esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Chinese HY-2D satellite was launched on 19 May 2021, carrying a Ku-band scatterometer. Together with the operating scatterometers onboard the HY-2B and HY-2C satellites, the HY-2 series scatterometer constellation was built, constituting different satellite orbits and hence opportunity for mutual intercomparison and intercalibration. To achieve intercalibration of backscatter measurements for these scatterometers, this study presents and performs three methods including: (1) direct comparison using collocated measurements, in which the nonlinear calibrations can also be derived; (2) intercalibration over the Amazon rainforest; (3) and the double-difference technique based on backscatter simulations over the global oceans, in which a geophysical model function and numerical weather prediction (NWP) model winds are needed. The results obtained using the three methods are comparable, i.e., the differences among them are within 0.1 dB. The intercalibration results are validated by comparing the HY-2 series scatterometer wind speeds with NWP model wind speeds. The curves of wind speed bias for the HY-2 series scatterometers are quite similar, particularly in wind speeds ranging from 4 to 20 m/s. Based on the well-intercalibrated backscatter measurements, consistent sea surface wind products from HY-2 series scatterometers can be produced, and greatly benefit data applications.  
radar backscatter; satellite intercalibration; scatterometer; sea surface wind +
Footnote †: journal: remote sensing  
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## 2 Model and Equations
### The Stationary GPP System  
Stationary GPP equations are constructed by assuming spherical symmetry and that the order parameter can be rewritten as \(\Psi(t,\vec{x})=\psi(r)e^{-i\omega t}\), with \(\omega\) an eigenfrequency, and \(\psi(r)\) a real function of the radial coordinate \(r\). With these assumptions, the GPP system is written as follows, according to [13; 25]:  
\[-\frac{1}{2r^{2}}\frac{d}{dr}\left(r^{2}\frac{d\psi}{dr}\right)+V\psi+g\psi^{3 }=\omega\psi, \tag{14}\]  
\[\frac{1}{r^{2}}\frac{d}{dr}\left(r^{2}\frac{dV}{dr}\right)=\psi^{2}. \tag{15}\]  
To ensure physically acceptable solutions, we impose certain boundary conditions. For the stationary order parameter \(\psi\), we require that \(\psi(0)=\psi_{c}\), \(\psi(0)=0\), and \(\lim_{r\rightarrow\infty}\psi=\lim_{r\rightarrow\infty}\psi^{\prime}=0\).  
For the gravitational potential \(V\), we set \(V(0)=V_{c}\) and \(V^{\prime}(0)=0\). The choice of \(V_{c}\) can be arbitrary since shifting this condition to \(V_{c}+V_{a}\) is equivalent to finding an eigenvalue \(\omega+V_{a}\) for some arbitrary value \(V_{a}\). These boundary conditions ensure physically meaningful solutions that satisfy the requirements of regularity and isolation.  
Since this set of equations is solved numerically, it is convenient to write it as a first-order system by defining the variables \(\phi=r^{2}\psi^{\prime}\) and \(M=r^{2}V^{\prime}\), where \({}^{\prime}=d/dr\) is the derivative operator. The above system is then rewritten as:  
\[\psi^{\prime} = \frac{\phi}{r^{2}}, \tag{16}\] \[\phi^{\prime} = 2r^{2}\Big{(}V+g\psi^{2}-\omega\Big{)}\psi,\] (17) \[V^{\prime} = \frac{M}{r^{2}},\] (18) \[M^{\prime} = r^{2}\psi^{2}. \tag{19}\]  
with the boundary conditions \(\psi(0)=\psi_{c}\), \(\phi(0)=0\), \(V(0)=V_{c}\), \(M(0)=0\), and \(\lim_{r\rightarrow\infty}\psi(r)=\lim_{r\rightarrow\infty}\psi^{\prime}(r)=0\). This set of equations along with the boundary conditions define an eigenvalue problem, where the eigenvalue is \(\omega\).  
For ease, it is convenient to define the vector \(u=(\psi,\phi,V,M)\) and the right side of the system as \(f(r,u)=\left(\frac{u_{2}}{r^{2}},2r^{2}(u_{3}+gu_{1}^{2}-\omega)u_{1},\frac{u_ {4}}{r^{2}},r^{2}u_{1}^{2}\right)\). The system can be written compactly as:  
\[\begin{cases}\frac{du}{dr}&=&f(r,u),\\ u(0)&=&u_{0},\end{cases} \tag{20}\]  
where \(u_{0}=(\psi_{c},0,V_{c},0)\).
