esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion  
### Isolation of Strains Producing L-Asparaginase and Free of L-Glutaminase Activity  
A total of 268 _Bacillus_ strains from 32 Korean fermented soybean foods survived from acid and bile salt solution. To screen the strains that produce L-asparaginase and thosethat are free of L-glutaminase, these strains were allowed to grow in the MCD medium containing L-asparagine or L-glutamine, respectively, as a sole nitrogen source. The pink zone around the bacterial colony indicates pH alteration, which originated from ammonia accumulation in the MCD medium. The 162 strains showed the presence of a pink colony in the MCD medium containing L-asparagine, which exhibits L-asparaginase activity. These strains showed no color change in the MCD medium containing L-glutamine, implying the absence of L-glutaminase activity.
# Rolling Bearing Fault Diagnosis Based on CEEMDAN and CNN-SVM
## 4 Experimental Validation  
### Experimental Data Sources  
This experimental data acquisition platform is shown in Figure 4, which is mainly composed of a high-speed variable frequency motor, a load balancing disc, a radial loader and an acceleration sensor. Among them, the high-speed variable frequency motor drives the whole system to rotate according to the set rotational speed. The load balancing disc is used to maintain the dynamic balance of the system during rotation to reduce vibration and noise. The radial loader is used to simulate the radial pressure that the bearings may be subjected to in the actual working environment. The acceleration sensor, model 356A15, is used to collect bearing vibration signals.  
The rolling bearing model chosen for this experiment is NSK6205, which has four different states: normal state, outer ring failure, inner ring failure and rolling element failure. Among them, each failure state has two failure diameters of 0.2 mm and 0.5 mm. The conditions of this experiment are a radial pressure of 600 N, motor speed of 1800 r/min, sampling frequency of 10.24 kHz and acquisition time of 60 s. A total of seven types of rolling bearing vibration signals were collected for the normal condition, outer ring failure, inner ring failure and rolling element failure states.  
Figure 3: Fault diagnosis flowchart.
# Retinitis Pigmentosa Classification with Deep Learning and Integrated Gradients Analysis
###### Abstract  
Inherited retinal diseases (IRDs) are genetic disorders affecting photoreceptors and the retinal pigment epithelium, leading to progressive vision loss. Retinitis pigmentosa (RP), the most common IRD, manifests as night blindness, peripheral vision loss, and eventually central vision decline. RP is genetically diverse and can be categorized into non-syndromic and syndromic. Advanced imaging technologies such as fundus autofluorescence (FAF) and spectral-domain optical coherence tomography (SD-OCT) facilitate diagnosing and managing these conditions. The integration of artificial intelligence in analyzing retinal images has shown promise in identifying genes associated with RP. This study used a dataset from Portuguese public hospitals, comprising 2798 FAF images labeled for syndromic and non-syndromic RP across 66 genes. Three pre-trained models, Inception-v3, ResNet-50, and VGG-19, were used to classify these images, obtaining an accuracy of over 80% in the training data and 54%, 56%, and 54% in the test data for all models. Data preprocessing included class balancing and boosting to address variability in gene representation. Model performance was evaluated using some main metrics. The findings demonstrate the effectiveness of deep learning in automatically classifying retinal images for different RP-associated genes, marking a significant advancement in the diagnostic capabilities of artificial intelligence and advanced imaging techniques in IRD.  
machine learning; deep learning; inherited retinal diseases; retinitis pigmentosa; gene classification +
Footnote †: journal: Applied sciences  
Article
are noteworthy studies that have used conventional machine learning methods in CAD in order to diagnose lung cancer. Since deep learning methods are independent of complicated handcraft feature extraction methods and additionally are able to extract low-level to high-level features from a large amount of datasets, in recent years, they have become more successful compared to conventional machine learning methods in CAD. Therefore, deep learning methods in CAD systems help physicians in the diagnosis of cancer by improving the accuracy of diagnosis and cost efficiency in a short time [5].  
