esa-sceva/satcom-chunk-collection huggingface dataset:
The 3 GPP model [29] describes the azimuthal APD as wrapped Gaussian, while the zenithal spread is described using a Laplacian distribution. In the model developed by [PERSON] [32], the azimuth APD is considered to have a normal distribution, while the elevation AOA is Laplacian. This model introduces the concept of spatial lobe (SL) that will be used later in this paper. Some other distributions may also be considered in describing the APDs [33]. Beam-switching and beamforming techniques were first proposed in [5; 34] as a solution to compensate the high propagation losses, which include free space propagation losses and human body link blockage, in the 60 GHz frequency band. As these techniques try to combine the angular spread energy, the combining improvement depends on the scattering of the radio channel [35]. Coherent and non-coherent beam-combining techniques are compared in recent works [36; 37]. Results show that beam-combining improvement increases with the number of beams combined, and this improvement is larger for coherent than for non-coherent combining. Improvements up to 11.2 dB and 24.9 dB are reported at 73 GHz and 28 GHz, respectively. Later publications even present some millimeter-wave beamforming prototypes [6]. These studies mainly focus on urban environments, where a dense use of 5G communication systems is expected. However, as 5G communication systems would provide a global coverage in a large variety of scenarios, other environments should be considered. In this paper, in addition to the traditional urban environment, we extended the study to three other propagation scenarios: open, wooded, and suburban areas, where multipath conditions may differ. We obtained the APDs and quantified the angular spread for the four environments. This will help to incorporate these propagation environments to millimeter-wave 5G channel models. We also calculated the maximum improvement that could be achieved by combining the different angular contributions and analyzed its dependency on the number of combined lobes. We provide the values for the combining improvement in the four environments. The rest of the paper is organized as follows: In Section 2, we describe the measurement system and measurement environments. We used a wideband sounder with angular scanning capabilities at the mobile end to be able to obtain the APDs in four different types of macrocellular scenarios. We present the results in Section 3 and discuss the APDs modelling and the improvement by angular combining in Section 4. Finally, in Section 5 we summarize the main results.
# A Review of Prediction Methods for Ultra-Low Cycle Fatigue Damage of Steel Piers Under Earthquakes
## 2 Research Status and Development Trend at Home and Abroad
### Continuum Damage Mechanics (CDM) Model
#### 2.4.1 The Principle and Development of Traditional CDM Model
They found that while the CDM model could predict the crack initiation position, initiation life, and crack propagation path, the predicted life was lower than the test life, indicating a need for improved prediction accuracy.
## 3 Methodology
### Point Features Extraction
#### 3.1.1 Height Variation (\(Hv\))  
Height Variation is the difference between the maximum and the minimum elevations of neighboring points of a LiDAR point. The water points were assumed to have high \(HV^{\text{C3}}\) values when using the green wavelength in C3 due to the recorded returns from water surfaces and benthic layers. Also, the water points were assumed to have low \(HV^{\text{C1 or C2}}\) values when using the infrared wavelengths in C1 or C2 due to the recorded returns from water surfaces only.
## 3 Results
### Professional Characteristics and Resin Material Selection
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Total Sample**} & \multicolumn{5}{c}{**Clinical Experience (in Years)**} \\ \cline{2-6}  & \multicolumn{2}{c}{**Up to 5 Years**} & \multicolumn{2}{c}{**Over 5 Years**} \\ \cline{2-6}  & \multicolumn{2}{c}{**N**} & \multicolumn{2}{c}{**\%**} & \multicolumn{2}{c}{**N**} & \multicolumn{2}{c}{**\%**} \\ \hline What are the selection criteria for the resin materials you choose for anterior tooth restorations? & & & & \\ \hline It depends on the anatomical features I want to achieve & 47 & 54.0\% & 31 & 70.5\% & 16 & 37.2\% \\ \hline It depends on the interdental space that exists & 14 & 16.1\% & 10 & 22.7\% & 4 & 9.3\% \\ \hline It depends on the color of the neighboring teeth & 44 & 50.6\% & 25 & 56.8\% & 19 & 44.2\% \\ \hline It depends on the transparency and opacity of the & & & & & \\ neighboring teeth & 49 & 56.3\% & 29 & 65.9\% & 20 & 46.5\% \\ \hline I do not have a specific criterion, I use what I have in stock & 6 & 6.9\% & 4 & 9.1\% & 2 & 4.7\% \\ \hline I only use special anterior resins & 32 & 36.8\% & 10 & 22.7\% & 22 & 51.2\% \\ \hline What are the selection criteria for the resin materials you choose for posterior tooth restorations? & & & & & \\ \hline It depends on the anatomical features I want to achieve & 48 & 55.2\% & 29 & 65.9\% & 19 & 44.2\% \\ \hline It depends on the interdental space that exists & 24 & 27.6\% & 15 & 34.1\% & 9 & 20.9\% \\ \hline It depends on the color of the neighboring teeth & 25 & 28.7\% & 14 & 31.8\% & 11 & 25.6\% \\ \hline I do not have a specific criterion, I use what I have in stock & 24 & 27.6\% & 10 & 22.7\% & 14 & 32.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Selection criteria for resin materials in anterior and posterior tooth restorations.
