esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multiobjective Decision-Making Model for Risk-Based Maintenance Scheduling of Railway Earthworks
## 1 Introduction
The increased availability of data such as LiDAR for geometry, embedded sensor and satellite data combined with improved weather forecasts provides an opportunity for infrastructure managers to address this problem of quantifying safety and make better-informed maintenance investment decisions [22]. Having established the safety level, asset maintenance planning is a process of deciding the scope, timing, costs, and benefits of future maintenance activities on a specific asset. Due to long lifespans and constraints on budgets, optimization of maintenance investments considering technical and economic objectives is essential for railway owners to fulfill societal expectations. Implicit in any decision-making process is the need to construct, either directly or indirectly, the preference order so that alternatives can be ranked, and the best alternative can be selected [23]. For some decision-making problems, this may easily be accomplished. For example, in the case of a decision based on a cost-minimization rule (where the lowest-cost alternative is chosen), the preference order is adequately represented by the natural order of real numbers (representing costs). Multiciteria decision-making (MCDM) methods provide a systematic approach to evaluate multiple conflicting criteria in decision-making. Conflicting criteria are typical in evaluating options; for example, the cost is usually one of the main criteria, and some measure of quality (performance level) is typically another criterion, usually in conflict with a cost. MCDM is used to identify and quantify decision-maker and stakeholder considerations about various (mostly) non-monetary factors in order to compare alternative courses of action [24]. Alternatively, the multiple performance criteria can be combined into a so-called utility function, in which all the criteria are brought into a single scale. This paper demonstrates the applicability of MAUT for proactively determining the optimum maintenance schedule of earthwork assets on a network scale. The approach is demonstrated using the Irish Rail network. Multiple performance objectives or goals are considered looking at a range of attributes, failure scenarios and maintenance options. The result is a ranking of a network's earthwork maintenance schedule, in order of maximum return on investment, where the return is based on the tradeoff of multiple performance goals as selected by the infrastructure manager. An example would be to increase the structural safety and availability of a network while minimizing the cost of interventions [25; 26; 27; 28]. It allows for the consideration of environmental impacts and user costs and can easily be augmented further by additional performance goals as they become relevant. It explicitly allows the user to integrate choice and risk preferences.
# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 4 Experiment
### Experiment Design  
To verify the effectiveness of the pruning strategy for improving tracking speed, and to verify whether the online updating algorithm can improve the model accuracy (APE and AOR), special experiments are designed, mainly including: (1) comparison experiments on tracking accuracy and speed of tracking models with or without pruning during the training process; (2) comparison experiments on accuracy and speed of tracking models of secondary pruned feature network and pre-pruned feature network; (3) comparison experiments on accuracy and speed of tracking models with or without adding alternative kernels; (4) comparison experiments on accuracy and speed of tracking models with or without online updating of feature network during tracking.
## 3 Push-Pull Converter Sizing and Parameterisation
### Transformer Sizing and Parametrisation  
#### 3.1.1 Transformer Core  
When sizing a transformer, many variables are involved to estimate the proper core size. It is a complex task that requires consideration of the non-linear relationship of the operating power with respect to the area product, core volume and heat dissipation capacity [29]. To facilitate this task, magnetic core manufacturers propose their own methodology for the correct choice of core size for various applications. These methodologies are based on the determination of the minimum core area product (\(AP\)), defined by the area of the magnetic section (\(A_{E}\)) and the window area (\(A_{W}\)) which guarantees the required magnetic flux capacity of the transformer core, assuming its operation in the linear region, for a design power and frequency [30]. In this case, the empirical Equation (2), provided by Unitrode(Merrimack, NH, USA) and Texas Instruments(Dallas, TX, USA), will be used to size the cores for the different cases under study. This equation is commonly used in the design of power transformers for switching power supplies [29].  
\[AP=A_{E}\cdot A_{W}=\left(\frac{P_{o}}{K\cdot\Delta B\cdot f_{sw}}\right)^{ \frac{4}{3}} \tag{2}\]  
For the design of the different transformers, E-type core (widely used in the design of power transformers) of N27 and N87 ferrite materials (MnZn) from TDK Electronics(Munich, Germany) have been chosen. Zinc-based ferrites are characterised by high saturation flux densities and high magnetic permeability, which favours the reduction of core size and cost [31]. The choice of TDK Electronics(D) products is justified by the fact that it is a well-known manufacturer in the industry, which also has intuitive and powerful design tools for the calculation of the main parameters of the transformer core.
