esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation Method
### Comprehensive Evaluation and Decision Analysis
#### 4.2.4 Fuzzy Comprehensive Evaluation (FCE)  
The FCE method is based on fuzzy mathematics and utilizes the concept of fuzzy relationship synthesis to measure factors that lack clear boundaries and are difficult to quantify. This method quantifies fuzzy indicators that pertain to the entities under evaluation (i.e., it assesses the degree of membership) by creating hierarchical fuzzy subsets, followed by applying the fuzzy transformation principle to consolidate each indicator. It proves highly applicable in tackling non-deterministic challenges and improves the efficiency of addressing non-quantitative matters. By converting these hard-to-quantify elements into numerical evaluations, the FCE method allows for a more logical appraisal of GSEUUS. The procedure commences with identifying the evaluation objects and factors, defining evaluation levels, and forming a fuzzy relationship matrix through single-factor assessments. Afterward, the weights for each factor are established, and a synthesis operation is executed on the weight vector alongside the fuzzy relationship matrix to produce a comprehensive evaluation outcome, which is subsequently analyzed and interpreted based on the results obtained.  
The FCE method presents various benefits, such as the capacity to handle fuzzy evaluation elements, deliver comprehensive evaluation data, and exhibit significant applicability to both subjective and objective variables. Nevertheless, it is accompanied by certain drawbacks, including intricate calculations and a somewhat subjective approach to defining the index weight vector. Furthermore, despite its robustness, the method faces challenges in handling the correlation issue among evaluation elements. When the index set is extensive, a super-fuzzy phenomenon may occur, which can ultimately affect the precision of the evaluation outcomes (Figure 7).  
Numerous studies have explored the use of the FCE method for the GSEUUS. A 3D evaluation of rock mass quality is accomplished by refining the FCE method, which enhances the accuracy and reliability of the assessment [51]. Comparable approaches have been utilized for the suitability evaluation of UUS development in Ningbo [134]. When evaluating UUS's suitability for development and use, the integration of the cloud model with the analytic hierarchy process effectively addresses the ambiguity and uncertainty present in the evaluation process [135]. In assessing the appropriateness of UUS development in Qingdao's main urban area, an index system incorporating rock and soil features, groundwater, and geological structure was established, producing scientifically valid zoning outcomes via the fuzzy comprehensive evaluation method which provides a crucial basis for effective UUS development and use [128]. Overall, these investigations illustrate the efficacy and practical utility of the fuzzy comprehensive evaluation method in the context of assessing UUS development and utilization (Figure 8c and Table 1).
## 5 Discussion
### Wake-Up Tasks and [PERSON]'s Motivation-Ability-Prompt (MAP) Model  
The study results are well linked with [PERSON]'s MAP behavior model [14], which explains human behavior through three elements: (1) motivation, (2) ability, and (3) prompts, as shown in Figure 4. Inthis model, if at least one of the three elements is missing, the anticipated behavior is rarely triggered. This model can thoroughly explain mobile alarm users' usage behavior. A user's motivation can vary depending on the next morning's schedule or their willingness to build the habit of waking up early. Furthermore, the ability in MAP corresponds to how much the user is confident in waking up on time. According to the MAP theory, the activation threshold for awakening on time can basically be determined by motivation and ability. Additionally, the wake-up tasks in the mobile alarm app can be seen as prompts.  
For example, in the context when a user's motivation is low, and their ability is high, the wake-up tasks do not need to be that hard. The study results show that most users frequently used the easiest task (_normal_task_) and mostly set it to recur. This probably indicates that motivation is usually lower than ability for most users. Even the easiest task can trigger the targeted behavior (awakening on time) because it may not be that important. However, the regression analysis results reveal that hard-taskers usually seem less pressured to wake up on time (e.g., late and irregular alarm hour, many snoozes, and relatively less use in weekdays), so they do not need to build the habit of waking up early (i.e., the ability is relatively low). However, probably, they occasionally face situations that require them to get up early (i.e., the motivation becomes higher), and the hard tasks become more valuable at this time.  
Developing the ability requires time and effort, so diverse wake-up tasks in mobile alarm app scan be useful to deal with different activation levels depending on contextual motivation and individual ability. One further idea would be a wake-up task recommendation based on the user's schedule. For example, a mobile alarm app can be connected with a calendar app and encourage the user to set a challenging wake-up task only if there is an important event early in the next morning. Such flexible interaction can reduce the overall pressure or burden on the app user and convince them to continue using the app.
