esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Cost Solution for Kinematic Mapping Using Spherical Camera and GNSS
## 2 Materials and Methods
### Case Study--Lipoud Street, Kopidno  
The measured street is in the small town of Kopidno (50.3362019 N, 15.2695339 E) in the north of the Czech Republic (Figure 3). In this place, a detailed survey was recently carried out by the Cadastral Office for the purposes of the cadastral, which uses only XY coordinates. The cadastral points in this area were used as a basis for referencing because  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**St. Dev. XY [mm]** & **St. Dev. Z [mm]** & **St. Dev. XYZ [mm]** \\ \hline
11 & 17 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Standard deviations of the GNSS RTK point field: Fleming Square.  
Figure 2: Map of the test site: Fleming Square.  
several checkpoints (e.g., fence and building corners) were surveyed using a Trimble S8 total station (Precision Laser & Instrument, Inc., Columbus, OH, USA) with reflectorless EDM, directly referencing these cadast markers. Since the official cadastral coordinates are defined in the national S-JTSK coordinate system, they were transformed into ETRS89. The GNSS RTK method was used to independently verify the coordinates of 10 selected points. The standard deviations of the differences between the cadastral coordinates and the GNSS-measured coordinates are given in Table 2. Only the first 7 points were used for referencing and checking, as the others had a higher error due to the tall trees along the street.  
The area was also mapped by photogrammetry using a Mavic 3E drone (DJI Enterprise, New York, NY, USA) with an RTK module. The point cloud was calculated in the Agisoft Metashape software ver. 2.0.1 [31]. It was aligned using 3D coordinates derived from 7 cadastral points that were verified and supplemented with height information from GNSS RTK measurements. Among these, 3 points were used as GCPs for referencing, while the remaining 4 were used as independent checkpoints. The standard deviations of the differences in the checkpoints can be found in Table 3.
# Functional Analysis of Two Terpene Synthase Genes Isolated from the Flowers of _Hosta_ 'So Sweet'
## 3 Results
### Functional Characterization of Recombinant HsTPS1 and HsTPS2  
The recombinant proteins were obtained from the _E. coli_ Rosetta (DE3) strain using a pET-28a expression vector. The soluble portion of the protein induced by low temperature was purified and incubated with GPP and FPP substrates, and the products were analyzed by GC-MS (Figure 3). The two recombinant proteins were active when incubated with GPP substrates and produced monoterpenes. Incubation of recombinant Hs_TPS1_ with GPP mainly produced linalool, citronelene, and nerol. In addition, minor amounts of geranial were detected. Incubation of recombinant Hs_TPS2_ with GPP produced linalool, nerol, and geranial. After incubation with FPP, both recombinant proteins showed activity and produced sesquiterpenes in lower amounts. Recombinant Hs_TPS1_ used FPP to produce (_E,E_)-farnesol and (_E,E_)-farnesal. In addition to the above two sesquiterpenes, recombinant Hs_TPS2_ protein also produced a small amount of nerolidol.  
Figure 3: Total ion chromatogram of the products formed by incubating extracts of empty vector (control) with GPP and FPP. GC-MS analysis of products formed by recombinant Hs_TPS1_ and Hs_TPS2_in the substrate GPP and FPP. Only sample peaks that were significantly higher than the negative control were marked with letters. Corresponding compounds are: 1 = linalool (retention time = 7.087 min); 2 = citronellene (retention time = 9.229 min); 3 = nerol (retention time = 9.639 min); 4 = gerani (retention time = 10.100 min); 5 = (_E_,_E_)-farnesal (retention time = 15.587 min); 6 = nerolidol (retention time = 15.796 min); 7 = (_E_,_E_)-farnesol (retention time = 15.961 min).
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 3 Approaches for Summarizing Personalized Videos  
To achieve a concise and condensed presentation of the content of a video stream, video summaries have several audiovisual cues [11]. The goal of each personalized video summary is to keep the audiovisual elements that they incorporate intact. The audiovisual cues used for personalized video summaries are shown in Figure 3 and are classified as follows:  
* _Keyframe cues_ are the most representative and important frames or still images drawn from a video stream in the sequence of time [49]. For example, the personalized video summarization from [PERSON] and [PERSON] [32] is configured as a keyframe option that maximizes a scoring function based on sentiment characteristics.
