esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Carbon Source Optimization  
In order to evaluate the effect of different carbon sources and their concentration, glycerol was tested and compared to glucose, individually and in combination as a carbon source to grow _Aurantiochytrium_ sp. (Figure 2)  
Figure 1: Batch growth of _Aurantiochytrium_ sp. AF0043, in MB10 and MT1, supplemented with two distinct trace metal solutions (TM1 and TM2). Error bars represent the standard deviation between three biological replicates.  
Figure 2: Batch growth of _Aurantiochytrium_ sp. AF0043 strain in MT1 culture medium with different carbon sources and concentrations: 50 g/L glucose, 100 g/L glucose, 50 g/L glycerol, 100 g/L glycerol and 25 g/L glucose + 25 g/L glycerol. Error bars represent the standard deviation of three biological replicates.  
All treatments displayed similar growth performances, reaching a final DW around 10.88-11.83 g/L at day 5 (\(p>0.05\); Figure 2). The exceptions were the cultures grown with 50 and 100 g/L of glycerol, which attained, respectively, 9.86 and 5.66 g/L of DW, which indicates that increasing glycerol concentrations inhibit the growth of this strain. Overall, biomass productivities between 1.89 and 2.08 g/L/day and growth rates between 0.39-0.43 day\({}^{-1}\) were obtained.  
Glucose was not fully consumed by day 5 in any of the conditions (data not shown). However, glucose was consumed at a much faster rate when glucose was the sole carbon source; conversely, when a mix of carbon sources was employed (glucose and glycerol; Figure 2), glucose remained almost constant throughout the growth period, indicating a preference of _Aurantiochytrium_ sp. for glycerol over glucose.
# Performance Analysis of D2D Communication with Retransmission Mechanism in Cellular Networks
## 5 Queuing Stationary Analysis and Performance Parameters
### Performance Parameters  
Here, with the explicit formulation of the steady-state distribution, the system performance was evaluated by the average queue length, average throughput, average delay, and dropping probability. \(\overline{Queue}\) denotes the average number of packets in the D2D user buffer. The expression can be written as:  
\[\overline{Queue} =\sum\limits_{i=0}^{L_{\max}}{i\pi\left(i\right)}=\sum\limits_{i=0 }^{L_{\max}}{\left[{i\cdot\frac{{{p_{0,1}}{{p_{1,2}}\cdots{p_{i-1,i}}}}}{{{p_{i -1}}\cdots{p_{2,1}}{{p_{1,0}}}}}\left({1+\sum\limits_{i=1}^{L_{\max}}{\frac{{{p_{ 0,1}}{{p_{1,2}}\cdots{p_{i-1,i}}}}}{{{p_{i,i-1}}\cdots{p_{2,1}}{{p_{1,0}}}}}}} \right)^{-1}}\right]}. \tag{20}\]  
The average throughput of the queue can be acquired as:  
\[\overline{Thr}=\left({1-\pi\left(0\right)}\right)\cdot{p_{suc}}{=}\frac{{\sum \limits_{i=1}^{L_{\max}}{\frac{{{p_{0,1}}{{p_{1,2}}\cdots{p_{i-1,i}}}}}{{{p_{ i-1}}\cdots{p_{2,1}}{{p_{1,0}}}}}}}}{1+\sum\limits_{i=1}^{L_{\max}}{\frac{{{p_{0,1}}{{p_{1,2}} \cdots{p_{i-1,i}}}}}{{{p_{i,i-1}}\cdots{p_{2,1}}{{p_{1,0}}}}}}}\cdot{p_{suc}}. \tag{21}\]  
The average delay, which was the average time before a packet departed from the queue, can be expressed according to [PERSON]'s law [37] as follows:  
\[\begin{array}{l}\overline{Del}=\frac{{\overline{Queue}}}{{Thr}}=\frac{{ \sum\limits_{i=0}^{L_{\max}}{i\pi\left(i\right)}}}{{\left({1-\pi\left(0\right) }\right)\cdot{p_{suc}}}}\\ =\sum\limits_{i=1}^{L_{\max}}{\left[{\frac{{{ip_{0,1}}{{p_{1,2}}\cdots{
## 3 Multi-Link Channel Access Schemes and Coexistence Issue
### Design Variants of Synchronous Channel Access Operation  
We consider two design variants of the Sync scheme for better exploitation of multi-link transmission opportunities:  
* _Sync-PL_: This scheme resembles the conventional wideband operation of IEEE 802.11 [6; 18], as illustrated in Figure 6. In this operation scheme, an MLD runs a single BO in a primary link (PL) only. When the backoff count of the primary link is about to reach zero (within one point coordination function interframe space (PIFS) before the backoff completion time), the MLD performs a short CCA for a PIFS on each of the other links to check the availability of each. At the time of backoff completion, the MLD transmits frames using the primary link and the other link(s) sensed to be idle such that the transmissions on the used links will finish at the same time. If no other link is available at this time, the MLD uses the primary link only. In this manner, the transmit opportunities (TXOPs) of different links are synchronized with each other.
