esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Lithoficies/Rock Types of Analysis  
The lithology in E11 was affected by diagenesis, as observed during the core description Figure 3. Three main basic lithologies are present in the E11 field: limestone 53%, dolomite 11%, and claystone 3%. The correlation among wells can only be achieved based on gamma-ray, neutron-, and density logs. The correlation was made in terms of flooding intervals. This led to the division of the carbonate into four porous zones (SS1, SS2, SS3, and SS4 separated by four tight layers FS1, FS2, FS3, and FS4, which are interpreted as major flooding intervals Figure 5. The E11 field can be subdivided into six basic rock types.  
* **Dense argillaceous shale RT1**, from fair to poor reservoir rocks porosity of 10-25%, permeability 1-100 Md.
* **Tight argillaceous limestone RT2** is considered the poorest reservoir rock porosity 2-8% and permeability of 1-5 Md. From the core observation, these intervals are considered tight thin intervals, formed by the abundant occurrence of massive crystalline corals or intermittent emergence horizons (Figure 6B).
* **Mouldic limestone RT3**, a rock type that forms thick intervals of good reservoir quality. Porosities are 20-40% and permeabilities 10-400 mD (Figure 6C).
* **Chalkified limestones RT4** are widespread and constitute reservoirs with porosities ranging between 10 and 25% and permeabilities between 5 and 50 Md. Their porosity originated from fine interparticle microsolution chalkification (Figure 6D).
* **Sucrosic dolomites RT5**, from equally good reservoir rocks as mouldic limestones but, on a regional scale, they are less abundant than the latter. The sucrosic dolomites originated from mud-supported carbonates and have porosities of 15-30% and permeabilities of 10-500 mD (Figure 6E).
* **Mouldic sucrosic dolomites RT6** exhibit fossil mould in addition to their intercrystalline porosity. They form excellent reservoir rocks but have a limited distribution not exceeding 10% of the total rock volume. Porosities are 25-40% and permeabilities 100-3000 mD (Figure 6F).  
\begin{table}
\begin{tabular}{c|c c}  & Dense argillaceous shale & R1 \\
2 & Cemented argillaceous limestone & R2 \\
3 & Moudic limestone & R3 \\
1 & Chalkified limestones & R4 \\
5 & Sucrosic dolomites & R5 \\
6 & Moudic sucrosic dolomites & R6 \\ \end{tabular}
\end{table}
Table 5: List of 6 Lithofacies types used for IPSOM map construction.  
Figure 5: The lithological distribution model of the E11 platform and stratigraphic intervals.  
Figure 6: _Cont_.
## 3 Methods
### Statistical Evaluation of Satellite Precipitation Products  
To evaluate the accuracy of the GPM IMRG-E product against rain gauge observations in both at basin and grid-cell scale that corresponds to the GPM pixel covering the rain gauge. Four statistical indices were adopted; The Pearson's correlation coefficient (CC) describes the agreement between the satellite precipitation and ground observations (Equation (1)). Mean error (ME) measures the average error magnitude of satellite precipitation (Equation (3)). Root-mean square error (RMSE) indicates the average error magnitude between satellite data and observed precipitation data (Equation (4)). Relative bias (BIAS) reflects the systematic bias between the SPPs and the ground observations (Equation (2)). These indices are expressed as follows:  
\[C=\frac{\sum_{i=1}^{n}\left(G_{i}-\overline{G}\right)\left(S_{i}-\overline{S} \right)}{\sqrt{\sum_{i=1}^{n}\left(G_{i}\ -\overline{G}\right)^{2}}\times\sqrt{\sum_{i=1}^{n}\left(S_{i}\ -\overline{S}\right)^{2}}} \tag{1}\]  
\[\text{{Bias}}=\frac{\sum_{i=1}^{n}\left(S_{i}\ -G_{i}\ \right)}{\sum_{i=1}^{n}G_{i}} \ \times 100 \tag{2}\]  
\[\text{{ME}}=\frac{\sum_{i=1}^{n}\left(S_{i}\ -G_{i}\ \right)}{n} \tag{3}\]  
\[\text{{RMSE}}=\sqrt{\frac{\sum_{i=1}^{n}\left(S_{i}\ -G_{i}\ \right)^{2}}{n}} \tag{4}\]  
where \(n\) is the sample size; \(S_{i}\) is the satellite precipitation estimate; \(G_{i}\) is the gauge observation; \(S\) is the mean satellite precipitation estimate; \(G\) is the mean gauge observation.  
