esa-sceva/satcom-chunk-collection huggingface dataset:
# VIIRS Day/Night Band--Correcting Striping and Nonuniformity over a Very Large Dynamic Range
## 3 Results
### Evaluating Causes of Striping and Nonuniformities
#### 3.2.2 Residual Uncorrected Stray Light in the Earth-View  
The DNB stray light correction works very well in removing the effects of stray light, but it is not perfect [1; 10; 22]. There are at least four shortfalls to this method:  
1. The correction uses \"dark\" new moon scenes to characterize the background stray light, but in reality these scenes are not completely dark, since they contain variable amounts of nightglow. The nightglow is estimated from areas where there is no stray light, but since nightglow is not uniform over the scene [31] this variation leads to over/under-correction.
2. The correction look-up tables are constructed as a function of solar zenith angle. In reality, stray light intensity is also a function of solar azimuth angle, and this changes seasonally over the year. This azimuthal dependence is handled empirically; via monthly updates of look-up tables but for times between these synoptic updates the changes in actual stray light cause over/under-correction. This can be somewhat alleviated by taking advantage of the year-to-year 9-days shift in the lunar cycle and using tables created from the previous years. This assumes, however, that the stray light does not change long-term from year to year for other reasons.
3. The stray light cannot be characterized for scenes with twilight illumination, so correction in these areas are estimated either through extrapolation [22] or by setting it to a constant based on the solar zenith angle [10]. Neither of these estimates are perfect, and so this results in residual over/under-correction.
4. Changes in offset between the time the correction tables are made and when they are applied can cause additional striping due to drifts in offset described previously. This is because there is striping in the signal used to estimate airglow, and these offset errors become a part of the correction tables. When the offsets drift, their effect remains in the tables and it creates additional striping in the stray light corrected scene.  
The combined effect of all of these factors are seen in Figure 6. Regarding item #3, there is a stray light feature in the twilight region that appears for at least part of the year that is seen only in the southern hemisphere and becomes most prominent in from September through December [35]. It does appear, however, in a weaker form in other months and was first observed in an image from 19 July 2012 [22]. Figure 8 shows an NCC image containing this feature. The artifact affects about 1.2 million km\({}^{2}\) per orbit, and appears in every orbit over the same solar zenith angles. Because of the twilight background, it cannot be easily characterized, and thus cannot be easily corrected [35].
## 1 Introduction
[22] investigated the effects of the nozzle position and angle on flow characteristics and cleaning efficiency through simulation and experiments. Such nozzles are small-diameter pipe sections installed on tools. When the fluid velocity is high, the jets used for cleaning cannot achieve the highest energy conversion efficiency and optimal cleaning effect. Therefore, the development of conical straight nozzles has attracted attention. The converging section of a conical straight nozzle can accelerate the fluid and concentrate fluid energy, which has been mainly applied in the petroleum field [23] ([PERSON] et al., 2021). [PERSON] et al. [24] analyzed the influence of jet key parameters on jet flow characteristics based on the SST \(k\)-\(\omega\) model. The results show that increasing the jet depth and cone angle will increase the air entrainment rate. [PERSON] et al. [25] found that under the same conditions, the jet generated by a streamlined nozzle performs significantly better than a conical nozzle in practical applications. [PERSON] et al. [26] further demonstrated through numerical simulations that the energy loss of streamlined nozzles is lower than that of conical nozzles. Red tide is a common ecological phenomenon in coastal waters. In areas where red tide occurs, marine organisms such as fish, shrimp, and shellfish will die in large numbers due to poisoning or lack of oxygen, leading to severe damage to the marine ecosystem [27]. A special clay used for red tide control is mixed with water and sprayed onto the water surface to achieve the purpose of controlling red tide in China [28]. It is necessary to study the influence of nozzle structure and jet parameters on jet flow characteristics to improve the spraying effect. In the study, the computational fluid dynamics (CFD) numerical simulation method is used to investigate the flow characteristics of high-pressure water jet in the air using a model [29; 30]. The changes in the flow velocity, turbulent characteristic parameters, and air content of the jet are analyzed by controlling the nozzle diameter, cone angle, and orifice diameter. The equivalent diameter classification results of the influence range at the jet outlet under different conditions are discussed by using statistical methods such as systematic clustering analysis. The study identified the optimal parameters of the nozzles, and the results provide scientific guidance for the selection of jetting devices for the control of red tides at sea.
