esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 _Article_
### _Ansuring Security and Energy Efficiency of Wireless Sensor_
###### Abstract  
With the advancement of new technology, security is the biggest issue nowadays. To solve security problems, blockchain technology will be used. In recent work, most of the work has been done on homogeneous systems, but in our research, the primary focus is on the security of wireless sensor networks using blockchain. Over the last few decades, the Internet of Things (IoT) has been the most advancing technology due to the number of intelligent devices and associated technologies that have rapidly grown in every field of the world, such as smart cities, education, agriculture, banking, healthcare, etc. Many of the applications are developing by using IoT technologies for real-time monitoring. Because of storage capacity or low processing power, smart devices or gadgets are vulnerable to attack as existing cryptography techniques or security are insufficient. In this research work, firstly, we review and identify the privacy and security issues in the IoT system. Secondly, there is a solution for the security issues, which is resolved by blockchain technology. We will check the wireless sensor network to see how data work on distributed or decentralized network architecture. Wireless sensor network clustering technique was introduced by researchers for network efficiency because when the workload spreads, the system will work faster and more efficiently. A cluster comprises a number of nodes, and the cluster head manages the local interactions between the nodes in the cluster (CH). In general, cluster members connect with the cluster head, and the cluster head aggregates and fuses the data acquired in order to save energy. Before approaching the sink, the cluster heads may additionally create another layer of clusters among themselves. The clustering concept divides data traffic into several groups similar to the other data points in the same data point. In contrast, this data point is dissimilar from other data points in another group. All results are presented at the end of this study paper, in which we will see the network or nodes' performance in the specific area of the network, how it works, and how efficient it is. Likewise, Blockchain also works in a distributed manner.  
Internet of Things; blockchain; wireless sensor network; security; privacy; consensus mechanism +
Footnote †: journal: Applied Sciences
# Privacy Auditing in Differential Private Machine Learning:
## 4 Privacy Auditing Schemes
### Privacy Audits  
When we focus on auditing DPML models, we first need to know whether we have enough access to information to perform a _white-box audit_ or a _black-box audit_. A white-box auditing can be difficult to perform on a large scale in practice, as the algorithm to be audited needs to be significantly modified, which is not always possible [109]. Nevertheless, auditing DPML models in a white-box environment requires minimal assumptions about the algorithms [43]. Instead, black-box audits are more realistic in practice, as our attacker can only observe the final trained model. Privacy auditing _schemes_ empirically evaluate the privacy leakage of a target ML model, or its algorithm trained with DP [42; 46; 47; 62; 96]. Such schemes use the DP definition a priori to formalize and quantify the privacy leakage [174]. Currently, most auditing techniques are based on simulating different types of attacks [114] to determine a \((\epsilon,\delta)\) lower bound on the privacy loss of a ML model or algorithm [62]. Privacy auditing can be performed using different attacker schemes (processes), which can be broadly categorized as follows (Table 2):  
* Membership inference auditing. * Poisoning auditing. * Model inversion auditing. * Model extraction auditing. * Property inference auditing. In summary, privacy auditing schemes leverage various techniques to strike a balance between privacy, data utility and auditing efficiency. The comprehensive privacy auditing methodology, privacy guarantees with references can be found in Appendix A. We review the most important works, starting with the use of black-box and white-box privacy attacks.
## 3 Overview and Development of the Proposed Test System Considered for the Study
### The Large-Scale Wind Power Plant Considered for the Study
#### 3.2.1 Preparation of the Large-Scale Wind Power Plant  
The simulation of the LSWPP requires fundamental tuning of wind speed (m/s) and pitch angle adjustment (degrees). See Figure 26 for logic adapted from the same reference [36]. The control components shown in the figure are logically connected to the wind turbine (WT) model and are accessible for control on the RSCAD runtime module. In the same figure, _WT1 PitchAngle_ is the signal name to control the change in the pitch angle of the wind turbine, while _WindSpd1(kn/h)_ is the input wind speed to the wind turbine. The pitch angle is adjusted using a dial selector switch WT1 PitchAngleAdjust with figures or positions 1 to 5. The wind speed is adjusted with a slider labeled WindSpd1 Adjust. This logic is the same for all other WTs in the LSWPP model.  
Other parameters like Default Air Temperature (DegC), Default Barometric Pressure (millibar), and Default Relative Humidity (%) do not need any additional control components to connect to them logically. Figure 27 shows the setting of these parameters on the WT model as viewed on the RSCAD draft module. By default, these parameters are accessible and can be tuned on the runtime module. Reference [36] can be visited for more information on how the LSWPP was modeled.  
