esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experiments, Results, and Analysis
### Dashboard  
To inform the user when and why a given forecast should be trusted, we built a dashboard (see Figure 5). The dashboard has six sections, each of which provides specific information to the user. At the top of the dashboard (Figure 5A), we provide information regarding the time series identifier, the dataset they belong to, the forecast date and value, if considered an outlier, and the most relevant features to that particular forecast. We then created a line plot of the time series, where the last value corresponds to the forecast (Figure 5B). We use a gray dashed vertical line (see the Figure 5(B2) arrow) to indicate the start of the forecasting horizon, and a red dashed vertical line (see the Figure 5(B3) arrow) to highlight the occurrence of an anomalous forecast. We provide a short explanation regarding the criteria applied to the forecast to determine if it is anomalous or not (Figure 5C). Given that the machine learning models that we consider assume manually crafted features, insights regarding the time dimension can be conveyed only through the features and their metadata. Inspired by related work [91; 93], we map features metadata regarding the time dimension and compute how strongly that the referenced time windows and points-in-time are from the most relevant features to a particular forecast overlay. Based on that information and the values of the time series data points within each range considered, we overlayed a heatmap to the time series line plot, showing how influential those data points are to the forecast. In particular, in Figure 5(B1), we show a heatmap with a heat space ranging from yellow (low relevance) to red (high relevance). The relevance heatmap's area is determined by the time windows of the relevant features and the values of the data points that fall within the aforementioned time window. In Figure 5D, we display a set of training instances that probably influenced the forecast (prototype local explanations), followed by two counterfactual examples: a set of instances that would result in a non-anomalous forecast (Figure 5E). We created our utility code to compute the counterfactual examples (see algorithm's pseudocode in Listing 1). To compute them, we restricted the values search to the most significant features identified by LIME and ensured that the values were plausible. To decide whether there was a significant change in the forecasted value when computing counterfactual examples, we created a feature vector with the mean values of all the features, except for the meaningfulones identified through LIME, which received values of their own by ninety thousand values from a normal distribution. The normal distribution was parameterized with a mean equal to the mean of past time series values, and a standard deviation equal to three times the standard deviation measured in the past for that same time series. We then used the GTSMLFM to issue a forecast and the anomaly detector to determine if the proposed sample instance qualified as an outlier or not, and randomly selected a subset of them to show them to the user.
# An Assessment of Environmental RF Noise Due to IoT Deployment
###### Abstract  
The advent of the Internet of Things (IoT) has contributed to an increase in the production volume of RF-featured equipment. According to statistics from the literature, the IoT industry will soon deploy billions of products. While the concept behind these applications seems exciting, this paper sought to assess the effects the radio emissions produced by IoT products would have on the ambient radio noise levels within the unlicensed frequency bands of 433 MHz, 868 MHz, and 2.4 GHz. The study extended to three environments: industrial, urban, and suburban. This study developed an IoT noise generator (ING) device to emulate RF noise signals in the desired IoT radio transmission band. The paper presents a simplified radio noise surveying system (RNSS) for data collection of ambient radio noise from five South African candidate sites. The statistical and empirical analysis agree that the level of ambient radio noise was directly proportional to the rate of IoT radio activities. The slopes of the regression lines demonstrate that 80% of the analyzed data developed augmenting trends. Approximately 20% of the data show declining trends.  
environment RF noise; equipment RF noise; IoT; ISM bands; noise generators; RF noise survey; software-defined radio +
Footnote †: journal: Sensors
## 5 Mean Field Power Control Game
### Mean Field Game
#### 5.1.2 Mean Field Utility Function  
The formulated utility function (20) needs to be adapted to accommodate a large number of players in the mean field domain. Interference dynamics defined by (1) and (2) can be easily modified to conform with large number of MTC users as:  
\[i_{k}^{\ m}(t)=\ \sum_{i=1,\ i\
eq k}^{K}p_{k}(t)G_{i,k}(t)\approxeq(K-1)\ p_{k}^{ \ m}(t)\overline{G}_{i,k}^{\ m}(t). \tag{26}\]  
Here, \(p_{k}^{\ m}(t)\) is the transmit power for by \(k_{th}\) MTC user required while estimating parameters for the MFG. \(\overline{G}_{i,k}^{\ m}(t)\) is defined as the mean field channel gain for MTC devices. The power received at the BS is defined as:  
\[P_{k}^{\ RX}(t)=\ \ P_{k}^{\ m}(t)G_{k}(t)+\ i_{k}^{\ m}(t), \tag{27}\]  
where \(G_{k}(t)\) is the channel gain and \(P_{k}^{\ m}(t)G_{k}(t)\) is the total received signal power. Further, \(i_{k}^{\ m}(t)\) is the induced interference to the intended signal. The mean field SINR can be defined using (26) and (27) as:  
\[\Gamma_{k}^{\ m}(t)=\ \frac{p_{k}(t)g_{k}(t)}{(K-1)P_{k}^{\ m}(t)G_{k,i}^{\ m}(t )+\ \sigma^{2}}. \tag{28}\]  
Hence, the modified mean field utility function can now be formulated as:  
\[\partial_{k}^{\ m}(t)=(\Gamma_{k}^{\ m}(t)-\ \Gamma_{th}(t))^{2}+\ \Omega p_{k}(t). \tag{29}\]  
Formulation of the power control MFG can now be completed after including the mean field approximations in the interference dynamics and utility function for players.
