esa-sceva/satcom-chunk-collection huggingface dataset:
# Marine Bioactives and Their Application in the Food Industry: A Review
## 3 Marine Bioactive Compounds and the Food Industry
### Bioactive Marine Polysaccharides  
The biological properties of marine-derived polysaccharides are determined by their chemical composition and structure. They can be derived from a wide range of marine organisms and possess several properties that make them ideal ingredients to incorporate into food. Algae, crustaceans, and most other marine organisms produce fican/fucoidan, carrageenan, hydrocolloids, and glycosaminoglycans like polysaccharides that are widely used in numerous commercial applications, particularly in food, beverages, and supplements [49]. For example, algal polysaccharides such as alginate, agar, and carrageenan are used as gelling, emulsifying, clarifying, thickening, stabilizing, and flocculating agents in various food products, including ice cream, candy, yogurt, meat product, and functional beverages [56]. Most of their functional properties, such as gelling and thickening, are due to the presence of polysaccharide-polysaccharide interactions as their unique structural characteristics. Agar forms stable gels and is widely used as an additive in dairy, bakery, and canned meat/fish products, soups, sauces, and beverages, and also in some culinary applications such as Japanese foods like \"Tokoroten\" (noodelike agar gel) and \"Mitsumame\" (canned fruit salad with agar jelly) [144]. Furthermore, due to the high sugar content of agar, it can be used in confectionery industries, particularly in jellies, jams, fruit candies, puddings, and custards. Like agar, alginate also possesses the ability to form a firm and rigid gel that can be used in jams and puddings, and it is also important as a thickening agent in ice cream, sauces, ketchup, mayonnaise, and pures. Apart from that, the film-forming ability of alginate is used in pastries and fruit fillings to prevent cake moistening [49]. Moreover, some polysaccharides can bind large amounts of water and disperse it in food products. This property is exhibited in carrageenan-like polysaccharides that are used to modify the texture of food products by changing their water-binding and foaming attributes [4]. For example, carrageenan finds application in a variety of water-based gelled desserts and cake frosting. Additionally, it is employed in dairy products both on its own (e.g., processed cheese, flan, sterilized chocolate, and evaporated milk) or in conjunction with other gums such as locust bean gum (e.g., ice cream and cream cheese) [49,145]. Fuoidans are another complex sulfated polysaccharide isolated from different marine species, and because of their structural sulfated groups, they improve biological properties and make them suitable to incorporate into dairy products. Apart from fucoidan, other marine polysaccharides such as chitosan are ideal raw materials for edible and biodegradable films and as antimicrobial agents, additives, and nutraceuticalals. In addition, meat products, including sausages, dairy products, and beverages, can be supplemented with polysaccharides like carrageenan, chitosan, and chito-oligosaccharides [7,8,51,128].
## 3 HGV Tracking with Multiple Model Algorithms
### HGMM Tracking Algorithm
#### 3.3.2 Determination of the Fine Subset  
Two classes of approaches are presented in [36], which are simpler and easier to implement. The two approaches are: fine grid with fixed grid distance, and fine grid with adaptive grid distance. We use the first approach in this paper.  
We need to get an attack angle estimate \(\hat{\alpha}_{k}\) based on \(M\) using IMM algorithm and then we can form the fine grid by quantizing the region whose center is \(\hat{\alpha}_{k}\) as following:  
\[A_{k}=\left\{\hat{\alpha}_{k}-d,\hat{\alpha}_{k},\hat{\alpha}_{k}+d\right\} \tag{36}\]  
where \(d\) is the fixed grid distance, and in this paper we set \(d=1^{\circ}\).
## 4 Experimental Setup
### Scenarios  
To automate the execution of the experiment, we developed a Python 3.8.10 script that remotely configured both the base station and the attenuator with the appropriate settings. The script used the paramiko SSH module to connect to each testbed component, ensuring secure and reliable communication. After applying the desired configuration (e.g., adjusting gNB parameters and setting attenuation levels), the script verified whether the changes had been correctly applied. Depending on the testing phase, latency, throughput, or both were measured.  
To assess latency, the script adjusted the ICMP packet size and started monitoring the traffic exchange between the LiDAR client and the remote controller. Once a stable request-response pattern was detected, indicating a successful and valid test run, a brief warm-up period was triggered. During this time, ping ran continuously to eliminate transient fluctuations that typically occur immediately after connection establishment. This helped to ensure that the collected results were accurate and not distorted by initial anomalies.  
