esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### In Situ Allometric Equations--The Relationship between Biomass of Tree Components and Their DBH  
The total dry biomass of individual trees was correlated with their DBH in order to obtain site-specific allometric equations (Figure 3). Fitted non-linear regression parameters suggest that total dry biomass is highly correlated with DBH-the coefficient of determination exceeded 0.9. A power-type regression was used, since this function type best fits the data. The approach of relating total biomass of the tree to its DBH will hereinafter be referred to as the \"site-specific method M1\".  
Figure 2: Scheme of tree components used for in situ biomass investigation of five model trees collected from the main research area. AGB–Above Ground Biomass, BGB–Below Ground Biomass.  
To estimate the biomass of the different tree components, particular functions can be fitted. However, [PERSON] et al. (2012) suggested that these should be used together (equation matrix) rather than separately (simple sum) when calculating total tree biomass ([PERSON], 1998). In our work, the parameters were obtained for individual components based on direct in situ measurements; these were summed to derive the whole tree biomass. Stem increment of the average tree determined from dendrometer measurements was used to relate the biomass of individual tree components to DBH. The resulting set of empirical equations is presented below (Equation (1)), hereinafter referred to as the \"site-specific method M2\".  
\[\begin{array}{c}0.3932\ DBH^{1.7298}-a\\ 0.0122\ DBH^{2.5525}-b\\ \end{array} \begin{array}{c}\begin{bmatrix}R^{2}=0.99\\ R^{2}=0.72\\ R^{2}=0.75\\ R^{2}=0.70\\ R^{2}=0.87\\ R^{2}=0.67\\ \end{bmatrix}\\ \end{array} \tag{1}\] \[\begin{array}{c}0.3932\ DBH^{1.7298}-a\\ 0.0122\ DBH^{2.5525}-b\\ \end{array} \begin{bmatrix}R^{2}=0.99\\ R^{2}=0.72\\ R^{2}=0.75\\ R^{2}=0.70\\ R^{2}=0.87\\ R^{2}=0.67\\ \end{array}\]  
where DBH--diameter at breast height, \(a\)--stem + bark biomass, \(b\)--thick branches biomass, \(c\)--needles biomass, \(d\)--thick roots biomass, \(f\)--small roots biomass. The coefficient of determination (\(R^{2}\)) for the obtained equations is given in square brackets.  
In addition, we compared the results obtained from in situ allometric equations with those from [PERSON] and [PERSON] (2008), which were determined to be decent empirical estimations for comparison. Their research area was located about 50 km northwest of the Mezyk site, and their equations were derived for 82-year-old Scots pine. The climatic and meteorological conditions of both ecosystems are very similar.
## 2 Methods
### Measurements
#### 2.2.4 Data Analysis  
Gait termination was defined as when the center of mass (COM) progression velocity was less than 0.05 m/s in the anteroposterior (AP) direction [18]. In this study, gait termination was divided into three phases based on the identifiable vertical ground reaction force (GRF-z) landmarks [21]. Phase 1 was set from the time when the heel of the trailing limb made contact with the first force plate until the GRF-z of the trailing limb reached the mid-stance valley. Phase 2 was set from when the GRF-z of the trailing limb reached the mid-stance valley until the heel of the leading limb reached the second force plate. Phase 3 was set from when the heel of the leading limb made contact with the second force plate until the COM progression velocity falls below 0.05 m/s in the AP direction. Therefore, Phase 1 corresponds to the pre-stopping phase, in which gait deceleration occurs, and Phases 2 and 3, where the main braking occurs, correspond to the initial and terminal stopping phases, respectively (Figure 2). We mainly analyzed the data of the leading limb in each phase because the leading limb plays a more important role in gait termination than the trailing limb. The gait termination time was defined as the time interval between Phases 1 and 3. The gait termination step length was calculated as the AP distance between the trailing and leading limb heel contacts. The gait termination step length was normalized to an individual's leg length. Gait velocity was calculated by referring to a marker placed on the sacrum [22]. In addition, the kinetic and kinematic parameters of the leading limb were analyzed during each phase. For the walking trial data, only Phases 1 and 2 were analyzed because data from Phase 3 could not be obtained.  
Figure 1: Flowchart of the experimental procedure.
