esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Scheme for Modulation of Data Transmission Module in Endoscopic Capsule
## 5 Conclusions  
This paper addresses issues such as high power consumption, significant signal interference, and low data transmission rates in capsule endoscopy communication. It proposes a modulation optimization scheme (MIP-MDCSK) aimed at improving performance through three main aspects: BER performance, EE, and SE. The performance of the proposed scheme is evaluated for WBANs.  
Through Monte Carlo simulations, the BER is evaluated in the in-body channel. The results reveal that the in-body channel yields favorable BER performance, especially as the \(n_{c}\) value increases, indicating better robustness against noise. The gap between 2 IP-4 DCSK and 4 IP-4 DCSK is 1 dB, whereas the gap between 2 IP-4 DCSK and 2 IP-8 DCSK at the same BER is 2.5 dB at a BER of \(10^{-2}\). Moreover, the BER of the system can be significantly reduced to \(10^{-5}\), indicating that it meets the BER performance requirements of in-body channel devices that need to be synchronized in medical image transmission environments.  
Therefore, this optimized solution can address the low-cost and low-power communication transmission of endoscopic capsules while improving transmission performance, thus promoting the widespread adoption and development of endoscopic capsules.  
Conceptualization, M.M. and C.Y.; methodology, M.M.; software, M.M. and Z.X.; validation, Z.X.; formal analysis, M.M.; investigation, M.M.; resources, Z.X.; data curation, M.M.; writing--original draft preparation, M.M.; writing--review and editing, C.Y.; visualization, M.M.; supervision, M.M.; project administration, M.M., J.Y. and L.Z.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 62301277, 62471244), the Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications (No. NY223019, NY223134), the Jilun Medical Intelligent Technology (Nanjing) Co., Ltd. (No. 2024-298).  
Not applicable  
See [PERSON] Performance Analysis of Differential Chaotic Shift Keying Ultra-Wideband Systems in In-Body Channels. Master's Thesis, Xiamen University, Xiamen, China, 2022.  
The authors declare no conflicts of interest
## 5 Discussion
The reason is that since SMAP SMC estimates are derived from its own BSR and VOD products, the resulting CYGNSS SMC estimates will not be affected by employing distinct BSR and VOD products. Concerning the SMC validation strategies, additional reliable reference datasets are necessary. For example, in some particular conditions and geographical areas not frequently affected by clouds, reliable SMC estimates could be obtained by means of optical multispectral techniques, e.g., MODIS or Sentinel 3 products, which spatial and temporal resolutions can fulfill the GNSS-R SMC requirements. Moreover, since there are no satellite sensors that measure SMC directly, processing errors present in all these satellite-based SMC retrieval techniques requires a reliable calibration. In response to this problem, in situ measurements from reliable monitoring networks (e.g., National Soil Moisture Network, [[http://nationalsoilmoisture.com/](http://nationalsoilmoisture.com/)]([http://nationalsoilmoisture.com/](http://nationalsoilmoisture.com/))) can be employed to calibrate the current satellite-based techniques, and more accurate and reliable results will be obtained. Other factors to take into account are those related to instrument calibration as pointed out in [37]. However, this question is beyond the scope of the here presented model design and implementation.
## 3 Results
### Relationships Between Sprint, MAT, and Y-SRA Performance  
The study revealed significant correlations between sprint and agility performance. A strong positive correlation was observed between the 15 m sprint time and the total Y-SRA time (r = 0.740, \(p<0.01\)), as well as a moderate correlation between the planned MAT and the total Y-SRA time (r = 0.387, \(p<0.05\)). Sprint distances also exhibited moderate-to-strong correlations with the MAT performance (r = 0.464 for 5 m, 0.457 for 10 m, and 0.599 for 15 m, all \(p<0.01\)) and the total Y-SRA time (r = 0.485 for 5 m, 0.506 for 10 m, and 0.740 for 15 m, all \(p<0.01\)). Additionally, the initial 5 m sprint time within the Y-SRA test was moderately correlated with all the sprint distances (r = 0.360 for 5 m, 0.502 for 10 m, and 0.544 for 15 m, all \(p<0.05\)), while the COD-phase time showed weaker, non-significant relationships with the sprint distances at 5 and 10 m (\(p>0.05\)). These findings highlight the interplay between sprinting ability, agility, and reaction-based components in sports performance assessments (Table 3).  
