esa-sceva/satcom-chunk-collection huggingface dataset:
# Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review
## 2 Research Objectives  
Over the past several years, the optimization of load management in next-generation mobile networks has been pursued with the aim of enhancing the performance and QoS provided. Several applicable techniques have been identified for this purpose; however, most of them focus on describing possible heuristic or metaheuristic algorithms based predominantly on typical parameters obtained from conventional network management systems, without considering the dynamic and random behavior of the environments where mobile networks are deployed. For instance, in scenarios with user concentration hotspots, traditional algorithms do not account for social information or human behavior (such as mobility routes, dwell times, interactivity, most demanded applications, energy consumption, etc.). As a result, algorithms that overlook these sources of information may only partially solve the problem, leading to the omission of external factors that could help reduce excessive energy consumption or prioritize radio resource allocation. Therefore, it is crucial to identify which algorithms are currently being used and which ones can be adapted to integrate measurable parameters outside the mobile network. The purpose of this study is to conduct a systematic literature review (SLR) to identify and evaluate the most frequently used load balancing algorithms in mobile networks, and to use this as a foundation for adapting them with new parameters that more accurately represent real-world network deployment scenarios.  
Finally, the significance of this study lies in classifying the types of algorithms according to their area of applicability, level of automation, and relevance within the structure of new radio resource management schemes.
## 3 Enhanced Name Resolution System
### System Model
In current TCP/IP-based networks, the Domain Name System (DNS) plays the role of global name resolution, which resolve URLs to IP addresses. Some researchings have tried to use DNS to realize the name resolution service in ICN, for example, information-centric DNS (iDNS) [47] and NDN DNS (NDNS) [48]. ENRS maps and resolves EIDs to NAs. Both DNS and ENRS are hierarchical strutures, and the functionalities of DNS and ENRS are similar, but different. The main differences between DNS and ENRS are divided into four aspects, as listed below:  
(1) They are different in working layers of the Internet. The DNS works on the application-layer, while our ENRS work on the network-layer. (2) The binding methods for them are different. DNS uses the early-binding technology so that the bindings of URL and IP are static. If the IP is unreachable when routing data, the session is interrupted. While ENRS is supported for the late-binding technique [49], the bindings of EIDs and NAs are dynamic. If the IP is unreachable, the equipment can initialize a new query with EID to get the latest IP destination. In addition, URL is a hierarchical name and is related to the position the content resides, while EID is permanent and location-independent. (3) Their name record caching methods are different. Both DNS and ENRS can cache name records, however, DNS's cache is effected by user requests, while ENRS employs a proactive name caching method to realize the nearest-replica-routing of ICN, which will be introduced in Section 3.3. (4) Their message forwarding methods are different. DNS is in favor of recursive querying, while ENRS supports constant hop resolution. In addition, our ENRS is also different from existing hierarchical and nested ICN NRS, e.g., DONA [9], HDHT [26], H-Pastry [28], and CURING [50], which organize the hierarchical and nested structure based on the hierarchical structure of the underlying inter-domain topology, i.e., at theAutonomous System (AS) level, the Point of Presence (POP) level, or the Access Node (AN) level. Our ENRS not only considers the underlying topology, but also quantifies the hierarchical and nested areas by transmission latency constraints from the user nodes to the serviced resolvers. ENRS divides the service area into finer grains and helps to solve the administrative domain partitioning problem. Metrics such as the worst-case latency and nodes with a bound on latency are utilized as deterministic constraints.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 8 Case Studies
### Adopting Content for Healthcare Service Providers  
#### 8.2.1 National Health Service, UK  
The National Health Service (NHS) is the taxpayer-funded healthcare provider in the United Kingdom and one of the world's largest publicly funded healthcare systems, carrying out over 500 million appointments per year and providing most of the healthcare delivered in the UK. Given the combination of the UK's large reliance on the NHS as a service and its vast scale, it becomes cumbersome to roll out operating system updates and security patches across the service as a whole. As a result of these factors, it has often been a target of security breaches in recent times, such as patient data leaks and ransomware attacks. Indeed, in the previous two years, over 3000 data breaches were reported, with many more expected to have gone unreported. In 2017, the NHS fell victim to a large-scale attack propagated by the WannaCry ransomware, disrupting administration tools and the care of patients. Estimates of the financial damage caused by this attack are upwards of GBP 19 million, with an additional GBP 73 million in IT costs and 19,000 canceled appointments [54].  
