esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Multiscenario Planning Approach for Urban Drainage Systems
## 1 Introduction
The simplified overall system level (Distribution A in Figure 1) in previous studies did not consider individual components in the RC-calculation process. Thus, to compare the compromise and individual solutions, RC calculations must also consider the individual components (e.g., pipe sizes and manhole depth) (Distribution B in Figure 1). Simultaneously accounting for stable UDS performance and the various rainfall patterns would provide critical insight into an effective UDS design and planning for fluctuating future conditions. Figure 1: Schematic of regret cost (RC) concept at two levels. (**A**) Overall system; (**B**) individual components. In this study, the pipe design and layout for a given set of future scenarios were optimized. The following future scenarios were developed: (1) different spatial and temporal rainfall patterns and (2) specifications of manholes for drain-inflow distribution in the nodes of the network. These scenarios were applied to obtain an optimal UDS solution to provide guidelines and assist with decision making during the UDS planning. The proposed model consisted of a two-phase optimization process as follows: (1) A scenario-optimal solution was obtained in the scenario-optimal planning model (Phase I) and (2) the common elements across all scenarios were identified, and the total RC was calculated considering individual component-wise RCs to obtain a compromise solution (Phase 2). In the proposed model, to obtain an optimal solution at each phase, a hydraulic-hydrologic simulation linked with a metaheuristic algorithm was implemented for each phase of the optimization process. The proposed model was validated in the UDS planning of a hypothetical grid-type network. Finally, the compromise solution in the proposed approach was compared with those in the scenario-optimal planning model with respect to cost effectiveness and stability of the UDS performance.
# Assessment of Imaging Protocol and Patients Radiation Exposure in Computed Tomography Colonography
## 2 Materials and Methods  
### Population  
In this study, conducted over the period January 2017 to June 2019, all CTC procedures were reviewed retrospectively (via the hospital information system, HIS, the radiological information system, RIS, and the picture archiving and communication system, PACS). The study sample size is 55 procedures, 25 males (45%), and 30 females (55%). The mean, standard deviation, and range of the patient age (years) were 57.96 \(\pm\) 13.6 (24-83), 16.3% of the cohort being below 50 years of age. The mean \(\pm\) sd and range of patient weight (kg) is 76 \(\pm\) 22 (50-120). The patients in this study were classified in several ways: (i) into two groups based on the CT machine; (ii) four groups based on the applied protocol, and; (iii) three groups based on the procedure results. The researchers obtained Institutional Review Board (IRB) approval from the ethics and research committee at KFMC (No: FWA00018774). The patients in this study, all adults, were referred to the radiology department as a result of the usual clinical criteria. The CTC procedure clinical indications are screening, limited colonoscopy, colonoscopy contraindication, or known cancer/follow up.
# Multi-Feature Extension via Semi-Autoencoder for Personalized Recommendation
## 5 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multirow{2}{*}{**Methods**} & \multicolumn{6}{c}{**Proportion of Training Data**} \\ \cline{3-6}  & & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** \\ \hline \multirow{6}{*}{MAE} & SVD++ & 0.752 & 0.747 & 0.741 & 0.726 & 0.722 \\  & I-AutoRec & 0.729 & 0.723 & 0.714 & 0.704 & 0.698 \\  & HCRSA & 0.727 & 0.724 & 0.713 & 0.711 & 0.703 \\  & GraphRec & **0.721** & **0.714** & 0.709 & 0.703 & 0.701 \\  & PRKG & 0.729 & 0.723 & 0.714 & 0.704 & 0.698 \\  & MFSAE & 0.723 & 0.715 & **0.706** & **0.700** & **0.692** \\ \hline \multirow{6}{*}{RMSE} & SVD++ & 0.979 & 0.965 & 0.949 & 0.932 & 0.924 \\  & I-AutoRec & 1.021 & 1.003 & 0.979 & 0.964 & 0.953 \\ \cline{1-1}  & HCRSA & 0.927 & 0.921 & 0.907 & 0.891 & 0.897 \\ \cline{1-1}  & GraphRec & **0.916** & **0.905** & 0.899 & 0.893 & 0.887 \\ \cline{1-1}  & PRKG & 0.928 & 0.917 & 0.913 & 0.899 & 0.895 \\ \cline{1-1}  & MFSAE & 0.917 & 0.907 & **0.899** & **0.890** & **0.882** \\ \hline \hline \end{tabular}  
* Tip: The bolder ones mean better.  
