esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
## 2 Related Work
### Data Association  
Current inter-frame target association methods often use target motion and appearance features. [PERSON] et al. used Kalman filtering (KF) [21] to calculate the prediction frame with the detection frame obtained by Faster R-CNN [22] for intersection over union (IOU) calculations to associate the target [10]. [PERSON] used convolutional networks to model movement patterns and interaction relationships [11]. [PERSON] et al. used a graph convolutional network to fuse multiframe image information for target location prediction [12]. This method causes significant degradation in tracking performance in dense scenes because it uses only motion information. [PERSON] et al. extracted target appearance features using GoogLeNet and used the k-nearest neighbor algorithm to associate targets [13]. [PERSON] and [PERSON] enhanced the target discrimination by fusing feature pyramids, thus enabling inter-frame target associations [14]. This method is prone to problems, such as tracking frame drift in the face of the small size and similarity of features of the moving vehicles in the satellite video. [PERSON] et al. proposed a graph neural network for joint multi-target tracking with an optimized detection and tracking phase [23]. [PERSON] et al. proposed a self-correcting KF position prediction algorithm combined with recurrent neural networks for inter-frame target association [24]. This method is usually slow to track, owing to the high complexity and computational effort of the network. In summary, existing algorithms do not exhibit good applicability in the case of small sizes, large numbers, and similar features of moving vehicles in satellite videos.
# GNSS Positioning Using Mobile Devices with the Android Operating System
## 4 Results and Discussion
### Positioning Performances Considering Different Environments
For verifying the repeatability of these results, another dataset in the same places, with the same techniques, was collected. Considering only the Huawei P10+ and the RTKLIB (Table 4), it seems that there were no evident differences with those obtained in the other data collection. The same dataset was also processed with the Leica Geo Office 8.3 software, to obtain another independent solution to demonstrate that the performances obtainable today with smartphones are independent of the software used for processing. Table 5 shows the results obtained with Huawei P10+ smartphone and demonstrates that the results obtained with this commercial software were generally slightly better than those obtained with RTKLIB, even if the behavior in terms of session length and environmental conditions was the same. Even if no results are reported here, it is important to underline that no differences could be found when the Samsung Galaxy S8+ smartphone was used. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sites** & **Session Length** & **Method** & **East** & **North** & **Up** \\ \hline \multirow{3}{*}{A} & \multirow{3}{*}{10} & Static & 8.221 & 7.985 & 11.121 \\  & & Kin & 11.452 & 9.768 & 16.433 \\  & & SPP & 15.109 & 14.344 & 19.674 \\ \hline \multirow{3}{*}{B} & \multirow{3}{*}{10} & Static & 2.011 & 1.974 & 3.858 \\  & & Kin & 3.986 & 3.884 & 4.112 \\  & & SPP & 6.101 & 7.254 & 8.114 \\ \hline \multirow{3}{*}{A} & \multirow{3}{*}{30} & Static & 3.985 & 4.465 & 6.110 \\  & & Kin & 6.158 & 6.552 & 8.764 \\  & & SPP & 8.552 & 7.986 & 9.992 \\ \hline \multirow{3}{*}{B} & \multirow{3}{*}{30} & Static & 1.107 & 2.213 & 4.101 \\  & & Kin & 3.201 & 2.120 & 2.988 \\  & & SPP & 5.124 & 4.442 & 5.899 \\ \hline \multirow{3}{*}{A} & \multirow{3}{*}{60} & Static & 4.231 & 5.102 & 8.101 \\  & & Kin & 7.532 & 6.422 & 10.101 \\  & & SPP & 11.365 & 9.105 & 13.421 \\ \hline \multirow{3}{*}{B} & \multirow{3}{*}{60} & Static & 0.123 & 0.113 & 0.201 \\  & & Kin & 1.101 & 1.632 & 2.115 \\ \cline{1-1}  & & SPP & 4.102 & 3.986 & 5.221 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Precision of the positioning results using Samsung Galaxy S8+ in meters.
