esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 3 Study Cases
### Application 3: Coastal Area Monitoring
#### 3.3.3 Sea Surface Salinity  
Sea surface salinity (SSS) represents the quantification of salts dissolved in seawater [162]. It is thus a key parameter to measure variations of the fresh water amount within the ocean, as, for instance, a sudden entry of non-salty water through precipitation or melted ice [163] can affect ecosystems. SSS is fundamental for tracking horizontal and vertical water mass movements and/or mixing [163; 164; 165]. In particular, SSS, together with SST, can be used to derive ocean density, and, therefore, salinity allows to estimate the long-term variations of the water cycle, ocean-atmosphere exchanges, and the density-driven global ocean circulation [163; 165].  
The first map of SSS on a global scale, at 40-150 km spatial resolution, was made possible via remote sensing by means of the ESA Soil Moisture and Ocean Salinity (SMOS) mission, launched in 2009, the NASA Aquarius mission, launched in 2011, and the NASA Soil Moisture Active-Passive (SMAP) mission, launched in 2015 [164; 166]. SSS is derived from the polarized brightness temperature, which is measured by a radiometer at the L-band, once the dielectric constant of sea water is known [167]. By combining the measurements obtained by sensors onboard all these missions, it is possible to enhance by \(\sim\)30% the global salinity map [162].
## 3 Comparison of Finite Element Simulation of Almen Test and Almen Test
### FE Simulation of Almen Test
#### 3.1.4 Impact Sequence and Position  
With the quantitative description of peening coverage, the impact sequence and position of shots for FE simulation could be determined. Figure 5 shows the schematic of the impact sequence and position in 1 impact cycle. The impact cycle is repeated to simulate 6 layers impacting irregularly on the Almen strip surface. In each layer, the distance between the two shots is 0.6 mm. To study the influence of high peening coverage on Almen intensity and RCS, higher peening coverage could be got by increasing the number of impact cycles. In the Almen strip test, impact velocity is 25, 30, and 35 m/s, respectively, dent size is 0.125, 0.14, and 0.16 mm in FE simulation.
# Energy Aware and Quality of Service Routing Mechanism for Hybrid Internet of Things Network
## 3 System Model
### Network Model  
We represent mesh-connected visual sensor nodes in the hybrid IoT network by a directed graph \(G=\{V,\ E\}\), where \(V\) is the set of visual sensor nodes, and \(E\) is the set of wireless links between the set of those nodes. We consider single radio mesh-connected visual sensor nodes and that all the visual sensor nodes are in a static condition. The visual sensor nodes can be source nodes, relay nodes, and gateway nodes. For a given pair of nodes in the network, there is a link \((i,j)\)\(\epsilon\)\(E\) if both are in the transmission range of each other. We consider the same transmission range for all nodes in the network.
# Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
## 2 Experimental Method
### Feature Extraction  
The main purpose of feature extraction was to reduce the dimension of the original surface sEMG signal to reduce the complexity of the pattern recognition and classification, and then to improve the efficiency. Therefore, it was crucial to extract features with good distinction, low complexity, and high efficiency [27]. At present, the main feature extraction methods were time domain, frequency domain, and time-frequency domain analysis. In the field of human-computer interaction, time domain features are widely used, due to their excellent signal classification performance and lower computational complexity in a low-noise environment. [28; 29]. In this paper, we chose the root mean square (_RMS_) [12] and integrated EMG (_iEMG_) as the input features, where the _RMS_ reflected the amplitude variation of the surface EMG signals on the time scale, and the _iEMG_ referred to the total area of the sEMG signal curve after being rectified and filtered: this reflected the changes in the sEMG signal strength in the time dimension [29], and they can be expressed as in Equations (1) and (2).  
\[RMS=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\,X_{i}^{2}} \tag{1}\]  
\[iEMG=\frac{1}{N}\sum_{i=1}^{N}\left|X_{i}\right| \tag{2}\]  
where \(X_{i}\) is the _i_th sEMG time samples sequence. \(i\) = 1, 2..., \(N\). \(N\) is the number of sampling points in each analysis window.  
