esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Discussions
### Validation of the MAP Model's Efficiency  
The client users' interaction operations on the map (zoom in/out and pan) will generate a sequence of spatial/temporal/attribute filters to enable retrieval of the _st-tiles_ from the underlying HBase database. To simulate the real interaction process, 16 filters with different spatial and temporal size were defined in this experiment; i.e., four spatial rectangles (illustrated in Figure 11), three temporal spans (day/month/year), and all/single attribute(s).  
The average database response time is recorded in Table 3. From the table, it can be seen that as the size of the spatial temporal extent increases, the response time remains at around 10 ms and does not show a significant increase. This indicates that the MAP model can scale well with different spatiotemporal query sizes and can also guarantee a millisecond-level response. Therefore, the pre-aggregation model guarantees a constant query time for different spatial-temporal filters generated by any interaction event (zoom in/out or pan).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Group** \\ **No.** \\ \end{tabular} & \begin{tabular}{c} **Spatial** \\ **Extent** \\ \end{tabular} & \begin{tabular}{c} **GeoHash** \\ **Search Depth** \\ \end{tabular} & \begin{tabular}{c} **Temporal** \\ **Span** \\ \end{tabular} & \begin{tabular}{c} **Attribute** \\ **Filter** \\ \end{tabular} & \begin{tabular}{c} **Tile** \\ **Number** \\ \end{tabular} & \
\begin{tabular}{c} **Time Size** \\ **(MB)** \\ \end{tabular} &
\begin{tabular}{c} **Query** \\ **Time (ms)** \\ \end{tabular} \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{0.58 \(\times\) 0.22} & \multirow{3}{*}{12} & 1 year & All & 32 & 0.874 & 6 \\  & & & All & 32 & 0.391 & 6 \\  & & & Single & 32 & 0.255 & 6 \\  & & & 1 day & All & 32 & 0.155 & 6 \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{0.08 \(\times\) 0.03 (urban)} & \multirow{3}{*}{15} & 1 year & All & 32 & 3.808 & 10 \\  & & & All & 32 & 2.204 & 10 \\ \cline{1-1}  & & & 1 month & Single & 32 & 1.311 & 9 \\ \cline{1-1}  & & & 1 day & All & 32 & 0.370 & 6 \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{0.08 \(\times\) 0.03 ( suburb)} & \multirow{3}{*}{15} & 1 year & All & 36 & 0.889 & 7 \\  & & & 1 month & All & 36 & 0.238 & 7 \\ \cline{1-1}  & & & 1 month & Single & 36 & 0.097 & 7 \\ \cline{1-1}  & & & 1 day & All & 36 & 0.041 & 6 \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{0.018 \(\times\) 0.008} & \multirow{3}{*}{17} & 1 year & All & 40 & 5.538 & 12 \\  & & & All & 40 & 1.853 & 12 \\ \cline{1-1}  & & & Single & 40 & 0.609 & 9 \\ \cline{1-1}  & & & 1 day & All & 40 & 0.110 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Query time of different spatial-temporal filters.  
Figure 11: The different spatial extents for queries displayed on OpenStreetMap.
## 6 Recommended Procedure for Using _Rmr_ and \(Q\) Systems and their Correlation  
After _RMR_ and \(Q\) systems had been used in the field of tunneling worldwide, [PERSON] and [PERSON] provided the \"ten commandments\" for the proper use of _RMR_ and \(Q\) systems [53] as follows:  
* Use standard procedure of measurement for the parameters instead of only description.
* Determine the ranges and average values of _RMR_ and \(Q\) per their classification procedure.
* Use _RMR_ and \(Q\) systems and check them with published correlation.
* Estimate the support and rock-reinforcement requirements.
* For preliminary modelling, determine the stand-up time and modulus of rock-mass.
* For checking, perform numerical modelling.
* If the available information is inadequate, extend the exploration work.
* Consider the construction process.
* Include the characterization information of rock-mass along with the design procedure, specifications and assumptions in the Geotechnical Baseline Report.
* In the construction phase, calculate _RMR_ and \(Q\) for design verification and modification.  
