esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Mobility Model for Improving Transmissions in Multi-UAVs Enabled WSNs
## 3 Literature Review  
Mobility models that define velocity, direction and change in acceleration over the course of simulation or real-time environments in ad hoc networks have been around for quite some time. Mobility models find specific applications while testing new protocols and applications, which imposes a restriction on them for being real-world feasible and easy to implement. We segregate the literature into two parts. Initially, major ad hoc mobility models are described in brief. Then, recent contributions towards the field of UAV mobility as well as the data dissemination techniques that rely on the mobility paradigm are discussed in detail. The Random Walk model derived from the Brownian motion is the most widely used mobility model. The movements of the random walk model are characterized either by a constant time or constant distance, i.e., the mobile nodes can change direction either after traveling a certain distance or a certain time interval. Because of its simplicity and ability to simulate the spontaneous behavior of the mobile nodes, many variants of the model have been proposed in the literature [13; 14; 15; 16]. The Random Way-point model is another memory less mobility model. The only addition to the random way-point model is that it maintains a miniature pause time between swaps. The mobile node moves from one destination to another with the speed chosen between a predefined maximum and minimum speed after the pause interval [13; 17]. The random way-point model without the mandatory pause has also been implemented in literature [18]. The Gauss-Markov model for ad hoc networks can handle the inherent randomness of the mobile nodes more efficiently. The nodes are initialized with specific speed and time. Then, the speed and direction are updated regularly with the help of a tuning parameter. To keep the nodes from edges, they are pushed away when nodes reach within a specific distance from the edge [13; 19; 20]. Table 1 presents some of the ad hoc mobility models proposed in the literature, which are centered around UAVs as relays. [PERSON] and [PERSON] [3] presented a framework for multi-UAV guided ground ad hoc networks. The authors have discussed how the network is formed among the multiple UAVs and an efficient mechanism for making the search non-redundant. Bayesian Kalman filtering is employed for the purpose of estimating the location of the Concepts, which, in turn, is used for updating previous maps. The new concept of Topology Organizing Maps is also introduced by the authors who determine the positioning of the nodes with respect to the introduced Virtual Concepts, which are identified during cognitive mapping. The authors have successfully demonstrated the cooperative network formation with the help of constant prediction and estimation with the help of neural models and Kalman filtering.
# Time Management in Wireless Sensor Networks for Industrial Process Control
## 3 Results
### Time Synchronization
Unlike our proposal, it follows a layered mesh network topology in which the coordinator propagates the time to each layer via router nodes. In [22], they also proposed a lightweight approach (SA-MAC) based on the DW1000 transceiver from Decawave that uses UWB (ultra-wide-band) communication that reduces sync-related traffic. The DW1000 uses hardware-based timestamps and precise internal oscillators to send frames at specific times with utmost accuracy. Our work builds on these by integrating deterministic time division with beacon-based alignment, minimal protocol overhead, sub-GHz communication, and COTS components to achieve time drifts comparable to the ones obtained in [20] 0.6 milliseconds vs. 0.256 milliseconds.
# Dense Small Object Detection Based on an Improved YOLOv7 Model
## 4 Experiments and Analysis of Results
### Visual Analysis  
Figure 15 compares the detection effects of YOLOv7 and our proposed algorithm in different scenarios. As can be seen from (a), in a dark environment, YOLOv7 cannot detect vehicles with roadside occlusion, nor can it detect vehicles with similar colors to the background. There is a lack of detection, but our algorithm can detect objects well. And in the dense small target image (b), YOLOv7 has a large number of missed targets, but our algorithm reduces the rate of missed detections. In the scenario with a light change (c), the difference between the two detection effects is not large, but the detection of YOLOv7 has missed detection and false detection. These fully demonstrate the effectiveness of our improved algorithm.  
Figure 15: Detection results between YOLOv7 (**left**) and the proposed algorithm (**right**). (**a**) Dark environment. In front of the store, where there is light, because the object is too small, the left picture has an obvious missed detection. In the dark environment, the color of the target image is close to the background color, especially in the place where the bottom of the tree is blocked, and the left image is also missed. (**b**) Dense small-target image. In the dense small-target image, the missed detection is obviously very serious. Although the right image also has a missed detection, it is significantly improved compared with the left image. (**c**) Scene with a light change. In the scene with the light change, the target-detection effect of the two figures is not very different, and the problem of the left figure is still the target density caused by missed detection, as well as the obvious false detection in the middle of the figure.  
