriptors and the applied machine learning techniques. A QSAR model is considered interpretable when its descriptors have clear, well-defined meanings that correspond to specific physicochemical or structural properties. Examples include structural keys, molecular fragments, and 3D molecular interaction field descriptors like CoMFA and GRID. In contrast, descriptors such as topological indices or hashed fingerprints lack straightforward interpretations and are better suited for black-box QSAR models. Machine learning techniques also play a crucial role in model interpretability. Nonlinear methods like neural networks (NNs) [159] and Support Vector Machines (SVMs) [139] generally do not provide simple interpretations, making them more suitable for black-box applications [160; 161]. Conversely, Partial Least Squares (PLS), a linear machine learning method, is widely used in QSAR modeling because it generates regression equations with coefficients that directly indicate how specific descriptors influence the biological activity [162]. However, PLS assumes a linear relationship between descriptors and biological activity, which can be a limitation when the true relationship is non-linear. In such cases, it becomes necessary to introduce square and cross-terms of descriptors by multiplying pairs of descriptors to create \"cross-term blocks\". While these terms can enhance the model's predictive accuracy, they also make interpretation more complex, especially when higher-order terms are included.
## 5 Spatio-Temporal Big Data Collaborative Storage Mechanism
### Spatio-Temporal Big Data Collaborative Storage Scheme  
The spatio-temporal big data collaborative storage scheme proposed in this paper includes the following phases: Initial setup, Query phase, Verification phase, and Update phase. The work of each phase is described in detail below. The scheme designed in this chapter is shown in Figure 2.  
Initial setup: the data owner selects a security parameter, outputs the public parameter, initial summary and initial state through the probabilistic bootstrap algorithm, verifies its legitimacy using a smart contract, and after passing the verification, stores the initial summary, and the initial state in the blockchain and the off-chain storage server, respectively, and sends the initial data together to the off-chain storage server to be stored in the off-chain storage server.  
Query phase: the data verifier submits a request to query the subscript collection. The off-chain storage server queries the data in the database according to the requested subscript collection, submits the proofs to the blockchain, performs aggregation using smart contracts, and returns the generated aggregated proofs and stored data to the data verifier after the aggregation is completed.  
Verification phase: the data verifier based on the proof and data returned from the off-chain storage server, the initial digest stored in the blockchain. Run the validation algorithm; if it passes the validation, then it indicates that the data is legitimate.  
Figure 2: Spatio-temporal big data collaborative storage scheme.  
Update phase: the data owner first calculates the corresponding auxiliary information and signature locally based on the data to be updated, and then sends the auxiliary information to the blockchain for upload, the off-chain storage server receives the updated data uploaded by the data owner, the blockchain runs a smart contract to verify the information and the signature, and after the verification passes, the signature is stored in the blockchain, and the off-chain storage server updates the data in the database.
## 5 Experiment Conclusions
### Metrics
#### 5.2.2 Kendall's Tau (\(\tau\))  
Kendall's tau (\(\tau\)) is a statistical method for determining the relationship between two permutations. It is commonly used to assess the consistency between different ranking methods. Let us consider two sequences \(A\) and \(B\), both containing \(N\) elements: \(A=(a_{1},a_{2},\ldots,a_{N})\) and \(B=(b_{1},b_{2},\ldots,b_{N})\). For any pair of two elements \((a_{i},b_{i})\) and \((a_{j},b_{j})\) (where \(i\
eq j\)), if \(a_{i}>a_{j}\) and \(b_{i}>b_{j}\) or if \(a_{i}<a_{j}\) and \(b_{i}<b_{i}\), these two pairs are referred to as concordant pairs. Conversely, if \(a_{i}>a_{j}\) and \(b_{i}<b_{j}\) or if \(a_{i}<a_{j}\) and \(b_{i}<b_{i}\), these two pairs are referred to as discordant pairs. Notably, if \(a_{i}=a_{j}\) or \(b_{i}=b_{j}\), then these two pairs are neither concordant nor discordant [46]. The formula for calculating the [PERSON]'s tau (\(\tau\)) between these two permutations is as follows:  
\[\tau=\frac{2(n_{+}-n_{-})}{N(N-1)} \tag{14}\]  
where \(n_{-}\) and \(n_{+}\) are the numbers of discordant and concordant pairs. [PERSON]'s tau (\(\tau\)) quantifies the similarity between node ranking sequences under various comparative algorithms and DNC. A higher \(\tau\) values signifies a stronger resemblance between the node ranking sequences of the two methods.