A back-analysis methodology for _RMR_ and \(Q\) calculation, as shown in Figure 3, was proposed using the installed support and used to evaluate and extend the application of these classification in the field of tunneling [70]. The variation of rock-mass quality in underground excavation is always significant. However, the forecast of _RMR_ ahead of the excavation face in tunneling, up to certain distance, is reliable using the rock-mass quality of the current working face of the tunnel [130]. It is worth emphasizing that use at least two empirical classification systems in the tunnel-support design process [131]. When using both systems, they should be checked through correlation [53]. After the development of _RMR_ and \(Q\) systems, a linear correlation based on the regression analysis of _RMR_ and \(Q\) values was presented by [PERSON] [75], as shown in Equation (70), where \(A=9\) and \(B=44\).  
\[RMR=A\times\ln Q+B \tag{70}\]Since then, several researchers have presented other correlations but there is no scientific basis to assume a universally valid regression between the two systems. The limitations in correlation is due to the differences between the rock-mass characterizations and rating rules of _RMR_ and \(Q\) system [132]. Due to this constraint of correlation, some scholars propose local, project-based, or geological-based correlation between the two systems [132; 133; 134; 135; 136]. Another suggested approach for the correlation is between the truncated _RMR_ and \(Q\) values [54; 128]. The latest version of _RMR_, that is, _RMR\({}_{14}\)_, is not as popular as _RMR\({}_{89}\)_; however, an excellent correlation between the two versions exists [23; 58; 137], as shown in Figure 4.
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
## 4 Electrical System Design  
In terms of electrical design, standards must be set that regulate the electrical circuit. It is important to know the distribution of energy in the ROVs, and to reach an electrical circuit that works with the suitable efficiency and the least capabilities. This section aims to develop a customer-oriented design that would optimize performance while maintaining modularity and flexibility. The electrical system consists of the following primary components: (i) surface control station, (ii) tether system, and (iii) onboard electronics [27]. Figure 6 shows the flowchart of the electrical system integration diagram (SID).  
Figure 5: (a) Hydraulics gripper arm, (b) camera enclosure.  
The ROV was controlled from the surface using a laptop and a gaming controller. Firstly, the electrical circuit was drawn as a hand sketch, enabling each component to be placed in the electric circuit, extending each element that needs power. Subsequently, the desired area of the electrical circuit was calculated, and the electrical circuit was modeled using Eagle and Easy EDA software programs, and every part was determined in the electrical circuit as shown in Figure 7.  
Figure 6: Flowchart of electrical system (SID).  
Figure 7: Electrical Circuit.  
Control data was sent from the Arduino to a passive high-definition composite video interface (HDCVI) transceiver connected by another passive HDCVI transceiver in the ROV's electric enclosure using a Category 6 cable connecting to the onboard Arduino mega through two wires. A Category 6 cable was utilized because it provides high data. Through put for the Ethernet cameras on the ROV. Since Arduino boards already have support circuitry built in, the carrier board design was made simpler by using them throughout the system. Additionally, because of their low cost, it is sometimes simpler and less expensive to replace a whole defective Arduino board rather than isolated components.  
A 48 V power supply was delivered to the ROV's PCB using 14 American wire gauge (AWG) wires with an in-line 25-A fuse. The electric enclosure consists of three levels. The first level contains the Power PCB, the second has the electronic speed controller (ESC), and the third includes the Arduino Mega with a shield and a passive HDCVI transceiver. The 48 V enters the Power PCB and is converted to 12 V through the Murata DC-DC converter [28]. Subsequently, the 12 V is distributed by the Power PCB to the ESCs, cameras, and a power converter from 12 V to 5 V to feed the Arduino [3].
# A Millimeter-Wave Radar Tunnel Obstacle Detection Method Based on Invalid Target Filtering
## 2 Materials and Methods
### Filtering Methods for Invalid Millimeter-Wave Radar Targets
The millimeter-wave radar is mounted in the middle of the mine tramway locomotive, and the intersection of the millimeter-wave radar vertically downwards and horizontally on the ground is taken as the origin of the coordinate system, assuming that the x-axis of the coordinate system is the direction of travel of underground electric locomotive and the y-axis is perpendicular to the longitudinal direction of mine tramway locomotive. A total of four obstacles are assumed to exist in the area of operation of underground electric locomotives and the adjacent area, as shown in Figure 3.  