As previously mentioned, first, we aimed to run multiple configurations prepared beforehand based on the configurations proposed by the base station manufacturer with additional parameters. The analysis and results are presented in Section 5.  
There were four baseline configurations: one focused on minimizing latency, while the other three aimed at maximizing throughput. Their modifications involved parameters discussed in the Section 2.3. Ultimately, 24 configurations were tested: 4 baseline, 20 derived. Not all parameters were tested with each baseline configuration. The main parameters that were modified include the following:  
* Bandwidth: 20 and 60 [MHz];
* RX to TX latency (parameter that defines the minimum allowed latency in slots between RX and TX): 1, 2, 4, and without this parameter;
* Scheduling Request Period (the time that User Equipment (UE) waits to obtain UL grant): we used the value of 1;
* PRACH (The Physical Random Access Channel--used to carry random access preamble for the UE toward the next-generation Node B (gNB)): 128 and 160;
* Disabling TRS (Tracking Reference Signal);
* Number of PDCCH candidates in USS (possible scheduling opportunities for the UE on the control channel): we used 8 for Aggregation Level 2 (AL2).  
Figure 4: Diagram of the test setup.  
In Table 3, we specified the parameter values used at both stages. As mentioned in Section 3, Stage I aimed to identify which configurations offer the greatest potential for minimizing latency and maximizing throughput. Due to the time-consuming nature of the tests, we limited the number of parameter values examined, as presented below. In Stage II, we thoroughly examined the capabilities of the selected configurations under varying environmental conditions and with different packet sizes.
# Wireless Positioning in IoT: A Look at Current and Future Trends
## 3 Designing an IoT Positioning System
### Comparing IoT Technologies and IoT Enablers
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Technology**} & \multirow{2}{*}{**Frequency Bands**} & \multirow{2}{*}{**Channel Bandwidth (MHz)**} & \multicolumn{2}{c}{**Modulation Type**} \\  & & & **(UNB/NB/SS/OFDM/UWB)** \\ \hline
**5G** & GHz, mmWave & \(<\)100 & OFDM \\ \hline
**ANT+** & GHz & 1 & NB \\ \hline
**BLE mesh** & GHz & 1 & NB \\ \hline
**Dash7** & sub-GHz & 0.025, 0.200 & NB \\ \hline
**EC-GSM-IOT** & sub-GHz & 0.2 & NB \\ \hline
**EnOcean** & sub-GHz & 0.0625 & NB \\ \hline
**Ingenu** & sub-GHz and GHz & 1 & SS \\ \hline
**ISA101.11a** & GHz & 5 & SS \\ \hline
**LoRa** & sub-GHz & 0.125, 0.500 & SS \\ \hline
**LTE-M** & sub-GHz and GHz & 1.08, 1.4 & OFDM \\ \hline
**MiWi** & sub-GHz and GHz & 0.040, 0.250 & NB \\ \hline
**NB-IoT** & sub-GHz and GHz & 0.18 & NB, OFDM \\ \hline
**RFID** & sub-GHz and GHz & 0.2 & NB \\ \hline
**Sigfox** & sub-GHz & 0.2 & UNB \\ \hline
**Telensa** & sub-GHz & 0.1 & NB \\ \hline
**Thread** & GHz & 5 & NB \\ \hline
**Weightless-N** & sub-GHz & 0.2 & UNB \\ \hline
**Weightless-P** & sub-GHz & 0.0125 & NB \\ \hline
**Weightless-W** & sub-GHz & 5 & SS \\ \hline
**WirelessHART** & GHz & 0.25 & SS \\ \hline
**WiFi802.11 af** & sub-GHz & 8 & OFDM \\ \hline
**WiFi802.11 ah** & sub-GHz & 1, 2, 4, 8, 16 & OFDM \\ \hline
**WiFi802.11 az** & GHz, mmWave & 20, 40, 60, 80, 160 & OFDM \\ \hline
**WiFi802.11p (V2X)** & GHz & 10 & OFDM \\ \hline
**Wirepas** & sub-GHz and GHz & 0.126, 0.5 & NB \\ \hline
**WiSUN** & sub-GHz and GHz & 0.2–1.2 & NB, SS and OFDM \\ \hline
**ZigBee** & sub-GHz and GHz & 0.6, 1.2, 2 & SS \\ \hline
**ZigBee-NaN** & sub-GHz & 0.6, 1.2, 2 & SS \\ \hline
**Z-Wave** & sub-GHz & 0.2 & NB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of key physical layer parameters for several IoT protocols. To further complement our study, we end with a perspective on what the achievable positioning accuracy is. The next two sections focus on measurement-based and simulation-based studies, respectively. We introduce simulation-based results from two systems whose performance was difficult to find as benchmarks in the existing literature, namely IEEE 802.11 az and LoRa. Then, we present measurement-based results from an office environment of a positioning system built on top of the Wirepas' mesh solution.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 2 Current Status of Lunar Relay Navigation Satellite Networks
### U.S. Lunar Navigation and Communication Plan
Fully enable integration and interoperability with other space and terrestrial infrastructure--lunar, terrestrial, and cislunar space;
3. Cloud-based data distribution, open architecture, software definition, and end-to-end user management;
4. Realize communication between users and space;
5. User data are distributed directly to different cloud storage facilities (hosts), corporate sites, and universities/laboratories through existing interconnections between space stations and ground stations.
