esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Wireless sensor networks have attracted great attention for applications in structural health monitoring due to their ease of use, flexibility of deployment, and cost-effectiveness. This paper presents a software framework for WiFi-based wireless sensor networks composed of low-cost mass market single-board computers. A number of specific system-level software components were developed to enable robust data acquisition, data processing, sensor network communication, and timing with a focus on structural health monitoring (SHM) applications. The framework was validated on Raspberry Pi computers, and its performance was studied in detail. The paper presents several characteristics of the measurement quality such as sampling accuracy and time synchronization and discusses the specific limitations of the system. The implementation includes a complementary smartphone application that is utilized for data acquisition, visualization, and analysis. A prototypical implementation further demonstrates the feasibility of integrating smartphones as data acquisition nodes into the network, utilizing their internal sensors. The measurement system was employed in several monitoring campaigns, three of which are documented in detail. The suitability of the system is evaluated based on comparisons of target quantities with reference measurements. The results indicate that the presented system can robustly achieve a measurement performance commensurate with that required in many typical SHM tasks such as modal identification. As such, it represents a cost-effective alternative to more traditional monitoring solutions.  
Keywords:Raspberry Pi; smartphones; wireless sensor networks; vibration measurements +
Footnote †: This paper is an extended version of our paper published in Proceedings of the 7 th Asia-Pacific Workshop on Structural Health Monitoring 2018, Hong Kong, China, 12-15 November 2018.
## 3 Results and Discussion
### Statistical Tests Results  
By performing the Friedman test on the validation dataset results for \(MAE\), \(R^{2}\), and \(RMSE\), \(p\)-values of \(3.4744\times 10^{-149}\), \(6.0304\times 10^{-187}\), and \(6.0304\times 10^{-187}\) were obtained, respectively, which were all below the 0.05 threshold. These results led to the rejection of the null hypothesis, confirming that the models exhibit statistically significant differences in performance. To further investigate these differences, the Nemenyi test was applied to compare the models in a pairwise manner. Table 7 presents the mean ranks of each model for \(MAE\), \(R^{2}\), and \(RMSE\), where lower values indicate better rankings for \(MAE\) and \(RMSE\), while higher values are preferable for \(R^{2}\). Figures 10-12 illustrate the distribution of rankings across the models.  
The results indicate that although the stacking models did not surpass individual neural networks in testing, the Nemenyi test on the validation dataset suggests that Stacking 1 and Stacking 2 achieved the highest mean rankings across all metrics, followed closely by Stacking 3 and Stacking 4. However, since the critical distance calculated for the test was \(CD=0.96\), and multiple models ranked within this range, no statistically significant differences were observed among the stacking models or between most neural networks. The GA-ANN-2 (50), AGA-ELM-3 (20), and GA-ELM-4 (50) models fell within the critical distance for all metrics, while AGA-ANN-1 (20) and AGA-SVR (20) were also within this range for \(MAE\).  
In contrast, the tree-based models exhibited consistently lower predictive performance when compared to other approaches. The Nemenyi test results reinforce that these models behave similarly, with less capacity to predict \(ELOS\) accurately for the studied dataset. The  
Figure 11: Model validation performances for the Nemenyi test considering the \(R^{2}\) metric.  
Figure 12: Model validation performances for the Nemenyi test considering the \(RMSE\) metric.  
findings confirm that while the stacking models performed well, they did not provide a statistically significant advantage over the best individual neural networks.
# Quarantining Malicious IoT Devices in Intelligent Sliced Mobile Networks
## 1 Introduction
A first detection procedure, executed by a lightweight SDN application, performs a coarse-grained identification of suspicious sets of end devices which are believed to include malicious nodes. The data flows of the suspicious nodes are diverted to a quarantine slice, in which they can be analyzed in depth to set malicious devices apart from legitimate ones. This two-step process reduces the computational load of the DDoS detection system of the mobile network, since traffic is only inspected in depth on demand on a reduced set of data flows. Summing up, the main contributions of this paper are:  
* A two-step SDN-based architecture for detecting and mitigating DDoS attacks in IoT cellular networks. * The analysis of the detection capabilities of the proposed solution through simulations. * The evaluation of the reassignment of IoT devices to quarantine slices in a real-world testbed. The rest of the paper is organized as follows. Section 2 discusses related work. Our proposed solution is described in Section 3 and evaluated in Section 4. Finally, Section 5 concludes the paper.