## 11 Future Goals and Challenges  
It is encouraging to witness the economic growth of Bangladesh in recent years and the general improvement of the livelihoods of millions of people despite a significant inequality among rich and poor people. It is encouraging that Bangladesh has formulated many sound policies for sustainable water resource management. A few of the challenges that need to be tackled in attaining sustainable water management in Bangladesh are listed below:  
Climate change impacts: increased flooding, more severe droughts, increased water, and land salinity in the coastal areas, sea-level rise, internal migration of people, loss of productive lands, and unplanned urbanization.  
Water sharing: inadequate water-sharing treaties/agreements between Bangladesh, India and other neighboring countries will increase salinity in the coastal areas, groundwater lowering, and the lack of surface water irrigation in dry seasons.  
Drinking water: unsafe drinking water to most people due to water pollution (arsenic and pathogens), groundwater lowering, a lack of proper maintenance, and a lack of investment.  
Agricultural water: unsustainable groundwater use due to increased demand for irrigation in the dry periods, a lack of surface water irrigation projects, waterlogging, salinity, and inefficient irrigation systems.  
Quality fish production: Poor water quality can contaminate fish with heavy metals and micro-pollutants, including plastics, which can affect the human food chain. In this regard, the \"good water means good fish\" principle should be promoted in Bangladesh, as fish is the main source of protein for the majority of Bangladesh people.  
Institutional and socio-political issues: Lack of coordination and transparency occasionally lead to poor construction practice.  
Recreational water: most urban rivers are highly polluted due to unregulated effluent discharge from industries and indiscriminate solid waste disposal by the general public, municipalities, and industries, preventing the use of water for water parks, water fountains, boating, fishing and high-profile water-front housing projects.  
Social issues: population displacement from coastal areas in the short term; however, in the long-term a significant part of Bangladesh will be underwater, which will need mass migration (in the form of water-refugees) within the country and from Bangladesh to other countries.  
Sustainable water resources: rainwater harvesting, solar distillation, greywater reuse, artificial recharge, water-sensitive urban design, water-efficient irrigation systems, artificial-intelligence-based urban irrigation to generate cooling effects, the use of remote sensing data to manage the urban canopy and green space using stormwater and greywater, the circular economy, and public participation are important components in sustainable water resource management for Bangladesh.
## 5 Functional Verification of Structural Genes Discovered in Endophytic Fungi  
Although more and more plant bioactive metabolites are being found to be produced by endophytic fungi, the real challenge consists in the discovery of the underlying biosynthetic pathways, which in turn would enable us to consider genetic engineering of fungal producers and for synthetic biology approaches to create systems able to synthetize these compounds on a large scale and in a sustainable manner.  
After the discovery of a structural gene by an NGS approach, its function needs to be verified and proven experimentally. This could be done either by a gene disruption approach through a knock-out mutant, by inducing stable or transient gene overexpression, or via gene activation by using different triggers to activate silent pathways.  
Several successful examples of functional verification of a structural gene function exist in the field of fungal endophytes able to produce plant metabolites. The role of _swnK_ was understood after inactivating it in _Metartizium robertsii_ through homologous gene replacement to generate a _swnK_ mutant unable to produce Swn, then complementing the mutant with the wild-type gene to restore this biosynthetic aptitude [70]. The role of gene _hyp3_ encoding 1,8-cinee synthase in _Hypoxylon_ sp. was determined by heterologous expression in _Escherichia coli_ overexpression strain [82]. Likewise, functional analysis of the copper amine oxidase and lysine decarboxylase genes from the above-mentioned endophytic strain E5026 has been done by constructing a stable, high yielding Hup system in _E. coli_ resulting from the overexpression of _CgCAO_ and _CgLDC_[74]. Afterwards, the function of two _LDC_ genes from _C. globesporoides_ Cg01 was validated by the split-marker homologous recombination knockout method. It has been found that knock-out of single candidate biosynthetic-genes did not influence Hup production, while knock-out of both _LDC_ genes led to increased Hup production, which revealed the existence of differences in Hup biosynthesis between endophytes and plants [75].