## 4 Parameter Sensitivity Analysis  
### The Impact of Burial Depth on Frost Heave and Thaw Settlement Displacement  
Simulations of three freeze-thaw cycles were conducted for pipeline burial depths of 1 m, 1.5 m, and 2 m in frozen soil regions, as shown in Figure 13. The results indicate that as the burial depth increases, the surface frost heave displacement gradually increases while the displacement at the pipeline boundary significantly decreases. This phenomenon reveals the important regulatory role of burial depth in the soil's frost heave behavior. The underlying mechanism can be attributed to the thermal insulation effect of the overlying soil layer.  
When the pipeline is buried at a shallow depth (e.g., 1 m), heat exchange between the pipeline and the surface soil is stronger, allowing temperature fluctuations to rapidly propagate to the surrounding soil. During the freezing stage, the significant temperature gradient in the surrounding soil causes strong moisture migration effects, resulting in considerable frost heave deformation at the pipeline boundary. At the same time, the surface soil layer experiences buffering of temperature fluctuations, leading to relatively smaller frost heave displacement at the surface.  
As the burial depth increases, the overlying soil layer provides greater thermal insulation, significantly reducing the intensity of heat exchange around the pipeline. This leads to a more stable temperature distribution in the surrounding soil, thereby reducing the  
Figure 13: Time-dependent variation of surface and pipeline surrounding displacements for different pipeline burial depths.  
localized frost heave effects. However, in conditions with greater burial depth, heat is primarily transferred from the soil layer to the surface, causing a more significant temperature gradient in the upper soil layer, which increases surface frost heave displacement. Additionally, pipelines buried at greater depths cause less disturbance to the stress distribution in the surrounding soil, which helps to reduce the deformation at the pipeline boundary and enhances the stability of the surrounding soil.
# An Improved YOLOX Model and Domain Transfer Strategy for Nighttime Pedestrian and Vehicle Detection
## 3 Improved YOLOX Detection Algorithm
### Loss Function Design
#### 3.5.2 Bounding Box Regression Loss Function Design  
The border regression loss in the YOLOX model is mainly calculated by the IoU. When the overlap area between the predicted box and the ground truth box is larger, the IoU value is larger and the localization effect is better. However, IoU loss has the following drawbacks: When there is no overlap between the prediction box and the ground truth box, the IoU is 0, and it does not reflect the relative distance between the prediction box and the ground truth box, and the gradient of IoU loss is 0 at this time, so it cannot be optimized. In addition, IoU is calculated using the overlap area only, and when the overlap area is determined, its IoU value is the same, so it cannot distinguish between the prediction box and the ground truth box alignment mode, which affects the convergence speed.  
To address the above problems, CIoU [67] loss was introduced as the bounding box loss function to improve the convergence speed and model detection accuracy. The CIoU loss integrates the overlap area, centroid distance, and aspect ratio between the prediction box and the real box, which directly minimizes the relative distance between the prediction box and the ground truth box and accelerates convergence. The problem that IoU loss does not accurately reflect the overlap of the two boxes was solved, making the regression more accurate and faster when there is overlap or even inclusion with the target box. The formula for calculating CIoU loss is shown in Equations (3)-(5).  
