esa-sceva/satcom-chunk-collection huggingface dataset:
Recognizing the significant influence of emotions on thoughts and actions, this study focuses on identifying fundamental emotions through both audio and visual cues. The model distinguishes between happy, sad, angry, disgusted, surprised, frightened, and neutral emotions. Facial expression and current sentiment are analyzed from video input to predict video accuracy. A multilayer perceptron (MLP), a feedforward artificial neural network, processes input datasets to generate relevant outputs. The Haar cascade algorithm is employed for classification, distinguishing between positive and negative data points related to detected objects. Audio feature extraction identifies the user's emotional state from their speech using mel-frequency cepstral coefficients(MFCCs) and a spectrogram model. Both video and audio accuracy are calculated, and the highest accuracy is used to determine the dominant emotion. This information drives a knowledge-based music recommendation system designed to shift negative emotions towards positive ones by selecting songs appropriate to the user's identified mood. Automated music recommendation systems driven by human emotion are gaining increasing popularity. Recognizing the rich information content of both audio and lyrics, the paper explores a multi-modal fusion model to enhance classification accuracy in music mood classification [15]. The primary objective is to investigate the impact of various attention mechanisms--specifically, self-attention (SA), channel attention (CA), and the hierarchical attention network (HAN)--within a multi-modal music mood classification system. The experimental results demonstrate that multi-modal architectures incorporating attention mechanisms achieve higher accuracy than those without attention and uni-modal architectures. Inspired by the effectiveness of these attention mechanisms, a novel network architecture, termed HAN-CA-SA, is proposed for multi-modal classification, achieving an accuracy of 82.35%. The efficacy of the proposed model is further evaluated through receiver operating characteristic (ROC) analysis and Kappa statistic calculation. K-fold cross-validation is employed to ensure robustness. The performance of the proposed model is benchmarked against XLNet and CNN-BERT systems. Finally, [PERSON]'s statistical hypothesis test is conducted to validate the significance of the proposed approach.
# Assessing the Techno-Economic Feasibility of Bamboo Residue-Derived Hard Carbon
## 2 Materials and Methods
### Discounted Cash Flow Analysis  
A TEA was conducted to evaluate the economic viability of producing hard carbon from bamboo residues at an industrial scale. The analysis was based on a DCF framework, following established methodologies widely applied in biomass-to-materials and biofuel systems from the U.S. National Renewable Energy Laboratory [25]. The model integrates both CAPEX and OPEX across the full production chain from bamboo residue procurement to hard carbon output at the factory gate. The objective was to determine the MSP of hard carbon required to achieve a target IRR of 15%.  
The DCF model considers time-adjusted cash flows over a project lifetime, and accounts for installation, inflation-adjusted equipment costs, working capital, tax effects, and depreciation schedules. Cost estimation was performed using standard process engineering approaches, including the six-tenth scaling law (we used a scaling factor of 0.7) for capacity adjustment and CEPCI-based cost normalization. Region-specific inputs such as feedstock cost, transportation distance, and electricity price were incorporated for each of the ten bamboo-rich provinces. In addition, a one-way sensitivity analysis was conducted to examine the impact of key technical and financial parameters on MSP outcomes. The details of capital cost estimation, operational cost structure, financing assumptions, and sensitivity scenarios are described in the following subsections.
