esa-sceva/satcom-chunk-collection huggingface dataset:
### Introduction Internet of Things (IoT)  
The Internet of Things (IoT) has transformed the way objects are monitored and data are collected over a network [2]. It allows us to connect and monitor things locally and remotely. The IoT has empowered systems for automatic data collection rather than depending on human participation during its process. Generally, humans are error-prone and are highly inefficient in comparison to computing systems, especially in the field of data collection. IoT's application is now widespread, and it is seen in various fields, including smart healthcare, smart homes, automated supply chain management systems, remote environmental monitoring, smart city monitoring, etc. The IoT leverages machine-to-machine (M2M) communication, replacing the need for humans to provide data input, which ensures scalability, reliability, and accuracy. The IoT has a significant advantage because of its interoperability with many existing systems and networks. It is transforming healthcare applications, and such IoT systems that are used in medical-related monitoring and control are termed the Internet of Medical Things (IoMT).
# An Image Compensation-Based Range-Doppler Model for SAR High-Precision Positioning
## 3 Experiments
### Experiments on Spaceborne SAR Images  
#### 3.1.1 Experimental Data  
The X-band TerraSAR-X images and GF3-C images were selected for the spaceborne SAR experiments.  
(1) TerraSAR-X Images  
The TerraSAR-X images and reference points used in this study have also been previously used in the literature [41] as an experimental data set. This image data set was acquired on 5 September 2012, at a center longitude and latitude of E 120.78\({}^{\circ}\) and N 50.37\({}^{\circ}\), respectively. The test area is located near Yigen Farm to the east of Eerguna City in the Inner Mongolia Autonomous Region, China. The average elevation of the study area is 706 m. This area is characterized by relatively flat terrain, consisting of bare soil, farmland, and woodland. The image resolution is 2.614 m in the azimuth and 0.909 m in the slant range direction. A total of 21 corner reflectors (numbered 01 to 21) were strategically placed along approximately straight lines in both the range and azimuth directions. Figure 2 illustrates the image, including the distribution of corner reflectors (a), and a part of the TerraSAR-X image (b) indicated by the blue box in (a).  
(2) GF3-C Image  
GF3-C is China's first C-band multi-polarization synthetic aperture radar imaging satellite, with a maximum resolution of 1 m. The image selected for the test was acquired on 27 September 2019, with dimensions of 16,886 \(\times\) 24,878 pixels, and was captured from a descending orbit. The slant range resolution of the image is 1.54 m, and the azimuth resolution is 1.12 m. The test area is located in the northeast corner of Beijing, China, characterized by flat and mountainous terrains with elevations ranging from 10 m to 400 m. In the area covered by the image, 12 uniformly distributed reference points (labeled A01-A12) are shown, as illustrated in Figure 3a. A part of the image is shown in Figure 3b.
## 3 Methods
### Spectral Angle Mapper
#### 3.2.2 Improved Spectral Angle Features  
The traditional SAM uses different bands in a single time-phase image as inputs to construct a spectral angle feature. Although this type of spectral angle feature can suppress the shadow effect to a certain extent and highlight the target reflectance, it has limitations in the Zhaotong City area, where the terrain is complex and cloudy. Therefore, we propose an improved spectral angle feature based on the SAM. Compared to the traditional SAM, this method uses the VV and VH bands of Sentinel-1 images of multiple time phases as inputs to obtain two spectral angle features with time-series characteristics that are unaffected by clouds and rain.  
As the growth cycle of apple trees is different from that of other crops in a year, the VV and VH bands of the monthly average Sentinel-1 images from January to December 2021 were used as the basis for this study to obtain two characteristic images, SAM-VV and SAM-VH, through the SAM.  
Using SAM-VV as an example, the steps are as follows:  
1. The VV bands of monthly averaged Sentinel-1 images from January to December 2021 were obtained.
2. Ten randomly selected image elements were used as standard image elements.
3. In the same month, the VV values of the ten standard image elements were averaged to obtain the reference mean values of the VV bands from January to December 2021.
4. Using the SAM, the VV band from January to December 2021 was used as the input to calculate the original spectrum \(\overset{\rightarrow}{\alpha}\) of the individual image elements.
5. Using the SAM, we constructed a reference spectrum \(\overset{\rightarrow}{\beta}\) of the VV using the VV averages from January to December 2021 as input.
