esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work and Our Motivations
### Our Contributions  
We put forward an anonymous IBBE against a continual-leakage attack. First, for the first time, we use state division technology to obtain the leakage resilience of a broadcast encryption scheme. The main advantage is that it can ensure that the scheme has the ability to resist side-channel attacks and has relatively high computational efficiency at the same time. The computational efficiency is also one of the important considerations of the cryptographic scheme. Second, the scheme has anonymity, which protects the privacy of users. For example, for a health diagnosis and treatment system based on cloud storage, if the data owner (a hospital) wants to encrypt the data about coronary heart disease in the Department of Cardiology for the relevant patients, if there is no anonymity, a bystander can infer that a user accessing this data is suffering from heart disease. Thus, the identity information of the user is virtually leaked. Therefore, anonymity is also a very important aspect. In fact, ref. [53] provided an anonymous broadcast encryption. Although its efficiency is considered, a side-channel attack is not considered. Thirdly, our scheme has a good ability to resist a side-channel attack. The side-channel attack is a new cryptosystem attack form in the past 10 years. Therefore, if the designed cryptographic algorithm can capture the side-channel attacks, the security for the cryptographic scheme is better.  
Figure 2 shows a whole framework about an anonymous leakage-resilient IBBE for cloud services. The system involves four entities: private key generator (PKG), cloud storage server (CSS), data user (DU) and data owner (DO). The PKG offers private keys for all DUs based on DUs' identities. The PKG sends the system's parameters to the DO and DU. The DO will authorize the data user in the target set as the receiver and encrypt the symmetric encryption key through anonymous IBBE. The DO encrypts its information by the session key and places the ciphertext on CSS. The symmetric encryption key is broadcast by the data owner to the target user set. The target user decrypts the ciphertext with their private key and obtains a symmetric encryption key. Next, the target user decrypts the ciphertext with the symmetric encryption key. In this process, the user cannot obtain the information of other users, so the system has anonymity.
# A Comprehensive Review of Acoustic Methods for Locating Underground Pipelines
## 5 Factors Affecting Acoustic Methods in Pipeline Localization  
According to the principles behind acoustic methods, to successfully locate buried pipes, three aspects are necessary in the process of detection, including generation of elastic waves, wave propagation through pipe-soil systems, and signals collection after wave travel. Several factors can be controlled for wave generation, including the frequency range, the coupling between the wave generator to the points to be vibrated, as well as the pressure of in-pipe medium [10]. Once the sound is introduced, wave propagation through pipe-soil systems goes beyond the technician's control, due to the differences/uncertainties in coupled pipe systems including the pipe material, pipe size, burial depth, surrounding soil and in-pipe medium. In addition, the quality of collected signals may also be affected by the condition of ground surface and background noise. The availability of adequate information concerning the condition of buried pipes provides the prerequisite for selecting appropriate acoustic methods. In this section, several main factors are briefly discussed as follows.
For instance, the endophytic bacteria _Pseudomonas stutzeri_ E25 and _Stenotrophomonas maltophilia_ CR71 inhibited the growth of _Botrytis cinerea_ by releasing VOCs, with dimethyl disulfide (DMDS) being the main component [18]. VOCs produced by _Bacillus subtilis_ GB03 can directly inhibit the mycelial growth of Arabidopsis gray mold and interfere with the attachment of pathogenic fungi on hydrophobic foliage [19]. A small number of VOCs were found to inhibit growth of _R. solani_ specifically, such as sulfide, 1-methyl-4-(1-methylethenyl)-cyclohexene, methyl-2-methylvalerate, and 1,3,5-trichloro-2-methoxybenzene [14; 19; 20]. Overall, information on the identity and inhibitory effect of VOCs released by rice rhizosphere bacteria is fragmented. In the present study, we searched for novel VOCs from rice rhizosphere bacteria with efficacy against rice sheath blight. To achieve this objective, bacteria from rice rhizosphere suppressive to _R. solani_ were screened via a double Petri dish assay, VOCs were identified and quantified with GC-MS, and relative contribution of each VOC was estimated by constructing a random forest model and was further verified using pure compounds.
