esa-sceva/satcom-chunk-collection huggingface dataset:
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 3 AMAN Air Traffic Controller Support
### Dynamic Severe Weather Visualization on a Traffic Display  
Since convective cells should be visualized on a 2D display, the current and forecasted cells can be represented by discrete sets of simple polygons. Each polygon of a set that is valid for some time period has an identification number that provides unique correlation between sets of cells. The polygons have to be displayed without covering essential traffic information, such as aircraft labels or additional flight plan information. For animation of actual weather development, two methods for comparison were implemented with different demands in terms of the level of detail for weather information. In addition to a linear extrapolation of the last weather development, this also includes a procedure based on a morphing algorithm which, in addition to the last meteorological measurement, also considers nowcasts in the animated presentation.
# Towards DC Energy Efficient Homes
## 4 Possible Scenarios for Electrical Installations at Home
### Scenario 4: DC Distribution and DC/AC Inversion in Essential Cases  
In this case, the power is converted into DC at the entrance point of the house. Inside the house, DC is the only type of power used, unless it is for some essential purposes (e.g., washing machine). The house may have a HESS, based on renewables or not, but most of the household devices, such as EV chargers and electronic devices, are powered directly in DC. The advantage here is that there is just one distribution system of power around the house (instead of two as in the previous case) and that there are no losses due to AC to DC conversion though the disadvantages may be the voltage drop with the distances, as well as the prices of a completely new installation. In this scenario, DC devices and EV are going to have the same consumption as in the third one. However, AC devices would have a higher consumption than in the previous scenarios, due to the specific inversion of DC/AC needed. The consumption of AC devices is according to Equation (3) in this scenario.  
\[E_{AC}\big{]}_{4}=E_{AC}\big{]}_{1}-E_{AC}\big{]}_{1}\cdot(1-\rho_{r})+E_{AC} \big{]}_{1}\cdot(1-\rho_{i}) \tag{3}\]  
where \(E_{AC}\big{]}_{X}\) is the energy consumed by an AC device in the scenario X. This DC home configuration can be seen in Figure 11.  
Figure 10: Block diagram of the AC and DC distribution scenario with PV, HED, and EV.
# A Hybrid Soft Actuator Inspired by Grass-Spike: Design Approach, Dynamic Model, and Applications
###### Abstract  
This paper presents the bio-mimetic design approach, the dynamic model, and potential applications for a hybrid soft actuator. The proposed hybrid soft actuator consists of two main parts: a cylinder-shaped rigid core and soft silicone spikes wrapped around the core's surface. The key idea of the proposed design approach is to mimic the movement of a grass-spike at a functional level by converting the vibration force generated by a small electric motor with a counterweight in the rigid core into a propulsion force produced by the elastic restoration of the spikes. One advantage of this design approach is that the hybrid soft actuator does not need to be tethered by a tube line from an air compressor and is more amenable to fine control. In addition, the hybrid soft actuator can be modularized with a wire and a tubular passage, which in turn work as a linear actuator. The dynamic model of the hybrid soft actuator can be derived by applying Lagrangian mechanics, and unknown system parameters can be identified by the optimization process based on the empirical data. Two applications--an elbow manipulator and a robotic hand grasper--demonstrate the feasibility of the proposed actuator to perform a muscle-tendon action successfully.  
soft robotics; bio-mimetic design; hybrid soft actuator; hybrid soft actuator module; elbow manipulation; robotic grasping Article
# Multi-Device to Multi-Device (MD2 MD) Content-Centric Networking Based on Multi-RAT Device
## 2 Related Works and Problems
### Content-Centric Networking  
The architectures of the existing Internet and CCN are shown in Figure 2. CCN differs from a conventional IP-based network in that an IP-based network uses the IP to indicate the location of the content, while, in CCN, the device acquires content based on the name of the content [6]. In other words, CCN focuses on the content itself, rather than the location of that content.  
In CCNx, an interest message (which is a request message) and a content object (which is the response data) are used to process requests for content and the subsequent responses. An interest message contains information about the content requested by the user. The content object is the content transmitted in response to a request.  
A CCN node consists of a forwarding information base (FIB), pending interest table (PIT), and content store, as shown in Figure 3. The FIB is a table used for the efficient transfer of interest messages. This table stores the information on the face to be transferred in response to a request for specific stored content. The PIT stores interest messages and information on the requested face. The content store is a cache that stores the content object, which can potentially be reused [7]. By virtue of this architecture, when the content store of a router is stored to a cache, it is possible to prevent the duplication of a request for the content in the CCN. Also, the overall amount of traffic is reduced by receiving data from the cache [6].  