As a result of these attacks, the NHS has been forced to improve security and it adopted the Cyber Security Program in the wake of the WannaCry attacks, which act as a series of security measures. These include the completion of independent assessments, which are conducted upon ensuring that companies associated with NHS services register to cyber alert monitoring services and increase investment. Security services invested in include Virtual Perimeter Security (VPS), vulnerability scanning through the Vulnerability Monitoring Service (VMS), and the BlueFort intrusion detection systems, which are also widely used by other large companies with a large technology infrastructure. BlueFort automated intrusion detection systems combine network intrusion detection (NIDS) systems and host system intrusion detection systems (HIDS) in tandem to create a multilayer threat mitigation service. The NIDS monitors inbound packets at the network perimeter, sending alerts when potential breaches are identified based on a set of defined criteria or activity. The HIDS monitors the traffic within the network and host system and, similarly to the HIDS, sends alerts to the networking team.
# A Brief Review on Challenges in Design and Development of Nanorobots for Medical Applications
## 2 Challenges in the Design and Development of Nanorobots
### Powering and Navigation
#### 2.3.7 Flexible Magnetic Composites  
As mentioned above, eukaryotic flagella exhibit bending waves via the distribution of activities and self-assembly of motors. A sperm cell can move autonomously even when it is detached from the body [109]. However, incorporating these natural phenomena into artificial bending microactuators is a challenge, as they function with one-sided bending and require external control for propagation. Thereby, magnetic actuation with distributed magnetization can be used to achieve propagation of bending waves in artificial systems. For example, it was demonstrated that a flexible filament could be aligned with an external magnetic field by linking the magnetic microparticles of the filament with DNA and red blood cells. One end of the filament attached to the red blood cell will break the system, which produces the propagation of bending waves [111]. The magnetically guided beating of the filament propels the microbot, dragging the red blood cell body in the direction of the tip of the filament, which is distinct from the pressing motion that flagella exerts on the sperm cell body [111].
# Rolling-Element Bearing Fault Diagnosis Using Advanced Machine Learning-Based Observer
## 5 Experimental Results  
In this section, the fault diagnosis capabilities of the proposed technique are validated against two state-of-the-art approaches that have reported results for the same publicly available dataset. Specifically, the first employed method is the ASMO and the second approach used for the comparison is the SMO. The final fault diagnosis performance is expressed in terms of the average classification accuracy (ACA) as follows:  
\[ACA=\frac{\sum_{m=1}^{4}TP_{m}}{N_{samples}}\times 100\% \tag{37}\]  
where \(T_{p}\) is the number of true positive predictions (i.e., the number of data samples of the specific class \(m\) correctly identified as ones belonging to the class \(m\)), and \(N_{samples}\) is the total number of samples available in the particular dataset. To provide a comprehensive performance evaluation, the two case-studies are considered in this result sections: evaluating fault diagnosis accuracies on crack-variant datasets provided by CWRU and the created custom load-variant datasets.
# Three-Dimensional Formation Control for Robot Swarms
## 5 Covering the Sensing Holes Inside the User-Specified Shape
### MATLAB Simulation of Algorithm 1  
One demonstrates the effectiveness of Algorithm 1 through MATLAB simulations. We consider the case in which the user-specified 3D shape is a sphere with radius 100, centered at the origin. Figure 2 shows the path of each maneuvering AR until the user-specified 3D shape is entirely covered. Here, Algorithm 1 is utilized for formation controls. The path of a maneuvering AR is depicted with yellow line segments. The final position of a maneuvering AR is depicted as a blue diamond. See that blue diamonds are marked at the end points of each line segment.  
Figure 3 presents the ARs' final positions under Algorithm 1. Among 100 ARs, 66 ARs maneuver for covering the user-specified 3D shape, while (100-66) ARs are located at the leader. In other words, Algorithm 1 is finished as \(i=100-66\) since no frontierPt is detected at this instant. The ARs consume 4115 s until the 3D shape is entirely covered. MATLAB takes 42 s to run the simulation.  