## 3 Methodology and Results
### Miniaturisation  
As mentioned before, there are advantages in miniturising the sensors. The main advantage is that smaller sensors could be integrated into the pipe wall, without affecting its integrity, in some manner that is more convenient to the utility owners, provided that the sensors can be powered and communicated with effectively. To demonstrate a possible method of powering and communicating with MEMS sensors, a simple compliant parallel plate capacitor with a capacitance of 5 pF was integrated with a small inductor loop to form an oscillating circuit similar in nature to a RFID tag. This formed a passively powered pressure sensor. The power and communication comes from a nearby network analyser, which is connected to another inductor loop, inductively coupled to the sensor. The principle is that a change in pressure will deflect the capacitor changing the resonant frequency of the oscillating circuit. The network analyser, which measures the resonant frequency, can therefore measure the pressure without needing to be directly connected to the sensor, or for the sensor to have its own power source. This means the sensor and associated circuit could be inside the pipe or integrated into the pipe wall, whether it be in the lining or otherwise. For instance, in Figure 16 it can be seen that the frequency of the oscillating circuit changed by 4 MHz when pressure was applied.  
However, the sensor in this circuit was connected to a 5 turn, 10 mm diameter inductor loop with a ferrite core soldered to a surface mount capacitor with a fixed capacitance of 10 pF. This is, of course, too large to be integrated into the pipe wall. Therefore, an investigation into the possibility of miniaturising the sensor and circuit was carried out whereby an oscillating circuit was fabricated using MEMS technology as seen in Figure 17.  
Initial testing did not detect a signal from the MEMS circuit. Further tests on larger resonator circuits showed that there is a limit to how small an oscillating circuit can be made that is detectable  
Figure 16: Change in resonant frequency of a sensor due to changes in pressure as measured through the pipe wall.  
Figure 17: **(a)** Schematic of and **(b)** Scanning Electron Microscope image of the MEMS oscillating circuit [29] and **(c)** potential application on a pipe.  
from a reasonable distance. For instance, Figure 18 shows how the signal strength reduced with decreasing inductor loop diameter. This implies that the range over which the circuit can be detected is limited by the coil size using this method. This suggests that either an alternative power supply or communication system may be required which will increase the size of the device. This will be subject of further investigation.
## Mathematical Formulation
### [PERSON] Criterion  
The effective stress, as shown in Equation (5), is evaluated by substituting the state of stresses obtained into [PERSON] criterion, as follows:  
\[\sigma_{e}=\frac{1}{\sqrt{2}}\Big{[}(\sigma_{t}-\sigma_{r})^{2}+(\sigma_{r}- \sigma_{z})^{2}+(\sigma_{z}-\sigma_{t})^{2}\Big{]}^{1/2} \tag{9}\]  
Substituting Equation (3) into Equation (8) results in axial stress,  
\[\sigma_{z}=0.5(\sigma_{r}+\sigma_{t}) \tag{10}\]  
Using this value of \(\sigma_{z}\) in Equations (6) and (7), one gets,  
\[\dot{\epsilon}_{r}=\frac{3}{4}\frac{\dot{\epsilon}_{e}}{\sigma_{e}}(\sigma_{r }-\sigma_{t}) \tag{11}\]  
and Equation (7) becomes.  