However, there is no deep learning model that is sufficient in terms of classification or detection of all kinds of real-life problems. Therefore, in recent papers, hybrid architectures of deep learning models using Support Vector Machine (SVM) as a well-known classification algorithm were proposed and achieved significant results in various classification problems [6; 7].  
In this study, by considering the advantage of deep learning methods as one of the state-of-the-art machine learning methods in terms of automatic feature extraction and the promising results of hybrid architectures in diagnosing lung cancer in early stages, different architectures of Convolutional Neural Network (CNN), as one of the significant models of deep learning methods, were investigated. In this regard, in order to classify Data Science Bowl and Kaggle lung Computed Tomography (CT) scan images, two 3D-CNN architectures were proposed. Straight 3D-CNN was the proposed CNN architecture which used softmax as its conventional classifier and additionally hybrid 3D-CNN was the second proposed CNN architecture which utilized Radial Basis Function (RBF)-based SVM as its classifier instead of softmax. It is noticeable that this method is the first applied method in terms of the classification of lung cancer. Moreover, the altered 3D architectures of AlexNet and GoogleNet, the two well-known architectures of Convolutional Neural Network (CNN), were used for the classification of lung CT scan images as well. The performance of both proposed 3D-CNN, 3D-AlexNet and 3D-GgoogleNet architectures have been evaluated in lung cancer diagnosis.  
The rest of the paper is organized as follows: An overview of the other studies which have used Deep learning methods on different lung datasets are presented in Section 2; a brief description of Convolutional Neural Network and its related concepts and also RBF-based SVM classifier are presented in Section 3 as well. Section 4 presents the description of the used dataset and all proposed 3D-CNN architectures in order to diagnose lung cancer. Eventually, experimental results, a discussion and the conclusion are presented in Sections 5 and 6 as well.
###### Abstract  
Dental implants designed with platform switching have been used clinically to reduce crestal bone resorption. The aim of this study was to determine the biomechanical effects of loading types, diameter of platform, and implant diameter in bone strain around immediately loaded implants with platform switching concept. Platform-switching features of dental implants with various diameters of implant body and implant platform (named as RP5.0, RP4.3, and NP3.5) were inserted into artificial bone blocks. The initial implant stability was confirmed using a Periotest device before the loading test. Rosette strain gauges were placed on the alveolar region around the implants, and peak values of the bone strain during a 190-N vertical load or 30-degree lateral load were measured by a data acquisition system. The Kruskal-Wallis test and post-hoc pairwise comparisons were performed as statistical analyses. The median Periotest values of the RP5.0, RP4.3, and NP3.5 implants ranged from \(-6.59\) to \(-7.34\). The RP5.0 implant always showed the lowest bone strain around the implant, regardless of whether a vertical or lateral load was applied. Relative to the RP4.3 and NP3.5 implants, the RP4.3 implant produced a higher bone strain (by approximately 8%) under a vertical load but a lower bone strain (by approximately 25%) under a lateral load. This study confirmed that using a wider implant could relieve the bone strain around an immediately loaded implant with platform switching concept especially under lateral loading.  