## 2 Results and Discussion
### Volatile Constituents Profile of Fresh True Lavender Leaves
In less amounts cumin aldehyde (1.92% \(\pm\) 0.59), \(\tau\)-cadinol (2.04% \(\pm\) 0.55), \(m\)-cymen-8-ol (2.09% \(\pm\) 0.25), camphor (2.09% \(\pm\) 0.92), \(p\)-cymene (2.58% \(\pm\) 0.33), caryophyllene oxide (3.31% \(\pm\) 0.18), limone (3.42% \(\pm\) 1.16) and 1-octen-3-pl acetate (3.80% \(\pm\) 0.52), which have a significant influence on true lavender leaves' fragrance quality, were identified. The most characteristic and valuable constituents for true lavender (flowers), linalool and linalyl acetate, represented 0.42% \(\pm\) 0.03 and 2.21% \(\pm\) 0.73 of the total amount of volatile constituents, respectively. Similar findings were reported in recent studies where eucalyptt (8.50% and 31.9%), borneol (15.21% and 24%), camphor (2.00% and 16.1%), cumin aldehyde (0.50% and 2.2%) were identified as main volatile components of a true lavender leaves sample [33; 34]. Also, one of these studies, by [PERSON] et al. [34] pointed out low amounts or even a lack of linalool (0.7%) and linalyl acetate. This result is contrary to the one obtained in this study, however it may be related to the slightly different plant chemotype or due to the fact that in [PERSON]'s study leaves essential oil was analyzed, not headspace volatiles. [PERSON] and [PERSON] [35] have identified linalool and linalyl acetate in a similar ratio (1:5), and furthermore they also found higher amounts of \(\gamma\)-cadinene (3.4 \(\pm\) 0.1) and caryophyllene oxide (7.2% \(\pm\) 0.2). Unfortunately, there is a lack of reports in literature including HS-SPME analysis of true lavender leaves volatile constituents. Most of available ones takes as study object lavender flowers or whole aerial parts of the plant, where linalool and linalyl acetate dominate in the chromatographic profile of the volatile constituents [36; 37; 38]. [PERSON] and [PERSON] [39] reported high amounts of eucalyptt (41.37%), camphor (15.83%), borneol (12.32%), \(\alpha\)-pinene (4.66%), and \(\gamma\)-cadinene (1.07%) found by HS-SPME analysis of true lavender samples. At the same time they did not find any traces of linalool or linalyl acetate, suggesting that the major part of their samples were lavender leaves.
# An In-Depth Review on Blockchain Simulators for IoT Environments
## 2 Background
### Blockchain Layers
#### 2.2.2 Consensus Layer  
The consensus layer is very important in the DLT state system as its role is to get all the nodes of the system to reach an agreement. Two main consensus algorithms used by DLT systems are proof based and PBFT [12].  
The proof-based consensus was first introduced on the bitcoin network as PoW or proof of work. This is the core mechanism on which the bitcoin network was based, a mechanism which relies on competition between nodes to compete to be the first to solve a mathematical problem [13]. This calculated block would then be broadcast by a node to other nodes, which must then mutually confirm the correctness of the hash value. Once this has been achieved, other miners would add this new block to their own blockchain. Quite similarly, this PoW is used by other cryptocurrencies such as Ethereum and Dogecoin. A drawback would be the waste of considerable computing resources. Proof of Stake (PoS) and Proof of Authority (PoA) are other two proof-based consensus algorithms.  
PBFT or Practical Byzantine Fault Tolerance is an algorithm intended to handle up to 1/3 malicious byzantine replicas. The PBFT algorithm is divided into three phases: pre-prepare, prepare and commit. The pre-prepared and preparation phases are used to order requests sent in the same view even when the primary is faulty. The prepare and commit phase is used to ensure that requests that commit are totally ordered across views. For each phase, a node needs 2/3 votes from all nodes to proceed from one phase to the next. The PBFT algorithm relies on a fault tolerance calculated by the formula \((n-1)/3\) to ensure activity and safety. An important feature of the PBFT consensus algorithm is that nodes are only partially trusted [14]. Hyperledger Fabric is an example that uses this type of consensus.
# Contribution of Climate Change and Grazing on Carbon Dynamics in Central Asian Pasturelands
## 4 Discussion
### Socio-Economic Impacts and Future Implications  
Central Asia has great carbon sequestration potential, but it is still among the most poorly known area in global carbon cycle [1]. Central Asian countries experienced one of the most widespread land use changes in the 20 th century due to the collapse of the former Soviet Union in early 1990s. Large tracts of arable land were abandoned to grassland or left unutilized, which led to remarkable carbon sinks of 70 g C m\({}^{-2}\) year\({}^{-1}\) in Ukraine, Belarus, and European Russia [40], 240 g C m\({}^{-2}\) year\({}^{-1}\) in Kazakhstan [41], and 95 g C m\({}^{-2}\) year\({}^{-1}\) in Russia [42]. Nevertheless, the impacts of land use change are still highly uncertain, because due to the shortage of refined ground observational data, most modeling studies had to rely on sensitivity analysis rather than solid validation [43], and long-term, spatially-explicit land use data that could reflect the large scale land dynamics were also highly limited [44]. Recently, using integrated frameworks were proven to be effective choices to solve complex interactions between ecological and economic processes, especially for those lacking parameter values [45; 46; 47]. These studies generally combined field measurements, mathematical cooperative models, and geographic information system models. This study attempted to use climate forcing data ensembles to minimize the simulation uncertainties; however, the uncertainties in other input data, as well as in model structure and parameters, deserve no less attention.  