Figure 2: The path of each maneuvering AR until the user-specified 3D shape (sphere with radius 100) is entirely covered (\(r_{sc}=50\) m). The path of a maneuvering AR is depicted with yellow line segments. The final position of a maneuvering AR is depicted as a blue diamond. See that blue diamonds are marked at the end points of each line segment.  
Once a swarm of ARs are aggregated into a user-specified 3D shape, one can control all ARs so that they maneuver in a desired direction while maintaining the network connection. For the flocking maneuver, all ARs maneuver while synchronized to the leader. Under Assumption (A4), the velocity command of the leader can be transmitted to all ARs inside the shape in real time. The velocity of each AR is synchronized to that of the leader, utilizing the communication command transmitted from the leader.
# Detection of Household Furniture Storage Space in Depth Images
## 2 Related Research
### Indoor Scenes and Furniture Synthesizing  
In order to apply a deep learning approach, which requires large annotated datasets for training, we address in this work the generation of synthetic scenes and the automatic annotation of the bounding boxes of the storage space in these scenes. In [15], the authors created a large synthetic dataset of indoor 3D scenes with dense per-pixel annotations. In [16], a physics engine was used to randomly generate scenes from metrically scaled ShapeNet [1] objects. Finally, they obtained rendered RGB-D scenes and their ground truth annotation in a large video dataset. The authors of [17] manually created their own large dataset of synthetic 3D scenes with dense occupancy and semantic annotations. These 3D scenes contain 84 object categories, including furniture to which clutter was added to the shelves under supervision. In [18], ATISS, an autoregressive transformer architecture for creating indoor environments, based on room type and floor type, was presented. The input to the proposed model is a collection of 3D labeled bounding boxes of the scene objects with their corresponding room shapes. The output is the realistic furnished room with objects contextually belonging in a certain room type. However, these datasets were not suitable for network training in our research because they do not contain information about the storage space bounding boxes. We provide a dataset containing models and depth images of storage furniture with associated bounding boxes of the storage space, which was used for neural network training. This dataset was obtained by the proposed algorithm for automatic detection of empty spaces on voxelized 3D models, which does not require training. The whole process presented in our work is fully automatic and does not require any manual labeling.  
The idea of creating assistive robots is presented in [19], where a simulation platform, Habitat 2.0 for training virtual robots in interactive 3D environment with rich physics, is presented. The authors presented the interactive dataset ReplicaCAD, consisting of apartment layouts with dynamical parameters of objects, their semantic classes, collision proxies, etc. However, there are no annotations of storage volumes, only surface annotations. In this simulator, robots can rearrange objects from a given list.
## 3 Results and Discussion
### Contaminants Biodegradation at Microcosm Scale  
Concerning the soil, it should be considered that this test takes place under conditions of \"still dynamic\" contamination, leading to a natural reduction in the level of hydrocarbons resulting from the oil spill. This is a markedly different condition than in a site where those substances have reached a kind of equilibrium with all soil components, both biotic and abiotic (i.e., degradation processes with much slower kinetics).  
For this reason, the behavior of hydrocarbons C \(\leq\) 12 and C \(>\) 12 in non-vegetated soil (Snv) was also evaluated to control the action of different plant species (Sv) in the microcosm test performed compared to the initial values detected in each soil (Ss) (Figure 2).  
Looking at the trends in Figure 2 and considering the average concentration values, a downward trend of these compounds in the microcosm test period emerges (Snv), which seems to be further favored by the presence of plants (Sv). Although the results obtained are not always statistically significant due to the considerable variability of the analytical data, this observation can be quantified as an increase in degradatio  
Figure 2: Variation of C \(\leq\) 12 (**a**) and C \(>\) 12 (**b**) hydrocarbon content in the soil (mg kg\({}^{-1}\)d.m.) for different soils (A–D) during microcosm tests. Data reported as the mean of replicates with the related standard deviation. Ss = starting soil, Snv = non-vegetated soils; Sv = vegetated soil; C, L and A denote _Z. mays_ (Corn), _L. albus_ (Lupine) and _M. sativa_ (Alfalfa), respectively.  
planted soils in comparison to bare soils, confirming the generally positive effect of the presence of plants in reducing hydrocarbons in soil.  
