esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Ozonated Fruit Leather Solution/Suspension Sample Analysis  
This is the first study to apply on-fiber derivatization SPME on carbonyls generated from ozonolysis treatment. The ozonated FL solution/suspension is a complex sample matrix with a large amount of suspended particles. Generation of the four major potentially toxic carbonyls, ethanal, hexanal, 2-furfurral  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Compound** & **Calibration Curve** & **R\({}^{2}\)** & **LOD (\#g/L)** \\ \hline
**Ethanal** & y = 1.2 \(\times\) 10\({}^{3}\)x + 70324 & 0.9831 & 0.030 \\
**Hexanal** & y = 9.2 \(\times\) 10\({}^{2}\)x + 29024 & 0.9959 & 0.029 \\
**2-Furfural *** & y = 3.7 \(\times\) 10\({}^{4}\)x + 35313 & 0.9579 & 0.016 \\
**Benzaldehyde** & y = 4.1 \(\times\) 10\({}^{4}\)x + 38523 & 0.9874 & 0.016 \\ \hline \hline \end{tabular} Note: Calibration curves for all the compounds were generated as following: a PDMS/DVB fiber was exposed to the headspace of 17 mg/mL PFBHA aqueous solution for 15 min; and then the fiber loaded with PFBHA was exposed to the headspace of standard solution at gradient concentrations to perform on-fiber derivatization for 5 s. Two steps of extraction were both performed on 1 mL solution in a 4 mL vial at 60 \({}^{\circ}\)C, with a 1 cm PTFE coated bar stirring at 800 rpm. * 5-nitro-,4-(3-(diethylamino)propyl)semicarbazone, or 5-Nitro-2-furaldehyde 4-(3-(diethylamino) propyl) semicarbazone or 1-(5-Nitro-2-furfurrylidine)-3-_N_,_N_-diethylpropylaminourea hydrochloride.  
\end{table}
Table 4: Calibration curves of major potentially toxic carbonyl compounds.  
Figure 6: Chromatogram (SIM mode) of on-fiber derivatization SPME of ozonated fruit leather solution/suspension. 2. Ethanal at 0.620 mg/L, 3. Propanal, 4. Butanal, 5. Pentanal, 6. Hexanal at 67.6 mg/L, 7. Heptanal, 8. Octanal, 9. Nonanal, 10. Decanal. Note: Two isomeric oximes (_trans/cis_) were formed for each aldehyde (except for acetone with a symmetric structure). Peak areas of both isomers were summed up for quantification.  
and benzaldehyde was quantified in ozonated FL solution/suspension. In the post-treatment samples, 2-furfurfural and benzaldehyde were below LODs, _i.e._, 0.016 \(\mathrm{\SIUnitSymbolMicro g}\)/L each, while the maximum amounts of ethanal and hexanal were determined to be 0.644 \(\pm\) 0.188 mg/L and 80.0 \(\pm\) 22.0 mg/L, respectively (Figure 7). In our previous study, a primary health risk assessment was performed regarding ozonolysis byproducts in the recycled FL, where the existence of the four major potentially toxic carbonyls in recycled FL were determined to be far below reference doses [5]. The real and improved sample analysis results addressed byproduct toxicity concerns regarding ozone-based decolorization in FL recycling.
## 3 Material and Methods
### Auditory Stimuli  
A 5-s anechoic recording of a sentence in Swiss German pronounced by a male speaker was preconvolved with each of the BRIRs, either measured or generated by the models. A total of 27 stimuli, simulating 9 different sound source distances for both rendering models and 9 stimuli based on the measures for the same distances, were used. Each stimulus apparent distance was evaluated 4 times throughout the test trial, for a total of 108 stimuli presentations.
## 2 Materials and Methods  
### Sampling Site and Soil  
The experimental plot for dryland olive grove is located in Baena (Cordoba, southern Spain) in \"La Agusadera\" area (37\({}^{\circ}\)26\({}^{\prime}\)42.07\({}^{\prime\prime}\); 4\({}^{\circ}\)20\({}^{\prime}\)57.33\({}^{\prime\prime}\)) (Figure 1).  