In CCN, content name-based routing, hop-wise replication, and caching are all possible. By caching content in the content store of the router, which exists in the path, the dispersion of content requests and acquisitions is facilitated [8; 9; 11]. These characteristics of CCN can reduce the complexity of autoconfiguration mechanisms relative to the layered protocol stack-based approach, with CCN being suitable for an IoT environment [10]. Accordingly, various studies have been implemented  
Figure 1: Device-to-device (D2D) communication in a cellular network.  
Figure 2: Architectures of existing Internet and content-centric networking (CCN).  
regarding the applications of CCN in an IoT environment, such as CCN traffic optimization, video streaming, and secure sensing [19; 20; 21].  
CCNx is an open-source project of the Palo Alto Research Center (PARC). It is a developed CCN architecture [7].
###### Abstract  
In recent years, chaos-based secure communication systems have garnered significant attention for their unique attributes, including sensitivity to initial conditions and periodic orbit density. However, existing systems face challenges in balancing encryption strength with practical implementation, especially for multiple levels. This paper addresses this gap by introducing a novel N-level cascaded chaotic-based secure communication system for voice encryption, leveraging the four-dimensional unified hyperchaotic system. Performance evaluation is conducted using various security metrics, including Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Percent Residual Deviation (PRD), and correlation coefficient, as well as Field-Programmable Gate Array (FPGA) resource metrics. A new Value-Based Performance Metrics (VBPM) framework is also introduced, focusing on both security and efficiency. Simulation results reveal that the system achieves optimal performance at N = 4 levels, demonstrating significant improvements in both security and FPGA resource utilization compared to existing approaches. This research offers a scalable and cost-efficient solution for secure communication systems, with broader implications for real-time encryption in practical applications.  
hyperchaotic; cascaded chaotic system; security analysis; secure communication system; field-programmable gate array; voice encryption Article
# High Throughput and Acceptance Ratio Multipath Routing Algorithm in Cognitive Wireless Mesh Network
## 5 Backup Path Routing and Spectrum Allocation Algorithm  
The backup path routing construction algorithm (BPC), which is based on channel interference and reusability, and the backup path spectrum allocation algorithm (BPSR) are presented in this section.  
The link weight function based on channel interference and reusability was proposed. The weight function of link (\(u\),\(v\)) on channel \(k\) is given by  
\[f_{2}^{k}(u,v)=\frac{1}{(\left|\delta^{k}(u,v)\right|+1)(\left|\eta^{k}(u,v) \right|+1)} \tag{14}\]  
where \(\delta^{k}(u,v)\) and \(\eta^{k}(u,v)\) are the primary link interference degree and interference link reuse degree (\(u\),\(v\)) on channel \(k\), respectively. Then, \(\eta^{k}(u,v)\) is given by  
\[\eta^{k}(u,v)=\sum_{\forall\gamma_{1}\in\Delta}\sum_{(a,b)\in \alpha(s_{i},d_{i})}A\Big{\{}\delta^{k}(ab,uv)\Big{\}} \tag{15}\] \[(a,b)\in\alpha^{k}(u,v)\]where A{\(\bullet\)} is the condition function to judge whether it is true. \(q^{k}(ab,w)\) denotes whether the backup link (\(u\),\(v\)) and the primary link (\(a\),\(b\)) that is a link in the primary path \(\alpha(s_{i},d_{i})\) for the accepted request interfere each other.  
We can see that a higher primary link interference degree and the interference link reuse degree leads to a lower link weight.  