Central Asia is projected to suffer from above-average warming and drought occurrences in coming decades [48]. Meanwhile, the demands for socio-economic developments, such as urban expansion, mining exploitation, and transport infrastructure, are rapidly growing, which, together with future climate change, will inevitably affect the ecosystem functioning, land productivities, and production choices. These issues deserve high priority, because Central Asia is vulnerable to climatic and anthropogenic disturbances [1], and is already facing poverty and environment and ecosystem degradation. Unfortunately, in the absence of grazing and other specific land use information under future socio-economic scenarios, our present work could not thoroughly solve these issues.
# CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario
## 2 Materials and Methods
### IoMT, Microservices and Edge Intelligence  
The IoMT is progressing towards a smart, intelligent, reliable and automated infrastructure, thanks to the adoption of several evolving technologies, which make it possible to provide services everywhere and at any time in order to improve the human beings' quality of life. In particular, distributed and edge computing and Web of Things platforms are crucial to enabling a fast and responsive IoMT system and to facilitate the ubiquity, reusability and interoperability of available resources. On the one hand, the current generation of IoMT nodes can be independently deployed at the edge, stand alone or embedded in patient's devices, or deployed on infrastructure settings, both in hospital or home where the patient data needs to be monitored, where they collect diversified types of user-generated data such as emotional and physical health-related ones. The process of integration of the retrieved information is crucial, as their coordination and synchronization in order to provide advanced context-aware applications (such as behaviour monitors, fall detectors, automatic diary builders and so on). The storage and computing of these data can be done at the edge in the resource-constrained \"medical things\", which allows data from IoMT devices to be processed locally reducing latency and security-related issues [32; 37]. Edge computing and storage are forms of cloud parts that enable physically and logically moving the service-specific processing and data storage from the central to the edge network that is close to both the end users and providers. In 6G among the expected benefits the edge intelligence will contribute to all these aspects. The edge intelligence involves an increasing level of data processing and brings some new features linked to machine learning, evolutionary and self-organizing strategies and artificial intelligence and complex systems approaches on the clusters of nodes that can learn and share collectively [16]. On the other hand, thanks to the Web of Things, medical things can be seen as interconnecting devices into the Internet and web resources which can be easily discovered and accessed through traditional web standards (such as HTTP, REST, URI and so on) [20]. This paradigm, based on a resource-oriented architecture, improves the connectivity and reusability of context data and resources to deliver different kinds of health, wellness and ambient assisted living services. IoMT application can be split into independent execution units and microservices and therefore can be distributed across different medical things [38] for processing. In fact, in comparison with monolith software applications, whose modules cannot be executed independently and are unsuitable for distributed systems, microservices are cohesive, autonomic, replaceable and deployable independent processes interacting with each other through standardised interfaces [39].
## 4 ML Techniques
### Data Preprocessing  
The success of ML techniques depends mainly on the quality of the data fed to the algorithm; if the data contains irrelevant information, it may result in inaccurate results. Thus, data preprocessing is essential in the ML process, especially for AM applications where a large dataset is created. Data preprocessing can help solve problems of data redundancy, missing data, and noisy data [98]. Applying a digital filter may be necessary for AE signals to separate high- and low-frequency signals related to background noise [99]. For welding, cladding, and AM applications, transforming into fast Fourier transform are common preprocessing methods, as illustrated in Figure 9. Lately, the use of spectrogram is also becoming a common approach [78]. Some preprocessing for pyrometers included the use of discrete Fourier transform as well [100]. Preprocessing for image sensors includes background removal, filtering, and cropping [101]. For example, if meltpool is being monitored, the meltpool area will be measured, and the spatter area will be isolated for further investigation. Solid knowledge and experience in the process are necessary to identify digital representations and features in data acquired through images [102]. For example, when the part superelevated, the edge of the part appears after the fresh powder has been recoated. An experienced operator can detect this error and differentiate between it and other common errors such as debris or recoater hopping. Another common approach is to use principal component analysis (PCA) as a data preprocessing tool. However, if not used carefully, important information might be lost and affect the accuracy of the results [19].  
