esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In recent years, plants have become a great source of bioactive compounds, mainly due to their biological activities. Their applicability is vast, and is suitable in many areas of the pharmaceutical and cosmetic industries [1]. The literature suggests that natural products play an important role in photo-aging prevention (e.g., olive oil), topical application of antioxidants (e.g., vitamins C and E) in the protection of skin against UV-mediated damage [1; 2], among many others.  
_Sambucus nigra_ L. is one of medicinal plants that has been used since ancient times [3; 4]. In our previous study, some important biological activities have been found in twenty-two extracts of _S. nigra_ flowers (elderflowers) and berries (elderberries) and one of them suggested potential anti-inflammatory activity [5; 6]. Thus, the present study reports for the first time the combination of a methanolic extract of _S. nigra_ flowers with polymeric nanoparticles (NPs) for potential topical use. The strategy of using NPs as a carrier for extracts has been successfully developed in many fields, because NPs present several advantages, such as protection from moisture, light, heat and/or oxidation, as well as modifying the release of incorporated or loaded compounds [7]. In the current study, two polymers were used to prepare NPs: poly(lactide-co-glycolide) (PLGA) and poly-\(\epsilon\)-caprolactone (PCL). To the best of our knowledge, and specifically for _S. nigra_, a small number of studies combining _S. nigra_ and NPs are described in the literature. In one study, an acetone: water extract of the black fruit elderberries is reported to present a potential interest to the treatment of psoriasis lesions [8] and in other study the encapsulation of the fruit's extract in gold NPs is described for diabetes treatment [9]. The encapsulation of a dried aqueous extract of elderberries in liposomes is also mentioned in the literature [10]. The encapsulation efficiency was around 25% but no therapeutic applications of this combination were tested. In all three cited examples, none described the use of extracts obtained from elderflowers with NPs.  
Thus, taking advantage of the biological properties of the methanolic extract of elderflowers [5; 6] and of the combination with the carriers' nanoparticulate nature, a new nanoformulation is herein proposed as an alternative to the classical anti-inflammatory products in the market. Extract-loaded PLGA NPs and extract-loaded PCL NPs, both with oleic acid, were prepared and characterized in terms of mean size, size distribution, surface charge, morphology, encapsulation efficiency of the extract and release. Total polyphenol content (TPC), antioxidant activity (AA), collagenase (Coll) inhibition, safety and in vitro and in vivo anti-inflammatory (AIA) were also investigated.
# Fault Detection Method for Wind Turbine Generators Based on Attention-Based Modeling
## 2 Attention-Weighted Long Short-Term Memory Network (AW-LSTM)  
### Empirical Mode Decomposition (EMD)  
Empirical mode decomposition (EMD) is an effective method for processing non-linear and uncertain time series signals. The basic idea is to transform a waveform with irregular frequencies into a form of the accumulation of multiple single-frequency waves. In theory, EMD can be applied to any type of time series data decomposition. Therefore, it can decompose non-stationary, nonlinear, irregular, time-varying, and noisy multi-frequency bearing signals into independently layered data representations at different frequencies, through intrinsic mode functions (IMFs).  
For a given original time series \(\mathbf{x}(t)\), for each \(t\in(1,n)\), the specific steps of EMD decomposition are as follows:  
1. Calculate the instantaneous peak value and average value of the envelope of the function \(\mathbf{x}(t)\) at all data points, and then use cubic interpolation to fit the envelope lines \(\mathbf{x}_{up}(t)\) and \(\mathbf{x}_{down}(t)\) of the original data separately. Finally, calculate the average value \(\mathbf{m}(t)\) of the envelope line: \[\mathbf{m}(t)=[\mathbf{x}_{up}(t)+\mathbf{x}_{down}(t)]/2.\] (1)
2. Subtract the mean envelope line \(\mathbf{m}(t)\) from all data points in the original signal \(\mathbf{x}(t)\), to obtain a new data sequence \(\mathbf{h}(t)\): \[\mathbf{h}(t)=\mathbf{x}(t)-\mathbf{m}(t).\] (2)
3. Check if \(\mathbf{h}(t)\) satisfies the IMF constraint. If it does not, treat it as a new input sequence, and repeat steps (1) to (2) until the constraint is met. If it does, \(\mathbf{h}(t)\) is the first IMF component. Record it as \(\mathbf{imf}_{1}(t)\), separate the IMF component \(\mathbf{imf}_{1}(t)\) from the original sequence \(\mathbf{x}(t)\), and obtain the residual component \(\mathbf{r}_{1}(t)\): \[\mathbf{r}_{1}(t)=\mathbf{x}(t)-\mathbf{imf}_{1}(t).\] (3)
4. The residual component \(\mathbf{r}_{1}(t)\) is treated as a new sequence, and the above steps are repeated multiple times until \(\mathbf{r}_{n}(t)\) cannot be further decomposed, thus obtaining all IMF components \(\mathbf{imf}_{i}(t)\), where \(i\in(1,n)\).  
