esa-sceva/satcom-chunk-collection huggingface dataset:
# A Vision-Based Approach for Autonomous Motion in Cluttered Environments
## 3 Methods
### Motion-Planning System
#### 3.3.2 Security Guarantee  
Through Section 3.3.1, we can ensure that the distance between the path point and the obstacle is greater than the set threshold, \(d_{0}\). However, as shown in Figure 5, there may be cases where the path point does not collide with the obstacle, but the path collides with the obstacle. The path points \(Q_{1}\), \(Q_{2}\), \(Q_{3}\), and \(Q_{4}\) all keep a safe distance from the obstacle, but the path segment \(Q_{2}Q_{3}\) still collides with the obstacle. Therefore, in order to ensure the safety of the entire path, the conditions that need to be met are derived in this section.  
As shown in Figure 6, \(Q_{1}\) and \(Q_{2}\) are two adjacent path points, \(obs\) is any obstacles, and \(Q_{m}\) is a point on the path \(Q_{1}Q_{2}\). In order to maintain the safety of the path, we need to make the distance, \(d_{m}\), between any point, \(Q_{m}\), and the obstacle, \(obs\), greater than the safety threshold; that is, \(d_{m}\geq d_{thr}\). Based on the principle of triangle geometry, we can obtain \(d_{m}\geq d_{1}-l_{1}\geq d_{1}-(l_{1}+l_{2})=d1-\|Q_{1},Q_{2}\|\). Therefore, the safe distance between the path and the obstacle can be guaranteed if \(d_{1}-\|Q_{1},Q_{2}\|>d_{thr}\). In our case, the distance between path points can be less than \(l_{1}\) by setting \(l_{1}\) in Formula (12). Then \(d_{1}-\|Q_{1},Q_{2}\|>d_{thr}\) can be achieved via setting \(d_{0}>l_{1}+d_{thr}\) in Formula (11), thereby ensuring the safety of the path segment.  
Figure 5: Schematic of the collision between the path points and the obstacles. \(Q_{1}\) and \(Q_{2}\) are path points, and the gray square is an obstacle.  
The safety threshold, \(d_{thr}\), of the distance between the path and the obstacle is set to be 0.3 m. Because the radius of the robot is 17 cm, a safety margin of about 10 cm was added. The resolution of the grid map is 0.1 m, so the minimum distance between the path points obtained by the A* algorithm is 0.1 m, and the maximum value is 0.14 m. The maximum distance between the path points is set to \(l_{max}=0.2\) m. Since \(d_{thr}+l_{max}>d_{thr}+\|Q_{1},Q_{2}\|\), if the distance, \(d\), between the path point and the nearest obstacle satisfies \(d>d_{thr}+l_{max}=0.3\) m + 0.2 m = 0.5 m, the safety of the path can be guaranteed. Therefore, we set \(d_{0}\) in Formula (11) to 0.5 m, and \(l_{1}\) in Formula (12) to 0.2 m, so that the distance between the path point and the obstacle reaches up to 0.5 m, and the distance between the path points is less than 0.2 m. Thus, the distance between the path and the nearest obstacle is ensured to be greater than the safety threshold of 0.3 m.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 3 Engineering Background and Database Construction  
In this paper, a database for landslide prediction is constructed using the landslide disaster data of Kumamoto Prefecture in 2020, combined with the actual geographic and meteorological data, as the input data for the CNN-LSTM-SAM-Attention model. The process of database construction and processing includes data cleaning, normalization, dimensionality reduction, and sample equalization to improve the predictive ability of the model.