\end{table} 
Table 3: The performance of MAE and RMSE on MovieLens 100 K dataset.
###### Abstract  
Software-Defined Radio (SDR) technology has been a very popular and powerful prototyping device for decades. It finds applications in both fundamental research or application-oriented tasks. Additionally, the continuing rise of the Internet of Things (IoT) necessitates the validation, processing, and decoding of a large number of received signals. This is where SDRs can be a valuable instrument. In this work, we present an open-source software system using GNU Radio and SDRs, which improves the comprehension of the physical layer aspects of Internet of Things communication systems. Our implementation is generic and application-agnostic. Therefore, it can serve as a learning and investigation instrument for any IoT communication system. Within this work, we implement the open-source DASH7 Alliance Protocol (D7 AP). The developed software tool can simulate synthetic DASH7 signals, process recorded data sets, and decode the received DASH7 packets in real time using an SDR front-end. The software is accompanied by three data sets collected in controlled, indoor, and suburban environments. The experimental results revealed that the total packet losses of the data sets were 0%, 2.33%, and 16.67%, respectively. Simultaneously, the three data sets were received by a dedicated DASH7 gateway with total packet losses of 0%, 3.83%, and 7.92%, respectively.  
EPANET, LoRaWAN, LoRa; Sigfox; DASH7; GNU Radio; SDR
## 2 Methodology  
The methodology followed to obtain the objectives proposed in the article follows this sequence (Figure 1):  
1. The analysis of the methodological development towards obtaining the CEACE method (Certificado Ecologico Andaluz de Construccion de Edificios: Andalusian Ecological Certificate for the Construction of Buildings). The input data consists of the project budget, its quantity surveying, and cost assessment, while the output data involves the project footprints, CDW, and social aspects mentioned above;
2. The CEACE method will be applied and validated through the analysis of 15 projects defined as combinations of residential projects with different constructive solutions. A sample of 200 housing projects is used for the standardisation of indicators that lead to global, economic, environmental, and social value;
3. Obtaining results: Values of the following parameters will be obtained for each of the 15 projects: Costs, CF, EF, WF, and CDW (normalised by GFA). Subsequently, an aggregate indicator (I_CEACE) will be obtained, based on a standardisation6 and weighting process analysed in Section 3.3, for each of the 15 projects, which will contain environmental, economic, and social information on them. The percentage of influence of each of the work chapters in relation to the costs, CF, EF, WF, and CDW parameters will also be determined;
4. Study of the influence of the use of Environmental Product Declarations (EPD) in the CEACE method: The project of the 15 that has generated the highest value of the I_CEACE indicator will be analyzed. Based on the analysis obtained from the CEACE method, it will be possible to determine the construction unit that generates the greatest impact, subsequently selecting the materials that compose it. Alternatives to these materials that have EPD will be sought in the market [86]. Finally, different scenarios will be evaluated to analyse the influence of the use of materials with EPD on the I_CEACE indicator;
5. Discussion of results. A critical analysis of the results will be carried out. From this discussion, it will be demonstrated that the results are sensitive to changes in the foundation and typology of the building.