# Multi-Step Traffic Speed Prediction Based on Ensemble Learning on an Urban Road Network
## 2 Related Work
For example, the ensemble learning models have been proved to achieve much better performance in prediction accuracy than individual ones [26]. Nowadays, ensemble learning has been used in many fields of traffic prediction, such as traffic sign detection and recognition [27], traffic speed [28], short-term traffic volume [29], and traffic incident detection [30]. However, current ensemble methods are not explicitly designed to deal with spatiotemporal data, and how to effectively ensemble multiple models while utilizing the spatiotemporal information remains a challenging, but practical, problem. There is a tendency that more and more scholars draw their attentions on multi-step prediction. Usually, multi-step traffic prediction can provide drivers and traffic agencies more chances and time to pre-make better decisions than one-step prediction. [PERSON] et al. [31] reported a hybrid deep ensemble approach by integrating 3D convolutional neural network (CNN) with ensemble empirical mode decomposition (EEMD), and yielded the high performance regardless of prediction time step increases from 1 to 6. Notably, although the prediction time step increases, the evolving fuzzy neural network (EFNN) model with the consideration of the periodic pattern can also outperform other models (ANN, SVM, ARIMA, vector autoregressive model) with smaller prediction errors and slower raising rate of errors [5]. [PERSON] et al. [7] proposed a novel deep learning framework named attention graph convolutional sequence-to-sequence model (AGC-Seq2 Seq) to accurately capture the temporal heterogeneity in multi-step traffic speed prediction. [PERSON] et al. [32] embedded a microscopic traffic simulation of car-following model into dynamic multi-step traffic prediction and leaded to less than 10% error in speed prediction even for ten steps into the future.
# Review of Artificial Intelligence Adversarial Attack and Defense Technologies
## 2 Adversarial Samples and Adversarial Attack Strategies
### Adversarial Capabilities and Goals
#### Adversarial Goals  
The adversarial goals can be inferred from the uncertainties of models. According to the impact on models' output integrity, adversarial goals can be divided into four categories, i.e., confidence reduction, misclassification, targeted misclassification, and source/target misclassification.  
* **Confidence Reduction.** The adversaries attempt to reduce the confidence of prediction for the target model, e.g., the adversarial samples of a \"stop\" sign is predicted with lower confidence.
* **Misclassification.** The adversaries attempt to change the output classification of input to any class different from the original class, e.g., an adversarial sample of a \"stop\" sign is predicted to be any class different from the \"stop\" sign.
* **Targeted Misclassification.** The adversaries attempt to change the output classification of the input to a special target class, e.g., any adversarial samples inputted into a classifier is predicted to be a \"go\" sign.
* **Source/Target Misclassification.** The adversaries attempt to change the output classification of a special input to a special target class, e.g., a \"stop\" sign is predicted to be a \"go\" sign.
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 4 Data Processing
### Data Dissemination
#### 4.4.2 Early Warning and Alarms  
Early warnings are issued only if significant deformation has been detected. Depending on the amount of deformation observed, different early warning levels are issued. The number and value of thresholds used to define these levels are currently being determined, but early warning will most probably cover the range from mm per year up to cm or dm per hour. Based on how many and which neighbouring sensor nodes show deformation, the affected area and landslide mass are estimated and reported. If a further or sudden strong acceleration is detected, the system can issue an immediate (evacuation)-alarm using acoustic and/or light signals. Another factor that is important for the early warning are measurements of the trigger parameters, most importantly rainfall. It is measured on site by a weather station and a rainfall radar of the city of Medellin is used for forecasts. To make the connection between rainfall and groundwater levels, a hydrogeological model is used and combined with the data gathered during the test phase of the system. This test phase serves to make the system more accurate for predicting the influence of rainfall on groundwater levels, and therefore, hazard levels. Depending on the hazard state, deformation rate and the affected area different actors (experts, trained community members, first responders, whole population) are informed. Usually warnings will be checked by an expert before they are sent to the inhabitants. Only when at least two neighboring sensor nodes show very strong acceleration at the same time and the data has successfully been crosschecked according to the above data analysis procedure, the warnings are issued without review. Right now, we plan on warning the inhabitants using the Inform@Risk App, as well as multiple sirens which will be installed in the area. The exact definition of the warning levels, warning contents and the information dissemination paths are being developed in a participatory process, ensuring that, if possible, each actor gets the required information at the right point in time.
# Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models
## 4 Methods
### Dataset Description  
Eurecom/elasticmon5G2019 is the dataset used for training and evaluating the machine learning models that carry out a binary classification. The dataset contains the following attributes: 4G/5G MAC (Media Access Control), RRC (Radio Resource Control), PDCP (Packet Data Convergence Protocol) statistics. The dataset was constructed by monitoring 100 features per measurement, storing a total of 26082 examples. A pre-processing stage was executed, and 5 steps were followed: (1) a timestamp for the measurements was implemented, (2) stable features were deleted from the dataset, (3) overflows and metric spikes were removed using the mean or median of the other values, (4) a correlation matrix was applied in order to know the features that correlate the most with CQI, (v) the top 15 features were selected (see Table 1a), and (vi) the dataset was scaled between 0 and 1, which is required for training some models due to convergence being more difficult if data are not scaled.  
The dataset was divided into 70% for training and 30% for testing; however, a third set called _validation_ is contained inside the _training_ set since we used k-fold cross-validation with \(k=10\). The ML algorithm is trained with 9 out of 10 parts of the _training_, and the accuracy is measured over the remaining one-tenth of the set, which is the _validation_. This process is iterated 10 times, and a global accuracy for the _training_ is obtained by averaging the accuracy over the 10 _validation_ sets. The labels to classify the channel quality were assigned according to the technical specification of the 3 rd Generation Partnership Project (3 GPP) [48], where a CQI from 0 to 6 is considered low quality and a CQI from 7 to 15 is considered high quality, see Table 1b.  
Figure 5: Block Diagram and Module of the Sigmoid Function.  
Figure 6: Comparator Module of MLP and Logistic Regression.
###### Abstract  
The study of marine Lagrangian transport holds significant importance from a scientific perspective as well as for practical applications such as environmental-pollution responses and prevention (e.g., oil spills, dispersion/accumulation of plastic debris, etc.). In this regard, this concept paper introduces the Smart Drifter Cluster: an innovative approach that leverages modern \"consumer\" IoT technologies and notions. This approach enables the remote acquisition of information on Lagrangian transport and important ocean variables, similar to standard drifters. However, it offers potential benefits such as reduced hardware costs, minimal maintenance expenses, and significantly lower power consumption compared to systems relying on independent drifters with satellite communication. By combining low power consumption with an optimized, compact integrated marine photovoltaic system, the drifters achieve unlimited operational autonomy. With the introduction of these new characteristics, the Smart Drifter Cluster goes beyond its primary function of mesoscale monitoring of marine currents. It becomes readily applicable to numerous civil applications, including recovering individuals and materials at sea, addressing pollutant spills, and tracking the dispersion of marine litter. An additional advantage of this remote monitoring and sensing system is its open-source hardware and software architecture. This fosters a citizen-science approach, enabling citizens to replicate, utilize, and contribute to the improvement of the system. Thus, within certain constraints of procedures and protocols, citizens can actively contribute to the generation of valuable data in this critical field.  
Smart Drifter Cluster; marine current; marine drifters; wireless communication; marine litter dispersion; citizen science; IoT +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 2 Formulation of the Power Flow Problem  
### General Representation  
A general representation of a bipolar DC network is given in Figure 1. It consists of a grid with a positive pole \(p\), a negative pole \(n\), and a neutral pole \(o\) that may be solidly grounded. In a given node \(k\), the bipolar grid could contain multiple loads connected between positive and negative poles \(p_{dk,p-n}\) or between positive and neutral poles \(p_{dk,po}\) (analogously, a negative and neutral pole \(p_{dk,no}\)). Naturally, the system may be unbalanced. To deal with multiple constant power loads, we start with the analysis of the solidly grounded case and then extend the formulation to the non-grounded case.  
The bipolar DC grid is represented by an oriented graph \(\mathcal{F}=\{\mathcal{L},\mathcal{E}\}\), where \(\mathcal{L}=\{1,2,\ldots,l\}\) is the set of hypernodes and \(\mathcal{E}\subseteq\mathcal{L}\times\mathcal{L}\) is the set of hyperbranches. Each hypernode and hyperbranch has three components represented by the set \(\mathcal{B}=\{p,o,n\}\); these contain the positive, neutral, and negative poles. In this manuscript, the subscripts \(k\) and \(m\) are entries of \(\mathcal{L}\), and the electrical connections are represented by the nodal conductance matrix \(\mathbf{G}_{\text{bus}}\). Capital letters represent vector variables or constants, while capital bold letters represent matrix variables or constants, and their entries are lower-case letters. The main variables, constants, and indices are presented in the nomenclature below. In the present work, we call the loads connected between a positive and negative pole a C-type connection, and we call the loads connected between a positive and neutral pole an E-type connection (see Figure 2).  
Figure 1: Schematic representation of a bipolar DC network.
