esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Study Area and Data Used
Distance to roads, distance to rivers and distance to faults are divided into five categories by the equal distance method, as shown in Figure 2i-k, respectively. Lithology is one of the most important restrictive factors in landslide susceptibility evaluation; areas with highly resistant rocks or highly permeable subsoil material have low drainage density [81], which are natural factors essential to determine landslide occurrence [82; 83]. The geological map of this study was extracted from a geological map with 1:1,000,000 scale. The study area has 12 different lithological units, which are shown in Figure 2l. Rainfall is a widely recognized landslide-inducing factor, which not only increases the weight of the slope, but also reduces soil strength [84; 85]. From Figure 2m, it can be seen that the rainfall in the middle of the study area is significantly higher than that in the north and south. The rainfall map was reclassified with an interval of 100 mm/yr. Soil is the material composition of the slope, and different soils have different physical and mechanical properties. Soil type was extracted from the geological map with a 1:1,000,000 scale, as shown in Figure 2n, and there are nine kinds of soils in the study area. NDVI is a measure of surface reflectance and gives a quantitative estimate of biomass and the vegetation growth [86; 87; 88]. The NDVI values in this paper were reclassified into five categories: \(-\)0.21 to 0.21, 0.21 to 0.36, 0.36 to 0.44, 0.44 to 0.52, and 0.52 to 0.65 (Figure 2o). Land use is considered as the direct manifestation of human activities impacting on landslide probability. Land use types control the amount of infiltration and surface runoff generation [77]. In general, landslides are concentrated in human active areas [89; 90]. In this study, a land use map was extracted from regional land use maps with a 1:100,000 scale. Land use was divided into farmland, forestland, grassland, water, residential areas, and bare land (Figure 2p). Figure 2: _Cont_.
# Reporting Key Features in Cold-Adapted Bacteria
## 2 Metabolic Features Related to Energy Generation in Cold Environments  
Novel'multi-omic' approaches allow the acquisition of information related to the functionality of genotypic traits. Most of these works involving transcriptomic and proteomic analysis have highlighted the modifications of metabolic pathways that take place under cold conditions. As a general conclusion from this amount of information, it is known that many studied bacteria downregulate primary metabolism under cold conditions (Table 1). Oxygen solubility increases at low temperatures, generating the increase of reactive oxygen species (ROS), which leads to oxidative stress. For this reason, oxidative metabolism such as glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle (TCA) and the electron transport chain are generally depressed at low temperatures [19]. Although this adjustment of ROS-producing pathways is recognized, the underlying molecular mechanisms and the alternative metabolic pathways involved in bacterial cold-adaptation are still not fully understood. In this section, several selected examples of heterotrophic bacteria isolated from different cold environments are analyzed, trying to find support for the statement that cold-adapted bacteria can use shortened or non-central metabolic pathways to thrive in the cold. Reconstruction of carbon metabolism in _Psychrobacter articus_ 273-4, a Siberian permafrost bacterium able to grow at -10 \({}^{\circ}\)C, showed that it lacks glycolysis genes and a phosphotransferase system but possesses gluconeogenic enzymes, fructose-1,6-bisphosphatase and phosphoenolpyruvate synthase, suggesting that although it is not able to utilize sugars, other oxidized carbon sources are preferred [17]. Phenotypic analysis indicated that the energy metabolism of this bacterium is based on acetate, a compound found in that environment that can easily diffuse into the cell without cost-associated transport systems. A recent study of transcriptomic and proteomic responses to low temperatures in _Psychrobacter_ sp. PAMC 21119, isolated from Antarctic permafrost soil, also showed that pathways for acetyl-CoA metabolism were upregulated while proteins involved in energy production and conversion were downregulated [20]. The utilization of glyoxylate shunt can provide intermediate carbon compounds to fulfill biosynthetic requirements [17]. The induction of genes encoding enzymes involved in the glyoxylate cycle at 5 \({}^{\circ}\)C was also observed in _Nesterenkonia_ sp. AN1, a member of _Actinobacteria_ isolated from Antarctic soil [11]. In another bacterium isolated from permafrost--_Exiguobacterium sibiricum_--the presence of methylglyoxal synthase was proposed by [PERSON] et al. [10] as an important feature for bypassing the lower part of glycolysis, since it is an alternative catabolic pathway for triose phosphates. Glycosalase family proteins have also been recognized as key components in _Planococcus halocyophilus_ Or1, a non-spore forming _Firmicutes_ isolated from subzero temperatures from high Arctic permafrost [12]. The breakdown of glyoxal and
## 2 Related Work
### Information-Centric Networking  
ICN is a promising candidate for the future internet architecture (FIA). ICN forwarding engine comprises of three primary data structures: Content Store (CS), the cache space of content nodes; Pending Interest Table (PIT), which stores the pending incoming request and its ingress face; and Forwarding Interest Base (FIB), the table that stores the forwarding egress of the potential content provider.  