## 3 Results and Discussion
### Climatic Analysis
(**a**) SBIO1_Annual mean temperature, \(p\)-value = 0.0232847, [\(p\)(x \(\leq\) F) = 0.97675]; **(b**) SBIO2_Mean diurnal range, \(p\)-value = 0.00147094, [\(p\)(x \(\leq\) F) = 0.998529]; **(c)** SBIO3_Isotermality, \(p\)-value = 1.44085 \(\times\) 10\({}^{-7}\), [\(p\)(x \(\leq\) F) = 1]; **(d)** SBIO4_Temperature seasonality, \(p\)-value = 0.00000175783, [\(p\)(x \(\leq\) F) = 0.999998]; **(e)** SBIO5_Max_temperature of warmest month, \(p\)-value = 0.0108403, [\(p\)(x \(\leq\) F) = 0.98916]; **(f)** SBIO6_Min_temperature of coldest month, \(p\)-value = 0.0159335, [\(p\)(x \(\leq\) F) = 0.98406]; **(g)** SBIO7_Temperature annual range, \(p\)-value = 0.000327396, [\(p\)(x \(\leq\) F) = 0.999673]; **(h)** SBIO8_Mean_temperature of wettest quarter, \(p\)-value = 0.028066, [\(p\)(x \(\leq\) F) = 0.971934]; **(i)** SBIO9_Mean temperature of driest quarter, \(p\)-value = 0.0109722, [\(p\)(x \(\leq\) F) = 0.989028]; **(j)** SBIO10_Mean temperature of warmest quarter, \(p\)-value = 0.0247223, [\(p\)(x \(\leq\) F) = 0.975278]; **(k)** SBIO11_Mean temperature of coldest quarter, \(p\)-value = 0.0319085, [\(p\)(x \(\leq\) F) = 0.968092]. and Tame. We also noticed a distinct differentiation between the central region, considered a transitional climate zone, and the eastern and western clusters. A differentiation among clusters 9 and 4 is also evident, supporting a north-south pattern of contrasting climates.
# Numerical Laplace Inversion Method for Through-Silicon Via (TSV) Noise Coupling in 3D-IC Design
## 3 Results and Discussions  
In order to evaluate the effectiveness of the proposed method, simulation tests of the previous circuits were carried out. Simulations were performed with the Matlab and Pspice tools for all schemes, while the experimental tests of circuits 1 and 2 were taken from [13]. To take the measurements, the test vehicle in Figure 1 was fabricated using the Hynix via-last TSV process. The TSV circuit elements were calculated using the TLM-3D method; when the TSV diameter is 33 \(\upmu\)m, the TSV pitch is 250 \(\upmu\)m, the TSV dioxide thickness is 0.52 \(\upmu\)m, and the TSV height is 105.2 \(\upmu\)m. The RDL parameters were calculated using the method cited in [21]. Lumped circuit element values are listed in Tables 1-3. The accuracy and efficiency of the computing method were validated by simulations in Pspice and the measurements of [13].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Value** \\ \hline C\({}_{\text{tsv-equi}}\) & 201.3 fF \\ R\({}_{\text{tsv}}\) & 0.001 \(\Omega\) \\ L\({}_{\text{tsv}}\) & 20.7 pH \\ R\({}_{\text{sub-equi}}\) & 928.5 \(\Omega\) \\ C\({}_{\text{sub-equi}}\) & 11.2 fF \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lumped circuit elements of TSV-TSV noise coupling.  