# Bacteria-Mediated Modulatory Strategies for Colorectal Cancer Treatment
## 2 Bacterotherapy in CRC Treatment
### Mechanisms Used in Bacterotherapy in CRC
#### 2.1.2 Bacterial Peptides
\begin{table}
\begin{tabular}{|c c c c|} \hline
**Protein/Eptides** & **Bacterial Source** & **Mode of Action** & **Refs.** \\ \hline
**Lucentamycin** & _Nocardiopsis lucentensis_ &
\begin{tabular}{c} Shows cytotoxicity and induces apoptosis against \\ HCT-116 CRC cells. \\ \end{tabular} & [99] \\ \hline \multirow{6}{*}{**Arenamides**} & \multirow{6}{*}{_Salinispora arenicola_} & Subtypes A and B block TNF, nitric oxide and & \\  & & prostaglandin E2 and act cytotoxic on HCT-116 CRC & \\  & & cells. Investigation for chemopre and & \\  & & anti-inflammatory properties in HCT-116 CRC cells & \\ \hline \multirow{6}{*}{**Ohmyungsamycins**} & \multirow{6}{*}{_Streptomyces_ sp.} & Subtype A modulates Skp-p27 axis leading to cell cycle & \\  & & arrest (G0/G1 phase), apoptosis and selectively targeted & \\  & & reduction of proliferation in HCT-116 CRC cells. & \\ \cline{2-4}  & & Subtype A shows stronger activity against human & \\  & & cancer cells compared to subtype B. & \\ \hline
**Mixirins** & _Marine Bacillus_ sp. &
\begin{tabular}{c} Subtypes A, B and C act cytotoxic against cancer cells \\ and inhibit cell growth in HCT-116 CRC cells. \\ \end{tabular} & [103] \\ \hline
**Urukthapelstatin** & \
\begin{tabular}{c} _Mechercharimyces_ \\ _asporophorigenens_ \\ \end{tabular} &
\begin{tabular}{c} Subtype A inhibits DNA synthesis, growth and \\ proliferation of HCT-116 CRC cells. \\ \end{tabular} & [104] \\ \hline \end{tabular}
\end{table} 
Table 2: Collection of non-ribosomal peptides proposed for CRC therapy. However, these studies should be extended to gain further insights and to initiate clinical trials in the future.
# Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection
## 5 Results
### Results of the Scenario I
As the Hansen and Bowers test is the most commonly used overall balance test, its values are also presented in detail for the 100 datasets in Figure 3. The smaller the interquartile range of the box is, the less different the control groups are, and the higher the box is placed, the more similar the selected control groups are to the case group. Figure 3 clearly shows that the WNNSA method performed the best. Apart from a few outlier values, it selected the most similar control groups with high confidence. If we look at the outliers, it also performed better than the other methods. For better visibility, Figure 3 does not include the results of the SM method as its outlier values were too low. For the sake of completeness, the results of SM are presented in text format: the first quartile (Q1) of data for SM is equal to 0.8092, the median of the data (Q2) is equal to 0.9235 and the third quartile of data (Q3) is equal to 0.9730. Since the WNNSA method can be considered an improvement of the WNNEM method, it is worth comparing the results of these two methods in more detail. For the 100 datasets, the two methods gave the same results in 48 cases, the WNNEM method found more similar control groups in nine cases, while the WNNSA method outperformed the WNNEM method in 43 cases. In the nine cases where the WNNEM method produced better results than the WNNSA algorithm; the simulated annealing algorithm incorporated in the WNNSA method probably did not converge to the optimum. On the 100 datasets, the WNNEM method resulted in 0.9908 \(\pm\) 0.0290 and the WNNSA algorithm in 0.9976 \(\pm\) 0.0071 values on average for the Hansen and Bowers test. Summarizing the results, we can see that when tested on the widely used benchmark dataset, the WNNSA method was able to more reliably select more similar control groups than the widely used PSM, the nearest neighbour methods or even the WNNEM method.
## 3 Results and Discussion
### The EYB Eyebrow Image Results
#### 3.2.2 The Scale Space with Curvature-Based Method Results  
The same experimental conditions from the eye images in Table 4 were applied to the eyebrow images in this section, resulting in Table 8. The results of the eyebrow images in this experiment also displayed higher performance than the eye images. The conclusion from Table 3 for the accuracy results of \(O=0\) offered higher accuracy than \(O=1\), which was also repeated for the eyebrow images.  
