esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of the Effect of Different Pile Verticality Values  
Improper construction techniques, such as pile driving, pre-compression, and excavation, can result in the tilting of PHC pipe piles and lead to new engineering accidents in PHC pipe pile composite roadbeds. In these accidents, the piles typically exhibit a tilt in the same direction which is accompanied by lateral movement under vertical loads, ultimately resulting in lateral instability. Figure 6 illustrates the settlement cloud diagram of a roadbed with varying pile verticality values at a pile/soil load ratio of 7:3. The values in the legend indicate settlement.  
As can be seen from Figure 6, the roadbed exhibits different deformation characteristics under vertical loads depending on the verticality of the piles. Within the range of verticality values studied, the maximum settlement deformation consistently increases with the increase in verticality. When the pile is inclined, the deformation of the soil layer under the pile decreases as the inclination angle increases, resulting in a decrease in the reaction support provided. On the other hand, the deformation of the soil layer around the top of the pile increases, leading to an increase in the reaction force perpendicular to the pile. This means that the contribution of the deep soil layer to the bearing capacity of the roadbed decreases, while the contribution of the soil layer around the top of the pile becomes more significant. The inclination angle of the pile has a negative effect on the vertical bearing performance of the foundation, with the maximum deformation of the roadbed occurring gradually in the middle. This results in increasingly noticeable uneven settlements. The overall settlement and deformation of the roadbed move towards the lower left, and the area of soil movement increases with the inclination angle. This movement of the soil can have an adverse effect on the composite roadbed. When a PHC pipe pile composite roadbed with a consistent pile inclination angle is subjected to vertical loads, this inevitably causes lateral movement of the roadbed. If this lateral movement is excessive, it can lead to the destruction of the roadbed. The larger the inclination angle, the greater the pile side displacement and the poorer the lateral stability of the pile side. As a result, the composite roadbed may experience significant settlement and loss of bearing capacity under the action of overlying fill, leading to slip and collapse.
# Comparison of Thermal Response for RF Exposure in Human and Rat Models
## 2 Models and Methods  
### Computational Rat and Human Models  
One rat and four human models were considered as computational models. The computational rat model (8-week-old rat) was developed from computed tomography (CT) images [7]. The rat model was composed of the following six tissues: muscle, skin, fat, eye, bone, and brain. These tissues were classified in a semi-automatic procedure and then additional partial manual editing by an expert using a similar method to [26] was performed. This model had a resolution of 0.25 mm, and the whole-body weight used in the rat model corresponded to 265 g.  
Realistic anatomical human models for different ages and gender were considered. The Japanese male model (TARO) [27], and the European models of the Virtual Family ([PERSON], [PERSON], and [PERSON]) [28] were used. These computational models had a resolution of 0.5-2 mm. They were segmented into 51-77 anatomical regions, such as skin, muscle, and bone. The human models were truncated at the bottom of the neck because the rest of the body marginally affects the interaction with the dipole antenna [29]. A major source of computational error in the finite-difference time-domain (FDTD) method is the discretization error, which depends on the ratio between the cell size and the wavelength in biological tissue. A rule to suppress the numerical dispersion error in FDTD simulations is that the maximum cell size should be smaller than one-tenth of the wavelength [30]. Thus, the model resolution was adjusted to 0.5 mm to ensure computational accuracy.