Moreover, to quantitatively evaluate the precipitation detection ability of the satellite precipitation against the rain gauge observations, we used two indices that are based on the contingency table of satellite precipitation estimates with observations. The probability of detection (POD) (Equation (5)) measures the fraction of precipitation events correctly detected by the satellite. The false alarm ratio (FAR) (Equation (6)) describes the fraction of false events detected by the satellite. (Table 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Observation**} \\ \cline{3-5}  & & **Yes** & **No** & \\ \cline{3-5}
**GPM IMRG-E** & **Yes** & A: Hits & B: False & A + B: Observed by satellites \\ \cline{2-5}
**No** & & C: Misses & D: Rejection & C + D: Not observed by satellites \\ \cline{2-5}  & & A + C; Observed by rain gauges & B + D: Not observed by rain gauges & N: Total of events \\ \hline \hline \end{tabular}
\end{table}
Table 2: The contingency table used to estimate the precipitation detection ability of the satellite precipitation. POD: probability of detection, FAR: false alarm ratio.  
\[FAR=B/(A+B) \tag{6}\]
# Low-Cost RSAC and Adsorption Characteristics in the Removal of Copper Ions from Wastewater
## 2 Materials and Methods
### Effect of Time on Adsorption  
RSAC particles were weighted and divided into five groups: 2, 4, 6, 8, and 10 g. The groups were put into conical flasks, respectively, with 150 mL of 100 mg/L copper ion solution. Then, all the samples were mixed at room temperature (25 \(\pm\) 1 \({}^{\circ}\)C). The concentration of residual copper ion in the supernatant of each sample was measured at a specific time and the relationship was investigated.
## 4 Hybrid Ant Colony Optimization for Optimization of the Machining Line Balancing Problem
### Introduction of Heuristic Task Set Filtering Module for Hybrid Ant Colony Optimization  
When the element sequence is formed by ant colony optimisation, the candidate element set is generated in step \(s\), and then the elements are selected by the heuristic ant colony hybrid search rule until the selection of \(n\) elements is completed, forming a complete element processing chain.  
1. Time constraint rules: comprehensively consider 'or' constraint, as well as conventional precedence relationship constraint and cycle time constraint. The process of determining whether a certain element \(j\) can be added to the initial set of optional elements is as follows: (1.1) Set the current workstation to \(k\), set Q\(j=\{i\mid a_{ij}=1\) or \(a_{ij}=2\) or \(a_{ij}=-2\}\) and establish Q\(j\in\cup_{k=1}^{k}\)S\({}_{h}\). This indicates that all the pre-ordering tasks of element \(j\) have been assigned, and then proceed to the next step as follows. (1.2) Assume that the scheduled task completion time of the current workstation \(k\) is \(t_{\text{temp}}\). Then, the following candidate task set D\({}_{s}\) can be preliminarily screened according to the beat time \(C\) constraint by D\({}_{s}=\{j\mid t_{\text{temp}}+t_{j}\leq C\}\).
2. Tool constraint rules: Consider the difference between the turret tool of the current workstation \(k\) and the tool used by task \(j\) to be assigned to consider the tool change time and further filter the time constraint set D\({}_{s}\) in (1): (2.1) If \(j\) is the first task of the current workstation, the station preparation time p\({}_{x}\) should be considered. (2.2) If \(j\) is not the first task of the current workstation, check whether the tool used in task \(j\) is the same as the tool at the end of the current workstation; otherwise, consider the tool change time. (2.3) Based on the above conditions, the set Ts conforming to the tool constraints is obtained.  