This trend of reducing hydrocarbon concentrations in soils shows further accentuation in the presence of PGPB. A direct comparison is shown in Figure 3, where the percentage reduction in the level of the C > 12 fraction in soils during microcosm tests with and without PGPB is reported.  
Although the percentage of reduction in the concentration of C > 12 does not vary significantly with the addition of PGPB, Figure 3 suggests a general improvement, quantifiable as 5 to 10 percent of C > 12 further eliminated from the soil compared to the initial concentration.  
The results obtained are generally in line with what was expected. Although the plant can aid soil remediation via the absorption of contaminants and accumulation at the root level [42], the main action that plants perform in the presence of hydrocarbons is the stimulation and promotion of microorganisms capable of oxidizing hydrocarbon through the production of root exudates [44]. The direct activity of hydrocarbon-oxidizing bacteria, in addition to reducing contamination levels, promotes plant growth under stressed conditions. Thus, the interaction between plants and microorganisms is crucial in removing soil contaminants [26].
# Using Modeling to Select the Type of Microwave Field Emitter for Dense-Layer Grain Dryers
## 3 Results
### Studies of Rectangular Waveguides with Slot Radiators  
On the basis of this waveguide, a slot radiator was designed [34; 41]. For this purpose, transverse slots were cut in the broad waveguide's wall. The transverse slot in the broad wall of the waveguide becomes excited by the direct-axis component of the current. On account of the required volume of the section, a radiator length of 400 mm was selected. Slots in the back side of the radiator are spaced 1/2 \(\lambda\) apart (Figure 3).  
Radiator parameters were first conformed and then were input into CST Microwave Studio software for mathematical simulation of the electromagnetic field distribution from the radiator's slots. The results of this simulation are presented in Figures 12 and 13.  
Mathematical simulation results show that the application of a rectangular waveguide makes it possible to achieve better homogeneity of the electromagnetic field distribution. Additionally, the rectangular waveguide features SWR equal to 1 and increased radiated power values.  
That is why the use of a rectangular 55 \(\times\) 110 waveguide in convective-microwave zones for processing grain is more preferable.  
Apart from the rectangular 55 \(\times\) 110 waveguide, mathematical simulation of the electromagnetic field distribution was performed for a waveguide with a semicircle cross-section (circular sector) and a slot radiator (Figure 4).  
Figure 12: Frequency dependences obtained by mathematical simulation for microwave field in rectangular (55 \(\times\) 110) radiator: (**a**) frequency dependence of the standing wave ratio (SWR) for rectangular slot type radiator and (**b**) frequency dependence of the radiation efficiency for rectangular slot radiator.
# Review Rating Prediction Based on User Context and Product Context
## 2 Related Work
### Review-Based Recommendation  
When the user-item rating matrix is sparse, the recommendation performance will be significantly reduced. Therefore, some research work considers review content information to improve missing rating predictions in user-item rating matrix. The effectiveness of using review content information in the recommendation has been extensively discussed and demonstrated in many existing studies [13; 25; 26; 27].  
While considering user review content information, some research works generate latent factors for users and items by integrating topic models in the collaborative filtering framework [28; 29; 30; 31; 32; 33]. One of the early studies of using review content to improve missing rating predictions in a user-item rating matrix was proposed in [34]. This work found that reviews often include information, such as price, service, positive, or negative sentiments that can be used for missing rating predictions in use-item rating matrix. Hidden factors as topics were proposed to discover potential aspects from item or user reviews using topic model in [30]. This approach achieved significant improvements over models that only use user-item ratings matrix or review content.  
Existing review-based recommendation methods predict the missing ratings in the user-item rating matrix from the history of review texts written by a user. While in our paper, we mainly study the rating prediction of an existing review. Existing review-based recommendation methods provide a common RRP recommendation model for all the users. In contrast, our approach builds a user-specific and product-specific review rating prediction model for each user and each product.
