esa-sceva/satcom-chunk-collection huggingface dataset:
# Guineensine: Isolation, Synthesis, and Biological Activity
## 3 Total Synthesis and Reactions
### Total Synthesis
#### 3.1.1 The [PERSON] et al. Approach (1979/1984)  
[PERSON] et al. developed a convenient route for the synthesis of guineensine (Scheme 1). The protocol was initially published in 1979 [53] and republished in 1984 in an easier-to-access literature source with more experimental details [23].  
The acylation of 1,2-(methylenedioxy)benzene **22** with azelaic acid monomethyl ester chloride **23** gave the intermediate ester **24**, which subsequently reduced with NaBH\({}_{4}\) and dehydrated to afford the styryl ester **26**. Ester **26** was transformed to the corresponding aldehyde in two steps by LiAlH\({}_{4}\) reduction and then modified Collins oxidation [54] of the produced alcohol **27**. However, the authors did not provide a description of these modifications. Next, the _trans_-double bonds, along with the terminal carbonyl group were introduced by Wittig olefination. Finally, the acid **31**, which was obtained from ester **30** by base hydrolysis, reacted with excess isobutylamine in the presence of SOCI\({}_{2}\) to afford guineensine **1**. The final product was purified by preparative TLC.  
This method uses mild conditions, commercially available reagents and reactants, and easy handling of the final and intermediate products. Moreover, no tedious purifications steps are needed making the protocol easy to replicate. However, it suffers by low overall yields, mainly due to low yields of the Wittig and amidation reaction steps.  
3.1.2. The [PERSON] et al. Approach (1980)  
One year later, [PERSON] et al. reported an alternative route for the synthesis of guineensine, which is also based on Wittig reactions--two in this case--as key steps (Scheme 2) [55].  
The authors utilized 1,8-octanediol **32** as the starting material. Diol **32** was partially protected with the tetrahydropropylanyl (THP) group and oxidized to the corresponding aldehyde **34** to serve as one of the two Wittig components. Reaction with the triphenyl phosphorane derivative **36** resulted to the formation of the intermediate **37**, which contained the tail group of the target molecule **1**. In the next steps, the THP-protected alcohol **37** was deprotected using PTSA and oxidized to afford **28**, which gave a modified Wittig olefination with phosphonocrotonate reactant **39** to incorporate the diene moiety. The final two steps were in line with the previously reported method and included the hydrolysis of ester **40** and amidation of the carboxylic acid **31**, using POCI\({}_{3}\) this time, to finally form guineensine **1**. The final product was purified using column chromatography and crystallization.  
The Vig methodology is as convenient and easy to perform as the previously reported method by [PERSON] et al., since both of them are performed in eight steps and require no difficult techniques or hard to obtain reagents. Typically, this protocol seems to be more efficient, although no yield for the final step is reported.
## 6 Results and Analysis
### Robotic Grasping
#### 6.4.2 Robot Grasping in Cluttered Scenes  
To further validate the effectiveness of our grasp detection algorithm in challenging cluttered environments, we conducted grasping tests by randomly placing 10 to 15 different objects to create a cluttered scenario. The robot executed grasping tasks in these cluttered environments. As shown in Figure 17, in 200 attempts at cluttered grasping, our system achieved an impressive success rate of 91% (182/200). This can be attributed to the model's pixel-level grasp configuration learning, enabling the extraction of grasp-relevant features and facilitating generalization. Thus, the model can effectively infer grasp configurations in cluttered scenes, and the robot also executes the optimal grasp based on the inference results. The experimental results demonstrate the effectiveness of our grasp detection model even when the targets are cluttered.  
Figure 15: Single-object grasping experiment.  
Furthermore, Table 5 presents a comparative analysis of different algorithms' performances when handling the objects in cluttered scenes. It is clear that the proposed method outperforms the existing similar methods, which effectively grasps unknown objects in cluttered settings. The main reason for the suboptimal performance of these methods lies in the insufficiency of feature extraction. The network design in [4] is overly simplistic, leading to insufficient expressive capability of the model. The researchers in [8] utilized the Region of Interest (ROI) feature extraction method from Fast R-CNN. However, the ROI extractor resized each candidate box to a fixed size, which could potentially lead to information loss or deformation, thus impacting subsequent feature extraction. In the studies [19; 40], a cascading model approach was utilized, where errors may accumulate gradually during the concatenation process, potentially resulting in larger final output errors compared with our approach with single-model output [41]. Although the grasp success rate is close to ours, their feature extraction network utilized six dense blocks, each containing convolutional modules, whereas our backbone extraction network only employed four convolutional modules [42] and two branches in the grasping network, with one branch dedicated to generating bounding boxes. However, if the detector fails to detect objects, the grasp will become ineffective, leading to performance degradation. Our model overcomes the shortcomings of the aforementioned methods.  