# An Efficient Approach to Manage Natural Noises in Recommender Systems
## 3 The Proposed Approach of Managing Natural Noises
### Detection of Natural Noise
#### 3.1.1 The Classification of Ratings  
In order to detect natural noise, we first divide the ratings into three classes, i.e., low ratings, medium ratings, and high ratings. To precisely describe these three types of ratings, two thresholds \(k\) and \(v\) are defined as  
\[k=\min R+round\left(\frac{1}{3}\times(\max R-\min R)\right), \tag{1}\]  
\[v=\max R-round\left(\frac{1}{3}\times(\max R-\min R)\right), \tag{2}\]  
where \(\max R\) and \(\min R\) are the maximum and the minimum value for all possible ratings, respectively, and the function \(round(x)\) returns the nearest integer value to \(x\). The threshold \(k\) is used to differentiate low ratings and middle ratings, and the threshold \(v\) is used to differentiate middle ratings and high ratings. That said, the ratings smaller than \(k\) are defined as low ratings, the ratings larger than \(v\) are defined as high ratings, and the ratings between \(k\) and \(v\) are defined as medium ratings.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 3 Design of Current Controllers
### Supplementary ADP (Adaptive Dynamic Programming) Controller Design  
The input of the action network is designed as: \(X(t)=[x_{1},x_{2},x_{3},x_{4},x_{5},x_{6}]\), where \(x_{1}\) is the error signal between \(i_{d}^{*}\) and \(i_{d}\), \(x_{2}\) and \(x_{3}\) are the one-time step and two-time step delayed signals, \(x_{4}\) is the error signal between \(i_{q}^{*}\) and \(i_{q}\), and \(x_{5}\) and \(x_{6}\) are one-time step and two-time step delayed signals as shown in Figure 6a. Therefore, the architecture of the action network in ADP is designed as a 6-6-2 multi-layer perceptron (MLP) structure, and the critic network is designed as an 8-6-1 MLP structure with six hidden neurons for each network. The output of the ADP controller is the control action, \(u(t)=[u_{d}(t),u_{q}(t)]\), where \(u_{d}(t)\) and \(u_{q}(t)\) are control actions for the \(d\)-axis and \(q\)-axis, which supplements the output of the PI controller. The reward signal is designed as:  
\[r(t)=-c\sum_{i=1}^{6}a_{i}x_{i}^{2}, \tag{11}\]  
where \(c\), \(a_{i}\) for \(i\) = 1,...,6 are the coefficients of this quadratic equation. The weights of the two networks are updated using the gradient descent algorithm described in [49]. The typical learning process of the ADP controller includes two trials, as described in [50]. In the first trial, the neural networks were initialized with random weights, and the supplementary control action outputs may not be optimal. The simulations were repeated until there was no further improvement in the total harmonic distortion (THD). At the end of this offline learning process, the ADP learns information about the system and state-action pairs. In the second trial, or online learning, fully-trained control policies in terms of trained weights were used that obtain better control performance over time. After enough training, the actor produced the optimal control action and the critic evaluated the performance of the actor over time.