6. The angle between the original spectrum \(\overset{\rightarrow}{\alpha}\) and reference spectrum \(\overset{\rightarrow}{\beta}\) of a single image element to obtain the spectral angle coefficient \(\theta\) of a single image element was calculated using the following formula: \[\theta=\cos^{-1}(\overset{\rightarrow}{\alpha}\cdot\overset{\rightarrow}{ \beta}\overset{\rightarrow}{\beta}),\theta\in(0,\frac{\pi}{2})\] (1)  
Finally, the operation in Step (6) was performed for each image element, and the calculated spectral angle coefficients constituted the SAM-VV features.  
The improved spectral angle features of SAM-VV and SAM-VH are shown in Figure 6.
## 3 Auction-Based Cooperative Rescue Plan
### Bidders
#### 3.2.1 The Utility Function of Bidders  
The utility \(u_{i}\) bidders gain from task \(T_{i}\) indicates the task's rescuing priority. The more urgent the task is, the higher the utility would be. The utility \(u_{i}\) could be measured by buried depth, number of buried victims and severity of injury. In this paper, the utility is calculated through fuzzy comprehensive evaluation (FCE).  
Let \(Z=\left\{z_{1},\,z_{2}\,\,\ldots,\,z_{q}\right\}\) be the factor set, where \(z_{a}(a=1,\ 2,\ \ldots,\,q)\) denotes the a-th factor, \(q\) denotes the number of factors. Let \(V=\left\{v_{1}\,\,v_{2}\,\ldots,\ v_{p}\right\}\) be the evaluation set, where \(v_{b}(b=1,\ 2,\ \ldots,\,p)\) denotes one evaluation level, \(p\) denotes the number of levels.  
We calculate the membership degree of each factor to \(V\) according to membership function. For example, the membership degree of \(z_{a}\) is \(R_{a}=\left(r_{a1},\ r_{a2},\ldots,\ r_{ap}\right)\), which is also called single factor evaluation set. The overall fuzzy evaluation matrix of all \(q\) factors is \(R=\left(R_{1},\ R_{2},\ldotsldots R_{q}\right)^{\prime}\). Let \(W=\left(w_{1},\ w_{2},\ldots,\ w_{q}\right)\) be the weight vector, where \(w_{a}\) denotes the relative importance of a-th factor. Consequently, the evaluation result would be  
\[H=W\times R=\left(h_{1},\ h_{2},\ldots,\ h_{p}\right) \tag{5}\]  
When evaluating the utility of task \(T_{i}\), we first calculate the comprehensive score of task \(T_{i}\)\(U_{i}^{T}=\frac{\sum\limits_{j=1}^{T}h_{b}\cdot b}{\sum\limits_{j=1}^{P}h_{b}}\), and then obtain the utility of task \(T_{i}\ u_{i}=\frac{U_{i}^{T}}{n_{i}}\).
## 3 Design of Synchronous Multi-Link Distributed Coordination Functions
In this situation, a link with the largest backoff counter becomes the primary link. On the auxiliary link, the station checks whether the link has been idle during _T_th before TXOP starts and determine participating in multi-link transmission. In the case of MLA-A, as shown in Figure 4d, similar to conventional DCF, each station tries to commence TXOP when the backoff counter elapses. A link where the backoff counter firstly elapses is set to the primary link. While the backoff counter is decrementing on the other link, the station can initiate a transmission if its backoff counter becomes zero while transmission on the primary link, and thus, the link is set to the auxiliary link. For example, the backoff counters of links 1 and 2 are 5 and 2, respectively. The backoff counter of link 2 will be expired earlier, and link 2 becomes the primary link. Since there is a remaining counter on link 1, the rest of the counter will also be expired soon. If there is ongoing transmission within the same MLD at the time, then the additional transmission will occur. In MLA-A, we need to focus on three things. First, since the ongoing transmission on link 2 precludes station on link 1 from checking the channel, which is shown in Figure 2a, channel check during _T_th does not work. Second, to avoid in-device interference between links for non-STR cases, the end moments of TXOPs need to be aligned. If not, the following frames, such as acknowledgments, may occur in-device interference on the  
Figure 4: Illustrations of synchronous multi-link DCFs for supporting non-STR multi-link devices (MLDs). (**a**) Single-link access (SLA), (**b**) multi-link access with the shortest backoff (MLA-S), (**c**) multi-link access with the longest backoff (MLA-L), (**d**) multi-link access with end-time alignment (MLA-A). links being received, as seen in Figure 2b. Lastly, MLA-A may not be used with RTS/CTS exchange when the CTS reception on the primary link and the transmission on the auxiliary link overlap.