Figure 16: Grasping in multi-object scenes.
## 6 Results
### DTN Routing
The idea is to increase the message delivery ratio and decrease the message dropping, i.e., throwboxes forward messages. They can be placed where there is heavy traffic or for a sparsely populated network, at the point of contact with most nodes. It can be random or planned, fixed, or mobile. The mobile throwbox resulted in a higher delivery rate; less latency was found in the planned placement of the mobile throwbox and the same for contacts per hour [29]. Ad-hoc networks are not stable; therefore, they can take advantage of opportunistic contact to communicate. This can be applied in socially aware networking, especially because of the store-carry-and-forward capability and the need to make better predictions. Exploring the social relationships of people, animals, and vehicles carrying mobile devices can help predict mobility patterns and contact opportunities. Social Aware Networking (SAN), social opportunistic networks, vehicle social networks, and opportunistic Internet of Things (IoT) share the theory behind mobile nodes and social networks [7]. Using DTN-based robots with sensor localization can create topological maps as they work as relays for disconnected parts of the network [48]. As we can see, terrestrial applications for DTN are extensive and most important to point out is the smart cities' use. DTN has not only been experimented on in rural areas with limited or no telecommunication infrastructure but also in city environments together with other technologies. From our point of view, a user-friendly and common framework for this technology is lacking to pass the experimentation phase and be adopted massively.
# Axial Bearing Mechanism of Post-Grouted Piles in Calcareous Sand
## 3 Analysis of the Loading Test Results
### Load Transfer Response of a Single Pile  
The load transfer response is a comprehensive reflection of the axial bearing behavior of a single pile, which can reflect not only the mobilization of shaft resistance but also the bearing performance of the pile tip. The axial force distribution of single piles MP1 to MP6, under different loading levels, can be obtained by the data collected from strain gauges. The strain is measured by the strain gauge arranged on each pile section, and the average strain value \(\varepsilon_{i}\) of pile section \(i\) can be calculated as follows:  
\[\varepsilon_{i}=\frac{(\varepsilon_{i1}+\varepsilon_{i2})}{2} \tag{1}\]  
where \(\varepsilon_{i1}\) and \(\varepsilon_{i2}\) are the strain values measured by the strain gauges on both sides of pile section \(i\).  
After the average strain value of pile section \(i\) is obtained, the axial force \(N_{i}\) of pile section \(i\) can be calculated as follows:  
\[N_{i}=E_{\text{m}}A_{\text{m}}\varepsilon_{i} \tag{2}\]  
where \(E_{\text{m}}\) is the elastic modulus of a single pile and \(A_{\text{m}}\) is the cross-sectional area of a single pile.  
The axial force distributions of each pile under different loading levels are shown in Figure 5.  
Figure 5 shows that the axial force of the pile decreases with increasing depth at different loading levels. The slope of the axial force curve is larger during the initial load increments, which shows that the mobilized shaft resistance of the pile and the axial force transferred to the lower segment of the pile shaft are small. With increasing load, the slope of the axial force curve decreases, which indicates that the shaft resistance of the pile gradually develops, that axial force is gradually generated at the lower part of the pile shaft, and the tip resistance is also gradually mobilized. This also fully illustrates that the side friction of the soil around the pile gradually develops from the head of the pile to the bottom of the pile during the downward transmission of the axial load along the pile shaft, and the attenuation rate of the axial load reflects the distribution of the shaft resistance. In addition, grouting will change the stress path of the soil around the pile, which will affect the load transfer responses of the pile. The attenuation rate of the axial force of grouted piles is higher than that of ungrouted piles. Thus, grouting at the side  
Figure 5: Measured axial force distributions for test piles at different loading levels: (**a**) MP1; (**b**) MP2; (**c**) MP3; (**d**) MP4; (**e**) MP5; (**f**) MP6.  
of the pile increases the effective horizontal stress in the soil surrounding the pile, which increases the shear strength of the soil, and the increase in shaft resistance can effectively offset the transmission of the axial force of the pile shaft.