# Towards Robust Neural Rankers with Large Language Model: A Contrastive Training Approach
## 6 Discussion
### Real-World Applicability  
The method proposed in this paper can be flexibly adapted to the training processes of all IR models based on PLMs, whether they are built upon the Dense Encoder or Reranker architectures. It is worth noting that while our method indeed presents vast long-term application prospects and reliable performance, these outcomes are contingent upon dependable training data: specifically, high-quality LLM-generated query variations.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Real Query and Its Relevant Document** & **Original (MAP/MRR)** & **Our (MAP/MRR)** \\ \hline
**Q**: How can I keep my rabbit indoors? \#4473331 & & \\
**D**: just bring the cage indoors and keep it clean...Oh, and don’t confuse the cocoa puffs with the rabbit droppings!! & & \\ \hline
**Q**: why do my baked potatos never taste near as good as when I get them in a nice restaurant? \#402514 & & \\
**D**: Due to the cooking time a lot of restaurants bake potatos twice. Wrap them in foil, with some course sea salt for the first bake. Allow them to & \\ \hline
**Q**: how do I go about getting copies of letters of commendation presnet & & \\ to me from city of san diego? \#100653 & & \\
**D**: Use a copy machine. Or scan the letter into your computer and print it out in color mode to pick up any logos on the original letterhead. & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Case studies. Three cases are selected from ANTIQUE to qualitatively analyze the ranking effectiveness of our proposed method under a query attack.
###### Abstract  
Nowadays, the popularity of the internet has continuously increased. Predicting human body dimensions intelligently would be beneficial to improve the precision and efficiency of pattern making for enterprises in the apparel industry. In this study, a new predictive model for estimating body dimensions related to garment pattern making is put forward based on radial basis function (RBF) artificial neural networks (ANNs). The model presented in this study was trained and tested using the anthropometric data of 200 adult males between the ages 20 and 48. The detailed body dimensions related to pattern making could be obtained by inputting four easy-to-measure key dimensions into the RBF ANN model. From the simulation results, when spreading parameter \(\sigma\) and momentum factor \(\alpha\) were set to 0.012 and 1, the three-layer model with 4, 72, and 8 neurons in the input, hidden, and output layers, respectively, showed maximum accuracy, after being trained by a dataset with 180 samples. Moreover, compared with a classic linear regression model and the back propagation (BP) ANN model according to mean squared error, the predictive performance of the RBF ANN model put forward in this study was better than the other two models. Therefore, it is feasible for the presented predictive model to design garment patterns, especially for tight-fitting garment patterns like activewear. The estimating accuracy of the proposed model would be further improved if trained by more appropriate datasets in the future.  
