esa-sceva/satcom-chunk-collection huggingface dataset:
# Heavy Metals in Post-Exploitation Reservoirs--The Bagry Lake Case Study (Poland)
## 5 Relationships between Metals in Supernatant and Sediments
Zinc forms a number of soluble compounds, and the correlation between its presence in the sediment and in the supernatant is much higher (R = 0.2600) than for iron and manganese; however, the link distance between zinc in the supernatant and metals in the sediment and other metals in the supernatant is still weak (cluster O, \(w\) = 3.570). Calcium in the sediment is mainly linked with the fine volatile fraction, particles \(<\) 0.06 mm (cluster N, \(w\) = 3.566; Figure 18). Therefore, it may be concluded that calcium originated from natural calcareous sediments with organic matter: these can be mollusk skeletons (a group of mussels, snails). The link is not strong but definitely stronger than between the mineral fraction with particles \(\geq\) 0.06 mm (sand) (cluster P, \(w\) = 30.407). Formally, mollusk skeletons may be classified as mineral matter, as the organic concentration in exoskeletons is less than 5% ([PERSON] _et al._, 2008). However, mollusk skeletons are directly related to living matter; hence, cluster O (\(w\) = 3.570) is slightly stronger than cluster P (\(w\) = 30.407). Cluster analysis shows that the metals concentration in the sediment and in the supernatant is indirectly related to a calcium concentration and the mineral fraction of the sediment as well as the fraction of particles \(<\) 0.06 mm in the sediment (cluster N, \(w\) = 3.566; Figure 18). Looking at relationships between metals in the supernatant and in the sediment as well as different types of sediment, the strongest link was found between copper and lead in the supernatant (cluster A, \(w\) = 0.312, Figure 18; R = 0.9547, Table 5); it can be explained by copper alloys sunken in the lake or lead coatings of copper electrical wires found in the lake sediment. Another strong cluster was found between iron and manganese in the sediment and in the supernatant (clusters: D, E, \(w\) = {0.680; 0.757}, R = {0.8271; 0.7974}, respectively, Table 5). Such a strong relationship may also be caused by sunken iron alloys as well as the natural co-existence of these metals in the sediments and in the supernatant. The strong link between cadmium and Fe, Mn, Cu and Pb (cluster G, \(w\) = 0.787, Figure 18) in the supernatant and the high correlation coefficients R {0.7249, 0.6087, 0.6919 and 0.7859, respectively} (Table 5) result from the presence of cadmium in alloys with other metals or in metallic coatings of the sunken materials; there might be a common source of contamination with these metals. The relationship between metals in the sediments or in the supernatant (cluster M, \(w\) = 3.109) is several times weaker than between metals within the sediment or supernatant phases (Figure 18). It may be explained by a poor migration of metals between the sediment and the supernatant, their poor solubility or corrosion resistant materials deposited at the bottom of Bagry Lake.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 4 Technology Trends to Achieve the Requirements in the 5G Network
### Cognitive Radio  
It is another fundamental technology to be considered for the next generation network 5G [75]. Cognitive radio technology might be an essential solution for enabling the WRAN scenario [76]. The users are classified into two categories in the cognitive radio network. The first one is the primary user and the second one is the secondary user. A cognitive system can operate in two modes. The first one is spectrum overlay and the second one is spectrum underlay. During the first approach, secondary users and primary users can transmit simultaneously, while the power of transmission for the secondary user is limited, such that the primary user's interference is less than a given threshold. In the second approach, the secondary users are allowed to opportunistically utilise the white spaced that are allocated to the primary network in the spectrum using dynamic spectrum access [77]. The Cognitive Network is more appropriated for WRAN scenario.
## 1 Introduction  
Vulvovaginal candidiasis (VVC), caused by _Candida_ species, is the second most common fungal infection of the genital tract [1]. _Candida albicans_ (_C. albicans_), the major etiologic agent of VVC, is present, with a prevalence of up to 78% [2, 3]. The onset of VVC is mostly associated with the use of antibiotics, lack of _Lactobacillus_ spp. in the vagina, sexual activity, and use of hygiene products [2, 4]. The common symptoms of VVC include itching, burning, and redness [5].  