Using EMD decomposition, it is possible to decompose the bearing fault signal from the wind turbine gearbox into different trend components and time-domain signals with different frequencies, forming a series of subsequence components of different timescales. Compared to the original data, these subsequence components have stronger stability, making them more suitable for normalization. Additionally, they facilitate the analysis of effective data in the bearing signal by LSTM, while ignoring the interference caused by irrelevant component signals.
# A Survey on Secure WiFi Sensing Technology: Attacks and Defenses
## 2 Background
### Taxonomy  
Research works on the topic of secure WiFi sensing can be categorized into a two-level structure. The methods are first categorized by their purposes: attack or defense.  
For the attack techniques against WiFi sensing, they are further divided into two categories according to whether the attacker interferes with the sensing system.  
* **Active attack.** (1) Definition: Attacks that involve direct interaction with the WiFi sensing system to alter, disrupt, or damage the wireless channel or its operations. (2) Objective: The attacker aims to interfere with the normal functioning of the sensing system, causing it to produce inaccurate or unusable results;
* **Passive attack.** (1) Definition: Attacks that focus on monitoring or eavesdropping on the wireless channel or sensing results without altering them. (2) Objective: The attacker aims to gather sensitive information or infer private data without being detected.  
The defense techniques for WiFi sensing are also divided into two categories according to whether the defense strategies are adopted by the sensing target.  
* **Active defense.** (1) Definition: Protective measures taken directly by the sensing target (e.g., a user, device, or system) to safeguard against attacks. (2) Objective: The target actively engages in its own protection to mitigate potential threats;
* **Passive defense.** (1) Definition: Protective measures adopted by external parties, such as users of the sensing data or third-party entities, to secure the sensing information. (2) Objective: These measures protect the sensing data or system without requiring direct involvement from the sensing target.
## 5 Machine Learning Algorithms
### Clustering and Reconstruction Loss  
As per Equation (3), we train the DAEs with the Mean Squared Error (MSE) used for reconstruction loss. This metric helps to measure the difference between the original input data and the reconstructed output from the autoencoder. The reconstruction loss is  
Figure 5: HD-DNCAE architecture.  
important since it directly affects the capacity of the autoencoder to capture and reproduce the underlying data distribution.  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}\bigl{(}Y_{i}-\hat{Y}_{i}\bigr{)}^{2} \tag{3}\]  
We then add a clustering lossbased on the HDBSCAN algorithm. This is an unsupervised learning technique that helps to find density-based clusters within the latent space features generated by the autoencoder. The loss is simply the clustering loss based on the score given by the cluster validity scores of HDBSCAN. It applies a penalty if the clustering results in only one cluster, as it is necessary to form multiple, distinct clusters to distinguish among the different patterns inside the data. The total loss function of the HD-DNCAE model is a combination of these three kinds of losses (outer DAE Loss, inner DAE Loss, and clustering loss), where the scheme's control hyperparameters are \(\alpha\), \(\beta\), and \(\gamma\). Such a scheme allows fine-tuning between accurate data reconstruction and effective clustering. Balance in the importance of each of these aspects acquired in the developed model is achieved by adjusting these weights, hence optimising its performance for some datasets. We performed a grid search using the values 0.1, 0.5, and 1 for each hyperparameter to select their best value.