# Toward a Modern Last-Mile Delivery: Consequences and Obstacles of Intelligent Technology
## 4 Challenges of Modern LMD
### Feedbacks  
The literature provided touches on a variety of topics, one of which is the feasibility of using a robot-based delivery system. A customer places their order and a local delivery robot makes its way to the vendor to pick up said order. Then, the robot transports it to the customer's front door, and the delivery robot can be tracked using an app, which also unlocks the secure cargo compartment. This topic has been discussed from both theconsumer and societal perspectives, and the results of those discussions will be presented in the following section. The fact that pedestrians must share sidewalks with delivery robots poses a potential challenge for the operation of a robot-based delivery system in congested areas [37]. The authors did, however, emphasize the importance of considering this in future studies, stating that it will play a significant role in the future implementations of robot-assisted delivery trucks as an LMD system. This was an issue that their study did not address, but the authors did emphasize the importance of including it in future studies [37]. Incidents involving pedestrians and autonomous robots sharing sidewalk space have already occurred in some locations, according to a separate article written by [PERSON] and [PERSON] [39].  
Another example is that autonomous drones do not recognize other types of objects as well as manned aircraft, posing a risk to public safety [101]. According to [PERSON] and [PERSON] [39], great technologies almost always face widespread opposition before society accepts them. It is possible that this will also be the case with future land-based robot deliveries. The disparities in legal systems that exist between nations may make the use of robots more difficult in the near future, rather than making it easier. As a result, it has been argued that in the relatively near future, patience will be required for society to recognize the benefits of this new technology [39].  
[PERSON] et al. [41] investigated VRPTWDR, an issue known as vehicle routing problem with time windows and delivery robots. According to the writers of this article, one of the most essential features of deliveries is that not all customers are able to accept deliveries made by delivery robots; instead, some clients will have to be supplied by a regular van. This means that the addition of delivery robots to traditional LMD methods may be beneficial in the future. It is possible that this will change in the future as consumers become more accustomed to this novel and cutting-edge method of delivery [41].
# Multi-Body Dynamics Modeling and Simulation of Maglev Satellites
## 2 Modeling of Three Body Dynamics of Maglev Satellite  
Unlike traditional satellite attitude control platforms, LFMP breaks the limitation that imaging satellites can only image after their attitude is stable. It combines the \"instability\" of attitude maneuver with the \"stability\" of high-precision imaging, allowing for continuous control of payload stability and pointing during ultra-agile maneuvers. LFMP inherits the non-contact advantages of the DFP platform and is divided into a multi-body structure with an SM (Support Module), MM (Maglev Module), and PM (Payload Module). At the same time, it uses Lorentz force magnetic bearings (LFMBs) support to stabilize and suspend the PM inside the MM. LFMP is jointly controlled by two poles, three rotating Lorentz force magnetic bearings (RLFMBs), coaxial two orthogonal Lorentz force magnetic bearings (OLFMBs), and a linear Lorentz force magnetic bearing (LLFMB) to complete the 7-DOF universal motion. The RLFMB controls the 2-DOF universal agile maneuver, the OLFMBs and LLFMBs control the 5-DOF stable suspension, and the three types of magnetic bearings work together to complete the complex 3-body 7-DOF motion. The structure of the maglev satellite is shown in Figure 1.  
To improve the accuracy of dynamic modeling for satellite systems containing LFMBs, this paper establishes dynamic models for platform subsystems and payload subsystems based on the second type of Lagrange equation.  
As shown in Figure 2, based on the constraint methods between the various components of the satellite system containing the LFMB, in the dynamic modeling process, it is considered that the SM and MM constitute the platform subsystem, while the PM and imaging payload constitute the payload subsystem. This section establishes dynamic models of the platform subsystem and payload subsystem based on the principle of the second type of Lagrange equation by formulating corresponding assumptions.  