Figure 9: Preprocessing of AE signals: (**a**) a typical AE signal, (**b**) wavelet denoising of the signal, (**c**) FFT of the signal.
## 3 Results
### Amoebicidal Activity of E. globulus Labill. (EEO)  
A gradual increase in amoebicidal activity from the first hour of exposure emerged for all the EEO concentrations tested (\(p\) = 0.0007 for 0.5 xMIC, \(p\) < 0.0001 for 1 xMIC and 2 xMIC) compared to the untreated sample. Cells treated with an EEO concentration of 2 xMIC showed a decrease of 57.33% (compared to untreated cells) and a significant difference compared to the EEO concentrations of 0.5 xMIC (\(p\) < 0.0001) and 1 xMIC (\(p\) = 0.0002) (Figure 2a).  
At 24 h post-treatment we observed a decrease in _A. polyphaga_ cells compared to the untreated control of 57.3% for 0.5 xMIC (\(p\) = 0.0008), 1 xMIC (\(p\) = 0.0003), and 1 xMIC (\(p\) = 0.0001) (Figure 2b).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**EO** & \begin{tabular}{c} **MIC EOs** \\ **(ug/mL)** \\ \end{tabular} & \
\begin{tabular}{c} **MIC EO/EO** \\ **(ug/mL)** \\ \end{tabular} &
\begin{tabular}{c} **FIC Index** \\ \end{tabular} \\ \hline TTO & 16 & 4 & \\ EEO & 32 & 8 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Minimum Inhibitory Concentration (MIC) of _M. alternifolia_ Cheel (tea tree) and _E. globulus_ Labill. (ug/mL) against _A. polyphaga_ and Fractional Inhibitory (FIC) Index value.  
Figure 1: The cell viability of _A. polyphaga_ trophozoites after (**a**) 1 h and (**b**) 24 h of exposure to _M. alternifolia_ Cheel (tea tree) (TTO) essential oil at concentrations of 0.5 xMIC, 1 xMIC, and 2 xMIC. Each bar represents the mean \(\pm\) SD of the three determinations (error bar = S.D.; n = 3). \(p\) < 0.05 (*), \(p\) < 0.01 (**), \(p\) < 0.001 (***), and \(p\) < 0.0001 (***) were considered significant during \(t\)-tests and ANOVA with the Bonferroni correction. ns stands for not statistically significant.
# Immobilization of Levocyetririzine on Mesoporous Silica for Antiallergenic Gel Formulation
###### Abstract  
Levocyetrizine dihydrochloride is an effective antiallergenic drug applied mostly orally; however, developing a topical formulation for localized treatment could be beneficial. To achieve this, a modified formulation technique is necessary to enhance bioavailability efficiency and minimize possible side effects. Therefore, levocyetrizine particles were prepared by immobilization on mesoporous silica material. Both the dihydrochloride form and its free base of levocyetrizine were fixed on a silica-type Syloid support. Immobilization of the active ingredient levocyetrizine in a free base form on a Syloid support by mixing in a dichloromethane solution provides better surface coverage (65.5%) than immobilization in the dihydrochloride form in water or methanol (24.5% for both). The successful binding of levocyetrizine was confirmed by X-ray photoelectron spectroscopy and infrared measurements. The active ingredient in the form of hydrochloride is more likely to be in the pores, while the free base is bound to the surface in larger quantities. The time-dependent levocyetrizine release showed that the liberation of the active ingredient from the Syloid is slower than the dissolution of the starting active ingredient itself, so it may be suitable for exerting a more reliable and prolonged local effect. A gel containing a Syloid-fixed levocyetrizine free base was tested in vivo in a cartoon oil-induced era edema mouse model. When compared to a reference gel, the half-dose formulation containing levocyetrizine free base demonstrated a similar efficacy to Fenitsil gel, indicating that the new formulation may offer superior effectiveness at lower doses.  
levocyetrizine; mesoporous silica material; Syloid; dissolution; mouse model; edema
# Global-Entropy Driven Exploration with Distributed Models under Sparsity Constraints
## 3 Distributed Cooperative Exploration  
In this section, our goal is to build upon the algorithms discussed in the previous section to derive optimal exploration strategies, i.e., optimize swarm trajectories to increase the efficiency of data collection. To this end, we adopt ideas from optimal experiment design [24] and cast them in the distributed swarm exploration setting.  