## 5 Link Budget Calculations  
From previous section we know that sensitivity of the sensor, limited by radio noise, is \(-\)160 dBW. For accurate measurements, a minimum SNR of 20 dB is needed. When we assume that fading minima of \(-\)35 dB may occur, the system has to be designed for a mean input power of \(-\)160 + 20 + 35 = \(-\)105 dBW.  
Figure 5: (**a**) The sensor; (**b**) One of the probes. Distance between probe and sensor is 50–200 km.  
The antenna gain at both ends of the link is 3.7 dBi. The ionospheric losses are low: we estimate them at 4 dB. The path length through the ionosphere can be approximated by assuming an ionospheric refraction height of 220 km and a horizontal distance of 100 km. A wave travelling in a straight line to the reflection point would cover a path length \(R\) of:  
\[R=2\sqrt{220\;km^{2}+\left(\frac{150\;km}{2}\right)^{2}}=451\;km \tag{7}\]  
As the real path is a curved and not a straight line, approximately 1.2% must be added to its length, so that we arrive at a path length of 457 km. Probe frequency is 7 MHz. Using the Friis transmission equation for free space propagation [39] complemented with the ionospheric losses, we can calculate the required probe transmit power:  
\[P_{probe}=P_{sensor}-G_{sensor}-G_{probe}+20\log_{10}R-20\log_{10}\left(\frac{ c}{4\pi f}\right)+A_{i} \tag{8}\]  
where \(P_{probe}\) and \(P_{sensor}\) are in dBW and \(G_{probe}\) and \(G_{sensor}\) are the antenna gains in dBi and \(A_{i}\) are the ionospheric losses in dB. \(c\) is the speed of light (\(2.997\times 10^{8}\) m/s) and \(f\) is the frequency in Hz. Substituting these values in (9), the minimum power that the probe must deliver to the antenna is:  
\[P_{probe}=-105-3.7-3.7+20\log_{10}\left(457\times 10^{3}\right)-20\log_{10} \left(\frac{2.9979\times 10^{8}}{4\pi*7\times 10^{6}}\right)+4=-5.9\;\mathrm{dBW} \tag{9}\]  
According to these calculations, a minimum probe power of 0.26 Watt will produce 55 dB SNR at the sensor in a 30 Hz bandwidth. When the probe power is increased to 1 Watt, the sensor power will become \(-99\) dBW, and the SNR in a quiet rural environment will be 61 dB.
## 2 Materials and Methods
### Analysis
#### 2.3.2 Determination of Tannins by HPLC  
To carry out the determination of tannin concentration and composition, the phloroglucinolysis method was used, following the [PERSON] et al. (23) protocol for sample preparation and analysis, using as reagents a mixture of phloroglucinol with ascorbic acid dissolved in hydrochloric acid 0.2 N and a solution of sodium acetate 200 mM. A Waters 2695 system (Waters, Milford, MA, USA) equipped with a Waters 2996 photodiode array detector was used for the analysis. The column used was Atlantis dC18 (250 \(\times\) 4.6 mm, 5 \(\upmu\)m packing) protected with a guard column of the same material (20 mm \(\times\) 4.6 mm, 5 \(\upmu\)m packing) (Waters, Milford, MA, USA). The solvents used in the elution were water/formic acid (98:2, \(v/v\)) (A) and acetonitrile/solvent A (80:20 \(v/v\)) (B), employing a flow rate of 0.8 mL/min at an oven temperature of 30 \({}^{\circ}\)C. Elution started with 100% A for 5 min, linear gradient from 100 to 90% A in 30 min, and gradient from 90 to 80% in 30 min, followed by washing and re-equilibration of the column. The injection volume was 10 \(\upmu\)L. By this method, the total tannin concentration (TTp) was determined as well as the mean degree of polymerization (mDP), calculated as the sum of all subunits (flavan-3-ol monomer and phloroglucinol adducts, in moles) divided by the sum of all flavan-3-ol monomers. The percentage of galloylation (%Gal) was also determined, as well as the percentage of epigallocatechin (%EGC) subunit.