**Assumption 1:**: _Except for the bilateral solar wings of the satellite body, all components of the satellite system containing LFMP are assumed to be rigid bodies._  
**Assumption 2:**: _When the PM is actively maneuvering, the rotational speed is low, and the changes in the moment of inertia during the rotation of the PM and MM are ignored._
## 4 Results
### Performance
#### 4.2.2 LBM Performance  
The performance \(P\) of LBM implementations is often measured in _lattice updates per second_ (LUPS), which define the number of processed nodes per unit of time. However, direct comparison of \(P_{LUIPS}\) values for different implementations is difficult since the amount of processed data per node depends on both the lattice arrangement and the data type, usually either single (32 bits) or double (64 bits) precision floating-point number. Since LBM implementations are usually bandwidth-bound on available machines, in this work, we then define LBM performance as a theoretical, minimal bandwidth \(P_{B}\) required to achieve given LUPS for specific lattice arrangements and data types. Let \(s_{d}\) denote the size (number of bytes) required to store a single number (\(s_{d}\in\{4,8\}\) for single- and double-precision floating-point numbers). Assuming that, in an ideal case, processing of a lattice node requires only read and write of all \(q\) functions \(f_{i}\), the minimum amount of transferred data per single node is \(B_{node}=2\cdot q\cdot s_{d}\) bytes. The LBM implementation performance can be then defined as \(P_{B}=B_{node}\cdot P_{LUIPS}\). We can also define a theoretical bandwidth utilisation \(U_{B}=P_{B}/B_{peak}\), where \(B_{peak}\) denotes the maximum theoretical memory bandwidth of a given machine. For the GTX Titan Xp GPU, \(U_{B}=C\cdot P_{GLUPS}\), where \(C\in\{0.131,0.263\}\) for single and double precision, respectively, and \(P_{GLUPS}\) is performance in \(10^{9}\) LUPS. The values of \(U_{B}\) can be then compared for different machines showing how much room for potential improvements is still available. However, the \(U_{B}\) coefficient does not take into account the additional limitations of the machine that prevent it from achieving full memory bandwidth. The performance of the LBM kernel was measured for dense and sparse geometries, different data structures, and single and double precision numbers. Results for sparse data structures for dense geometry can be treated as an estimation of introduced overheads compared to the dense memory layout. Measurements were conducted using Python _time.perf_counter_ for 1000 kernel calls. Before measurements, each kernel was called 100 times to force the runtime compilation and warm up the whole system. For sparse data layouts, we turned on the experimental _async_mode_ available in Taichi that disables analysis of geometry sparsity before each kernel call because, in our case, the geometry is static during computations. This mode allowed increase in performance, but we observed sporadic problems with stability. Performance for Dense Geometries  
As an example of dense geometry, we used the cavity case at different mesh resolutions. Measurements for each data point require more than 20 s because of kernel compilation. Thus, we limited the number of checked mesh resolutions to about 100 on a logarithmic scale. To keep results comparable, all measurements for different data types and layouts are for the same set of geometry resolutions. The obtained performance is shown in Figure 6 and Table 2.
## 3 Models and Methods
### Proposed Approach for Collectively Summurizing Text Streams
```
1:INPUT: MS[1, 2,..., m] // Message Streams
2:OUTPUT: RTC[1, 2,..., \(\epsilon\)]; HTS[1, 2,..., m] // Real-Time Event Cluster
3: Begin
4:for each MS[i]
5:for each RTC[j]
6:if(d(\(\cdot\)) - \(\Theta\) < 0) then
7: add RTC[j] into HEC and revom RTC[j];
8:else