human body dimension; tight-fitting garment pattern making; activewear; radial basis function; artificial neural networks +
Footnote †: journal: Applied Sciences
# AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks
## 4 Experimental Results
### Performance Results
#### 4.3.2 Energy-Efficiency  
The \(x\)-axis in Figure 7 represents the network size in term of the number of nodes, and the \(y\)-axis shows energy consumption per round in joules. We observe that, for the average energy consumed per round, performance metrics among CCEF, DEF, and AEF show similar energy consumption. The trivial increase in energy cost of the proposed scheme is due to energy-aware routing instead of using greedy or shortest path routing. Our method also considers current attack information to respond accordingly. However, our scheme performs better as compared to CCEF in energy efficiency. At large, energy efficiency demonstrates similar performances. Moreover, the energy consumption per round decreases for all schemes as the network size increases. On average, energy consumptions for CCEF, DEF, and AEF are \(4.55^{-4}\), \(4.32^{-4}\), and \(4.60^{-4}\). A summary of detection capacity comparison is shown in Table 3. Considering negative exponents and significant increases in network lifetime, we can state that the energy efficiencies have similar performances.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**FTR** & **FCO** & **10 PCO** & **LCO** & **Avg.** \\ \hline CCEF & & & & \\ \hline
50\% & 2.92 & 2.64 & 2.36 & 2.64 \\
70\% & 2.81 & 2.73 & 2.37 & 2.63 \\
90\% & 2.71 & 2.74 & 2.41 & 2.61 \\ \hline DEF & & & \\ \hline
50\% & 1.25 & 1.11 & 1.05 & 1.14 \\
70\% & 1.20 & 1.05 & 1.06 & 1.10 \\
90\% & 1.15 & 0.95 & 1.06 & 1.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network lifetime gain of adaptive en-route filtering (AEF; times) over commutative cipher-based en-route filtering (CCEF) and dynamic en-route filtering (DEF).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**FTR** & **CCEF** & **DEF** & **AEF** \\ \hline
50\% & 4.84\({}^{-4}\) & 4.47\({}^{-4}\) & 4.66\({}^{-4}\) \\
70\% & 4.54\({}^{-4}\) & 4.29\({}^{-4}\) & 4.59\({}^{-4}\) \\
90\% & 4.26\({}^{-4}\) & 4.19\({}^{-4}\) & 4.55\({}^{-4}\) \\ avg. & 4.55\({}^{-4}\) & 4.32\({}^{-4}\) & 4.60\({}^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Energy efficiency comparison (joules).  
Figure 7: Energy efficiency (FTR 50, 70, and 90% at first cut-off (FCO)).
# Using Plastic Waste in a Circular Economy Approach to Improve the Properties of Bituminous Binders
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Wax Obtained from Plastic Waste  
The wax used in this research was obtained from an experimental system developed by co-authors [PERSON] et al. [33; 34; 35], where the main objective was to produce light gaseous hydrocarbons and/or liquid fuels (gasoline in the most part). This reaction system (described in Section 2.2.1) is used for the thermal pyrolysis of polymers, namely for HDPE from plastic waste (for example, from crushed plastic waste obtained in the mechanical and biological treatments (MBT), landfills or source-separated packaging waste PE). Initially, various HDPE wastes were processed, and then, to have a basis for comparison and to ensure a higher reproducibility in the quality of the wax, fresh HDPE pellets were used. This material had 950 kg\(\cdot\)m\({}^{-3}\) (@2 3\({}^{\circ}\)C) density, 27 MPa tensile strength, 850% elongation, and melt indexes (@190 \({}^{\circ}\)C) of 0.23 g/10 min (MFI\({}_{2.16}\)) and 20 g/10 min (MFI\({}_{21.6}\)).  
HDPE was selected as the source material as it decomposes solely into relatively simple hydrocarbons, including gaseous and liquid hydrocarbons, and, for the purpose of this work, waxes (no other toxic products are produced). As it has been previously studied by several researchers [7; 36; 37; 38; 39], the characteristics of these waxes can be affected because of the reaction conditions (type and rate of gas used, temperature, pressure, duration, type of feedstock material, reactor type, and possible inclusion of catalysts [40]).  
The developed composite binder that included this wax (in 2 wt.%) was based on a PMB with SBS and finished with other functional additives. These additives allow the mitigation of less attractive trends (such as ageing) and improve the following binder properties: anti-oxidation, adhesiveness, rutting resistance, and elastic recovery. From now on, this blending is identified in this manuscript by the expression \"PMB + wax\".