Figure 8: Block diagram of the proposed method.  
Based on the results reported in the figures, it can be seen that the proposed method is in good agreement with the experiments. By analyzing these results, one can see that the proposed method is valid.
# Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints
## 5 Results and Discussion
### Mission Coupled Constraints  
1. In an area where a fire broke out due to collapse of structures, cooperation of both engineering vehicles and firefighting vehicles will be required. To allow the engineering vehicles to begin clearing the wreckage efficiently, the fire needs to be first extinguished. _Wreckage clearance task is dependent on the response to fire and must begin after the fire has been extinguished._
2. In a fire outbreak, some casualties may be trapped in the fire. Assuming that the rescue vehicles are well-equipped to withstand some levels of heat such that casualty extraction is possible when firefighting vehicles are on site to provide assistance in controlling the fire. Furthermore, casualty extraction will not be possible after the fire has been extinguished as the casualties will have likely suffocated during the process of firefighting. _Casualty rescue is dependent on firefighting operation and must begin during firefighting._
3. The rescue vehicles do not possess the heavy lifting capabilities required to rescue casualties trapped in a wreckage. Assistance from the engineering units is required. _Casualty rescue is dependent on wreckage clearance task and must begin after the wreckage have been cleared._  
Figure 2: Agent-Task compatibilities in a disaster relief operation [30; 31; 32; 33; 34]. The figure is a composite image constructed from the various sources cited.  
These constraints are only applicable when the tasks are in the same position (e.g., casualties trapped in a burning house.) The mission coupled constraints for these sites with multiple types of disaster can be reflected in the dependency matrix (Table 3) and temporal matrix (Table 4) as follows:
## 2 Materials and Methods  
### Bacterial Strain and Inoculum Preparation  
Enterotoxigenic _S. aureus_ strains (harboring SEA gene) were previously isolated from dairy product samples by the culture method on Baird-Parker agar and identified using conventional biochemical method and PCR. The isolates were inoculated in Brain Heart Infusion (BHI) (BBL 11407, Lansing, MI, USA) broth and incubated at 37 \({}^{\circ}\)C for 24 h. Before inoculation in the milk, the inoculum was washed twice in phosphate buffer saline (PBS) (Oxoid, Basingstoke, UK) and then re-suspended in skim milk.  
Preparation of the Nanoparticles Suspensions and Determining the Minimum Inhibitory Concentration (MIC) of NiO NPs  
Nickel oxide nanoparticles (20 nm in size) used in this study with 99.9% of purity were commercially purchased from Nan-Tech Company, Egypt. To prepare different concentrations of such particles, a solution of 10\({}^{4}\) ug/mL NiO NPs was used as an initial stock. Fifteen concentrations of NiO NPs (10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 70, 80, 90 and 100 ug/mL) were examined in the present study to detect the minimum inhibitory concentration (MIC) that could inhibit _S. aureus_ strains using agar well-diffusion method according to [PERSON] et al. [28] with some modifications. Briefly, 100 \(\mu\)L of the fresh bacterial culture having 10\({}^{6}\) CFU/mL was spread on brain heart infusion agar plates and left for 10 min to be absorbed. One-hundred \(\mu\)L of the nanoparticle's suspensions were poured onto the wells (8 mm). After overnight incubation at 35 \(\pm\) 2 \({}^{\circ}\)C, the diameters of the inhibition zones were observed and measured in mm.
## 4 Experimentation and Discussion
### Glasses Lens Detection Results and Analysis  
#### 4.4.1 Training and Evaluation  
The loss curve provides an intuitive way to assess the training progress and convergence of a neural network. In order to demonstrate the effectiveness of the model in a more intuitive way, this article used the TensorBoard visualization tool to record the details of the model's learning during the training process and plotted the relationship between iteration number and test set loss, as shown in Figure 12. During the experiment, a total of five loss curves were recorded, including: loss_basis_sem, loss_focs_cls, loss_fcos_loc, loss_mask, and the final total_loss curve. Among them, the loss_focs_cls curve evaluates the model's classification performance, which can show the specific details of the classification training process. The loss_fcos_loc curve evaluates the degree of position offset of the target detection box, where larger offsets correspond to higher loss values. The loss_mask curve evaluates the effect of pixel-wise segmentation of the image during instance segmentation. The total_loss curve is the sum of loss_basis_sem, loss_focs_cls, loss_fcos_loc, and loss_mask, which can provide a comprehensive evaluation of the overall performance.  