The relationship between the 15 m sprint and the total Y-Shaped Reactive Agility times (R\({}^{2}\) = 0.231, \(p<0.01\)) are illustrated in Figure 4.  
\begin{table}
\begin{tabular}{l c} \hline \multicolumn{1}{c}{**Parameter**} & **Mean \(\pm\) SD** \\ \hline Speed (s) & 1.22 \(\pm\) 0.10 \\ Sprint 10 m & 2.22 \(\pm\) 0.19 \\ Sprint 15 m & 3.07 \(\pm\) 0.27 \\ MAT (s) & 8.05 \(\pm\) 0.73 \\ Y-SRA (s) & 1.34 \(\pm\) 0.09 \\ COD-Phase Time & 1.47 \(\pm\) 0.17 \\ Total Y-SRA Time & 2.96 \(\pm\) 0.20 \\ \hline \end{tabular}
\begin{tabular}{l} \hline MAT: Modified Agility T-Test; Y-SRA: Y-Shaped Reactive Agility. \\ \end{tabular}
\end{table}
Table 2: Mean \(\pm\) SD of sprint (5 m, 10 m, 15 m), Modified Agility T-Test (MAT), and reactive agility (Y-SRA) test (N = 33).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \multicolumn{1}{c}{} & \multicolumn{5}{c}{**Correlations**} \\ \hline  & & **Speed** & \multicolumn{2}{c}{**COD**} & \multicolumn{2}{c}{**Reactive Agility**} \\ \hline
**Test** & **5 m** & **10 m** & **15 m** & **MAT** & **Y-SRA Init.** & **Y-SRA** & **Y-SRA** \\  & & & & **5 m (s)** & **COD** & **Total** \\ \hline
5 m & & 0.614 ** & 0.690 ** & 0.464 ** & 0.360 * & 0.104 & 0.485 ** \\
10 m & 0.614 ** & & 0.692 ** & 0.457 ** & 0.502 ** & 0.275 & 0.506 ** \\
15 m & 0.690 ** & 0.692 ** & & 0599 ** & 0.544 ** & 0.450 ** & 0.740 ** \\ \hline Y-SRA & 0.464 ** & 0.457 ** & 0.599 ** & & 0.429 * & 0.292 & 0.387 * \\ Y-SRA Init. 5 m & 0.360 * & 0.502 ** & 0.544 ** & 0.429 * & & 0.614 ** & 0.639 ** \\ \hline Y-SRA COD & 0.104 & 0.275 & 0.450 ** & 0.292 & 0.614 ** & & 0.500 ** \\ Y-SRA Total & 0.485 ** & 0.506 ** & 0.740 ** & 0.387 * & 0.639 ** & 0.500 ** & \\ \hline \end{tabular}
\end{table}
Table 3: [PERSON]’s correlation between reactive agility (Y-SRA), sprint speed, and Modified Agility T-Test (MAT) in U12 male soccer players.
## 3 Microdrilling Fused Silica
### Effect of Tool Shape  
The shape of the tool influences the cutting force and surface quality [23]. To investigate the effect of tool shape on F\({}_{peak}\), in this study, a cylindrical tool and D-shaped tools were used. Figure 4a is a SEM image of the D-shaped tool, which was fabricated by WEDG. Figure 4b shows the thrust forces in the drilling with a cylindrical tool and two D-shape tools of different width. Compared to the result of cylindrical tool, the F\({}_{peak}\) of D-shaped tools were lower, and thrust forces were more stable. This is because of the edge of the tools. At the edge of the D-shape tool, the workpiece can more easily crack; consequently, the force decreases. The D-shaped tool with a width of 180 \(\upmu\)m showed lower F\({}_{peak}\) than the case of 240 \(\upmu\)m. This is because the smaller bottom area of the tool causes higher stress and introduces cracks easily.  