* _Sync-FT_: This scheme is a hybrid of the Sync and Async schemes for faster transmission (FT) when a multi-link transmission opportunity is available, as illustrated in Figure 7, with the aim of providing both STR and non-STR MLDs with high multi-link utilization [19; 20; 21]. As in Async, the links of an MLD are allowed to run individual BOs. When some of the links finish their BOs earlier than the others, the MLD performs a short CCA on each of the other links and begins to transmit on the set of links that are sensed to be idle, as in Sync, freezing the backoff counts of the links. Once the links complete transmission, they resume their individual BOs; those that have completed their BOs at the time of transmission pick a new backoff count, while the others resume their BOs with their remaining (frozen) backoff counts. This operation scheme lessens the failure of link utilization in non-STR MLDs due to RF power leakage, thus enhancing their multi-link utilization. As illustrated in Figure 7, Sync-FT is applicable to both STR and non-STR MLDs but with a subtle difference in behavior.  
Figure 4: Asynchronous operation (Async) scheme for multi-link channel access. (**a**) STR MLD. (**b**) Non-STR MLD.
## 5 Discussion  
### Side Lengths and Angles of the Distribution Envelope Simplified by SM-ADSL  
The SM-ADSL method in this paper can simplify the number of boundary vertices of the original envelope to improve the prediction efficiency. The model was designed to simplify the distribution envelope by increasing the side lengths and changing the angles simultaneously. Here, we used the 17 May result as an example to analyze the side lengths and angles of the simplified distribution envelope using the SM-ADSL method (see Figure 10). Compared with the original side lengths and angles in Figure 3, the range of side lengths and angles changed significantly, with a more extensive range of angle values and much larger side lengths. Figure 8 shows that the side lengths range from 3 to 5 km and the angles range from 110 to 180\({}^{\circ}\). The side lengths are mostly around 3 km when the angle is less than 140\({}^{\circ}\), and the side lengths are mainly distributed around 4 km when the angle is 140\({}^{\circ}\)-180\({}^{\circ}\). We can find that small side lengths correspond to small angles (large azimuth differences), and large side lengths corresponds to large angles (small azimuth differences), which is consistent with the side-angle relationship in the model.  
Figure 10: Side lengths and angles of green tide distribution envelope simplified by SM-ADSL on 17 May.  
Figure 9: The whole process of green tide development and drift in 2021, where (**A**) shows the first half of the process, and (**B**) shows the second half.