## 2 Sideband Component Feature Identification Based on DPWM
### Analysis of Sideband Current Harmonics and Radial Electromagnetic Force Characteristics
By substituting Equations (5)-(7) into (4), the sideband radial electromagnetic force density can be presented as follows:  
\[ \begin{split} f_{r}\left(\theta,t\right)&=\frac{1}{2 \mu_{0}}\left[\sum_{\
u=1}\frac{3\sqrt{2}NkI_{d/q}}{\pi\
u p}\cos\left(\
u p \theta\pm\omega\right)+\sum_{\rho=1}^{\infty}\frac{3\sqrt{2}NkI_{d/q}}{\pi\mu p }\cos\left(\mu\,p\theta\pm\omega\,+\,\varphi\right)\right]^{2}\\ &=\frac{9N^{2}k^{2}}{\mu_{0}\pi^{2}p^{2}}*\sum_{\
u}\sum_{\
u} \frac{I_{d/q}}{\mu\
u}^{2}\begin{cases}\cos\left[\left(p-\
u N_{\
u}\right) \theta-\left(\alpha_{0}-\omega_{c}\right)t\right]+\\ \cos\left[\left(p+\
u N_{\
u}\right)\theta-\left(\alpha_{0}+\omega_{c}\right) t\right]\end{cases}\end{split}  \tag{8}\]  
where \(N_{t}\) denotes the unit motor, numerically taking the maximum common factor of the motor pole pair number \(p\) and slot number \(z\). \(k\) denotes the winding coefficient. In Equation (8), it can be seen that the sideband electromagnetic force has obvious time-frequency characteristics. The frequency distribution of the major sideband harmonics can be expressed as \(n\omega_{c}\pm k\omega_{0}\), where \(n\) and \(k\) are combinations of odd and even parities. Combining the sideband current harmonic characteristic frequencies near the first carrier frequency, when \(n=1\), the spatio-temporal characteristics of sideband components near the carrier frequency domain of the DPWM are shown in Table 2. Based on the above, the analytical model of the sideband harmonic components and radial electromagnetic force characteristics introduced by the conventional DPWM technique is completely established.
# MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection
## 4 Experiment
### Datasets
#### 4.1.2 UBI-Fights  
The UBI-Fights dataset represents a novel large-scale benchmark for anomalous behavior detection, comprising 1000 carefully curated surveillance videos totaling 80 h of footage. Distinguished by its frame-level annotation precision, this dataset offers detailed temporal localization of violent incidents while maintaining comprehensive scene diversity.
###### Abstract  
Non-centrifugal cane sugar (NCS) is produced from sugarcane syrup via thermal evaporation. This study aimed to assess the effects of different temperatures during the evaporation process on the physicochemical characteristics and Maillard reaction products (MRPs) of NCS. Evaporation was tested at three final heating temperatures (120, 130, and 140 \({}^{\circ}\)C). The moisture content, water activity, L*a*b*c color spaces, and ICUMA (International Commission for Uniform Methods of Sugar Analysis) values of the NCS were determined. Volatile MRPs of NCS were extracted using polyethylene (PE) membrane and retronasal aroma simulator (RAS) techniques, and their components were measured using gas chromatography. A higher evaporation temperature produced NCS with less moisture content and water activity. However, it also led to a darker color, as indicated by lower L* (brightness) and b* (yellow) values in the color spaces. Additionally, higher evaporation temperatures resulted in greater ICUMA values. Moreover, higher heating increased the amounts of volatile MRPs, such as 2,3-dihydro-3,5-dihydroxy-6-methyl-4H-pyran-4-one, 2-furanmethanol, 2-methylpyrazine, 2,5-dimethylpyrazine, and 2,6-dimethylpyrazine. Negative correlations were observed between moisture content, water activity, brightness, yellow color, and the total MRPs obtained by PE and RAS extractions. Additionally, positive and significant correlations were confirmed between ICUMA values and most MRPs. Thus, the evaporation temperature alters the key physicochemical traits and volatile compounds of NCS, affecting its physical stability and flavor quality.  