Regarding ICN concept, Content-Centric Networking (CCN) is a well-known research project in ICN initiated since 2007 [1]. The main idea of CCN is that a content request broadcasted by a user (namely Interest packets) contains the desired content name. When an Interest packet arrives, the CCN forwarding engine checks the information base to find the requested content's possible providers which are the nodes with original content or its valid replica. Then the Interest packet is forwardedthrough any outgoing interface that moves it closer to the user location [4]. In this research, we utilize Named-Data Networking (NDN) as it is an enhanced version of the CCN architecture, which has started in 2010 with detailed protocol and forwarding algorithms to form the fully functional prototype in ICN.
## 3 Materials and Methods
### Computing the Movement Parameters  
Having the spot's coordinates of each image, the rotation speed and movement directions can be computed using geometry. So, in this last phase of the algorithm, different important information is achieved, supporting the desired assessment of the evaluated motors.  
In the first place, every spot is associated with an angular distance in relation to the center of the disc. The angular distance \(A(c)\) is defined as expressed in Equation (1). The value of \(A(c)\) will be within the range from \(0^{\circ}\) to \(360^{\circ}\), which means that it will be in one of four possible quadrants (I, II, III or IV), as specified in Figure 1 (the values of the angles increase in a counter-clockwise fashion).  
\[A(c)=\text{atan2}(y_{(c)}-Py,x_{(c)}-Px) \tag{1}\]  
With the value of \(A(c1)\) and \(A(c2)\), for \(c1\
eq c2\), the angular distance \(D_{(c1,c2)}\) can be computed as presented in Equation (2). When the disc is rotating, \(A(c)\) may assume any value, even suddenly changing from a small angle value (Quadrant I) to a high angle value (Quadrant IV). Since the motor can move in both clockwise and counter-clockwise directions, the correct computation of the angular distance between two points have to be performed considering a quadrant test, \(Q(c)\): for clockwise movement, if \(Q(c1)=1\) (which means that \(c1\) is in Quadrant I) or \(Q(c1)=2\), while \(Q(c2)=3\) or \(Q(c2)=4\), the angular distance computation needs a compensation by \(360^{\circ}\), and the opposite is valid for counter-clockwise movement, as expressed in Equation (2).  
\[D_{(c1,c2)}=\begin{cases}|A(c1)+(360-A(c2))|,&\text{if $clockwise$ and $Q(c1)=1$ or $2$,}\\ &\text{and $Q(c2)=3$ or $4$}\\ |A(c1)+(360-A(c2))|,&\text{if $counter$-clockwise$ and $Q(c1)=3$ or $4$},\\ &\text{and $Q(c2)=1$ or $2$}\\ |A(c2)-A(c1)|,&\text{otherwise}\end{cases} \tag{2}\]  
After being able to compute the angular distance between any two spots, different movement parameters can be computed. Initially, considering previous works in this area [13; 14; 27], it is desired to compute the greatest angular movement of the motors, defined as the engaging angle (\(\alpha_{e}\)), which is the greatest value of \(D_{(c1,c2)}\) in a forward/backward cycle. Additionally, we also want to compute the disengaging angle (\(\alpha_{d}\)), which is the greatest value of \(D_{(c1,c2)}\) in the opposite direction of the same movement cycle.  
