esa-sceva/satcom-chunk-collection huggingface dataset:
# A Spectral Gap-Based Topology Control Algorithm for Wireless Backhaul Networks
## 4 SGTC: The Spectral Gap-Based Topology Control Algorithm
### SGTC with Optimized MBS Placement  
To address Problem Formulation 2, where the network designer can determine the location of the MBSs, we propose to find a set of locations that minimizes the maximum distance between any SC and its closest MBS. More formally, let \(dist(l_{S}(u),M)=\min_{m\in M}\{dist(l_{S}(u),l_{M}(m))\}\) be the distance between an SC \(u\in S\) and the set \(M\) of MBSs. We propose to find a function \(l_{M}\) that assigns locations to the MBSs that minimize \(M\)'s _covering radius_\(r(M)\), which equals \(max_{u\in S}\{dist(l(u),M)\}\).  
This latter formulation corresponds to the vertex k-center problem (for \(k=|M|\)), a well-known NP-hard problem originally proposed by [PERSON] in 1964 [42]. Since the vertex k-center problem cannot be solved in polynomial time within an approximation factor of \(\rho<2\)[43], unless P = NP, we propose using the Critical Dominating Set algorithm that has shown good performance in practice [44; 45].  
Once we have determined optimized locations for the MBSs, we can use the SGTC algorithm to look for a topology with a large Laplacian Spectral Gap.
# Digital Twin in the Maritime Domain: A Review and Emerging Trends
## 5 Developing a Ship Digital Twin Architecture
### Objectives of a Marine Digital Twin Framework  
Overall, the DT framework should reflect the behavior of the vessel's response to the environmental inputs such as forces of waves, current, wind, temperature, etc. The input sensors will be limited as per the requirement only with relevant design patterns based on the target evaluations of the vessel. These design patterns for DT-based vessels are extensively discussed by [PERSON] from NTNU, Norway [93], based on the original 23 design patterns derived by [PERSON] et al. in their classic book _Elements of Reusable Object-Oriented Software_ in the year 1994 [94]. In this book, the following four design patterns for a ship DT have been proposed.  
Figure 6: Direct Stakeholders of a Ship DT System.  
1. Structural Patterns: These are the main constituents of the DT package. It consists of the Product Data Model (PDM) of the DT that is formulated along with sensor-based big data of the vessel. The baseline is also a formulated source model derived from physics-based model behavior. Load-based sensor data are also acquired from the environment. The benchmark of the DT is to be designed to simulate the DT in parallel to the ship to compare anomalies. Finally, a Machine Learning Proxy layer can also be incorporated for DT to assist its behavior through ML rather than depending on the standard physics.
2. Creational Patterns: This is the realization of the DT as a proxy simulation tool. It will be a dynamic hybrid system with the ability to switch intelligently between ML and physics as per the situation. This can be either one vessel or an entire unmanned vessel fleet.
3. Insight Patterns: It is the layer of utilizing the DT that is operated with the ship. Anomaly patterns will be monitoring the deviation from normal operation and reverse analyzing to find the root cause for any abnormality in the system.
4. Computational Pattern: Designing the DT to perform efficiently with smart storage by a high-fidelity controlled regression to avoid data misconceptions. Offloading of data will also be performed with controlled dynamic regression offloading in physics analysis.