## 2 Methodology
### Instruction Disturbance Test  
In order to further verify the effectiveness of the proposed EEA-ESKF-MPC strategy in actual power plant operation, this section tests its performance under command disturbance through simulation experiments. Three typical disturbance scenarios were designed in the experiment: throttle opening disturbance (3000 s), coal volume disturbance (6000 s), and water volume disturbance (9000 s), respectively, to simulate the interference of valve adjustment, fuel change, and water volume fluctuation in actual operation. Figure 6 shows the response of process outputs and control inputs, and Table 3 shows the IAE and ME metrics for different controllers.  
Figure 6: Simulation comparison of control strategies in the uncertainty disturbances case. (**a**) Processes outputs; (**b**) control inputs.  
Under the throttle opening disturbance (Case 1), the pressure IAE1 (7.473) of EEA-ESKF-MPC is lower than that of ESKF-MPC (7.837) and MPC (8.423), and the load IAE2 (47.529) and temperature IAE3 (37.314) are also significantly better than other controllers. In particular, it shows a strong error suppression ability in temperature control (IAE3 is about 41.7% lower than ESKF-MPC). This shows that the adaptive mechanism effectively improves the response ability to fast disturbances.  
Under the coal feed disturbance (Case 2), the load IAE2 (37.170) of EEA-ESKF-MPC is lower than that of ESKF-MPC (38.490) and EA-ESKF-MPC (95.535), and the temperature IAE3 (44.765) is also better than that of other controllers. However, pressure IAE1 (3.058) was slightly higher than ESKF-MPC (2.973), possibly due to the insufficient adjustment of pressure errors by adaptive weights. The control input (Figure 6b) shows that the EEA-ESKF-MPC achieves good tracking performance while maintaining smoothness.  
Under water feed disturbance (Case 3), EA-ESKF-MPC performs well in three indexes: the pressure IAE1 (9.540), load IAE2 (116.083), and temperature IAE3 (73.964) are all lower than ESKF-MPC and MPC, indicating its robustness to complex disturbances. The change in the ME index is small, indicating that the maximum deviation control ability is comparable to that of other controllers.  
Overall, the EA-ESKF-MPC demonstrated excellent dynamic performance and robustness in the command perturbation experiments, especially in temperature and load control, which significantly reduced error accumulation. The adaptive weight mechanism enhances the adaptive ability of the system to the disturbance by dynamically adjusting the prediction error weight, and the exponential smoothing filter avoids the overreaction of the control input. These results further validate the potential application of EEA-ESKF-MPC in multivariable coupling control in supercritical power plants.
# Thermometric Characterization of Fluorescent Nanodiamonds Suitable for Biomedical Applications
## 3 Results and Discussion  
### NV Centre Electronic Structure  
In general, the electronic structure of the NV centre has six electrons. Two coming from the nitrogen atom, and another three are dangling bonds from the three carbon atoms surrounding the vacancy. The sixth electron is captured from the lattice, resulting in NV\({}^{-}\) charge state [56]. During the FNDs crystal growing others NV charge states may occur, such as NV\({}^{0}\) and NV\({}^{+}\). Among the created centres, NV\({}^{0}\) and NV\({}^{-}\) are optically active but only NV\({}^{-}\) is both magnetically and optically active. The electron density of those centres is distributed in a plane vertical to the central NV axis, with the highest density on the three carbon atoms and the vacant site [57; 58]. In this scheme, the electronic structure of NV\({}^{0}\) has a \({}^{2}\)E ground state, \({}^{2}\)A\({}_{2}\) optical excited level, and an intermediate metastable \({}^{4}\)A\({}_{2}\) level. The \({}^{2}\)A\({}_{2}\) has no orbital degeneracy and exhibits no zero-field fine structure, but the ground state \({}^{2}\)E has orbital degeneracy causing a spin-orbit fine structure [59]. For NV\({}^{-}\) centre, the ground base (\({}^{3}\)A\({}_{2}\)) is a spin-triplet that at zero field state split in three sub-spin levels: a doublet (m\({}_{\text{s}}=\pm\) 1) and a singlet (m\({}_{\text{s}}=0\)). Moreover, the NV\({}^{-}\) centre presents an additional\({}^{1}\)A\({}_{1}\) metastable state [60]. In contrast, the excited electronic state \({}^{3}\)E exhibits a complex fine structure composed mainly by spin-spin and spin-orbit interactions [61]. Figure 1a. Thermal energy enhances all the luminescent relaxation processes but also actives surface or defects states. These nonradiative relaxation channels are activated as temperature rises. The thermal relaxations may compete directly with the radiative response from ZPL in FNDs, diminishing their fluorescence signal [62].