Figure 26: Logic for pitch angle and wind speed adjustments.
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 4 Challenges of VNF Placement in Edge Environments
### Heterogeneity of SFC Requirements  
In addition to the variety of computational resource capacities that edge nodes provide, SFCs and their VNFs also present different requirements. SFCs can demand different end-to-end delay tolerance, bandwidth, jitter, and SLA according to their VNFs and service types. Furthermore, the same SFC may have different requirements depending on the user requesting it. Moreover, VNFs of the same SFC may require different CPU and/or memory capacities according to their roles in the SFC.  
SFCs also have multiple and conflicting priorities that should be addressed in the VNF placement solution. These priorities regard latency, capacity, and energy consumption. If an SFC prioritizes latency, the VNF placement approach should be aware of selecting edge nodes that better satisfy the VNF requirements regarding the delay for executing the SFC. Then, the other priorities should also be verified to serve the SFC according to the priority order. Therefore, these different priorities further contribute to increasing the heterogeneity of SFCs.
# Tectonic Subsidence and Its Response to Geological Evolution in the Xisha Area, South China Sea
## 1 Introduction  
The Xisha area is an extraordinary continental slope located on the northwestern margin of the South China Sea, as shown in Figure 1. Structurally, it is located at the elongated offshore end of the Red River Fault Zone in the Yinggehai Basin and close to the influence area of the Hainan mantle plume [1; 2; 3; 4; 5]. Multiple perspectives, including characteristics of faults, crust structure, and tectonic simulation, have been applied to study the tectonic evolution of the Xisha area [6; 7; 8]. The tectonic subsidence was also discussed in previous studies, which suggest at least three periods of rapid subsidence and one period of weak subsidence have been experienced in the Cenozoic [9; 10]. The first and second rapid subsidence occurred during the rift period stage and the most recentrapid subsidence began at the beginning of the Late Miocene [9; 10]. However, previous studies mainly concentrated on the northern margin of Qiongdongnan Basin, the tectonic subsidence around the Xisha area has not been reported in the literature, and the regional tectonic subsidence process is unknown.  
With the increasing demands of petroleum exploration in the Xisha area, the acquisition of high-resolution seismic data and well-logging data provides a good opportunity for the analysis of tectonic subsidence history. There are two goals to study the history of tectonic subsidence in the Xisha area. One is to clarify the spatiotemporal distribution of tectonic subsidence of each tectonic unit and subdivide the subsidence phase. The second is to discuss the relationship between subsidence mechanisms, especially the hypothesis of reversal of Red River Shear Zone during the Middle Miocene, and regional geological factors, including tectonics, volcanism, and sedimentation.
###### Abstract  
The aim of the study was to obtain biological active compounds during biotransformation. Three bicyclic halolactones with methylcyclohexane ring (2-chloro-4-methyl-9-oxabicyclo-nonan-8-one, 2-bromo-4-methyl-9-oxabicyclo[4.3.0]nona- 8-one and 2-iodo-4-methyl-9-oxabicyclo[4.3.0]nonan-8-one) obtained from the corresponding \(\gamma\),\(\delta\)-unsaturated acid were subjected to a screening biotransformation using 22 fungal strains. Two of these strains (_Cumningham japonica_ AM472 and _Fusarium cultorum_ AM10) were able to transform halolactones into 2-hydroxy-4-methyl-9-oxabicyclo[4.3.0]nonan-8-one by hydrolytic dehalogenation with good yield. The biotransformation product was structurally different from its synthetically prepared analog. All halolactones and hydrox lactones were tested for their biological activity. The chlorolactone inhibited growth of _Staphylococcus aureus_ (max \(\Delta\)OD = 0), _Escherichia coli_ (max \(\Delta\)OD = 0.3) and _Candida albicans_ (max \(\Delta\)OD = 0) strains. Bromolactone caused inhibition of growth of _Staphylococcus aureus_ (max \(\Delta\)OD = 0) and _Fusarium lini_ (max \(\Delta\)OD = 0) strains. Iodolactone limited growth of _Staphylococcus aureus_ (max \(\Delta\)OD = 0), _Escherichia coli_ (max \(\Delta\)OD = 0.25), _Candida albicans_ (max \(\Delta\)OD = 0.45) and _Pseudomonas fluorescens_ (max \(\Delta\)OD = 0.42) strains. Hydroxy lactose caused inhibition of growth of _Staphylococcus aureus_ (max \(\Delta\)OD = 0.36) and _Pseudomonas fluorescens_ (max \(\Delta\)OD = 0.39) strains only. The test performed on aphids _Myzus persicae_ (Sulz.) showed that chloro- and bromolactone exhibited deterrent activity after 24 h (\(ID\) = 0.5 and 0.4, respectively), while hydroxylactone was a weak attractant (\(ID=-0.3\)).  