non-centrifugal cane sugar; evaporation temperature; physicochemical traits; Maillard reaction products; polyethylene membrane extraction; retronasal aroma simulator 2023
## 3 Results
### Bioaccumulation in Indigenous Organisms  
Mercury levels were usually higher in strictly sediment feeding (detritivorous) oligochaetes than in chironomids (Table 2). However, median values (0.395 vs. 0.302 mg kg\({}^{-1}\), n = 7) were not statistically different (\(p\) = 0.602, Rank Sum Test). Results for site Carmine deep (CARM-P2) were excluded from the statistics because only oligochaetes could be collected for Hg analysis at this site. Mercury concentrations in indigenous chironomids and oligochaetes were not correlated with total Hg content in sediments (Spearman rho = 0.75, \(p\) = 0.066, n = 7 for chironomids; rho = 0.619, \(p\) = 0.105, n = 8 for oligochaetes) and between themselves (Spearman rho = 0.321; \(p\) > 0.5, n = 7 considering only sites where both groups could be sampled and analysed--Table 2). The largest potential for Hg transfer from sediment to benthic organisms was observed at stations Baveno sublittoral (BAV-SL), Baveno, Pallanza and Carmine deep (BAV-P, PAL-P, CARM-P2) and Carmine sublittoral (CARM-SL) that showed low to moderate Hg enrichment in sediments. At these sites, biota-sediment accumulation factors (BSAF), defined as the ratio between Hg concentrations measured in organisms and in sediments, ranged from about 0.5 to 4 (Table 2), while BSAF for organisms collected at the more contaminated sites PAL-SL and PAL-01 did not exceed 0.4. The situation for chironomids from Lake Mergozzo may be peculiar and deserves further study.  
Differences in Hg accumulation among indigenous chironomids and oligochaetes at a given site reflect the combined outcome of organisms' ecology and patterns of Hg contamination in surface sediments. Results obtained from sediment cores LM16, LM17 and LM 51 collected in the Borromean Gulf in 2011 (Figure 1 and Table 1) provide a good example. The three cores have similar sedimentation rates of about 0.7 cm year\({}^{-1}\) (Table S2), but different Hg contamination profiles over the period 2005-2011 covering three separated sediment layers. Hg levels in core LM16 were homogeneous (0.247 \(\pm\) 0.037 mg kg\({}^{-1}\) for the three layers). Cores LM17 and LM51 had similar Hg levels in the top 3 cm of sediments (2007-2011), which overlaid more contaminated sediment layers deposited in 2005-2007 (Table S2). Vertical variability of Hg levels in (sub)surface sediments can be particularly important in explaining bioaccumulation differences between tube-building head-up chironoids feeding on the surrounding sediment surface or intercepting drifting food and tube-building head-down oligochaetes that burrow deeper acting as bioturbators. On the other hand, chironomids can feed on suspended matter freshly deposited around their tubes [17]. Such additional route of exposure may be of variable importance depending on the actual sedimentation rates in the different areas.
# Underground Disposal of Coal Gauge Backfill in China
## 3 Gangue Backfilling Technologies
### Fully Mechanized Mining with Solid Backfilling Technology  
Fully mechanized mining with solid backfilling technology [60; 61; 62] (Figure 4c) is a suitable method for coal mining under buildings (structures), railways, and water bodies. The gangue produced by underground coal mining is initially crushed mechanically and then transported to the working face for in-situ backfilling. The filling materials in the goat are compacted by the ramming mechanism [63; 64]. The integrated underground mining-dressing-backfilling technology that combines solid backfilling mining and underground gangue dressing has appeared in response to China's policies of green mine construction and to accommodate deep coal resources [65]. On this basis, the technical concept of \"mining, dressing, backfilling + X\" integrated green mining (Figure 4d) has been proposed. The \"X\" consists of active control for strata movement, gob-side entry retaining, gas extraction, disaster prevention and control, and water conservation mining [66; 67; 68].
# KAN-Transformer Model for UltraShort-Term Wind Power Prediction Based on EWMA Data Processing
## 2 Research Methodology and EWMA-KAN-Transformer Model  
### Method of the Present Research  
To clearly show the research methods used in this paper, an overall framework diagram of the research methods is presented in Figure 1.  