# Strategies for Reducing Automobile Fuel Consumption
## 5 Transportation Policies, Alternatives, and Externalities
### Electric Mobility
These use electric power in short-range trips (e.g., inside cities) and fuel for longer trips. The integration of advanced _driver-assistance systems_ (ADASs) and further efforts to improve autonomous driving technology are also being increasingly incorporated into the designs of EVs and HEVs [90]. Similar to vehicles powered by ICEs, electric vehicles also experience significant friction losses. According to a report by [PERSON] and [PERSON] [43], approximately 57% of the total electrical energy supplied to an electric vehicle (equipped with lithium-ion batteries and driven by an electric motor that functions as a generator) is consumed to overcome friction losses. These losses are distributed as follows: 1% in the 75 kW electric motor, 3% in the transmission (single-stage mechanical transmission), 41% in rolling resistance, and 12% in the brakes. Considering that tire technology used in vehicles with ICEs is similarly applied in current EVs, rolling resistance loss values are similar. Moreover, vehicle hybridization allows for _automatic engine shutdown_ when the vehicle is stationary (i.e., utilizing the start-stop system). In such vehicles, the use of technologies such as _regenerative braking_, combined with the optimization of the combustion engine's operating conditions, should result in reduced fuel consumption [7; 28]. _Regenerative braking_ technology enables the recuperation of a portion of the vehicle's kinetic energy during deceleration and uses it to recharge the battery [45]. This process is achieved by reversing the electric motor's function to act as a generator, thus capturing the energy usually lost as heat in friction brakes and storing it in the battery. Managed by sophisticated electronic control systems, regenerative braking optimizes energy recovery based on vehicle speed and battery capacity, enhancing energy efficiency, extending vehicle range, and reducing the need for frequent recharging. Therefore, while traditional braking contributes indirectly to fuel consumption by wasting pre-acquired kinetic energy, regenerative braking in electric vehicles ensures a cyclic utilization of energy and minimizes overall energy (and fuel) consumption. Innovative methodologies aimed at harnessing energy from the vehicles' oscillatory movements are gaining importance [46]. A critical constraint for energy recuperation is the peak current that the generator-battery system can withstand, given a steady nominal voltage of 12 V. Consequently, it is imperative to select mechanical transmission ratios that facilitate the motor's operation at peak efficiency, in awareness of the driving constraints. Achieving this may require unique tailoring of gear ratios to suit diverse speed profiles, characteristic of specific driving cycles inherent to individual cities. Such strategic optimizations could bolster energy recuperation, culminating in an augmented powertrain efficiency.
# VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT
## 4 Experiments
### Ablation Studies
This effectiveness stems from their complementary capabilities: Baichuan2 excels in handling complex multi-turn text dialogues, while MiniGPT-v2 is adept at detailed multimodal dialogues. This synergy maximizes the text comprehension ability of LLMs and the video understanding capacity of LMMs, thereby enhancing the overall performance of our framework. **Proposal generator. ** In our study, top-\(k\) and the continuity threshold \(\lambda\) within the proposal generator play a critical role. The parameter \(k\), acting as a count threshold in our dynamic mechanism, directly influences the identified length of relevant proposals. In contrast, \(\lambda\) determines the number of irrelevant consecutive frames. To optimize these parameters, we conducted a series of ablation experiments on the proposal generator, as illustrated in Figure 7. The visualized results indicate that a combination of \(k=8\) and \(\lambda=6\) yields the most favorable outcomes. This specific pairing strikes a balance between segment length and threshold sensitivity. It skillfully avoids the drawbacks of excessively low thresholds, which could incorporate irrelevant frames into prediction results. Simultaneously, it averts the \"tolerance trap\" where an overly high number of discontinuous frames makes it difficult to determine when the segment ends. **Proposal scorer. ** To balance the quality and length of segments, we conduct experiments on our proposal scorer, as shown in Figure 6b. We explore integrating the length score \(S_{l}\) into the scoring mechanism. Initially, without including the length score (\(\alpha=0\)), mAP Avg. is 30.45. Upon incorporating \(S_{l}\), mAP Avg. peak at 30.91. Similarly, [EMAIL_ADDRESS] increases from 51.21 to 54.24, indicating that incorporating a length-based scoring mechanism is crucial for generating the final segment scores. Figure 6: Ablation experiments on the QVHighlights _val_ split focus on [EMAIL_ADDRESS] and mAP Avg. (**a**) Utilizing debiased queries can enhance model performance, yet increasing the number of debiased queries (\(N_{q}\)) does not always lead to better results. The model achieves optimal performance when \(N_{q}\) is set to 5. (**b**) In the proposal scorer, proposal length significantly impacts the final outcomes, with the model performing optimally when \(\alpha=0.5\). **IoU threshold \(\mu\). ** Finally, we assess the effectiveness of IoU thresholds \(\mu\) in the NMS process, focusing on their role in reducing segment overlap. It is important to note that NMS does not alter the values of [EMAIL_ADDRESS] and [EMAIL_ADDRESS]. Therefore, we report only the mAP metrics in Table 6. As illustrated in Table 6, setting \(\mu\) to 0.75, compared to not employing NMS (\(\mu=1\)), results in an increase of +0.53 in mAP Avg. This increment underscores the significance of eliminating excessively overlapping segments, affirming that reducing such overlaps can notably enhance the model's performance.