**Keywords:** lactones; hydrolytic dehalogenation; antimicrobial activity; deterrent activity
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 2 CDRs and Development  
A climate data record (CDR) is defined as a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change [4]. A Climate Information Record (CIR) is a time series derived from CDRs along with related _in-situ_ and/or model data that provide specific information to address a specific climate application.  
Satellite observations have been used to produce CDRs due to their global coverage and sufficient length of temporal coverage. In practical terms, this amounts to transforming satellite sensor data records (SDR) across decades from multiple satellite platforms into uninterrupted and coherent long-term SDR through re-calibration and reprocessing. The time series of inter-calibrated SDRs for a family of sensors together with the ancillary data used to calibrate them is a fundamental climate data record (FCDR). Then, a geophysical parameter (e.g., sea ice, clouds, _etc._) is derived from a FCDR, which creates a thematic CDR (TCDR) often by blending satellite observations with _in-situ_ data and model output.  
Considering that there are users who require regular access to CDR data with low latency (e.g., daily or monthly updates) rather than a fully mature CDR product, an interim climate data record (ICDR) can be produced. This is accomplished by forward processing the CDR algorithm using near-real time observations. While this decreases the maturity of the CDR (by not having the retrospective calibration), the ICDR can be improved after the fact through retrospective calibration and reprocessing. Figure 1 shows the production pathways schematically for various CDR products (ICDR, FCDR, TCDR, and CIR) and their relative dependence, which is produced in either shortly-delayed or delayed fashions (compared to near-real time production) to meet the needs from different users. More details on the development and production of various types of CDR products in a sustainable way are provided in the following subsections.
## 3 Hybrid FE-SEA Modeling Parameter Identification Algorithm
### Deterministic Subsystem Response Derivation of Three-Coupled Systems
\begin{split}& E\big{[}qq^{H}\big{]}=D_{tot}^{-1}\bigg{[}S_{ff}+ \sum\limits_{k=1}^{N}E\Big{(}f_{rev}^{(k)}f_{rev}^{(k),H}\Big{)}\bigg{]}D_{tot}^ {-1H}=\\ & D_{tot}^{-1}\Big{[}S_{ff}+E\Big{(}f_{rev}^{(1)}f_{rev}^{(1),H}+ f_{rev}^{(2)}f_{rev}^{(2),H}\Big{)}\bigg{]}D_{tot}^{-1H}\end{split}  \tag{31}\]  
where Equation (28) is substituted into Equation (31) to obtain the displacement response of the deterministic subsystem of the three-coupled system in cross-spectral form:  
\[ \begin{split}& S_{qq}=E\big{[}qq^{H}\big{]}=D_{tot}^{-1}\bigg{[}S_{ ff}+\sum\limits_{k=1}^{N}\Big{(}\tfrac{4E_{k}}{\omega\pi n_{k}}\text{Im}\Big{\{}D_{ dir}^{(k)}\Big{\}}\Big{)}\bigg{]}D_{tot}^{-1H}=\\ & D_{tot}^{-1}\Big{[}S_{ff}+\Big{(}\tfrac{4E_{1}}{\omega\pi n_{1} }\text{Im}\Big{\{}D_{dir}^{(1)}\Big{\}}+\tfrac{4E_{2}}{\omega\pi n_{2}}\text {Im}\Big{\{}D_{dir}^{(2)}\Big{\}}\Big{)}\bigg{]}D_{tot}^{-1H}\end{split}  \tag{32}\]  
Equation (32) relates the displacement cross-spectral response of the deterministic subsystem in the three-coupled system to the energy density of the random subsystem, and the motion response of the deterministic subsystem in the hybrid FE-SEA method can be obtained. In turn, the deterministic subsystem response model of multiple systems can be derived. In the process of solving the motion response of a deterministic subsystem in a coupled system, the following matters should be noted:  
1. The displacement response of the deterministic subsystem obtained by this method is based on the overall average response, and the response of a certain position cannot be predicted. 2. One of the derivation conditions of the equation is that the random subsystems in the system conform to the reciprocity relation of the diffusion field, and the interaction between the random subsystems can be ignored. 3. The premise of formula derivation is the energy density (\(\tfrac{E_{k}}{n_{k}}\)) of the random subsystem, so whether the prediction of the deterministic subsystem response cross spectrum (\(S_{qq}\)) is accurate is affected by the accuracy of the random subsystem \(K\) energy response (\(E_{k}\)).