The highest accuracy result of 94.93% occurred when \(O=1\) and \(L=1\) with \(SS+H\). The equal highest accuracy result was also produced with \(SS+N\) when \(O=0\) using _SVM-1_. The difference between \(O=0\) and \(O=1\) was within the Gaussian kernel's width and the images' size. In \(O=0\), the image's size was bigger than that in \(O=1\) by half due to the decimation in Equation (17). Figure 6 displays the results from \(SS+K\), \(SS+H\), \(SS+X\), and \(SS+N\). Like the eye images, the combination with \(H\) and \(X\) resulted in richer features in the eyebrow images. Additionally, for the eyebrow images, the combination with \(N\) also performed well.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**Base**} & \multicolumn{4}{c}{**Curvature-Based**} \\ \cline{3-6}  & & \(K\) & \(H\) & \(X\) & \(N\) \\ \hline
1-\(NN\) & 63.27 & 18.29 & 52.65 & 72.74 & 72.86 \\ _SVM-1_ & 92.63 & 56.68 & 86.64 & 86.64 & 91.24 \\ _SVM-2_ & 64.52 & 35.94 & 47.93 & 31.34 & 35.94 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The accuracy (%) results from the EYB dataset for the eyebrow images and the extracted curvatures.
## 2 Design of the Robotic Catheter System
### Communication Modality  
The master-slave platform facilitates data exchange via an open-source communication protocol from Modbus (Schneider Automation Inc., Rueil-Malmaison, France) and a wireless network protocol as shown in Figure 4. The RCS intercommunicates through the Modbus remote terminal unit, based on the RS485 standard, with a controller area network (CAN) and expansion board that allows transmitting/receiving data between Raspberry PI and other devices via RS485/CAN functions. Typically, the slave robot executes a given action and performs an error check before feedback of the completed action to the master device for subsequent commands. An 8-bit analog-to-digital (A/D) and digital-analog (D/A) converter chip, PCF8591 module (NXP Semiconductors, Eindhoven, Netherlands), aids A/D-D/A sampling in Raspberry PI via an inter-integrated circuit communication interface. Both linear and rotary data are intra-processed by the system module within the master-slave Raspberry PI and transmitted to the corresponding motor drivers, which actuate the linear or rotary motors for an axial or rotary drive of the catheter/guidewire.  
The interventionalist relies on sensory feedback such as visual feedback and inter-communication between the master-slave devices for precise tool navigation. The system employs an onboard control system wherein the slave device receives control actions via preprogrammed instructions in a Raspberry PI operating system and thus could perform routine tool movements with discrete motion commands.  
Figure 4: The master-slave robotic platform communication scheme.
## 4 Results of Mission Analysis
### Thermal Analysis  
Thermal analysis using Finite Element Method (FEM) analysis was conducted using Siemens NX software [29]. The results produced indicate that the temperatures are within the operating temperature ranges for all of the components including the payload and the batteries. Extensive thermal testing will be conducted using a thermal vacuum chamber to verify the simulation results.
# ZSM Framework for Autonomous Security Service Level Agreement Life-Cycle Management in B5G Networks
## 4 ETSI PoC
### Security Assets
#### 4.1.4 Fifth Generation Core Agent  
The 5G core agent developed in this work functions as a middleware component between the ZSM-based framework and the 5G core. Its primary role is the internal management of various network functions (NFs) within the 5G core network. Specifically, the agent dynamically adapts the configurations and versions of these NFs based on the policy intent defined by the Security Orchestrator (SO). This dynamic adaptation ensures that the 5G core network operates in compliance with the specified security requirements and service parameters dictated by the SO. By intelligently managing NF configurations, the system maintains the desired security and trust levels in response to evolving network conditions and security threats. A key feature of this security asset is its repository of NF images. When a vulnerability is identified in any of the images, a more reliable image is on-boarded, with configurations tailored to the image version and the current status of the 5G core. This proactive image management enhances the overall resilience and security of the network. In addition, the 5G core agent can initiate a request toward the management plane of the RAN, for instance, requesting the incorporation of a PLMN to be broadcast, and register/delist serving AMFs.  
The policy model has been extended to introduce the Secured Service MANO capability for the 5G core. This capability ensures that the 5G core provides a mechanism (e.g., an agent) for managing its internal components and features with security in mind. In this context, the 5G core agent becomes a necessary dependency for secure 5G core deployment. The plugin translates the policy into configurations required by the 5G core agent driver. This driver equips the framework with a REST API that facilitates a wide range of actions to implement procedures, such as registering a new AMF in the gNB. The plugin models configurations using a lightweight JSON format, enabling the specification of a diverse array of actions--including redeployment, NF registration, and UE identifier retrieval--along with the associated action-specific parameters such as endpoints, protocols, identifiers, and versions. The Data Services provide additional support for these configurations.  
