esa-sceva/satcom-chunk-collection huggingface dataset:
# An Object- and Shapelet-Based Method for Mapping Planted Forest Dynamics from Landsat Time Series
## 2 Materials and Methods
### Methods
#### 2.2.5 Planting Year Estimation  
The establishing year of the planted forest can be estimated based on the interpretability of the time-series shapelets. As shown in Figure 7, the NDVI time-series data of the sample were segmented into shapelets and non-shapelets, corresponding to a low-vegetation-cover time comprising the site preparation and a high-vegetation-cover period for the growth of trees, respectively. In view of the definition of the planting year of rubber plantations in [20], in our study, the year of the planted forest establishment was defined as the last vertex on the time-series shapelet of a planted forest object. A vertex (\(t_{v}\)) was decribed as a node of shapelet satisfiying the condition of \(t_{v}=\{t_{v}<t_{v-1}\) and \(t_{v}<t_{v+1}\}\), which is lower than the neighboring nodes. Since the trees started to grow after the transplanting of seedlings (or sowing), we chose the last vertex among the multiple ones that met the condition to characterize the time of planting. In addition, the Landsat imagery cutouts provide a visual interpretation of the changes in the land cover at the object level during the study period to illustrate the establishment of the planting year.  
Figure 5: Five typical cases of NDVI trajectory patterns of forest objects: (**a**) intact natural forest; (**b**) clouds and cloud-shadow noise; (**c**) recovered natural forest; (**d**) planted forest converted from cropland; and (**e**) planted forest established on bare land. Notes: For each case, the time series (red broken line) is stacked via the object-level NDVI mean values, whereas the range of values for the homogenous pixels is represented by the shadow area. The shapelet is composed of the time nodes covered by the blue area, and a statistic \(\chi^{2}\) calculated using the m-test explains the significance of the difference between the shapelet and non-shapelet.
# Optimal Base Station Density of Dense Network:
###### Abstract  
Network densification is attracting increasing attention recently due to its ability to improve network capacity by spatial reuse and relieve congestion by offloading. However, excessive densification and aggressive offloading can also cause the degradation of network performance due to problems of interference and load. In this paper, with consideration of load issues, we study the optimal base station density that maximizes the throughput of the network. The expected link rate and the utilization ratio of the contention-based channel are derived as the functions of base station density using the Poisson Point Process (PPP) and Markov Chain. They reveal the rules of deployment. Based on these results, we obtain the throughput of the network and indicate the optimal deployment density under different network conditions. Extensive simulations are conducted to validate our analysis and show the substantial performance gain obtained by the proposed deployment scheme. These results can provide guidance for the network densification.  
network densification; heterogeneous network; interference; load; base station density +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Physical Properties  
2.5.1. Evaluation of Odor Intensity  
Following the procedure of [PERSON] et al. [23], a total of 5 g of sample was taken in a 50 mL conical flask, the lid closed with parafilm and fitted with an odor concentration meter (XP-329, New Cosmos Electric Co. Ltd., Osaka, Japan). The odor intensity was calculated by observing the signal (peak), which is expressed as an arbitrary unit.  
2.5.2. Calculation of Weight Loss  
The Goulas and Kontominas [24] method was used to calculate the weight loss of CM fillets. It was done by evaluating the differences between the weight of the samples before and after oven drying.  
2.5.3. Texture Analysis  
The texture of CM fillets was assessed by Brookfield Texture Analyzer (Massachusetts, USA), equipped with a computer software system (Texture PRO CT, Middleboro, USA). A series of parameters, explicitly hardness, chewiness, cohesiveness and springiness, were estimated by following the experiment of [PERSON] and [PERSON] [25].  
2.5.4. Evaluation of Color  
The CM fillet color was assessed by the CM-700d Konica Minolta (Tokyo, Japan) instrument. Using a Hunter system value, the surface color of each sample was calculated as in previous reports [26; 27; 28].  
2.6. Chemical Properties  
2.6.1. Determination of pH  
The homogenized CM sample (4 g) was thoroughly mixed with 45 mL of distilled water (DW) for 2 min and well centrifuged (SHG-15D, SciLab, and Seoul, Korea). The pH of the sample was measured using a pH meter (OHAUS STARTER 2100, Seoul, Korea) with a glass electrode.