## 1 Introduction
Figure 2 shows the actual cases of damage to the rubber expansion joints, with the leading causes being traffic and vehicular loads (54.9%), debris accumulation (22.8%), and material degradation (22.3%), as summarized in Table 3[2]. Despite this damage, rubber waterproofing materials are treated as consumable parts, and these parts are replaced after the damage occurs. Therefore, research on the maintenance of rubber index materials is not widely conducted in Korea. This study proposes an innovative approach to address the limitations of rubber expansion joints in existing bridge designs. This research explores the use of superelastic shape memory alloys as a substitute for conventional rubber materials in expansion joints. These alloys can uniquely return to their original shape after plastic deformation, offering adaptability to bridge expansions. Additionally, these alloys can be tailored to various bridge types and possess properties akin to general steel, improving maintenance efficiency. Previous preliminary research has confirmed the efficacy of superelastic shape memory alloys for use in smaller bridges as an alternative to rubber expansion joints. Displacement control was performed on the superelastic shape memory alloy, wherein it reverted to its original shape without residual displacement [3]. However, in the preliminary research, the approximate shape of a small-sized water-stop material applied to small-scale bridges was created and reviewed. Based on this foundation, the current study extends the application to larger bridges, modeling both rubber and superelastic shape memory alloy expansion joints similar to those used in real-world scenarios. Furthermore, theoretical verification was conducted through finite element analysis to evaluate the practical applicability, without performing separate field application. Figure 1: Distribution of primary damage causes in expansion joints.
# Efficient Expiration Date Recognition in Food Packages for Mobile Applications
## 4 Results
### The Overall Performance  
Figure 7 illustrates the functionality of the proposed method on local product packages. It shows that the area indicating the expiration date can be detected, with the date subsequently recognized and converted into a standardized format. Dates lacking a specific day are automatically adjusted to the start of the month; for instance, in the first row of Figure 7, 01 2025 is translated to 2025-01-01. The second row in Figure 7 depicts that dates including the full year are accurately processed. The third row demonstrates that dates using a two-digit format for the year can also be correctly interpreted, as seen with 30.03.25, which is processed as 2025-03-30.
## 2 Methodology
### Link Budget Analysis
#### 2.6.3 Bit Rate  
The bit rate is an important parameter in communication systems as it determines the maximum amount of data that can be transmitted or received within a given time period. It is often used as a performance metric to evaluate the efficiency and capacity of communication systems, such as wireless networks, digital television broadcasting and internet connectivity. Bit rate is a measure of how quickly bits are moved between two locations. It is the product of symbol rate, code rate and spreading factor [41], as shown in Equation (11), where symbol rate (Equation (12)) is the transmission rate of signals along certain connections and code rate (\(CR\)) is the proportion of transmitted bits that actually carry information. For LoRa communication, the code rate has four values [42], which are 4/5, 4/6, 4/7 and 4/8, and they are shown in Equation (13), where n is between 1 and 4.  
\[Bit\ rate(bit/s)=Symbol\ rate\ *\ CR\ *\ SF \tag{11}\]  
\[Symbol\ rate=\frac{BW}{2^{SF}} \tag{12}\]  
\[CR=\frac{4}{4+n}\ where\ n=1,2,3,4 \tag{13}\]