## 2 Two-Bidirectional Buck-Boost Converter for Capacitor Voltage Balancing  
### Capacitor Voltage Imbalance in a Five-Level Switch-Sharing-Based Multilevel Inverter  
A topology of a three-phase five-level switch-sharing-based multilevel inverter is considered here and its circuit configuration is shown in Figure 1. It consists of 12 switches in which six bidirectional switches are used with four DC-link capacitors, wherein each capacitor voltage is equal to \(\mathrm{V_{in}}/4\). \(\mathrm{V_{in}}\) is the total DC-link voltage across the four capacitors. To optimize the operation of bidirectional switches, each switch is shared among the three phases. In order to avoid a short circuit across the DC source, only one switch is allowed to be turned ON while the others are turned OFF. The details of the topology structure and operational principles of the inverter can be found in [14]. Due to unequal capacitance leakage currents and unequal delays in semiconductor devices, the inverter suffers from the capacitor voltage imbalance problem. In Figure 2, it can be observed that the outer capacitors (\(\mathrm{C_{1}}\) and \(\mathrm{C_{4}}\)) continue to charge (i.e., \(\mathrm{V_{1}}\) and \(\mathrm{V_{4}}\) increase) and the inner capacitors (\(\mathrm{C_{2}}\) and \(\mathrm{C_{3}}\)) continue to discharge (i.e., \(\mathrm{V_{2}}\) and \(\mathrm{V_{3}}\) decrease) when the DC source, \(\mathrm{V_{in}}\) is set at 200 V and the modulation index is 0.9 during the period of no voltage balancing control in place. This will result in unbalanced voltages with a compromised quality to be observed in the inverter's output waveforms.  
Considering that the switch-sharing-based multilevel inverter has some similarities to DCMI, it would be reasonable that somewhat similar solutions of capacitor voltage balancing in DCMI could be considered and applied in the switch-sharing-based multilevel  
Figure 2: Unbalanced capacitor voltages in a five-level switch-sharing-based multilevel inverter when no voltage balancing control in place.  
inverter. Since redundant switching states are not available for this inverter [14], modification of the PWM strategy is therefore not applicable. Hence, the proposed buck-boost converter is considered to serve the purpose of voltage balancing circuit in a five-level switch-sharing-based multilevel inverter. As mentioned in Section 1, the balancing circuit based on the parallel-switch-based chopper uses a smaller number of switches as compared to that of the buck-boost converter. However, a parallel-switch-based chopper is not suitable for the switch-sharing-based multilevel inverter since the simulation study shows that the chopper is not able to balance the capacitor voltages at high modulation indexes. Therefore, the buck-boost converter is considered for capacitor voltage balancing in the switch-sharing-based multilevel inverter.
# Concealed Object Detection and Recognition System Based on Millimeter Wave FMCW Radar
## 2 Materials and Methods
### Synthetic Aperture Radar (SAR) and Multiple-Input Multiple-Output (MIMO) Radar Antennas Technique
#### 2.2.1 Radar Enabled Three Transmitting Antennas and Four Receiving Antennas  
In the first version, the radar uses a single transmitter and single receiver mode, and the sampling interval needs to be controlled at 0.9495 mm in the \(Y\) direction, requiring multiple scans, which will increase the error of longitudinal movement of the mechanical slide rail, and it is very time-consuming.  
In the second version, in order to ensure the sampling interval and improve the resolution of the image, this paper started with three transmitting antennas and four receiving antennas enabled [15]. Therefore, the concept of the virtual channel can be constructed. A total of 12 virtual channels are arranged linearly in the \(Y\)-direction.  
In the actual test, it was found that 12 virtual channels are used for data analysis at the same time to generate image blur, which will lead to the decline of resolution. In order to improve the quality of information carried by pixels in the longitudinal direction of the image, this paper removes the virtual channel with a higher interference on the upper edge and lower edge, selecting 8 virtual channels to construct 3D data blocks; the scan length on the \(Y\)-axis is estimated to be \(D_{y}\approx N_{y}(M-1)\frac{\lambda}{4}\) where \(M=8\), \(N_{y}\) is the number of scans in the \(Y\) direction. After using the MIMO-SAR radar antennas technology, the mechanical slide moves \(2\lambda=7.590\) mm each time in the longitudinal direction, the image resolution between each virtual channel is \(\lambda/4\), as shown in the Figure 4a. A comparison of scanning time and equivalent antenna aperture of the single \(Y\)-direction scan between the second version and the first version are shown in Figure 4b.  
Figure 3: \(Z\) scanning track.