## 4 Results and Discussion
### Pruning Performance Comparison
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Schemes** & \(\mathbf{\alpha}\) & **Max mAP \%** & **Rounds for 80\% mAP** & **Param. Saving** \\ \hline \multirow{3}{*}{FedAvg [1]} & 0.5 & 84.32 & 8 & \\  & 0.7 & 87.32 & 6 & 0 \\  & 10 & 90.34 & 5 & \\ \hline \multirow{3}{*}{FedLP [48]} & 0.5 & 84.33 & 15 & 1.01 M (31\%) \\  & 0.7 & 85.4 & 14 & 0.84 M (24\%) \\  & 10 & 86.79 & 12 & 0.37 M (11\%) \\ \hline \multirow{3}{*}{DualPFL} & 0.5 & 89.75 & 6 & 2.36 M (67\%) \\  & 0.7 & 91.53 & 5 & 2.15 M (60\%) \\ \cline{1-1}  & 1.0 & 92.82 & 4 & 1.65 M (51\%) \\ \cline{1-1}  & 10 & 94.37 & 3 & 1.29 M (40\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of detection performance on COCO dataset. Figure 3: Test accuracy on CIFAR-10 under different Dirichlet (\(\alpha\)). (**a**) \(\alpha=0.5\); (**b**) \(\alpha=10\).
## 1 Introduction  
Valpolicella is a wine producing region characterized by the traditional practice of post-harvest withering for the production of dry and sweet red wines, among which Amarone is the most famous [1]. Valpolicella is located in the north-east of Italy close to Verona city, with Corvina, Corvina and Rondinella grapes being the traditional varieties employed for local wines [1]. At ripening, grapes are harvested and stored in a specific warehouse traditionally called a \"fruttaio\", where they undergo slow dehydration [2]. The duration of withering varies depending on the wine type being produced, and it is generally monitored by assessing grape weight loss. In the case of Amarone or Recioto, withering generally lasts 2-3 months, with a weight loss of approximately 30% of the initial weight [3]. In the case of other wines such as Valpolicella Classico superiore as well as different IGT wines, a milder withering is usually carried out, lasting 4-8 weeks with weight loss of 10-15%.  
Grape withering has a deep impact on the formation of the characteristic aroma of Amarone wine [4,5]. During the withering process, an increase in sugar content due to water loss is not the only transformation taking place. Phenolic and aromatic composition of grapes and wines is also affected [6; 7; 8; 9; 10; 11; 12], and skin wall composition as well as grape mechanical properties are also modified [13; 14]. Interestingly, some of these processes are not due to dehydration but are the result of ongoing metabolic activities in the berry, resulting in peculiar gene expression patterns contributing to changes secondary metabolism [15; 16; 17].  
Different grape withering techniques have been developed in the past, depending to local environment, grape variety and technical issues, entering in the local tradition and history as community heritage [18]. The different techniques can be divided into natural, forced and on-vine withering [18]. An example of a natural withering technique is the exposure of grapes to the sun, while in the forced method, grape dehydration is obtained using ventilated rooms like in the case of a modern fruttaio where withering conditions like temperature, humidity and air flow are controlled. On-vine withering can be obtained by practicing late harvest, cane cutting, or peduncle twist [18]. Though withering in Valpolicella is traditionally made in a fruttaio, there is an increasing interest to explore other postharvest methods to support traditional practices, in particular for mild withering processes requiring a short duration.  
The aim of this research was to evaluate the effect of the two withering systems, in \"fruttaio\" and on-vine with peduncle twist, on the volatile profile of wines. The results were compared with those of wines obtained from not-withered grapes. Wines were also assessed after a period of aging, to evaluate the influence of the different withering practices on aging patterns of the resulting wines.
# Cytotoxicity and Antimicrobial Activity of BioAktTM and Phytic Acid: A Laboratory-Based Study
## 3 Results  
Statistical tests showed a statistically significant difference in cell viability between 4 h and 24 h for 1% IP6 (4 h: 0.485 \(\pm\) 0.058, 24 h: 0.074 \(\pm\) 0.009; \(p\) = 0.031), 3% IP6 (4 h: 0.398 \(\pm\) 0.063, 24 h: 0.069 \(\pm\) 0.009; \(p\) = 0.031), 5% IP6 (4 h: 0.331 \(\pm\) 0.058, 24 h: 0.071 \(\pm\) 0.004; \(p\) = 0.031), and BioAkt (4 h: 0.250 \(\pm\) 0.016, 24 h: 0.013 \(\pm\) 0.002; \(p\) = 0.031).  