# Domain Knowledge Graph Question Answering Based on Semantic Analysis and Data Augmentation
## 1 Introduction
The corpus data of various consultation questions involved in this study is derived from the records of the consultation system of the government service hall. Since the QA system uses speech recognition technology to convert the user's speech content into a text form of consultation questions, the input of the QA system often has problems with missing sentence components or unclear descriptions. This makes it difficult to correctly identify the event entity and further complicates answer retrieval. The effectiveness of a QA system heavily depends on the quality of its training data. Due to the high cost of sample data acquisition, it is difficult to obtain a sufficient number of training samples. The main contributions of this paper are summarized as follows:  
* This paper uses compound value types (CVT) nodes to store household registration events. Since CVT nodes collect multiple attributes of events and more accurately model complex relationships between entity nodes, this approach simplifies queries with multiple constraints in a knowledge graph (KG) into simple queries;
* This paper comprehensively uses KGs and text similarity technology to improve the accuracy of the QA system. It leverages a corpus of query questions to train a RoBERTa-BiLSTM-MultiHeadAttention (RBMA) model to classify query intent. When the intent is clear, it utilizes the language technology platform (LTP) [7] to extract semantic role subjects from queries, and further retrieves the answer from the KG. When the intent is ambiguous, it uses text similarity techniques to match input queries with a corpus of queries and outputs the most similar answers;
* This paper applies the LLM to enhance the training data to solve the problem of data imbalance and improve the accuracy of intent classification. We use the GPT-3.5-turbo language model to augment the dataset size by replacing synonyms and randomly inserting irrelevant words. The experiment results show that data augmentation techniques greatly improve the performance of QA systems. The structure of the remaining sections of the paper is as follows: Section 2 discusses related works, Section 3 describes the process of constructing a household registration domain KG, Section 4 details the framework structure of the QA system, Section 5 presentsthe experiment results and analysis, and Section 6 concludes the paper and discusses future work.
# A Distributed RF Threat Sensing Architecture
## 5 Performance Evaluation
### Jamming Detection  
The proposed RF sensor has been able to detect reliably constant and sweep jamming, forwarding RF raw data, and alarms over the RF threshold to a SOC for further processing. The jamming detection has been demonstrated up to 500 m, even with low-power jamming signals. The system capability to profile the deployment environment in time slots and implement the ATL algorithm allows for minimization of false-positive alarms, while, in the collaborative setup, it provides additional capabilities to the SOC for simultaneous monitoring of numerous critical infrastructures. The significant improvement of the false-positive rate (FPR) is depicted in Tables 6 and 7, which summarize the analysis of the data retrieved during the experiment. In a number of recorded 2912 RSS measurements, involving 87 real jamming events, the ATL with k = 8 outperforms as follows:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Triggers** & **False Positives** & **False-Positive Rate (FPR)** & **Precision (\%)** \\ \hline Env. Max & 169 & 82 & 2.90\% & 51.48\% \\ \hline ATL (k = 2) & 120 & 33 & 1.17\% & 72.50\% \\ \hline ATL (k = 4) & 113 & 26 & 0.92\% & 77.00\% \\ \hline ATL (k = 6) & 109 & 22 & 0.78\% & 79.82\% \\ \hline ATL (k = 8) & 89 & 2 & 0.07\% & 97.75\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between Env. Max and ATL algorithm configurations (2.4 GHz band).  
Figure 11: DJI Mavic 3 Pro RF signals at 150 m.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Triggers** & **False Positives** & **False-Positive Rate (FPR)** & **Precision (\%)** \\ \hline Env. Max & 162 & 75 & 2.65\% & 53.70\% \\ \hline ATL (k = 2) & 147 & 60 & 2.12\% & 59.18\% \\ \hline ATL (k = 4) & 125 & 38 & 1.35\% & 69.60\% \\ \hline ATL (k = 6) & 102 & 15 & 0.53\% & 85.29\% \\ \hline ATL (k = 8) & 101 & 14 & 0.50\% & 86.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison between Env. Max and ATL algorithm configurations (5.8 GHz band).  
Furthermore, by utilizing directional antennas and distributed RF sensors in the overall architecture, one can also estimate the direction and/or the location of the jamming source. This is the next step in future work. The SOC operators can also centrally utilize AI/ML models to better detect anomalies and increase the confidence level of a jamming attempt.
## 3 Results  
Many characteristic habitats and large-scale surface structures of the tidal flats are clearly reproduced by the TerraSAR-X data. They can be visually identified and analyzed from high-resolution (HRS), spotlight (SL), and, depending on the size of the structure, even in stripmap (SM) images. Figure 3 gives an overview of the main study area, the tidal flats south of the island of Norderney, reproduced by TerraSAR-X. The added in situ photographs illustrate some of the macrostructures imaged by the SAR sensor. Some of them, such as mussel beds or fields of shell detritus, are usually displayed very clearly due to their outstanding surface roughness and specific textures. Edges also, in particular the steeper slopes of channels and gullies or the steeply sloping edges of high sandflats, are clearly shown depending on their orientation relative to the sensor.  