\[L_{\text{ciou}}=1-\left(\frac{S_{i}}{S_{u}}\right)+\frac{\rho^{2}(b,b\texttt{ s}^{\text{t}})}{c^{2}}+\alpha v \tag{3}\]\[\alpha=\frac{v}{\left(1-\frac{S_{i}}{S_{u}}\right)+v} \tag{4}\]  
\[v=\frac{4}{\pi^{2}}\bigg{(}\arctan\frac{w^{\text{gt}}}{h^{\text{gt}}}-\arctan \frac{w}{h}\bigg{)}^{2} \tag{5}\]  
where \(\rho\) denotes the Euclidean distance between two center points; \(b\) and \(b^{\text{gt}}\) denote the center points of the prediction box and the ground truth box, respectively; \(c\) denotes the diagonal distance between the minimum closed area of the prediction box and the ground truth box; \(S_{i}\) is the area of the overlapping area of the prediction box and the ground truth box; \(S_{u}\) is the merging area of the prediction box and the ground truth box; \(w\) and \(w^{\text{gt}}\) denote the width of the prediction box and the ground truth box, respectively; \(h\) and \(h^{\text{gt}}\) denote the height of the prediction box and the ground truth box, respectively.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### Position-Based Solutions
#### 4.4.1 Kalman Positioning RPL (KP-RPL)  
The RPL provides energy-efficient routing strategies having a slow response to topology changes making it inefficient in keeping up-to-date routes in presence of mobile nodes due to high signaling cost. Hence, the authors [54] proposed Kalman Positioning RPL (KP-RPL) considering position inaccuracies and disconnections among static and mobile nodes by providing reliable routing. The proposed Kalman filter takes into account the velocity estimates by enhancing mobile node position accuracies resulting in better routing decisions. A position-based approach is followed for the routing of static and mobile nodes. A confidence region is generated by a mobile node using RSSI measurementsfrom static nodes. Then mobile nodes define their most likely position by considering the location with the highest probability in the confidence region. Further, the position is refined using Kalman filtering by combining velocity estimates. Before making a routing decision, a blacklist is generated by mobile nodes to discard unreachable nodes due to positioning errors. Similarly, blacklisting provides robustness to inevitable mobile node positioning errors due to RSSI measurement and velocity estimates inaccuracies. It helps in avoiding disconnection because of positioning errors. Finally, the best routing paths are selected by nodes in each transmission slot using end-to-end ETX estimates. Simulation results show KP-RPL is robust to channel conditions and provides reliable routing. The average ETX of KP-RPL with Kalman filtering increases the reliability of the network to 25% as compared to KP-RPL without Kalman filtering. The large density of static nodes increases network reliability and enhances the positioning accuracy of mobile nodes but also increases infrastructure costs. So, KP-RPL helps in reducing the number of static nodes, thus lowering infrastructure costs. KP-RPL helps in enhancing the energy consumption of the overall network because of its high reliability. Hence increasing the lifetime of both static and mobile nodes.
## 5 Conclusions  
The present study explored the detection of SCA through the statistical comparison of ECG features, such as heart rate and R-R interval related markers, between normal and patients at risk of SCA and the identification of the ideal time-frame for detection spanning up to three hours prior to the onset of SCA. Overall, there was a statistically significant change in heart rate 3 h and 6 min prior to SCA onset. There was also a statistically significant change in R-R interval duration the observed 9 min to 3 h prior to SCA onset and for RMSSD at 3 min. Conversely, there was no statistically significant change observed in SDSD at any time-frame. Following classification, the 2-min time-frame exhibited the highest accuracy across all three classifiers. However, reasonable accuracies were obtained at the 40 and 50-min time-frames which, in turn, may provide sufficient time for optimal patient response. The main limitation presented in this study was the small cohort numbers. As only 10 patients were used in each cohort, this may have attributed to the low performance measurement values. This can be addressed through further testing on a larger cohort prior to clinical application. Future studies may also aim to explore ECG feature detection specifically in the SCA patient cohort through the analysis and comparison of time-frames prior to, during, and possibly after the onset of SCA. Overall, the present study further contributes to the growing field of SCA detection by analyzing time-frames three hours prior to SCA onset, in which clinically-useful information can be obtained and utilized in the clinical setting to improve overall patient survival.  
The following are available online at [[http://www.mdpi.com/2076-3417/7/9/954/s1](http://www.mdpi.com/2076-3417/7/9/954/s1)]([http://www.mdpi.com/2076-3417/7/9/954/s1](http://www.mdpi.com/2076-3417/7/9/954/s1)), Source Code for All Steps Requiring MATLAB Processing.  