Besides the values of \(\alpha_{e}\) and \(\alpha_{d}\), it is also desired to compute the RPM of the assessed motors, allowing comparisons with the specifications provided by the manufacturers. For that, we take the number of images that comprises the cumulative sum of the angular movements of the motors until it reaches \(360^{\circ}\), associating it with the defined value of \(f(s)\). For \(Im\) images being processed to indicate a \(360^{\circ}\) cumulative angular movement, Equation (3) stands for the computation of the RPM (for time in seconds).  
\[RPM=\frac{f(s)\times 60}{Im} \tag{3}\]
# Design and Simulation Analysis of a New Type of Assembled UHPC Collision Avoidance
## 2 Device Construction Design and Finite Element Modeling  
### Device Construction Design  
The collision avoidance is composed of 18 independent UHPC collision avoidance floating boxes connected by closed high-strength bolts [27]. The gaps between adjacent boxes are filled with asbestos cushion plates. Each single box is closed and can float at a fixed water level. Each collision avoidance floating box consists of a two-layer UHPC floating box with two-way dense reinforcement and internal supporting elements. The concrete protective layer on the wall of the box is 10 mm thick and RB500E steel bars with 100 mm spacing are used. A modular design is adopted for the box body. The device can be divided into three modules: Box No. 1 is the water distribution tank, Box No. 2 is the adjustment tank, and Box No. 3 is a sidewall standard box. The structural arrangement of the three modules is shown in Figure 1 while Figure 2 shows the detailed structure of the device end. The following three types of UHPC collision avoidance floats are briefly introduced.
# Distributed Detection of Large-Scale Internet of Things Botnets Based on Graph Partitioning
## 6 Discussion
### Threats to Validity  
**Internal Validity:** Our experiments employed controlled variables and cross-validation to ensure the validity of the experimental results. However, the graph datasets we used may have introduced some unexpected topological noise, affecting the effectiveness of model training. Further efforts are needed to gradually examine the quality attributes of the data. Although the experimental results indicate that our model is effective, more work is needed to understand what types of nodes the model struggles to distinguish.  
**External Validity:** The graph datasets we used are attribute-free graphs, so our model is based on specific botnet topologies for botnet detection. Therefore, the trained model can be generalized to botnet detection in real-world scenarios with similar topological structures. However, the model may fail to detect specific botnet topologies that are not present in the training dataset.
## 5 Calibration and Application
### Stability of Rock Slope with Two Joint Sets and a Rock Bridge  
Example 3 is an artificial rock slope containing two joint sets and a rock bridge, as shown in Figure 17, in which \(J1\) is a set of non-persistent joints, and \(J2\) is a set of fully persistent joints. The quadrangle \(ABCD\) is a rock bridge with a width of 3.0 m that is not incised by joint set \(J1\). The height of the rock slope is 70 m, the dip angle of joints \(J1\) is \(150^{\circ}\), the dip angle of joints \(J2\) is \(105^{\circ}\), and the two spaces of the parallel joint plane in two joint sets are 20.0 m. The physical and mechanical parameters of the rock slope are listed in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material Name** & **Unit Weight (\(\delta\)N/m\({}^{3}\))** & **Cohesion (\(\delta\)Pa)** & **Internal Friction Angle (\({}^{\star}\))** \\ \hline Inact rock mass & 25.00 & 500.00 & 30.00 \\ Rock bridge & 25.00 & 500.00 & 30.00 \\ Jointed plane & / & 120.00 & 24.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical and mechanical parameters of example 3.  
Figure 16: Yield zone of slope with different joint connectivities \(k\) (\(c\) = 1000 kPa): (**a**) \(k\) = 70%, (**b**) \(k\) = 75%, (**c**) \(k\) = 80%, (**d**) \(k\) = 85%, (**e**) \(k\) = 90%, (**f**) \(k\) = 95%.  
Figure 17: Sketch of rock slope containing two joint sets of example 3 (unit m).  
The mixed numerical discrete method is used to divide the rock slope. The rock bridge is divided into 207 triangular finite elements, and the rock blocks are divided into 45 rigid block elements. The strength reserve coefficient calculated using the lower bound method is 1.342, which is close to that (1.289) of the _Janbu_ method. The distribution of the yield zone under the condition of the shear parameters is shown in Figure 18. It can be seen that the true failure mode of this rock slope is the shear failure occurring along the polygonal line _ABCDE_ (as shown in Figure 18).