Other intertidal structures, however, are specifically reproduced due to the contrasting water and sediment surfaces. Most obvious, water level lines delineate the sub-littoral from the tidal area at low tide or flooded areas from exposed flats in the course of the tides. But also residual water caught in troughs and depressions helps to recognize the relief of tidal flat surfaces indicating structures such as intertidal bedforms, depressed areas, or mud fields.  
Figure 2: In situ verification of land-water-lines: (**a**) GPS measurement of channel edges synchronous to satellite overflight at 14 min. after low tide (yellow line); (**b**) location of study area (rectangle) in the tidal flats of Spiekeroog (SL of 17/05/2011, ascending orbit).
# ML-Empowered Microservice Workload Prediction by Dual-Regularized Matrix Factorization
## 4 Performance Evaluation  
This section evaluates the TDDRMF model in microservice workload prediction tasks. The experiments aim to answer the following research questions:  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Training Complexity** \\ \hline TDDRMF & \(O(k((T-d)\cdot r^{2}\cdot T))\) \\ TAMF & \(O(k((T-d)\cdot r^{2}\cdot T))\) \\ ARIMA & \(O(n\cdot p^{2}\cdot T)\) \\ Random Forest & \(O(L\cdot n\cdot(T-p)\cdot p\cdot\log(n\cdot(T-p)))\) \\ LSTM & \(O(k\cdot n\cdot(T-p)\cdot(p\cdot r_{h}+r_{h}^{2}))\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training complexity comparison.  
RQ1: How does TDDRMF compare to state-of-the-art methods in terms of prediction accuracy?  
RQ2: How effective are the temporal and nuclear norm regularization components in improving model performance?  
RQ3: Can TDDRMF achieve efficient and robust predictions in challenging scenarios, such as cold-start or dynamic dependency environments?
## 2 Conceptual Model
### Experimental Setup  
A prototype amplification mechanism was constructed that consists of a lead-zirconate-titanate (PZT), brass-reinforced bimorph bender (Mide Technology, T220-series), a 3D-printed compliant amplification structure, and a passive rigid beam. A schematic view of the amplification structure is shown in Figure 6, while Figure 7 shows an illustrative photograph of the system. The compliant amplification structure was designed as two thin beams parallel to the active bender with a torsion bar connection on which to mount the passive output beam, providing both translational and rotational compliance. The rigid element, in this case a second, un-actuated PZT beam, is used as the impact point to ensure a high CoR between active and passive components. The assembly's total size is 55 mm \(\times\) 22 mm \(\times\) 1 mm.  
The compliant mechanism was fabricated from polylactic acid (PLA), printed with an extrusion-based 3D printer (Comgrow Creativity Ender 3). Epoxy was used to mount the active bender and compliant frame to an anchor, and to attach the passive output beam to the torsion link of the compliant structure.  
The prototype was excited by sine and square wave excitation from 0 to 1000 Hz using a function generator. Input signal was amplified to 40 V driving amplitude by an amplifier (TEGAM, Model 2350). Motion was measured using a vertical displacement profiler (Keyence, LJ-V7001P) with sampling rate up to 6.4 kHz. The behavior of motion was calculated from the height of the central point of either the active bender or passive output beam when measured in profile as a function of time. The motion amplitude and dominant frequency were extracted from the fast Fourier transform (FFT) of resulting time-series measurements.
# A Cache Policy Based on Request Association Analysis for Reliable NAND-Based Storage Systems
## 3 Motivation  
To improve the performance of NAND-based storage systems, DRAM is usually added to NAND-based storage systems as cache to speed up data reading and directly absorb a large amount of data written, because DRAM supports in-place updates and has higher access speed and better reliability than NAND flash memory. When the limited cache space is full, how to select the appropriate victims to be removed from the cache has a great impact on the performance of NAND-based storage systems. The typical cache methods (such as LRU, ARC and VBBMS) are mainly to distinguish the important data  
Figure 4: An example of FP-Growth.  
based on temporal locality or spatial locality. However, we observe that there are certain associations between the accessed data for some specific workloads in the experiments. Therefore, we suggest that a cache policy should consider the temporal/spatial locality and the association rules between the accessed data to improve the cache hit ratio. The above observations are the motivation behind the paper.