# Scattering of Plane Waves by Cylindrical Cavity in Unsaturated Poroelastic Medium +
## 2 Model and Solution
### Wave Field Analysis and Solutions
Substituting the expressions of the potentials of the total wave field, Equation (29), or Equation (30) into the boundary conditions with the stress components being expressed by the potentials of the total wave field, a system of linear equations about unknown quantity, \(A_{n,j}\)\((j=1,2,3)\) and \(B_{n}\), can be obtained as follows,  
For P\({}_{1}\) wave incidence:  
\[\left[ \begin{array}{cccc}S_{1}&S_{2}&S_{3}&S_{5}\\ W_{1}&W_{2}&W_{3}&W_{5}\\ X_{1}&X_{2}&X_{3}&X_{5}\\ Y_{1}&Y_{2}&Y_{3}&Y_{5}\end{array} \right]\left|{}_{r=a}\right.\left[ \begin{array} []{c}A_{n,1}\\ A_{n,2}\\ A_{n,3}\\ B_{n}\end{array} \right]=\left.\left[ \begin{array}{c}-S_{4}\\ -W_{4}\\ -X_{4}\\ -Y_{4}\end{array} \right]\right|_{r=a} \tag{32}\]  
For SV wave incidence:  
\[\left[ \begin{array}{cccc}S_{1}&S_{2}&S_{3}&S_{4}\\ W_{1}&W_{2}&W_{3}&W_{4}\\ X_{1}&X_{2}&X_{3}&X_{4}\\ Y_{1}&Y_{2}&Y_{3}&Y_{4}\end{array} \right]\left|{}_{r=a}\right.\left[ \begin{array} []{c}A_{n,1}\\ A_{n,2}\\ A_{n,3}\\ B_{n}\end{array} \right]=\left.\left[ \begin{array}{c}-S_{5}\\ -W_{5}\\ -X_{5}\\ -Y_{5}\end{array} \right]\right|_{r=a} \tag{33}\]  
The detailed expressions of coefficients \(S_{j},W_{j},X_{j},\) and \(Y_{j}\) as provided in Appendix A. By solving Equations (32) and (33) for different kinds of incident waves, respectively, the undetermined wave amplitude coefficients \(A_{n,j}\)\((j=1,2,3)\) and \(B_{n}\) can be obtained by solving the system of linear equations. Once the undetermined wave amplitude coefficients are obtained, the scattered wave field can be calculated. Finally, the displacement and stress components can be obtained by the superposition of the incident wave and the scattered wave field.
# Utilizing Multi-Dimensional MmWave MIMO Channel Features for Location Verification
## 2 Problem Formulation and System Model  
### Problem Formulation  
Similar to previous works [16; 17; 18], we also use a three-entity model including a legitimate transmitter ([PERSON]), a malicious attacker ([PERSON]) and a legitimate base station ([PERSON]), where [PERSON] and [PERSON] are both equipped with a single antenna and [PERSON] is equipped with \(N_{r}\) antennas, as shown in Figure 1. [PERSON] intends to spoof [PERSON] by using the identity of [PERSON] and thus may launch some more advanced attacks, such as a man-in-the-middle attack and session hijacking attack while [PERSON] communicates with [PERSON].  
Figure 1: System model.  
Suppose [PERSON] transmits one data frame to [PERSON] at time \(t-1\) and this data frame was validated by cryptography-based higher layer authentication approaches. In this process, [PERSON] measures and stores physical layer features information, e.g., mmWave MIMO channel features, with the help of signal estimation techniques. Subsequently, at time \(t\), an unknown transmitter (either [PERSON] or [PERSON]) sends another data frame to [PERSON] and [PERSON] needs to validate the data frame origin (i.e., the current transmitter identity) to avoid impersonation attack.  
[PERSON] has data frame structure information by probing the transmitted signals between [PERSON] and [PERSON] and some publicly known information (e.g., training sequences and pilot signals). The estimation techniques and authentication schemes that [PERSON] utilizes cannot be known by the attacker, [PERSON]. Further, we exploit the carrier sense multiple access/collision detection protocol.
## 3 Results
### Validation  
The final segmentation method is based on the J48 algorithm using the entropy filter with a range of sigma from 1 to 16 and subsequently excluding areas with a size smaller than 4000 pixels from the binary image. Results of the classification process--available as binary masks--were multiplied with the original images. For validation, three external test sets (ETS1-3) were used consisting of 117, 121, and 137 images, respectively. The segmentation method shows a high specificity and ACC of more than 90% for all three external test sets (Figure 6). This indicates that background areas of the image were correctly assigned giving a high TNR. A total of 67-75% of areas classified as worms were correctly assigned, which is eminently proficient giving the fact that manual segmentation is subject to inter-operator variations of approximately 20% [41; 42; 43]. Moreover, the automated segmentation reduces the time of user interaction by 75%.  