For an obstacle target within the travel area I on a motor vehicle track to be hazardous, this is measured by a set longitudinal distance threshold. Let \(X_{0}\) be lateral, and the longitudinal coordinates x of the non-threatening target need to satisfy the following conditions:  
\[|x|>X_{0} \tag{8}\]  
The threat of targets in the adjacent areas II and III of the trackside danger zone can be measured by setting the lateral coordinates y of non-threatening targets by means of a lateral distance threshold. Let \(Y_{0}\) be the lateral:  
\[|\,y|>Y_{0} \tag{9}\]  
The test was conducted with a car instead of a motor car, using a roadway on the ground to simulate a coal mine underground mine tramway locomotive; the car size was  
\begin{table}
\begin{tabular}{c c c} \hline
**Stage** & **Status Parameters** & **Practical Implications** \\ \hline \multirow{3}{*}{Generate} & FindCount + 1 and & FindCount starts to accumulate when a front-end target is detected, and \\  & LostCount = 0 & LostCount is set to 0. \\ \multirow{3}{*}{Continuous} & FindCount \textgreater{} \textless  
1800 mm \(\times\) 1000 mm \(\times\) 1500 mm, and the driving route of the motor car was relatively simple, for track driving, so the transverse threshold Y\({}_{0}\) was set to 2 m. The ARS 408-21 XX millimeter-wave radar can detect obstacles up to 250 m, and the most underground locomotive operation speed is generally low, about 10 km/h. The longitudinal distance threshold is set to 100 m, which is sufficient for the locomotive to avoid obstacles and other operations, taking into account the 5% measurement error of the radar. Based on the above invalid target filtering method, this paper designs the invalid target filtering algorithm flow, as shown in Figure 4.  
The algorithm processes the data collected by radar, directly filters the empty target whose distance is zero in the radar data, and directly filters the non-threatening target whose distance exceeds the transverse range threshold Y\({}_{0}\) and the longitudinal range threshold \(X_{0}\), and filters the false target generated by the radar performance or the instability of the echo signal through the radar-effective target life cycle method. Finally, output the valid target.
## 3 Linear Deviations of the Four Scanner Configurations  
From Figure 2, for each of the four scanner configurations, the maximum and minimum linear deviations can be obtained, corresponding to the respective angular deviations deduced above for the \(\varphi=0\) and for the \(\varphi=\pi\) case, respectively. Both these maximum and minimum linear deviations consist of two components, one within the prism system, \(y_{P}\), and the other one from the scanner to the screen, \(y_{S}\), as shown for example in Figure 2(a1), but specific for each of the four configurations, (a) to (d):  
\[y_{j}^{max,min}=y_{jp}^{max,min}+y_{js}^{max,min},\ j=a,\ b,\ c,\ d. \tag{8}\]  
From the minimum deviations, \(y_{jp}^{min}\), one obtains, for identical prisms, with \(n_{1}=n_{2}=n\) and \(\theta_{1}=\theta_{2}=\theta\) (therefore for \(k=1\)), the radius \(\Delta_{j},\ j=a,\ b,\ c,\ d\) of the blind spot, i.e., of the central disk of the scan patterns (Figure 2(a2,b2,c2,d2)). These blind spots are confirmed further on for the \(k=1\) cases of the scan patterns in Section 4 and are analyzed in Section 5. For the small angle approximation, simplified (i.e., linearized) expressions of these blind spot radiuses, \(\Delta_{j}^{lin},\ j=a,\ b,\ c,\ d\), can be obtained.  
All the maximum deviations, \(y_{jp}^{max}\), have for the particular case above (with identical prisms and the small angles approximation) the simple expression \(2(n-1)L\). However, this approximate expression can be utilized only if the distance \(L\) is very large, for example in the order of km, as in satellite positioning, in Security and Defense, or in Remote Sensing. In contrast, for biomedical imaging, for example, but also for other scanning applications that utilize close range scanning, the exact expressions that are developed in the following should be employed.  
The discussions are carried out further on in this section using the above procedure for each of the four scanner configuration, for the maximum deviations--column (1) in Figure 2, as well as for the minimum deviations--column (2) in Figure 2.