* _Video segment cues_ are a dynamic extension of keyframe cues, which are video segment cues [11]. These are the most important part of a video stream, and video summaries produced with dynamic elements in mind manage to preserve both video and audio [49]. These video summaries have the ability to preserve the sound element and movement of the video, making them more attractive to the user. A major drawback is that the user takes longer to understand the content of these videos [11]. [PERSON] et al. [48] proposed a framework that checks the relevance of a shot to its importance in the context of the video and in the user's query for inclusion in the summary.
* _Graphical cues_ complement other cues using syntax and visual cues [49]. Using syntax as a substitute for other conditions and visual cues shows an extra layer of detail. Users perceive an overview of the content of a video summary in more detail due to embedded widgets that other methods cannot achieve [11]. This is illustrated by [PERSON] and [PERSON], who presented a method according to which the annotation tool learns the semantic idea, either from other sources or from the same video sequence [13].  
* _Textual cues_ use text captions or textual descriptors to summarize content [49]. For example, the multilayered Probabilistic Latent Semantic Analysis (PLSA) model [50] presents a personalized video summarization framework based on contemporary feedback in time offered by multiple users.
* _Social cues_ are connections of the user image through the social network. [PERSON] et al. [51] proposed an automatic video summarization framework in which user interests are extracted from their image collections on the social network.
# Vehicular Alarm System Using mHealth Data and Lightweight Security Algorithms
## Security Algorithms with Lightweight Security
### Trust Calculation for Member Nodes in mHealth Networks  
The basic trust calculation methods are described for both networks in this section. Both SUBS and PUBS calculate trust for their member nodes depending on past behaviors. We give an overview of the trust measurement approach below.  
**Direct trust calculation**: The base station computes the trust value of a member node in its mHealth network if a past interaction experience exists between the member node itself. This is denoted by TDirecttrust.  
**Indirect trust calculation**: Indirect trust calculation is used to establish the trust value of the member node by the base station in case of the absence of any direct past interaction experience between the base station and member node. The base station will send a request to other member nodes and seek assistance to send recommendations to it about the requesting node. Upon receiving the recommendations from all member nodes, the trust value of the candidate is computed. The TIndirecttrust denotes the computed trust value.  
**Integrated trust calculation**: The base station combines both the direct and indirect trust values in order to compute the trust value of the candidate node in situations where both exist, according to the preferences of the nodes. This is done by the integrated trust calculation method and the TIntegbrust denoted the computed trust value.  
Both base stations keep storage of these calculated trust values for their member nodes and send the values to CA. Thus, the CA will have a trust repository for keeping the trust record of every member node in the mHealth network. In this study, we used a digital signature to ensure authenticity of each mHealth member node so that any unauthorized user cannot have access to the network communication. We also use the trust value to check the requesting member node's previous behavior.
# Self-Organized Fast Routing Protocol for Radial Underwater Networks
## 8 Conclusions  
In this paper, we have presented a routing protocol adapted for acoustic underwater networks with a hybrid radial/linear topology. It can also be easily used in hexagonal or rectangular grids by just allowing the formation of multiple connections via one intermediate node. Such networks are an interesting topology for monitoring medium-size oceanic regions such as estuaries, fishing zones, or geological features of interest. The protocol is designed to minimize the delay for sensitive data transmitted by the nodes, minimize the costs of managing such a network, and to provide strong resilience. We have reduced the energy consumption by designing the protocol in such a way that we avoid collisions that may occur during the initialization and maintenance of routes. Furthermore, it does not require additional sensing devices.  
As a proactive protocol, this proposal achieves a low packet forwarding latency compared to reactive protocols. Although the convergence time at initialization is high due to the effort made in the design to avoid collisions, once the routing paths have been formed, the packet forwarding delay is quite low, thus reducing computational cost.  
The gateway initiates the path formation process, assigns a unique identification for each string, and serves as a connecting point with external networks. The paths are created in a hybrid radial/linear way, by forwarding a Route Request packet that travels along the network and, upon reaching the end of each string, a response is sent back. Cross-layer techniques have been used to minimize the odds that some node is mistakenly considered down during path initialization.  
To counteract node failures, the protocol includes mechanisms for creating alternative paths, thus making the protocol fault-tolerant. The full set of possible scenarios has been analyzed in the paper. The protocol provides viable solutions for all of them without compromising the transmissions of the other nodes.  