## 3 Methodology
### Fine-Grained Text Region Annotation Strategy
The target region annotation map is defined as follows:  
Figure 3: Comparison of annotation strategies. The green rectangle denotes the bounding box. The box-based mask annotation (gray-green area) labels all pixels within the bounding box as foreground. In contrast, the stroke-level mask annotation (light green area) includes only the pixels corresponding to actual character strokes, providing more precise supervision. \[M(x,y)= \begin{cases}1,&\text{if }(x,y)\in\mathcal{R}_{\text{stroke}}\\ 0,&\text{otherwise}\end{cases} \]  
where \(\mathcal{S}_{\text{stroke}}\) denotes the set of pixels corresponding to character strokes as defined by the segmentation labels, and \((x,y)\) represents a pixel location in the image. The contrast between the two approaches is visually summarized in Figure 3. The box-based annotation treats the entire bounding box as foreground, while the stroke-level annotation selectively includes only the actual stroke pixels. This difference significantly impacts the supervision signal used during training. The stroke-level mask offers a more semantically aligned and spatially precise indication of target regions, allowing the model to focus directly on the most informative parts of the image. From a technical perspective, this shift from coarse to fine-grained annotation also affects the distribution of training samples. In box-based masks, the foreground occupies a larger area, potentially leading to class imbalance when the foreground is overrepresented by semantically irrelevant pixels. In contrast, stroke-level masks reduce this imbalance by restricting positive labels to meaningful regions only. This enhances the signal-to-noise ratio in the supervision and prevents the model from being biased toward background characteristics. In addition, stroke-level supervision is inherently more suitable for character-level classification and recognition tasks. When used in conjunction with fully convolutional classification heads, these masks enable the model to associate local visual features with specific character categories on a per-pixel basis. This facilitates the learning of highly localized representations, which are critical for distinguishing between nuanced glyph structures, especially in low-quality or degraded inputs.
# Electro-Optic Sensor for Measuring Electrostatic Fields in the Frequency Domain
## 2 Method and Experimental Results
### Possible Sources of Measurement Error  
The experimental observations shown in the previous section match the analytical predictions well, with higher field intensity yielding more accurate results. The discrepancy for low values of the electric field can be explained by multiple factors discussed in the following paragraphs.
# Improved 2D Ground Target Tracking in GPS-Based Passive Radar Scenarios
## 3 Ground Target 2D Localization Scheme
### Estimation of the Initial Target Velocity  
Since targets of interest are ground vehicles and their movements are assumed to be confined to roads in the area of interest, an initial velocity estimate can be made by including external geographic information system data. The velocity vector can be estimated from the bistatic velocity of the targets (\(v_{b}\)), and the road orientation, with the resulting direction obtained by considering the previous location estimate. The target's bistatic velocity is obtained from the measured Doppler shift, extracting the Doppler components from satellite movement, as expressed in (15).  
\[v_{b}=-\frac{p}{\lambda}+\vec{v}_{sat}\cdot\vec{R}_{T}-\vec{v}_{sat}\cdot\vec {L} \tag{15}\]  
where \(\hat{R}_{T}\) and \(\hat{L}\) are the direction vectors from transmitter to target and receiver respectively, and \(\vec{v}_{sat}\) the satellite velocity vector.  
Therefore, the components of the target velocity vector \([v_{x},v_{y}]\) are computed according to the local orientation angle of the nearest road, \(\gamma\). If \(\tan(\gamma)=\infty\), the velocity vector is in the \(y\)-axis direction ( \([v_{x},v_{y}]=[0,v_{b}]\)). Otherwise, the system of equations in (16) must be solved.  
\[\begin{split} v_{b}&=v_{x}(x_{Tx}+x_{Rx})+v_{y}(y_ {Tx}+y_{Rx})\\ tan(\gamma)&=v_{y}/v_{x}\end{split} \tag{16}\]  
where (\(x_{Tx}\), \(y_{Tx}\)), and (\(x_{Rx},y_{Rx}\)) are the components of the direction vectors from the transmitter to the target location (\(\vec{R}_{T}\)), and from the receiver to the target location (\(\vec{R}_{R}\)).  
After substitution and simplification, the components of the velocity vector can be calculated with (17).  
\[\begin{split} v_{x}&=\frac{v_{b}}{x_{Tx}+X_{Rx}+tan (\gamma)(y_{Tx}+y_{Rx})}\\ v_{y}&=v_{x}tan(\gamma)\end{split} \tag{17}\]
# Anti-Fouling Properties of Phosphonium Ionic Liquid Coatings in the Marine Environment
## 2 Materials and Methods
### Field Testing  
Samples were coated on an epoxy-treated stainless coupon provided by Defence Technology Agency (New Zealand) and sent for field testing at Devonport Naval Base. The samples were submerged in coastal seawater inside a seachest simulator that allowed the slow exchange of seawater inside a dark environment protected from strong currents and flow. Control samples were included inside the seachest simulator and inspected at regular intervals for biofouling accumulation.