Figure 16 shows the confusion matrix of YOLOv7 and the algorithm proposed in this paper. It can be seen from the images that the accuracy of each type of object detected by our method is better than that of YOLOv7. Moreover, the omission rate of our algorithm for each type of object is lower than that of YOLOv7, which also proves the superiority of our proposed algorithm.  
Figure 16: Confusion matrix graph. The diagonal data in the figure are the probability of correctly detecting the target, the background FN is the missed rate, and the background FP is the false detection rate. (**a**) YOLOv7. (**b**) Method of this paper.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 4 Experimental Results
### Influence of the Filter Size  
For CNN models using a temporal guidance, it is also interesting to study the filter size. Considering the 2-day regular temporal sampling, a filter size of \(f\) (with \(f\) an odd number) will abstract the temporal information over \(\pm(f-1)\) days, before and after each point of the series. Given this natural expression in number of days, we name \((f-1)\) the _reach_ of the convolution: it corresponds to half of the width of the temporal neighborhood used for the temporal convolutions.  
Table 5 displays the OA values as a function of reach for TempCNN. We study five size of filters \(f=\{3,5,9,17,33\}\) corresponding to a reach of 2, 4, 8, 16, and 32 days, respectively.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & & **NDVI** & **SB** & **SB-SF** \\ \hline
**No guidance** & **Random Forest (RF)** & 88.17 \(\pm\) 0.59 & 90.02 \(\pm\) 1.44 & 90.92 \(\pm\) 1.22 \\  & **Fully-Connected (FC)** & 86.90 \(\pm\) 1.65 & 91.36 \(\pm\) 1.15 & 91.87 \(\pm\) 0.88 \\ \hline
**Recurrent Neural Network (RNN)** & 88.62 \(\pm\) 0.86 & 92.18 \(\pm\) 1.43 & 92.38 \(\pm\) 0.83 \\ \hline
**CNN with temporal guidance** & **90.16 \(\pm\) 0.94** & 92.74 \(\pm\) 0.80 & 93.00 \(\pm\) 0.83 \\
**CNN with spectral guidance** & 88.24 \(\pm\) 0.63 & 95.34 \(\pm\) 0.88 & 93.24 \(\pm\) 0.83 \\
**CNN with spectro-temporal guidance (TempCNN)** & 90.06 \(\pm\) 0.88 & **93.42 \(\pm\) 0.76** & **93.45 \(\pm\) 0.77** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Averaged overall accuracy (\(\pm\) one standard deviation) over five folds for four levels of help in spectral and/or temporal dimensions. Three feature vectors are used here: (1) Normalized Difference vegetation index (NDVI), (2) spectral bands (SB), and (3) SB and three spectral features (SB-SF). Bold values show the highest performance for each type of features. CNN/ COnvolutional Neural Network.  
Table 5 shows that the maximum OA is obtained for a reach of 8 days, with a similar OA for 4 days. The F-Score values per class corroborate this results where maxima are often achieved for a reach of 4 or 8 days. Although the OA values are close for the different reach values, there are some observable differences per class: conifer, sorgo, and pea have F-Score differences that range from 2.8 to 8.9%. This result shows the importance of the high temporal resolution SITS, such as the one provided at five days by both Sentinel-2 satellites. The frequency of acquisition indeed allows CNNs to abstract enough temporal information from the temporal convolutions. In general, the reach of the convolutions will mainly depend on the patterns that need to be abstracted at a given temporal resolution. For example, even though the F-scores are very similar w.r.t. reach, the conifer class seems to require the lowest reach and the deciduous to require the highest reach.