## 3 Results
### Correlations between GPP and Driving Factors
#### 3.4.2 Effects of Phenological Parameters and FPAR on GPP
Apart from phenological parameters, FPAR, which refers to the fraction of absorbed PAR in vegetation photosynthesis, may also be an important driving factor for GPP, because it can directly impact the total vegetation productivity during photosynthesis. Correlation analysis, as depicted in Figure 9, showed a significant positive correlation between GPP and FPAR in the Mohe County, along the GKMR, and in part of the southern GKM, meaning that FPAR in the areas mention above was an important factor for AGG; however, little significant correlation was found in the central GKM. A comparison between Figures 8 and 9 showed a great difference of driving factors in different regions. It is necessary to combine all of the factors mentioned above to explore the spatial distribution of the dominant factor affecting GPP across the GKM; this will help us to further understand the underlying mechanism of vegetation dynamics in our study area over the past 34 years.
# Architecting Software for the Internet of Thing Based Systems
## 6 Phases and Trends of Research for Architecting IoT Software
### Active and Futuristic Research on Architectural Solutions for IoT Software
#### 6.2.2 Context-Sensitive and Dynamic Adaptive IoTs  
Autonomous and adaptive systems can dynamically adapt the behavior of the system to cope with the changing requirements and conditions of operational environments that also involve self-organization and self-healing [S6; S21]. Context-aware computing has been exploited to develop systems that can exploit contextual information to adapt systems on the go with the recent emergence and fast adoption of the mobile computing technologies, as per the needs of the context [S58; S59]. The emerging and futuristic trends suggest that possible unification of the IoTs and context-aware computing can lead to context-sensitive IoTs that can rely on contextual (i.e., temporal, environmental, geographical, etc.) information to adapt their behavior and operations. The usage and application of context-sensitive IoTs can range from Big Data to Crowd Sensing and Agent Systems. In the context of big data systems that process and transmit large amount of data, the IoT gateway with limited resources of data (pre-) processing may face performance or bottleneck issues [18]. In such scenarios, IoT gateway consumes more time than expected, in pre-processing the collected data and/or transmitting the data to the connected servers. Therefore, quality attributes or non-functional properties, such as performance, scalability, and availability, must be explicitly addressed while architecting IoT-driven big data systems. IoTs that support crowd sensed data, such as real-time traffic flows and environmental circumstances, can exploit contextual information to support advanced recommender systems.  
For context-sensitive and dynamic adaptive IoTs, one of the major challenges is mobility management, which relates to connectivity and performance issues for IoT devices and applications that move from one geographic location/network to another one. The solution in [19] provides a distributed computing architecture (based on Edge/Fog/Cloud monitoring system and a Capillary Container Orchestrator) to handle highly dynamic IoT environments to address the challenges of mobility management. In this architecture, container-based microservices running on the Edge node will be offloaded to the upper layer, called the Fog node, to manage situations where IoT devices are moving from one geographical point to another one.
# An Investigation of Optically Very Thin Ice Clouds from Ground-Based ARM Raman Lidars
## 4 Results
### Global Cloud Fraction  
The RL with higher sensitivity detects more clouds than CALIPSO. However, the RL is confined to a point-location, whereas CALIPSO provides near-global cloud measurements. The strengths for both instruments offer the ability to infer what the global cloud fraction is by estimating what the cloud fraction undetected by CALIPSO is and comparing that to what CALIPSO observes for the cloud fraction globally. The cloud fraction undetected by CALIPSO is estimated by averaging the difference between the RL and CALIPSO cloud fraction at SGP and TWP. CALIPSO's global cloud fraction is determined by considering all available CALIPSO profiles for the time period considered (2006-2016).  
Figure 8 shows the cloud fractions from the RL and CALIPSO over SGP and TWP, and CALIPSO over the globe. Cloud fractions are derived by using all profiles (upper panel) and transparent-only profiles (lower panel) for all clouds (left) and ice clouds (right). Ice clouds are those only containing randomly oriented ice particles. The RL resolution is consistent with CALIPSO's feature detection (i.e., CALIPSO-like feature detection resolution), which is 5 min/60 m over SGP and 20 min/60 m over TWP ([PERSON] et al., 2016). Cloud fractions are constructed by only considering low to high confidence cloud layers. However, the results are nearly identical when including no-confidence cloud layers or when restricting this to medium and high confidence cloud layers, especially for ice clouds.  