Thus, the input vector \(Z\) is constructed:  
\(Z_{i}=\left\{RMS_{i1},iEMG_{i1},RMS_{i2},iEMG_{i2},RMS_{i3},iEMG_{i3}, \ldots,RMS_{i\bar{9}},iEMG_{\bar{9}}\right\}\), in which, \(i\) = 1,2,3,..., \(K\), \(K\) is the number of analysis windows; \(j\) = 1,2,3,..., \(M\), and \(M\) is the number of the channels.  
In our experiment, four muscles were selected for collection of the sEMG signals, which meant that there would be eight features in each time window to be used as input vectors. The outputs were composed of five gait phases (pre-stance, mid-stance, terminal stance, pre-swing, and terminal swing).
# An Accelerated Method for Protecting Data Privacy in Financial Scenarios Based on Linear Operation
## 5 Discussion
### Visualisation of Encryption Effects and Robustness Validation  
In this section, to more visually discuss the impact of our approach on computational data privacy protection, we visualise the original data and its encrypted data distribution as shown in Figure 5. In addition, to verify the robustness of our approach, the dataset used in this section is the negative financial information and subject determination dataset provided by DataFountain. The results are shown in Table 10.  
From the figure, we can see that the original data distribution is a normal-like distribution with a strong statistical distribution pattern. After encryption by this method, the distribution is approximately uniform, i.e., the distribution pattern that may reveal statistical information has been masked. After decryption, the distribution can be effectively restored, and the obtained distribution information is consistent with the original  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Inference Model** & **Without Protection** & **With Our Method** \\ \hline LSTM-CRF & 82.5\% & 82.5\% \\ BERT-CRF & 86.2\% & 86.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Accuracy quantisation on our method with named entity recognition dataset.  
Figure 4: Illustration of the scheduling model.  
Figure 5: Visualisation of data distribution based on the encryption and decryption of raw data by the method in this paper.  
data. As shown above, the visualisation process once again proves the effectiveness of this paper's method and explains why this method does not lead to the decrease of the accuracy of the inference model. Moreover, the experimental results in the above table also demonstrate the good robustness of our method.
# The Case for a More Holistic Approach to Dry Eye Disease: Is It Time to Move beyond Antibiotics?
## 3 Prebiotics  
### Definition  
Prebiotics were defined about twenty years ago as \"a non-digestible food ingredient that beneficially affects the host by selectively stimulating the growth and/or activity of one or a limited number of bacteria in the colon and thus improves host health\" [74]. Throughout the last decade, in parallel with improved sequencing technologies leading to better understanding of the human microbiome, the concept of prebiotics has been modified several times. Finally, in 2016 the International Scientific Association for Probiotics and Prebiotics (ISAPP) published a consensus statement for prebiotics as \"a substrate that is selectively utilised by host microorganisms conferring a health benefit\" [75].  
Antibiotics, minerals and vitamins can alter the gut bacterial composition but these are not considered as prebiotics. Prebiotics harbour distinct features. First, they are selectively utilised which means not utilised by the pathogenic microorganisms; second, evidence supports their beneficial effects on host; and third, the host enzymes should not degrade the compound [75].  
Prebiotics alter the composition of the gut microflora and studies have shown the modification of faecal microbiota by adding non-digestible energy sources in the diet [76]. Clinical studies have demonstrated that the primary group of bacteria which are stimulated by prebiotics are Lactobacillus and Bifidobacterium both of which are commonly used as probiotics. Hence, prebiotics can contribute to the functional improvements of probiotics. It is also important to note that prebiotics do not influence pathogenic bacteria such as _Clostridia_ and _Escherichia coli_[77; 78; 79].  
Traditionally, non-digestive carbohydrates have been considered prebiotics and much research has focussed on three major groups; inulin, fructoligosaccharides (FOS) and galactooligosaccharides (GOS). According to the recent definition of prebiotics, particular non-carbohydrate compounds such as plant polyphenols may fit the prebiotic definition although more investigation needs to be done regarding this matter [75]. Further, [PERSON] and colleagues [80] make a convincing case for omega-3 polyunsaturated fatty acids (PUFA) to be considered prebiotics for their ability to modulate gut bacteria by supporting growth of lipopolysaccharide (LPS)-suppressing bacteria while concomitantly decreasing LPS-producing bacteria and _Enterobacteriaceae_.