\[\dot{\epsilon}_{t}=-\frac{3}{4}\frac{\dot{\epsilon}_{e}}{\sigma_{e}}(\sigma_{ r}-\sigma_{t}) \tag{12}\]  
Using Equation (10) in Equation (9),  
\[\sigma_{e}=\frac{\sqrt{3}}{2}(\sigma_{t}-\sigma_{r}) \tag{13}\]  
The strain rates in radial and tangential directions are represented through Equations (14) and (15), with \(\mathbf{u}\) is the radial displacement at any radius \(r\),  
\[\dot{\epsilon}_{r}=\frac{\delta\dot{u}}{\delta r} \tag{14}\]  
\[\dot{\epsilon}_{t}=\frac{\dot{u}}{r} \tag{15}\]  
Eliminating \(\dot{u}\) from Equations (14) and (15), one gets,  
\[r\frac{\delta\dot{\epsilon}_{t}}{\delta r}+\dot{\epsilon}_{t}-\dot{\epsilon} _{r}=0 \tag{16}\]  
Using Equations (11) and (12), in Equation (16), one gets,  
\[nr\bigg{(}\frac{\delta\sigma_{t}}{\delta r}-\frac{\delta\sigma_{r}}{\delta r }\bigg{)}+2(\sigma_{t}-\sigma_{r})=0 \tag{17}\]Eliminating \(\sigma_{t}\) from Equations (1) and (17), one gets,  
\[nr^{2}\frac{\delta^{2}\sigma_{r}}{\delta r^{2}}+2r\sigma_{r}+2\rho r^{2}\omega^{2 }(n+1)=0 \tag{18}\]  
Solving the differential Equation (18), which is a Cauchy-Euler Equation, we get.  
\[\sigma_{r}=-\frac{\rho\omega^{2}}{2}r^{2}+C_{1}+C_{2}r^{\frac{-2}{n}} \tag{19}\]  
where, \(C_{1}\) and \(C_{2}\) are constants determined from the boundary condition of the problem.  
Substituting Equation (19) in Equation (17), one gets,  
\[\sigma_{t}=-\frac{\rho\omega^{2}}{2}r^{2}+C_{1}+\bigg{(}\frac{n-2}{n}\bigg{)}C _{2}r^{\frac{-2}{n}} \tag{20}\]  
Then substituting Equations (19) and (20) in Equation (10)  
\[\sigma_{z}=-\frac{\rho\omega^{2}}{2}r^{2}+C_{1}+\bigg{(}\frac{n-1}{n}\bigg{)} C_{2}r^{\frac{-2}{n}} \tag{21}\]
## 4 Results and Performance Evaluation
### Dichotomous Statistical
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Radar**} \\ \cline{2-4}  & & **Rain** & **No Rain** & **Total** \\ \hline \multirow{3}{*}{**Satellite**} & Rain & s1 & s2 \\ \cline{2-4}  & No Rain & s3 & s4 \\ \cline{2-4}  & Total & s1 + s3 & s2 + s4 & s1 + s2 + s3 + s4 = \\ \hline \hline \end{tabular}  
* POD measures the fraction of observed events that were correctly identified, the best value is one.
* FAR measures the fraction of estimated rainy pixels that were not raining, its best value is zero.
* CSI measures the fraction of estimated events that were correctly identified, its best value is one.
* BIAS measures the overestimation or underestimation of the method, then a BIAS greater than one indicates an overestimation, while a BIAS lower than one indicates an underestimation.
* POFD indicates the fraction of pixels incorrectly identified by the satellite and its optimal value is zero.
* PC is the percentage of correct estimations and its best value is one.  
A pixel is considered “rainy” if the rain rate is greater than 0.2 mm/h. In Table 11 the statistical results are listed.  
\end{table} 
Table 11: Dichotomous scores for neural network technique (NN) and PR-OBS-3A (H03) algorithm.