In order to evaluate the algorithm's ability to recognize and determine the position of the glasses lenses, we used seven key metrics: precision rate (\(P\)), recall rate (\(R\)), metric function (\(F_{1}\)), intersection over union (IOU), average precision (AP), mean average precision (\(mAP\)), and mean intersection over union (mIOU). Precision rate (\(P\)) represents how many true positive samples were correctly predicted among all samples predicted as positive, while recall rate (\(R\)) represents how many true positive samples were correctly predicted among all actual positive samples. The precision rates (\(P\)) and recall rates (\(R\)) can be defined as follows:  
\[\begin{array}{l}P{=}\frac{TP}{TP+FP}\\ R{=}\frac{TP}{TP+FN}\end{array}\, \tag{10}\]  
where \"true positive (\(TP\))\" and \"false positive (\(FP\))\" indicate the number of positive and negative results detected as positive, respectively, and \"false negative (\(FN\))\" indicates the  
Figure 12: The loss value changes with the training steps.  
number of positive results detected as negative. The metric function (\(F_{1}\)) of the precision and recall rates can be defined as follows:  
\[F_{1}=\frac{2P\times R}{P+R}. \tag{11}\]  
To more extensively evaluate the model algorithm, the intersection over union error (IOU) was considered to examine the measurements. The detection performance of the model is evaluated considering the mean accuracy (\(mAP\)) [38]. The \(mAP\) can clearly reflect the performance when it is related to the target position information and category in the image. The IOU and \(mAP\) were defined as follows:  
\[\begin{array}{l}\text{IOU }=\frac{\text{Area Overlap}}{\text{Area Union}}\\ mAP=\frac{1}{N}\cdot\sum\limits_{i=1}^{N}AP_{i}\end{array}. \tag{12}\]
# Estimating Critical Latency Affecting Ship's Collision in Re-Mote Maneuvering of Autonomous Ships
###### Abstract  
Estimation of the critical latency that can cause collision in remote maneuvering of autonomous ships can provide a clue to avoid collisions. The concept of estimating the critical latency was established using the turning circle formed by the turning maneuver of the own ship, and critical latency was estimated using the radius of the turning circle with the turning time ratio. The turning circle was observed using the turning trajectory of the give-way vessel measured in the ship maneuvering simulation experiment. Experimental results demonstrated that the proposed method is capable of identifying both the location and time of the collision due to critical latency. As a result, a clue to avoid possible collision in remote maneuvering caused by critical latency was deduced.  
Keywords:critical latency; ship's collision; turning circle; remote maneuvering; autonomous ship +
Footnote †: journal: Applied Sciences
# Route Optimization for UGVs: A Systematic Analysis of Applications, Algorithms and Challenges
## 1 Introduction
The primary contributions of this research include (1) a detailed hardware comparison highlighting the key differences in processing power and memory; (2) performance benchmarks across a range of tasks, including image processing, machine learning and IoT applications; and (3) a comprehensive analysis of the future trends of path planning optimization in different application domains. In Section 2, the methodology used for benchmarking and comparison is presented; Section 3 presents the results and analysis of the performance evaluation, while Section 4 concludes the paper and discusses future research directions.
# Investigation of Microseismic Characteristics of Rock Burst Based on Fractal Theory
## 6 Conclusions  
In this study, we analyzed the characteristic relationship between the number of microseismic events and time by monitoring their temporal occurrence. We also used fractal theory to investigate the results. To verify the feasibility of the early warning method, we analyzed the spatial distribution characteristics of microseismic events and established an observation model in the field. The results of the study are as follows:  
1. The temporal fractal dimension is consistent with the distribution of microseismic events over time, which is related to the accelerated accumulation of events. The growth of the fractal dimension of the rock burst incubation process was consistent over time, and a large amount of microseismic time data can be simplified using the fractal dimension to facilitate field judgment.
2. Based on the spatial distribution characteristics of the microseismic time used to build the observation model, the location of microseismic events can be visualized, and the occurrence of rock bursts can be predicted through the density of events for study and judgment. The refined model can highlight the key area that require attention and facilitate long-term observations.
3. The reliability of the early warning method was verified in the field, proving that this study is significant for subsequent rock burst monitoring and safe operation of the Maoping mine.  