# Optimization of Sensor Placement for Modal Testing Using Machine Learning
## 6 Conclusions  
Machine learning represents an appealing solution to the issue of sensor selection for modal testing. Current algorithms used for sensor selection are iterative when implemented and, for large geometries and complex models, the computational time can be substantial. The method presented here exploits the ability of the random forest approach to select the most important feature of a database for modal analysis sensor selection based on finite element models of the system. A database is created in the frequency domain; the responses of interest at every nodal location of the model are considered possible sensor locations. The output of the random forest feature selection approach is defined based on a global output that characterizes the system. Three options are evaluated in the paper: the ODS of the system, a normalized ODS, and the average FRF at the specific frequency. The approach is applied to a one-dimensional model of a vibrating cantilever beam. The optimal sensor locations identified by the proposed approach are compared with the sensors selected by the Effective Independence Method and appear to yield similar results to the proposed approach. Within the proposed approach, the choice of the operational deformed shape (ODS) as a global parameter appears to be more robust than the other proposed options. The approach is evaluated for sensitivity with respect to mesh size and type of excitation signal and is robust to any changes in these parameters.  
The work presented in the paper offers several insights into the use of machine learning approaches for sensor selection. As the research progresses, an experimental application of the approach will be conducted, as well as the application of the methodology to more complex components.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **EIM** & **(a) ODS** & **(b) Norm ODS** & **(c) Avg FRF** \\ \hline Computational time \([10^{-3}\cdot\text{s}]\) & 173.671 & 5.653 & 7.619 & 7.146 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of computational time between EIM and the random forest feature selection approach for case 2 (Simulations run on 13 th Gen Intel(R) Core(TM) i7-1365U 1.80 GHz).  
**Author Contributions:** Conceptualization, M.C. and T.K.; methodology, M.C. and T.K.; software, M.C. and T.K.; validation, M.C. and T.K.; formal analysis, T.K.; investigation, M.C. and T.K.; resources, M.C. and F.D.K.; data curation, T.K.; writing--original draft preparation, M.C. and T.K.; writing--review and editing, M.C., T.K. and F.D.K.; visualization, M.C. and T.K.; supervision, M.C. and F.D.K.; project administration, M.C.; funding acquisition, M.C. and F.D.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** The data presented in this study are available in article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges
## 2 Fog and Edge Computing: Modelling and Simulation Challenges
### Mobility  
Recent studies focus on the emergence of 5G networks and the interaction between these networks and fog and edge computing. 5G networks offer network improvements through optimization of mobile resource usage, large data pre-processing, and context-aware services (using cell load, user location, and allocated bandwidth as information) [20]. Notwithstanding these improvements, as each fog and edge application may have different latency requirements and may generate different types of data and network traffic, a mechanism may be required to differentiate delay-sensitive flows such as network slicing [26].  
Modelling user mobility aspects requires the implementation of geographic awareness logic, for example calculation of the nearest mobile access point based on user coordinates at each simulation timestep. Furthermore, availability and access to real-world data on end user mobility is problematic both legally and technically. Additional calculations further increase the complexity and computational resource demands of a given simulation platform. Intelligent model generators are one solution for creating fog and edge infrastructure workload models based on 3 rd party socio-demographic and geographic data that can be used for simulation purposes [27].
# A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV
## 2 Materials and Methods
### Two-Step Ensemble Method Based on MetaCost Framework  
#### 2.3.1 MetaCost Framework  
MetaCost is an ensemble framework used to convert cost-insensitive classifiers into cost-sensitive classifiers. The basic idea of MetaCost is to train multiple meta-classifiers organized by bagging first, then calculate the expected cost according to the prediction probability and misclassification cost matrix based on Bayesian theory, relabel training set based on the expected cost, and finally train an ensemble modified classifier on relabeled training data [23]. The key work of MetaCost is as follows:  
Given a solid cost matrix, an instance should be classified into a class that generates the minimum expected cost. The expected cost of classifying an instance \(x\) into class \(C_{i}\) can be expressed as the following:  
\[E(C_{i}|x)=\sum_{j}P(C_{j}|x)\cdot c_{ij} \tag{5}\]  
where \(P(C_{j}|x)\) is the probability of class \(C_{j}\), which is the actual class of instance \(x\).  