## 8 RAN Decomposition Scheme
### Fronthaul Interface
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Symbol** & **Typical Value** \\ \hline Number of antennas & \(M\) & 1–16 \\ Number of sectors & \(N_{s}\) & 1–3 \\ Line code & C & 10/8 \\ Control overheads & \(C_{w}\) & 16/15 \\ Sampling rate (MHz) & \(R_{s}\) & 15.36/10 \\ Bit resolution & \(N_{res}\) & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Typical transmission parameters. Figure 26: Required MFH capacity for supporting different RF bandwidths (\(B_{RF}\)). rates in the 5G and beyond technologies could render conventional CPRI-based MFH implementation impractical [47; 421]. Consequently, these call for disruptive RAN infrastructural change and redesign. In [47], we give a comprehensive discussion on different potential approaches such as bandwidth compression, SDN/NFV, mobile data offloading, split-processing, and Radio over Ethernet. In addition, one of the cost-effective approaches for alleviating the requirements is the RAN FSOn scheme [47]. The scheme enables 5G service requirements accomplishment by facilitating the RAN functionality split between the CU and the DU. Consequently, this disruptive approach prefers an efficient and flexible architecture capable of assigning different elements of the RAN signal processing chain appropriately to either the CU or the DU. The employed split point could be based on different 5G deployment/use cases such as mMTC, eMBB, and ultra-reliable and low latency communications (URLLC). Moreover, based on the split point, the RAN FSOn exhibits many trade-offs regarding complexity, latency, bandwidth demand, and joint processing (JP) support. Therefore, the MNOs have to weigh the trade-offs to choose suitable split option(s) that could best serve the intended deployment scenarios [23; 36; 37; 38]. As explained in Section 3.3, for effective service provision, 5G FWA implementation could require significantly more cell sites and the related increase in the per connected-site requirements, compared with the conventional macro deployments. Consequently, this presents different challenges on the transport network (i.e., backhaul/fronthaul networks). As aforementioned, the required ISD varies and depends on the actual 5G use cases and radio deployment scenarios. For instance, a number of FSOns have been defined between the CU and DU in the 5G network as discussed in the subsequent Section 8.2.
# Cu&Si Core-Shell Nanowire Thin Film as High-Performance Anode Materials for Lithium Ion Batteries
## 2 Materials and Methods  
### Cu@Si Core-Shell Nanowire Thin Film (CSNWF) Preparation  
Cu nanowires were purchased from Hongwu Nanometer Material CO., LTD.,Xuzhou China. Cu@Si CSNWFs were prepared by three steps as schematically illustrated in Figure 1. Firstly, 2 mg Cu nanowires (CuNWs), as received without any pretreatment, were uniformly dispersed into 10 mL isopropanol (IPA), and then the slurry was casted onto 50 \(\upmu\)m-thick Cu foil, followed by heating at 280 \({}^{\circ}\)C for 2 h under the reductive atmosphere of 5 \(\mathrm{\SIUnitSymbolMicro\%}\) Hz + 95 \(\mathrm{\SIUnitSymbolMicro\%}\) Ar to evaporate IPA and reduce the oxide on the surface of the CuNWs. As a result, the Cu foil was coated with a layer of cross-linked CuNW sediment. Finally, the Si layer was deposited onto them without intentional heating or cooling to obtain Cu@Si core-shell nanowire thin films by direct current magnetron sputtering of the Si target. The sputtering power is about 120 W, the distance of the target to the substrate is about 50 mm and the work pressure is about 0.4 Pa. For comparison, pure Si film was simultaneously deposited on Cu foil and glass substrate, respectively, under the same sputtering parameter. The thickness of the pure Si films is determined as 456 nm through observing its cross-section thickness using SEM. The loading density of the as-deposited pure Si thin films was calculated under the assumption that the density of Si thin film is 2.33 g cm\({}^{-3}\), as reported in literature [19], which is the highest density achievable for the as-deposited films and will give the most conservative estimate for the specific capacity of our electrode materials.