## 3 Results
### Signal Stability  
The stability of the optical signal of the 2-LED-\(\upmu\)Spectrophotometer was characterized by an hour scan of various LEDs (Figure 3). The signal drift was determined, and values of \(-1.0\times 10^{-7}\) (430 nm), \(-2.0\times 10^{-9}\) (530 nm), \(-4.0\times 10^{-9}\) (630 nm), and \(3.0\times 10^{-9}\) (690 nm) were calculated. As visible in the histogram plots (Figure 3d) determining the short-term noise, signal variations of \(\pm 0.000027\) and below were observed.  
In order to test the suitability of the chosen approach of a 2-LED-\(\upmu\)Spectrophotometer for monitoring the gold nanoparticle aggregation, an AuNP solution inside the cuvette of the spectrophotometer was subjected to aggregation by salt addition (Figure 4a), and continuously monitored using two LEDs of different wavelengths. The various wavelengths showed different behavior, in some spectral ranges (colors), the absorbance increased (such as in red), while in others (like blue), it decreased. This effect is the result of the localized surface plasmon resonance intensity and the plasmon peak shift to higher wavelengths upon salt addition. The behavior over time of an aggregation for the absorptions at separate wavelengths can also be observed. The example shows a gold nanoparticle solution  
Figure 3: Characterization of the signal stability and measurements: (**a**) stability of the optical signal of various LEDs of different wavelength over a time of 3 h; (**b**) histogram plots of the short-term noise.  
Figure 2: Schematic representation of the DNA sensing strategy. In the presence of DNA and an aggregation promoter (such as salt, acid), oligonucleotide-functionalized AuNPs aggregate, leading to a color change from red to blue, and simultaneously to a shift in the spectrum to higher wavelengths as a result of interparticle plasmonic coupling.  
and is comparable to the corresponding spectra measured by a benchtop spectrometer (Figure 4b). These measurements confirmed the ability of a 2-LED-\(\upmu\)Spectrophotometer to monitor the reaction and other possible reactions at different wavelengths sufficiently.
## 2 Steady-State Model of Energy Flow in the Electric-Gas Interconnection System
### Dynamic Energy Charging and Discharging Model of the Energy Storage (ES) System  
Energy storage equipment is an important equipment of energy hub, which can transfer energy in a time dimension. Excess energy or cheap energy can be stored by energy storage equipment and released when the energy demand is large or the energy price is high, which can make efficient use of energy and reduce energy cost.  
In a broad sense, energy storage includes not only electricity storage (battery), but also heat storage (the hot water storage system), gas storage (gas storage tank), cooling storage (ice storage, water storage, etc.). Their processes of energy charging and discharging are similar, which need to consider the constraints of multiple periods, mainly including the constraints of charging and discharging state, energy storage and so on. The general dynamic model of generalized energy storage can be expressed as follows:  
\[u_{t}^{\text{charge}}+u_{t}^{\text{discharge}}\leq 1 \tag{7}\]  
\[u_{t}^{\text{charge}}p_{\text{min}}^{\text{charge}}\leq P_{t}^{\text{charge}} \leq u_{t}^{\text{charge}}P_{\text{max}}^{\text{charge}} \tag{8}\]  
\[u_{t}^{\text{discharge}}p_{\text{min}}^{\text{discharge}}\leq P_{t}^{\text{ discharge}}\leq u_{t}^{\text{discharge}}P_{\text{max}}^{\text{discharge}} \tag{9}\]  
\[Q_{t+1}^{\text{storage}}=Q_{t}^{\text{storage}}(1-\delta^{\text{storage}})+( \eta^{\text{charge}}P_{t}^{\text{charge}}-\frac{P_{t}^{\text{discharge}}}{\eta^{ \text{discharge}}})\Delta t \tag{10}\]  
\[Q_{\text{min}}^{\text{storage}}\leq Q_{t}^{\text{storage}}\leq Q_{\text{ max}}^{\text{storage}} \tag{11}\]  
\[Q_{24}^{\text{storage}}=Q_{0}^{\text{storage}} \tag{12}\]  
where, \(u_{t}^{\text{charge}}\) and \(u_{t}^{\text{discharge}}\) represent the charging and discharging states of energy storage equipment respectively, which are 0-1 variables. 1 represents the working state, and 0 represents the non-working state. Equation (7) shows the limit of charging and discharging energy of energy storage equipment.  