Statistically significant different cell activity emerged between 4 and 24 h for 1% IP6 (4 h: 55,207 \(\pm\) 3973.63, 24 h: 30,431.2 \(\pm\) 25,493.4; \(p\) = 0.031), 3% IP6 (4 h: 8043.33 \(\pm\) 1381.21, 24 h: 5495.8 \(\pm\) 350.96; \(p\) = 0.031), and BioAkt(4 h: 4389.83 \(\pm\) 440.84, 24 h: 0 \(\pm\) 0; \(p\) = 0.031).  
Figure 1: Crystal Violet Assay Results; * indicates statistically significant difference between 4 h and 24 h.  
After 4 h, cell viability was statistically significantly lower in BioAkt(tm (0.233 \(\pm\) 0.250) than in 1% IP6 (0.508 \(\pm\) 0.532) (\(p\) = 0.002). After 24 h, BioAkt(tm (0.013 \(\pm\) 0.002) reported lower cell viability compared to 1% IP6 (0.074 \(\pm\) 0.009, \(p\) = 0.002), 5% IPS (0.071 \(\pm\) 0.004, \(p\) = 0.024). After 24 h, BioAkt(tm) reported a statistically significant difference in cell viability compared to 5% IP6 (0.071 \(\pm\) 0.004, \(p\) = 0.024). Overall, 1% IP6 gave the compound with the lowest reduction in cell viability. After 24 h, only BioAkt(tm) reached the same reduction in cell viability as 1.5% NaOCI in both tests.  
Cell activity in BioAkt(tm) was lower after 4 h (4389.83 \(\pm\) 440.84) and 24 h (0 \(\pm\) 0) compared to 1% IP6 (4 h: 55,207.33 \(\pm\) 3973.63, \(p\) = 0.014; 24 h: 30431.2 \(\pm\) 3320.86, \(p\) < 0.0001). Statistically significant differences also emerged on comparing BioAkt(tm) and 3% IP6 (5495.80 \(\pm\) 350.96, \(p\) = 0.006) (Figure 2).  
For antimicrobial activity, the diffusion test showed no statistically significant differences among the four solutions (Figure 3). In contrast, after 1 h of treatment, 1% IP6 reported the lowest antimicrobial activity compared both to BioAkt(tm) (\(p\) < 0.05) and 1.5% NaOCI (\(p\) < 0.05), while BioAkt(tm) showed similar antimicrobial activity as that demonstrated by 1.5% NaOCI (Figure 4).  
Figure 3: Diffusion test.  
Figure 2: Cell-Titer Glo® 2.0 Assay—* indicates statistically significant difference between 4 h and 24 h.
## 2 Research Methodology
### Study Selection  
The selection of relevant studies followed a structured multi-stage process based on the criteria summarized in Table 2:  
1. Title Screening. Articles with titles clearly unrelated to price prediction or machine learning were excluded.
2. Abstract Screening. The remaining articles were reviewed at the abstract level to assess their relevance to agricultural price forecasting using machine learning methods. Papers that did not meet the core focus were excluded at this stage.
3. Full-Text Review. Full texts of the remaining articles were reviewed in detail to ensure alignment with the inclusion criteria and overall objectives of the review. Only studies with a direct focus on forecasting the prices of wheat, corn, or rice using machine learning or time series techniques were retained.  
After applying this process, a final set of 50 studies was selected for in-depth analysis. This systematic review adhered to the PRISMA 2020 reporting guidelines. Two reviewers independently performed the screening of the titles, abstracts, and full texts. Any disagreements were resolved through discussion. The studies were grouped according to the forecasting techniques identified (ML, EL, DL, TS) and the selected agro-commodities (wheat, corn, rice). No formal risk of bias assessment was performed, as the review focused on descriptive and comparative analysis of forecasting methods. This review was not registered, and no protocol was prepared.  