## 3 Results and Discussion
### Validation
#### 3.1.2 Experimental Results  
For the purpose of comparison, we normalized the scale of experimental results according to the magnitude of the impact force. Figure 4d illustrates that the two data sets, e and m, were close in INT. The difference between the two data sets in DL1 is illustrated in Figure 4e. The \(DI\) was 3.59, and the damage was of scale 5 mm. Figure 4f displays that the two data sets differed considerably in DL2. The \(DI\) was 5.67 for the case with severe damage (of scale 10 mm). Similar to the numerical results,  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Location 1** & **Location 2** & **Location 3** & **Location 4** \\ \hline Case 1-1 & Intact & Intact & Minor damage & Minor damage \\ Case 1-2 & Intact & Intact & Moderate damage & Moderate damage \\ Case 1-3 & Intact & Intact & Severe damage & Severe damage \\ Case 2-1 & Intact & Minor damage & Intact & Minor damage \\ Case 2-2 & Intact & Moderate damage & Intact & Moderate damage \\ Case 2-3 & Intact & Severe damage & Intact & Severe damage \\ Case 3-1 & Severe damage & Minor damage & Intact & Intact \\ Case 3-2 & Intact & Intact & Minor damage & Severe damage \\ Case 3-3 & Intact & Severe damage & Intact & Minor damage \\ \hline \hline \end{tabular}
\end{table}
Table 2: Damage severity at different locations on the pipe.  
Figure 4: Numerical results of (**a**) INT (intact), (**b**) DL1 (mildly damaged), and (**c**) DL2 (severely damaged); experimental results of (**d**) INT, (**e**) DL1, and (**f**) DL2; \(``e\"\) and \(``m\"\) denote expanded and measured strains.  
the experimental \(DI\) also effectively identified the degree of damage. The \(DI\) was greater when the damage was more severe.
# Large Language Models as Evaluators in Education: Verification of Feedback Consistency and Accuracy
## 4 Experimental Setup
### Evaluation Metrics  
In our LLMs-as-evaluators experiment, we employed two metrics to measure agreement between responses: _inner agreement_ and _inter-agreement_.  
Inner agreement is a metric that measures the consistency of responses when a single LLM generates multiple answers, allowing us to evaluate the reliability of the evaluator. An overview of the calculation process can be found in Figure 6(a), and the pseudocode is detailed in Algorithm 1.  
A single LLM evaluator (\(E\)) generates \(R\) responses (\(E_{R}\)). If all responses \(E_{1}\) to \(E_{R}\) yield the same results, a score of 1 is assigned; otherwise, a score of 0 is given. In other words, a score of 1 is assigned only in the case of unanimous agreement. The formula for calculating inner agreement is as follows:  
\[\text{Inner-agreement}=\begin{cases}1&\text{if }\sum_{r=1}^{R}E_{r}=0\text{ or}\\ &\sum_{r=1}^{R}E_{r}=R,\\ 0&\text{otherwise}.\end{cases} \tag{1}\]  
The inter-agreement measures the agreement between responses from different evaluators. This metric demonstrates how similarly various evaluators judge the same query. A high inter-agreement signifies that diverse evaluators provide stable and consistent results. An overview of the entire calculation process can be found in Figure 6(b), and the pseudocode is detailed in Algorithm 2.  
Suppose there are \(M\) feedback evaluators (\(E^{M}\)), and each evaluator generates \(R\) responses (\(E^{M}_{R}\)). For each evaluator, a majority voting is performed on the \(R\) responses (\(MV^{M}\)). Then, if all majority voting results from \(MV^{1}\) to \(MV^{M}\) are the same, a score of 1 is assigned; otherwise, a score of 0 is given. That is, similar to inner agreement, a score of 1 is assigned only in the case of unanimous agreement. The formula for calculating inter-agreement is as follows:  
\[\text{Inter-agreement}=\begin{cases}1&\text{if }\sum_{r=1}^{M}MV^{m}=0\text{ or}\\ &\sum_{m=1}^{M}MV^{m}=M,\\ 0&\text{otherwise}.\end{cases} \tag{2}\]  
Figure 7: Calculation process for inner and inter-agreements. We conducted the experiment with \(R\) and \(M\) set to 5. The tick represents cases where the agreement is 1, while the cross represents cases where the agreement is 0.