# Vibration Energy at Damage-Based Statistical Approach to Detect Multiple Damages in Roller Bearings
## 4 Results and Discussion
### Inner Race Damage  
A bearing with an inner race fault was replaced with an intact bearing at the outboard bearing housing. The accelerometers were installed on the outboard and inboard bearing housing in the horizontal and vertical direction. To analyze the inner race defect, the simulator was operated at 1002, 1500, 2400, and 3000 RPM rotating speed, and the data were acquired with two loading conditions: (1) without installing a bearing loader, and (2) with installing a bearing loader. The histograms for the outboard bearing housing in the vertical direction at each rotating speed for intact and inner race bearing damage were compared, without installing a bearing loader and with installing a bearing loader, as shown in Figures 2 and 3, respectively. The kurtosis values for the outboard bearing  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Rotating Speed** **(RPM)**} & \multicolumn{2}{c}{**Rotor Inboard Bearing Housing**} & \multicolumn{2}{c}{**Rotor Outboard Bearing Housing**} \\ \cline{2-5}  & **Horizontal** & **Vertical** & **Horizontal** & **Vertical** \\ \hline
1002 & 0.2757 & 0.1337 & 0.2771 & 0.1741 \\
1500 & 0.5939 & 0.3116 & 0.6544 & 0.5724 \\
2400 & 1.2863 & 0.7680 & 1.2607 & 1.0588 \\
3000 & 2.0655 & 1.1951 & 2.6264 & 2.3558 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall velocity-RMS (mm/s) without installing a bearing loader at no-fault condition.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Rotating Speed (RPM)**} & \multicolumn{2}{c}{**Rotor Inboard Bearing Housing**} & \multicolumn{2}{c}{**Rotor Outboard Bearing Housing**} \\ \cline{2-5}  & **Horizontal** & **Vertical** & **Horizontal** & **Vertical** \\ \hline
1002 & 0.3557 & 0.1557 & 0.2675 & 0.1548 \\
1500 & 0.6291 & 0.2669 & 0.4674 & 0.2990 \\
2400 & 1.1643 & 0.9012 & 0.7484 & 0.5704 \\
3000 & 2.7166 & 1.5035 & 1.7238 & 1.4493 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall velocity-RMS (mm/s) with installing a bearing loader at no-fault condition.  
housing in the vertical direction at each rotating speed, without installing a bearing load, and with installing a bearing loader, are shown in Table 4.  
Figure 2: Histograms for an inner race damaged bearing without installing a bearing loader.  
From Figures 2 and 3, it can be seen that the bars are high for an inner race damaged bearing for both of the loading conditions. At all rotating speeds, the frequency of the inner race damaged bearing data is high as compared to the intact bearing data. The kurtosis values shown in Table 4 are also high in the case of an inner race damaged bearing for all rotating speeds. As the kurtosis values give a better idea of vibration energy at damage; the  
therefore, the histograms and kurtosis values concluded that the energy at damage-based approach is an efficient way to detect inner race damage in the rolling bearing.
## 2 Methodology for C/V of Radar Altimeters  
### Lake Issykkul C/V Site Description  
Lake Issykkul (42\({}^{\circ}\)10\({}^{\prime}\)-42\({}^{\circ}\)40\({}^{\prime}\) North, 76-78\({}^{\circ}\) East, Republic of Kyrgyzstan, Figure 1), is the 7 th deepest lake in the world. It is situated in Central Asia in the Tien-Shan Mountains at a height of, approximately, 1607 m above sea-level. It is the second largest mountain lake in the world, after Lake Titicaca in South America. In the north and south, it is surrounded by Kungei-Alatoo and Terskei-Alatoo ranges, elevating up to 2000-4500 m above the lake [13]. The length of the lake is 178 km, width is 58 km, maximum depth is 668 m, and its area is 6247 km\({}^{2}\).  
Since the launch of T/P in 1992, Lake Issykkul, has been overpassed by all of the satellites, carrying an altimeter onboard: GFO (Geosat Follow On), T/P, Jason-1,2,3, Envisat, ERS-1,2, Cryosat-2, Saral/AltiKa, HY2A, Icesat-1, and Sentinel-3A/B. In the near future, the Sentinel-3B, Jason-CS/Sentinel-6, and IceSat-2 will also measure Lake Issykkul's water-level. In 2021, the SWOT (Surface Water Ocean Topography [14]) mission will be launched, for which Lake Issykkul has been already chosen by NASA and CNES, as a dedicated C/V site for hydrology.  
