esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method  
### PSMS-SLAM Framework  
Our approach designs a multi-sensor fusion SLAM framework, PSMS-SLAM, combined with panoptic segmentation, which mainly contains five modules, and its main structure is shown in Figure 2.  
The IMU module uses the preintegration [35] method to perform IMU data pre-processing and provide the data to other pipelines. The LiDAR-inertial system module extracts feature points from the LiDAR point cloud, performs feature extraction and matching, and combines the IMU preintegration data to estimate the pose. The panoptic segmentation module is responsible for the panoptic segmentation of the image to obtain the panoptic information and to track the dynamic target pose in real time with this and global pose information. The visual-inertial system module accepts the image data using the Lucas-Kanade (LK) optical flow method to track the feature points of the previous frame, then uses RANSAC for the pairwise polar constraint to remove the exclusion points, and finally extracts the features from accelerated segment test (FAST) feature points in the image. In this module, the visual-based loop-closure, which utilizes the improved version of distributed bags of words library (DBOW2) method known for its high accuracy and robustness, is also implemented. Specifically, the IMU measurement constraint, visual odometry constraint, and LiDAR odometry constraint are jointly optimized using the factor graph method to obtain the final system poses, and the data from each module are accepted to complete the construction of the map.  
The modules are also coupled to enhance the performance of each other: the visual-inertial system module receives data from the LiDAR to register the depth information into the feature points, completes the initialization using the estimated poses from the LiDAR-inertial system module, and receives information from the panoptic segmentation to reject the interference of dynamic objects on the accuracy.
###### Abstract  
Collaborative strategies for mobile sensor nodes ensure the efficiency and the robustness of data processing, while limiting the required communication bandwidth. In order to solve the problem of pipeline inspection and oil leakage monitoring, a collaborative weighted mobile sensing scheme is proposed. By adopting a weighted mobile sensing scheme, the adaptive collaborative clustering protocol can realize an even distribution of energy load among the mobile sensor nodes in each round, and make the best use of battery energy. A detailed theoretical analysis and experimental results revealed that the proposed protocol is an energy efficient collaborative strategy such that the sensor nodes can communicate with a fusion center and produce high power gain.  
mobile environments; mobile sensing schemes; collaborative weighted clustering algorithm; weighted clustering algorithm; oil leakage monitoring +
Footnote †: journal: Sensors
# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
###### Abstract  
Underwater wireless sensor networks (UWSNs) can provide a promising solution to underwater target tracking. Due to the limited computation and bandwidth resources, only a small part of nodes are selected to track the target at each interval. How to improve tracking accuracy with a small number of nodes is a key problem. In recent years, a node depth adjustment system has been developed and applied to issues of network deployment and routing protocol. As far as we know, all existing tracking schemes keep underwater nodes static or moving with water flow, and node depth adjustment has not been utilized for underwater target tracking yet. This paper studies node depth adjustment method for target tracking in UWSNs. Firstly, since a Fisher Information Matrix (FIM) can quantify the estimation accuracy, its relation to node depth is derived as a metric. Secondly, we formulate the node depth adjustment as an optimization problem to determine moving depth of activated node, under the constraint of moving range, the value of FIM is used as objective function, which is aimed to be minimized over moving distance of nodes. Thirdly, to efficiently solve the optimization problem, an improved Harmony Search (HS) algorithm is proposed, in which the generating probability is modified to improve searching speed and accuracy. Finally, simulation results are presented to verify performance of our scheme.  
target tracking; node depth adjustment; improved harmony search; underwater wireless sensor networks +
Footnote †: journal: Journal of Sensors
## 2 Materials and Methods  
### Equipment Used  
The characteristics and specifications of the materials and equipment used are described below. A DJI Mavic 2 Pro quadcopter drone [56; 57] was used to conduct the photogrammetric survey. It is a small, foldable, and easy-to-transport drone with a built-in 20-megapixel camera. Its camera quality, flight duration capabilities (30 min), and portability make this drone a suitable option for photogrammetric studies [57; 58].  