Antibiotic therapy is an effective clinical strategy for controlling _C. albicans_ infection. However, the continuous use of antibiotics can increase drug resistance, relapse rates, and interrupt the composition of vaginal microbiota [6, 7]. Recent in vitro and in vivo studies have demonstrated lactobacilli-mediated immune defense against C. _albicans_ colonization [8, 9]. Thus, interest in the development of alternative strategies that involve indirect treatment of VVC using lactobacilli has increased.  
_Lactobacillus_ species represent the predominant bacterial group in healthy vaginas. _Lactobacillus_ species are a major source of lactic acid and bacteriocins that maintain the vaginal microflora in an acidic environment, contributing to reducing the invasion of pathogenic infections by competing to the adhesion site [10; 11]. Moreover, these vaginal lactobacilli regulate the innate immune system through controlling the secretion of cytokines and antibody responses against pathogenic infections [12]. Prior in vivo and in vitro studies on _Lactobacillus_ spp. have reported that _L. reuteri_ and _L. rhamnus_ can modulate inflammatory immune responses to counteract against _C. albicans_[13; 14; 15].  
The _Weissella_ was separated from _Lactobacillus_ and categorized as a genus in 1993 [16]. _Weissella confusa_ WIKIM51 (Wilac D001) was isolated from dandelion kimchi, a traditional Korean fermented food [17]. Several studies have revealed that _Weissella confusa_ produces diverse components such as lactic acid, exopolysaccharideacharide (EPS), and bacteriocin, which facilitate anti-inflammatory and antioxidant activities in the colon [18; 19; 20]. Moreover, _Weissella_-abundant women deliver at full term in _bacteroides_-dominated communities [21]. The oral administration of _Weissella_ into mice showed that the abundances of _Lactobacillus_ species were increased and modulated regulatory T cells [22; 23]. However, the antifungal effect of Wilac D001 has not been fully explored in comparison with _Lactobacillus_ spp. in the vagina. In this study, we examined the antifungal efficacy of Wilac D001 against C. _ albicans_ infection in vaginal epithelial cells (VECs) and compared it with that of _L. reuteri_ and _L. rhamnus_.
## 6 Experimental Evaluation
### Scenarios
#### 6.3.2 Scenario 2: Federated Workload Profiling on Various Resource Parameters at the Node Level
present cumulative disk space usage before and after the implementation of FWP. This separation prevents data from overlapping with the other, ensuring that both trends can be easily examined and compared. The graph suggests that the difference in cumulative disk space between before and after the implementation of FWP is generally negligible. In practical terms, this indicates that FWP has not significantly impacted the cumulative disk space usage across the ten nodes. Overall, the negligible difference in cumulative disk space suggests that FWP has moderately reduced but not substantially increased or decreased the total storage requirements of the system, at least within the observed scope of these ten nodes. Figure 8: Cumulative memory usage before and after FWP. Figure 9: Cumulative disk space consumption before and after FWP. 3. **FWP's influence on network I/O with increasing federation nodes. ** In this experiment, we investigate the effects of FWP on network I/O statistics as the number of nodes within the federation expands. Figure 10 offers a comprehensive perspective on network I/O (Input/Output) statistics for ten nodes, featuring four key components. The blue bars denote cumulative bytes sent before the introduction of Federated Workload Profiling (FWP), while the green bars represent cumulative bytes received before FWP. Similarly, the orange bars depict cumulative bytes sent after FWP, and the red bars showcase cumulative bytes received after FWP. Additionally, the graph incorporates purple bars, which represent the cumulative difference in bytes sent, and cyan bars that represent the cumulative difference in bytes received. The two y-axes in the graph serve distinct purposes: the primary y-axis represents cumulative network traffic volumes, while the secondary y-axis illustrates the cumulative differences in network activity before and after the implementation of Federated Workload Profiling (FWP). The cumulative difference bars on the secondary y-axis appear relatively higher, mainly because they are scaled using a factor to emphasize even subtle variations in network behavior. This comprehensive visualization aids in understanding the impact of FWP on network data flow across multiple nodes. The graph demonstrates a notable network traffic pattern. Initially, there is a reduction in data bytes sent and received at Node 1 after implementing FWP. However, as more nodes join the federation, there is a consistent increase in network activity. Notably, the cumulative difference bars (purple and cyan) remain positive, highlighting FWP's ability to effectively manage and optimize data traffic as the network scales. This trend suggests significant network traffic savings when FWP is employed with a larger number of nodes, enhancing network efficiency and resource utilization.
# Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array
## 2 System Requirement and Coding Theory
### Standard Digital FPA System  
The digital FPA system is an important direction for its development. According to the different ways in which the analog-to-digital converter is integrated into the readout circuit, the digital readout scheme is divided into three architectures: chip-level, row/column-level, and pixel-level ADC. Among them, the pixel-level ADC digital readout circuit is the most advanced architecture at present.  
Figure 1 depicts a block diagram of a standard digital FPA system. The sensor component is analogous to a photodiode (PD), which converts light intensity into an output current signal. The readout circuit utilizes a pixel-level ADC design, which converts the analog current signal to a digital signal before outputting it via the peripheral circuit. The digital signal is transmitted to the image processing module, processed by the digital morphological neural network, and then the recognition and classification tasks for the target are completed.  
Digitizing the signal within the FPA pixels is an efficient method of processing it; however, such systems still have limitations for artificial vision tasks.  
First, based on the reusability of the pixel circuit, the digital encoding method of every pixel in the complete FPA is standardized. This implies that the image accuracy acquired in any area of the FPA cannot be configured and is instead determined by the number of conversion bits of the ADC at the outset of the system design. This goes against the pattern of \"scan-gaze\" in biological systems. Since the FPA has already digitalized the signal, it can only process the image encoding using a digital morphological neural network, such as ANNs or CNNs. The actual brain and nervous system utilize more efficient bioelectric impulse signals to transmit and process information, which are mechanically distinct from digital morphological neural networks. The final method for digitizing the signal within the FPA is to use multiple series-connected D flip-flops and SRAM to form an N-bit counter, which occupies up to 80% of the pixel area in existing product designs. This is an impediment to the further miniaturization of FPA pixels.  
Figure 1: Block diagram of a standard digital FPA system.
## 3 Results and Discussion
### PXRF (Portable X-ray Fluorescence): Spatial Survey  
Preliminary analyses were performed on fine blackish and greyish fine fractions (less than 2 mm) obtained by sieving the materials collected from the waste hills in place in site 1 before the rearrangement works. Strong contamination, exceeding CSC3, was found for Pb and Cd (Pb from 1860 to 15,121 mg/kg and Cd from 24 to 150 mg/kg), while for Sn (Sn from 57 to 273 mg/kg), values did not exceed CSC3 values. Furthermore, among the pXRF measurements performed on the samples collected on the regular grid at two depths (Table 5), only the mean values of As, Cd, Pb and Sb exceeded the CSC3 at both depths. Cu showed hot spots (3382 mg/kg), mainly in the topsoil, as well as Sn (1682 mg/kg). Bivariate Pearson analysis applied to pXRF measurements at 0-10 cm depth showed significantly positive correlation coefficients (two-tails, ** at 0.01 level and * at 0.05 level) between Pb and Sb (0.950 **), Cd (0.840 **), Ni (0.742 **), As (0.750 **), Sn (0.715 **), Fe (0.620 **), Zn (0.546 **) and Cu (0.290 **) (Table S1A). At the next depth (10-40 cm), some correlation coefficients notably increased, such as that between Pb and Sn (0.772 **), Fe (0.753 **), Zn (0.671 **) and Cu (0.533 **) (Table S1B). On the contrary, negative correlations were found at 0-10 cm between Pb and Mn (\(-\)0.709 **), Ti (\(-\)0.659 **), K (\(-\)0.637 **), Zr (\(-\)0.535 **), V (\(-\)0.420 *), Sr (\(-\)0.370 *), Rb (\(-\)0.324 *) and Th (\(-\)0.301 *). The positive correlations found between Pb and many PTEs suggested a common source of contamination. Indeed, different types of batteries were stocked as waste in site 1 of the studied area, including Pb (which contained the conductive support grid made by Pb-Sb alloys), Ni-Cd and Ni-Zn batteries. Concerning As: considering the high levels reached (mostly in site 1), anthropogenic sources, including residues of industrial processes of battery assemblage, must be taken into account [73]. Indeed, even though natural origin has been reported for As in these environments [60; 74], levels in the study site were excessively high and attributable mainly to pollutant sources. Regarding the negative correlations found between Pb and Ti, Th, Zr, and Nb, these elements are known to naturally enrich volcanic soils of the Phlegrean Fields, Mt. Roccamonfina volcano and Nola-Pomigliano areas (north of Mt. Somma-Vesuvius volcano) [60].