The soil is characterised by a clay-loam texture, low organic carbon content (1.26%), basic pH (8.3), non-saline conditions (0.46 dS/m) and a cation exchange capacity of 44.8 cmol\({}_{*}\) kg\({}^{-1}\). It is classified as Haplic Calciol [14].  
The hydroinfiltrator is a cylindrical mesh-like shell structure primarily filled with biochar--obtained from oak wood and olive pruning waste--which was semi-buried, in 2020, next to the tree (or shrub) to capture and retain rainwater or irrigation water in the root zone, thereby enhancing infiltration and reducing losses due to evaporation or runoff [2; 15]. Furthermore, it can be used in conjunction with fertilisers or advanced irrigation systems, increasing water use efficiency and olive grove production. This system also contributes to soil improvement and carbon sequestration, representing a sustainable tool in the context of climate change.  
The characteristics of the studied plot, soil properties and olive grove management practices have been previously described by [PERSON] et al. (2023) [2].  
Figure 1: Location of the experimental plot in La Agusadera area, near Baena town (Cordoba, Spain).  
Metagenomic Analysis of Soil at Different Depths near Olive Trees with and Without Biochar Hydroinfiltrators  
Four soil samples were collected 20 cm away from the hydroinfiltrator (soil material surrounding the hydroinfiltrator) or equivalent position in control trees without the hydroinfiltrator, which was installed 0.5 m upstream from the base of each olive tree. Samples were taken in October 2022: SC20 (control soil at 20 cm depth), SC40 (control soil at 40 cm depth), SH20 (soil with hydroinfiltrator at 20 cm depth) and SH40 (soil with hydroinfiltrator at 40 cm depth).
# Robust Sampling Frequency Offset Estimation for OFDM over Frequency Selective Fading Channels
## 3 Conventional Estimation Scheme
### Conventional Scheme B  
The second conventional method [13] uses the differential relation among pilot subcarrier indexes specified in the DRM standard, i.e., \(k_{2}-k_{1}=2(k_{3}-k_{2})\) and \(k_{3}-k_{1}=3(k_{3}-k_{2})\). Denote \(x^{Q}\) be the imaginary term of \(x\). By using \(D\)-symbol delay estimation, the resulting expression leads to \[\hat{\Delta}_{s}=\frac{N}{4\pi DN_{u}(k_{3}-k_{2})}\text{arg}\left\{\sum_{m=1}^{3} \sum_{\begin{subarray}{c}n=1\\ n<m\end{subarray}}^{3}\hat{R}_{l}(k_{m})\hat{R}_{l}^{*}(k_{n})\right\} \tag{7}\]  
with  
\[\sum_{m=1}^{3}\sum_{\begin{subarray}{c}n=1\\ n<m\end{subarray}}^{3}\hat{R}_{l}(k_{m})\hat{R}_{l}^{*}(k_{n})\approx 3(E_{X})^{ 2}e^{i\pi(k_{3}-k_{2})\Delta_{s}DN_{u}/N}+\sum_{m=1}^{3}\sum_{\begin{subarray}{ c}n=1\\ n<m\end{subarray}}^{3}Z_{l}(k_{m},k_{n}) \tag{8}\]  
where \(Z_{l}(k_{m},k_{n})\) is the appropriate noise contribution. By using the high SNR approximation, the variance of the estimate is  
\[E\left[\left|\hat{\Delta}_{s}-\Delta_{s}\right|^{2}\right]\approx\left(\frac{ N}{12\pi(k_{3}-k_{2})E_{X}^{2}DN_{u}}\right)^{2}E\left[\left|\sum_{m=1}^{3} \sum_{\begin{subarray}{c}n=1\\ n<m\end{subarray}}^{3}\left\{Z_{l}(k_{m},k_{n})e^{-jv}\right\}^{Q}\right|^{2 }\right] \tag{9}\]  
where \(v=4\pi(k_{3}-k_{2})\Delta_{s}DN_{u}/N\). The MSE has the form after some simple computations [13]  
\[E\left[\left|\hat{\Delta}_{s}-\Delta_{s}\right|^{2}\right]\approx\frac{N^{2}} {144\pi^{2}D^{2}N_{u}^{2}(k_{3}-k_{2})^{2}}\left(\frac{1}{\gamma}+\frac{1}{2 \gamma^{2}}\right)\left[8+3\left(\frac{1}{\gamma}+\frac{1}{2\gamma^{2}} \right)\right] \tag{10}\]