# Design Trends and Comparative Analysis of Lightweight Block Ciphers for IoTs
## 4 Related Work
### Feistel Network (FN)
It achieves a throughput of 437.31 Kbps with low execution time (146.35 \(\upmu\)s) and passes advanced cryptanalytic tests, including biclique and avalanche analysis. Importantly, ANU-II is highly efficient in hardware, requiring only 1010 gate equivalents (GEs)--significantly lower than PRESENT (2756 bytes of flash and higher GE) and other standard ciphers. Despite its compactness and security, ANU-II has certain limitations. While it removes complex diffusion layers such as P-layer to save memory, this simplification may reduce flexibility in adapting the cipher to newer threat models or future applications requiring stronger diffusion mechanisms. Additionally, its architecture is highly tailored for 64-bit data blocks, which may not scale efficiently to larger block sizes needed in modern secure communication systems. Moreover, although ANU-II uses a single S-box for efficiency, the lack of variety in non-linear operations could be a potential vulnerability if future attacks exploit S-box-specific weaknesses. Hence, while ANU-II is an excellent fit for  
Figure 22:4 The encryption of SCENERY. low-end IoTs devices, its design choices may limit broader applicability and long-term cryptanalytic robustness.
# Selection of Production Reliability Indicators for Project Simulation Model
## 2 Literature Background
### Reliability Indicators and Risk Analysis  
Reliability is a complex concept, and many definitions can be interpreted under it. Is a system reliable when it does not produce a bad quality product? Or is this terminology used for providing information about the condition of a machine? However, it can be stated that both approaches aim at providing quality product for the customers, as well as smooth operation for the production including maintenance and other components of production. According to [PERSON]'s book [7], there are 4 major categories for the mostly used indicators in production reliability:  
1. Faultlessness: these indicators are created in order to examine the smooth operation of the production system. These are usually time-bounded/calculated ratio: failure rate, mean operation time, probability of failure, probability of faultless operation and mean time between failure.
2. Reparability: it includes all the indicators which are calculated when a job breaks down and needs to be repaired. These indicators are usually time-bounded or a calculated ratio: mean repair time, mean downtime, recovery intensity, probability of recovery, mean time to repair.
3. Durability: this category reflects on the durability of the jobs, such as: mean operation time, mean lifespan, q-percent operation.
4. Storability: this category stands for the storability of the product. This is also an important factor for the customer, as it also has an effect on the perception of quality: mean storage life, percentage of storage time.  
Such reliability indicators are in the focus of risk analysis and provide the foundation of the research. In other words, these 15 indicators are analyzed later in the meta-analysis part. As these analyses are concerned, qualitative or subjective calculations are very popular and widely applied in the professional life. Examples for them are the Risk Matrix [8] or the Failure Mode and Effects Analysis (FMEA) [9]. A common drawback is the subjectivity which can mislead the entire analysis, because they do not conform to the need of reproducibility and consistency [10]. That is why quantification and simulation-based methods are emerging [11; 12]. However, professionals rely more on data-driven solutions, see papers [13; 14]. That is why complex indicators should be created. The previously listed indicators are investigated in the following sections of the authors' work, which are proven quantitative indicators.
## 3 Background and Definitions
### Fuzzy Logic  
**Definition 9**.: _[PERSON] [29] gives the following definition: a set \(A\), is defined as a membership function \(f_{A}(x)\) that maps the elements of a universe \(X\) to elements in the interval \([0,1]:f_{A}(x):X\mapsto[0,1]\) and represent a degree of membership of \(x\) in \(A\)._  
Meaning that the close \(f_{A}(x)\) values to 1 implies a higher the degree of membership of \(x\) in \(A\).  