# An Overview of Emerging Nuclear Sensor Technologies: Challenges, Advancements and Applications
###### Abstract  
Nuclear sensors are essential for detecting and measuring nuclear radiation in various applications, including nuclear power plants, medical imaging, and environmental monitoring. Traditional nuclear sensors have served these fields for decades, but recent advancements in emerging sensor technologies offer novel improvements in accuracy, sensitivity, and reliability. This review presents an up-to-date overview of recent progress in the advancements of nuclear sensor technologies, their diverse applications, challenges in implementation, and opportunities for future research.  
nuclear sensor; advanced manufacturing; nuclear power plant; environmental monitoring; medical imaging 2020 IEEE/RSJ Web Conference 2020  
In nuclear power plants (NPPs), advanced sensor technology improves monitoring of reactors and cooling systems for early detection of anomalies [6]. In medical imaging, advanced sensor technology enhances the detection of gamma rays for precise localization of tumors [7]. Environmental monitoring benefits from nuclear sensor's ability to detect low levels of radioactive contaminants in air, water, and soil [8].  
Despite significant progress in the advancements of nuclear sensors, challenges remain in optimizing these technologies for widespread use. Issues such as scalability, integration with existing systems, and manufacturing costs need to be addressed. Ongoing research focuses on overcoming these obstacles to make the sensors more practical and accessible for industrial use.  
This review presents an overview of recent advancements in graphene-based, quantum dot-based, neuromorphic-based, and solid-state nuclear sensors. It examines their advantages, limitations, and current development status. This paper also discusses challenges in the field and outlines future research directions.
# Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network
## 4 Discussion
When the number of offloading tasks is small, the number of network switching times based on the Q-learning computation offloading method is the least; when the number of offloading tasks exceeds 130 Mbit, the number of network switching times based on the DQN computation offloading method is the least. It can be seen that the Lyapunov algorithm does not have the learning characteristics of the Q-learning algorithm and the DQN algorithm, and is not able to monitor the offloading environment in real-time for policy learning and updating, resulting in the highest number of network switches during the task offloading. Therefore, compared to Q-learning and DQN algorithms, the Lyapunov algorithm is not suitable for application in the complex Space-Air-Ground Integrated Network. For the Q-learning algorithm, the learning environment of the offloading network is relatively simple with a lower algorithm complexity than the DQN algorithm when the number of offloading tasks is small. However, considering the complexity, dynamic characteristics, and the large number of tasks in the Space-Air-Ground Integrated Network, the DQN-based computation offloading method can select the optimal computation offloading decision and offloading network, ensuring fewer network switches and effectively reducing the additional cost in the computation offloading process. For the three different mission offloading modes of the Space-Air-Ground Integrated Network, as shown in Figure 8, the task has the lowest energy consumption for edge-only computation offloading, with the workload between 20 and 80 requests, which is close to the energy consumption for edge backbone nodes mixed computation offloading but is saturated with edge computing workloads above 80 requests. Only the backbone nodes have the largest computation offloading energy consumption and the fastest increasing. In contrast, the offloading task using the mixed computation offloading mode of edge and backbone nodes has lower energy consumption and high workloads, which effectively meets the work demands of a large number of offloading tasks in the Space-Air-Ground Integrated Network. Figure 9 shows that the offloading task using a mixed-mode of edge and backbone nodes computing has the lowest delay cost, and the delay cost tends to increase more slowly than other offloading modes as the workloads increase. Specifically, when the number of the network work requests exceeds 100, the proposed mixed offloading approach is 61.54% lower in energy consumption and 71.01% lower in delay cost than the full offloading to the network backbone nodes approach, and is able to satisfy a large number of task offloading requests without saturation. Therefore, selecting the mix of edge and backbone nodes for computation offloading is more suitable for the task offloading environment of the Space-Air-Ground Integrated Network than selecting a pattern of offloading sites individually.
## 2 Materials and Methods
### Requirements of the Interface
#### 2.1.1 Emg  
The Delsys(r)TrignoTM wireless EMG system (Natick, MA 01760, USA) was used to acquire the EMG signals from the muscles directly [16]. The Delsys system has several advantages, including the following:  
* The system uses a time synchronisation wireless protocol, which reduces the waiting time for data transmission.