## 4 Results
### Comparative Experiment
#### 4.4.1 Comparison on the ISBI2017 Data Set
Table 5 is an objective comparison between this network and other reference networks on the ISBI2017 data set. In this paper, MSREA-Net is the best in Dice and Jac, and Acc ranks second. The DAGAN network uses dense extended convolution blocks to generate depth representation that retains fine-grained information. In addition, two discriminators are used to jointly determine whether the discriminator input is true or false. CMM-Net mainly fuses the global context features of multiple spatial scales at each shrinking convolution network level of U-Net. In addition, an extended convolution module is developed, which can expand the acceptance fields at different rates according to the size of feature maps in the whole network and obtain larger acceptance fields to obtain high-resolution feature maps and improve the segmentation accuracy. In this paper, the reverse residual attention of the network, by mining the potential relationship between data sets, is that the network pays more attention to the lesion area, making the Dice similarity coefficient and Jac index the highest. By comprehensive comparison, our proposed network can fully extract feature  
Figure 9: Different network segmentation results on ISBI2017 data set. Images (**a–f**) represent 6 different images of skin diseases. From left to right, the images are Image, mask, U-Net segmentation results, CE-Net segmentation results, DeepLabv3+ segmentation results, CPF-Net segmentation results and the segmentation results of our algorithm. Details of each network partition are shown in red circles. information and accurately locate the lesion area and has a better segmentation effect on skin disease images.
## 6 Conclusions  
This paper investigates the elastic critical buckling of prismatic steel beams subjected to transverse loadings at the top flange with negative end moments. In addition, the elastic critical buckling of steel beams was analyzed by using the FEA program ABAQUS. Here, 336 models in total were used to study the influence of the length-to-height ratio (_L/h_) on the elastic critical LTB of steel beams, and simply supported beams were simultaneously subjected to negative end moments and transverse loadings on the top flanges. In addition, the end moment variation was taken into account with the investigated end moment ratios of 0, 0.5, 0.75 and 1. Additionally, to consider the effect of the variation in the negative end moments and transverse loadings, the \(M_{q}\)/\(M_{1}\) and \(M_{p}\)/\(M_{1}\) ratios were considered, ranging between 0.5 and 3, with a gap of 0.5, applied in this study. After performing the comparisons between the FEA, existing designed procedures and [PERSON]'s investigation, some conclusions can be drawn as follows:  
1. The ratio of \(M_{\sigma}\)/\(M_{\alpha cr}\) for beams with LC\({}_{1}\) and LC\({}_{2}\) increased as the ratio of _L/h_ increased. The values of \(M_{cr}\)/\(M_{\alpha cr}\) were greater than 1 and changed with the values of the \(M_{q(rp)}\)/\(M_{1}\) ratio.  
2. The predicted values obtained from the current design standards and [PERSON]'s study were conservative for most cases and tended to be too conservative as the _L/h_ ratio increased. AISC 2016, BS 5950, AS 4100 and [PERSON]'s study give maximum differences for conservative values of 50.7%, 59.3%, 55.3% and 64.8%, respectively.  
3. A new equation with a factor, \(F_{b}\), taking into account the effect of the length-to-height ratio and transverse loading on the top flange was proposed. The proposed equation provided reasonable accuracy and less conservative values for most cases, with maximum differences of 35.1% and 32.7% for LC\({}_{1}\) and LC\({}_{2}\), respectively.  