Lake Issykkul is moreover a good C/V site because in situ measurements are available and some instruments have been installed along the coast, over the last 15 years. We used, for example, the data from a weather station located in Cholpon Ata (see map on Figure 2), to calculate the Dry Tropospheric Correction (DTC). The cooperation with the Institute of Water Problem and Hydropower (IWPH) of Kyrgyzstan, since 2003, also allows for having facilities during the fieldwork, including a long vessel (named Multur) which is capable of navigating for many consecutive days on the lake and performing the C/V measurements. It is also under the framework of this cooperation that we have been authorized to perform in situ measurements and install permanent instruments--two permanent GPS receivers near the lake, two weather stations, and radar on the southeast bank of the lake, providing atmospheric pressure, air temperature, and humidity, and water-level every 5 min, since September 2010.  
Figure 1: Map of the central Asia region around Lake Issykkul.  
In the past, this lake has already been used to determine the absolute bias of T/P, GFO, Jason-1,2, and Envisat altimeters and results were published and detailed by [PERSON] et al. (2016, 2017, 2018). Results have been compared to other studies performed with ocean C/V sites: Harvest platform in California (2018); Corsica site (2018); Gavdos Island (2018); and Bass Strait in Australia (2018).
## 4 Experiments
### Comparative Analysis with Existing Methods
We opted for the MGP method's tiny version after initially validating its base version, which failed to converge. CRNN and TRBA, employing CNN-based feature extraction methods, excel in scenarios with limited training data. TRBA, in particular, demonstrates outstanding performance. CDistNet, a technique combining CNN and Transformer sequential encoding, also yields better outcomes in scenarios with more scarce training data than other self-attention encoding methods. PARSeq, utilizing a Permutation Language Modeling strategy, achieves favorable results on the Raw + A1 + A2 dataset by establishing associations between arbitrary characters. However, based on the Transformer architecture, its performance is slightly inferior to that of CRNN, TRBA, and CDistNet.
## 5 Numerical Results and Discussion
### Investigation of the Ratcheting Behavior of the Pipe Considering the Assembly Contact Effect
This is due to the effect of assembly contact between the sleeve and pipe, which plays an important role in inhibiting the hoop ratcheting strain at Position 2. As change in the outside diameter at Position 1 was found to be less than the initial clearance between the sleeve and the pipe during 45 cycles under an internal pressure of 10 MPa (shown in Figure 8), the ratcheting response at Position 1 does not appear to be greatly affected by the assembly contact between the sleeve and pipe, and is instead mainly controlled by the bending moment (as mentioned above). Further, the bending moment at Position 2 was smaller than that at Position 1. So the ratcheting strain at Position 2 was also smaller than that at Position 1 under the internal pressure 10 MPa during 45 cycles (Figure 9a). In addition, as a consequence of the increase in internal pressure, Position 2 deviated slowly from the sleeve end in the axial direction; thus, the inhibition effect of the assembly contact on the ratcheting strain at Position 2 was gradually weakened. Moreover, increasing internal pressure led to increasing hoop ratcheting strain, resulting in the increase of outside diameter at Position 1. Hence, the clearance between the sleeve and the pipe decreased, and the inhibition effect of the assembly contact between the sleeve and pipe on the ratcheting response at Position 1 was enhanced by a number of degrees. As a result, the ratcheting strain at Position 2 had a trend beyond the ratcheting strain at Position 1  
Figure 9: Ratcheting response under different internal pressures: (**a**) 10 MPa, (**b**) 21 MPa, and (**c**) 28 MPa. under the 21 MPa internal pressure over 45 cycles, as shown in Figure 9b. Lastly, the ratcheting strain at Position 2 exceeded the ratcheting strain at Position 1 within 45 cycles under the internal pressure of 28 MPa, as illustrated in Figure 9c. The ratcheting strain at Position 1 was greater than that at Position 3 due to the larger bending moment at Position 1 under the internal pressure of 10 MPa. However, the inhibition effect of the assembly contact between the sleeve and the pipe on the ratcheting response at Position 1 gradually increased, and thus the hoop ratcheting strain at Position 3 also tended to exceed that at Position 1 under the internal pressure of 10 MPa during 45 cycles. As presented in Figure 9b, the hoop ratcheting strain at Position 3 further exceeded that at Position 1 during 45 cycles when the internal pressure was 21 MPa, and the hoop ratcheting strain at Position 3 exceeded that at Position 1 under 28 MPa internal pressure at a faster rate than under 21 MPa internal pressure.