# Hamming Distance Optimized Underwater Acoustic OTFS-IM Systems
## 3 Hamming Distance Optimized Index Mapping Model
### Hamming Distance Optimization Model
When the hamming distance between different index combinations is obtained by calculating the hamming distance between codes, the corresponding maximum Hamming distance is calculated as:  
\[d_{\text{max}}(H)=\underset{2^{P_{1}}}{\text{max}}\sum_{i=1}^{C_{1}^{k}}\sum_ {j=1,j\
eq i}^{C_{1}^{k}}HD\left(X_{i}^{\prime},X_{j}^{\prime}\right) \tag{18}\]  
Figure 3: Maximum Hamming distance indexed combined sequence selection flowchart. Figure 2: Transmitter of the Hamming distance optimization modeling system. where \(HD(\cdot)\) denotes the Hamming distance between combinations and \(X_{i}^{\prime}\), \(X_{j}^{\prime}\) is the encoded sub-grid group. Equation (18) allows for the selection of the \(2^{p_{1}}\) combinations with the greatest Hamming distance from the \(C_{n}^{k}\) index combinations to form the final sequence of index combinations at \(C_{n}^{k}>2^{p_{1}}\). Taking \((n,k)=(4,2)\) as an example, all combinations of \(C_{n}^{k}=6\) belong to \(\left[0,C_{n}^{k}-1\right]\) and satisfy \(C_{n}^{k}>2^{p_{1}}\). We need to select \(2^{p_{1}}=4\) indexed combinations from 6 combinations to form the final indexed combination sequence. The four combinations with the maximum Hamming distance index were obtained by the calculation of equation (18). As shown in Table 2, the Hamming distance optimization model removes two redundant combinations from the original six index combination sequences, and the average Hamming distance of the selected combinations is 2.67, which has the maximum average Hamming distance. The greater Hamming distance between different combinations, the smaller the probability of errors during demodulation, thus achieving the purpose of improving the BER performance of the system.
# Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM
## 3 Experiments and Results
### Data Processing
#### 3.3.2 Data Length Normalization and Expansion  
Due to inconsistencies in vector lengths among the experimental data, all vectors were standardized to a length of 10 (using the longest dimension as the standard) to facilitate SVM model processing. For the missing parts, zeros were used as padding. The zero values indicate that the recorder was damaged, and no subsequent experiments were conducted, effectively skipping a particular experiment. It can be inferred that varying the position of the zero-padding does not alter the experimental results. However, the position of the zero elements can affect the SVM training outcomes. This allows for data expansion, as illustrated by the transformation, shown in Table 6 (Data 01), to the expanded format in Table 7.
## 3 Stationary Localization Algorithms
### Distributed Localization Algorithms
#### 3.2.6 Localization Scheme for Large Scale underwater networks (LSLS)  
Integrating UPS and USP, a Localization Scheme for Large Scale underwater networks (LSLS) is proposed in [32], where any localized node can work as reference node to further localize neighbor nodes. Similar to RLA and LSHL, LSLS is a hierarchical localization approach. LSLS includes three phases: sea surface anchor localization, iterative localization, and the complementary phase. In the first phase, three surface anchor nodes send their beacon messages sequentially to calculate the range difference as described in UPS. Then, unknown nodes are localized as described in USP. In the second phase, certain localized nodes are selected to serve as reference nodes. More unknown nodes are localized as described in the first phase. If a node fails to be localized in the first two phases, it can initiate a location request in the third phase. A new group of reference nodes are then selected to localize the unlocalized node.  
Similar operations in the first two phases are applied in [33]. However, the remaining unlocalized nodes are not considered in [33]. Compared with UPS, LSLS can localize a large-scale UWSN with short-range acoustic communication. Moreover, LSLS localize more unknown nodes than USP does. However, more energy and communication overhead are consumed to implement the three phases localization.