implant diameter; platform switching; immediately loaded dental implant; bone strain; strain gauge analysis +
Footnote †: journal: Applied Soft. 2019, 9, 1998; doi:10.3390/APP9101998
## 1 Introduction
In detail, telomeres are the non-coding terminal areas of the chromosomes, which comprise of extremely recurring sequences. Their length is conditioned by some physiologic, lifestyle, and environmental factors, as well as aging, smoking habit, psychological stress, overweight/obesity, and contact with xenobiotics [32; 33; 34; 35; 36; 37; 38; 39]. Decreased telomere length has been as well correlated with SW [40; 41; 42]. Furthermore, telomere length at the time seems to be the most promising to assess the biological age in shift workers. Telomeres are nucleotide replicates (TTAGG) n with connected proteins at the conclusions of human chromosomes. Telomeres play a decisive role in preserving the reliability and solidity of the genome and as well contribute to the development of cellular DNA injury and repair [40]. Inside proliferating cells, telomere length is active and undergoes shortening at every cycle of cell separation [39]. Telomere length assessed in surrogate tissues, for example, buccal cells, peripheral blood lymphocytes, mononuclear cells, and white blood cells, has been helpful as one of the appropriate susceptibility biomarkers for epigenetic clock and aging [27]. The circadian rhythm of telomerase activity has some significant clinical impacts in shift workers. Telomerase activity fluctuates with the circadian rhythm and is below the control of _CLOCK_ genes [40]. Telomerase is accountable for maintaining the length of telomeres, and disruption in the rhythmic telomerase action gives shortened telomere length. Telomere shortening is associated with SW [40]. DNA damage produced by oxidative stress is a further process implicated in increased telomere shortening [43] and stimulates the relationship among telomere length and aging. Telomeres, as a starter of DNA damage reactions [44], play a preeminent role in discriminating unprocessed DNA terminations from double-strand break ends that provoke DNA damage checkpoints and consequent repair [45]. As a result of the high quantity of guanines, telomeres are mainly sensitive to the growth of reactive oxygen species (ROS)-induced 8-oxo-7,8-dihydrodeoxyguanosine (8-oxodG) DNA-strand interruptions [46; 47; 48]. Oxidative stress can generate an elevated level of 8-oxodG development in the telomere sequences than in further non-telomere sequences [49; 50]. Moreover, the immediate outcome of oxidative damage on telomeric DNA, it has been suggested that reduced DNA repair capacity may endorse telomere erosion. Increased single-strand breaks formed by oxidative stress have been acquired to be under powerfully repaired at telomeres than in the end of the genome [51; 52]. This systematic review aims to elucidate the usefulness of telomere length as a biomarker of biological aging in shift workers.
## 4 Simulation and Analysis
### Simulation on Pair-Wise Neighbor Discovery  
We implemented the BR-DA protocol to validate the neighbor discovery process between a pair of UAVs. We traced the discovery delay for the different number of sectors and compared it with the ODND protocol presented in [24]. \(P_{a}\), \(P_{b}\), \(I_{a}\), and \(I_{b}\) were randomly generated. The clock drift between UAV \(a\) and UAV \(b\) was randomly generated from [0, 1000] slots. Both UAV \(a\) and UAV \(b\) were assigned 8-bit random IDs. The worst-case discovery delay and average discovery delay are the maximal delay and average discovery delay of 10,000 runs, respectively. Additionally, we computed the duration of 10,000 runs and compared it with the ODND protocol.  
The simulation results, based on 10,000 runs, demonstrate that the BR-DA protocol exhibits lower calculation complexity compared to the ODND protocol. In Figure 23, the duration of 10,000 runs for BR-DA with different numbers of sectors is consistently lower than that of the ODND protocol.  
The worst-case discovery delay for different numbers of sectors is shown in Figure 24, clearly showing that the BR-DA protocol achieves a lower worst-case discovery delay compared to the ODND protocol.  
Figure 23: The duration of 10,000 runs for different numbers of sectors.  
Figure 24: The worst-case discovery delay for different numbers of sectors.  