## 3 Results
### Mapping of Hydrothermal Alteration Zones
#### 3.1.3 Contribution of Aster Data  
The results of the extracted band ratios for mapping clay alteration minerals (kaolinite, alunite and montmorillonite), phyllite alteration (sericite, muscovite and illite), propylitic alteration (epidote, chlorite and carbonates) and iron oxide alteration (hematite, goethite and jarosite) are shown in Figure 5.  
The band ratio (b4 + b6)/b5 shows the spatial distribution of clay alteration minerals (blue pixels), which are mostly mapped in the Lower Cryogenian turbidites, in the Upper Cryogenian units, in the Lower Ouarzazate Group units and very locally in the Quaternary unit along the Oueds (Figure 4a).  
The band ratio (b5 + b7)/b6 illustrates the surface distribution of phyllic alteration minerals in yellow pixels (Figure 4b), which are mapped in the Upper Cryogenian conglomerate and pelite units with intercalations of cinerites, rhydolites and andesites and the Lower Ouarzazate Group granite unit. These bands are mapped because of their mineralogical composition's high content of sericite, muscovite, and illite. The band ratio (b7 + b9)/b8, corresponds to the propylitic alteration mineral index shown in green pixels; it shows a distribution more or less similar to the phyllic alteration mineral index and is generally related to the Upper Cryogenian volcano-sedimentary unit and the Lower Ouarzazate Group granite unit (Figure 4c).  
The band ratio (b5/b3) + (b1/b2) shows the surface distribution of iron oxides in red pixels (Figure 4d). Compared with the geological map, the high abundance of all these mapped minerals is typically associated with Cryogenian and Lower Ouarzazate Group units. It shows a strong correlation with the mineral occurrences and prospects in the study area.
# Compensation of Limited Bandwidth and Nonlinearity for Coherent Transponder
## 1 Introduction
The known symbol sequence is identified by a sliding window, and the address of the LUT is formed accordingly. Although those developed methods can mitigate the nonlinearity, they rely on the high-speed photodiode to perform optical-to-electrical conversion, and the high-speed digital-to-analog converter (DAC) to perform the measurement. The setup is complicated and cannot be easily integrated within the coherent transponder. In this article, we demonstrate a novel method to compensate for the limited bandwidth and mitigate the nonlinearity. The most significant advantage of the proposed technique is a simple setup using the low-speed photodiode integrated into the coherent transponder. First, we establish an accurate mathematical model describing the output from the MZM, taking into consideration the limited bandwidth and the nonlinearity. Next, during the initial phase of the coherent transponder, we demonstrate the simultaneous measurement of the limited bandwidth, the nonlinearity, the power imbalance, and the bias point. From the measurement result, we determine the coefficients of the FIR filter and the memoryless Volterra filter. Finally, we compensate for the limited bandwidth and mitigate the nonlinearity of the coherent transmitter. The article is organized as follows: In Section 2, we establish the mathematic model; in Section 3, we show the measurement result and demonstrate the compensation of the limited bandwidth and the nonlinearity; in Section 4, we discuss multiple aspects of the proposed technique; in Section 5, we draw the conclusions. The presence of bandwidth limitation and nonlinearity is not unique to the coherent optical transponder. In other types of application, such as radio over fiber [11; 12] and millimeter wave band communication [13; 14], the transmitter also suffers from the penalty due to bandwidth limitation and nonlinearity. Thus, the novel method demonstrated in this paper can be adopted, modified, and applied to different types of communication systems.