# Wavelet Packet Decomposition to Characterize Injection Molding Tool Damage +
## 3 Results and Discussion
### SFS Feature Selection  
The dimensions of the feature vector cannot be arbitrary large. One reason is the computational complexity, and the second reason is that the dimension ultimately causes a decrease of performance [14; 24]. Our goal is to reduce the dimensions of the data by finding a small set of important features that can give good classification performance. With feature selection we find a subset \(F_{j}(W)=\{z_{w}\mid w{=}1,\,...,\,W\}\) that outperforms all other subsets with dimension \(W\) as:  
\[J(F_{i}(W))\geqslant J(F_{j}(W))\;\;\text{\emph{for}}\;\;\;all\;\;\;je\{1, \,\ldots,\,q(W)\} \tag{19}\]  
where \(q(W)\) is the number of evaluations of performance measure \(J(F_{j}(W))\).  
We used sequential forward selection (SFS). The method starts at the bottom (the root of the tree) with an empty set and proceeds its way to the top without backtracking. At each level of the tree, SFS adds one feature to the current feature set by selecting from the remaining available measurements the element that yields a maximal increase of the performance. A structure of feature selection is shown in Figure 7, where \(z_{n}\) is the designation of a feature.  
We used a \(k\)-Nearest Neighbor (\(k\)-NN) classification algorithm as a criterion. With \(k\)-NN the input consists of the \(k\) closest training examples in the feature space. In our research \(k\) = 3. The output of the algorithm is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its \(k\) nearest neighbors.  
The major focus of our research was detection of damage in an injection molding tool during the injection molding process. This was the reason for using SFS with \(k\)-NN on feature vectors of AE burst signals acquired during the holding phase of the injection molding cycle on intact and damaged molds. We selected feature subset \(Z_{s}\) with a size of 10. Feature subset was used for neural network pattern recognition of AE signals during the full time of the injection molding cycle. The selected features based on SFS with \(k\)-NN are: \(Z_{s}=(R_{5}^{8},R_{5}^{16},\,R_{5}^{24},\,\,R_{5}^{21},\,R_{5}^{18},\,R_{5}^{ 20},\,d,\,R_{5}^{6},\,R_{5}^{32},\,A_{p})\).
## Energy-Efficient Data Reduction Techniques for Wireless Seizure Detection Systems  
**[PERSON] * and [PERSON]**  
Department of Electrical and Computer Engineering, The University of British Columbia, 2332 Main Mall, Vancouver, BC V6T 1Z4, Canada; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-604-822-2872; Fax: +1-604-822-5949.  
_Received: 2 December 2013; in revised form: 31 December 2013 / Accepted: 17 January 2014 / Published: 24 January 2014_  
**Abstract:** The emergence of wireless sensor networks (WSNs) has motivated a paradigm shift in patient monitoring and disease control. Epilepsy management is one of the areas that could especially benefit from the use of WSN. By using miniaturized wireless electroencephalogram (EEG) sensors, it is possible to perform ambulatory EEG recording and real-time seizure detection outside clinical settings. One major consideration in using such a wireless EEG-based system is the stringent battery energy constraint at the sensor side. Different solutions to reduce the power consumption at this side are therefore highly desired. The conventional approach incurs a high power consumption, as it transmits the entire EEG signals wirelessly to an external data server (where seizure detection is carried out). This paper examines the use of data reduction techniques for reducing the amount of data that has to be transmitted and, thereby, reducing the required power consumption at the sensor side. Two data reduction approaches are examined: compressive sensing-based EEG compression and low-complexity feature extraction. Their performance is evaluated in terms of seizure detection effectiveness and power consumption. Experimental results show that by performing low-complexity feature extraction at the sensor side and transmitting only the features that are pertinent to seizure detection to the server, a considerable overall saving in power is achieved. The battery life of the system is increased by 14 times, while the same seizure detection rate as the conventional approach (95%) is maintained.  