Figure 4: Bars represent the mean performance (\(\pm\)SD) of attribute subsets 1–8 derived from three test sets TS1-3. The performance was calculated as the mean value of TPR, TNR, ACC, and MCC.  
Figure 5: Histogram of all classified areas measured in test sets TS1-3 based on attribute subset 1. Bars represent the number of objects with a certain area (pixel); the red bar indicates the minimum between two distributions.
# An Efficient Blockchain Transaction Retrieval System
## 2 Literature Review
### Bloom Filter  
[PERSON] proposed the Bloom filter [33] in 1970. It is a probabilistic approximate retrieval structure that can be used for large-scale data. It can use a small amount of memory space to quickly and efficiently check whether an object is likely to exist in the set. With the explosion of data growth, Bloom filters have been widely used in many research fields, such as databases, distributed systems, and web caches [34], due to their high efficiency, low utilization of resources, and high flexibility between the bloom filter bits and the false positives probability. Specific optimization works include network packet filtering [35], p2p lookup [36], and metadata lookup [37], and many derivative types of Bloom filters, including counting Bloom filters [38], 2D Bloom filter, multidimensional Bloom filter [39], and so on.  
The Bloom filter's core structure contains a bit array whose length is m with k hash functions. All bits in an empty Bloom filter are 0. When adding an element to the Bloom filter, each hash function maps the element to the bit array, and the bit array corresponding position is set to 1. When checking whether an element exists, the Bloom filter passes the element through k hash functions. If any related k positions is 0, the test element is not in the Bloom filter. As shown in Figure 1, item A sets positions 1, 5, and 6 to 1 through three different hash functions, hash1, hash2, and hash3. Item B sets positions 3, 5, and 6 to 1 through hash1, hash2, and hash3. When checking the new item C, the bit array is obtained, through hash1, hash2, and hash3. When the positions set to 1 are consistent, it proves that the element is stored.  
The Bloom filter cannot produce false negatives but may produce false positives. False positives mean that the Bloom filter seems to store a item that does not actually exist in the Bloom filter. The k positions indicate whether an element is in the set as described above. Still, this method may cause the algorithm to mistakenly assume that an element that is not originally in the set is detected as being in the set. There is a mutually restrictive relationship between false positives and the Bloom filter bit array. We can see a connection between the two, whereby increasing the size of bit array in the Bloom filter reduces the false positives rate:  
\[\mathrm{k}=\frac{m}{2^{\lceil\log_{2}n\rceil}}*\ln 2 \tag{1}\]  
\[\epsilon=\left(1-\left(1-\frac{1}{m}\right)^{kn}\right)^{k} \tag{2}\]  
where \(\epsilon\) is the false positives rate, \(k\) is the hash functions number, \(n\) is the resolved features number in the Bloom filter, and \(m\) is the Bloom filter bits number.
# Rateless Coded Uplink Transmission Design for Multi-User C-RAN
## 3 Rateless Coded Uplink Transmission Scheme
### Rateless Encoder at the User  
Each user applies Raptor code to encode its intended messages. Explicitly, to generate the coded bits \(c_{i}\) at each user \(i\), the message \(m_{i}\) is firstly encoded by LDPC code with rate \(R_{p}\), and then by LT code with an output node degree profile:  
\[\Omega_{i}(x)=\sum\
olimits_{d=1}^{d_{c}}\Omega_{i,d}x^{d},\ i=1,2, \tag{2}\]  
where \(d_{c}\) is the maximum output node degree and \(\Omega_{i,d}\) is the probability that a coded bit \(c_{i}\) (also named 'output bit' in the following) is with degree \(d\).  
Each output bit is generated as follows. The degree of each output bit is randomly chosen according to the degree profile (2). For an output bit with degree \(d\), \(d\) bits are randomly picked from the LDPC coded message (also named 'input bits' in the following). These \(d\) bits are XORed and the result is the value of this output bit. Through the above encoding process, the output bits can be generated infinitely. As for the modulation, for simplicity, we apply binary modulation, i.e., bits 0 and 1 are mapped to 1 and \(-1\), respectively. The users continuously send the modulated signals until an ACK is received.