esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Introduction and Analysis of Experimental Results of the Demonstration System
### Analysis of Running Effect and Experimental Waveform
From the waveform of the demonstration operation, it can be seen that the ESS is charged from the utility at the moment when the night electricity price is low. At the time when the early peak electricity price is high, photovoltaic power generation cannot meet the load power consumption, and the ESS discharges to supplement the power shortage. In the afternoon, the ESS is charged at the parity time of the utility. First, it is charged with photovoltaic power, then the shortage is supplemented by the utility. The ESS will be discharged during the period when the peak electricity price is high, late at night, to supply power for the load.
# Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
## 2 Related Work
### Outliers, Anomalies, and Forecasting  
Multidimensional pollutant data and meteorological data consist of multivariate data which is collected in chronological order from monitoring stations at a particular interval of time. This data has various complications such as dimensional explosions, periodic trends, etc. Due to these problems, simple outlier removal methods result in poor spotting of outliers. Hence, there is a need to remove these outliers/anomalies from the dataset before prediction. There are two types of anomalies in air pollution datasets: unwanted data and others depending on the event of interest. Unwanted data are cleaned by using simple outlier removal methods, such as the inter quartile range, z-score, Grubb's test, Tietjen-Moore test, and Hampel's test methods [32]. In later cases, outliers/anomalies have been removed by machine learning-based models, such as KNN, ARIMA, and SVM [33], and deep learning models, such as variational models based on autoencoders [34] and LSTM autoencoders [35]. Detecting anomalies using a combination of the robust projection pursuit and Mahalanobis distance method implemented in [36] showed that anomaly detection is important. Before removing the anomalies in the dataset, the missing values were replaced by a simple column median calculated from available data.
## 5 Dynamic Equation for the Anti-Swaying System in the Frequency Domain
### Under-Damping Hydraulic Anti-Swaying System  
When \(0<\xi<1\), that is, when \(0<\varepsilon<2\omega_{n}\), the anti-swaying system is called an under-damping hydraulic anti-swaying system. In this situation, Equation (13) has two conjugate complex characteristic roots:  
\[s_{1,2}=-\xi\omega_{n}\pm j\omega_{n}\sqrt{1-\xi^{2}}. \tag{14}\]  
Then, the time domain function of the load swaying angle can be expressed by Equation (15):  
\[\varphi(t)=\frac{\varphi_{0}}{\sqrt{1-\xi^{2}}}e^{-\xi\omega_{n}^{t}}\sin( \omega_{n}\sqrt{1-\xi^{2}}t+\cos^{-1}\xi). \tag{15}\]  
It is clear that Equations (10) and (15) are equivalent when the hydraulic anti-swaying system is an under-damping hydraulic anti-swaying system. When \(\varphi_{0}\) and \(\omega_{n}\) are known, Equation (15) shows that the change in the swaying angle with time is only affected by the damping coefficient, \(\xi\). Based on Equation (10), the entire oscillatory attenuation process experienced by the swaying angle is always contained between a pair of envelope surfaces, \(\pm\frac{\varphi_{0}}{\sqrt{1-\xi^{2}}}e^{-\xi\omega_{n}t}\).  