This concise JSON-based approach simplifies the configuration process while maintaining flexibility and precision in defining management and security actions. By enabling seamless interaction between the ZSM-based framework and the 5G core, the 5G core agent ensures that trusted NF versions are effectively deployed in the 5G core.
# Efficient Conjunctive Keywords Search over Encrypted E-Mail Data in Public Key Setting
## 5 Performance Evaluation
### Experimental Results  
Our experiment is run on Intel(R) Core(TM) i7 CPU at 3.40 Ghz processor with 16 GB memory size. The experiment is based on Java language because Java is a cross platform development language and the bilinear pairing technique is realized by adopting the Java Pairing Based Cryptography (JPBC) library [32]. The experiment is based on a real-world e-mail dataset called Enron e-mail dataset [33]. We randomly select 1000 e-mail messages from the dataset and extract 5\(\sim\)25 words from each document as its keyword list. By utilizing these e-mails and its associated keywords, we implement the schemes introduced in References [7,8,10] and our scheme in the same environment to demonstrate the advantages of the proposed scheme in term of time and space overhead (The source code can be accessed through the website [[http://www.inforstation.com/webservers/SPE-CKS/](http://www.inforstation.com/webservers/SPE-CKS/)]([http://www.inforstation.com/webservers/SPE-CKS/](http://www.inforstation.com/webservers/SPE-CKS/))).
# A Practical Group Symbol and Group Name Approach (PSNA) according to EN ISO 14688-2:2018
## 2 Standards
### ASTM D2487-17e1  
ASTM is one of the most widely used standards globally. According to ASTM D2487-17e1 [25], the boundary of coarse and fine (silt and clay) grain material is 0.075 mm, and the boundary of sand and gravel is 4.75 mm. Symbols are given, such as G, S, M, and C for gravel, sand, silt, and clay, respectively. This standard is based on the determination of particle size characteristics, liquid limit, and plasticity index and can be used when precise classification is required. A brief chart of ASTM D2487-17e1 is presented in Table 4.  
Figure 1: Plasticity chart of EN ISO 14688-2:2018 [21].  
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{4}{c}{**X Liquid Limit, WL**} & \multicolumn{2}{c}{**Y Plasticity Index, Ip**} \\ \hline Soil type & \multicolumn{4}{c}{Plasticity} \\ \hline \multicolumn{4}{c}{Liquid limit} \\ \hline Cl & Clay & L & Low & \textless{}35 \\ \hline Si & Silt & M & Medium & 35 to 50 \\ \hline \multicolumn{4}{c}{H} & High & 50 to 70 \\ \hline \multicolumn{4}{c}{V} & Very high & \textgreater{}70 \\ \hline \multicolumn{4}{c}{O} & Organic &
\begin{tabular}{c} append to classification for organic \\ material (e.g., ClHO) \\ \end{tabular} \\ \hline U line & Ip = 0.9 (WL \(-\) 8) & A line & Ip = 0.73 (WL \(-\) 20) \\ \hline \end{tabular}
\end{table}
Table 3: Key for the plasticity chart of EN ISO 14688-2: 2018 [21].  
The standard gives a single classification group symbol and group name except when soil contains 5 to 12% fines or when the PI (Plasticity Index) versus LL (Liquid Limit) plot falls 4 \(\leq\) PI \(\leq\) 7 on the plasticity chart (Figure 2). In these two cases, a dual symbol is employed, such as SW-SC or CL-ML. ASTM D2487-17e1 also uses a cumulative particle size plot (Figure 3) for the procedure of classification of coarse-grained soils.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Criteria for Assigning Group Symbols and Group Names Using Laboratory Tests \({}^{\mathbf{A}}\)**} & \multicolumn{2}{c}{**Soil Classification**} \\ \cline{3-6} \cline{5-6}  & & & \(\mathrm{C_{u}\geq 4.0}\) and \(1\leq\mathrm{C_{c}\leq 3.0}\)\({}^{\mathrm{D}}\) & \multicolumn{1}{c}{GW} & \multicolumn{1}{c}{Well-graded} \\  & & & \(\mathrm{C_{u}\geq 4.0}\) and \(1\leq\mathrm{C_{c}\leq 3.0}\)\({}^{\mathrm{D}}\) & \multicolumn{1}{c}{GW} & \multicolumn{1}{c}{gravel \({}^{\mathrm{E}}\)} \\  & & & & & \\ \cline{3-6}  & & & \(\mathrm{C_{u}<4.0}\) and/or [\(\mathrm{C_{c}<1\) or \(\mathrm{C_{c}>3.0}\)] \({}^{\mathrm{D}}\)} & \multicolumn{1}{c}{GP} & \multicolumn{1}{c}{Poorly graded} \\  & & & & & \\  
All tables, identifications, group symbols, and group names about soil classifying are presented detailed in the standard (ASTM D2487-17e1) [25].