# Median U-Turn Intersection Critical Parameter Research and Operational Performance Evaluation
## 3 Critical Parameter Calculation of MUT Intersections
### The Calculation of the Separation Distance of MUT Intersections
Thus, the turn-around process of the detoured traffic can be considered as following the \(M/E_{k}/1\) queuing model. Assuming the number of vehicles turning around via the median U-turn is n (n \(\geq\) 1), \(t_{i}\) is the time at which the \(i\)th vehicle completely merges onto the major road, and \(t\) is the time headway of the traffic flow on the major road. When the time headway of major traffic meets the requirement for n vehicles to turn around, then \(n\) vehicles can turn around [31]. Therefore, we can conclude that:  
\[t_{i}\leq t\leq\sum_{i}^{i+1}t_{i}, \tag{4}\]  
\[p_{n}=\int_{t_{i}}^{\sum\limits_{i}t_{i}}p(t)dt=\int_{t_{i}}^{\sum\limits_{i} t_{i}}\frac{\lambda k(\lambda kt)^{k-1}}{(k-1)!}e^{-\lambda kt}dt, \tag{5}\]  
\[u=\sum_{n=1}^{\infty}(1-\lambda)p_{n}, \tag{6}\]where \(p_{n}\) is the probability of the detoured traffic that merges with the major road, \(k\) is the order of the Erlang distribution, and \(u\) is the service rate of the median U-turn. According to the \(M/E_{k}/1\) queuing model, the queuing length of vehicles outside the median U-turn, \(L_{q}\), can be calculated:  
\[L_{q}=\frac{(k+1)\big{(}\frac{\lambda}{u}\big{)}^{2}}{2k(1-\frac{\lambda}{u})}. \tag{7}\]  
Considering traffic safety, it should ensure that the separation distance, \(L\), is slightly greater than the sum of the weaving length, \(L_{w}\), and the queuing length of vehicles outside the median U-turn, \(L_{q}\), because this can provide sufficient space for vehicles to complete the necessary weaving and queuing maneuvers and prevent congestion. Above all, the separation distance, \(L\), can be determined as follows:  
\[L\geq max\Big{\{}L_{f},\big{(}L_{W}+L_{q}\big{)}\Big{\}}. \tag{8}\]
Performance of the Oncomine(tm) Lung cfDNA Assay for Liquid Biopsy by NGS of NSCLC Patients in Routine Laboratory Practice  
[PERSON]  
1 Molecular Diagnostic Unit, IRCCS Ospedale Policlinico San Martino, Lgo [PERSON] 10, 16132 Genova, Italy; [EMAIL_ADDRESS] (G.D.L.); [EMAIL_ADDRESS] (S.L.); [EMAIL_ADDRESS] (R.C.); [EMAIL_ADDRESS] (S.Z)  
2 Pathology Department, IRCCS Ospedale Policlinico San Martino, Lgo [PERSON] 10, 16132 Genova, Italy; [EMAIL_ADDRESS]  
3 Lung Cancer Unit, IRCCS Ospedale Policlinico San Martino, Lgo [PERSON] 10, 16132 Genova, Italy; [EMAIL_ADDRESS] (C.G.); [PERSON]EMAIL_ADDRESS] (M.T.); [EMAIL_ADDRESS] (S.C.); [PERSON].B.)  
4 Correspondence: [EMAIL_ADDRESS]; Tel.: +39-010-555-89404  
circulating tumor DNA (ctDNA) derived from tumor cells from normal cells can be challenging [4]. Additionally, since molecular findings by genomic characterization of ctDNA are able to drive treatment decisions, clinical laboratories have been adopting highly sensitive and accurate assays for a precise identification of different DNA alterations.  
In this scenario, next-generation sequencing (NGS) approaches provide interesting application choices to deal with rare ctDNA molecules diluted within cfDNA and are rapidly gaining traction as a valid assay for cancer.  
Since many tumors, including non-small cell lung cancer (NSCLC), may harbor subclonal mutations with molecular allele frequencies (MAFs) down to 0.1%, it is advisable to use NGS assays sensitive as well as accurate enough to detect such low-abundance alterations.  
At present, some technology improvements have allowed researchers to develop NGS panels able to improve the performance levels of the NGS approach in liquid biopsy. Molecular barcoding is one of the possible solutions when dealing with challenging clinical samples since unique molecular tags, also known as unique molecular index (UMI), are added into every DNA molecule, prior to library amplification [5; 6; 7; 8; 9; 10]. This has the great advantage to increase the sensitivity by keeping track of the DNA molecules intercepted, meanwhile reducing polymerase chain reaction (PCR) and/or sequencing artefacts [11; 12; 13; 14], also when compared to amplicon-based NGS [15].  
The OncomineTM Lung cell-free DNA Assay (OLcfA, ThermoFisher Scientific) is an NGS panel based on molecular tagging technical method, that we have already wet-lab tested with the main goal to detect low MAFs of \(EGFR\) Thr790 Met mutation in plasma samples from tyrosine kinase inhibitors (TKIs)-treated NSCLC patients [16]. We also showed that the NGS results for the search of Thr790 Met mutation were comparable with those obtained with a digital PCR (dPCR) approach, achieving a 100% of concordance in terms of sensitivity and specificity [16].  
Here, we extended the assessment to a large cohort of 76 NSCLC patients and demonstrated, through a deep NGS data analysis of covered target regions, that the OLcfA performance makes this test suitable and reliable when used in the routine liquid biopsy practice in the NSCLC clinical setting.