\(P_{\text{max}}^{\text{charge}}\)(Unit: MW), \(P_{\text{min}}^{\text{charge}}\)(Unit: MW), \(P_{\text{max}}^{\text{discharge}}\)(Unit: MW) and \(P_{\text{min}}^{\text{discharge}}\)(Unit: MW) represent the upper and lower limits of the charging and discharging energy of the ES system respectively. Equation (10) shows the energy change before and after charging and discharging of the ES system. \(Q_{l+1}^{\text{storage}}\)(Unit: MWh) and \(Q_{t}^{\text{storage}}\)(Unit: MWh) represent the energy storage of the ES system in the \(t+1\) and \(t\) period respectively. \(\eta_{\text{E}}^{\text{charge}}\)(Unit: %) and \(\eta_{\text{E}}^{\text{discharge}}\) represent charge efficiency and discharge efficiency of the ES system respectively. \(Q_{\text{max}}^{\text{storage}}\)(Unit: MWh) and \(Q_{\text{min}}^{\text{storage}}\)(Unit: MWh) represent upper and lower limits of the energy storage in the ES system respectively. Equation (12) shows that the energy storage of the energy storage equipment is equal at the end time and the initial time.
## 2 Results and Discussion
### Identification of CYP450 Unigenes Putatively Involved in Diosgenin Biosynthesis
zingiberensis_ leaf tissue whereas was not observed in the rhizome (data not shown). It still could not be ruled out that there exist CYP enzymes catalyzing the sterol C-16 oxidation for diosgenin biosynthesis. Phylogenetic analysis of the unigene c63041_g1 revealed a relatively closer relationship to genes encoding the sterol C-22 hydroxylases and a \(\beta\)-amyrin (a triterpenoid) C11-oxidase [25]. The c63041_g1 shows similarity to genes encoding enzymes belonging to CYP88D group, and from this family some members have already been reported to participate in the biosynthesis of SGA [26] or triterpenoids [25]. Thus, it will be a priority to test whether the unigene c63041_g1 participates in diosgenin biosynthesis as a C-16 oxidase. The rest of the CYP unigenes were grouped into many other P450 subfamilies (i.e., CYP76, CYP75A, CYP7D1D55, CYP93E, CYP78A5,CYP73A, CYP77A, CYP701, CYP74A, CYP716A, and CYP707), which are known to participate in several other pathways, including indole alkaloid, flavonoid, sesquiterpenoid, fatty acid, gibberell, and Abscisic acid (ABA) metabolisms (Figure 3).
# Demand Control Strategies of a PCM Enhanced Ventilation System for Residential Buildings
## 3 Constant Ventilation Airflow Rate  
### Method  
For the simple constant airflow ventilation strategy, the influence of the ventilation airflow rate on the PCM energy storage saving potential is investigated. The house is simulated as a single thermal zone. The average internal heat load from equipment in the house is 3.5 W/m\({}^{2}\)[29]. The heat load schedule shown in Figure 9 is based on previous work [14]. The house is occupied by four people. The occupant schedule of the house is based on the recommendation in ASHRAE standard 90.1 [30], as seen in Figure 10. The time here refers to New York time.  
The ventilation strategies shown in Figures 3-6 are simulated. The required minimum ventilation flow rate of the house is 102 m\({}^{3}\)/h according to US Building Codes and Indoor Air Quality [31]. To investigate the influence of the airflow rate on the PCM energy storage saving potential, three ventilation flow rates are examined: 102 m\({}^{3}\)/h (0.385 h\({}^{-1}\)), 180 m\({}^{3}\)/h (0.679 h\({}^{-1}\)) and 300 m\({}^{3}\)/h (1.132 h\({}^{-1}\)), for summer and winter applications, respectively.