## 5 R-DPAP: Resource-Constrained Directed Path Allocation Problems
### Theoretical Complexity
The maximum lateness is lower than or equal to \(l\) in the machine scheduling problem if and only if there exists a valid allocation \(\pi\) with a utility greater than or equal to \(n\). Indeed, to reach such a utility value, the paths selected in the \(n\) graphs must each have a utility equal to \(1\). The selection of such paths indicates that all activities \(v_{a}\) can be scheduled on the unique resource while satisfying the release date and the due date, to which is added the lateness bound \(l\). **Proposition 4**. : _The R-DPAP-lex-dec problem, which consists in determining whether, for a given R-DPAP problem, there exists an allocation \(\pi\) and start time functions \(\tau_{\pi,g}\) such that \(\pi\) is valid and its leximin evaluation is greater than or equal to a given utility vector, is NP-complete._  
Figure 7: R-DPAP part generated for each activity \(a\) in \(A\). (**a**) Graph generated for each activity \(a\) in \(A\). (**b**) Description of nodes in the graph generated for each activity \(a\) in \(A\). Proof. : By using the same encoding as in the previous proof, there exists a solution such that \(L_{\max}\leq l\) if and only if the leximin-optimal allocation has a value greater than or equal to \((1,1,\ldots,1)\). Further, the leximin evaluation of an allocation \(\pi\) can be computed in polynomial time, hence the NP-completeness result.
## 3 Analysis of Original Research Articles
### Time Series Models
[63] employed EMD and VMD in combination with ARIMA and BPNN to forecast prices of corn, crude oil, and gold, finding that the VMD-ARIMA model achieved the smallest predictive errors for corn. However, EMD's contribution to the predictive ability of the BPNN model was limited. Similarly, [PERSON] et al. [64] found that while the ARIMA and SARIMA models were effective for short-term wheat price forecasts, neural network models like BPN and LSTM performed better for long-term predictions, especially with weather data. Advanced neural network models and sentiment analysis have emerged as promising tools for time series forecasting. [PERSON] and [PERSON] [65] utilized a nonlinear auto-regressive neural network to forecast yellow corn prices in China, delivering stable and accurate results, outperforming all comparison models (RW, AR-GARCH, SVR, RT, LSTM). [PERSON] [24], in their study, compared the ARMA, ANN, and RW models for forecasting Indian wheat futures. The random walk model delivered the lowest RMSE and MAE. [PERSON] et al. [66] used stochastic volatility models and Bayesian MCMC to assess oil price volatility and its spillover effects on corn and wheat futures. Results showed significant post-2006 volatility transmission from oil to corn and wheat due to ethanol-driven interdependence. [PERSON] et al. [67] used a comprehensive econometric framework involving AR, VAR, VEC, ARCH, and GARCH models to forecast wheat, soybean, and corn prices. Their results highlight the predictive value of macroeconomic variables--particularly real exchange rates (REER)--and the effectiveness of VEC models. The previous section highlighted how the techniques have been applied in practice, their effectiveness in different forecasting scenarios, and the advancements made in integrating multiple methods to enhance prediction accuracy. Table 7 presents a unified comparison of selected forecasting models applied to wheat, corn, and rice price prediction. The studies included in this table were selected based on the availability of clearly reported performance metrics (e.g., MAPE, RMSE, R\({}^{2}\), or techniques comparison) and their focus on directly comparing models using quantitative evaluation. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Refs.** & **Year** & **Commodity** & **Model Type** & **Technique** & **Evaluation Metric** & **Performance** \\ \hline
[68] & 2021 & Rice, & & & & & \\  & Corn, Soybean & ML & SVM & RRMSE & 7.45 (avg) \\ \hline
[21] & 2016 & Corn & ML & BPNN & MAPE & 1.31\% (in), 2.26\% (out) \\ \hline
[27] & 2023 & Rice & EL & Random Forest & MAPE & 0.0093573 \\ \hline
[29] & 2024 & Corn & EL & Bagging SVR & R\({}^{2}\)/MAE/RMSE & 0.961/0.234/0.315 \\ \hline
[30] & 2022 & Rice & EL & Random Forest & R\({}^{2}\) & 0.864 \\ \hline
[31] & 2021 & CORN & EL & ML–KNN & MAE/RMSE/MAPE & 0.0372/0.0512/ 0.6908 \\ \hline
[34] & 2023 & Corn, Palm Oil, Soybean Oil & DL Hybrid & VMD–TDNN & MAPE/RMSE/DSTAT & 0.0345/9.49/90.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Unified performance comparison table.