## 2 Basic Theory of Signal Noise Reduction Method
### EMD Noise Reduction Method by Dominant Eigenvalues
#### 2.3.2 New IMF Component Extraction Method by Dominant Eigenvalue  
Because the actual noise is variable, the effective IMF component should be obtained by the adaptive strategy. Therefore, the IMF component extraction method according to the dominant eigenvalue is proposed and carried out by the following two steps:  
Figure 3: Three kinds of threshold function curve.  
Calculate the eigenvalues \(\lambda_{1},\lambda_{2},\cdots,\lambda_{n}\) of each order IMF component \(s_{i}(t)\) and the residual component \(r_{n}(t)\) by the Singular Value Decomposition (SVD) [21].
2. Extract the effective IMF component by the Dominant Eigenvalue Method (DEM) [22].  
The variation tendency demarcation point of the ratio \(\mathrm{K}_{i}\) of adjacent singular eigenvalues is used in this paper to determine the boundary between the dominant and non-dominant eigenvalues to then extract the effective IMF components. The expression of \(\mathrm{K}_{i}\) is defined as Equation (7)  
\[\mathrm{K}_{i}=\frac{\lambda_{i}}{\lambda_{i+1}},i=1,2,\cdots,n-1 \tag{7}\]  
The variation tendency demarcation point of \(\mathrm{K}_{i}\) is expressed as Equation (8)  
\[\Delta_{i}=\frac{\mathrm{K}_{i}}{\mathrm{K}_{i+1}},i=1,2,\cdots,n-1 \tag{8}\]  
For the Equation (8), the first point of \(\Delta_{i}\) less than 1.0 is the variation tendency demarcation point of \(\mathrm{K}_{i}\).  
From Equation (8), it can be seen that if \(\Delta_{m}\) is smaller than 1.0, the corresponding eigenvalues \(\lambda_{1}\), \(\lambda_{2}\),..., \(\lambda_{m+1}\) are dominant. In other words, the components from IMF\({}_{1}\) to IMF\({}_{m+1}\) are effective.  
3.3 New Noise Reduction Method Based on the Improved Threshold Wavelet and EMD by Dominant Eigenvalue  
The newly proposed method by combined use of the improved threshold wavelet and EMD with the dominant eigenvalue can be carried out according to the process shown in Figure 5. Firstly, the improved threshold wavelet denoise and EMD are carried out in sequence, then the IMF component is extracted. Secondly, the eigenvalue is calculated and then the dominant eigenvalue is obtained. Finally, the effective IMF component is determined and then the signal is reconstructed.  
Figure 4: EMD flow chart.
## 3 Data and Application
### Method 3: Machine Learning Algorithms (e.g., Support Vector Machines)
(**Bottom**) The green line represents the true labels indicating the presence of changes in the signal, while the red dashed line represents the predicted labels obtained using a Support Vector Machine (SVM) classifier.
# Implementation of a Fast Link Rate Adaptation Algorithm for WLAN Systems
## 2 Link Adaptation Algorithm Basics
To cope with the case that the actual RX performance is different than the performance PBLA assumes, the adaptive PBLA or APBLA algorithm takes advantage of the ACK information of the previous packets to amend the channel SNR. If ACKs have frequently occurred, the channel condition is regarded as better than what the table predicts whereas if ACKs have not occurred often, the channel condition is counted as worse than that expected by the table. Thus when many ACKs have occurred, the channel SNR is incremented and conversely when ACKs have not occurred many times, the channel SNR is decremented. More specifically, the SNR offset value renewed every packet on the basis of the ACKs of the previous packets and this renewed SNR offset is applied to each subcarrier SNR, whereby the discrepancy between the actual RX and the PBLA internal model is overcome.
## 3 Application Domains
### Product Monitoring  
Directly connected to supply chain management is product monitoring, which refers to the activities that are associated with the product from the moment it actually becomes a product within the manufacturer's premises, to the one it is purchased and used by customers. This includes _real-time monitoring of location and condition_, _logistic tracking_ and _quality checking_, which are explained above in the previous subsection. Besides the objective parameters related to the product quality, mainly regarding defect tracking, product monitoring also refers to the subjective assessment of their quality. To this end, _customer feedback_ and _market analysis_ are required [38; 39]. Thanks to modern smart devices, these activities are performed in an easier, more efficient and cost-effective way: feedback will be gathered using the mobile crowdsourcing paradigm, data coming from customers will be integrated with market-related data and, based on them, advanced analysis techniques will be used to generate relevant KPIs (Key Performance Indicators). Furthermore, thanks to the integration of customer feedback with product tracking, _data-driven demand prediction_ will be enabled [40]. Therefore, it will be possible to optimize production and supply chain processes.