**Keywords:** electroencephalography; wireless sensor networks; seizure detection; compressive sensing; feature extraction
# Study on the Optimization of Pile Length of Micropiles in Soil Landslides
## 2 Methods
### Seismic Design Calculation Method of Micropile
Considering the randomness of earthquake occurrence, this study suggests that in engineering practice, the seismic design calculation of micropiles should focus on verification. The verification process is similar to the static design process of micropiles. Compared to static design calculations, seismic design calculations can appropriately reduce the safety factor in order to balance engineering safety and economy. The specific value of the seismic design safety factor should be referenced from relevant structural seismic codes.
## Abbreviations  
BSSBusiness Support System  
CFSCustomer Facing Service  
DNSDomain Name Server  
ETSIEuropean Telecommunications Standards Institute  
IaaSInfrastructure-as-a-Service  
ISPInternet Service Provider  
IXPInternet eXchange Point  
MDMulti-Domain
# Hard Disk Failure Prediction Based on Blending Ensemble Learning
## 3 Materials and Methods
### Strong Learners
#### 3.2.4 AdaBoost  
AdaBoost (adaptive boosting) was proposed in 1997 and is the first proposed ensemble learning algorithm to use a boosting strategy [20]. The basic idea is to increase the weight of the misclassified sample and correspondingly decrease the weight of the correctly classified sample during each serial iteration. Such an iterative method can make the model pay more attention to the samples that are classified incorrectly in the training and can better reduce the error and improve the performance of the model.  
Generally speaking, AdaBoost also uses the decision tree model as the base learner for training. This training strategy can significantly improve the training level and efficiency of the base learner without causing overfitting. However, the disadvantage of this method is that it will over-amplify the importance of some anomalous points, or noise, so it is greatly affected by noise.
###### Abstract  
Path planning in complex environments remains a challenging task for unmanned vehicles. In this paper, we propose a decoupled path-planning algorithm with the help of a deep reinforcement learning algorithm that separates the evaluation of paths from the planning algorithm to facilitate unmanned vehicles in real-time consideration of environmental factors. We use a 3D surface map to represent the path cost, where the elevation information represents the integrated cost. The peaks function simulates the path cost, which is processed and used as the algorithm's input. Furthermore, we improved the double deep Q-learning algorithm (DDQL), called retrospective-double DDQL (R-DDQL), to improve the algorithm's performance. R-DDQL utilizes global information and incorporates a retrospective mechanism that employs fuzzy logic to evaluate the quality of selected actions and identify better states for inclusion in the memory. Our simulation studies show that the proposed R-DDQL algorithm has better training speed and stability compared to the deep Q-learning algorithm and double deep Q-learning algorithm. We demonstrate the effectiveness of the R-DDQL algorithm under both static and dynamic tasks.  
pathfinding; deep Q-learning (DQL); 3D curved surface terrain; fuzzy logic Article
## 3 Results and Discussion
### Population Analysis  
Biomass samples from the bulk of digesesters R1 and R3, and electrodes of the outer cell of R2, were taken for microbiology analyses at the end of the experiments. Results are presented at the family level due to excessive uncertainties at the species level (Figure 8). There is a clear distinction between digester R2 and digesters R1 and R3. While in R2, the acetoclastic archaeal family _Methanosaetaceae_ constitutes up to 80% of the microbial community, in R1 and R3, this family accounts for only approximately 30% and 40%, respectively, which is less than half of its presence in R2. The higher presence of this family may be related to the amount of acetic acid produced during the beginning of the experiment in R2 and the influence of voltage to support the development under more adverse conditions that resulted in higher biogas production (Figure 6). Conversely, the dominant family in R1 and R3 is _Methanobacteriaceae_, associated with hydroperotrophic methanogenesis, representing about 50% in R1 and 45% in R3, but barely reaching 10% in R2. R1 and R3 show similar microbial compositions, with only minor differences among less abundant families. These results highlight the impact of voltage application in the R2 digester, leading to a higher proliferation of acetoclastic archaea. Thus, it can be inferred that the preferred methane synthesis pathway is closely linked to electron transfer, which is modulated by voltage application in R2 [44].  
Figure 8: Different families of archaea found in the extractions.  
Figure 7: Increase in installed power in Spain with the treatments proposed in this work. BES*: electrodes into reactor.