A TB 210 IR Giardino turbidity [59] was used to measure water turbidity and an LM-200 LED Amprobe lux meter [60] was used to control the luminosity rates. For the georeferencing of the photogrammetric survey, a Topcon global navigation satellite system (NGSS) [61] was used to take control points, enabling better information collection accuracy, as is shown in Table 2. This allows other researchers to extend the proposed methodology and apply it to other case studies. It is necessary to specify methodology to improve scientific research and make the scientific use of UVAs increasingly innovative [62; 63].  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{UAV Mavic 2 Pro [56]} \\ \hline Weight/maximum payload & 734 g/743 g \\ recommended & 25 min \\ Maximum flight time & 5 m/s and 3 m/s \\ Maximum ascent and descent & 5 m/s and 3 m/s \\ speeds & GPS-GLOAASS \\ Satellite positioning system & 20 MP \\ Camera & 30 min \\ Battery life & 30 min \\ Transmission distance & 8 km \\ \hline \hline \multicolumn{2}{c}{TB 210 IR Turbidity Meter [59]} \\ \hline Light source & Infrared LED (860 nm) \\ Accuracy & \(\pm\)2.5\% reading or \(\pm\)0.01 NTU (whichever is greater) \\ Handling & Acid and solvent-resistant polycarbonate membrane \\ Suitable measuring cuvettes & Round 24 mm cuvettes \\ Weight & 400 g \\ Measurement range & 0.01-1.100 NTU \\ Environmental conditions & 5–40 \({}^{\circ}\)C with a relative humidity of 30–90\% \\ \hline \hline \multicolumn{2}{c}{LM-200 LED Light Meter [60]} \\ \hline Measurement units & Lux or footcandle \\ Measurement range & 20,000 lux or 20,000 footcandle \\ Battery & 9 V \\ Accuracy & 3\% \\ External height/width/depth & 38 mm/63 mm/130 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the equipment used in this study.
# Secure Localization in the Presence of Colluders in WSNs
## 5 Correctness Analysis  
We demonstrate that a colluder is uncovered by Algorithm 1, despite its insertion in the _CCL_ of \(A\).  
Lemma 1: _A location-unknown sensor \(A\) executing Algorithm 1 detects all in-range colluders._  
Proof: Let \(A_{i}\) be a colluder. According to Lines 1-11 in Algorithm 1, the false coordinates of \(A_{i}\) are listed in the _CCL_ of \(A\). In the intersection of the discs \(d_{A_{i}}\) and \(d_{A}\), let \(l\) be the number of truth-telling sensors and \(m\) be the number of attackers colluding with \(A_{i}\). We assume that \(l\geq m+2\) and that each of these \(l\) truth-telling sensor node has _bi-directional_ communication with \(A\). In the worst case, the false coordinates of \(A_{i}\) are present in the _CCL_ of \(m\) colluders. It is proved in [16] Lemma 2 that \(A_{i}\) can fool a maximum of two truth-telling sensors (e.g., \(U_{i}\) and \(U_{j}\) in Figure 3). This means that there is potentially another truth-telling sensor node attacked by \(A_{i}\). If this happens, then node \(A_{i}\) is included in the _CCL_ of that truth-telling sensor node. Hence, \(A\) receives from its neighbours a maximum of \(m+1\)_CCLs_ including \(A_{i}\). According to Lines 12-18 in Algorithm 1, \(A\) gives a positive credit at most \(m+1\) times and a negative credit at most \(l-1\) times to \(A_{i}\). Since \(i\geq m+2\), we have that \(l-1\geq m+1\). Therefore, more negative credits than positive credits are attributed to \(A_{i}\). According to Lines 19-25 in Algorithm 1, \(A_{i}\) is declared a colluder by \(A\).  
Lemma 2: _A location-unknown sensor \(A\) executing Algorithm 1 detects all in-range truth-telling sensor nodes, when the number of truth-telling sensor nodes \(i\) is at least two more units greater than \(j\), the number of colluders._  
Proof: If \(B\) is a truth-telling sensor node, it should be present in the _CCLs_ of \(l\) truth-telling sensor nodes in the intersection of the discs \(d_{A}\) and \(d_{B}\). In the worst case, all \(m\) colluders remove the coordinates of \(B\) from their _CCLs_. According to lines 12-18 in Algorithm 1, \(A\) assigns \(l\) positive credits and \(m\) negative credits to \(B\). Given the number of truth-telling sensors \(l\) is at least two more units greater  
Figure 3: Let \(A_{i}\) be an evil-ring attacker and \(A_{i}^{\prime}\) be its fake location. Any sensor on the line that is perpendicular to \(A_{i}A_{i}^{\prime}\) and passing through \(A_{i}A_{i}^{\prime\prime}\)s midpoint cannot detect \(A_{i}\) as an evil-ring attacker. Given no three sensors are collinear (by assumption), there is no other location-unknown sensor other than \(U_{i}\) and \(U_{j}\) that can be fooled by \(A_{i}\).  