[PERSON] performed the data analysis and wrote the manuscript. [PERSON] advised regarding the data analysis and edited the manuscript. [PERSON] supervised the study, advised regarding the analysis, and edited the manuscript.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **50 Min Prior** & **40 Min Prior** & **8 Min Prior** & **2 Min Prior** \\ \hline Linear (LDA) & 0.76 & 0.79 & 0.79 & 0.77 \\ Linear SVM & 0.79 & 0.82 & 0.81 & 0.80 \\ Non-Linear SVM & 0.86 & 0.86 & 0.86 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Area under the receiver operating characteristic (ROC) curves generated for each classifier.
# \(k\)-Circle Formation and \(k\)-epf by Asynchronous Robots
## 4 Algorithm  
In this section, we propose a deterministic distributed algorithm that solves the \(k\)-circle formation problem for the remaining configurations. Each active robot will execute the proposed algorithm \(AlgorithmOneAxis(C(t))\) unless \(C(t)\) is a final configuration. Each robot will follow the following steps during an execution of \(AlgorithmOneAxis(C(t))\):  
1. The robots identify the current configuration. The robots agree upon the positive direction of the \(x\)-axis in some configurations.
2. One or two unsaturated fixed points are selected for the circle formation, referred to as target fixed points.
3. The robots identify one or two robots for each target fixed point, referred to as candidate robots.
4. Each candidate robot moves towards the \(k\)-circle centered at its target fixed point.  
First, we define progress in a half-plane. Let \(HL_{1}\) and \(HL_{2}\) denote the two half-planes delimited by the \(y\)-axis. Let \(f_{i}\) be the unsaturated fixed point, which has the highest rank in \(HL_{1}\) at time \(t\geq 0\). Similarly, suppose \(f_{j}\) is the the unsaturated fixed point, which has the highest rank in \(HL_{2}\) at time \(t\geq 0\). We say that there has been more progress in \(HL_{1}\) than \(HL_{2}\) at time \(t\) if one of the following conditions holds:  
1. \(\gamma(f_{i})<\gamma(f_{j})\);
2. \(\gamma(f_{i})=\gamma(f_{j})\) and \(N_{i}(t)<N_{j}(t)\);
3. \(\gamma(f_{i})=\gamma(f_{j})\) and \(N_{i}(t)=N_{j}(t)\) and \(d(f_{i},r_{1}(t))<d(f_{j},r_{2}(t))\) where \(r_{1}\) and \(r_{2}\) are candidate robots for \(f_{i}\) and \(f_{j}\), respectively.  
Otherwise, we say that there has been the same progress in both the half-planes.
# The Impact of Space-Based AIS Antenna Orientation on In-Orbit AIS Detection Performance
## Materials and Methods
### AIS Satellite Antenna
These include:  
* Monopole/dipole
* Helix
* Yagi-uda
* Patch  
The monopole/dipole antenna and the patch antenna offer linear polarization and are therefore able to introduce polarization diversity. The helix antenna suffers from a 3 dB gain loss due to polarization mismatch in addition to losing the benefit of polarization discrimination. LAPAN-A2 uses \(\lambda\)/4 antenna for its AIS receiver.
## 4 Result and Discussion
### Pipeline Stratum Model under 3D Effect
#### 4.1.5 Electrode Spacing  
To understand the relationship between the electrode spacing of the pipeline stratum and the 3D effect, and whether the electrode spacing could be used as the normalized parameter, this study analyzed the electrode spacing parameter, which was 3 m and 1.5 m, respectively. The model set n as 0.1, the section size of the pipeline was 1.5 (W) \(\times\) 2 (D) m, _dep_ = 2 m (the top of the pipeline), _dy_ = 3 m, _dx_ = 1.5 m or 3 m, and \(L\) = 42 m.  
We used the profiles of L1-L6 to compare the influence situation and influence distance of the 3D effect with different electrode spacings (_dx_ = 1.5, 3). As shown in Figure 8, when the electrode spacing was shortened, the spatial resolution improved. However, the influence range of the 3D effect between different electrode spacings was similar; after the distance from the edge of the pipeline increased to  
Figure 7: The influence of the pipeline embedding depth in the pipeline stratum model on the 3D effect.  
6 m, the 3D effect gradually became inconspicuous. This result shows that it is not appropriate to use electrode spacing as a normalized parameter for the 3D effect because the influence spacing does not decrease as the electrode spacing decreases.