# Federated Pseudo-Sample Clustering Algorithm:
## 3 Statement of Label Personalization Problem
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Time** & **Core Idea** & **Result** \\ \hline SCAFFOLD [19] & 2020 & Correct for “client drift” by using control variables & The convergence speed and effect are \\  & & (variance reduction). & better than FedAvg and SGD. \\ \hline FedDC [20] & 2022 & The weight difference between local and global is & Fast convergence speed, good \\  & & corrected by introducing an auxiliary term to & robustness, and high efficiency. \\  & & calculate the drift degree. & \\ \hline FedMA [21] & 2020 & A shared global model is built in a hierarchical & Fast convergence speed, small \\  & & manner by matching and averaging hidden & communication burden. \\  & & elements (i.e., channels of convolutional layers). & \\ \hline Per-FedAvg [23] & 2020 & Based on the global model, one or more gradient & The model performance and efficiency \\  & & descent steps are performed using the local data to & are better than FedAvg. \\ \hline pFedLA [24] & 2022 & Identify the importance of each layer from different & The personalization degree is better \\  & & clients to optimize personalized model aggregation & than the traditional method, and the \\  & & for clients with heterogeneous data. & communication cost is small \\ \hline FedAMP [25] & 2021 & More collaboration between clients with similar data & Improve the ability of clients to \\  & & through messaging mechanisms. & cooperate with each other \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._For personalized federated learning, the goal is to facilitate collaboration among each client and jointly train personalized models \(M_{i}\) for each client while not exposing \(D_{i}\) to other clients \(C_{j}(1\leq i,\,j\leq N,\,i\
eq j)\). Suppose client \(C_{i}\) obtains the optimal model parameters \(w_{i,fed}^{*}\) through federated training, that is:  
\[w_{i,fed}^{*}\overset{federated}{=}\underset{w_{i},w_{2},...,w_{n}}{\text{ argmin}}\frac{1}{N}\sum_{i=1}^{N}\underset{x\sim D_{i}}{\text{E}}\Big{[}f \Big{(}x,l_{x};M_{i},w_{i,loc}^{*}\Big{)}\Big{]} \tag{4}\]  
The performance of models trained through federated learning should not be worse than models trained locally, that is, when \(i=1,2,\ldots,N\):  
\[\underset{x\sim D_{i}}{\text{E}}\Big{[}f\Big{(}x,l_{x};M_{i},w_{i,fed}^{*} \Big{)}\Big{]}\leq\underset{x\sim D_{i}}{\text{E}}\Big{[}f\Big{(}x,l_{x};M_{i},w_{i,loc}^{*}\Big{)}\Big{]} \tag{5}\]  
For classification tasks, the model trained through federated learning should achieve higher classification accuracy compared with the local mode.
## 3 Materials and Methods
### Data Analysis Results
#### 3.3.3 Case Study Data Results
This research utilizes the actors as the nodes, and the messages that flow between the actors are abstracted in the form of the edges between the involved actors (nodes) on the _Gephi_ software to develop the network structure integrating the AVC actors. Digitalization of the Agricultural Value Chain in Enabling a Resilient and Inclusive Digital Economy  
The overall objective of this paper was to develop an AVC digitalization framework for implementing resilience principles in enabling an inclusive digital ecosystem. Resilience is the capability of any system to continuously offer services and adapt to ongoing changes [33]. Two datasets were collected and analyzed, one from the literature and a second from case study data collected from farmers, traders, and agricultural extension officers in Kisii County, Kenya. We integrated the literature review and data from Sections 3.3.1-3.3.3 to consider the elements of an AVC enabling an inclusive digital ecosystem. The food systems' value chains are crucial for livelihood sustainability, nutritional benefits, and other economic gains. Technological advances are revolutionizing agricultural sector operations, as discussed in Section 2.4. Based on the integrated data, Figure 6 presents a conceptual digitalization framework for the implementation of resilience principles in an AVC digital economy ecosystem. Figure 6 shows that the AVC digitalization framework integrates different stakeholders' processes and practices via technology. These processes and practices include production systems, processors (value addition) distribution channels, and market segments across various agricultural value chains. Digital technology infrastructures and capabilities are the critical enablers of the digitalization framework for artificial intelligence solutions and applications that utilize agricultural big data for efficient decision-making. The critical components of the AVC digitalization framework include system integration for efficiency in information access; blockchain for enforcing transparency and traceability; ubiquitous connectivity in enabling social networks activities; and edge computing for big data processing, including data mining aided by machine learning business model for artificial intelligence applications for enabling access to financial services and ubiquitous connectivity market accessibility. All these technologies facilitate digital services empowering all stakeholders in this ecosystem. The subsections below provide more detail on the digitalization framework. Figure 5: Stakeholders networks within the agricultural value chains for the Kisii County, Kenya case studies (Authors’ visualization).