Conceptualization, J.J.K.; methodology, X.T.N. and J.J.K.; software, X.T.N. and S.-H.P.; validation, T.N.M.N. and K.L.N.; formal analysis, T.N.M.N., S.-H.P. and K.L.N.; investigation, X.T.N. and K.-Y.Y.; resources, J.J.K.; data curation, X.T.N. and T.N.M.N.; writing--original draft preparation, X.T.N., S.-H.P. and K.L.N.; writing--review and editing, T.N.M.N., S.-H.P. and J.J.K.; visualization, X.T.N. and K.L.N.; supervision, J.J.K. and K.-Y.Y.; project administration, J.J.K.; funding acquisition, K.-Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (RS-2021-KA163626) from the Technology Advancement Research Program (TARP) funded by the Ministry of Land, Infrastructure, and Transport of the Korean government.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Proposed NIA
These are the casts that have defects and are correctly labeled by the model, meaning the classification aligns with the actual condition of the cast;  
Figure 5: NIA block diagram. 3. FP indicates the number of instances where the model incorrectly classifies a cast as \"ok\" when it is \"nok\". In this case, a defective cast is mistakenly identified as defect-free, which leads to severe quality control issues in manufacturing;
4. FN denotes the instances where the model incorrectly identifies a cast as \"ok\" when it is \"nok\", labeling a defect-free cast as defective, which results in unnecessary rejections or additional inspections. These parameters directly impact metrics such as _Precision_, _Recall_, and the _Average Precision_ (_AP_), which provide insights into how well the NIA is performing in distinguishing between defect-free and defective casts [65]:  
* _Precision_ is assessed by calculating the ratio of TP predictions (correctly identified defect-free cast) to the total number of casts predicted as \"ok\", and it is calculated with Equation (2). This metric ensures that NIA identifies only casts that are in good condition are classified as such. \[Precision=\frac{\text{TP}}{\text{TP}+\text{FP}}\times 100[\%]\] (2)
* _Recall_ indicates NIA's ability to correctly identify all casts, emphasizing its effectiveness in avoiding FN. \[Recall=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100[\%]\] (3)
* _AP_ is the area under the Precision-Recall curve, and it is completed with Equation (4). \[AP=\int_{0}^{1}Precision(Recall)dRecall[\%]\] (4)  
Employing these metrics, NIA's performance was evaluated to ensure it meets the requirements for accurate cast classification. We proposed a concept-level automated quality control process to highlight the advantages of using the Azure IoT Hub infrastructure. The applicability process is demonstrated through cast manufacturing. The authors emphasize that both the cast parts and the automated process are presented at a conceptual level, with the paper's primary focus being to demonstrate the proposed infrastructure's favorable results. The automation process involves initial quality assessment, Azure IoT Hub Infrastructure, and the NIA algorithm:  
* In the initial quality assessment stage, the ESP32-CAM captures high-resolution images of the cast in real-time. These images are sent to the Azure Custom Vision Service, where a custom-trained AI model identifies potential defects automatically without human intervention. The output includes defect classifications and probabilities, which are transmitted to the next stage;
* The Azure IoT Hub Infrastructure acts as a unified layer for data communication;
* The final stage involves the NIA algorithm, which aggregates defect probabilities from multiple frames captured at different orientations of the cast. The proposed concept of an automated decision-making process provides defect detection abilities with high applicability potential in manufacturing.