The attenuation time of the swaying angle is an important index for measuring the effect of the anti-swaying system. If the load swaying angle is required to attenuate to \(\Delta\) (rad) at \(t_{s}\) (i.e., the distance between the envelope surfaces is \(\Delta\)), then Equation (16) applies:  
\[\frac{2\varphi_{0}}{\sqrt{1-\xi^{2}}}e^{-\xi\omega_{n}^{t}}=\Delta. \tag{16}\]  
According to Equation (16), the attenuation time of the load swaying angle can be expressed by Equation (17):  
\[t_{s}=\frac{\ln\frac{\Delta^{2}(1-\xi^{2})}{4\varphi_{0}^{2}}}{2\xi\omega_{n}}. \tag{17}\]
# Validation of Copernicus Sea Level Altimetry Products in the Baltic Sea and Estonian Lakes
## 2 Material and Methods
### Geoid Model EST-GEOID2017  
Since sea-level variations of tidal origin are less than 0.10 m in the study area of the Baltic Sea [17], precise geoid model is one of the best reference surfaces for validating altimetric heights. The Estonian geoid model (EST-GEOID2017) is a quasi-geoid model covering 57\({}^{\circ}\) N-60\({}^{\circ}\) N and 20\({}^{\circ}\) E-30\({}^{\circ}\) E, with a spatial resolution of 1\({}^{\prime}\)\(\times\) 2\({}^{\prime}\) which gives geoid undulation (\(N\)) above the World Geodetic System 1984 (WGS84) reference ellipsoid. Its long-wavelength information refers to the GOC005s global gravity model [18]. Additionally, nearly 50,000 gravity points were used with an average formal uncertainty of 0.75 mGal, whereas within the Estonian mainland and islands, the uncertainty of gravity data is mostly within 0.5 mGal (see [19] for more details). The EST-GEOID2017 model is very smooth on the mainland (Figure 2) and its GNSS-levelling-based accuracy remains within \(\pm\)0.005 m [19]. This means that the accuracy of the geoid model in the coastal zones of the lakes should also be around \(\pm\)0.005 m. Note that the estimated discrepancy of \(\pm\)0.028 m between the model and the GNSS measurements is estimated in the Gulf of Finland [16]. Considering that the geoid model undulates over ca 5 m in Estonian territory (Figure 2), the 0.05-0.03 m overall accuracy in terms of standard deviation (STD) is superb, and the use of the EST-GEOID2017 model is therefore the main option for satellite altimetry validation in the coastal zone.
# Directed Clustering of Multivariate Data Based on Linear or Quadratic Latent Variable Models
## 6 Application on Speed-Flow Data  
### Model Selection  
We apply Algorithm 1 to the two datasets introduced in Section 4. We fit both the linear model and the quadratic model with different numbers of mixture components \(K\), where \(K_{max}=10\). Tables 3 and 4 show the BIC values for the speed-flow data from the left- and right-hand panels of Figure 1, respectively. Comparing the minimum BIC values of the fitted linear model and the fitted quadratic model indicates that the quadratic model provides a better fit for each of these two datasets; the quadratic model with \(K=4\) is selected for the first dataset, and the quadratic model with \(K=8\) for the second dataset.  
Figures 7 and 8 illustrate the fitting process of the quadratic model across the considered range of \(K\) values. They show that generally increasing \(K\) improves the model fit (decrease bias); however, as is known from the 'usual' nonparametric maximum likelihood estimation ([PERSON] et al., 2017), once it reaches a certain \(K\) value, increasing the number of mass points further will no longer make much improvement to the fitting. From Tables 3 and 4, one can see that the quadratic models achieve their optimal BIC values at larger values of \(K\) than the linear models. The reason for this behavior is that the quadratic shape of the curves allows capturing clusters, which would otherwise be masked when simply projecting ontoa line; this becomes intuitive when looking at the collection of clusters on the left part of each scatterplot in Figure 8.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & \(K=2\) & \(K=3\) & \(K=4\) & \(K=5\) & \(K=6\) & \(K=7\) & \(K=8\) & \(K=9\) & \(K=10\) \\ \hline Linear & 3831.80 & 3776.75 & **3751.58** & 3759.37 & 3766.83 & 3781.53 & 3786.85 & 3797.42 & 3808.69 \\ Quadratic & 3842.76 & 3751.23 & 3700.76 & 3709.41 & 3696.52 & 3694.48 & **3689.92** & 3696.85 & 3705.95 \\ \hline \hline \end{tabular} Note: The best fit for each model type is indicated in bold letters.  
\end{table}
Table 4: The BIC values for both the linear model and the quadratic model fitted with different mixture components, for the right-hand data from Figure 1.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & \(K=2\) & \(K=3\) & \(K=4\) & \(K=5\) & \(K=6\) & \(K=7\) & \(K=8\) & \(K=9\) & \(K=10\) \\ \hline Linear & **7545.65** & 7557.84 & 7570.03 & 7582.23 & 7594.42 & 7606.61 & 7618.80 & 7630.99 & 7643.18 \\ Quadratic & 7557.84 & 7391.20 & **7359.93** & 7371.79 & 7365.24 & 7364.84 & 7374.60 & 7382.78 & 7401.42 \\ \hline \hline \end{tabular} Note: The best fit for each model type is indicated in bold letters.  
\end{table}
Table 3: The BIC values for both the linear model and the quadratic model fitted with different numbers of mixture components, for the left-hand data from Figure 1.  