Then we relabeled the instance \(x\) based on the expected cost ranking of all classes as the following:  
\[Label_{x}=\text{argmin}E(C_{i}|x) \tag{6}\]  
Subsequently, we retrained the classifier to the relabeled training set in the end. The core advantage of the MetaCost framework is its suitability for almost all kinds of data sets, thanks to the ensemble approach.  
Aiming at the fault diagnosis task, the MetaCost-based cost-sensitive model can be improved in (1) the generation of training subsets for meta-classifiers; (2) the selection of meta-classifiers; and (3) the organizational form of meta-classifiers.
## 2 Materials and Methods
### Entropy Weight Hierarchy Combination Weight  
#### 2.1.1 Entropy Weight Method Weight Calculation  
A material system's degree of disorder can be quantified according to its entropy, which objectively measures the amount of information contained in known data without subjective bias. This method is based on a solid mathematical foundation and involves several stages for calculating weight using the entropy weight method.  
1. Setting up the initial data matrix An original data matrix is created, presuming an evaluation index system has \(n\) evaluation indexes and m evaluated items: \[A=\left(a_{ij}\right)_{m\times n}=\begin{bmatrix}a_{11}&\cdots&a_{in}\\ \vdots&\ddots&\vdots\\ a_{m1}&\cdots&a_{nm}\end{bmatrix}\] (1)
2. The original data is dimensionlessly processed: \[r_{ij}=\frac{a_{ij}-min\left\{a_{ij}\right\}}{max\left\{a_{ij}\right\}-min \left\{a_{ij}\right\}}\] (2) After dimensionless processing, \(S=\left(s_{ij}\right)_{m\times n}\). Then, S is normalized: \[s_{ij}^{\prime}=\frac{s_{ij}}{\sum_{i}\sum_{i}s_{ij}}\] (3)
3. Calculate the characteristic proportion \(f_{ij}\) of the characteristic object of the \(i\)-th evaluation object under the \(j\)-th index: \[f_{ij}=\frac{s_{ij}^{\prime}}{\sum_{i=1}^{m}s_{ij}^{\prime}}\] (4)
4. Calculate the entropy value \(Hj\) of the \(j\)-th index: \[H_{j}=k\sum_{i=1}^{m}f_{ij}Inf_{ij}\] (5) Among them, \(k=\frac{1}{ln(m)}\).
5. Introduce the difference coefficient \(a_{j}\) and calculate the difference coefficient of the \(j\)-th index: \[a_{j}=1-H_{j},\;\mathrm{j}\;=\;(1,\;2,\;\ldots,\;\mathrm{n})\] (6)
6. Determine the entropy weight of the \(j\)-th index, that is, the weight of the \(j\)-th index: \[x_{j}=\frac{a_{j}}{\sum_{j=1}^{n}a_{j}},\;x_{j}\in[0,1],\;\;\sum_{j=1}^{n}x_{i }=1\] (7)
7. Calculate the weight of each index. \[W=\begin{pmatrix}w_{1}\\ \vdots\\ w_{m}\end{pmatrix}=S\cdot\mathrm{X}=\begin{pmatrix}s_{11}&\cdots&s_{1n}\\ \vdots&\ddots&\vdots\\ s_{m1}&\cdots&s_{mn}\end{pmatrix}\begin{pmatrix}x_{1}\\ \vdots\\ x_{n}\end{pmatrix}.\] (8)
## 4 Arsenic Removal Processes
### Adsorptive Processes  
Adsorptive processes include ion exchange [94,95], iron oxide/hydroxide coated sand [89,90,111,112], iron-hydroxide coated alumina [91], granular ferric hydroxide (GFH) [113,114] and natural iron ones [115]. A desirable feature of the available adsorbents for developing countries is that they should be cheap, readily available and effective [116].  