Since chip adhesion on the tool leads to high drilling force and cracks, chip discharge is important [27]. The D-shaped tool is effective not only for decreasing F\({}_{peak}\) but also for flushing out chips. An experiment to verify the chip-discharge effect was conducted with a D-shaped tool possessing a cut length that is shorter than the drilling depth. Figure 5 shows the thrust force and the position of the tool during drilling. A is where the cut length of the tool is longer than the drilling depth. Since the chip discharged effectively from the drilling zone, the thrust force was low and stable. B is where the length of the tool is shorter than the depth of hole. Therefore, the thrust force begins to increase because there is no space for chip discharges between the tool and the workpiece.  
Figure 4: (**a**) A SEM image of a D-shaped tool and (**b**) thrust forces according to tool shapes (drilling conditions: axial feedrate 10 \(\upmu\)m/s; rotational speed of 50,000 rpm).
# Minimizing Travel Time and Latency in Multi-Capacity Ride-Sharing Problems
## 2 Preliminaries
### Notation
For each \((k,R_{k})\in M\) (\(k\in D\)) where \(R_{k}\) contains \(c\) requests, i.e., \(|R_{k}|=c\), we denote the minimum total waiting time of all requests in \(R_{k}\) by \(\text{wait}(k,R_{k})\), i.e., the sum of the times to reach all drop-off locations \(t_{r}\) (\(r\in R_{k}\)) following a path that visits all locations \(\{s_{i},t_{i}|\ i\in R_{k}\}\) starting from \(d_{k}\) while \(s_{i}\) is visited before \(t_{i}\). We view \(\text{wait}(k,R_{k})\) as consisting of two parts: one term \(c\cdot w(d_{k},s_{r})\) expressing the waiting time between \(d_{k}\) and the first pick-up location \(s_{r}\); another term \(\text{SHWP}(s_{r},R_{k})\) capturing the sum of waiting times from the first pick-up location \(s_{r}\) to every other drop-off location, minimized over all feasible ways of traveling through the locations in \(R_{k}\). The latency needed to serve requests in \(R_{k}\) (\(k\in D\)) is then given by:  
\[\text{wait}(k,R_{k})=\min_{r\in R_{k}}\{c\cdot w(d_{k},s_{r})+\text{SHWP}(s_{r },R_{k})\}. \tag{3}\]  
We denote the latency of an allocation \(M\) by:  
\[\text{wait}(M)=\sum_{(k,R_{k})\in M}\text{wait}(k,R_{k}). \tag{4}\]  
Thus, in CS\({}_{lat}\) and CS\({}_{lat,s=t}\), the goal is to find an allocation \(M\) that minimizes \(\text{wait}(M)\). Another variant of the ride-sharing problem considering the latency objective is counted with respect to the pick-up location rather than the drop-off location in CS\({}_{lat}\). In this setting, the drop-off location clearly becomes irrelevant to the objective, and our approximation results for CS\({}_{lat,s=t}\) become valid for this variant.
# A System for a Real-Time Electronic Component Detection and Classification on a Conveyor Belt
## 2 Materials and Methods
### Hardware Setup  
The hardware setup designed for the detection and counting of electronic components is shown in Figure 2. The system was designed to be inexpensive and meet the requirements of real-time detection in an industrial environment. The microcomputer was powered by a general power source. A camera was connected to the microcomputer for video input and an HDMI-connected screen was used to display results. SD memory was connected to store data and software for object detection. The user controls the microcomputer through the LAN interface and input devices. Additionally, a ventilator was connected to cool the microcomputer.  