## 4 Experiments and Results
### Features Selection (_Ivi_)  
Following the framework of our previous work [1], an FS technique was applied to all datasets, including the new dataset. These results are presented in Figure 4, and they were relevant for all ML algorithms, following the same methodology used in the companion paper [1] and showing consistency with the results previously described. In this figure, the relevant features (columns) are in green and those ones not identified as significant by the IVI algorithm (rows) are in red. According to the previous descriptive analysis [1], the features were classified as RFF if the feature had been selected in all the ML algorithms used, and MIF if the feature had been selected at least in two of them. In the synthetic dataset, Figure 4a, features \(f1\) to \(f4\) were all included with RFF filter, but \(f0\) was not identified as such due to the misclassification by SVC. In the same direction, features identified as relevant for at least two methods were understood to be informative for further analysis and so categorized within the MIFF group of variables. In Figure 4a, features \(f0\) to \(f5\) met the MIFF criteria and were included as members of this filter. These features perfectly match with the relevant features of the synthetic dataset (\(f0\) to \(f4\)), adding one of the redundant features (\(f5\)). Attending to these results, we can conclude that the IVI algorithm was consistent over the different ML methods, thus conferring it a valid potential feature selection capability. From the results obtained on the synthetic dataset, we can see how the MIFF filter discards non-informative and redundant features, allowing to increase the accuracy of the model. These results are extendable to real datasets, as it was analyzed in the companion paper [1]. For a more detailed analysis, see [1]. In the case of the new dataset, we can see in Figure 4b that there are three features which are selected by all the ML algorithms used, except by LDA, and these features are isFlaggedFraud, amount and oldBalanceOrg. For the new dataset, the results were consistent with the previous work, and again, FS using MIFF improved the training procedure in terms of computer efficiency, by reducing the number of features to reach higher accuracy, thus reinforcing the results in the previous work [1].
###### Abstract  
The development of corrosion mechanisms is a major issue that industrial experts are faced with while dealing with all phases of metal exploitation. Damage caused by corrosion affects the increase in both direct and indirect costs of manufacture. With adequate surface protection technology, it is possible to lessen the intensity of corrosion mechanisms' development. In order for the selected surface protection technology to provide its full protective action, it is necessary to be familiar with its process parameters. This paper deals with a surface protection system that creates a barrier between the aggressive environment and the base material. The tested barrier was created on a base material surface by electrostatic powder spraying. In this experiment, three types of base material (carbon steel, aluminum and galvanized steel) were coated by applying different combinations of operating parameters. Operating parameters, i.e., the voltage and strength of the electric current, were taken as numerical input variables in the experiment. Each experiment design was focused on measuring the thickness of the protective layer. Analysis of the obtained results and statistical data processing confirmed that appropriate parameters (input variables) provided the target thickness of the protective coating on each of the tested materials. Optimization of parameters contributes to the efficiency of production, shortens the processing time and ensures that coating thickness stays within the limits defined either by the manufacturer or the end user.  
corrosion; electrostatic spraying; powder coating; surface protection; parameter optimization Article
# Adaptive Seasonality Anomaly Detection Algorithm under Intricate KPI Profiles
## 4 Results
### Time Overhead Evaluation  
In this section, we firstly compare the time overhead of \(ASAD\) with Argus [3], EGADS [6] and Period [7]. Based on the single variable principle of the experiments, \(ASAD\) and Period both rely on EGADS as the anomaly detection model.  
As shown in Table 9, the overall KPI anomaly detection consuming time of \(ASAD\), Period, EGADS and Argus is shown. It needs to be emphasized that time overhead is the average consuming time for detecting 1-month KPI time series data. According to the testing data, the anomaly detection consuming time of \(ASAD\) is 82% lower than Argus and 24% lower than EGADS, but it is a little higher than Period. We think this is due to the different clustering algorithms, so we do extra experiments to compare the clustering performance of sub-sequences. Moreover, in Table 9, it can be seen that the clustering accuracy of \(ASAD\) has reached 84%, while the clustering accuracy rate of Period is only 57%; that is to say, the clustering accuracy rate of \(ASAD\) is 27% higher than Period algorithm. This shows that the \(ASAD\) algorithm is more accurate for data clustering under intricate KPI profile.  
According to the above experiments, we can see that time overhead of \(ASAD\) is the lowest of almost all algorithms. Although \(ASAD\) is a little slower than Period, it has a modest time overhead in return for more accurate sub-sequence clustering results. Furthermore, it also leads to the conclusion that the anomaly detection performance of our \(ASAD\) algorithm is better than Period algorithm in next section.