Figure 25 illustrates the average discovery delay for different numbers of sectors, indicating that as the number of sectors approaches \(2^{n}(2^{n-1}<N\leq 2^{n},n>1,n\in Z)\), the average discovery delay for the BR-DA protocol becomes significantly lower than that of the ODND protocol. In order to validate this conclusion, we conducted further simulations for the average discovery delay by varying the number of sectors from 8 to 32, as depicted in Figure 26. The results indicate that in cases where the number of sectors ranges from 12 to 16 and from 24 to 32, the average delay of the BR-DA protocol is less than that of the ODND protocol. Consequently, we can make the conclusion that the average discovery delay of the BR-DA protocol is less than that of the ODND protocol as the number of sectors is close to \(2^{n}(2^{n-1}<N\leq 2^{n},n>1,n\in Z)\). This result can be attributed to the diminishing randomness of the ODND protocol as the number of sectors approaches \(2^{n}(2^{n-1}<N\leq 2^{n},n>1,n\in Z)\), as stated in [24]. The average neighbor discovery delay of the random neighbor discovery protocol is minimal [15], but there is no upper bound on the neighbor discovery delay. The ODND protocol achieves a lower neighbor discovery delay by increasing the protocol's randomness, but this will increase the worst-case discovery delay.  
Figure 26: The average delay of the BR-DA and ODND protocols when ranging the number of sectors from 8 to 32.  
Figure 25: The average discovery delay for different numbers of sectors.
# Three-Dimensional Drone Cell Placement: Drone Placement for Optimal Coverage
## 6 Conclusions
As DC technology continues to evolve, it will be essential to validate these models in complex dynamic environments, ensuring their utility across diverse scenarios such as smart cities, disaster relief, remote or rural communication networks, etc. These real-world tests will help ensure that the proposed strategies are robust, scalable and adaptable across a wide variety of applications. Finally, integrating advance computational techniques with meta-heuristic approaches, creating a hybrid method, could further enhance the decision-makingprocess where DCs can adapt to real-time changes, enabling more responsive and efficient DC placement solutions. By addressing these challenges, future research will contribute to a more comprehensive understanding of how meta-heuristic algorithms can be utilized for DC placement, ensuring its relevance in increasingly interconnected and resource-constrained environments. This includes applications in 5G/6G networks, Internet-of-Things-based communication systems and emergency communication networks. Ultimately, these advancements will help shape the future of drone-assisted communication, making it more efficient, adaptive and sustainable across a wide range of industries and scenarios. Conceptualization, A.B. and M.M.A.-R.; methodology, A.B. and M.M.A.-R.; investigation, A.B. and M.M.A.-R.; simulation development, A.B. ; experiments, A.B. ; writing--original draft preparation, A.B. ; writing--review and editing, A.B., M.M.A.-R., H.O. and G.S. ; visualization, A.B. ; supervision, M.M.A.-R., H.O. and G.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
RANs  
Radio Access Networks  
DC  
Drone cell  
BS  
Base Station  
CH  
Coverage Hole  
DA  
Demanding Area  
QoS  
Quality-of-Service  
D2G  
Drone-to-Ground  
DA-RAN  
Drone-Assisted Radio Access Network  
3D  
Three-Dimensional  
4G  
Fourth-Generation  
DFO  
Dispersive Flies Optimization  
PSO  
Particle Swarm Optimization  
DE  
Differential evolution  
RS  
Random Search  
DMF  
Drone-cell Management Framework  
GTS  
Ground Terminals  
A2G  
Air-to-Ground  
UAVs  
Unmanned Aerial Vehicles  
UAV-BS  
Unmanned Aerial Vehicle Base Station  
IoT  
Internet-of-Things  
GWO  
Grey Wolf Optimization  
B-GWO  
Balanced Grey Wolf Optimization  
NOMA  
Non-orthogonal Multiple Access  
HDGWO  
Hybrid Discrete Grey Wolf Optimization  
MUPPEC  
multi-UAV planning model with energy constraints  
DEVIPS  
Differential evolution with a variable population size  
FBS  
Flying Base Station  
LoS  
Line of Sight  
NLoS  
non-Line of Sight  
LTE  
Long-Term Evolution  
UTM  
UAV Traffic Management  
CAGR  
Compound Annual Growth Rate  
C-RAN  
Cloud-RAN  
RRH  
Remote Radio Head