# Hepatoprotective Effect of Alpinetin on Thioacetamide-Induced Liver Fibrosis in Sprague Dawley Rat
###### Abstract  
Alpinetin is an original medicinal plant flavonoid derived from _Alpinia katsumadai_ and has several biological activities. The current research aimed to evaluate the hepatoprotective effects of Alpinetin against thioacetamide (TAA)-induced liver cirrhosis in rats. Five groups of rats were utilized in this study. Hepatic injury was measured macroscopically and microscopically for entire groups. The rats' body weight was significantly lower in the TAA control group, likened to rats fed with Silymarin or Alpinetin groups, while liver weight was significantly greater in the TAA control group when equated to rats nonwished with Alpinetin groups. A histopathological investigation of hepatic tissues displayed that TAA remarkably induced hepatocyte necrosis and gristly connective tissue propagation in the TAA control group. Alpinetin implicitly decreased the influence of TAA toxicity and diminished fibrosis of liver tissues. The TAA control group presented an increase in liver enzymes (ALP, ALT, and AST) and a decrease in total protein and albumin. Rats who were fed Alpinetin had significantly lower hepatic enzyme activity as well as augmented total protein and albumin, yet they were close to the normal range. Superoxide dismutase (SOD) and Catalase (CAT) enzymes in hepatic homogenate were significantly reduced, and malondialdehydedehyde (MDA) was meaningfully elevated in the TAA control group, while rats fed with Alpinetin had significantly increased SOD and CAT achievement and depressed MDA level. Alpinetin-gavaged groups had reduced levels of Tumor necrosis factor-alpha (TNF-\(\alpha\)) and Interleukin-6 (IL-6), significantly downregulated Proliferating cell nuclear antigen (PCNA), Alpha-smooth muscle (\(\alpha\)-SMA), and reduced hepatic stellate cell activity. However, the TAA control group significantly up-regulated PCNA and \(\alpha\)-SMA and increased the activity of hepatic stellate cells. Alpinetin was nontoxic and could improve defensive mechanisms against hepatic tissue injury. Acute toxicity tests discovered no evidence of any toxic signs or dead rats, which highlights the safety of Alpinetin. Consequently, the investigation's outcomes revealed that the hepatotoxicity effects of Alpinetin in TAA-induced hepatic impairment might be due to reduced TAA toxicity, increased protein and albumin, increased SOD and CAT levels, reduced MDA levels, and modulation of inflammatory cytokines and their anti-oxidant activities, and suppressed PCNA and \(\alpha\)-SMA.  
liver fibrosis; thioacetamide; Alpinetin; hepatoprotective; Silymarin; histopathology; \(\alpha\)-SMA +
Footnote †: journal: Applied Sciences
## 4 Security Threat Landscape in 5G Networks  
### Evolution of Threat Landscape  
The evolution of mobile networks from 1G to 5G technology presents an expanded threat landscape due to the network's complexity, enhanced functionalities, and the array of devices connected to it. As networks have become more sophisticated, so too have the potential threats [46]. The shift from 2G's digital framework to 3G's IP-based services expanded the threat landscape to include issues such as malware and phishing [50]. The advent of 4G, with its all-IP structure, heightened vulnerability to IP-specific attacks like DoS and MitM attacks. The decentralized, software-driven architecture of 5G further amplifies these risks, introducing advanced cyber threats related to edge computing and supply chain vulnerabilities. The large-scale integration of IoT devices into 5G networks also increases the potential for botnet attacks and data privacy breaches [68].  
In addition, security measures have adapted over time. Basic techniques like frequency hopping were sufficient for addressing 1G's primary concerns of eavesdropping and unauthorized access. The introduction of encryption algorithms in 2G provided a foundational layer of security, which had to be further reinforced in 3G through robust encryption and mutual authentication mechanisms. The complex threat environment of 4G required even stronger measures, incorporating advanced technologies like machine learning for anomaly detection [50; 76]. ENISA [35], the European Union's cybersecurity arm, released a threat landscape report for 5G networks, evaluating the risks associated with the fifth generation of mobile communication networks (5G), as shown in Figure 3.