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 5 Handover and Load Balancing in HWLNets
### Vho  
When there is evident loss of connection in HWLNets, a user is transferred from LiFi to WiFi, evidently changing the technology used to receive data, thus this is called vertical handover. Its phases are shown in Figure 10.  
There are two main reasons why this would happen:  
* light is blocked by an object;
* user equipment is oriented in such a way that LoS with AP is impossible.  
VHO probability has been researched [60], resulting in apparent reciprocity in the number of handovers and their delay. One scheme [61] proposes the use of Markov decision process to determine the implementation of VHO by using the queue length of WiFi and the channel conditions of LiFi as metrics. Another scheme [62] uses interruption duration, message size, and access delay as metrics recorded on user devices to make VHO. A third scheme [63] was developed using delay, queue length, data rate, and traffic preferences. These models relay on channel and traffic conditions to determine the best time to execute handover. User mobility and overhead also play a key role in the handover process and have different constraints than the above-mentioned models.
# The Assisted Positioning Technology for High Speed Train Based on Deep Learning
## 4 The Detection Model of Kilometer Post Based on an Improved SSD Algorithm
### The Detection Model of Kilometer Posts Based on an Improved SSD Algorithm  
To optimize the detection method of kilometer posts, we used the optimization methods described in reference [31] to maintain the superiority of the detection model. By analyzing specific application scenarios, the model was further simplified. The complex Z-style structure and residual network in [31] were deleted, and only the backbone network was extracted for modeling. In addition, in this study, the number of anchor boxes was reduced from 4 to 2 to further improve the detection efficiency. Finally, the kilometer post detection model based on an improved SSD algorithm was established, as shown in Figure 6.  
The detection model mainly includes a backbone network (BBN) and backbone detection network (BDN). BBN includes the optimized VGG-16 basic network and the designed auxiliary convolutional layer. The optimization of the VGG-16 basic network is mainly reflected in the last two fully connected layers, which are replaced by a convolutional layer with different settings. According to the analysis of reference [31], this not only reduces the number of parameters and the network running time, but also enhances the network discrimination ability. Compared with the input image, the resolution of C3, C4 and C5 layers is reduced by 8, 16 and 32 times, respectively. In addition, in order to further enlarge the receptive field, capture more deep semantic information, and carry out effective multi-scale detection, two additional convolutional layers were added to the end of the reduced VGG-16, named C6, which forms multi-scale feature maps with C3, C4 and C5. The detection mode of BDN is similar to SSD. Multiple feature layers (C3, C4, C5 and C6) are used for generating bounding box offsets and scores. A fixed number of detection results can be produced by applying convolutional layers. For the  
Figure 5: Network framework of SSD.  
stability of the training process and robustness of detection results, a set of anchor boxes are introduced on each location of the grid cell. Based on the analysis of application scenarios, in this paper, the model design was simplified and the number of anchor boxes was reduced from 4 to 2, which ensures the detection accuracy, and further improves the efficiency. The specific detection process can refer to the detailed description in [31], which is not repeated here. The specific experimental results are analyzed in Section 5.