Figure 5: Ant colony optimization with heuristic rules.  
3. Based on (1) and (2), compact constraint conditions are further considered to generate the compact constraint set \(\rm{j_{s}}\), and the heuristic ant colony search rule is used to select the processing elements. Through this heuristic task set filtering module, a feasible set of optional tasks can be provided when the task processing sequence is constructed, and then the ant colony algorithm is used to randomly select tasks to form a feasible solution. Similarly, this heuristic task set filtering module can also be combined with the standard particle swarm algorithm used in [40] to solve the research problem of this paper. Therefore, it can be used as a comparison of the enhanced hybrid ant colony algorithm in this paper.
# Analyzing Zone-Based Registration Using a Three Zone System: A Semi-Markov Process Approach
## 3 New Mathematical Model
### Semi-Markov Process Model for 3Z
#### 3.2.1 Definition of State by the Position of Each Zone in a 3Z System  
Among the three zones, let us call the two zones at each end edge zones and the middle zone center zone and mark them as E and C, respectively. Sometimes, it is simpler to mark the two edge zones as 1 and 3, and the center zone as 2. Note that the edge zone, marked as 1 (E\({}_{1}\)), is where location registration occurred more recently than the other edge zone (E\({}_{3}\)).  
For example, let us assume UE moves in the order of zone X (and registers) \(\rightarrow\) zone Y (and registers) \(\rightarrow\) zone Z (and registers) \(\rightarrow\) zone Y, as shown in Figure 3. In this situation, zone X and zone Z are the edge zones, and zone Z is where location registration occurred most recently, we define this as state 1.  
Let us define the state where we apply our semi-Markov process model. We define the following states with respect to the regular case.  
\[\text{E}_{ij}\;(i=1,\,2,\,3;j=1,\,2,\,3)\]  
\[\text{C}_{ij}\;(i=1,\,2,\,3;j=1,\,2,\,3)\]  
E and C indicate that the most recently registered zones are an edge or center zones, respectively. The first subscript \(i\) (\(=1,\,2,\,3\)) represents the UE's immediately previous zone, the second subscript \(j\) (\(=1,\,2,\,3\)) indicates the UE's current zone.  
For example, for the case of the UE shown in Figure 3, its state is E\({}_{12}\) since the current zone is the center (\(j=2\)) and the last zone is the edge zone where it registered its location (\(i=1\)). If the UE in Figure 3 returns to zone X, its state will be E\({}_{23}\) since the last zone is the center (\(i=2\)) and the current zone is the edge zone where it did not register its location most recently (\(j=3\)). In addition, if the above UE with a state of E\({}_{23}\) returns to zone Y, its state will be E\({}_{32}\) since the last zone is the edge zone where it did not register its location most recently (\(i=3\)) and the current zone is the center (\(j=2\)).  
On the other hand, if the UE in Figure 3 returns to zone Z, its state will be E\({}_{21}\) since the last zone is the center (\(i=2\)) and the current zone is the edge zone where it registered its location most recently (\(j=1\)).  
Figure 4 presents a collection of examples for states E\({}_{ij}\).  
Example for \(C_{ij}\) (\(i=1,\,2,\,3;j=1,\,2,\,3\)) will be given in the next subsection for better understanding.  
Figure 3: Edge and center zones in a 3Z system.
# Design of Positioning Mechanism Fit Clearances Based on On-Orbit Re-Orientation Accuracy
## 4 Experiment and Discussion
### Experimental Results and Discussion  
The optical unit model was removed from the frame and then inserted back into it. After insertion, it was fixed on the frame by positioning mechanisms. At that time, the corresponding azimuth and pitch angles of the theodolites were recorded (as Table 1). The above steps were repeated 10 times.  