# 6G Cognitive Information Theory: A Mailbox Perspective
## 6 Data Life Cycle Extensions for Information Value
### Data Fusion for Data Analysis
#### 6.4.3 Data-Fusion Targets  
Diverse intelligent IoT applications need to rely on the analysis and mining of heterogeneous multi-source data sets. The data fusion targets for these applications are summarized and mainly include the following aspects:  
* _Improving data quality_: Preprocessing (with the data cleaning and filtering technologies) is applied to various quality related problems caused by the environment and hardware, such as data loss, errors, redundancy, noise, etc.; and the data integrity, reliability, and analyzing efficiency are improved;
* _Multi-source data integration_: Multiple segments of information from a single sensor or information provided by different types of sensors are fused (with the data-generation and integration technology), redundancies and contradictions of information that may exist between multiple sensors are eliminated, and complementation is conducted to mine the correlation between the data and deep meaning of data;
* _Deep information mining_: Data types include structured, semi-structured, and unstructured data. For semi-structured and unstructured data, which account for 80% of all data, there is still a lot of room for the adoption of more intelligent algorithms to conduct deep analysis and mining. This is more dependent on strong feature-extraction support provided by data-fusion technology based on AI;
* _Strengthening decision-making and evaluation_: For intelligent IoT, distributed hardware and software systems lead to a condition where each subnet or each layer of the computing unit has its own analytical and decision-making capabilities. However, for decisions or services provided by the system to really meet the needs of users is still a very difficult challenge. Therefore, reinforced learning or tagless learning schemes can be adopted to conduct fusion for historical decisions and remarks, or centralizedfusion can be conducted to distributed decision-making. Both methods can effectively enhance the decision-making effect, and the service quality can be better evaluated.  
The appropriate fusion algorithm can be intelligently selected according to the fusion target and fusion level, fusion can be conducted for multi-source data, and more efficient deep information mining and analysis can be done on this basis to obtain a more accurate representation or estimation for the specific target and application.
## 3 Methods
### Integrated Matching Model Framework
Once the model is fitted, the algorithm evaluates the consistency between all point pairs and the estimated model by computing the geometric error for each correspondence. The Sampson distance, a commonly used error metric, is employed due to its close approximation of the epipolar constraint residual, along with its strong geometric interpretability and numerical stability. The computation is provided in Equation (9)  
\[d_{\text{Sampson}}(p_{1},p_{2},F)=\frac{{({p_{2}}^{\text{T}}F{p_{1}})}^{2}}{{( F{p_{1}})}_{1}^{2}+{(F{p_{1}})}_{2}^{2}+{(F{}^{\text{T}}{p_{2}})}_{1}^{2}+{(F{}^{ \text{T}}{p_{2}})}_{2}^{2}}, \tag{9}\]  
Unlike traditional RANSAC, MAGSAC++ does not distinguish inliers from outliers using a fixed threshold. Instead, it maps each residual error to a continuous score based on a marginalized likelihood function. This approach, rooted in the probabilistic framework of maximum likelihood estimation, employs a weighted loss function to marginalize the residuals of all point correspondences. The continuous scoring mechanism avoids the  
Figure 7: Epipolar geometry constraint. instability caused by threshold selection and provides a more accurate reflection of each point pair's compatibility with the model. After each sampling iteration, MAGSAC++ calculates the model score and selects the model with the highest score as the optimal solution. Once the best model is determined, it re-evaluates the geometric error for all matched points and uses a probabilistic scoring function to assess the confidence of each point being an inlier. This process significantly improves model estimation robustness under high noise and high outlier ratio conditions. By incorporating both geometric error and the statistical distribution and uncertainty of residuals, MAGSAC++ exhibits superior inlier selection performance in practical visual matching tasks.