All clouds from all profiles provide an estimate for the global cloudiness considering all observations. CALIPSO's global cloud fraction is 0.66 and the average cloud fraction difference between the RL and CALIPSO at SGP and TWP is +0.13. For transparent profiles, the CALIPSO global cloud fraction is 0.42, with the average cloud fraction difference between the RL and CALIPSO at SGP and TWP being +0.20. The difference in the RL and CALIPSO for all cloud fractions are largely caused from the differences in instrument sensitivity: the optically very thin clouds are only detected by the RL.  
The results for ice clouds for all profiles (top right in Figure 8) should be interpreted with caution since the CALIPSO, which is in space, samples different ice clouds than the ground-based RL. The case study highlights the differences where CALIPSO observed ice clouds above liquid clouds, which is more challenging to detect by RLs. Ice clouds for transparent profiles afford the ability to estimate what the global cloud fraction might be if CALIPSO detected optically very thin ice clouds which the higher sensitivity RL observes in greater numbers. Globally, CALIPSO observes an ice cloud fraction of 0.36 for transparent profiles, while the average cloud fraction difference between the RL and CALIPSO at SGP and TWP is +0.17.
# A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites
## 3 Model
### Interference Avoid Model [7]  
According to the SNR formula, the interference power can be considered negligible when the noise power is much larger than the interference power.  
The Formula (4) can be expand to:  
\[SNR_{k}=\frac{\left|h_{k,n}\right|\cdot P_{n}}{\sigma^{2}}=\frac{P_{\Gamma} \times G_{\Gamma}\times G_{r}}{Loss\times k\times T\times B} \tag{11}\]  
\(\frac{P_{\Gamma}\times G_{\Gamma}\times G_{r}}{Loss}\) is the magnitude of the complex channel coefficient.  
Figure 2: The normalized radiation pattern of three sizes of beams.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Radius (km)** & **Gain (dBi)** \\ \hline Largest size beam & 80 & 31.89 \\ \hline Middle size beam & 40 & 37.89 \\ \hline Smallest size beam & 20 & 43.89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Beams size and its gain.  
Formula (3) can be expanded to:  
\[SINR_{k}=\frac{\frac{P_{l}\times G_{l}\times G_{r}}{Loss}}{\left(k\times T\times B +\frac{P_{l}\times G_{l}(\theta)\times G_{r}}{Loss}\right)} \tag{12}\]  
Considering \(d=\frac{\lambda}{2}\), the antenna array factor can be deduced:  
\[G_{l}(\theta)=\frac{\sin\left(\frac{N\pi}{2}\sin\left(\theta\right)\right)}{ Nsin\left(\frac{\pi}{2}\sin\left(\theta\right)\right)} \tag{13}\]  
When \(k\times T\times B\gg\frac{P_{l}\times G_{l}(\theta)\times G_{r}}{Loss}\), the interference power can be neglected, and considering the above equations, the interference avoidance angle can be solved:  
\[\hat{\theta}=G_{l}^{-1}(\frac{k\times T\times B\times\text{ Loss}}{P_{l}\times G_{r}}) \tag{14}\]  
It is worth noting that \(\hat{\theta}\) is the minimum lower bound, and \(\theta\) should be larger than that to avoid stronger interference. According to the above equation and system value, we can visualize the value relationship between noise power and interference power in Figures 3-5.  
We can infer from Figure 3 that the largest beams can provide the largest coverage for users to access the Internet, but on the other hand, it they will lead to stronger interference than other beams because their gain drops slowly. Therefore, the placement of the largest beam is the first kind of beam to take into account; otherwise, the users' Qos can not be guaranteed. In addition, the position and the time at which the largest beam is placed are the most difficult problems that we will address in this article.  
Figure 3: The relationship between noise power and interference caused by the largest-sized beam.  
It can be seen that the smallest beam has the smallest off axis angle to avoid interference thanks to its fast-decaying gain curve. It is not easy to cause interference; the reverse is true as well. The experimental results are in line with existing research findings.  
Figure 4: The relationship between noise power and interference caused by a middle-sized beam.  
Figure 5: The relationship between noise power and the interference caused by the smallest-sized beam.