In addition to the measurement results, the re-orientation errors expressed by the 3\(\sigma\)-confidence interval (calculated by the following equation [23]) are also listed in Table 1.  
\[\Delta(x)=3\sigma, \tag{19}\]  
where \(\sigma\) is the standard deviation of a group of measurement results.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Azimuth Angle of** & **Azimuth Angle of** & **Pitch Angle of** & **Pitch Angle of** \\
**Theodolite1** & **Theodolite2** & **Theodolite2** & **Theodolite1** \\
**(Relating to the** & **(Relating to the** & **(Relating to the** & **(Relating to the** \\
**Rotation Angle** & **Rotation Angle** & **Rotation Angle** & **Rotation Angle** \\
**Around the \(x\)-Axis** & **Around the \(x\)-Axis** & **Around the \(y\)-Axis** & **Around the \(z\)-Axis** \\
**of the Model)** & **of the Model)** & **of the Model)** \\ \hline \multicolumn{3}{c}{**Test Results (”)**} & **Test Results (”)** & **Test Results (”)** \\ \hline
1 & \(-\)22.3 & \(-\)51.2 & 12.8 & 3.3 \\
2 & \(-\)24.2 & \(-\)56.2 & 13.1 & 7.7 \\
3 & \(-\)26.4 & \(-\)54.4 & 11.6 & 8.4 \\
4 & \(-\)18.4 & \(-\)52.8 & 13.0 & 8.5 \\
5 & \(-\)20.9 & \(-\)49.5 & 13.3 & 4.3 \\
6 & \(-\)21.2 & \(-\)52.2 & 9.0 & 4.4 \\
7 & \(-\)22.3 & \(-\)54.0 & 10.1 & 12.5 \\
8 & \(-\)24.8 & \(-\)56.5 & 7.2 & 3.8 \\
9 & \(-\)22.0 & \(-\)52.3 & 11.0 & 8.9 \\
10 & \(-\)21.7 & \(-\)52.1 & 10.4 & 8.7 \\ \hline \multicolumn{3}{c}{Re-orientation error} \\ \multicolumn{3}{c}{(3\(\sigma\)-confidence interval)} \\ \hline \end{tabular}
\end{table}
Table 1: Experiment results.  
Figure 7: Photograph of the experimental system.  
As shown in Figure 8, the re-orientation errors in the experiment basically accorded with the theoretical derivation. However, there were some acceptable errors between the experimental results and theoretical derivation due to errors in machining, installation, instruments, and personnel, etc. Besides, because the hoisting point of the optical unit model did not completely coincide with the center of mass and there were errors in the gravity unloading device which sometimes led to gravity unloading instability, the standard deviation \(\sigma\) of the measurement results around the \(z\)-axis got larger. As a result, the re-orientation error around the \(z\)-axis was higher than the theoretical limit.
## 4 Results
### Input Interactions
#### 4.1.2 System Usability  
We identified and compared participants' subjective usability, on how easily and efficiently they used each input interaction through the system usability scale (SUS), evaluated on a scale of 0 to 100 in increments of 10. SUS scores of more than 71 points indicate that the system is acceptable with five steps anchored with \"Strongly Disagree\" and \"Strongly Agree\" [49].  
The one-way repeated measures ANOVA tests shows significant differences \(F\) (2, 62) = 5.69, \(p\) < 0.005 in SUS. TI scored highest (\(M\) = 75.31, \(SD\) = 16.97), followed by VI (\(M\) = 71.87, \(SD\) = 15.83) and G-HI (\(M\) = 63.35, \(SD\) = 22.12), as shown in Figure 7. The SUS score of TI was significantly higher than in G-HI.  
Figure 5: Average of reaction time for each input interaction (gaze-head input, touch input, voice input).  
Figure 6: Reaction times for three input interactions considering each AV action in two driving scenarios.  