In optimal experiment design, the goal is to select an experiment setting that optimizes (reduces) some measure of an estimator's variance. Translated in our context, this corresponds to selecting optimal measurement locations, which reduce the uncertainty of model parameters. There are a number of criteria for optimizing the uncertainty of parameter estimates [24]. In this paper, we use a so-called D-optimality criterion, which attempts to minimize the log-determinant of the estimator's covariance matrix. In [24], the author showed that this is equal to the minimization of the differential Shannon entropy. Therefore, we will refer to D-optimality and entropy interchangeably.  
In a distributed setting, this criterion cannot be computed in a simple fashion, since the required information is shared among agents. In what follows, we therefore develop schemes for a distributed computation of the D-optimality criterion separately for the SOE and SOF distribution paradigms. Similarly to Section 2, we will begin with a centralized setting and then extend the developed concepts to SOE and SOF.
# An Automatic Participant Detection Framework for Event Tracking on Twitter
## 3 Materials & Methods
### Framework
#### 3.1.3 Filtering  
As we show in Section 4, the pre-event discussions on Twitter are very noisy. Generally, the tweets mention many named entities, and not all of them are participants. The top named entities are likely to be discriminative and temporally-relevant since they are mentioned frequently before the event. Therefore the filtering step retains the \(k\) most frequent named entities from the ranking produced in the previous step.  
We found 50 to be a good value for \(k\) in football matches and basketball games because it is close to the number of participants in both types of events. Furthermore, we note that the rankings become far noisier after the first 50 items, with many spurious named entities that could mislead the rest of the APD process. At this stage, the retained named entities are not participants, but candidates that are accepted or rejected in the next step: Resolution.
# A Low-Complexity Lossless Compression Method Based on a Code Table for Infrared Images
## 5 Results  
The mean square error (MSE) between the original and reconstructed images is used to verify that the compression is lossless. MSE is defined as  
\[MSE=\frac{1}{M\cdot N}\sum_{i=1}^{M}\sum_{i=1}^{N}[p^{\prime}(i,j)-p(i,j)]^{2}, \tag{9}\]  
where \(p^{\prime}(i,j)\) is the pixel at row \(i\) and column \(j\) of the reconstructed image, and \(p(i,j)\) is a pixel at row \(i\) and column \(j\) of the original image. MSE describes the reconstruction error between the reconstructed image and the original image. The MSEs of the 53 scenes calculated during our experiment are all 0, indicating that the proposed algorithm achieves lossless compression.  
Additionally, SSIM (Structural Similarity Index) is a metric used to measure the similarity between two images, defined as follows:  
\[SSIM(p,p^{\prime})=\left[\frac{2\mu_{p}\mu_{p^{\prime}}+C_{1}}{\mu_{p}^{2}+\mu _{p^{\prime}}^{2}+C_{1}}\right]\cdot\left[\frac{2\sigma_{pp^{\prime}}+C_{2}}{ \sigma_{p}^{2}+\sigma_{p^{\prime}}^{2}+C_{2}}\right], \tag{10}\]  
where \(\mu_{p}\) and \(\mu_{p^{\prime}}\) are the mean values of the original and reconstructed images, respectively. \(\sigma_{p}^{2}\) and \(\sigma_{p^{\prime}}^{2}\) denote the variances of the original and reconstructed images, respectively. \(\sigma_{pp^{\prime}}\) represents the covariance between the original and reconstructed images. \(C_{1}\) and \(C_{2}\) are small constants used to stabilize the computation and prevent the denominator from approaching zero. All 53 images have an SSIM of 1, further confirming that the compression is lossless and the reconstructed images are structurally identical to the originals, with no loss in image quality.  
The code table is calculated from the 53 scenes, so we need another scene to verify the algorithmic generality. We capture another 37 scenes for validation, partially displayed in Figure 7.  
The compression ratio is defined as  
\[Cr=\frac{Source\ file\ size}{Compressed\ file\ size}, \tag{11}\]  
We use a 16-bit space to store a 14-bit pixel, so the theoretical limit compression ratio is defined as  
\[TCr=\frac{16}{Image\ information\ entropy}, \tag{12}\]