For the hardware setup described, an Nvidia Jetson Nano microcomputer was chosen [63]. It has a 128-core Maxwell GPU, 4-core ARM A57 CPU, 4 GB LPDDR4 memory, HDMI and display connectors, USB, and other interfaces. To capture moving objects, a Raspberry Pi NoIR Camera v2 CSI camera was connected [64]. The camera has an 8-megapixel sensor and supports 1080p30 and 720p60 video formats. SD card size was 64 GB. SD card containing a dataset and data for training epochs. Power supplied by micro-USB T6712 DV. The user interface was communicated through the LAN, using VNC Server software. Additionally, for customization, a mouse and keyboard were connected via USB. Ventilator power was 2.5 W, which was supplied by USB from the microcomputer.  
Nvidia Jetson Nano microcomputer was chosen because of its ratio between price and performance. The device can implement the currently available deep learning algorithms. Moreover, the device supports Nvidia JetPack, which included board supports package, Linux OS, CUDA, cuDNN, and TensorRT deep learning libraries, dedicated for object detection. Additionally, the device has many input and output ports, power is supplied through USB, and has various interfaces, such as GPIO, CSI.  
The Raspberry Pi Noir CSI camera was connected to the microcomputer. The camera has a high-quality 8 megapixel Sony IMX219 image sensor, which is capable of working with various illuminance conditions. The camera is capable of capturing 3280 \(\times\) 2464 px static images, and also supports 1080p30, 720p60 and 640 \(\times\) 480p90 video modes [64]. The small size and light weight of the camera module allow it to be easily mounted on the conveyor. In case of low illuminence, the No Infrared lens filter allows one to capture high-quality images.  
During the testing phase of our hardware setup, high temperatures (beyond 65 \({}^{\circ}\)C) of the microcomputer were observed. To maintain the working temperature of the CPU  
Figure 2: Hardware setup schematic of object detector. Implementation for a SMD component conveyor.  
and GPU, an external 2.5 W ventilator was connected. The size of the ventilator was 160 \(\times\) 150 \(\times\) 100 mm, with a 5 V USB power supply. With the connected ventilator, the temperature of the system did not rise above 35 \({}^{\circ}\)C. For stable illuminence, a 4400 K color and 6600 lm brightness was used.
## 2 Results
### Chromosomal Location and Gene Duplication Analysis of EhFPS  
To reveal the distribution of the _FPS_ genes, their sequences were aligned to the genomic data of _[PERSON]. [PERSON]_, and chromosome mapping was performed using gene location information, as shown in Figure 1. These genes were unevenly distributed on the four chromosomes, with one member each on Ch1, Ch3, and Ch8. The number of _EhFPS_ genes in Ch6 was the highest, with five members. A tandem gene cluster comprising _EhFPS4_ and _EhFPS7_ was identified on chromosome 6 within a 1 Mb region, along with fragment replication involving _EhFPS1_ (Figure 2A). Evolutionary analysis of these tandem repeat genes revealed that their Ka/Ks ratio was significantly less than 1 (Figure 2B). A low Ka/Ks ratio suggests that nonsynonymous mutations are largely excluded, likely because of their detrimental effects on protein function. This phenomenon is typically observed in functionally important and conserved genes such as those encoding metabolically critical enzymes or structural proteins. Furthermore, analysis of gene duplication events indicated that the _FPS_ genes of both _E. Hirta_ and rubber trees contained a pair of duplicate genes. Specifically, _EhFPS4_ on Ch6 in _E. Hirta_, _EhFPS1_ on Ch3, and _EhFPS2_ on Ch5 in _Heeva brasiliensis_ were identified as fragment repeat genes (Figure 2C). These findings suggest that homologous gene pairs emerged through gene replication during evolution.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{\begin{tabular}{c} **Gene** \\ **Name** \\ \end{tabular} } & \begin{tabular}{c} **No. of** \\ **Amino** \\ **Acids** \\ \end{tabular} & \begin{tabular}{c} **Molecular** \\ **Weight** \\ **(Kda)** \\ \end{tabular} & \begin{tabular}{c} **Isoelectric** \\ **Point** \\ **(PL)** \\ \end{tabular} & \begin{tabular}{c} **Total Average** \\ **Hydrophobicity** \\ \end{tabular} & \begin{tabular}{c} **Alpha** \\ **Helix** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Beta Turn** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Extendendendendend & \