To determine the most influential item in the SUS result, we analysed the 10 increment items of SUS for each input (**Q1**. I would like to use this system frequently, **Q2**. This system unnecessarily complex, **Q3**. This system was easy to use, **Q4**. I would need the support of a technical person to be able to use this system, **Q5**. The various functions in this system were well integrated, **Q6**. There was too much inconsistency in this system, **Q7**. Most people would learn to use this system very quickly, **Q8**. This system was very cumbersome to use, **Q9**. I was very confident using the system, **Q10**. I needed to learn a lot of things before I could get going with this system.), as shown in Figure 8. The ANOVA test found a significant difference in the SUS item **Q4** (\"I think that I would need the support of a technical person to be able to use this system\"), \(F\) (1.62, 50.34) = 6.32, \(p\) < 0.006, where TI (\(M\) = 80.46, \(SD\) = 26.74) scored significantly higher than G-HI (\(M\) = 65.6, \(SD\) = 29.61, \(p\) < 0.004) and VI (\(M\) = 72.65, \(SD\) = 27.20, \(p\) < 0.04). Also, SUS item **Q6** (\"I thought there was too much inconsistency in this system\") showed a significant difference, where F (2, 62) = 9.07, \(p\) < 0.000, where G-HI (\(M\) = 51.56, \(SD\) = 31.7) scored significantly lower than TI (\(M\) = 75.0, \(SD\) = 21.99, \(p\) < 0.001) and VI (\(M\) = 69.53, \(SD\) = 25.97, \(p\) < 0.013). The least influential item in SUS is item **Q9** (\"I felt very confident using the system\"), \(F\) (1.89, 58.7) = 6.39, \(p\) < 0.004, where TI (\(M\) = 78.1, \(SD\) = 23.54) was highly confident compared to G-HI (\(M\) = 57.03, \(SD\) = 33.1, \(p\) < 0.010), as shown in Table 1.  
Figure 8: Ten increment items of system usability scale scores for the three input interactions.  
Figure 7: Average system usability scale (SUS) scores for the three input interactions.
## 2 Methods
### Alignment to North
There are four different situations for the relative position of sites A and B, which can be expressed in quadrants as: NE-quadrant, NW-quadrant, SW-quadrant, and SE-quadrant, as illustrated in Figure 4. The rotation angle \(=\theta_{i}\) (\(i=1,2\)) when the relative position is in NE-quadrant and NW-quadrant while rotation angle \(=180^{0}-\theta_{i}\) (\(i=3,4\)) in SW-quadrant and SE-quadrant.
## 4 Energy Measurement Results  
### Required Energy Results and Calibration of Absorbing Cup  
The test results of the drop-weight impact test are shown in Table 1. The total mass of the drop-weight in tests no. one to three is 8.8 kg, while in tests no. four to 11 it is 5.8 kg.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Preset Impact** & **Compression** & **Impact Velocity** & **Actual Impact** \\  & **Energy (J)** & **Length (mm)** & **(m\(\cdot\)s\({}^{-1}\))** & **Energy (J)** \\ \hline
1 & 50 & 2.50 & 3.162 & 44.054 \\
2 & 80 & 5.96 & 4.164 & 76.298 \\
3 & 110 & 7.94 & 4.612 & 93.582 \\
4 & 110 & 3.94 & 4.617 & 61.813 \\
5 & 110 & 6.00 & 5.268 & 80.471 \\
6 & 140 & 7.24 & 5.701 & 94.238 \\
7 & 170 & 9.80 & 6.302 & 115.178 \\
8 & 200 & 11.46 & 6.641 & 127.879 \\
9 & 250 & 12.80 & 7.282 & 153.798 \\
10 & 20 & 0.20 & 2.424 & 17.045 \\
11 & 15 & 0.00 & 2.069 & 12.411 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the drop-weight impact test.  
Figure 14: The pin-rod.  
Figure 13: The test tooling of the pin-rod type of measuring device.  
When the preset energy of the machine was 15 J in test no. 11, the pin was sheared, and the pin-rod just moved in place while the absorbing cup was not deformed. It can be seen that at this time, the kinetic energy of the hammer is the energy required to shear the pin, and the required energy is 12.411 J.  
According to the test results of no. one to no. 10 in Table 1, the compression length-energy curve of the energy absorbing cup is fitted, as shown in Figure 15.  