9: simOfMS&RTEC \(\leftarrow\) sim(MS[i], RTEC[j])*d(\(\cdot\));
10:if(simOfMS&RTEC > maxSimOfMS&RTEC) then
11: maxSimOfMS&RTEC \(\leftarrow\) simOfMS&RTEC;
12:idOfMaxC \(\leftarrow\) j;
13:else continue;
14:endfor
15:if(maxSimOfMS&RTEC < \(\Theta\)) then
16: create a new RTEC for MS[i];
17:else
18: merge MS[i] to RTEC[idOfMaxC];
19:endfor
20:until no messages is posted
```  
**Algorithm 2** Self-adaptive stream clustering  
The second part of the event-message accumulation and summarization algorithm is to filter the event information through the BERT classifier, and then use the vector of the filtered message obtained by the Sentence-BERT model and put it into the DBSCAN algorithm to cluster the event information into For each sub-event, the summary content is finally obtained through the T5 model. The proposed method for event-message accumulation and summarization algorithm is shown in Algorithm 3. ```
1:INPUT: TM[1, 2,..., n] // Text Message
2:OUTPUT: HT[1, 2,..., \(\epsilon\)]; HTS[1, 2,..., m]; EHT[1, 2,..., k] // Hot Topic
3:Begin
4:for each TM[i]
5: Text \(\leftarrow\) BERT Classifier Model(TM[i])
6: Sentence Embedding \(\leftarrow\) Sentence-BERT Model(Text)
7: HT \(\leftarrow\) DBSCAN(Sentence Embedding)
8:for each HT[j]
9: HTS \(\leftarrow\) T5 Text Summarization Model(j)
10:endfor
11:end
```  
**Algorithm 3** 2 nd Pass event-message accumulation & summarization
# Energy-Aware Security Adaptation for Low-Power IoT Applications
## 4 Dynamic Security and Energy Adaptation
### Analytical Evaluation in the Context of LoRaWAN
\begin{table}
\begin{tabular}{l l r r r r r r} \hline \hline \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \hline \multirow{3}{*}{**Scenario**} & \multirow{3}{*}{**Algorithm**} & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multirow{3}{*}{**Random**} & Logic & 306,698,115 & 208,301,610 & 306,698,150 & 208,301,610 & 379,677,515 & 224,068,215 \\ \cline{2-9}  & Logic & 131\% & 57\% & 131\% & 57\% & 186\% & 69\% \\ \cline{2-9}  & Delta & 148,947,695 & 117,738,690 & 148,947,770 & 117,738,710 & 149,992,340 & 117,797,305 \\ \cline{2-9}  & Logic & 12\% & \(-11\%\) & 12\% & \(-11\%\) & 13\% & \(-11\%\) \\ \hline \multirow{3}{*}{**High 90\%**} & Logic & 398,231,705 & 362,629,505 & 398,231,705 & 362,629,475 & 623,803,770 & 539,511,990 \\ \cline{2-9}  & Logic & 200\% & 173\% & 200\% & 173\% & 369\% & 306\% \\ \cline{2-9}  & Delta & 157,904,815 & 137,406,450 & 157,904,925 & 137,406,470 & 165,193,705 & 142,853,115 \\ \cline{2-9}  & Logic & 19\% & 3\% & 19\% & 3\% & 24\% & 8\% \\ \hline \multirow{3}{*}{**High 80\%**} & Logic & 468,039,540 & 390,846,515 & 468,039,545 & 390,846,515 & 1,124,969,280 & 604,671,550 \\ \cline{2-9}  & Logic & 252\% & 194\% & 252\% & 194\% & 747\% & 355\% \\ \cline{2-9}  & Delta & 254,661,730 & 153,298,930 & 254,661,750 & 153,298,950 & 297,894,645 & 160,122,900 \\ \cline{2-9}  & Logic & 92\% & 15\% & 92\% & 15\% & 124\% & 21\% \\ \hline \multirow{3}{*}{**Medium 40\%**} & Logic & 889,946,800 & 553,140,925 & 889,946,820 & 553,140,930 & 3,337,300,455 & 1,660,158,380 \\ \cline{2-9}  & Logic & 570\% & 316\% & 570\% & 316\% & 2412\% & 1149\% \\ \cline{2-9}  & Delta & 889,946,660 & 360,221,480 & 889,946,780 & 360,221,720 & 883,662,985 & 439,623,500 \\ \efficient regarding energy usage. Ref. [19] also compares how different spreading factors affect power consumption in LoRaWAN, with SF = 12 being the most expensive in terms of energy usage and the worst case in power consumption. For that reason, the chosen results for this evaluation consider a SF = 12, given that it provides support for a worst-case scenario analysis of the behavior of the algorithm. Transmission costs in terms of energy are thus used together with the cost of the various security levels decided by the controller. We start by addressing the configuration of the scenarios considered in this evaluation.