# A Fully-Automatic Gap Filling Approach for Motion Capture Trajectories
## 3 Proposed Method
### Initialization  
#### 3.1.1 Auxiliary Markers  
This initialization step intends to find a set of auxiliary markers (\(A_{i}\)) that posses semi-rigid behaviour with marker \(i\), a property which the gap filling algorithm will later take advantage of. Markers placed on a rigid body (i.e., a body that will not deform or change shape) will ideally have their inter-marker distance unchanged during the course of the movement. In this paper, we refer to semi-rigid behaviour to define markers for which their inter-marker distance remains (roughly) constant, without requiring them to be placed on the same (previously defined) rigid body. The semi-rigid behaviour relaxes the assumption of markers being placed on the same rigid body but still presenting characteristics compatible with a rigid body movement assumption.  
The auxiliary markers of a certain marker \(i\) will be defined by analysing how constant the distance between \(i\) and the other markers \(j\) are considering their trajectories over time. Every marker \(j\) for which the standard deviation of the distance between \(i\) and \(j\) over all frames is lower than \(d_{thr}\) cm will be considered as a potential auxiliary marker of \(i\). Marker \(i\) can only have up to \(a_{thr}\) auxiliary markers. In the case where there are more potential auxiliary markers than \(a_{thr}\), only the \(a_{thr}\) with lower standard deviation will be considered.  
In order to find the auxiliary markers, the trajectories of the current sample (potentially with gaps) may be used. Alternatively, any other calibration trajectory (without gaps) that uses the same marker model may be used for initialization.
# Structural Analysis and Design of Reinforced Concrete Bridge Corbels
## 1 Introduction  
The refurbishment of existing infrastructures is becoming very important due to their aging or lack of maintenance [1, 2, 3, 4]. A common problem of existing bridges is the installation or substitution of safety barriers that must be replaced due to their aging or increased demand [5, 6, 7]. The substitution of safety barriers is usually associated with the partial reconstruction of the upper part of the concrete curb and the addition of post-installed reinforcing or anchoring bonded systems.  
In the last decades, these types of connections have been largely used thanks to their flexibility and to their wide range of applications. Several researchers investigated the limits of the field of application (exploiting the performances in low- and high-strength concrete [8, 9, 10, 11] and the reliability of these systems [12, 13, 14, 15, 16]).  
Quite frequently, however, this affordable connection does not satisfy code requirements for the design of connectors considered as overlapped reinforcing bars (according to [16, 17]) or as post-installedanchors (according to [18; 19]). The main challenges are the reduced thickness of the concrete corbel, which often does not guarantee enough bonded length, and the limited edge distance, preventing the development of the full concrete capacity according to anchor theory [20; 21; 22].  
Nevertheless, the use of post-installed bonded anchors is very attractive due to their high versatility and strength [23]. This study considered a new solution presented in [6] that meets the common refurbishment practice requirements in Italy (with slight modifications) and is based on the removal and rebuilding of the damaged top concrete layer of the curb. The novelty consists in the installation of additional U-shaped post-installed bonded rebars (with a prescribed spacing along the whole corbel length) before the new, top, concrete layer is cast. The safety barriers are then installed into the new reinforced curb via three post-installed bonded bars (according to one of the most typical layouts adopted in Italy). The adoption of this layout represents the minimum viable solution for the application investigated in this study and that the use of a larger number of anchors, while affecting the design parameters, would not hinder the applicability of the proposed model. To evaluate the influence of the U-shaped rebars in the connection system, three full scale specimens were tested.  