## 3 A Brief Overview of AHL Systems in _Burkholderia_
### Burkholderia cepacia _Complex (BCC)_  
The taxonomy of the _Burkholderia cepacia_ complex refers to closely related species (previously called genomovars) that have been isolated from clinical samples and are opportunistic human pathogens. BCC is composed of at least 17 species, including _Burkholderia cepacia_, _B. multivorans_, _B. cenocepacia_, _B. vietnamiensis_, _B. ambifaria_, _B. stabilis_, _B. dolosa_, _B. anthina_ and _B. pyrrocina_[53]. The AHL QS in the BCC group consists of _luxI/R_ homologs known as _cepI_ and _cepR_. CePI synthesizes two AHL signals, the C8-HSL (_N_-octanoyl-L-homoserine lactone) and C6-HSL (_N_-hexanoyl-L-homoserine lactone) in greater and lesser amounts respectively. CepR binds and consequently responds to the abundant cognate C8-HSL [53]. The _cepI_ and _cepR_ genes are divergently transcribed and the intergenic region most commonly contains an _rsaM_ homolog. This kind of arrangement is termed as M1 ( \(\vec{R}\,\vec{M}\,\vec{I}\) ) [31]. The intergenic gene _rsaM_ has previously been shown to be a negative regulator of the AHL QS system in _P. fuscovaginae_[35]. Another QS system, called _ccil/R_, has recently been identified in _B. cenocepacia_ strains harboring pathogenicity islands as is the case in _B. cenocepacia_ K56-2, _B. cenocepacia_ J2315 and _B. cenocepacia_ MC0-3. This system produces primarily C6-HSL and minor amounts of C8-HSL [54,55]. _ccil_ and _cciR_ are transcribed in the same direction and accordingly the topology is named as R1. All _B. cenocepacia_ members contain a solo or orphan _luxR_ homolog named as _cepR2_[29,56]. All QS systems are present on chromosome 2 except in some cases--_bviI/R_ of _B. vietnamiensis_ (X5 topology) and _luxI/rsaM_ of _B. ambifaria_. QS topologies in BCC members are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Species** & **QS system** & **ID** & **Gene Topology** & **Chr. ** & **Major AHL** & **Comments** \\ \hline \multirow{5}{*}{_B.
## 6 Conclusions  
The results of the analyses presented above allow for the formulation of certain concluding remarks, which, however, require a cautious approach when being applied in future research. The remarks can be written as follows:  
* Good accordance was achieved for longitudinal derivatives. The biggest difference was noticed for \(C_{mn}\), which is associated with lack of a cross-flow modeling in PANUKL--linear moment characteristics without pitch-up. The difference becomes lower with Mach number, where the pitch-up effect is less present.
* In the case of directional derivatives, the biggest difference was noticed for \(C_{n\beta}\). This is associated with the geometry of the side plates that have a significant contribution in the yaw moment. Due to a significant dihedral angle, the flow around the side plates should be considered in the nonlinear range with a vortex (Figure 11); therefore, PANUKL underestimated these derivatives.
* Good accordance was achieved for all dynamic tests for all source data, despite some differences in aerodynamic characteristics and stability derivatives; for all data sets, the qualitative results were the same (acceptable or not, according to stability criteria), and the differences were only quantitative:
* For the phugoid mode, a higher damping ratio was noticed for the wind tunnel data. In the pure numerical data, the difference in damping for low speed was negligible, while for higher speed, the damping was stronger for the MGAERO case due to presence of the compressible effect on the drag coefficient. The phugoid was the mode where the differences obtained between results were the biggest.
* For the short-period mode, the differences in both the damping ratio and undampened natural frequency were negligible. The short period was the mode where the differences obtained between results were the smallest.
* For the Dutch roll mode, a stronger damping ratio was noticed for the MGAERO data set, while the lowest damping ratio occurred for the wind tunnel data.  
Figure 19: Yaw rate (R) and roll rate (P) in Dutch roll oscillation.  
* The potential method is quite efficient and good enough to estimate dynamic characteristics for the subsonic range and moderate angles of attack.
* The use of the potential flow method to assess handling qualities of even an unconventional configuration can be utilized in the preliminary stage of design.  
Despite the relatively old age of potential methods, they are still an attractive tool in the design process and, despite their lower fidelity, can be used as aerodynamic solvers in MDO processes.  