A performance analysis has also been presented, and it has been validated through simulations. The analysis of results shows that extending the network, both in number of nodes per string or in number of strings, has a linear impact on the performance.  
[PERSON] came up with the main idea of the protocol, and carried out the simulations. [PERSON] helped with the protocol mechanism, as well as the structure and revisions of the paper. [PERSON]-[PERSON] made suggestion to state of the art and pointed out some scenarios to be considered. [PERSON] revised the manuscript and suggested the main focus of the paper.  
This research has been partially funded by \"Universidad de Malaga\" and R&D Group Applied Communications Engineering for UnderWater (ACEuw).  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Effect of Immobilization and Freeze-drying on Cell Viability during Storage
#### 3.2.1 Storage at RT
**_plantarum_ F4**} \\  & **logcfu** & & & & **\% Survival** & & \\  & **d0** & **d40** & **d90** & **d180** & **d40** & **d90** & **d180** \\ \hline  & W & 8.3 \(\pm\) 0.1 & 4.8 \(\pm\) 0.1 & \textless{}1 & \textless{}1 & 57.1 \(\pm\) 0.4 & NC & NC \\ zea flakes & FD(ww) & 8.2 \(\pm\) 0.1 & 6.0 \(\pm\) 0.1 & 4.0 \(\pm\) 0.1 & 2.7 \(\pm\) 0.1 & 73.0 \(\pm\) 0.5 & 48.3 \(\pm\) 0.8 & 32.6 \(\pm\) 0.2 \\  & FD & 8.5 \(\pm\) 0.1 & 6.5 \(\pm\) 0.1 & 4.1 \(\pm\) 0.1 & 3.0 \(\pm\) 0.1 & 77.1 \(\pm\) 1.0 & 48.3 \(\pm\) 1.5 & 34.9 \(\pm\) 0.8 \\  & W & 8.2 \(\pm\) 0.1 & 6.4 \(\pm\) 0.1 & 5.2 \(\pm\) 0.1 & 2.3 \(\pm\) 0.1 & 77.3 \(\pm\) 1.3 & 63.3 \(\pm\) 1.4 & 28.4 \(\pm\) 0.7 \\ pistachios & FD(ww) & 8.2 \(\pm\) 0.1 & 6.1 \(levels ranged 7.0 and 7.2 logcfu/g (survival rates of 82.5% and 86.6%) for immobilized cells on pistachios and zea flakes, respectively, underlying the positive effect of immobilization on maintenance of cell viability, as also reported elsewhere [43]. Additionally, at 40 days of storage, freeze-dried immobilized cells on raisins were detected at levels \(<\) 1 logcfu/g. The survival of presumptive probiotictic cells on raisins for only a short period of time is probably related to the antimicrobial activity of certain fruit components, like phenolic compounds [38]. Indeed, specific polyphenols, such as catechin, procyanidins, and quercetin have been associated with antimicrobial activity [44; 45] (Table 2a-c). Storage at room temperature could facilitate distribution and vast usage of probiotics, yet scarce studies focus on the subject. Freeze-dried encapsulated _L. acidophilus_ LA02 ID 1688 cells using sodium alginate, magnesium hydroxide, and chocolate coatings as immobilization agents, resulted in increased cell viability throughout the final products' (cereal bars) shelf life and survival rates \(>\) 94% and \(>\)90% during storage at 25 \({}^{\circ}\)C, for 60-90 and 120 days, respectively [46].
## 7 Superframe Duration Allocation Schemes  
Finally, we derive a set of Superframe Duration Allocation (SDA) schemes, whose target is to improve the throughput of convergecast traffic in cluster-tree networks. The underlying reasoning is to estimate adequate values for the superframe durations and buffer sizes of each cluster coordinator, considering both the network requirements and the protocol and timing constraints. These allocation schemes can help system designers in what concerns the definition of network parameters, configuration of message streams and the need of using techniques such as data fusion or aggregation [46].  
Two proportional allocation schemes for setting-up the superframe durations are proposed: (1) Load-SDA, based on the traffic load imposed by the cluster descendant nodes; and (2) Nodes-SDA, based on the number of descendant nodes. Load-SDA scheme is suitable for cluster-tree networks, where both the topology and the data traffic behaviour are known, whereas Nodes-SDA scheme is suitable for cluster-tree networks where only the topology is known.  