## 2 Materials and Methods
### Bsrn, Ceres, and Aeronet Data  
The input and output data of the RTM were evaluated through comparisons against corresponding reference data to ensure their reliability, which also guarantees the quality of the computed solar fluxes. The input MERRA-2 aerosol optical depth data at 550 nm were compared to AOD data from 31 AERONET (AErosol RObotic NETwork [58]) stations located in the broader MB (Figure S1). The AERONET data were interpolated from the available wavelengths of 440 nm and 675 nm, whereas hourly mean values of AERONET measurements were calculated to compare with the corresponding MERRA-2 data. The RTM solar fluxes at the Earth's surface were compared against corresponding fluxes from 7 Baseline Surface Radiation Network (BSRN [59]) stations located within or near the dust export area (Figure S2) and provided measurements for the study period (16-18 June 2016). Since the FORTH SSR fluxes are computed at 3-hourly time steps, the corresponding time intervals were chosen for stations. The RTM fluxes at the top of atmosphere (TOA) were compared to all-sky Clouds and the Earth's Radiant Energy System--s synoptic 1\({}^{\circ}\) (CERES-SYN1 deg-Level 3 [60]) hourly fluxes, choosing the nearest time step in CERES fluxes after the RTM time. Additionally, the original CERES data, which are available at a 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) latitude and longitude equal-angle resolution, were regridded to 0.5\({}^{\circ}\)\(\times\) 0.625\({}^{\circ}\) grids using the first-order conservative remapping scheme [61] along with the operator \"remapcon\" from the Climate Data Operators (CDOs). It should be noted that this regridding method,  
Figure 1: The study region, along with the sub-region (yellow frame) for which 3-hourly spatial mean dust DREs (which are shown in Figure 9) are calculated during the three days of the examined DAEC (16–18 June 2016).  
when applied from low to high resolution, preserves the original coarse grid structure and does not affect the comparison.
## 1 Introduction
### Contributions  
The main contributions of this paper can be summarized as follows:  
* Three dual-hop DF cooperative relaying protocols are proposed. In H-PRS, the best relay can be selected by using the CSIs of the first or second hop. On the other hand, C-ORS and B-ORS select a relay that has the highest e2e channel gain and the highest e2e SNRs, respectively, to convey the data transmission from secondary source to secondary destination.
* It is noteworthy that the PB-assisted cooperative CR relaying systems using H-PRS, B-ORS, or C-ORS have their own mathematical analysis challenges since the energy harvested from the beacon and the interference constraint of the primary users (PUs) affect the transmit power of the secondary source and relays. Moreover, due to the correlation between SNRs of the first and second hop, the analysis of the performance in the C-ORS scheme becomes much more challenging, compared with that in the H-PRS and B-ORS schemes.  
Assuming independently and identically distributed (i.i.d.) Rayleigh fading environment, exact closed-form expressions and asymptotic analysis of OP and TP for H-PRS, B-ORS and C-ORS are derived. Monte Carlo simulations are performed to validate our derivations.  
The rest of paper is organized as follows. Section 2 describes the system model used in this paper. Section 3 provides the performance evaluation. Section 4 gives the simulation results while Section 5 concludes the paper.
# Towards Mobile Federated Learning with Unreliable Participants and Selective Aggregation
## 4 Evaluation Design  
The final goal of this paper was to implement a multimobile Android-based solution for federated learning. To reach this goal, we outlined a succession of tests to perform.  
First, the dataset to be used for experimental evaluation was selected, and a baseline method was run to serve as a reference for the remaining results. Then, tests were run with 2, 4, and 8 clients, with the selected dataset evenly distributed  
In a real-world implementation, clients do not have the same quantity of data available for training. Therefore, we also tested an uneven distribution of the dataset across all clients to get a clearer picture of how a federated solution would perform in such scenarios.  
In all the tests described previously, the arithmetic mean was used as the aggregation method. Alternatively, we repeated these tests using an aggregation method that used a weighted average. The weighted average benefited clients with a larger dataset over ones that had less data, so conclusions about the effects of selective aggregation could be drawn by directly comparing the results.  
We then performed cross-validation tests that allowed us to test the generalization capability of the implemented solution, i.e., we evaluated the results obtained with different parts of the dataset.  
Finally, we experimented with another, more complex dataset to validate the implemented solution and the proposed alternative aggregation method. Figure 5 shows a diagram of the experimental methodology for the results discussed in Section 5.  
In all experiments, the performance measure used was the average accuracy after multiple rounds. This was calculated by the server, which progressively recorded the arithmetic mean of the accuracy achieved in each round. Moreover, each test configuration was repeated for five trials.  
Figure 5: Experimental Methodology.