# A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps
## 1 Introduction
Therefore, AD and FD algorithms are equally important in supporting predictive maintenance. Although there are some intelligent methods including both AD and FD [22; 23], or methods focused on unknown faults [24], they are currently not considering how to select the most suitable model among existing models quickly, and the question of how to fuse the different outputs of UAD and FD lacks research. This paper aims to provide an intelligent condition monitoring framework for ESWPs. To achieve this aim, a unique condition monitoring framework based on UAD and FD parallel implementation is proposed, and a model pump of an ESWP with five-channel vibration is built for essential data collection and model testing. Multi-channel data put together (together) or fused by mean fusion (fusion) are two attempted signal processing methods for UAD and FD. A model preselection algorithm based on the highest validation accuracy has been proposed to select the best UAD model from common UAD models and the best FD model from common classifiers. Expert intervention is suggested to support the updating of models for persistence. The contributions of this paper include the following:  
1. A model preselection algorithm is proposed to select the best model among existing models. 2. An information integration algorithm is proposed to integrate the output of UAD and FD to avoid misdetection and misdiagnosis. 3. The condition monitoring framework obtained by the parallel connection of UAD and FD provides a new method for obtaining the status of complex critical equipment. The remainder of this paper is organized as follows: The main theory related to basic UAD and FD models is introduced in Section 2. The test bench, dataset, and the proposed intelligent condition monitoring method are described in Section 3. All module and overall experiment results and related discussions are presented and analyzed in Section 4. Finally, the main conclusions are given in Section 5.
# Thermal Analysis of Radiation Heat Transfer of Improved Fractal Solar Collectors
## 1 Introduction
Parabolic-based, fractal-structured solar collectors have the potential to offer higher efficiency compared to traditional designs. Such designs increase the heat transfer surface area and optimize the fluid flow [38]. As a result, studies to increase the efficiency of solar energy systems focus on fractal-structured collectors, advanced parabolic concentrators, nanofluids, and innovative materials. The development of concave-based, fractal-structured solar collectors with different absorber surface designs is an important research topic in this field. Radiative thermal analyses and computational fluid dynamics (CFD) simulations [39] play a critical role in evaluating the performance of these new-generation collectors. In this study, the thermal properties of improved toroidal fractal solar collectors with parabolic dishes were evaluated. In addition, it is investigated how different numbers of toroidal absorbers in the fractal arrangement on the parabolic concentrator affect the efficiency of the proposed solar collector. In the experimental study conducted by [40], positive results were observed in obtaining hot water at low solar radiation thanks to the fractal arrangement of 3-row toroidal absorbers on a parabolic concentrator. The methodological basis for determining the transmission-absorption capacity in this study was taken as the basis of the power balance equation in the solar radiation heating mode with zero water consumption. It was found that the obtained energy indicators and the maximum water temperature significantly depend on the dimensions of the fractal absorbers. It was stated that in the spring and autumn periods, it is necessary to increase the absorption area in order to heat the water to higher temperatures and to provide the desired hot water flow. In order to increase the radiation surface absorption area stated in the experimental findings of the 3-row fractal structure, first the experimental results of the 3-row fractal structure were compared with the numerical analysis data. The 96.84% surface absorption area obtained from the 3-row toroidal fractal structure was also applied to the 4-row and 5-row toroidal fractal structures, and the numerical results obtained were presented comparatively in tables and graphs. When the numerical analysis results were compared, it was seen that the highest temperature increase was in the 5-row toroidal structure. With this proposed study, it was revealed that the dish-integrated fractal solar collector design can provide high efficiency in solar water heating applications.
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
## 6 Evaluation
### Multiple Queue Evaluation  
The multiple queue scenario is more common than the single queue scenario. Although the multiple queue scenario is complex, and it is not easy to find a representative case for evaluation, we still try to evaluate our scheme using the experimental topology shown in Figure 13. We use Ixia to send 1000 different flows for each NIC port separately, and the data plane hashes the five-tuple of received packets and sends them to the corresponding Tx queues based on the computed hash values. Each network port has the same input rate. We measured the forwarding latency metrics for each scheme when the total input rate is increased from 1 Gbps to 15 Gbps.  
Figure 14a shows the performance of each scheme in terms of average forwarding latency. In the [0, 8] Gbps interval, our scheme's maximum reduction in forwarding latency is about 4.35% relative to the original data plane (at an input rate of 4 Gbps). And in the [9, 15] Gbps interval, the maximum reduction is about 55.54% (at an input rate of 13 Gbps). In terms of the original data plane, there is a significant increase in forwarding latency (up to 32%) for input rates above 9 Gbps relative to the single queue scenario. This is because the average forwarding latency of the software data plane under the spin-polling mechanism rises with the number of queues, which is discussed in detail in paper [6]. Since this is not the focus of this paper, we do not go into depth. The forwarding latency performance of the Smart Batching scheme is also comparable to, or even slightly worse than, that of the original data plane. Figure 14b shows the CPU utilization of each scheme. The CPU utilization of our scheme is slightly more improved, but still not enough to fully utilize the performance of the software data plane at low input rates.  
Figure 14: The performance of each scheme on multiple queue scenario. (**a**) The average latency of each scheme under different input rates. (**b**) The CPU utilization of each scheme under different input rates.  
Figure 13: The topology of multiple queue evaluation.