If the link (\(u\),\(v\)) is allocated to channel \(k^{*}\) before, then \(f_{2}(u,v)=f_{2}^{k^{*}}(u,v)\). If the link (\(u\),\(v\)) is not allocated to any channel, then \(f_{2}(u,v)\) is set to the minimum of \(f_{2}^{k}(u,v)\) on all of the channels, which is given by  
\[f_{2}(u,v)=\min_{k\in K(u,v)}\left\{f_{2}^{k}(u,v)\right\} \tag{16}\]  
The allocated channel of link (\(u\),\(v\)) when \(f_{2}(u,v)\) obtains the minimum is \(x_{2}^{*}(u,v)\), which is given by  
\[x_{2}^{*}(u,v)=\argmin_{k\in K(u,v)}\left\{f_{2}^{k}(u,v)\right\} \tag{17}\]
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 3 Results
### Common Wearable Sensors Used in Gait, Balance, and RoM Analysis
#### 3.1.4 Insole Pressure and Force Sensors  
Besides measuring body movement with IMUs and smart devices and measuring muscle electrical activity with EMG, there are sensors that can measure ground reaction forces applied by the subject. The pressure sensor is typically located in the inside of the subject's shoes, and it can measure the plantar foot surface in 3 dimensions. The most common insole sensors are capacitive, resistive piezoelectric, and piezoresistive sensors, and which is selected depends on the range of pressure it can stand and it's sensitivity [8]. Insole pressure sensors are known for being unobtrusive and for their potential in monitoring daily activities since people wear shoes for multiple hours a day. They are typically used in gait analysis to count steps and extract time and distance based parameters. In balance analysis, they are typically used to measure center of pressure to evaluate postural stability [19; 26; 42; 43; 71].  
The most common wearable devices used by the studies included in this review are IMUs (71.43% of studies). 21.43% of articles used smart devices in their studies, while 8.93% and 8.93% used EMGs and pressure/insole sensors, respectively.
# Microbes as Medicines: Harnessing the Power of Bacteria in Advancing Cancer Treatment
## 2 Mechanism of Tumor Suppression
### Bacterial Cell Death Inducing Agents
#### 2.1.2 Bacterial Enzymes  
Essential amino acids are vital for normal cell growth and cellular metabolism. These amino acids can serve as a limiting factor, capable of regulating the uncontrolled, rapid growth of tumor cells. Deprivation of these essential amino acids is an alternative therapy for cancer. To do so, bacteria produce a wide array of enzymes that act on these essential amino acids and prevent various cellular activities required for tumor growth. L-Asparaginase, arginine deaminase, and arginine decarboxylase are the commonly investigated bacterial enzymes [22]. L-Asparaginase (L-ASNase) catalyzes the hydrolysis of asparagine, enzymes such as arginine deaminase and arginine decarboxylase are involved in arginine catabolism. These enzymes result in deprivation of the amino acids required in protein synthesis and subsequently cause tumor cell death [23].  
_Escherichia coli_, _Erwinia_ species, _Streptomyces_, and _Bacillus subtilis_ species are the sources of L-ASNase. In a study by [PERSON] et al., L-ASNase enzyme caused lowering of blood asparagine levels thereby inhibiting protein synthesis and causing cell cycle arrest in G1 phase of leukemia cells [24]. L-ASNase has been proved effective in the treatment of acute lymphoblastic leukemia, lymphomas, neoplasia, and other malignancies [24; 25]. Commercial asparaginase have L-glutaminase activity, are antigenic, and cause hypersensitivity reactions. The L-glutaminase coactivity is responsible for the side effects such as hepatotoxicity and immunosuppressive effect. It has been reported by [PERSON] et al. that _Erwinia chrysanthemi_ derived L-ASNase is highly effective in the treatment of acute lymphoblastic leukemia demonstrating lower side effects and is less toxic as compared to the other FDA approved asparaginase as it lacks L-glutaminase coactivity [24].  
\begin{table}
\begin{tabular}{p{10.0 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline \hline
**Methodics—Category** & **Material** & **Material** & **Material** & **Material** & **Material** & **Material** \\ \hline \hline \multirow{6}{*}{Electerial Teoxins} & _Carpobacterium_ _diphtheria_ & Diphtheria toxin (DT) & DATA is responsible for the cytotoxic enzymatic activity and industries the ADP-ribosylation of elongation 2.2 Dirk facilitates cell entry by binding to surface receptors and subsequent translocation to cytoplasm by undergoing endocytosis & Ovarian cancer (SKU-3), Pancreatic cancer (CRL-1687, CRL-2119, CRL-1997, and CRL-2547), Lung cancer (NCL-H460, and CRL-2547), Lung cancer (NCL-H460, and CRL-2547), and Lung cancer (NCL-H460, and CRL-2547) & **References** \\  & _Carpococcus_ & **Carpobacterium O (SLO)** & SLO binds to cholesterol in the plasma membrane, _Carpobacterium kidney fibroblasts_ (293T, HEK), _Carpococcus_ & **Carpobacterium to form aggregates which form large pores. This results in cytolysis and cell death & **Carpobacterium to form aggregates which form large pores.