The fitted straight line's equation is:  
\[y=10.152x+17.566 \tag{8}\]  
where, \(x\) is the compressed length of the energy absorbing cup and \(y\) represents the corresponding transferred energy, and the correlation coefficient of the fitted straight line is 0.989.
# Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland
## 5 Results  
Red and Blue reflectances are highly variable because of snow presence. When the ROI is 0% snow covered, it is 100% covered by grassland. The Red band had higher values when the images show vegetation and lower when there is snow present. This is in contrast to the Blue band that had lower values when there was no snow (Figure 10).  
Figure 10: (**a**) Band reflectance variation, (**b**) picture without snow presence and (**c**) with snow presence in Oakville.  
The Red and Blue bands had different behaviors when there was no snow, but with snow present, they had similar curves. We assumed that the ratio (B/R) could be used to differentiate between snow and no-snow and used different threshold values in the Blue band image. The ratio was compared with the experts' FSC estimations. Figure 11 shows that in both cases, the R\({}^{2}\) was higher than 0.9, showing a strong relation between them. In most of the cases, when FSC = 0, the ratio value was lower than 1.0.
# Review on Free-Space Optical Communications for Delay and Disruption Tolerant Networks
## 3 Delay-/Disruption-Tolerant Networking
### LTP Enhancements  
In Reference [52], the authors propose an enhancement of the LTP protocol that reduces the delays caused by the losses of Report Segments. The improvements were validated byemulations carried, showing that there is a reduction in the difference between the average and worst case scenario of losses.  
In the state of the art of LTP studies, Reference [45]'s researched an implementation of LTP with a Protocol Layer Forward Error Correction (PL-FEC) that decodes errors from unstable links and limits LTP retransmissions to the unlikely case of decoding failures. This FEC protocol is inserted immediately below LTP and called the erasure coding link service adapter (ECLSA). This protocol was upgraded for high-bandwidth-delay links. The High-Speed LTP (HSLTP) \"enforces almost one-to-one correspondence between LTP blocks and FEC codewords, and never requires LTP segment retransmissions\" [46]. The main advantage is that it requires only one Rx buffer, ideal for nodes limited in memory and optical links in space.
# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
## 3 Experimental Setup and Dataset
### Measurement Campaigns
The estimated position of the two PCIs that were detected with highest average RSRP--one per operator--is also shown on Figure 2. The distance between the center of the DIET Department and Op1 PCI (resp. Op2 PCI) is of about 60 (132) meters. The specific locations at which measurements were taken are shown on Figure 3. Green, blue, and red lines represent walls, windows, and doors, respectively. Table 3 reports the summary of performed measurement campaigns for each operator and location in terms of measurement duration and total number of channel measurements considered. Measurements were performed at all eight locations; however, we did not detect any SSB from Op 1 at locations 5, 6, 7 and from Op2 at location 3. As previously mentioned, such samples refer to those PCIs with highest average RSRP across all locations under analysis, and such RSRP values refer to the SSB signals that 5G PCIs transmit, in order to synchronize with and provide information to a 5G UE. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Characteristics**} \\ \hline \multirow{2}{*}{**Location**} & DIET department building, second floor, \\  & Sapienza University of Rome, Rome, Italy \\ \hline \multicolumn{2}{c}{**Building type**} & Office \\ \hline \multicolumn{2}{c}{**Thickness of the window glass**} & Total 10 mm \\ \hline \multicolumn{2}{c}{**Height of buildings**} & About 50 m \\ \hline \multirow{2}{*}{**Surrounding**} & Typical office building, window-to-wall ratio is \\  & about 2:1. \\ \hline \multirow{2}{*}{**Wall thickness**} & Internal walls \(\approx\) 15 cm \\  & Perimeter walls \(\approx\) 30 cm \\ \hline \multirow{2}{*}{**Inside arrangements**} & Each office contains chair, tables, shelves \\  & attached with walls. \\ \hline \multicolumn{2}{c}{**Building materials**} & Concrete \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of the building where measurements were performed. Figure 2: Google Earth map showing the DIET Department and the estimated position of the PCIs for Op1 and Op2 detected with highest RSRP.