## 3 Experimental Results
### Qualitative Evaluation  
The qualitative evaluation was performed on various groups of tongue images with different circumstances to assess the robustness and effectiveness of the proposed system. As mentioned in Section 1, tongue images can be divided into the following groups: (1) Tongue with an apparent gap in the mouth, (2) Tongue with abnormal color, (3) Tongue with abnormal texture, (4) Tongue with teeth showing, (5) Tongue with irregular poses, (6) Tongue not completely protruding, (7) Tongue with teeth imprints on the edges, and (8) Tongue closely surrounded by lips. Figure 10 shows the segmentation results of the above groups on some samples from the dataset. The highlighted regions in each sub-figure are the segmentation results. As can be seen in each group, tongue regions are segmented precisely by TongueNet under different circumstances, which demonstrates its robustness. In particular, the edge pixels are predicted well, indicating that TongueNet is capable of handling various changes on the tongues. Furthermore, the segmented tongue regions are well separated from its surroundings, such as the lips and teeth. Overall, the proposed TongueNet system achieves a promising segmentation performance.  
Figure 9: Samples from the mask set corresponding to the tongue images in Figure 8.  
Figure 11 displays eight pairs of comparison groups with the with the proposed TongueNet (lowercase) and the results of REF [18] (uppercase). Noticeably, we made comparisons with REF as it is the most representative and currently the best tongue-oriented segmentation method among various state-of-the-art methods. From Figure 11a-e,g,A-E,G, it can be observed that TongueNet exactly segmented the tongue without teeth and lips, while REF failed to produce a clean segmentation. This clearly shows the superiority and robustness of the proposed method when the surroundings are complicated. If the color, shape, and texture of the tongue are abnormal, TongueNet can also perform a smooth segmentation (refer to Figure 11f,F,h,H).  
Figure 10: Segmentation results of TongueNet in qualitative evaluation (highlighted). (**a**) and (**b**) results of a tongue with an apparent gap in the mouth; (**c**) and (**d**) results of a tongue with abnormal color; (**e**) and (**f**) results of a tongue with abnormal texture; (**g**) and (**h**) results of a tongue with teeth showing; (**i**) and (**j**) results of a tongue with irregular poses; (**k**) and (**l**) results of a tongue not completely protruding; (**m**) and (**n**) results of a tongue with teeth imprints on the edges; (**o**) and (**p**) results of a tongue closely surrounded by lips.
# The _E. coli_ Global Regulator DksA Reduces Transcription during T4 Infection
###### Abstract  
Bacteriophage T4 relies on host RNA polymerase to transcribe three promoter classes: early (Pe, requires no viral factors), middle (Pm, requires early proteins MotA and AsiA), and late (Pl, requires middle proteins gp55, gp33, and gp45). Using primer extension, RNA-seq, RT-qPCR, single bursts, and a semi-automated method to document plaque size, we investigated how deletion of DksA or ppGpp, two _E. coli_ global transcription regulators, affects T4 infection. Both ppGpp\({}^{0}\) and \(\Delta\)_dksA_ increase T4 wild type (wt) plaque size. However, ppGpp\({}^{0}\) does not significantly alter burst size or latent period, and only modestly affects T4 transcript abundance, while \(\Delta\)_dksA_ increases burst size (2-fold) without affecting latent period and increases the levels of several Pe transcripts at 5 min post-infection. In a T4_motA\({}^{\textit{min}}\)_ infection, \(\Delta\)_dksA_ increases plaque size and shortens latent period, and the levels of specific middle RNAs increase due to more transcription from Pe's that extend into these middle genes. We conclude that DksA lowers T4 early gene expression. Consequently, \(\Delta\)_dksA_ results in a more productive wt infection and ameliorates the poor expression of middle genes in a T4_motA\({}^{\textit{min}}\)_ infection. As DksA does not inhibit Pe transcription in vitro, regulation may be indirect or perhaps requires additional factors.  
bacteriophage T4; DksA; MotA; RNA-seq; transcriptome analysis +
Footnote †: journal: Physics Letters B
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 2 LoRaWAN
### Medium Access  
The LoRaWAN specification defines three modes of MAC layer operation, namely, Classes A, B, and C. Depending on the needs of a LoRaWAN application, the mode for a low-power operation (Class-A), for a lower latency (Class-B) or for a high data availability (Class-C) can be selected.  