# Fragility Analysis of Transmission Towers Subjected to Downburst Winds
## 2 Fragility Analysis Framework  
The task of wind-induced fragility analysis of a transmission tower is to determine the conditional probability of the tower reaching a certain failure limit state under the case of a certain wind speed. This can be described by a fragility curve, which is associated with the wind load strength and the failure limit state. Generally, the abscissa of this curve is the wind speed while its ordinate is the probability of structural responses exceeding the specified failure limit state. As mentioned previously, the current wind-induced fragility analysis generally focuses on the synoptic wind. Nonetheless, insufficient attention has been paid to that under downburst winds. In this section, a fragility analysis framework of transmission towers under downburst winds will be presented. First, three important parts of this framework, including the uncertain structural model of transmission towers, the downburst wind load model, and the capacity curve of transmission towers, will be introduced separately. The fragility analysis framework of transmission towers under downburst winds is then presented in detail.
## 3 Probability of Chip Error in Coherent O-QPSK
### Without Pulse Shaping
#### 3.1.2 Case (b): \(\sqrt{C/2}\leq\sqrt{I}<\sqrt{C}\)  
When \(\sqrt{I}\) is larger than (or equal to) \(\sqrt{C/2}\), the demodulator will start deciding erroneously, because of the stronger signal energy of the interfering component, which forces the received signal to be sampled in one of the adjacent quadrants on the constellation plane, as shown in Figure 2b. In such a condition, the probability of symbol error will be given by:  
\[P_{s}=\frac{S_{0}}{\pi}=2\frac{2\alpha}{2\pi}=\frac{2\alpha}{\pi}\]where \(S_{0}\) is the arc of the circle shown in Figure 2b, the angle, \(\alpha\), in radians, is equal to \(S_{0}/2\), and \(\alpha\) is:  
\[\alpha=\arctan\sqrt{\frac{I-(C/2)}{\sqrt{C/2}}} \tag{11}\]  
The sampling point of the received signal can only pass to the adjacent quadrants, and the receiver makes a one chip error per each transmitted symbol; as a result, \(P_{c}\) will be the half of \(P_{s}\):  
\[P_{c}=\frac{4\alpha}{4\pi}=\frac{\alpha}{\pi}\]
## 5 Evaluation
### Results  
The experimental results indicate that the XGBoost algorithm based on operating condition recognition and classification can predict the content of f-CaO with relatively higher accuracy (Figure 6) in comparison with that without the preprocessing based on image classification (Figure 7). As illustrated in Figures 6-8, the \"ground truth\" (red) lines represent the actual values of the f-CaO content in cement clinker, i.e., the results obtained from laboratory tests, while the \"predicted\" (blue) lines represent the predicted f-CaO content in cement clinker. As shown in Figure 6, the overall mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE) of the predictions with preprocessing based on image classification are approximately 0.21, 0.08, and 0.28, respectively; a 16%,27%, and 15% improvement than that in Figure 7 without the preprocessing. The prediction accuracy improvement of the proposed method over LSTM is also significant, specifically, a 25%, 38%, and 22% upgrade than that in Figure 8 are observed in our evaluations.  
To illustrate the robustness and generalizability of the proposed solution, we present in Figure 9 the training loss and validation loss of the adopted models. The results in Figure 9 show that: (i) the training loss first plunges down with every epoch, hinting that the models learn and absorb information from the datasets; (ii) the validation loss, during the same epochs, is slightly higher than the training loss and also drops smoothly, indicating that the training gets the model more seasoned with the training datasets and is not under-fitting; and (iii) as the epochs continue, the training loss reaches the plateau, and the validation loss keeps decreasing and becomes smaller than the training loss, showing that the models are not over-fitting. In sum, the trained models both have a good fit to the training datasets and generalize well to be used for new datasets.  
Figure 8: f-CaO content prediction results of LSTM.  
Figure 6: f-CaO content prediction results of ResNet50 + XGBoost.  
Figure 7: f-CaO content prediction results of XGBoost.
## 6 Experimental Results
### Performance Evaluation
#### 6.3.3 Denning-Sacco Protocol  
In our analysis of the Denning-Sacco protocol (Figure 5), we assigned the following delay values: \(D_{1}=2\), \(D_{2}=3\), \(D_{3}=4\), \(D_{4}=5\). We found two paths that exhibit a Man-in-the-Middle behaviour, where locations 47 or 87 were reachable. Additionally, we discovered two paths representing an honest execution, with locations 3 or 7 being reachable, and two paths representing an execution involving the Intruder, with locations  
Figure 4: Andrew Lowe Protocol.  
Figure 3: Andrew Protocol.  