## 3 Results and Discussion
### Free Radical Scavenging  
Among the methods used to estimate the antioxidative properties in vitro, the most popular are those based on the scavenging of free radicals. The most prevalent are cheap, simple, and fast assays exploiting ABTS**+** and DPPH**+** radicals; however, the structure of artificial radicals varies from the naturally occurring reactive oxygen species, therefore, the in vivo significance of the results can be disputed [36]. Therefore, the activity against three radicals of major biological significance was also determined. Detailed results are presented in Table 1 while a summary and interpretation of results are shown in Table 4.  
Extracts from _Aronia melanocarpa_ showed comparable scavenging properties in both assays implementing the use of artificial radicals (ABTS**+** and DPPH**+**). The activity of phenolics was approximately two times smaller than that exhibited by Trolox. Both tests showed a similar activity range and a high correlation with the concentration of phenolic compounds.  
The scavenging capacity of the hydroxyl radical was also well correlated with the phenolic content. However, the results were affected by the properties of this reactive oxygen species; it is excessively reactive and can oxidize the vast majority of organic molecules [37]. All compounds present in the extracts (including non-phenolics) increase the scavenging activity against OH**+**, however, the concentrations required for efficient protection are not possible to achieve in living cells. Therefore, the only effective method of defense against the hydroxyl radical is by hindering its formation from other reactive oxygen species.  
Research on the scavenging activity against the superoxide radical showed that chokeberry extracts are exceptionally good neutralizers of these molecules. A concentration of phenolics as low as 1.6 mg/L (expressed as equivalents of gallic acid) was able to scavenge more than 50% of radicals. This parameter showed the highest correlation with total phenolic content (\(R^{2}\) = 0.9803).  
Nitric oxide is a distinguished reactive oxygen species. It shows very limited activity against macromolecules, although it can react with other radicals, which induces the formation of more dangerous molecules, e.g., peroxynitrite (ONOO\({}^{-}\)) [38]. The phenolic compounds obtained from _Aronia melanocarpa_ turned out to be weak inhibitors of NO**+**. The correlation between the TPC and activity against nitric oxide was moderate (\(R^{2}\) = 0.6249).
# Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval
## 3 Geo-DMP Mobile System  
In this section, we will elaborate on each constituent part of our work in the succeeding subsections, respectively. Specifically, we start with introducing the framework of Geo-DMP. Thereafter, the workflow and mechanism of delay-tolerant content retrieval are described in detail. Next,we present the method of partitioning map into regions by taking administrative boundaries into consideration, along with the usefulness of segmented regions in the design of routing protocol.
# BioShare: An Open Framework for Trusted Biometric Authentication under User Control
## 5 Evaluation and Analysis
### Security Analysis of BioShare  
We evaluate the security level of secure communication channels between the terminal TEE and the server enclave by analyzing the cryptographic algorithms used in BioShare, as shown in Table 10. In the implementation, we specify key lengths of 128/192 for the AES algorithm; thus, the security strength of symmetric encryption is 128/192. We specify key lengths of 3072/7680 for the RSA algorithm and key lengths of 256/384 for the ECC algorithm, and therefore, the security strength of asymmetric encryption is 128/192. We use SHA-256 as the hash algorithm, and the security strength is 256. In conclusion, the security strength of secure communication channels is 128/192, which fulfils and even exceeds the values recommended by the National Institute of Standards and Technology (NIST) [57].  
The terminal TEE empowers the Trusted Application with secure hardware, secure OS, secure storage, and secure provisioning, etc. The server enclave empowers the Enclave Module with isolated execution, encrypted RAM, secure storage, sealing and attestation mechanisms, etc. The TEEs on both terminal and server sides provide trusted environments to process and store biometric data at a high security level, and Table 11 lists some security properties of trusted environments.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Provider** & **Platform** & **Notification Service** & **Delay (ms)** \\ \hline Apple & IOS & Apple Push Notification Service & 400 \\ \hline Google & Android & Firebase Cloud Messaging & 500 \\ \hline Aurora Mobile & Android/iOS & Aurora Mobile JPush & 415 \\ \hline \end{tabular}
\end{table}
Table 9: Normal Delay of Notification Services.  
Figure 9: CDF of End-to-End Communication Delays.