To represent the fuzzy set \(A\) consider a pair of values: each element \(x\in X\) having a degree of membership to \(A\).  
\[A=\{(x,f_{A}(x))|x\in X\}\]  
**Definition 10**.: _Fuzzification converts a value or quantity to fuzzy quantity._  
One of the most common fuzzifiers is the triangular function, next described:  
* **Triangular function:**\(f_{A}(x)=max[min(\frac{x-L}{C-L},\frac{R-x}{R-C}),0]\).  
\(L\),\(C\) and \(R\) values delimit the fuzzy set \(A\), where \(C\) is the most substantial input value to \(A\).  
Definition 11: Defuzzification converts a fuzzy quantity into a value or quantity.  
Defuzzification is done by applying the _weighted average_ method, using the next algebraic equation:  
\[\frac{\sum f_{A}(a_{c})(a_{c})}{\sum f_{A}(a_{c})}\]  
where \(\sum\) denotes algebraic summation and \(a_{c}\) is the centroid of each symmetric membership function.  
A Brief Description of FIS  
A fuzzy inference system uses fuzzy logic to model almost anything into an input space to an output space. Using FIS, one can formalize human language into a simple mathematical model. FIS consists of four modules, next described:  
* _Fuzzification module:_ This module transforms anything that is modelled by crisp numbers into a membership function and transforms them into fuzzy sets using a fuzzification function.
* _Knowledgebase:_ stores the rules that are given by experts in the form of if-then rules.
* _Inference engine:_ makes inferences from the inputs and tries to simulate human reasoning by using if-then rules
* _Defuzzification module:_ this module gives as output a crisp value that can be easily interpreted by humans or computers; it transforms membership functions into fuzzy sets. The most known type of FIS are the Mamdani and the Sugeno [30].
* If \(x\) is \(A\) and \(y\) is \(B\) then \(z\) is \(V\)
* If \(x\) is \(A\) and \(y\) is \(B\) then \(z\) = \(f(x,y)\)
Computational Evaluation of Cardiac Function in Children Supported with Heartware VAD, HeartMate 2 and HeartMate 3 Left Ventricular Assist Devices  
[PERSON]  
1 Department of Mechanical Engineering, Yeditepe University, Istanbul 34755, Turkey;  
[EMAIL_ADDRESS] (K.K.S.); [EMAIL_ADDRESS] (M.C.A.)1  
[PERSON]  
1 Department of Mechanical Engineering, Yeditepe University, Istanbul 34755, Turkey;  
[EMAIL_ADDRESS] (K.K.S.); [EMAIL_ADDRESS] (M.C.A.)1  
[PERSON]  
2 School of Engineering, Ulster University, Newtownabbey BT37 OQB, UK1  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/1](https://www.mdpi.com/journals/pdf/1)]([https://www.mdpi.com/journals/pdf/1](https://www.mdpi.com/journals/pdf/1))  
body surface area [8]. Experiences of using different continuous-flow VADs in children have mainly been reported for the following three devices: the HeartMate 2 (HM2) device (Thoratec, Pleasanton, CA, USA), which is a suitable option for children with a body surface area over 1.3 m\({}^{2}\)[7]; the Heartmate 3 (HM3) ventricular assist device (Thoratec, Pleasanton, CA, USA), which is a centrifugal pump and can be used in patients with 0.8 m\({}^{2}\) body surface area [9]; and the HeartWare HVAD continuous-flow device (HeartWare, HeartWare Systems, Framingham, MA, USA), which can be used in patients with a BSA under 1.0 m\({}^{2}\)[10].  
Current challenges in continuous-flow VAD support in children include patient selection, the influence of risk profiles, small body sizes for continuous-flow VADs, surgical modifications to mitigate patient-device size mismatch, and selection of the most suitable device for each patient considering these factors [11; 12; 13]. A lack of data in children under VAD support poses further challenges in treating paediatric patients; hence, health centres implant continuous VADs to children using data from adults [11]. Moreover, HVAD and HM3 devices have modes with variable operating speeds such as the Lavare cycle and artificial pulse; however, studies focusing on these features consider mechanical circulatory support in adults [14]. A direct comparison of the haemodynamic outcome during continuous-flow VAD support in children may help better the understanding of VAD therapy in children. The aim of this study was to evaluate the cardiac function in children supported with HVAD, HM2 and HM3 devices by utilising computational modelling.