In the Class-A operating mode, LoRaWAN nodes utilize the simple pure ALOHA medium access method. Hence, the nodes transmit their payloads, in a scheduled or in an event-based manner, towards a gateway without channel-activity sensing. The gateway can, in turn, communicate towards the nodes via downlink connection. The downlink packets include mostly the acknowledgement packets utilized for confirmed messaging and the MAC commands. For the downlink data, as illustrated in Figure 2, two reception time slots on the node, often referred to as RX1 and RX2, are activated in Class-A mode. The reception times are scheduled 1 s or 2 s after the transmission of an uplink LoRaWAN payload has been finished. This way, LoRaWAN nodes can spare energy,y as the microcontroller and the LoRa modem of a node can enter sleep mode between uplink packets and between the transmission and the reception windows.  
The LoRaWAN Class-B operation mode is devoted to higher data availability. As an extension to Class-A, Class-B introduces periodical reception time windows, which can be helpful to implement long-range applications, where a continuous bidirectional  
Figure 1: Overview of the LoRaWAN network architecture.  
data transfer is required. Nodes synchronize themselves to the broadcast synchronization beacon message.  
Class-C extends data availability beyond the options given by Class-A and Class-B. With an unlimited reception time window, nodes can immediately receive downlink packets, which is beneficial in terms of low-latency applications. As nodes are required to be activated periodically for receptions in Class-B/C modes, the energy consumption is higher as activation of sleep modes become more sparse. Thus, these modes are better suited for mains-powered, always-on applications, such as smart meters.  
Next to data packets, the LoRaWAN specification defines several MAC command packets that are delivered bidirectionally between a node and a gateway. These commands can be utilized to, e.g., set the maximum aggregated transmit duty cycle of the node, inquire the status of the node and request to change downlink reception frequency. The complete list of MAC commands can be found in [6].
## 5 Experimental Tests Results, and Discussion
### Effect of Water Flow Rate on the Cold Plate  
The water flow rate inside the cold plate and its effect on the final temperature is plotted as a function of time in Figure 15. The water flow rate was varied to 3.04, 2.16 and 1.80 L/min at a constant current supply of 4 A. It can be observed from Figure 14 that the temperature drops as the flow rate is decreased. Furthermore, the temperature at a different time is for the various water flow rate is presented in Table 6.  
Figure 12: Total heat removal rate from the cold side of the TEC module at various inlet air flow rates.  
Figure 13: COP of the system at various inlet air flow rate.  
Figure 14: Temperature distribution inside the closed room at various inlet air flow rates.  
Figure 15: Temperature distribution at various water flow rates inside cold plate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time (s)** & **3.04 L/min** & **2.16 L/min** & **1.80 L/min** \\ \hline
5000 & 13.4 \({}^{\circ}\)C & 12.5 \({}^{\circ}\)C & 12.2 \({}^{\circ}\)C \\
10,000 & 11.3 \({}^{\circ}\)C & 10.1 \({}^{\circ}\)C & 9.9 \({}^{\circ}\)C \\
15,000 & 10.7 \({}^{\circ}\)C & 9.4 \({}^{\circ}\)C & 9.2 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 6: Temperature distribution for various water flow rates inside the cold plate.  
It can be observed from the table that the temperature decreased with a decrease in the water flow rate. One of the reasons for this can be that once the flow rate drops, it has more time to interact with hot surface and the heat removal rate increases as the amount of heat carried by the water increases based on the heat capacity of the liquid. Overall, there is a 15% decrease in the temperature noticed when the flow rate decreased from 3.04 to 1.80 L/min. Furthermore, there is no significant change for the bulk mean temperature when the flow rate varies (see Figure 16). Moreover, it maintained to lower temperature in the range of 20 \({}^{\circ}\)C-22 \({}^{\circ}\)C which also implies a good heat removal rate. Additionally, the graph follows the same pattern as variable fan speed; first, the temperature rises, then it drops to its minimum value, and finally, it remains constant.  
There is no significant effect on the heat removal rate by changing the water flow rate inside the cold plate. The graph is plotted as a function of time for the closed room temperature in Figure 17. It can be observed that there is no significant change inside the temperature. The temperature is almost similar for the 2.16 and 1.80 L/min flow rates. However, the temperature varies between 21 \({}^{\circ}\)C-23 \({}^{\circ}\)C at variable water flow rate inside the cold plate changes from 3.04 to 1.80 L/min.  
Figure 16: Bulk mean temperature at various water flow rates inside the cold plate.