The use of proportional allocation schemes to define the superframe durations ensures that adequate network resources (bandwidth) will be allocated to each cluster-head. Therefore, the use of such allocation schemes may avoid the network congestion and message discard issues that usually occur near the PAN coordinator [27]. Moreover, by defining adequate bandwidth and buffer sizes for cluster coordinators, the proposed allocation schemes may also guarantee a minimum level of QoS for message streams and a smaller energy consumption level for each of the network cluster-heads.
## 5 Experimental Progress
### Experimental Results  
Before the experiment, four tests were carried out, including remote image processing, network server transfer test, satellite networking test, and joint test of data processing and transfer. Through these tests, the ability of data transmission and analysis, near real-time image processing, remote operation, etc., was verified, and the test duration and latency were evaluated.  
On 5 June 2022, the NRRS task was uploaded to the satellite BP01 at the TT&C station in Kashgar (39.49\({}^{\circ}\) N, 75.75\({}^{\circ}\) E), and the image data were downloaded at the Lijiang station (26.70\({}^{\circ}\) N, 100.03\({}^{\circ}\) E). The task uploading and data transmission were completed in a single orbital cycle of BP01. The satellite implemented imaging of the target at 92.06\({}^{\circ}\) E and 27.7\({}^{\circ}\) N and completed near real-time data downloading. The whole process took about 15 min. It took 141 s for image extraction and 158 s for image processing.  
Figure 14: Key timing nodes of the NRRS experiment.  
Figure 13: Workflow of the NRRS task planning.  
On 3 September 2022, the 0.45 m broadband STOM terminal was used to establish a communication link with GS2 and accessed the ICRS application system. The TT&C station uploaded the planned task to BP01 through the Lijiang station, then BP01 implemented imaging and data transmission when passing by the data receiving station in Hulunbuir. The coordinates of the target location are 119.7\({}^{\circ}\) E and 49.2\({}^{\circ}\) N, and the whole process took about 18 min.  
On 18 October 2022, satellite BP01 implemented imaging and data transmission when passing by the data receiving station in Qitaihe (45.84\({}^{\circ}\) N, 130.98\({}^{\circ}\) E). The coordinates of the target location are 120.3\({}^{\circ}\) E and 36.5\({}^{\circ}\) N. The image files were analyzed in the ICRS application system. The test personnel used a 0.45m broadband STOM terminal to access the ICRS application system via the established communication link with AP03 and obtained the remote sensing image. The whole process took about 30 min.  
Another experiment was completed in Nantong (31.89\({}^{\circ}\) N, 120.95\({}^{\circ}\) E), China, on 7 November 2022. Using a broadband communication terminal, the test personnel accessed the ICRS application system through satellite GS2, and uploaded the planned task to BP01 via the Hulunbuir station (50.98\({}^{\circ}\) N, 121.04\({}^{\circ}\) E). BP01 implemented imaging and data transmission when passing by the data receiving station in Tongchuan (34.93\({}^{\circ}\) N, 108.89\({}^{\circ}\) E). The coordinates of the target location are 108.89\({}^{\circ}\) E and 34.93\({}^{\circ}\) N. The image files were processed in the ICRS application system. The test personnel used the broadband communication terminal to access the ICRS application system via the established communication link with AP03 and obtained the remote sensing image. The whole process took less than 25 min. The time nodes can be found in Table 4.
# Topological Navigation and Localization in
## 4 Development and Testing Methodology  
The methodology used for the development and testing of the proposed navigation system consists of four steps. The first is the development of the automation system in a simulated environment, which is a safe and cost efficient platform for that purpose. The second is the use of scale models to verify the behaviors that are too complex or impractical to be tested on a simulated environment. The third is validation and testing in real equipment, using a safe location intended for that purpose. Fourth is the validation and testing in a real operation environment under controlled conditions before moving on to production. Details are discussed further in the following section.
## 2 Materials and Methods
### Soil Electrical Conductivity Prototype Design
#### 2.2.3 Software Design for Soil Electrical Conductivity Detection  
The software for the soil electrical conductivity detection system primarily facilitates system initialization, time\(-\)sharing acquisition, and the processing of soil electrical conductivity and temperature data. It also enables the setting of prototype calibration coefficients, temperature compensation coefficients, and the configuration of prototype addresses and serial numbers.  