## 3 Results  
### CRN/PPN1 Strain Exhibits a Decreased PolyP Level and Enhanced Resistance to Manganese  
The parent CRN strain (\(\Delta\)_ppm1_ mutant), the CRN/PPN1 strain (overproducing Ppn1 polyphosphatase), and the CRN/PPX1 strain (transformed by the same vector but carrying the _PPX1_ gene encoding Ppx1 polyphosphatase) showed no difference in the control growth in YPD medium (Figure 1A). The polyphosphatase activity in the cell-free extracts was higher in the respective overproducing strains (Table S1). The polyP level in CRN/PPN1 cells was lower as compared to CRN and CRN/PPX1 (Figure 1B). A decrease in the polyP level was more pronounced in the acid-soluble polyP fraction (Table S2), as it was observed in selective YNB medium [40]. In the presence of excess manganese, the polyP level was increased in CRN cells, remained almost unchanged in CRN/PPX1 cells, and showed a further decrease in CRN/PPN1 cells (Figure 1B).  
The manganese excess inhibited the growth of CRN and CRN/PPX1 strains (Figure 1A,C) similar to other _S. cerevisiae_ strains [42]. Surprisingly, the growth inhibition in the medium with manganese excess was dramatically lower for polyP-deficient CRN/PPN1. Furthermore, manganese excess induced a notable cell death of the CRN and CRN/PPX1 strains (Figure 1D and Figure S1), while CRN/PPN1 was more resistant and accumulated a lower amount of manganese (Figure 1E).
# Unified Ciphertext-Policy Weighted Attribute-Based Encryption for Sharing Data in Cloud Computing
## 4 System Model and Security Model of UCP-WABE
### System Model of UCP-WABE  
Figure 2 displays the data-sharing system using UCP-WABE in the cloud environment. This system model is improved from the system model of CP-WABE [16]. The system model consists of five types of entities: a weighted attribute authority (WAA), a mutual information extractor (MIE), a cloud storage platform, numerous data providers, and data consumers. The cloud storage platform is managed by CSP and offers data storage service due to its massive storage ability. The provider's data are encrypted with its weighted access policy and uploaded to the cloud. The data consumer downloads the ciphertexts and decrypts them with his/her secret key to recover the data. All providers and consumers are supervised by WAA. WAA is responsible for authenticating the attributes of every data consumer. WAA also assigns different weights to attributes according to their importance. The MIE assists the providers in encrypting their data. For illustration, we consider two providers in the system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbb{S}\) & \(\Gamma_{1}(\mathbb{S})\) & \(\Gamma_{2}(\mathbb{S})\) \\ \hline Attr1(3), Attr2(2), Attr3(4), Attr4(2) & FALSE & FALSE \\ Attr1(3), Attr2(3), Attr3(2), Attr4(2) & FALSE & TRUE \\ Attr1(3), Attr2(3), Attr3(4), Attr4(1) & TRUE & FALSE \\ Attr1(3), Attr2(3), Attr3(4), Attr4(2) & TRUE & TRUE \\ \hline \hline \end{tabular}
\end{table}
Table 1: Valuation of the weighted access trees in Figure 1.  
In Figure 2, WAA is a trustworthy entity that is in charge of producing the data consumers' secret keys. CSP is honest but curious. This means that the cloud implements the manipulations dutifully and gives true outcomes; whereas, it also tries its best to get sensitive information. A data consumer could be compromised by an adversary; thus, this consumer could make attempts at obtaining data beyond his/her access range. The communication channels among the providers, the consumers, as well as the cloud are unsafe. In other words, the data in these channels could be eavesdropped by the adversary.
## 1 Introduction
Section 2 analyzes the self-interference suppression performance of the CCFD system from three domains and presents the current total self-interference suppression level. Section 3 provides a detailed analysis of the spatial configuration of the BDS-3 satellite navigation constellation, constructing a dynamic link constraint model based on satellite visibility and antenna directivity. Section 4 conducts a comprehensive link budget analysis of the BDS-3 ISL. Section 5 systematically introduces the proposed two-way measurement method for ISL, including the fundamental principle, system model, and key errors, theoretically evaluating the system performance under the new framework. Section 6 presents experimental simulations and analyses, autonomously constructing a BDS-3 ISL scenario and a digital simulation verification platform to capture inter-satellite topological characteristics, validate the feasibility of the full-duplex system, and analyze pseudo-code ranging performance under the new framework. Section 7 analyzes the experimental results and discusses potential directions for future research. Section 8 provides a comprehensive summary and synthesis of the research findings presented in this paper.