Figure 7: The calspeedflow data from the northbound freeway SR57-N, fitted with a quadratic curve (in blue) and mass points represented by red triangles, with different numbers of mixture components.
## 1 Introduction
The ability to monitor temperature fluctuations in near real-time with improved spatial resolution will allow farmers to better implement preventive measures, such as activating frost protection systems, to mitigate crop damage [29]. Agricultural strategies to combat the effects of frost are crucial for enhancing the resilience of crops sensitive to low temperatures, such as citrus [25]. Among the most effective active techniques are the use of vegetative covers and sprinkler irrigation [30], which help form a protective layer around the plants, reducing potential frost damage, and the use of heaters and wind machines [31; 32; 33], which aim to prevent excessive temperature drops in the fields. While these techniques are effective, they require precise implementation and constant weather monitoring to be truly useful and are not always economically feasible [3]. Passive methods, such as proper site and species selection and adequate pruning, can offer sufficient benefits to avoid the need for active methods and are generally less costly [18]. In this context, having precise and up-to-date knowledge of the territory and the frost risk affecting the area is essential to help farmers improve their adaptive capacities and resilience, reducing their vulnerability to adverse climatic events [22; 34; 35]. We focused on citrus crops, given their significant economic and nutritional value [36; 37], as well as their widespread global consumption. Consequently, we decided to monitor the frequency and intensity of frosts over the past 20 years in the Valencian Community, a region where the citrus agricultural sector is highly developed, representing more than half of Spain's total citrus cultivated area [8], and positioning Spain as the world's sixth-largest producer of fresh citrus [38]. To achieve this goal, we propose a practical remote sensing method to quantify the extent of frost damage, using the minimum temperature reached and the duration of the frost as key indicators.
## 2 Results and Discussion
### Nephroprotective Effect of APTM
#### 2.4.7 Effect of APTM on Renal Malondialdehydedehyde (MDA) Level  
Numerous investigations have identified lipid peroxidation in renal tissue as the damaging process of GM injection on renal function [40]. Increased malondialdehydedehyde level in renal tissue suggests increased oxidative stress in GM-induced nephropathy [41]. The increased free radical generation has been linked to the degradation of several structural macromolecules, which can lead to cell lesions and tubular necrosis [40; 42]. To this end,  
Figure 6: Effect of aqueous APTM extract on serum gamma-GT (**b**) and ALP (**a**) concentrations in GM-poisoned rats. \({}^{\#\prime}\)\(p\)\(<\) 0.01 related to NCC. \({}^{*}\)\(p\)\(<\) 0.05, \({}^{**}\)\(p\)\(<\) 0.01 compared to GGM. Ns: not significant compared to GGM. Ns: not significant.  
we assessed the impact of APTM aqueous extract on lipid peroxidation in GM-treated experimental animals. As shown in Figure 7, rats exposed to GM had a substantial increase (\(p<0.001\)) in MDA rates compared to rats in the normal control group. The groups treated with aqueous APTM extract at two doses administered, with the GM-injection, had a marked decline (\(p<0.05\) and \(p<0.01\), respectively) in MDA concentration compared to rats in the GGM (80 mg/kg). These findings are consistent with prior studies, in which plant extract reduces lipid peroxidation to protect against GM-induced nephrotoxicity [25; 43].
# The Impact of Underlying Opaque White Coating Parameters on Flexographic Print Quality
## 2 Multi-Layer Packaging for Food Products  
The research was carried out as part of designing a test print for multi-layer polymer flexible packaging for a food product Kulen, produced by Podravka, a company in Kopruncia, Croatia. Food product is packed in a thermoformed base on which the flexible packaging material, the subject of this research, is sealed.  
The packaging material used for research in this paper consists of a multi-layered polymer material. Polymeric materials are commonly used for the production of food product packaging for several reasons: they are easy to manufacture and have excellent characteristics such as flexibility, strength, stiffness, and barrier properties, and they provide protection against unwanted microorganisms [22; 23; 24].  