* The main architecture is intended to support high-resolution EMG signals, along with the corresponding biofeedback signals.
* The internal amplification circuit has a low-pass filter to reduce the noise and increase response performance.
* All 16 sensors can be used simultaneously to stream the data to the base station.  
However, a disadvantage of the Delsys(r) system is its high cost. In addition, an external analogue-to-digital circuit needs to be designed to convert the output signal.  
EMG signals from the Delsys were collected by two systems: a Vicon(r) motion capture system (Oxford, UK) which is standard in the lab and routinely used in clinical practice, and the other was the developed device.  
It should be noted that it was not aim of this study to develop EMG equipment with electrodes and sensors. Using Delsys(r), the analogue EMG signals, which are almost equal to original muscle activities, were obtained; using the Vicon(r) motion capture system, Delsys(r) EMG data could be processed and exported to validate the proposed system.
# Hand Joint Hypermobility among Dental Students--A Cross-Sectional Study
###### Abstract  
(1) Background: Joint hypermobility refers to joint movement beyond normal range. Density requires fine motor skills and manual dexterity; thus, the presence of hand joint hypermobility requires early assessment and intervention. The aim of this cross-sectional study was to assess the prevalence of hand joint hypermobility among first-year dental students and investigate correlations between the severity of their hypermobility and muscular strength. (2) Methods: First-year dental students were evaluated by an occupational therapy team composed of certified hand therapists. The extension of distal interphalangeal, proximal interphalangeal, and metacarpophalangeal finger joints was assessed with bilateral testing of digit hypermobility (with a cut-off of \(>\)20\({}^{\circ}\) for hypermobility) and classified as high, moderate, or low risk according to number /types of joints involved. Muscular strength was assessed by comparing grip strength, lateral pinch, and three-point pinch to norm values. Right- vs. left-handed individuals, and females vs. males were compared with an independent \(t\)-test; the relationships between the variables were investigated using the Pearson correlation. (3) Results: Out of 141 dental students (24.6 \(\pm\) 3.0, 70% females, 87% right-handed), 15% and 16% were classified as having a high and moderate risk of hypermobility, respectively, with no sex difference. Right-handed students scored significantly higher in the right lateral pinch than left-handed students (18.2 \(\pm\) 4.8 vs. 13.2 \(\pm\) 4.1, \(p\) = 0.045). A higher severity of hypermobility was associated with bilateral lower grip and pinch strength (\(p\) < 0.05). (4) Conclusions: One third of the first-year dental students exhibited moderate to severe hand joint hypermobility. Moreover, those identified as having moderate to severe hand joint hypermobility showed a decreased grip strength.  
occupational therapy; ligament hyperlaxity; hand joint hypermobility; dental students; multidisciplinary approach
## 3 Static Analysis
### Fatigue Life Analysis Results  
The nCode DesignLife is integrated on Workbench platform and is positioned as an integrated fatigue durability engineering solution. Its functions are mainly divided into fatigue data acquisition, post-processing of stress output results, and fatigue cloud generation, providing users with advanced fatigue analysis methods. The fatigue analysis module can be initiated after completing the static calculation. Through the analysis type selected in Workbench, the S-N static fatigue analysis process is automatically established in nCode DesignLife. After setting the parameters of each module, the fatigue life nephogram of the rudder stock mechanism is obtained, as shown in Figure 16.  
It can be seen from the figure that:  
* The severely damaged area of the rudder stock mechanism is located at the connection between the guide rod and the connecting rod. With the temperature decreasing in a gradient, the fatigue hotspot position is transferred from the connection between the guide rod and the connecting rod to the front end of the guide rod.
* When the temperature is 0 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C, there is no significant difference in the fatigue life of the rudder stock mechanism.
* When the temperature is reduced to \(-\)60 \({}^{\circ}\)C, compared with the previous temperature, the fatigue life suddenly decreases from 6.72 \(\times\) 10\({}^{6}\) to 3.74 \(\times\) 10\({}^{6}\). The reason may be that the substantial increase in the equivalent stress is related to the material properties of the steel structure itself.