# Evaluation of Malolactic Starters in White and Rose Winemaking of Moschofilero Wines
## Materials and Methods
### Experimental Design and Winemaking Conditions  
Grapes of the Greek Moschofilero variety were harvested from the same vineyard during the 2015 vintage, in the PDO wine zone of Mantineia in Peloponnese, at an altitude of 650 m. Dostemming and crushing were performed manually after storage of the grapes for 24 h at 4 \({}^{\circ}\)C. In the case of rose wine production, skin contact with the must was allowed for 15 h at 10 \({}^{\circ}\)C. In both cases 3 g/hL of sodium metabisulfite (Scharlab S.A, Barcelona, Spain) and 3 g/hL of petolytic enzymes (Safizym(r) Clean, Fermentis, Lambersart, France) were added to the must. After 24 h at 10 \({}^{\circ}\)C, the clarified must was inoculated with a commercial strain of _Saccharomyces cerevisiae_, UCLM S325, at approximately 6.5 log CFU/mL. Alcoholic fermentation (AF) took place in stainless-steel tanks of 50 L at 20 \({}^{\circ}\)C until residual sugar concentration was below 2 g/L. Alcoholic fermentation was performed in duplicate. Twenty-four hours after yeast inoculation, 200 mg/L of SpringFerm(tm) (Fermentis, France) were added. After the completion of alcoholic fermentation, the wine was transferred into stainless-steel tanks of 5 L each and the following five cases were assessed: 1. wine without malolactic fermentation (MLF), in this case 8 g/hL sodium metabisulfite was added to the wine in order to stop MLF (control condition-C); 2. wine with spontaneously driven MLF (S); 3. MLF driven by the _O. oeni_ strain Vinitilora(r) CH16 (CH16); 4. MLF driven by the _O. oeni_ strain Vinitilora(r) Oenos (Oenos); 5. MLF driven by the _O. oeni_ strain Vinitilora(r) CiNe (Cine). Each _O. oeni_ strain was inoculated at approximately 4.5 log CFU/mL. Each case was studied in duplicate from each AF tank, therefore MLF was assessed in quadruplicate. After the end of MLF (malic acid below 0.1 g/L), 8 g/hL sodium metabisulfite was added and the wines were then kept at 10 \({}^{\circ}\)C for one week and decanted into glass bottles for further chemical and sensory analyses.
## 4 Channel Selection Algorithm Based on Multi-Armed-Bandit Algorithm  
This section describes channel selection algorithms based on major MAB methods. That is the \(\varepsilon\)-greedy algorithm, UCB1-tunned algorithm, TOW dynamics algorithm, and the MTOW algorithm. The reason that we investigate these four algorithms is summarized as follows. The epsilon greedy method is the easiest to implement among the multi-armed-bandit (MAB) methods, while the UCB1-tunned can almost get the highest performance among the MAB methods. Moreover, TOW and MTOW proposed by the co-author of this paper can achieve higher performance than UCB1-tuned under certain environments. Hence, we investigated the effects of these four methods. Generally, the performance of the other MAB algorithm is between the UCB1-tunned method and the epsilon greedy method. In the rest of this section, the relationship between the MAB problem and the channel selection problem is presented first. Then, the details of the channel selection method are given.
# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
## 6 Results and Evaluation  
### Hardware Equipment and Deployment  
The RFID hardware used in our experiment includes an Impinj Speedway R420 reader, a UHF antenna, and a set of H47 UHF passive tags (52 mm \(\times\) 52 mm). The frequency of the signal that was emitted by the reader is between 902 MHz\(\sim\)928 MHz. The T360-A03 rotary table with radius of 30 cm is also equipped. The algorithms are executed on a Shinelon PC equipped with an Intel(R) Core(TM) i7-6700 HQ CPU (2.60 GHz, 4 cores), a GTX1060 6G GPU, and 16 GB RAM.  
The experimental scenario is deployed as shown in Figure 15. The antenna is mounted on a rotating table with wooden shelves on both sides, which are used to place tags. The reader and antenna are connected to the computer via Wi-Fi. The size of the bookshelf is 150 cm \(\times\) 89 cm \(\times\) 26 cm, the height between the layers is 30 cm, the center of the rotating table is 75 cm from the bookshelf, and the height of the center of the antenna is 130 cm. In the 3D localization scene, two antennas are placed on both sides of the three-dimensional tag sequence, and the distance between the antennas is 150 cm. The rotary table we chose is fully automatic by electronic motor. It can be rotated at a constant speed by turning on the switch. The rotation speed is 3 degree/s. During each rotation, about 500 data is collected for each tag. Both 2D and 3D are the same configuration.  