## 6 Analysis of the Findings of This Review
### The Practicality of the Primary Cobot Program and Control Methodologies Proposed in the Literature
#### 6.2.3 Virtual and Augmented Reality Systems  
Work in virtual and augmented environments is progressing; however, VR [93] and AR [95; 96] systems for cobot operations do not currently appear to be developed and tested to the point of practical deployment for the industry. What has been proposed in the literature are VR systems that allow users to simulate robotic movement in an immersive environment. However, each use case often requires creating a new virtual environment and current capability is limited to simulation rather than functioning as a practical programming method [93]. AR systems produce tracking inaccuracies, which can misdirect the cobot, and there are also visual constraints. A reduced field of view from the AR wearable devices and occlusion problems restrict the user's perspective of the computer-generated content and could impose risks when deployed in workplaces. In addition, users can be distracted as they continuously swap between augmented and real environments, disrupting the AR system's fluidity [95].
## 2 Repetitive Prime Number Labeling Scheme  
In this section, REP is explained, and then, we explain the drawbacks related to its inefficient method for performing reachability tests. Let \(v\) and \(w\) be nodes in a tree. If an edge \((v,w)\) exists, then \(v\) is the parent of \(w\). We denote by \(v=pa(w)\) the parent node of \(w\). Furthermore, \(w\) is reachable from \(v\) if there exists a sequence of nodes starting at \(v\) and ending at \(w\), where consecutive node pairs in the sequence are in edges. In REP, each node is assigned a prime number, called a self label.  
Definition 1: Self Label: \(self(v)\) is the self label of a node \(v\) such that \(self(v)\) is the \(s(v)\)-th prime number, where  
\[s(v)=\begin{cases}1&\text{if $v$ is root}\\ s(pa(v))+order(v)&\text{otherwise}\end{cases}\]  
where \(order(v)\) is the sibling order starting from \(0\). The \(1\)-th prime number is \(2\), which means that \(self(v)\) is \(2\) if \(v\) is a root.  
Note that a self label in PRM [6] is a globally unique prime number, whereas in REP, self labels are locally unique prime numbers within sibling nodes. We now provide the definition of the label of a node.  
Definition 2: Node Label: \(L(v)\) is the label of a node \(v\), which is a composite number computed as  
\[L(v)=\begin{cases}2&\text{if $v$ is root}\\ L(pa(v))\times self(v)&\text{otherwise}\end{cases}\]  
Lemma 1: Given a node \(v\), \(self(v)\) is equal to \(lpf(L(v))\), where \(lpf(\mathsf{n})\) is the largest prime factor of \(\mathsf{n}\).  
(B) in Figure 1 depicts an illustrative example labeled by Definition 2. For example, \(L(I)\) is computed by multiplying \(L(F)=2\times 3\times 3\) by \(self(I)=5\). A tree-labeling algorithm for this scheme is outlined in Algorithm 1.  
```
1:procedureAssignLabels(\(T\),\(p\))\(\triangleright\)\(T\) is a tree and \(p\) is a parent node (the root for the first-time call)
2:for the \(i\)-th child node \(c\) of \(p\) in \(T\)do
3:\(self(c)\leftarrow\) the \(i\)-th next prime number from \(self(p)\)\(\triangleright\) Definition 1
4:\(L(c)\gets L(p)\times self(c)\)\(\triangleright\) Definition 2
5:\(\textsc{AssignLabels(}T\),\(c\))
6:endfor
7:endprocedure
```  
**Algorithm 1** Labeling  
In [8], a prime factorization method to answer a reachability query in REP was described. Both labels of the given two nodes must be prime factored; subsequently, prefix matching is performed against the two prime factor sequences. For example, referring to (B) in Figure 1, \(H\) is not reachable from \(D\) as \(2\times 2\times 3\) is not prefix-matched in \(2\times 3\times 3\times 3\). On the other hand, \(H\) is reachable from \(E\) as \(2\times 3\) is a prefix. We argue that prime factorization is an expensive task, which led us to devise a more efficient method of answering reachability queries, as explained in the next section.