43 or 83 being reachable. Our observations showed that for the Denning-Sacco protocol, under the assumed delays, the minimum value allowing both honest executions but not permitting a Man-in-the-Middle attack is \(L_{1}=7\). Furthermore, we determined that the minimum value enabling a Man-in-the-Middle behaviour for the given delays (reachability of locations 47 and 87) is \(L_{1}=8\).
# Silane Modification of Crumb Rubber on the Rheological Properties of Rubberized Asphalt
## 3. Experimental Result and Discussion
### Specific Surface Area of Rubber Powder  
The combination of rubber powder and asphalt is mainly physical function. When silane coupling agent is introduced into the surface of rubber powder, the rubber powder is chemically combined with asphalt through the silane film attached to the surface of rubber powder. In order to further analyze the influence of silane coupling agent on the surface properties of rubber powder, and further explain the reasons of DSR and BBR test results, in this paper, the BET test of rubber powder was carried out to determine the effect of silane coupling agent on the specific surface area of rubber powder. The BET test results are shown in Figure 3.  
Figure 3 shows that the specific surface area of CR was 0.1754 m\({}^{2}\)-g\({}^{-1}\) and that area of MCR rose to 0.2618 m\({}^{2}\)-g\({}^{-1}\), which is 49.3% higher than CR after surface modification by silane coupling agent. Some studies have shown that the modification mechanism of silane coupling agent is to chemically attach a layer of silane film on the surface of modified material. The size, thickness, and shape of the silane film are different [25]. It can be seen that the specific surface area of MCR increased significantly because of the successful grafting of silane coupling agent molecules on its surface, which would change the surface properties of some modified materials or \"add\" some required functions.  
MCR, due to its larger specific surface area, firstly, absorbs more oil and resin within asphalt, which further raises the relative content of asphaltene and improves the ability to resist shearing stress; then, it facilitates the formation of stable chemical bonds between MCR and asphalt, which plays the role of elastomer to better improve mechanical properties at high temperatures; lastly, it still heightens friction action between MCR and asphalt, which helps to dissipate asphalt internal stress better.  
Figure 3: BET test results of normal rubber and modified rubber.  
MCR swells well and \"suspends\" within asphalt through chemical interactions, therefore, mechanical characteristics of MAR are further enhanced, which results in a better performance at high and low temperature environments.
## 1 Introduction
Given the widespread diffusion of this model, the development of identification procedures that can relate B-W parameters to the experimental data are crucial. In Ref. [39], dynamic tests were developed to characterize wire rope isolators, and then the Levenberg-Marquardt (L-M) algorithm [40; 41] was engaged to obtain the parameters. In Ref. [42], a novel method of attenuating vibration using vacuum-packed particles as the materials responsible for energy dissipation was investigated and a genetic algorithm was performed; in Ref. [43], the vibratory behaviour of a full-scale steel cantilever beam was studied and a hybrid evolutionary algorithm, which utilized selected stochastic operators, heuristics and problem-specific information, was involved. In this manuscript, a novel procedure to calculate CIKH model parameters was exploited. The procedure was carried out on three strain-controlled tests and one stress-controlled test. The cycle properties that were imposed were the hysteresis area, the stress range, the slope at the inversion points, the average stress and the average plastic strain. These were all extracted from the stabilized cycles of strain-controlled tests, and the ratcheting rate extracted from the stress-controlled test. This can be motivated considering that a good prediction of the global properties of the stabilized cycle is generally sufficient in fatigue analyses. Multiaxial fatigue criteria, in fact, generally require global quantities of the stabilized cycle to be applied. Once the CKH model parameters were obtained, the model was also refined with the introduction of a fourth backstress and then the VOC isotropic hardening law. In addition to this, the determination of CKH model parameters was conducted by using a classical pointwise fitting between the experimental data and the modeled curve. In addition, a B-W model was engaged to reproduce cyclic loadings. Cyclic plasticity is a nonlinear hysteretic behaviour, and therefore pertinent to the range of application of the B-W model. The parameters were computed using the L-M algorithm, as proposed in [44]. Section 2 is dedicated to explaining the theoretical background of the CIKH model and B-W model. In Section 3, the experimental data employed in this work are presented, and the proposed procedure to calculate CIKH model parameters, together with the algorithm used to determine Bouc-Wen constants, are formalized. In Section 4, the results of the calibration techniques are exposed. The results obtained with a classical pointwise fitting are initially shown. The results according to the proposed procedure are then presented, and, finally, the results obtained using Bouc-Wen are reported. In Section 5, a discussion and comparison between the three approaches are provided, together with suggestions for future studies about the modeling of cyclic plastic behaviour.