\begin{tabular}{c}
\begin{tabular}{c} **Randomcoli** \\ **Localization** \\ \hline \hline _EhFPS1_ & 342 & 39.52 & 5.5 & \(-\)0.264 & 63.16 & 2.92 & 7.02 & 26.90 & Cytoplasm \\ _EhFPS2_ & 350 & 40.50 & 5.11 & \(-\)0.255 & 65.43 & 3.14 & 5.71 & 25.71 & Cytoplasm \\ _EhFPS3_ & 389 & 45.24 & 5.91 & \(-\)0.305 & 59.90 & 3.34 & 6.17 & 30.59 & Chloroplast \\ _EhFPS4_ & 335 & 38.64 & 5.96 & \(-\)0.348 & 56.72 & 2.99 & 8.96 & 31.34 & Chloroplast \\ _EhFPS5_ & 323 & 37.44 & 5.14 & \(-\)0.293 & 65.94 & 2.48 & 7.74 & 23.84 & Cytoplasm \\ _EhFPS6_ & 342 & 39.73 & 5.49 & \(-\)0.353 & 65.50 & 2.92 & 6.43 & 25.15 & Cytoplasm \\ _EhFPS7_ & 336 & 38.97 & 6.52 & \(-\)0.317 & 63.99 & 2.98 & 7.14 & 25.89 & Cytoplasm \\ _EhFPS8_ & 346 & 39.95 & 6.52 & \(-\)0.323 & 61.27 & 3.76 & 5.49 & 29.48 & Chloroplast \\ \hline \hline \end{tabular}
\end{table}
Table 1: The basic information of _EhFPS_ genes.
# Calibration of Magnetometers with GNSS Receivers and Magnetometer-Aided GNSS Ambiguity Fixing
## 1 Introduction  
A precise attitude information is essential for numerous applications, e.g., for navigation, for flight control and for constructions. Magnetometers provide attitude information globally but are affected by sensor biases and measurement errors. The latter ones can be systematic due to metallic environment and/or stochastic due to measurement noise. Sensor biases and systematic errors can be removed by calibration.  
The calibration can be performed with three Global Navigation Satellite System (GNSS) receivers (e.g., GPS, Galileo, GLONASS, Beidou) being placed on the same platform as the magnetometer. Satellite navigation enables the determination of the relative position between both GNSS receivers in a local coordinate frame, e.g., the North-East-Down (NED) frame. The obtained relative position is related to the prior knowledge of the relative position in the _body-fixed_ frame to obtain the platform's _attitude_.  
The use of GNSS carrier phase measurements is required to obtain a _precise_ attitude information. The carrier phases are periodic, which requires an integer ambiguity resolution for each differential measurement. An efficient integer least-squares estimation of these integer ambiguities has been developed by [PERSON] in [1]. His \"_Least Squares Ambiguity Decorrelation Adjustment_\" (LAMBDA) method includes an integer decorrelation and sequential tree search. The success rate of integer ambiguity fixing can be substantially increased by including prior information on the baseline. [PERSON] has included prior information on the baseline length in [2,3] and [PERSON] and [PERSON] have additionally included prior information on the attitude in [4]. The prior information on the length and attitude angles can be either a \"hard\" or \"soft\" information.  
[PERSON] et al. have developed a method for determining magnetometer biases using two GNSS receivers in [5]. [PERSON] et al. [6] and [PERSON] et al. [7] used extended and unscented Kalman filters to estimate the sensor biases and scaling factors.  
The purpose of this paper is threefold: first, we describe a method for calibration of magnetometer biases using attitude information from three GNSS receivers. Second, we describe a method for fast and reliable integer ambiguity fixing using magnetometer-based attitude information. Finally, the proposed methods are verified with measurements of low-cost magnetometers and GNSS receivers in a test drive with a vehicle.