## 4 Validation Experiments
### Detection Tests for Multiple Terminals  
The detection time estimation in Section 3.6 is valid only in cases where there is a single MS. It was therefore necessary to study detection scalability as the number of terminals grows. We consider that a number between 5 and 100 terminals is realistic. In this situation another important contribution to total detection time must be taken into account: the worst-case contention time \(T_{C}\) for \(N\) MSs, due to the contention mechanism used in the random access channel (RACH), for the MSs to request a dedicated channel during channel changes [33]. This time is the time that it takes for the last MS to obtain an empty slot in the RACH.  
In short, all the terminals contend for an empty slot in the RACH in order to transmit a _Request Channel_ message. They transmit the message in a single slot and then wait for the response. When a collision occurs (when several MSs transmit their messages in the same slot), the affected terminals do not receive any response and a uniform distribution is used to determine the slot for the next attempt. This distribution depends on the \(T_{x-integer}\) parameter, which is broadcast on the BCCH and on the Common Control Channel (CCCH) configuration [33].  
We needed to estimate the average \(T_{C}\) in order to test the suitability of our proposal in a real emergency. Consequently, we simulated the behavior of \(N\) MSs requesting a dedicated channel, with a \(T_{x-integer}\) value of 14, assuming a combined CCCH, which resulted in a random uniform distribution for the number of slots between attempts in [41,54]. We simplified the model by eliminating the maximum number of attempts, assuming that all the MSs would initiate the requesting process at the same time, which is realistic in our scenario because the forced channel change triggers this process for all active MSs.  
We evaluated \(T_{C}\) by increasing \(N\) in [5,100] in steps of 5. For each step, we averaged samples until a confidence interval with a tolerance of 1% was achieved with a 99% confidence level at least. The number of simulations needed to satisfy these requirements was determined using the Batch Means method [43], since simulation samples exhibited low term correlation.  
Figure 16 shows the average \(T_{C}\) depending on the number of MSs in the scenario. The standard deviation of \(T_{C}\) varied between 0.01 and 0.02 seconds (corresponding to 10 and 100 terminals,  
Figure 15: Detection performance (\(N=1,024\), \(C/I=3\)\(dB\)).  
respectively). The results indicate that, as expected, the average contention time grows proportionally to the number of MSs, but it is reasonably low in the application scenario. This confirms the hypothesis that our proposal would satisfy the requirements in emergency situations, since the contribution of \(T_{C}\) to total response time is negligible compared with \(T_{D}\).
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 3 Common Classification of Photocatalysts
### Non-Oxide Photocatalyst
#### 3.2.2 CuS Series Photocatalyst  
CuS is a very important metal sulfide semiconductor material with excellent optoelectronic properties, and it is considered to be a typical P type semiconductor material. CuS has a forbidden band width of 2.2 eV, which has good visible light absorption capacity. When the CuS material absorbs the proper photons, electron-hole pairs are generated. Electrons and holes move to the surface of the material and react with water molecules or oxygen molecules on the surface of the material to produce several substances. These substances have high catalytic activity and can degrade the organic matter adsorbed on the surface of the material.  
Because of its excellent properties, CuS has found a wide range of optical, electrical, mechanical, and sensor applications [104; 105; 106; 107], including solar cells, lithium battery electrodes, photothermal conversion, and photocatalysis [108]. CuS nanomaterials have great potential for photocatalysis due to their appropriate energy band and absorption wavelength [109].
## 4 Results and Discussion  
### Stress of the Anchorage Zone at the Top Slab  
#### 4.1.1 Stress Distribution of Steel Bars  
The steel stresses of Sections A-A through I-I were measured. The steel bars at Sections A-A and I-I were under the tension condition. For the eight loading cases, the linear relationship between the stress and load of Section A-A can be observed in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Loading Case** & **10\%** & **20\%** & **40\%** & **60\%** & **70\%** & **80\%** & **90\%** & **100\%** \\ \hline Top slab specimen & 605 & 1211 & 2422 & 3633 & 4238 & 4843 & 5449 & 6054 \\ Bottom slab specimen & 371 & 742 & 1484 & 2226 & 2597 & 2969 & 3340 & 3711 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Loading forces of the specimens (Unit: kN).  