# Machine Learning-Based Methods for Code Smell Detection: A Survey
## 4 Findings and Discussion
### Rationalize the Research Questions
#### 4.2.7 RQ5: What Are the Performance Measurements of the Different ML Algorithms Used?  
Various performance measures are applied in code smell detection, as shown in Figure 11. Some performance measures are effectively used, such as precision (21 times), recall (21 times), accuracy (22 times), and F-measure (18 times). Some rarely used techniques are kept in miscellaneous. Table 6 shows that the performance measures precision, recall, accuracy, F-measure, root mean square error (RMSE), Matthew correlation coefficient (MCC), hamming score, mean absolute error (MAE), etc., are rarely used. ARTN15, ARTN29, ARTN32, ARTN35, ARTN37, and ARTN42 used AUC ROC for performance measurement. ARTN15 and ARTN37 used GMean for performance analysis. These performance measures are crucial in evaluating the effectiveness and efficiency of different ML algorithms in code smell detection.  
Figure 11: Performance measures used in various types of research.  
Figure 10: List of dataset languages used for code smell detection.  
Our research demonstrated the effectiveness of various ML algorithms in code smell detection. The SVM, Random Forest, J48, and Naive Bayes models have emerged as the most popular and accurate methods, as confirmed by their frequent use in research articles (Table 6). The accuracy, AUC RoC, precision, and recall are manually analyzed from different research articles. The accuracy score, in particular, is often used to measure the model's performance, providing a reliable benchmark for future ML algorithms in code smell detection.
## 4 Petrography  
Generally, the examined mantle units can be divided into serpentinites and serpentinized peridotites, based on their mineralogical composition. Petrographic examination of 31 samples was carried out at NMA using a polarizing microscope (Olympus bx53). Serpentinites are commonly sheared (carbonatized), and exhibit pseudomorphic textures after olivine and pyroxene minerals. Pseudomorph after olivine is indicated by common hourglass textures with less mesh, whilst bastite reflects pyroxene pseudomorph. Primary minerals such as olivine and pyroxene are completely replaced by antigorite, magnetite, and carbonate minerals. Antigorite occurs as dense sheds of fibro-lamellar, irregular aggregates, ranging from colorless to pale green, fractured, and occupied by iron oxides and carbonates. Abundance of bastites suggest pyroxene pseudomorph, providing evidence for harzburgite protolith (Figure 3a). Scattered patches of carbonate are the main component in the examined serpentinites. Opaque minerals are essentially represented by magnetite (Figure 3b). Magnetite is disseminated as fine, black, anhedral crystals.  
Figure 2: (**a**) Thrust contact between serpentinites and metasediments, and (**b**) pod of tal–carbonate within serpentinites along the shear zone.  
On the other hand, serpentinized peridotites are essentially composed of serpentines (antigorite and chrysotile) with variable relics of orthopyroxene, clinopyroxene, olivine,  
Figure 3: Photomicrographs of serpentinites—(**a**) sherds of antigorite (Atg) forming a bastite texture after pyroxene; (**b**) patches of carbonates (Cb) associated with disseminated magnetite (Mag)— and serpentinized peridotites: (**c**) fibro-lamellar antigorite enclosed between olivine (OI) crystals; (**d**) perfect veinlet of chrysotile (Ctl); (**e**) replacement of olivine by antigorite; (**f**) schiller structure of orthopyroxene (Opx); (**g**) two porphyroblastsats of green spinel (Al-Spl) engulfing apatite (Ap); and (**h**) fresh core of Cr-spinel (Cr-Spl) rimmed by secondary Cr-magnetite (Cr-Mag).  
and spinels. Tremolite, carbonates, and talc are the main secondary minerals. Fibroblastellar, irregular aggregate of antigorites form asbestiform and hourglass, reflecting their olivine pseudomorph (Figure 3c). Chrysotile commonly occurs as veinlets crosscutting olivine crystals (Figure 3d). Olivines reveal significant cracks, and are replaced partially to completely by serpentine, magnetite, and talc along their peripheries, forming ididinsite (Figure 3c-e). Orthopyroxene is replaced by serpentine, chlorite, tremolite, and magnetite, forming schiller structures (Figure 3f). Magnetite and red and green spinels are the main opaque minerals (Figure 3g). Red spinels occur as euhedral, unfractured grains, and are commonly zoned with fresh Cr-spinel cores (red) rimmed by Cr-magnetite (black in color, Figure 3h).