The composition of the packaging material from the research is the following: BOPET/PE/EVOH/PE (Figure 1). The upper external layer of the material that is printed on from the inside is a biaxially oriented polyester film polyethylene terephthalate (BOPET) that is 20 \(\upmu\)m thick. The issues that occur in the process of printing polymer films are: poor wettability, lack of absorbency, low surface free energy, lack of dimensional stability during stretching, and temperature changes [25]. Due to the mentioned characteristics, most polymer films need to be pre-treated and prepared for the printing process. BOPET is used in various combinations with polymer or combined flexible packaging materials. In the printing process, it has high dimensional stability and resistance to solvents [26]. BOPET is characterized by good mechanical strength, resistance to tearing and stretching, high transparency, and gloss. It is, therefore, often chosen as a packaging material.  
The following polymer layer in the mentioned packaging material is polyethylene (PE), i.e., PE/EVOH/PE, with a total thickness of 50 \(\upmu\)m. It is one of the most important and present polymers in the production of food industry packaging. A layer of EVOH (ethylene vinyl alcohol) is placed in a sandwich between two layers of PE. It is a copolymer of ethylene and vinyl alcohol, and it serves as a barrier layer. Its role is primarily to protect the food product from the influence of oxygen and to retain the aroma and smell of the product. EVOH is moisture sensitive and has to be combined with materials that are hydrophobic, such as PE. In some applications of multi-layer flexible packaging, it can replace an aluminum layer. Integrating the EVOH layer into the packaging extends the shelf life of products sensitive to external influences. EVOH also has high mechanical strength, high resistance to oils and organic solvents, and high thermal stability [27].
## 3 Technical Solutions for Collaborative Automations between Machines
### A Reference Architecture--Line, Modules, and Devices  
Different from self-organised ad hoc networks for WSN and lightweight computing devices, most digital manufacturing systems may find the following characteristics: powerful machines with reliable connection; heterogeneous machine platforms; ubiquitous messaging; high-throughput data transmission, and; data- and operation-centric tasks. Then, a reference system architecture is designed to depict the whole picture of manufacturing systems, as shown in Figure 2.  
Figure 2: Reference System Architecture.  
In the diagram, the machines are connected to a manufacturing network infrastructure to communicate with each other in an interactive way. The topology of machines in the network consists of three different hierarchies, namely, line, modules, and machines. The line includes all machines in the manufacturing line that fulfil all production processes and functionalities. The modules consist of one or several machines including a module controller to fulfil a comprehensive part of manufacturing functions. Then, devices are the basic functional units to achieve some specific functions.  
The machines can talk to each other, and flexibly broadcast to one or more selected modules containing several machines. Evidently, the flexible messaging between machines allows the collaborations between machines to maximise their efficiency. The ubiquitous interactions also allow the collection and integration of product data. With effective notification of events and commands, machines can request data from peer machines and respond with data to requesters, and carry out the manufacturing operations in a collaborative and autonomous way. The flexible messaging also enables an interoperable information architecture which handles the real-time management of manufacturing operations by a reasoning engine with minimum human interventions.  
Eventually, the collected data of products and operations can be used for data analysis, and the analytical insights can be used to optimise the processes and supervise the industrial operations.
## 2 Related Work
This reveals a significant knowledge gap: the lack of an adaptive selection strategy that accounts for accuracy contribution, energy constraints, privacy sensitivity, and heterogeneous devices all at once. Our proposed RBPS addresses this gap through a centralized strategy with cooperative intent, tailored to heterogeneous and battery-constrained environments. RBPS evaluates each client through a multi-objective reward function that dynamically balances three core dimensions: expected accuracy contribution, current battery level, and privacy sensitivity. Unlike prior methods, RBPS updates client eligibility at every round, ensuring that only suitable devices are selected. This centralized approach with cooperative intent reduces battery drain, avoids client overuse, and respects user-specific constraints. Furthermore, RBPS benefits from client heterogeneity rather than being hindered by it. While many SOA methods are optimized for uniform environments and thus tend to favor only one capability (e.g., maximizing accuracy at the cost of battery), our strategy uses heterogeneity to balance performance across all critical aspects. This makes RBPS more robust, generalizable, and practically viable for real-world FL systems. Table 1 summarizes this contrast. In the broader context, adaptive client selection strategies like RBPS are essential as FL continues to move toward deployment in edge and IoT scenarios, where device diversity and resource constraints are the norm. Moreover, with increasing interest in federated multi-task learning and privacy regulations, client selection mechanisms must become even more flexible and multi-objective. Our work contributes toward this evolving landscape by providing a concrete, multi-objective framework for practical FL systems.