Conceptualization, P.W. and Z.Z.; data curation, D.Z. and Z.C.; resources, P.W., D.Z. and Z.C.; visualization, P.W.; writing-original draft, Z.C. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Youth Science and Technology Innovation Fund, BGRIMM Technology Group (Grant No. 04-2228), and the National Key Research and Development Program of China (Grant No. 2017 YFC0602904).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
We thank [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for their assistance in improving this manuscript.  
The authors declare no conflict of interest.
## 3 Data Collection and Statistical Analysis  
By doing an extensive field survey in a real-world project in the Hai Phong city of Vietnam, namely Vinhomes Imperia housing, various information of soil was gathered [9]. The construction area was around 79 ha. The geological situation of the site was investigated by drilling 31 boreholes. Notably, the laboratory tests, as well as the geological survey, were carried out with respect to the Vietnam Standards of the TCVN9155:2012 and TCVN 9140:2012, considered for technical requirements for drilling machines and sample preservation, respectively. More details about the data provision process and the studied site are presented in ref. [9].  
Eventually, the coefficient of compression is considered as the target variable affected by twelve key factors of soil, including depth of sample (DOP), percentage of sand, percentage of loam, percentage of clay, percentage of moisture content (MC), wet density (WD), dry density (DD), void ratio (VR), liquid limit (LL), plastic limit (PL), plastic Index (PI), and liquidity index (LI) as independent variables. Figure 5 depicts the SCC versus the mentioned factors. Moreover, descriptive statistics are available in Table 1.  
Consisting of 496 samples, the whole dataset is divided into two parts by a random selection procedure. The proposed AI tools were fed with 80% of data (i.e., 397 instances) to analyze the relationship between the mentioned parameters. Then, the generalization power was evaluated by applying them to the remaining 20% of data (i.e., 99 instances). In other words, the input parameters of the second dataset were considered as stranger soil conditions with which the networks predicted the SCC for them.
## 1 Introduction
In this light, the monitoring of the prevalence of antibiotic-resistant bacteria are of significance regardless of the source (clinical or environmental) as antibiotic susceptibility profiles of microorganisms vary widely from country to country and from one geographical location to the other [20]. Furthermore, organisms are vulnerable to developing unpredictable resistance patterns, thus making it necessary for constant surveillance of the antibiogram profiles in order to be current with the trend [18]. However, there is a dearth of information on the presence of the aforementioned enteropathogens in animal manure procured from dairy farms and treated in anaerobic digesters, in the Eastern Cape Province, South Africa, as well as their antibiotic resistance patterns. It is well known that in developing countries, especially in rural areas, there is a high possibility of close contact of humans with food animals which in turn offers great chances for exposure to zoonotic bacteria. In particular, individuals living in both rural and urban areas in South Africa own and keep cattle for several reasons including generating income, providing food, producing manure which can be used as fertiliser, enhancing social status and presenting them as financial aids [21]. However, [PERSON] and colleagues [22] have noted the high prevalence of human immunodeficiency virus (HIV) in South Africa. In addition, it has been highlighted that HIV-positively diagnosed individuals are more vulnerable to develop enteric fever with frequent relapses [23]. Against this background, our study investigated antibiotic resistance in zoonotic bacteria, which is vital as the population in South Africa constitutes of a great proportion of immune-compromised individuals. Apparently, this study is first of its kind to assess the level of _Escherichia coli_, _Salmonella_ and _Campylobacter_ species as well as other Gram-negative bacteria (_Stigella_ species) in dairy cattle manure obtained from the Fort Hare Dairy Trust, Alice, Eastern Cape Province, South Africa, which was fed into a balloon-type digester for anaerobic digestion. In addition, the antibiotic resistance profiles, taking into consideration the multiple antibiotic resistance (MAR) index and predominant MAR phenotypes, were determined.
# Tuning Guidelines for an Adaptive-Gain Parabolic Sliding Mode Filter
## 4 Tuning Guidelines of AG-PSMF
### Tuning Guideline for \(H\)  
In Section 3.1, it has been discussed that the increase of \(H\) contributes to the suppression of overshoot but results in longer transient time. By carefully observing the response of AG-PSMF under different values of \(H\), as shown in Figures 4-6, one can notice that the optimum range of \(H\) is around \(5\leq H\leq 10\). Under such a value of \(H\), AG-PSMF provides short transient time by producing a low level of overshoot.