## 2 Methodology  
### Introduction of Variational Modal Decomposition  
The VMD algorithm is a decomposition method for vibration signal decomposition, which is completely non-recursive and adaptive [8]. Its basic process is the following: first, the sequence signal is decomposed into \(K\) modal components (IMF) so that the sum of the estimated frequency bandwidth of each mode is minimized, and the sum of the IMFs is the original decomposed signal; second, the bandwidth of each IMF component is estimated; lastly, the central frequency corresponding \(\omega_{k}\) to each IMF component is extracted. This mainly includes two parts: variational problem construction and variational problem solving. The steps of variable fraction problem construction are as follows:  
1. The original signal is decomposed to a series of intrinsic mode functions by VMD algorithm, each of which can be regarded as the AM-modulated signal. The symbol is \(u_{k}\), and its expression is: \[u_{k}(t)=A_{k}(t)\cos[\varphi_{k}(t)]\] (1)
2. The original signal is obtained via a one-sided spectrum by being Hilbert-transformed. Then, the frequency spectrum of IMFs is modulated to the corresponding baseband by mixing the predicted central frequency spectrum with the analyzed signal. In addition, the method to estimate bandwidth is by calculating the L\({}^{2}\) parametric square of the signal gradient. The constrained variational model of the VMD algorithm is [8]: \[\left\{\begin{matrix}\min\biggl{\{}\sum\limits_{K}||\partial_{t}\Bigl{[} \Bigl{(}\partial(t)+\frac{j}{\pi t}\Bigr{)}*u_{k}(t)\Bigr{]}e^{-j\omega_{k}t} ||_{2}^{2}\biggr{\}}\\ s.t.\;\;\sum\limits_{k}u_{k}=f\end{matrix}\right.\] (2) Then, the steps of variational problem solving are as follows:
3. The Lagrangian operator \(\lambda(t)\) and the quadratic penalty factor \(\alpha\) are introduced to the method to turn the constrained variational problem into an unconstrained variational problem. The extended Lagrangian expression is: \[\mathrm{L}(\{u_{k}\},\{\omega_{k}\},\lambda) =\alpha\sum\limits_{k}||\partial_{t}\biggl{[}\biggl{(}\partial(t)+ \frac{j}{\pi t}\biggr{)}u_{k}(t)\biggr{]}e^{-j\omega_{k}t}||_{2}^{2}+||f(t)- \sum\
olimits_{k}u_{k}(t)||_{2}^{2}+\langle\lambda(t),f(t)-\sum\
olimits_{k} u_{k}(t)\rangle\] (3) (4) The iterative solution by alternating multiplier method (ADMM) obtains each modal component \(u_{k}\) and its central frequency \(\omega_{k}\). The iterative update formulae for \(u_{k}\), \(\omega_{k}\), and \(\lambda_{k}\) are as follows [8]: \[u_{k}^{n+1}(\omega)=\frac{f(\omega)-\sum_{i\
eq k}u_{i}(\omega)+\frac{\lambda( \omega)}{2}}{1+2\alpha(\omega-\omega_{k})^{2}}\] (4) \[\omega_{k}
## 2 Results and Discussion
### Heatmap Analysis  
The heatmap in Figure 5 presents the expression levels of volatile organic compounds in microencapsulated jasmine instant tea, allowing for the comparison of profiles across different treatments and the control group. The color gradient on the heatmap, ranging from blue to red, indicates the relative intensity of the volatile organic compounds, with red representing higher expression levels and blue indicating lower levels. The dendrogram on the left side of the heatmap clusters the samples based on the similarity in their volatile organic compound profiles, providing insights into which samples have similar aroma profiles.  
Each row in the heatmap corresponds to a different volatile organic compound, while each column represents a different sample. The clustering algorithm groups the samples based on the similarity in their volatile profiles, which are influenced by the encapsulating agents used in their preparation. The 'Fresh' samples are distinctly separated from the microencapsulated samples, indicating a divergence in the aroma profile post-encapsulation. Among the encapsulated samples, those treated with gelatin are clustered closer to the 'Fresh' samples, suggesting that gelatin encapsulation more effectively retains the original aroma profile.  
The variability within the encapsulation groups, such as those seen in acacia and CMC, suggests that encapsulation with these agents results in a more heterogeneous retention of aroma compounds. conversely, the gelatin and maltodextrin samples demonstrate a more homogenous retention pattern, as reflected by the tighter clustering within these groups.