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 4 Hydrogen Transportation and Distribution Technologies
### Transport and Distribution by Ship  
Hydrogen transportation by ship is ideal when long distances, with possible routes across water, are considered [91]. According to [82; 91], three options to transport hydrogen by ship are the most viable, namely liquid hydrogen, ammonia and LOHC. In [82], it is stated that ammonia is the most cost-effective option of the three. Liquid hydrogen presents the advantage of already being a commercial technology for ship transportation, as well as not needing purification systems at the destination; nonetheless, boil-off and high energy losses occur during the transportation phase. LHOC present the advantages of being easily stored and presenting low capital costs, although its production needs to be scaled up to become a feasible transportation option.  
As previously mentioned, it is not possible to highlight only one specific technology as being the most cost-effective, since the transportation cost depends on two major parameters, namely distance covered, and volume transported. Figure 14 presents a systematic comparison between the most suitable transportation options for different distance and volume ranges [82].  
Figure 14: Systematic representation of the most cost-effective transportation and distribution options (adapted from [82]).
# A Hybrid Routing Protocol Based on Naive Bayes and Improved Particle Swarm Optimization Algorithms
## 3 Network Model and Assumptions  
### Network Model  
A wireless sensor network scenario is proposed in this paper. It is assumed that \(N\) sensor nodes are randomly deployed in a two-dimensional rectangular region of \(X^{\bullet}Y\), and the wireless sensor network is assumed to have the following properties [23; 24; 25]:The network is a static network with a high density, that is, the deployment of sensor nodes remains unchanged, and the node density is sufficient to ensure network connectivity and complete coverage of the monitoring area;
2. The base station is fixed and unique and has an unlimited energy supply, and its wireless transmitting power is controllable;
3. The sensor nodes are isomorphic and non-rechargeable. The initial energy is the same as \(E_{0}\);
4. The energy consumed by each sensor node is not equal, which makes the network's energy consumption heterogeneous;
5. Nodes have the ability of self-energy perception;
6. The nodes can adjust the transmission power [17; 25]; and
7. The nodes have the ability to fuse data.
# A Comparison of Deep Learning Algorithms for Anomaly Detection in Discrete Mechanical Systems
## 4 Network Architecture and Training
### Pre-Processing  
As previously mentioned, for the healthy structure, 1000 time histories of 70 s were recorded. In particular, the transversal accelerations were measured by the four accelerometers with a sampling frequency of 128 Hz arranged in a four-column matrix. Then, the dataset must be normalized [32]; to do this, the maximum absolute value for each channel was computed and stored in a vector G, that will be useful in the following experiments. In this way, each sample signal will range between \(-1\) and \(1\). Finally, the dataset was divided into training, validation, and test subsets composed of 800, 100, and 100 records, respectively.
# Urban Transportation Network Design for Fresh Fruit and Vegetables Using GIS-The Case of Bangkok
## 5 Result and Discussion  
### Results of LAP and VRPTW  
The results of the LAP of all scenarios are summarized in Table 5. The summary of VRPTW for all scenarios in terms of distance and time is presented in Table 6.  
Figure 8: Examples of vehicle routing problems with time window (VRPTW)in ArcGIS software (scenario 5).  
Regarding the VRPTW analysis, the results of the number of required trucks, total travel time, total travel distance, and fairness among drivers, scenarios 1 and 3 required 70 trucks, which is the minimum number trucks as compared to other scenarios. Although the number of required trucks in scenario 8 was higher than other scenarios, this scenario showed the best performance in terms of total travel time for 178.50 hr. Moreover, scenario 8 with 73 required trucks guaranteed the minimum total travel distance for 1109.12 km. However, scenario 7 showed the minimum value of the standard deviation of travel time (0.88) and distance (5.45). This means that scenario 7 guaranteed a better fairness among drivers and a well-balanced workload assignment.  
In addition, Table 6 shows the improvement in terms of travel time and travel distance as compared to scenario 1. Scenario 8 showed the highest improvement percentage for both time (71.76%) and distance (72.92%). Therefore, scenario 8 showed the best performance for both total travel time and distance. Moreover, the best performance in terms of fairness among drivers was observed in scenario 7. For the number of required trucks, most alternative scenarios (except for scenario 3) showed a lower performance than that of scenario 1.