# Fast Face Tracking-by-Detection Algorithm for Secure Monitoring
## 2 Related Work  
Numerous object detection and tracking algorithms have been proposed over recent decades. In this section, we discuss related work on object tracking, face detection and the tracking-by-detection method.  
We can divide object tracking methods into two types: generation methods and discrimination methods. Generation approach is template matching, which searches the most similar candidate region with the target as the tracking result [15; 16; 17]. Mean-shift algorithm [15] describes the object by color feature and searches the location with the largest color probability distribution through iteration. The algorithm is simple, but it does not perform well in fast motion. Particle filter algorithm [16] replaces the posterior probability distribution of state with random particles. Kalman filter algorithm [17] performs prediction and correction modules for steady tracking, which models an object with motion. Thus, the object motion is subject to linear Gaussian distribution. Discrimination method trains classifiers to distinguish between target and background [18; 19; 20; 21; 22; 23]. Compressive tracking (CT) algorithm [18] extracts features by compressed sensing, in which the naive Bayesian classifier is used to predict the target location. The support vector machine (SVM) is used to classify in the Struck algorithm [19]. Context tracker (CXT) [20] uses the random forest classifier. Tracking-learning-detection (TLD) algorithm [21] combines tracking, detection and learning, which is worthy of reference. But, the speed is slow.  
Compared with the traditional generation methods, the discriminant methods are more adaptable to environmental changes and long-term tracking, showing high robustness and high real-time applicability. In addition, in recent years, correlation filters and methods based on deep learning have far surpassed other tracking algorithms and achieved excellent results in various competitions [24; 25].
# Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects
## 2 Related Work  
### Geometric Representations of 3D Objects  
Considering the complexity of 3D city models, it is essential to have efficient geometric representations. In this paper, we convert the boundary representation (B-rep) of 3D buildings into constructive solid geometry (CSG) representation [14; 15]. In the CSG representation, the geometries are created by combining elementary geometric features (Figure 1). By using transformation parameters (rotation, scaling, etc.) as well as combining them (adding and subtracting), the geometries can model irregular shapes.  
CSG representation is widely used in building design (CAD/CAM) and provides the efficient storage of data for buildings that have a rectangular shape or share common parts, such as flat and gabled roofs. As [16] points out, CSG representation has the following advantages: (i) modeling using primitives and Boolean operations is much more intuitive than specifying boundary representation surfaces directly; (ii) the primitives can be parameterized, thus enabling reuse and collection in libraries, and reducing the file size; (iii) it can be associated with other, additional information; and (iv) the CSG modeling tree contains implicit information that can be used for many purposes. Another benefit of CSG is that it can easily ensure that objects are \"solid\" or water-tight if all of the primitive shapes are water-tight. By comparison, when creating geometry based on boundary representations, additional topological data is required, or consistency checks must be performed to ensure that the given boundary description specifies a valid solid object.  
If many buildings share similar features, it is possible to represent buildings with pre-defined prototypes. [PERSON] [17] introduces the CSG representation of 3D buildings and the CSG tree to form the building with Boolean operations such as intersection, difference and union. [PERSON] [16] compares the differences between B-rep and CSG representations, and proposes a method called weak CSG modeling which is a combination of the two representation forms. The main idea of weak CSG modeling is to package, or hide, the regularity constraints of objects inside the black boxes which appear to the user as traditional CSG primitives. Meanwhile, not all buildings can be represented as CSG. There are many buildings in real cities that cannot be described by CSGs. For these buildings, B-rep should be used. In this paper we use CSG to represent simple regular buildings composed by predefined structures (box, hip roof, gabled roof, tower and so on).