###### Abstract
Therefore, STC can be considered in power grid frequency regulation to reduce communication costs and make the utilization of communication resources more efficient. In this paper, we propose a novel self-triggered control scheme employed in a frequency-controlled power grid. A power grid consists of many subsystems that interact with each other through communication networks and power flow. The proposed self-triggered controller calculates the triggering period with each state point to ensure the system's exponential stability, input-to-state stability, and low communication cost. In the proposed model, the triggering interval is a function of system state and triggering rate is proportional to communication cost. This paper has three main contributions as follows:  
* A self-triggered control scheme is applied to the frequency regulation of the power grid;
* An online optimization method is used to extend the triggering period for reducing communication cost; and
* Communication cost and parameters of control system for power grid are estimated and optimized, so that the cost of control system can be guaranteed under a required level. This paper is organized as follows. Section 2 introduces the model of the power system and the basic concept used in this paper. Section 3 presents the proposed self-triggered control scheme in which the exponential stability theory with varying sampling rate, control performance synthesis algorithm for the control system under the consideration of communication, physical cost and onlineoptimization for searching the maximal triggering period are elaborated. A simulation of frequency regulation with a self-triggered control scheme is illustrated in Section 4. Conclusions are given in Section 5.
# GNSS-R Observations of Marine Plastic Litter in a Water Flume: An Experimental Study
## 2 Materials and Methods
### Generation of Waves in the Tank  
During the data acquisition, two independent environmental parameters were changed. The first one was related to the waves that were generated, while the second one referred to the kind of plastics thrown into the flume. The Deltares Atlantic Basin [12] flume at Delft, the Netherlands is 75 m long and 8.7 m wide, as shown in Figure 4.  
Two different wave spectra were generated: (1) regular waves describing a periodic sinusoidal shape (or as close as possible to it) with a fundamental frequency of 0.833 Hz, and (2) irregular waves with a JONSWAP spectrum [13]. This wave spectrum relies on the data collected during the Joint North Sea Wave Observation Project, and was chosen to represent ocean wave conditions [13].  
Four different amplitudes (5, 9, 12, and 17 cm) were generated using mechanical pedals on the left side of Figures 1 and 4, and in selected cases, capillary waves were also generated on top of the long waves by means of a fan. Figure 5 shows several JONSWAP spectra realizations with a significant wave height of 9 cm, as measured by the wave gauges in the tank.  
Figure 4: Sketch of the Deltares Atlantic Basin where the ESA marine plastic detection experiments were conducted with approximate location of measurement instrumentation [6].  
Figure 5: Sample irregular wave JONSWAP spectrum measured by six different wave gauges places at regular intervals at Deltares premises (exact location of wave gauges is described in [6]).  
Moreover, capillary waves were also generated on top of the long waves by means of a fan, as they are weaker and easier to be dampened. Finally, distinct wave amplitudes were generated.
## 3 Materials and Methods
### Datasets and Evaluation Metrics  
MOT16 [55; 56]: The goal of this publicly available benchmark dataset is to perform the detection of multiple objects present in a frame. MOT16 consists of 14 challenging video sequences with equal distribution of 7 training and 7 testing sequences, where video sequences are carefully annotated. Each video sequence is different from the others according to the camera's static view or movement, the camera's viewpoint, and conditions like weather, humidity, temperature, etc. MOT16 includes only detector Deformable Part Models (DPM), which provide pictorial structures of an elegant framework for object detection [57].  
MOT17 [56]: The objective of this benchmark dataset is multiobject tracking. It consists of equally distributed training and testing video sequences, but it provides more accurate ground Truth than MOT16. It consists of 7 sequences, 5316 frames for training and 7 sequences, 5919 frames for testing. The major difference between MOT17 with MOT16 is that it has three types of detectors used in video sequences; Faster R-CNN, Deformable part model (DPM), and Scale-Dependent pooling (SDP). Faster R-CNN [58] has three major components; bottom convolutional layers, a region proposal network, and a bounding box regressor or region-of-interest (ROI) based classifier. Due to its flexibility, Faster R-CNN can carry out instance segmentation [59]. Scale Dependent Pooling (SDP) [60] technique takes care of scale variation in object detection problems. SDP tackles the fixed input size requirement by selecting a proper feature layer depending on the size of the input image to describe an object proposal.  
The comparison with other existing methods was carried out by training two models separately using the training data of MOT16 and MOT17. These models were checked for generalization using MOT16 and MOT17 test data. The most widely used CLEAR MOT Metrics [61] include Multiobject Tracking Accuracy (_MOTA_), Multi-Object Tracking Precision (MOTP), Identity Switch (_IDSw_), the Mostly Tracked Trajectory (MT), False Positives (_FP_), False Negatives (_FN_), and Mostly Lost Trajectories (MLT), have been used to determine tracking performance.