than \(m\), namely, \(l\geq m+2\), and according to lines 19-25 in Algorithm 1, \(A\) will declare \(B\) as a truth-telling sensor.  
From Lemmas 1 and 2, we derive the following theorem:  
Theorem 4.1: _A location-unknown sensor \(A\) executing Algorithm 1 detects all in-range colluders and truth-telling node sensors._
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 3 Results  
To evaluate the efficacy of the micro-capsule network model in epileptic signal recognition, this study assesses its capabilities across three metrics: the number of network iterations, the recognition rate across different time intervals, and its performance in epileptic detection.
# Unsupervised Low-Light Image Enhancement in the Fourier Transform Domain
## 2 Related Work
### Enhancing Simultaneously Denoising Methods  
Traditional image enhancement methods are primarily built upon histogram equalization [32] or Retinex theory [33]. The Retinex model-based approach decomposes a low-light image into a reflection component and an illumination component by some kind of prior or regularization. Recently, learning-based methods have been proposed to learn illumination enhancement [34]. In terms of image denoising, there have been a lot of traditional methods, such as Filter denoisingand BM3D [35]. Most learning-based denoising models require paired data for training, but there are also deep unsupervised learning-based models, such as DnCNN [21] and Noise2 Noise [36]. Despite the rapid development of deep learning-based low-light image enhancement approaches, there are a lot of methods that only focus on luminance enhancement and neglect to remove the noise of low-light images [17; 24; 26]. RUAS [17] amplifies the noise when discovering low-light prior architectures from a compact search space. So, these methods would amplify the noise while enhancing the brightness of the image. Some methods consider the problem of denoising when enhancing luminance [22; 37], but they enhance luminance and remove noise in the spatial domain, leading to unsatisfactory results. It is meaningful work to study how to enhance the brightness of low-light images without amplifying the noise.
# Research Progress in the Flight Characteristics of Flyers Driven by Different Technologies
## 3 Research Progress in Flyer Velocity
### Line-VISAR  
There have been many improvements based on the ORVIS system involving imaging to provide spatially resolved velocimetry data along a line on the sample surface. This system is closely related to ORVIS and is referred to as \"line-VISAR\".  
The classic line-VISAR structure is shown in Figure 12. A convergent probe beam is incident on the flyer surface through the imaging system. The reflected beam, which contains velocity information, is collected and fed into a pair of interferometers. Doppler shifts appear as fringe shifts at interferometer outputs. The fringes are focused through a relay lens on a narrow slit at the entrance of a streak camera and recorded to determine the development of the flyer velocity over time [37; 38]. The line-VISAR has \(\upmu\)m-level spatial resolution and ps-level temporal resolution when compared to discrete multi-point measuring technology. Furthermore, the scanning image can directly display the speed difference with space-time resolution.  
[PERSON] et al. developed a line-imaging velocity interferometer with 50 ps temporal resolution and 20 \(\upmu\)m spatial resolution. In the velocity measurement experiment of the laser-driven Al flyer, the data processing results of the image recording provided a large amount of valuable information about the velocity and displacement distribution of a line on the target surface [39].