## 3 Results and Discussion
### Effect of Chitosan on Color Features and Anthocyanins Composition of Wine
Our experiments showed a value of \(\Delta\)E*ab of 11.93 when comparing C and KT, 8.5 between C and KTS and 3.49 for KT vs KTS, demonstrating that all samples were visually distinguishable from each other, especially between chitosan treatments and the control. Samples anthocyanins amount are showed in Table 4. In general, monomeric anthocyanins seemed not to be influenced by the presence of chitosan, as previously reported by some authors who used up to 1 g/L of chitosan [9; 10]. Vitisin B were a further class of compounds not affected by the treatment while vitisin A did demonstrate to be marginally but significantly lower in KT wines. Synthesis of vitisin A occurs through the reaction between malvidin-3-O-glucoside and pyruvate acid. At wine pH, this molecule is present in a mixture of neutral and anionic forms [37] that would facilitate its interaction with positively charged amine groups of chitosan. Together with the extended aromatic conjugation of vitisin A, such interaction may further stabilize adsorption onto chitosan surface which is in consonance to the outlined reduced co-pigmentation (Table 3). Slightly differences were also observed for hydroxyphenyl pyranoanthocyanins, particularly Mv-3-glc-4-vinyl-(epi)-catechin and Pn-3-glc-4-vinylphenol whose amounts were lower in samples treated with chitosan (Table 4). Together with vitisin A, these compounds contribute to the orange-yellowish nuance of red wines and their diminution, albeit modest, could be the reason for lower hue (Table 1), higher hab and red color vividness (Table 3) recorded for such samples. To give a general and qualitative view about the type of molecules adsorbed onto chitosan, one of the formulations (KTS, in particular) was removed from wine after fining and the adsorbed compounds extracted following polymer acid dissolution. Figure 1 shows a chromatogram of the identified compounds. As depicted, the main peaks were attributed to hydroxycinnamic acids, ellagic acid and quercetin while only traces of anthocyanins were observed, which perfectly match with the data coming from wine analysis (Table 2). Despite the adopted collection and extraction procedures did not permit to perform the accurate quantification of the molecules released from chitosan, a comparison with identical chromatograms from wine samples (not shown) allowed to evaluate the reciprocal affinity of these compounds with respect to the adjuvant. In this regard, ellagic and caftaric acids were the phenolics whose peak area ratio after/before the treatment increased the most, suggesting the highest affinity with chitosan. Furthermore, the presence onto the polymers surface of the identified molecules demonstrates that their diminution in wine samples is unlikely to be due to oxidation or other chemical modifications artificially induced by chitosan.
# A Cache Policy Based on Request Association Analysis for Reliable NAND-Based Storage Systems
###### Abstract  
Compared with the traditional hard-disk drives (HDDs), solid-state drives (SSDs) have adopted NAND flash memory and become the current popular storage devices. However, when the free space in NAND flash memory is not enough, the garbage collection will be triggered to recycle the free space. The activities of the garbage collection include a large amount of data written and time-consuming erase operations that can reduce the performance of NAND flash memory. Therefore, DRAM is usually added to NAND flash memory as cache to store frequently used data. The typical cache methods mainly utilize the data characteristics of temporal locality and spatial locality to keep the frequently used data in the cache as much as possible. In addition, we find that there are not only temporal/spatial locality, but also certain associations between the accessed data. Therefore, we suggest that a cache policy should not only consider the temporal/spatial locality but also consider the association relationship between the accessed data to improve the cache hit ratio. In the paper  
time when users are running many different programs. There is not only temporal/spatial locality, but also certain associations between the accessed data. Therefore, we suggest that a cache policy should consider the temporal/spatial locality and the association between the accessed data to improve the cache hit ratio. In the paper, we will propose a cache policy based on request association analysis for reliable NAND-based storage systems. According to the experimental results, the cache hit ratio of the proposed method can be increased by about 14.09\(\sim\)35.08% when compared with the typical cache methods.  
The rest of paper is organized as follows. Section 2 introduces the related work. Section 3 explains the motivation. In Section 4, we describe a cache policy based on request association analysis for reliable NAND-based storage systems. Section 5 provides a performance evaluation. Section 6 concludes the paper and suggests future work.