The basis of adsorption methods is the ability to achieve the attraction of soluble arsenic species to the surface of a suitable sorbent. These methods are cost-effective and have attracted a considerable interest for the development of adsorbents that are capable of cheap and efficient arsenic removal from drinking water. The two most commonly used sorbents are based on: (a) iron compounds, including akaganite (\(\beta\)-FeOOH), amorphous hydrous ferric oxide (FeOOH), goethite (\(\alpha\)-FeOOH) and poorly crystalline hydrous ferric oxide [111], and (b) aluminum compounds, including activated alumina \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) and gibbsite Al(OH)\({}_{3}\)[117].  
Among other sorbents considered are carbon from coconut husks [118], carbon from fly ash [119,120], hybrid polymeric materials [121], red mud [122,123], titanium dioxide [124,125], manganese dioxide [126,127], orange peel [128] and fungal biomass-based bio-sorbent [86,129].
# Using Hybrid LSTM Neural Networks to Detect Anomalies in the Fiber Tube Manufacturing Process
## 1 Introduction
This company specializes in the production and delivery of fiber optic technology solutions and is one of the leading manufacturers of fiber optic cables and telecommunication components in Central and Eastern Europe. The designed anomaly detection system allows for the reduction of disposal and repair costs for tube sections produced out of specification on the tube extrusion production line. The system described in the main section of the paper incorporates a hybrid Long Short-Term Memory (LSTM) network model, designed to process data streams collected from a tube extrusion production line. The input module, responsible for preprocessing, performs multiresolution analysis of the recorded process parameters. It subsequently assigns the current process state to one of the predefined classes, representing specific production anomalies, and directs it to the appropriate LSTM network modules. The three-channel network's training process enabled the effective identification of five distinct classes within the monitored tube production process. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors (Year)** & **Research Description** & **Applied Models** & **Key Results** \\ \hline [PERSON] et al. (2023) [6] & Anomaly detection in smart factories with sensor-to-sensor transfer & LSTM, Transfer learning, Neural networks & High anomaly detection accuracy, effective knowledge transfer between sensors \\ \hline [PERSON] et al. (2023) [10] & Digital twin for real-time production line monitoring & Digital twin, LSTM, Autoencoders & 40\% defect reduction, dynamic adjustment of production parameters \\ \hline [PERSON] et al. (2023) [5] & Production cost prediction in Industry 4.0 & LSTM, Decision trees & 92\% prediction accuracy for costs, reduction of defect-related losses \\ \hline [PERSON] et al. (2022) [7] & Anomaly detection in fiber optic monitoring & LSTM, SVM & 94\% anomaly detection accuracy, effective detection of optical cable damages \\ \hline [PERSON] et al. (2020) [12] & Automatic anomaly detection in production machines & LSTM, Linear regression, k–NN & 87\% anomaly detection accuracy, 25\% downtime reduction \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of key elements, applied models, and results for selected works on anomaly detection in production processes.
# Synchronizing Many Filesystems in Near Linear Time
###### Abstract  
Finding a provably correct subquadratic synchronization algorithm for many filesystem replicas is one of the main theoretical problems in operational transformation (OT) and conflict-free replicated data types (CRDT) frameworks. Based on the algebraic theory of filesystems, which incorporates non-commutative filesystem commands natively, we developed and built a proof-of-concept implementation of an algorithm suite which synchronizes an arbitrary number of replicas. The result is provably correct, and the synchronized system is created in linear space and time after an initial sorting phase. It works by identifying conflicting command pairs and requesting one of the commands to be removed. The method can be guided to reach any of the theoretically possible synchronized states. The algorithm also allows asynchronous usage. After the client sends a synchronization request, the local replica remains available for further modifications. When the synchronization instructions arrive, they can be merged with the changes made since the synchronization request. The suite also works on filesystems with a directed acyclic graph-based path structure in place of the traditional tree-like arrangement. Consequently, our algorithms apply to filesystems with hard or soft links as long as the links create no loops.  
file synchronization; algebraic model; optimistic synchronization; linear complexity 08A02; 08A70; 68M07; 68P05