## 4 Feasibility of Agro-Waste Utilization in Reinforced Concrete (RC) Elements
### Effects of Agro-Wastes on RC Elements
#### 4.3.1 Mechanical Properties and Structural Performance
\begin{table}
\begin{tabular}{c c c c c} \hline
**Agro-Waste Type** & **Experimental Research** & **Beam Geometry** & **Main Findings** & **References** \\ \hline \multirow{3}{*}{Coconut Shells} & The performance of steel fiber reinforced concrete with Class & \multirow{3}{*}{Standard dimensions} & Ultimate moment capacity & \multirow{3}{*}{[26; 73; 84]} \\  & F fly Ash was tested. & & & ratio is at an acceptable level. & \\ \hline \multirow{3}{*}{Palm Oil Shells} & The mechanical and insulation properties of lightweight concrete were investigated. & Comparative with conventional dimensions & The density and insulation properties of lightweight concrete are better, crack widths are acceptable. & \\ \hline \multirow{3}{*}{Wheat Straw Ash} & Different mixtures were tested under static loading. & The bending strength of the beams was tested. & 15\% Ash ratio improved the bending behavior, increased the load carrying capacity. & \\ \hline \multirow{3}{*}{Banana Fibers} & Effects on the flexural strength of reinforced concrete beams & Compared with conventional beams. & Flexural strength increased by 25\% crack propagation is less. & \\ \hline \multirow{3}{*}{Palm Kernel Shells} & Cracking and reinforcement stresses were analyzed. & Standard dimensions & Smaller crack widths and spacing, high ductility and low modulus of rupture were observed. & \\ \hline \multirow{3}{*}{Palm Oil Fuel Ashes} & Energy absorption and strength of concrete reinforced with adhesive additives were investigated. & It was compared with conventional beams. & It was found that it increased strength, contributed to zero waste targets, and improved structural integrity. & [21; 22; 83] \\ \hline \multirow{3}{*}{Fax Fiber Reinforced Coconut Fibers} & Flexural strength and energy absorption were analyzed. & Tested with different fiber contents. & Specimens with 3\% coconut fiber content absorbed the highest energy. & [76; 88] \\ \hline \multirow{3}{*}{Palm Oil Clinker} & Ductility ratio and crack formation were analyzed. & Standard dimensions & As the steel reinforcement ratio increased, a decrease in ductility ratio was observed. & [75; 82] \\ \hline \multirow{3}{*}{Coconut Shell (Torsion Test)} & The behavior of concrete under torsion was analyzed. & Comparison with control beams & Improvement in torsion reinforcement and ductility ratio; crack widths are smaller than control concretes. & [69; 88] \\ \hline \end{tabular}
\end{table} 
Table 4: Studies on the use of agro-waste in reinforced concrete beam production (own research).
## 2 Materials and Methods
### Data Sources
#### 2.2.2 Carbon Sequestration Driving Factors
Photosynthesis intensity: Sun-induced chlorophyll fluorescence (SIF) data, a crucial indicator of plant photosynthesis [59], exhibits both direct and indirect relationships with CS changes in urban contexts [60; 61; 62]. Research has validated the use of SIF and atmospheric CO\({}_{2}\) concentration as primary factors in estimating gross primary productivity (GPP) [63]. However, the integration of SIF with other factors, such as anthropogenic activities and climate change, in analyzing urban vegetation CS remains underexplored. Contiguous solar-induced fluorescence (CSIF) dataset provides high-resolution, long-term, daily global SIF data from 2001 to 2022, addressing previous limitations of short time series and low spatial resolution [64]. Its accuracy was validated against GOME-2 SIF datasets. Soil nitrogen and phosphorus: In contrast to other external drivers like human activities and climate change, less attention has been paid to how nitrogen (N) and phosphorus (P) in soil play fundamental roles in vegetation physiological activities and functions in city areas. However, the availability of these soil nutrients may be the possible reason to limit the increased intensity of plant photosynthesis [65]. Surface soil (0-10 cm) nitrogen (SoilN) and phosphorus (SoilP) data from the Chinese Terrestrial Ecosystem Nitrogen and Phosphorus Pool Model Database were used in this study [66]. This dataset is based on field sampling and machine learning predictions and has undergone model validation and uncertainty analysis. In the data pre-processing phase, the county (municipal district or outside) was used as the basic research unit, and the values of the sub-indicators came from raster data, except for the sub-indicator data of AAF (based on statistical yearbook accounting), GAR (land use data statistics), and WCR (land use data statistics). Lastly, all the raster data were transformed to a unified projected coordinate system (CGCS2000_3_degree_Gauss_Kruger_zone_35), and county-scale mean statistics were realized by the Zonal Statistics function of ArcGIS 10.5 software.