## 2 Methodology
### A Protocol for the Assessment of Gait of Parkinson's Disease Patients Using Wearable Sensors
#### 2.2.5 The FoG and Dual Tasking Test  
The FoG and Dual Tasking test is in line with a previously published work by [PERSON] et al. [45], for the assessment of FoG and motor and mental Dual Tasking. For this test, as shown in Figure 4, participants are requested to sit and relax in a chair for 30 s, then stand up and walk for 1 m till they place themselves in a 40 cm \(\times\) 40 cm square marked on the floor, where they need to perform two, in-position, turns of 360\({}^{\circ}\), one clockwise and one counter-clockwise; then, they walk for 2 m and walk through a narrow passage of 50 cm \(\times\) 60 cm (created with the use of two chairs); once they walk out of the passage they are requested to turn around (given a 1.2 m available space for turning); walk back through the narrow passage and walk in a straight route until they are in a position to sit back in the chair. This test is performed in three different versions: (a) the basic test, including only the walking route as described; (b) the motor-dual tasking, where they perform the walking route and at the same time hold a glass full of water in their hand; and (c) the mental-dual tasking, where they perform the walking route and at the same time perform loudly serial deductions of sevens starting at 100. The test is performed at a normal pace of walking as perceived by each participant.
###### Abstract  
The increasing integration of distributed generation (DG) and the rise of microgrids have reshaped the operation of distribution systems, introducing both challenges and opportunities for optimization. This study presents a methodology that combines network reconfiguration with the integration of buses with flexible power in order to improve the efficiency and cost-effectiveness of distribution networks. Flexible buses, which aggregate multiple microgrids or controllable distributed resources, function as control points that can dynamically adjust active and reactive power within predefined limits. This capability allows for more precise management of power flows, enabling the system to respond to fluctuations in generation and demand. The proposed optimization framework aims to minimize the total operational costs, including power losses and the use of flexible power, while adhering to system constraints. The methodology is evaluated through case studies on two distribution systems: the Kumamoto and IEEE-33 systems. The results indicate a 43.9% reduction in power losses for the Kumamoto system and a 66.6% reduction for the IEEE-33 system, along with notable cost savings in both cases. These outcomes demonstrate the potential benefits of incorporating flexible power buses in modern radial distribution networks, showing their role in adapting to various operational scenarios and supporting the integration of distributed generation and microgrids.  
distribution network reconfiguration; flexible power resources; operational cost minimization; power loss reduction
# A Comparison of an Adaptive Self-Guarded Honeypot with Conventional Honeypots
## 5 Design and Implementation
### Linux Command Parsing  
As shown in the sequence diagram Figure 3, after the request of the command execution, this command can be transmitted entirely to the target system and let the real shell handle it. In a normal execution flow, the shell will break it down into tokens of words and operators by obeying the quoting rules of the shell [32]. These tokens have to be parsedto construct a complete abstract syntax tree (AST) [33] that allows the shell to understand how to interpret them. The submitted command can be: a simple command optionally followed by arguments, compound commands (if, for,...), a command with a redirection, a piped command, etc. For example, if it is a built-in shell command (e.g., cd, break, exit, etc.), it will be executed by the shell itself, otherwise, a new process is forked to execute an external command. If there is a redirection of the standard output of a command, the shell has to create a pipe and link it to the redirected file accordingly. For example, this command ls -lh $(which ls) was seen as a way to detect the Cowrie system (as the command substitution was not implemented in the old version of Cowrie), and its AST representation is shown in Figure 4. This AST is made up of a command node as a root node, which consists of three child-nodes: a word node ls, a word node -lh and a command substitution node. The child-node of the command substitution is also a command node comprised of a word node which and a word node ls. Our objective is to take action on each individual command that can be made up of the original command. As a result, the parsing has to be carried out by the proxy system to produce an AST, and from that, it derives a list of commands that can be analysed and decided one by one.  
Figure 3: UML sequence diagram of the interaction between the attacker and Asgard/Midgard.