Figure 2 below presents the PRISMA 2020 flow diagram of the study selection process.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Criteria** & **Inclusion** & **Exclusion** \\ \hline Study Type & Peer-reviewed journal articles indexed in Scopus & Non-peer-reviewed articles, editorials, opinion pieces, gray literature, conference papers \\ \hline Commodity Focus & Studies focusing on agricultural commodities: wheat, corn (maize), or rice (paddy) & Studies on non-agricultural commodities (e.g., metals, energy) or financial markets \\ \hline Techniques & Studies using machine learning, deep learning, time series models, or hybrid forecasting methods & Studies using only traditional statistical methods (e.g., simple regression) or no forecasting \\ \hline Focus on Price Prediction & Studies explicitly focused on price forecasting or prediction & Studies focused on unrelated topics (e.g., yield, soil, climate) without price prediction \\ \hline Language & Published in English & Published in other languages \\ \hline Indexing & Indexed in Scopus & Not indexed in Scopus \\ \hline \hline \end{tabular}
\end{table}
Table 2: Study selection criteria.
###### Abstract  
Pore pressure prediction gives drillers an early warning of potential oil and gas kicks, enabling them to adjust mud weight pre-emptively. A kick causes a delay in drilling practices, blowouts, and jeopardization of the wells. Changes in pore pressure affect the type of fluid contact in the reservoir. This study predicted the pore pressure and determined fluid contacts within the Lower Cretaceous and early Upper Cretaceous (Barremanian to early Cenomanian) sandstone reservoirs of the Bredasdorp Basin using well logs and repeat formation test (RFT) data from three wells: E-BK1, E-AJ1, and E-CB1. [PERSON]'s method of developing a depth-dependent Normal Compact Trend (NCT), using resistivity and sonic wireline logs, as well as other methods including the Mathews and Kelly, Baker and Wood, and Modified Eaton and Bowers methods, were employed for pore pressure prediction. [PERSON]'s method provided reliable pore pressure results in all the wells when compared to alternative methods in this study. Overburden gradient and predicted pore pressures ranged from 1.84 gm/cc to 2.07 gm/cc and from 3563.74 psi to 4310.06 psi, respectively. [PERSON]'s resistivity and density/neutron log method results indicated normal pressure in E-BK1 and E-AJ1, as well as overpressured zones in E-AJ1. However, in E-CB1, the results showed only overpressured zones. The E-AJ1 significant overpressures were from 2685 m to 2716 m and from 2716 m to 2735 m in the pores exceeding 7991.54 psi. Gas-water contact (GOC) was encountered at 2967.5 m in E-BK1, while oil-gas contact (OGC) was at 2523 m in E-CB1, and gas-oil and oil-water contacts (GOC and OWC) were at 2699 m and 2723 m, respectively, in E-AJ1. In E-CB1, oil-water contact (OWC) was at 2528.5 m. Fluid contacts observed from the well logs and RFT data were in close agreement in E-AJ1, whereas there was no agreement in E-CB1 because the well log observations showed a shallower depth compared to RFT data with a difference of 5.5 m. This study illustrated the significance of an integrated approach to predicting fluid contacts and pore pressure within the reservoirs by showing that fluid contacts associated with overpressures were gas-water and oil-water contacts. In contrast, gas-oil contact was associated with normal pressure and under pressure.  
reservoir fluids; Bredasdorp Basin; well log analysis; fluid contact; effective stress; fracture pressure; fracture gradient
## 2 Materials and Methods
### Proposed Method
#### 2.3.3 SVM Classifier  
SVM is a supervised learning method which is often applied to classification or regression. It is a method for obtaining the optimal boundary of two sets in a vector space independently from the probabilistic distributions of training vectors in the sets. A support vector machine is used here to classify crackles and normal lung sounds. The RBF kernel function is used in this work to achieve an optimal result. In addition, the parameters of the SVM are optimized to obtain a better performance, which will be introduced in the third section.  
Support vector machines (SVMs) are the famous and widely utilized supervised classifiers developed by [61]. A support vector machine (SVM) is a powerful and versatile machine learning algorithm used primarily for classification and regression tasks. SVMs are particularly effective in scenarios where the data are not linearly separable, meaning that the classes cannot be separated by a single straight line or hyperplane. SVMs achieve this by transforming the input data into a higher-dimensional space where separation becomes possible through the use of a hyperplane.  
In a classification context, an SVM attempts to find the hyperplane that best separates different classes of data points while maximizing the margin between the two classes. The margin is the distance between the hyperplane and the nearest data points from each class. The SVM seeks to find the hyperplane that not only separates the classes but also maximizes this margin, which theoretically reduces the risk of overfitting and enhances the model's generalization capabilities.  