[PERSON], S et al. state that the transfer mechanism of the forces from the tensioned bars to the U-shaped rebars and their anchorage into the bridge deck was studied via a 3D strut-and-tie model and validated with numerical analyses [6]. This study considered the same geometry but was focused on the corbel behavior. The aim of the paper was to propose an analytical, simplified formulation that allows designers to verify the concrete corbel. This formulation derived from a strut-and-tie model, in which the geometry was defined on the basis of a finite element model (FEM) [24; 25; 26] considering a different specimen geometry.
## 2 Materials and Methods
### Validation of the Prototype  
In determining the electrical conditions of the prototype, NaCl salt solution (BIOTEC batch: 48943; Sao Jose dos Pinhais, Parana, Brazil) was used in a concentration of 2 mol \(\cdot\) L\({}^{-1}\), and from this, dilutions at concentrations of 12.5%, 25%, 37.5%, 50%, 75%, and 100% \(V/V\) with distilled water were made. For the bioimpedance measurement, 150 mL of sample was used. A portable conductivity tester (Hanna HI98304 Dist 4; Woonsocket, RI, USA) was used to measure electrical conductivity and temperature.
# Using the Hall Effect for Monitoring the Starter Condition in Motor Vehicles
## 3 Experimental Studies of Induction Distribution in the Magnetic Gap
### The Course of Measurements  
Testing the starter with the Hall sensors consisted in recording the voltage waveforms obtained from the Hall sensors, which was commensurate with magnetic induction (magnetic flux B is proportional to Hall voltage and inversely proportional to the supply current with a constant Hall sensor coefficient). The Hall voltage signal was amplified on the voltage amplifier.  
Figure 10: View of the measurement stand for testing the starter with the Hall sensor.  
Figure 9: Block diagram of the stand: 1—starter, 2—Hall sensor, 3—battery, 4—Hall sensor supply, 5—signal amplifier, 6—protective system, 7—computer with an analogue-to-digital converter, 8—monitor, 9—printer.  
As one Hall sensor was placed on the rotor and the other one on the stator, it can be assumed that both Hall sensors were in the air gap and thus recorded the course of induction in the gap. It turns out that such a course is different for different failures and differs from the course of an efficient starter. Induction distribution in the air gap can be a diagnostic parameter of the starter and can be used to assess its technical condition. The tests were carried out in such a way that the course of induction was recorded for the unloaded starter, i.e. at its idle running, and for the loaded one, but with the same load for all cases.
# Multivariate Statistical Process Control Using Enhanced Bottleneck Neural Network
###### Abstract  
Monitoring process upsets and malfunctions as early as possible and then finding and removing the factors causing the respective events is of great importance for safe operation and improved productivity. Conventional process monitoring using principal component analysis (PCA) often supposes that process data follow a Gaussian distribution. However, this kind of constraint cannot be satisfied in practice because many industrial processes frequently span multiple operating states. To overcome this difficulty, PCA can be combined with nonparametric control charts for which there is no assumption need on the distribution. However, this approach still uses a constant confidence limit where a relatively high rate of false alarms are generated. Although nonlinear PCA (NLPCA) using autoassociative bottle-neck neural networks plays an important role in the monitoring of industrial processes, it is difficult to design correct monitoring statistics and confidence limits that check new performance. In this work, a new monitoring strategy using an enhanced bottleneck neural network (EBNN) with an adaptive confidence limit for non Gaussian data is proposed. The basic idea behind it is to extract internally homogeneous segments from the historical normal data sets by filling a Gaussian mixture model (GMM). Based on the assumption that process data follow a Gaussian distribution within an operating mode, a local confidence limit can be established. The EBNN is used to reconstruct input data and estimate probabilities of belonging to the various local operating regimes, as modelled by GMM. An abnormal event for an input measurement vector is detected if the squared prediction error (SPE) is too large, or above a certain threshold which is made adaptive. Moreover, the sensor validity index (SVI) is employed successfully to identify the detected faulty variable. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms, and is hence expected to better monitor many practical processes.  
multivariate statistical process control (MSPC); bottleneck neural network (BNN); Gaussian mixture model (GMM); adaptive confidence limit (ACL); wastewater treatment plant (WWTP); benchmark simulation model No. 1 (BSM1) +
Footnote †: journal: _Algorithms_