## 4 Validation through BIM Application in Planning Stage  
### Validation of the Construction Cost Estimation Model with BIM  
As described in Section 3.3, the construction cost estimation model was validated through programming, but this validation simply indicates the reliability of the model. In addition, for the utilization of the architectural planning stage construction cost estimation model developed in this study, it is necessary for practicing engineers with programming knowledge to input data directly. In this case, considering the situations and available capabilities at actual construction sites, the model is unlikely to be used without encountering problems, and integration with the BIM process is also difficult. To overcome these limitations, a model that can be used for construction cost estimation in the early phase of a construction project based on BIM, assuming the architectural planning stage, is proposed, as shown in Figure 8. In this manner, the cost prediction model developed and selected in this study was validated. In the proposed process, it is first necessary to confirm the parameters operational in the architectural planning stage and to add the IfcOpenShell module to the developed cost prediction model to adjust the model to accept BIM information. In addition, the application of BIM using programs such as Revit and ArchiCAD can deliver digital model information in a consistent form, and the data need to be converted into an IFC format that features high compatibility. In this manner, various information in the architectural planning stage, including the definitions, relationships, and properties of the BIM object that must be input for construction cost estimation, can be delivered to the prediction model in an IFC format. The cost prediction model derives the construction cost estimates and error rate as results based on the input parameters. Accordingly, the predicted results throughout the process for each case are evaluated based on the accuracy range [8] presented by the AACE to verify and determine the adequacy and practical applicability of the cost prediction model. This validation process indicates that modeling can be performed by integrating the BIM design process and construction cost estimation process in the current situation, where separate programs do not exist.
## 6 Discussion  
The results clearly indicate the increased security and the data-collection efficiency of the UAV-assisted smart farming framework that utilizes energy constraints and an intrusion detection system. The UAVs have a limited battery and are only allowed to charge by transmitting data and recording transactions at the fog node. This approach incentivizes the UAVs to identify malicious activities that may result in excluding the UAVs from the trusted network.  
The proposed work is evaluated with various machine learning models as well as the other network parameters. In most of the existing works, authors are only focused on utilizing limited features for the machine learning model; thus, overlooking critical parameters such as UAV energy, transmission delay, and the impact of cyber-attacks on UAV's energy. This work presents an extensive framework that covers the data collection, sensors integrated environment, and role of fog nodes for recharging the UAVs.  
The CPU and memory are the important parameters to gauge the scalability of devices inside the framework. With the increased number of UAVs, a linear relation is observed in terms of CPU and memory usage. The utilization of resources reported with 600 UAVs in a simulation framework require only 9.1% memory, and corresponding CPU utilization is 39%. Further, we observed that the speed of UAVs has a direct impact on their energy and in order to cover large-scale areas, a 14 m/s speed needs to be maintained for maximum utilization of UAVs. Moreover, amongst the machine learning models, adopted, XGBoost showed the best performance with 99.77% accuracy.
## 3 System Model and Problem Formulation
### Problem Description  
In IoT-MEC networks, the dynamic SFC placement problem involves looking for or constructing a route path for IoT-SR so that it can minimize the implementation cost based on resource consumption and end-to-end delay of IoT-SR. It is important to emphasize that routing IoT-SRs to cloudlets in close proximity to IoT terminals can effectively reduce end-to-end latency. Since IoT networks change dynamically and IoT-SRs arrive stochastically, the difficulty of dealing with the dynamic SFC placement problem increases, including the placement of VNFs and selection of a traffic path. Based on NFV technology, VNFs can be flexibly and efficiently realized on VMs in the cloudlets. There are usually multiple VNF instances for the same type of VNF in different cloudlets in the network (\(G\)), leading to several combinations of candidate VNF instances with different performance for each IoT-SR. To meet the end-to-end delay requirement and efficiently utilize resources, we decompose the dynamic SFC placement problem into two parts: (i) determine the placement of VNFs and (ii) select the optimal path for each adjacent VNF instance pair.