## 3 Main Results
where \(\bar{L}^{0}=\frac{L^{0}+(L^{0})^{T}}{2},H^{\delta(t)}=L^{\delta(t)}+D^{\delta (t)},\mu=max\{\mu_{1},\mu_{2},\mu_{3}\},\delta=max\{\frac{3l_{1}+l_{2}}{2}, \frac{3l_{2}+l_{1}}{2}+1\}\), and \(\Omega,\Gamma_{m},\rho_{1},\rho_{2}\) will be given in the proof,  
then MAS (1) and (2) can achieve group consensus. Proof: From condition (1), we can see that  
\[\delta I_{N}-c\bar{L}^{0}-cD^{0}<0\]  
since \(\delta>1\); thus,  
\[c\bar{L}^{0}+cD^{0}-\frac{I_{N}}{2}>0 \tag{5}\]  
\(\square\)  
According to Lemma 1, we have  
\[ \begin{bmatrix}c(L^{0}+(L^{0})^{T})+2 cD^{0}&I_{N}\\ I_{N}&I_{N}\end{bmatrix} >0 \tag{6}\]  
When there is no DoS attack, namely, \(t\in[t_{i_{S}},t_{i_{S}+1})\), and we assume that there are \(n\) event-triggered time instants, denoted by \(\{t_{1}^{i},t_{2}^{i},\cdots,t_{n}^{i}\}\), when \(t\in[t_{k}^{i},t_{k+1}^{i})\), we choose the positive definite Lyapunov function as follows:  
\[V_{0}(t)=\frac{1}{2}\xi^{T}(t)( \begin{bmatrix}c(L^{0}+(L^{0})^{T})+2 cD^{0}&I_ {N}\\ I_{N}&I_{N}\end{bmatrix} \otimes I_{n})\xi(t)\]  
where \(\xi(t)=[\tilde{x}^{T}(t),\vartheta^{T}(t)]^{T}\). Further, based on Assumption 1 and [PERSON]'s inequality, we can obtain  
\[||\tilde{x}(t)||\cdot||f(x(t),v(t))-f(\tilde{x}(t),\tilde{v}(t))|| \leq l_{1}||\tilde{x}(t)||\cdot||\tilde{x}(t)||+l_{2}||\tilde{x}(t)|| \cdot||\tilde{v}(t)||\] \[\leq l_{1}\tilde{x}^{T}(t)\tilde{x}(t)+\frac{l_{2}}{2}(\tilde{x}^ {T}(t)\tilde{x}(t)+\theta^{T}(t)\tilde{v}(t))\] \[=(l_{1}+\frac{l_{2}}{2})\tilde{x}^{T}(t)\tilde{x}(t)+\frac{l_{2}} {2}\tilde{v}^{T}(t)\tilde{v}(t).\]  
At the same time, one can obtain that  
\[\tilde{x}^{T}(t)(f(x(t),v(t))-f(\tilde{x}(t),\tilde{v}(t)))\leq|| \tilde{x}(t)||\cdot||f(x(t),v(t))-f(\tilde{x}(t),\tilde{v}(t))||;\text{ thus, we have,}\] \[\tilde{x}^{T}(t)(f(x(t),v(t))-f(\tilde{x}(t),\tilde{v}(t)))\leq(l _{1}+\frac{l_{2}}{2})\tilde{x}^{T}(t)\tilde{x}(t)+\frac{l_{2}}{2}\tilde{v}^{T }(t)\tilde{v}(t)\]  
Similarly, we can obtain  
\[\tilde{v}^{T}(t)(f(x(t),v(t))-f(\tilde{x}(t),\tilde{v}(t)))\leq\frac{l_{1}}{2 }\tilde{x}^{T}(t)\tilde{x}(t)+(\frac{l_{1}}{2}+l_{2})\tilde{v}^{T}(t)\tilde{v }(t).\]  
Calculate the derivative of Lyapunov function as  
\[\tilde{V}_{0}(t)= \big{[}\tilde{x}^{T}(t)\quad\tilde{v}^{T}(t)\big{]}\Big{(} \begin{bmatrix}c(L^{0}+(L^{0})^{T})+2 cD^{0}&I_{N}\\ I_{N}&I_{N}\end{bmatrix} \otimes I_{n}\Big{)}\] \[\quad \begin{bmatrix}\tilde{v}(t)\\ f(x(t),v(t))-f(\tilde{x}(t),\tilde{v}(t))-(c(L^{0}+D^{0})\otimes I_{n})(\tilde {x}(t)+\tilde{v}(t))-ce^{0}(t)\end{bmatrix}
## 4 Experiment
### Experiment Planning  
We deemed it unnecessary to implement the experiment under different network configurations, as our previous research on Pareto-Q-learning has shown the approach to perform comparably under different network configurations [7]. We focused on a configuration of 50 nodes running three services.  
For this experiment, we chose to keep the players relatively unintelligent. Rather than build complex autonomous agents with sensors and underlying sub-goals, we have chosen to have an attacker that always randomly chooses an action and a defender that always follows the advice of the given algorithm.  