Figure 10: The arrangement of the strain gauges on the bottom slab specimen (unit: mm): (a) Section B-B; and (b) Section D-D.  
Figure 9: The arrangement of the strain gauges on the top slab specimen (unit: mm): (a) Section A-A; and (b) Section I-I.  
The stress level of the steel bars at the upper layer was higher than that at the lower layer at Sections A-A and I-I due to the eccentricity of prestressing tendons (approximately 0.1 m). The tensile and compressive stresses at Section A-A were higher than those of Section I-I, which indicates the stress level of tension section is higher than that of anchorage section. The effect of local bearing pressure is more remarkable at tension section.  
Test Points 3, 4, 10, and 11 at Section A-A and Test Points 161, 162, 168, and 169 at Section I-I were arranged at the mid-width of these two sections (see Figure 9). This causes their compressive stress levels to be higher than those of other test points under these eight loading cases. This result is a good agreement with principle of Saint Venant, as illustrated in Figure 2d.  
The steel stresses at Sections A-A, B-B, and C-C were 295.26, 204.54, and 169.89 MPa, respectively, when the load was equal to the prestressing force of 100%. It can be concluded that the compressive stress level and effect of local bearing pressure at each section were gradually decreased when the distance from the anchorage zone increased along the longitudinal direction of the slabs. Although no stress exceeded the yield strength of reinforcement and the steel bars were in good mechanical performance and elastic condition, the anchorage zone was seriously compressed.
# Influence of Printing Substrate on Quality of Line and Text Reproduction in Flexography
## 4 Results and Discussion
### Analysis of Line Deformation
It can be noticed that absolute line area increases with increases of line thickness, but percentage deviation decreases for all three printing substrates. The increase in line thickness has the least influence on the change in deviation on coated paper in negative shapes (\(-4.5\%\) for line thickness of \(85\)\(\upmu\)m and \(-2.3\%\) for line thickness of \(170\)\(\upmu\)m). Completely different tendencies in percentage deviations can be seen in the results of line perimeter measurements. Line perimeter is used to evaluate line raggedness, that is, line edge noise. Therefore, an assumption can be made that higher line perimeter values are indications of the higher line raggedness characteristics, which can be related to the absorbency of the printing substrate. All perimeter measurements show a positive deviation from target with significantly higher deviations on uncoated paper (over \(38\%\) regarding the target in positive and negative shapes for \(85\)\(\upmu\)m line thickness). Although the OPP film is a completely non-absorbent printing substrate, there are no significant differences  
Figure 4: Deviation of area and perimeter (%) for lines thickness of \(85\)\(\upmu\)m and \(170\)\(\upmu\)m printed in positive and negative shape on three different printing substrates. in the values of percentage deviation between coated paper and OPP film. It refers to each observed line thickness for each shape separately, for both positive and negative shapes (up to 1.5% at 85 \(\upmu\)m line thickness and up to 3.5% at 170 \(\upmu\)m). Significantly larger deviations on uncoated paper are the result of surface irregularities of the printing surface, which leads to the conclusion that the rough surface due to its better absorption characteristics has a favorable effect on the irregular spreading of lines, i.e., on the appearance of edge noise. The line quality in negative shapes is influenced by the same parameters as in positive shapes. However, the main characteristics of the line quality in a negative shape is openness. Thinner lines printed in negative shape are particularly sensitive to filling, which influences their visibility. Increased line edge noise, ink spreading on printing substrate and blurriness are the main reasons for the decrease of line quality in a negative shape. Ink spreading influences the line edge sharpness, which results in blurry edges. Figure 5 compares the quality reproduction of lines in positive and negative shapes. The line with least edge noise and sharpest edge is a line printed on coated paper, and it is visually the most homogeneous, which is directly related with the result of perimeter measurements. Significantly bigger edge raggedness on uncoated paper can be particularly easily noticed, visually.