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
## 2 Trends of the _Smart Metering_ Systems
### Architecture of a Smart Metering System
#### 2.1.1 Smart Meter  
The most recent evolution of the Smart Meter (_SM_) is based on the introduction of bidirectional capabilities and the progressive appearance of new applications. Bidirectional capabilities must be understood from two different points of view: _energy_ (energy flows towards/from consumption/generation points, mainly due to the _DG_, _DS_, and prosumers figures) and _communication_ (data travels from the _SM_s to the _CC_, but the _CC_ can also communicate with them, as the _SM_ includes an embedded communication node, within a configurable and multifunctional network).  
An _SM_ can present a wide range of features. Although there is not a directive or norm that defines them in terms of quantity or functionality, different bodies have established some guidelines. The _European Smart Meters Industry Group_ (ESMIG) has reduced the minimum features of an _SM_ to the following four:  
* Remote reading
* Bidirectional communication
* Support of advanced tariff systems and billing applications
* Remote energy supply control.  
On the other hand, the European Union extends the minimum desirable requirements for an electricity _SM_ as published in 2012/148/EU recommendation, described in Table 1.
## 2 Materials and Methods
### Front Detection and Segmentation Module
#### 2.2.1 Dataset  
To train a front detector and perform segmentation of the detected fronts, a suitable dataset needs to be collected. This dataset should include images with labeled fronts, along with annotated bounding boxes, segmentation masks, and assigned front classes (rotational or translational). Unfortunately, no publicly available dataset provides a sufficient number of such annotations. Therefore, a new dataset was created for the training, validation, and testing of the neural networks. Example annotations of the fronts of articulated objects from the training dataset are shown in Figure 2b.  
In certain cases, distinguishing between rotational and translational objects is challenging. This situation arises when the handle is located at the central point of the front, and the front is taller than it is wide or has a square shape. Determining whether the rotation axis is on the right or left side becomes difficult, as shown in Figure 3a. Another scenario occurs when it is challenging to determine whether the rotation axis is on the right, left, or bottom of the front, as depicted in Figure 3b. Additionally, the front's appearance may not match its actual class. We decided not to include these images in the training set because these objects require additional robot-object interaction to determine their class. Sample images used for training are displayed in Figure 2, and an overview of the dataset is presented in Table 2.  
Figure 3: Ambigious articulation: objects before and after interaction for objects with vertical axes (a) and vertical and horizontal axes (b).
## 4 Results
### Verification  
A verification of the model-based wedge detection algorithm is performed with a simulated thermogram with known position and size of the wedges. The studied thermogram contains three wedges, I, II and III, with the CNR values \(19,5\) and \(3\). The simulated thermogram with the consecutively numbered turbulence wedges of different sizes is shown in Figure 8a, while the reference turbulence wedge outline as well as the CNR value are shown in Figure 8b. Both of the wedge detection algorithms result in a flow transition line which includes all premature transitions across the width of the thermogram, see Figure 8d. To determine the area of the turbulence wedges with the GBA, the \(y\)-coordinates of the natural transition line are subtracted from the \(y\)-coordinates of the detected premature transition line. The results of the GBA and the novel MBA are shown in Figure 8c,d, respectively. The GBA detects wedge I, but fails to detect the other two wedges with lower CNR values. In contrast, the MBA correctly detects all three wedges. The results of the verification thus show that, with the introduced model-based algorithm, a reliable detection even at \(\text{CNR}=3\) is possible, which is a significant improvement compared to the state-of-the-art, gradient-based algorithm.  
A quantitative comparison of the areas found by the two wedge detection algorithms is shown in Table 2. The total area determined by the MBA has a relative deviation of \(1.5\%\) compared to the true value, while the state-of-the-art GBA detects a total area with a relative deviation of \(-38.6\%\) from the true value due to the undetected turbulence wedges. The MBA determines a larger-than-true area for the smallest turbulence wedge in the middle, but smaller areas for the larger turbulence wedges, which seems to be worth investigating further in the subsequent, more detailed characterization.  
Figure 8: Simulated thermogram with three different turbulence wedges with differing intensities, leading to different CNR values. Both the model-based algorithm as well as the state-of-the-art, gradient-based algorithm were applied to the image. (**a**) shows the simulated thermogram with the turbulence wedges numbered with roman numerals. (**b**) shows the reference transition line as well as the CNR value of the wedges noted above the respective wedge. (**c**) shows the result of applying the gradient-based algorithm (GBA) of [PERSON] et al. [14] to the image and (**d**) shows the result of the application of the model-based algorithm (MBA) on the simulated thermogram.