The primary objective of prototype initialization is to configure the MCU's internal resources and to set up the peripherals and pins utilized by each measurement module, as illustrated in Figure 6. The system operates at a frequency of 72 MHz, with the RS485 serial port configured to a baud rate of 9600. The EEPROM module and the TMP117 temperature module are assigned addresses of 0 \(\times\) 50 and 0 \(\times\) 48, respectively, through pin electrical connections. To ensure reliable operation despite potential electromagnetic interference or other factors that could cause the program to enter an abnormal state, the system employs the STM32's internal independent watchdog, set with a reset time of 1.6 s.  
Figure 5: Soil electrical conductivity prototype.  
The primary function of the prototype's main program is to facilitate the acquisition, processing, and storage of soil electrical conductivity and soil temperature data. Utilizing the Software Development Kit (SDK, ad5940-examples v0.2.1) provided by Analog Devices Inc. (ADI, USA), the control program for the AD5941 chip is developed. This includes configuring registers, the programmable switch matrix, the programmable AFE sequencer, and other resources of the AD5941 chip. Additionally, algorithms for impedance calculation, soil electrical conductivity conversion, and temperature compensation are implemented to ensure accurate data processing and analysis.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 2 The Energy-Efficient Internet-of-Things Model
### Radio Model and Energy Budget Computation
#### 2.2.2 Energy Budget for Data Aggregation by Cluster Heads  
One of the advantages of organizing the WSN into clusters is that the amount of data transmitted to the base station can be compressed and correlated. To determine the energy consumption of a cluster head for aggregating data, we use the following principle.  
Let \(E_{DA}\) denote the total energy dissipated by a cluster head node's digital electronics for aggregating \(k\) bits of data from \(m\) cluster members. Then,  
\[E_{DA}=(C_{tot}*V_{DD}+I_{leak}*\Delta t)V_{DD} \tag{4}\]  
This energy is the sum of the energy lost to switch capacitance (\(C_{tot}\)) and the energy lost in current leakage \(I_{leak}\)[13]. \(V_{DD}\) is the voltage supply, and \(\Delta t\) is the latency for aggregating \(k\) bits of data from each \(m\) cluster members. Using the experimental results of [18] and the parameters in [10], then the value of \(E_{DA}\) used in this paper is 5 nJ/bit/signal.
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 3 Results
### Spatial Distribution and Land Subsidence Rate
#### 3.1.1 Serang  
Serang refers to two different administrative areas, namely Serang City and Serang Regency. Geographically, Serang City is situated at 6.1104\({}^{\circ}\) S, 106.1630\({}^{\circ}\) E and Serang Regency at 6.1470\({}^{\circ}\) S, 106.1516\({}^{\circ}\) E, with populations of 700.000 and 1.6 million, respectively. Serang City is the capital of Banten Province, bordering with Serang Regency in the southern part and the Java Sea on the northern side [16]. Residential and business areas are concentrated in Serang City, while Serang Regency is predominantly agricultural. Geologically, Serang City is situated on loose volcanic products composed of andesite basalt from ancient volcanoes, which is a common feature of Banten Province; in contrast, the northern part lies within the alluvial plains [18].  
Several areas with apparent LS have been detected in Serang, as shown in Figure 3a. The regions with significant LS rates in the Serang area are marked by black rectangles (Figure 3(a.1, a.2)). The area with the highest rate of LS is located in Serang City (see Figure 3(a.1)), where it can reach up to 60 mm/year. A satellite image of the area reveals that it lies in a residential zone (Figure 3(c.1)). The cause of LS in this location has not been studied (Figure 3(b.1)); however, it is suspected to be caused by excessive use of groundwater for residential purposes.  
Based on the time series from 2017 to 2023, the areas of LS in Serang Regency are sporadic (Figure 3(a.2)), with an LS rate of 60 mm/year (Figure 3(b.2)). The LS area is located within industrial facilities (Figure 3(c.2)), which were established recently in 2012. Future development in this area could exacerbate LS, as the population growth drives increasing demand for housing and freshwater resources.  
The following are several examples of residential areas affected by LS based on field surveys. Figure 4a shows the densely populated environment in Serang City. Further studies are required to identify the underlying causes of LS in this area, enabling the development of effective measures to address this issue.