The players were given 10 moves per player. This was deemed to lead to sufficiently long games to expose the players to a variety of possible game states, while not having the game last unnecessarily long. We chose to have the game conclude after 10 moves, because in large networks the players would have to play extremely long games to meet any other winning conditions. In our network of 50 nodes, the attacker would have to make a minimum of 50 \(\times\) 3 = 150 moves to bring down all the services, or 50 \(\times\) 3 = 150 moves to infect all the nodes, and that is assuming that the defender does nothing to hinder the attacker's progress. Allowing for games with \"infinite\" game points led to games that were thousands of moves long.  
Our network consists of 50 nodes. Each node in the network has three services and can be flooded or exhausted. Ten percent of the possible services have been turned off (meaning that the service cannot be contacted, but also cannot be compromised). Likewise, 10% of the nodes require negligible CPU resources. Twenty percent of the nodes are high-value devices (app servers, sensitive equipment, etc.) and 80% of the nodes are low-value devices (client machines, mobile phones, etc.).  
The exact cost of flooding or exhausting a service is randomly calculated when creating a network. Table 1 contains the ranges for those values. Table 1 also details the rest of the network configuration values. The game does not require the attacker and defender to have the same value judgment allowing for different scalarization weights for the attacker and defender.  
The starting game network is randomly generated. Nodes are randomly connected with a number of edges given by the sparsity, where \(|\)edges \(|\) = sparsity \(\times\)\(|\)nodes \(|\)\(\times\)\((|\)nodes \(|\)\(-\) 1). Figure 1 shows an example of what the actual network may look like. Note that this figure contains just 10 nodes, and that there exists a path between all the nodes of the graph, but that the graph is not fully connected.
## 3 Results and Discussion
### Fluorescence Results  
#### 3.2.1 Fluorescence, Biological, and Humification Indexes  
In order to obtain a quick overview of the behaviour of fluorescent DOM in the DWTP, a set of three indexes was calculated: the fluorescent index (FI), biological index (BIX), and humidification index (HIX), results shown in Figure 3.  
By calculating the FI from the lagoon inlet and outlet (sites A and B, respectively), Po River's raw water (site C), and the inlet to the DWTP (site D, mixture of sites B and C), the FI is in all cases below 1.4, indicating that DOM sources are mostly terrestrial [28]. The FI values remained practically constant along the whole DWTP, indicating that it is not a sensitive parameter to analyse the aforementioned processes, although useful to rapidly characterise the raw water at the inlet points (sites A to D).  
The BIX of inlet sites is approximately 0.8, which indicates an intermediate autochthonous component on DOM sources [8,29]. The BIX drastically changed from the inlet points (sites A to D, where it was practically constant as mentioned before) to the ones that suffered a chemical/biological process (treatment lines Po1/Po2 and Po3). In fact, BIX values are inversely proportional to those obtained by UVA and NPOC (discussed above) and EEM-PARAFAC (_vide infra_), being much higher at those where a chemical (ozonation or chlorination) or biological process was applied. Although, in this case, it is clearly not related with the presence of \(\beta\) fluorophore (characteristic of autochthonous biological activity), due to its definition, the obtained results indicated that a degradation process produces an increment on a 380 nm emission signal and/or a reduction in the 430 nm one, which is in line with the expected reduction in the double bond conjugation  
Figure 3: Fluorescence background analysis: FI, BIX, and HIX values per site.  
Figure 2: Obtained values in each sampling point for: (**A**) UVA; (**B**) NPOC. Values represent the average of consecutive measurements and error bars the associated uncertainty.  
and production of smaller molecules (i.e., fluorescence shift to shorter excitation-emission wavelengths or blue-shift) ([PERSON] et al., 2018).  
In line with the results from the BIX, the HIX was negligible after the ozonation process (site F) but considerable after chlorination (site L). It is worth mentioning that a decrease in the HIX is correlated with a decrease in acute toxicity along water treatment plants ([PERSON] et al., 2018).