## 2 Data and Methods  
To quantify the geometric characteristics and coverage of MS-BiSAR Earth observation, it is necessary to establish precise geometric relationships between the Moon, the satellite, and Earth. This can be achieved by utilizing ephemeris and Earth orientation parameters (EOP) to determine the attitude and position of both celestial bodies. Subsequently, the positions of the satellites in the three orbits can be calculated using an orbit propagator. Once the attitude and position are transformed into a unified reference system, we can proceed with the quantitative analysis. Figure 2 illustrates the main workflow of this process.  
Figure 1: A conceptual illustration of MS-BiSAR Earth observation.
# Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model
## 4 Discussion
### Adaptability of Plant Community Photosynthesis to Spring Precipitation  
Warmer air temperature in spring can increase the rate of leaf out for the understory, which in turn can advance plant photosynthesis and may lead to greater carbon sequestration [32]. Under the background of seasonal spring warming, if there was no severe drought during the green-up phase, the photosynthetic capacity of the plant communities was unaffected, advancing the start of summer (SOP). One potential reason for this phenomenon may be the long-term regional climate; March is historically the second highest month of precipitation (Appendix A, Figure A1), and the plant community has already adapted to higher precipitation during this time, which allows for accelerated green-up. However, when short-term drought occurs in March, it delays the green-up phase, reducing productivity and delaying SOP.
# Caroenoid Production from Microalgae: Biosynthesis, Salinity Responses and Novel Biotechnologies
## 2 Health-Promoting Carotenoids from Microalgae and Their Biofunctions
### Health-Promoting Carotenoids and Their Production from Microalgae
#### 2.2.5 Fucoxanthin  
Fucoxanthin is a natural carotenoid that occupies nearly 10% of total natural carotenoid production [62]. The global production of fucoxanthin was about 500 tons in 2016 and was estimated to increase further with an annual rate of 5.3% [63]. As shown in Figure 2, it has a unique molecular structure with an allelic bond, a conjugated carbonyl, a 5,6-monoopoxide and acetyl groups. Fucoxanthin and its derivatives have shown potential anti-cancer [64], anti-inflammatory [65] and anti-obesity effects [46].  
Currently, the main natural sources of fucoxanthin rely on some macroalgae species, such as _Laminaria japonica_ and _Undaria pimmatifid_, which are common in Asian diets for iodine supplement [66]. However, the fucoxanthin contents in these macroalgae are relatively low (0.02-0.58 mg/g fresh weight) and not feasible for commercialization. Microalgae grow faster than macroalgae with higher productivity of carotenoids. Fucoxanthin is a primary light-harvesting carotenoid that transfers energy to LHC with high efficiency (>80%) [67] and may also protect microalgae from high light with its internal antioxidant property [47]. Up until now, research about producing fucoxanthin from microalgae is still relatively lacking.  
Microalgae are potential candidates for fucoxanthin production. Fucoxanthin mainly exists in heterokont and haptophyte groups of algae (>20,000 species, especially in Synuro-phyceae (up to 26.6 mg/g DW), diatoms (up to 21.67 mg/g DW) and Prymnesiophyceae (up to 18.23 mg/g DW) [45]. Under low light intensity, carbon skeletons are diverted into carotenoids, especially fucoxanthin in _Isochrysis zhanjiangensis_, which can produce 23.29 mg/g DW of fucoxanthin [46]. The marine diatom _Odontella aurita_ can accumulate 18.47 mg/g DW of fucoxanthin in a nitrogen-replete medium under low light [47]. Moreover, the purified fucoxanthin from _O. aurita_ is (_all-E_)-fucoxanthin, which owns strong antioxidant ability and bioavailability. [PERSON] et al. isolated a novel strain _Mallomonas sp._ (Synurophyceae) with the highest known content of fucoxanthin at 26.6 mg/g DW [45]. The highest ever reported fucoxanthin productivity was found in _Tisochrysis lutea_ with 9.81 mg/L/day under batch culture with continuous chemostat dilution [49].  
Diatom _Plaedactylum tricontrum_ also has a high fucoxanthin content at 16.33 mg/g DW [50]. As _P. tricontrum_ is a model system for investigations and its genome has been sequenced, further exploitations of key genes related to fucoxanthin, fucoxanthin hyperaccumulating mutants and genetic modifications of diatoms for fucoxanthin production are expected in the near future. Results of satisfactory fucoxanthin production from microalgae are summarized in Table 2 with details of contents and productivities.