In Figure 1, the process begins with the preprocessing of the dataset. The collected external variable data are denoised using the EWMA method, followed by Z-score normalization. The data are then divided into training, validation, and test sets in a ratio of 7:2:1. Next, the training set samples are extracted using a sliding window method within the Dataset class to create time-series forecasting samples. A DataLoader class is defined to facilitate data loading for model training. The model training process consists of five main parts: defining the improved KAN-Transformer model, loading data in batches using the Mini-batch method, inputting the loaded data into the KAN-Transformer model to obtain prediction values, calculating the loss by comparing the predicted values with the actual values using mean squared error (MSE), and passing the obtained loss to the backpropagation (BP) network for backward propagation. The model weights  
Figure 1: Overall framework diagram of this research.  
the adaptive moment estimation method (ADAM). Finally, the trained model weights are loaded for testing and evaluation. During the testing phase, data are loaded in batches into the model to obtain prediction results. After denormalizing the prediction results, the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R\({}^{2}\)) are calculated to assess the model's performance on the test set.
## 3 Semantic Expression of Relational Data Provenance
### Contribution Semantics  
In the view of query, relational schemas or relational data are the results of SQL execution in DBMS, and all inputs are the provenance data of the output. A super coarse-grained provenance has less significance and causes a large amount of provenance data storage. Therefore, separating the normalized data contributed to the output data from the input data as the provenance data represent a more rational approach. Currently, the provenance data research solves the division of provenance by contribution semantics, which includes three categories from a high-level perspective: input contribution semantics, copy contribution semantics, and influence contribution semantics [23]. In the input contribution semantics, all inputs are the provenance data. In the semantics of influence contribution, only the inputs that affect the existence of the outputs are the provenance data, and missing data items of these data will lead to unrecoverable provenance. For copy contribution semantics, only those data items (partial or complete) copied to the output are provenance data. For example, some items are only used for filtering data but not copied to the output. These data are provenance data in influence contribution semantics but are not subject to copy contribution semantics.  
The investigation of the provenance transformation process is the evaluation of the contribution semantics of provenance data. This is difficult in data provenance systems as SQL queries and relational algebra operations are transformation units in the transformation process. Input contribution semantics ignore the internal details of transformations, and thus the process is a black box. Unlike input contribution semantics, copy and influence contribution semantics analyze the details of the modifications, and influence semantics contain richer details than copy semantics. However, the influence semantics are complicated and cumbersome for understanding the relationship between data in a complex query language.  
Previous studies use how-prov and perm-transformations to express provenance transformation. However, how-prov's K-relation expression contains little information about the provenance transformation. The relational algebraic expression of perm-transformations, a tree structure that significantly relies on SQL syntax, is complicated and cumbersome for complex queries. Therefore, this paper proposes provenance explaining transformation (PET) to investigate this in more detail. PET begins with several basic operations (such as projection and join), providing the ability to describe conversions in SQL and stored procedures. To eliminate the influence of the complex features of languages, such as SQL, and explain the provenance transformation process concisely and clearly, we choose where-prov, a kind of copy contribution semantics, as the theoretical basis of the provenance data and define PET based on it.
# Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns
## 3 Overview of the Dataset
### Dataset Description  
This dataset is part of a survey administered to 383 randomly selected households in Brazil to collect data about monthly electrical power consumption and the characteristics of the households and occupants. The inputs consist of the building's geographical and structural data and data related to the occupants' social, economic and behavioral aspects. The outputs are low-, medium- and high-load-consumption profiles. Those profiles are formed by clustering the household power consumption with k-means, agglomerative hierarchical clustering (AHC) and self-organizing maps (SOM).  
Based on the research conducted by [36], selecting input variables for the dataset depends on expert opinions and initial testing. In this specific case, the selection of each variable results from a study on the factors that affect household electricity consumption.  
Various researchers have divided them into different categories to understand better the factors that affect indoor environmental quality. For instance, factors affecting household energy consumption have been categorized into seven groups: climate, building characteristics, social and economic factors, user presence, building service systems, occupant behavior and indoor environmental quality [37]. Additionally, these factors have been grouped into four categories: external conditions, physical characteristics of the dwelling, appliance and electronics stock, and occupant factors [38]. A simplified classification divides these factors into objective and subjective categories, where objective factors do not depend on the individual's intention, while subjective factors are related to an individual's intention [6].