The data points that are closest to the hyperplane and have the most influence on determining its position are referred to as \"support vectors\". These are the data points located at the edges of the margin or those that might be misclassified if the margin were to shift. The SVM focuses on these support vectors, making it a robust algorithm even in the presence of outliers or noisy data.  
SVMs offer flexibility in dealing with different types of data through the use of various kernel functions, which can implicitly map the data into higher-dimensional spaces. This allows SVMs to handle non-linear relationships between variables.  
Figure 5: Frequency response for the all-pole AR model using 6-th coefficients modelling the breathing sound excerpt for a healthy patient (top plot), an unhealthy frame with coarse crackle (middle plot), and an unhealthy frame with fine crackle (bottom plot).
## 4 Real Data
### Field Data from the BJI Observatory  
In this case, we consider inverting C-response data from BJI observatory, located in northern China, calculated by [PERSON] [41; 42], using the BIRRP code [43]. The data, including 20 periods, cover a period range from 1.33 to 117.78 days. The same priors are used for the interface number, conductivity, and interface depth as in the previous case. A total of five tempering chains are used. The collected sample is thinned as in previous cases, and used for later inference. The inversion result is shown in Figure 9. The 1D marginal profile shows a four-layer model, with increasing conductivity as the depth increases. The second and third layers are generally constrained well with narrow uncertainty, indicating that the data are sensitive to the structure defined by the two layers. For the structure below 1250 km, the uncertainty increases with the depth, which is physically understood as the diffusion of geomagnetic fields decreasing the sensitivity to the deeper structure. The marginal profile for the interface number in Figure 9 indicates that the probability peaks at 550, 950, and 1250 km, supporting a four-layer model.  
The fit of the data from inversion with the observation data is examined in Figure 10. As indicated in the figure, the predicted data both from the averaged model and the sampled ensemble are in good agreement with the measured C-response data. The sampling history of the Markov chain is shown Figure 11, indicating a well-mixing chain. The 1D marginal distribution in Figure 11 for the misfit indicates a relatively narrow high-probability region with a low-probability area covering a large area. The 1D marginal distribution for the interface number shows that the probability peaks at 7, covering a large area due to the possible fact that the data are less sensitive to the interface number (see Figure 11c).  
Figure 9: Marginal posterior probability profiles of (**a**) interface depth and (**b**) conductivity for BJI Observatory, located in northern China.
## 3 Proposed Work
### Node Selection and Slice Mapping Model
Furthermore, choosing the best path for the selected nodes requires satisfying the following inequalities,  
\[\psi_{i,length} \leq\Psi_{\mu}-1 \tag{6}\] \[f_{residual} \geq B_{NSR_{i}},\] (7) \[T_{round} \leq T_{NSR} \tag{8}\]  
where \(f_{residual}\) is the total residual flow (in terms of link capacity) for path \(i\) from the source node to the target node for \(NSR_{i,t}\), \(\psi_{i}^{length}\) is the length of path \(i\), \(\Psi_{\mu}\) is the average path length for all node connections, and \(T_{round}\) is the total roundtrip time for data transmission from the source to target node expressed as,  
\[T_{round}=T_{(n_{\alpha},n_{\Omega})}+T_{(n_{\Omega},n_{\alpha})} \tag{9}\]  
where \(T_{(n_{\alpha},n_{\Omega})}\) and \(T_{(n_{\Omega},n_{\alpha})}\) refer to the transmission times from the source node to the target node, and from the target node to the source node, respectively. These transmission times denote the amount of time required for the NSR to travel from the source node to the target node (vice-versa) and is affected by transmission delays. Algorithm 2 presents the details of the link mapping method. This approach aims to find the best path to the target node \(n_{\Omega}\) for \(NSR_{i,t}\). Equation (6) ensures that the length of this path does not exceed the average length of all paths in the network. Equation (7) guarantees that the residual flow meets the required link capacity for \(NSR_{i,t}\). In addition, Equation (8) ensures that the total time needed to transmit \(NSR_{i,t}\) from \(n_{\alpha}\) to \(n_{\Omega}\) (vice-versa) does not exceed the specified lifetime of the NSR. Furthermore, all identified augmenting paths are used as links for candidate network slices by the DCB-NS agent for NSR slice provisioning.