Figure 14: 3D localization principle analysis. (**a**) Top view; and, (**b**) Sectional view.  
Figure 15: Experimental scenarios. (**a**) two-dimensional (2D); (**b**) three-dimensional (3D); and, (**c**) three-dimensional (3D).
## 3 Method
### Functional-Type Identification of Buildings Not Containing POIs
#### 3.5.1 Calculating the Spatial Similarity of Buildings  
There are potential type-matching relationships between buildings located within the same road network mesh. These can be further identified by calculating the characteristic similarity between candidate matching buildings [40]. Spatial similarity is an important basis for determining whether two geographic entities have a type-matching relationship. In this paper, area, distance, shape, and orientation similarity indicators are used to determine the similarity relationship between buildings with identified and unidentified functions within the same road mesh.  
1. Distance similarity  
The distance similarity was taken as the ratio of the inter-building distance to the distance threshold. The distance threshold is determined by the Hausdorff distance [41] between the samples. In Formula (3), K denotes the distance threshold and h denotes the shortest distance between two buildings A and B, which is calculated by converting the building boundaries into point sets and finding the minimum Euclidean distance between any point in set A \((x_{a},y_{a})\) and any point in set B \((x_{b},y_{b})\), as shown in Formula (4).  
\[dissim=1-\frac{h(A,B)}{K} \tag{3}\]
2. Area similarity  
The area similarity of a building is calculated as the ratio between the areas of buildings A and B, as shown in Formula (5).  
\[\text{areasim}=\frac{\text{MIN}(\text{area}(\text{A}),\text{area}(\text{B}))} {\text{MAX}(\text{area}(\text{A}),\text{area}(\text{B}))} \tag{5}\]
3. Shape similarity  
In this paper, compactness is used to represent the shape characteristics of buildings (Formula (6)). The shape similarity is calculated using the compactness values of two buildings A and B, according to Formula (7), where C, L and F denote the building's compactness, perimeter and area, respectively.  
\[\text{C}=\frac{\text{L}^{2}}{4\text{F}\pi} \tag{6}\]  
\[\text{shapesim}=\frac{|\text{C}(\text{A})-\text{C}(\text{B})|}{\text{MAX}( \text{C}(\text{A}),\text{C}(\text{B}))} \tag{7}\]
4. Orientation similarity  
The angle between the long axis of the smallest outer rectangle of the building outline and the horizontal line is taken as the orientation of the building [42]. The similarity of the orientations of two buildings is the similarity angle, which is calculated using Formula (8), where \(\theta\) denotes the angle.  
\[\text{orisim}=1-\frac{2|\theta_{\text{A}}-\theta_{\text{B}}|}{\pi} \tag{8}\]
5. Overall similarity of buildings  
The total similarity between two buildings is obtained by weighted summation of several characteristics. When the total similarity between an unidentified building and anidentified building in the same road mesh reaches the maximum value, the two buildings are considered to have the same functional type. This is calculated using Formula (9).  
\[\text{sim}=\frac{\sum_{i=0}^{n}\omega_{i}X_{i}(A,B)}{\sum_{i=0}^{n}\omega_{i}} \tag{9}\]
## 4 Enabling Technologies for FSO Communication
### Modulation Schemes

Table 3: Literature summary on modulation formats and associated metrics for FSO.
## 5 Security Analysis of Strike
### Cryptanalysis Attacks
#### 5.3.2 Known-Plaintext Attack  
In this kind of attack, the attacker attempts to gain access to the complete list of ciphertexts and their mapped plaintext to retrieve _SKey_. In the Strike cipher, each sequence of _Ctext_ is obtained from an XOR operation between _Ptext_ and _Kstream_. A complete scrambling process is applied to every single parameter used in the keystream generator. Hence, the randomness of the behaviour of the generated strike will generate a completely different ciphertext, even for two identical plaintexts. Recall that each lightning strike is associated with different peak current intervals, stepped leader coordinates and jumps, all of which are extracted stochastically from _SKey_. Accordingly, the Strike cipher is found to be resistant to known-plaintext attacks.  
Figure 8: Avalanche effect on _Kstream_.  
\begin{table}
\begin{tabular}{l l} \hline \# of _Kstream_ & 500 \\ Minimum \% of changes on _Kstream_ & 70\% \\ Maximum \% of changes on _Kstream_ & 77\% \\ Avg. \% of changes on _Kstream_ & 73.56\% \\ \hline \end{tabular}
\end{table}
Table 5: Avalanche effect on _Kstream_.