## 4 Experimental Application
The reconstructed pressures for plate sound source based on the four methods are shown in Figure 12. It can be seen that for the source at 500 Hz, except TRESM and CESM, the other two methods could reconstruct the sound pressure distribution similar to that of the reference sound pressure. The unsatisfactory reconstruction results of TRESM was mainly due to insufficient measurement points and the influence of environmental noise. At the same time, the main reason for the unsatisfactory effect of CESM was that the basis of CESM was no longer suitable for the spatially extended sound source. Compared with CMESM, ERDCESM had a pressure distribution closer to the referenced. When the plate sound source was set at 2000 Hz, the reconstruction results of the four methods were deviated. This could be explained by the high order acoustic modes possessing high spatial frequency details, which were considered as the evanescent waves in NAH, and were more easily excited when the thickness of the plate was reduced, and thus the sparsity of the sound field was weakened. Another reason was that CS was based on the theory of signal compression, and the evanescent components always had lower weights. Therefore, when the measurement points were not enough, the evanescent components would be discarded. However, it did not affect the identification and location for the area where the radiated sound pressure was the strongest. Compared with the other three methods, ERDCESM realized that the reconstructed strongest radiated sound pressure area was the closest to that of the referenced area. Figure 11: Pressure distributions under experimental conditions of (**a**) ERDCESM with \(\
u=0\) at 500 Hz, (**b**) ERDCESM with \(\
u=1\) at 500 Hz, (**c**) ERDCESM with \(\
u=3\) at 500 Hz, (**d**) ERDCESM with \(\
u=0\) at 2000 Hz, (**e**) ERDCESM with \(\
u=1\) at 2000 Hz and (**f**) ERDCESM with \(\
u=3\) at 2000 Hz for a single source. Figure 13 demonstrates the results based on ERDCESM-\(\
u\). It can be seen that with the order \(\
u\) increasing, the coverage of the identified acoustic center on the reconstruction plane was narrowed gradually and the strongest radiated sound pressure area became more and more obvious. The identification and location for the main noise sources was thus more accurate. Combining the simulation with the application of the experiment, it was found that ERDCESM-\(v\) was effective and practical in reconstructing the radiated sound field of the plate. Figure 12: The Experimental results of **(a)** Theoretical map at 500 Hz, **(b)** TRESH map at 500 Hz, (c) CESM map at 500 Hz, (d) CMESM map at 500 Hz, (e) ERDCESM map at 500 Hz, (f) Theoretical map at 2000 Hz, (g) TRESH map at 2000 Hz, (h) CESM map at 2000 Hz, (i) CMESM map at 2000 Hz and (j) ERDCESM map at 2000 Hz for a plate sound source.
# On Sharing an FIB Table in Named Data Networking
## 2 Related Works
### Bloom Filter  
Due to their simplicity, Bloom filters [21] have been widely used in many applications [22; 23; 24] and a set of variant structures has also been introduced for different purposes [25; 26; 27]. The programming involves storing the given set into an \(m\)-bit vector by setting the bits indicated by hash indexes acquired from \(k\) hash functions for each element of the set, while the querying involves checking the membership information of an input by referring to the bits indicated by the same hash functions used in the programming. The query result can be identified as either _positive_ or _negative_. If any of the referenced bits is 0, then the query result is _negative_, meaning that the input does not belong to the given set. Otherwise, if all of the referenced bits are 1, the query result is _positive_, meaning that the input is an element of the given set. The negative results are always correct, but the positive results may be false. Let \(n\) be the number of elements programmed to an \(m\)-bit Bloom filter. The false positive rate can be obtained from Equation (1).  
\[f=(1-p)^{k}=\left(1-(1-\frac{1}{m})^{kn}\right)^{k} \tag{1}\]  
The valuable characteristic of a Bloom filter is that the Bloom filter for the union of plural sets is easily obtained. When the Bloom filter parameters are identical [22], the Bloom filter of the union set is obtained by performing the bitwise-OR operation of Bloom filters for each set, as shown in Figure 1: \(BF(S_{A}\cup S_{B})=BF(S_{A})\mid BF(S_{B})\). This characteristic is effectively used in our proposed method described in later sections.
# Multiple Network Fusion with Low-Rank Representation for Image-Based Age Estimation
## 2 Multiple Network Learning with Low-Rank Representation
### Definitions  
In age estimation with regression, given a set of images \(X=\{x_{1},x_{2},...,x_{n}\}\) and the corresponding labels \(Y=\{y_{1},y_{2},...,y_{n}\}\) with \(n\) pairs of samples, we try to learn a model that minimizes the loss:  
\[\underset{\delta}{\text{argmin}}\left|Y-\mathcal{F}(\overline{X})\right| \tag{1}\]  
where \(\mathcal{F}\) is the regression function, \(\delta\) is the regression parameter, and \(\overline{X}\) is the feature representation of \(X\). Therefore, to minimize Equation (1), we need a descriptive \(\overline{X}\) and a reasonable \(\mathcal{F}\). In the proposed method, we focus on \(\overline{X}\).