Conceptualization, A.K. and T.G.-G.; methodology, A.K. and T.G.-G.; software, T.G.-G.; resources, A.K.; investigation, A.K. and T.G.-G.; writing--original draft preparation, A.K. and T.G.-G.; writing--review and editing, A.K. and T.G.-G.; supervision, T.G.-G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in article.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
### Selection of Precursor Ions and Product Ions  
The _m_/z obtained in full scan mode for the selected analytes is shown in Supplementary Materials Figure S1. On the basis of the definition of identification points (IPs) in the Decision 2002/657/EC, 1.0 IP and 1.5 IPs are appointed to precursor ion and product ion during MS/MS analysis, respectively. An unambiguous confirmation of the analytes identity needs 3 or 4 identification ions [28]. Even though the definition was originated from the determination of organics in animal products, it has been used in the analysis of trace contaminants in environmental matrices [29].  
In the present research, the detected _m_/z range was set at 40\(\sim\)200 and the applied EI energy was fixed at 70 eV in MS full scan mode, those fragments with high abundance ratios and high _m_/z were selected as precursor ions in MRM mode [30]. Nitrogen was used as the collision gas during product ion scan mode, the product ion and the optimal collision energy for each of the selected analytes were determined. In order to make sure that the chromatographic peaks of the analytes under test were at least ten points and satisfy the analysis requirement, the test time is divided into seven periods, dwell time is set to 80\(\sim\)240 ms. For each of the selected analytes, one precursor ion with two different product ions at specific collision energy (CE) voltage were monitored (see Table S1).
## 2 Result and Discussions
### P-Lethality Suppression
This provides a better explanation for the lack of transformants (found by us and other authors) by plasmids capable of expressing P than does cell killing by P (_trans_ P lethality). P\({}^{\pi}\) proteins exhibit lower affinity for DnaB, compared to P [41], which may explain why elevated P\({}^{\pi}\) expression at 42 \({}^{\circ}\)C was needed to evoke the normal P phenotypes in the _grpD55_ strain in order for it to compete for and sequester DnaB away from DnaC [11], effectively creating the possibility for a slow-stop type inhibition in _E. coli_ replication initiation. A cautionary note here is that the dissociation of DnaB-DnaC complexes by P was only demonstrated _in vitro_. As well as interacting with \(\lambda\) P [42], DnaB interacts with many cellular proteins, e., g., with DnaA [43], DnaC [44], DnaG [45], SSB [46], the Tau subunit of DNA polymerase III [47], and RNA polymerase [48], and as noted the functional DnaB hexamer is present in limiting amounts, i.e., about 20 hexamers per cell [14,15]. Introducing two missense mutations into _dnaB_ will permit the cells to survive constitutive \(P\) expression (P-lethality), and can suppress P-inhibition of ColE1 replication, yet these mutations do not overtly impede cellular growth/replication. A single missense mutation in \(P\) can suppress P-lethality and plasmid loss at 37 to 39 \({}^{\circ}\)C. Therefore, we suggest that an interaction between P and DnaB is an important component of both P-lethality and P-inhibition of ColE1 replication. [PERSON] _et al._, [28,29] could transform _dnaA rpl_ mutants (resistance to _P le_thality) of the same strain we employed, namely 594, with plasmids expressing P, concluding that an interaction between P and DnaA, and not with DnaB, was important for suppressing P-lethality. Their _dnaA_-rpl8 [29] mutant did not inhibit \(\lambda\) plating [49], and thus has a different phenotype from _E. coli_ mutants arising in selections where \(\lambda\) development is inhibited, such as the one used by [PERSON] and [PERSON] [32]. Sequence analysis revealed that our 594 strain is wild type for _dnaA_[34] and therefore, the _grpD55_ mutation in _dnaB_ appears sufficient for cellular resistance to P lethality; however, we have not compared the two 594 strains by whole genome sequence analysis. We suggest that these possibly contradictory results are explained by the participation of DnaA in mechanistic events shared with DnaB, allowing the _dnaA-rpl_ allele(s) to modulate DnaB:P dependent P-lethality and P-inhibition. Alternatively, the reported effect of P protein on DnaA [28] is not at the level of DNA replication, but is in the context of transcription of genes regulated by DnaA.