# An Indoor Smart Parking Algorithm Based on Fingerprinting
## 4 Environment Setting  
For this work, let us imagine a scenario as the following: a multi-level car park within an airport. Clearly, as for the outdoor case, vehicles looking for a free space should be communicating with each other so as to ensure a reduction of traffic congestion and air pollution, but also to facilitate drivers in finding a parking slot which is suitable for his requirements.  
While in an outdoor setting drivers can exploit vehicular ad hoc networks to communicate with each other, in an indoor environment things get harder. Indeed, the idea is to perform an indoor localization of vehicles by relying on a slightly different version of fingerprinting approach.  
As you can see from Figure 1, the car park building presents several access points, used to measure the signal strength received from the red points, whose position has to be discovered. Those points should be placed in the proximity of each parking slot, so as to know their position in the building.  
Some interesting points should be also considered as possible destination points for drivers. More precisely, in a typical scenario, drivers looking for a free parking slot want to allocate their car and then reach some destination. In our airport setting, the interesting destination could easily be the terminals for departure. They are denoted by T in Figure 1.  
Moreover, the car park is supposed to be divided into areas with the final aim of associating drivers with an available parking slot belonging to the same area of the target terminal.
# Graph-Driven Deep Reinforcement Learning for Vehicle Routing Problems with Pickup and Delivery
## 2 Related Work
### Exact and Heuristic Methods  
Mainstream exact methods for VRP-PDs typically employ branch-and-cut, branch-and-bound, and their variants to systematically manage computational complexity. [PERSON] et al. [18] presented a branch-and-cut approach based on a set-partitioning formulation to solve VRP-PD with time windows. [PERSON] et al. [19] addressed real-world pickup and delivery challenges by introducing a branch-and-price-and-cut algorithm with specialized ad hoc label-setting strategies, achieving high-quality solutions for instances with fewer than 100 products within an hour of computation. [PERSON] et al. [20] formulated the VRP-PD as a relaxed mixed-inter programming problem and developed a branch-and-cut method based on an aggregated master model to optimize solutions for split-demand one-commodity VRP-PD. Although exact algorithms provide strong optimality guarantees, their high computational cost renders them impractical for large-scale routing problems, limiting their applicability in real-world scenarios.  
In contrast, heuristic methods exploit hand-crafted rules to guide the search process, enabling them to produce high-quality solutions for large-scale VRP-PDs. [PERSON] et al. [21] designed a two-stage simulated annealing approach for the conventional VRP-PD, aimed at minimizing the total cost of vehicle routes. [PERSON] et al. [22] concentrated on the green VRP with simultaneous linehaul and backhaul nodes, designing a hyper-heuristic framework that integrated adaptive local search to optimize energy consumption in oil-fueled vehicle fleets, addressing both conventional and improved VRP-PDs. [PERSON] et al. [23] implemented an advanced neighborhood search (i.e., LKH solver) for various routing problems, including TSP, CVRP, and VRP-PD. [PERSON] et al. [4] optimized electric vehicle routing in pickup and delivery logistics using an adaptive hybrid neighborhood search approach. Despite their effectiveness, heuristic methods are highly dependent on expert knowledge and problem-specific tuning, limiting their adaptability to diverse routing challenges.
## 1 Introduction
### Gap and Novelty  
Despite the growing body of research employing DPD to analyze oil-water emulsions, most existing studies focus either on reducing interfacial tension using conventional surfactants or on demulsification processes under simplified binary or ternary systems. Limited attention has been paid to the stabilization of HIPEs in water-in-oil configurations, particularly under conditions where the dispersed water content exceeds 70%. Moreover, few works have explored the cooperative role of emulsifier architecture and oil polarity in HIPE formation using a multiscale approach. The current study addresses this gap by employing a DFT-MD-DPD integrated simulation pipeline to systematically investigate how polyaromatic emulsifier structure, dipole moment, and oil phase composition collectively influence HIPE stability. The novelty of this work lies in identifying dipole moment as a predictive molecular descriptor for emulsifier performance and proposing four stabilization mechanisms observed directly through mesoscale simulation.