# Ten Years of TerraSAR-X--Scientific Results
## 2 Peculiarities of the TerraSAR-X Mission  
TerraSAR-X is characterized by numerous operational and experimental modes of its SAR instrument, unfolding a wide parameter space, e.g., multiple polarizations, along-track interferometry and a variety of SAR imaging modes with resolutions down to 0.5 m in range and 0.25 m in azimuth. Despite being well beyond their design lifetime of 5.5 years, both satellites are still fully functional, deliver high-quality images without any performance deterioration, and have enough consumables for operation into the 2020s.  
From the very beginning, the high resolution data of the TerraSAR-X mission complemented the European medium resolution missions in C-Band and the Japanese L-Band missions. Not quite unexpectedly, it was especially the high resolution that revealed many new insights into SAR scattering, which led to a large number of scientific publications. The expectation that the interferometric coherence over distributed scatterers and longer time spans would be low in X-band was confirmed. But it was more than compensated by a real surprise: the high resolution and the small wavelength revealed a very high number of point-like reflectors in urban and even in natural environments, whichgave rise to some ground breaking new methods such as urban SAR tomography and new applications in 3D point localization and deformation mapping of buildings and of infrastructure.
# Autonomous Intersection Management by Using Reinforcement Learning
## 4 Results and Discussion
### Experimental Results
#### 4.3.2 Comparison with FFS Method  
The fast first service (FFS) method is compared with our model by using a model trained over 50 epochs with a 0.5-s [5]. We use the same parameters as shown in Table 1 and use the four different inflow rates, including 25, 50, 75, and 100 vehicles per minute, to simulate for 200 s. We have tested each model 10 times for each four inflow rate. Table 10 depicts results. When compared to the FFS method, our model outperforms in terms of average waiting time and average queue length, as well as throughput. We think that the waiting time consideration in the suggested reward function, which results in a much more fair action selection strategy, causes the reduction in AWT by DRLAIM.
# Three-Dimensional Wide-Band Electromagnetic Forward Modelling Using Potential Technique
## 1 Introduction
To verify our approach, we simulate and compared our results with the analytical solution and also further tested the algorithm on two synthetic models. Additionally, different iterative solvers and preconditioners were also conducted to observe the most efficient with the example models.
## 2 Proposed PLL Architecture
### Programmable Prescaler for 32/33 Divider Ratio  
In the proposed PLL, the frequency divider is N = 5 at the input and M = 66 in the feedback loop. A programmable prescaler is used for the frequency divider circuit. Figure 11a  
Figure 10: s-Domain analysis block diagram.  
Figure 9: (a) Four-stage differential ring oscillator; (b) differential cross-coupled delay cell with multiband option.  
shows a dual-modulus prescaler circuit block that could divide by N or N + 1 according to the external control [2; 3; 4; 5]. Here, a dual-modulus prescaler cell is implemented to allow the multi-moduli frequency divider at the input, as well as at the feedback loop, to produce an output frequency equal to the reference frequency. The multi-modulus divider provides flexibility to the PLL architecture and maintains the future relevancy.  
The multi-modulus divider architecture is depicted in Figure 11. It consists of a chain of divide-by-2/3 dual-modulus prescalers in a cascade, connected similarly to a ripple counter. In every division period, the last cell of the dual-modulus prescaler in the chain generates signal mod\({}_{\text{in}}\)\(-\)1. The signal then propagates up the chain. Its simulation is shown in Figure 12.
## 5 Performance Analysis and Discussion
### Performance Metric
#### 5.2.2 Improvement Rate (\(I\!r\))  
It is defined as the percentage improvement obtained in the performance of the proposed algorithm in the overall execution of workflow DAG with respect to other algorithms. Reduction in the cost of the proposed algorithm over other considered algorithms can be calculated by \(I\!R(\%)\).  
\[IR(\%)=\frac{cost(other)-cost(proposed)}{cost(proposed)}\times 100. \tag{24}\]