## 2 Analytical Method
### Yield Function  
In this study, the Cazacu-Barlat 2004 yield function was applied [13]. It is an odd function of the principal values of the stress deviator \(S\). Therefore, the yield criterion is sensitive to the sign of the stress \(\sigma_{1}\), and it can capture the tension-compression asymmetry of the material. The Cazacu-Barlat 2004 yield function can be expressed as:  
\[f=\Big{(}{{{l}_{2}}^{\frac{3}{2}}-{{c}_{3}}}\Big{)}^{\frac{1}{3}}=\tau_{y} \tag{21}\]  
where \(\tau_{y}\) is the yield stress in pure shear, \(c\) is a material parameter and \({{{J}_{2}}}\) and \({{{J}_{3}}}\) are the second and third invariants of the stress deviator tensor, respectively. The material parameter \(c\) is expressed as:  
\[c=\frac{3\sqrt{3}\Big{(}\sigma_{t}^{3}-\sigma_{c}^{3}\Big{)}}{2\Big{(}\sigma_{ t}^{3}+\sigma_{c}^{3}\Big{)}} \tag{22}\]  
where \(\sigma_{t}\) and \(\sigma_{c}\) are yield stresses in tension and compression, respectively. The yield function in Equation (21) can be rewritten in the following form:  
\[(\frac{27}{3\sqrt{3}-2c})^{1/3}{{{({{{{{{{{{{{{{{{{{{{{{{ }}} }}}}}}}}}}}}}}}}}}^{2}-{{{{{{{{{{{{ {{{{{{{{}}}}}}}}}}}}}}}}}}}}}}})^{1/3}=\sigma_{t}. \tag{23}\]  
By assuming the uniaxial loading condition, the yield function is reduced to:  
\[f=\Bigg{(}\frac{27}{3\sqrt{3}-2c}\Bigg{(}\frac{|\sigma_{1}|^{3}}{3\sqrt{3}}-c \frac{2\sigma_{1}{3}^{3}}{27}\Bigg{)}\Bigg{)}^{\frac{1}{3}}=\sigma_{t} \tag{24}\]  
The sign function of \(\sigma_{1}\) is defined as:  
\[sgn(\sigma_{1})=\frac{d}{d\sigma_{1}}|\sigma_{1}|=\Bigg{\{}\begin{array}{cc} 1,&\sigma_{1}>0\\ -1,&\sigma_{1}<0\end{array} \tag{25}\]  
Therefore, we have:  
\[f=\Bigg{(}\frac{3\sqrt{3}-2c\,sgn(\sigma_{1})}{3\sqrt{3}-2c}\Bigg{)}^{\frac{1 }{3}}|\sigma_{1}|=\sigma_{t} \tag{26}\]  
From the above equation, we have:  
\[\beta=\Bigg{(}\frac{3\sqrt{3}-2c\,sgn(\sigma_{1})}{3\sqrt{3}-2c}\Bigg{)}^{\frac {1}{3}} \tag{27}\]  
where \(\beta\) is the function of stress sign, loading condition, and the material constant \(c\). In the case of symmetry, the material constant \(c\) is degenerated to zero, and \(\beta\) has the same value of unity for both tension and compression loading. Therefore, for uniaxial condition, and considering 1/\(\beta_{t}=\gamma_{t}\) and \(-\)1/\(\beta_{c}=\gamma_{c}\), we have:  
\[\gamma=\left\{\begin{array}{ll}\gamma_{t}=1,&\sigma_{1}>0\\ \gamma_{c}=-\left(\frac{3\sqrt{3}-2c}{3\sqrt{3}+2c}\right)^{\frac{1}{3}},& \sigma_{1}<0\end{array}\right. \tag{28}\]  
The values of \(c\), \(\gamma_{t}\), and \(\gamma_{c}\), for DP980 and SUS304 in rolling-direction (RD) and transverse-direction TD, are calculated from Equations (22) and (28) and listed in Table 1. Based on the experimental stress-strain curves available in the literature [6,7], the tensile and compressive yield points, which were determined using the commonly used 0.2% offset strain method, are also listed in Table 1.
# Inertial Parameter Identification in Robotics: A Survey
## 8 Introducing the BIRDy Matlab Toolbox: A Benchmark for Identification of Robot Dynamics
### Hardware Description and Experiment Setup
The detail of such method being out of the scope of this paper, the reader is redirected to [107] and the references therein for a more detailed review of friction modeling and identification. The reference trajectories used for the experiments were generated using the optimization method presented in Section 8.2 with the gains \(k_{1}=1\) and \(k_{2}=100\), and over a time horizon of 10 s. Two trajectories are considered for each robot, for identification and validation purposes, respectively. The generation of experiment data is carried out using Runge-Kutta fixed-step integration (RK4) of the close-loop DDM. The computations were realized using Matlab R2020a on an AMD-Threadripper 1920X workstation with 32 GB of RAM. When possible, the parallel computing toolbox from Matlab was used, along with the Matlab C/C++ Coder, to enhance concatenated for-loops execution speed, such as that found in sigma-point Kalman filters. The number of parallel threads allocated to each algorithm was limited to six for repeatability reasons. It is worth mentioning that the closed-loop model simulations required by some identification algorithms--such as IDIM-IV, DIDIM, CLIE, CLOE, or NKF--were carried out at the same frequency \(f_{c}\) as in the data generation process. Nevertheless, in this case, the DDM time integration was performed using an Euler (RK1) algorithm, as -- besides a higher computation time -- no significant difference was noticed in the identification results when using more advanced integration algorithms, such as RK2 or RK4. The obtained closed-loop simulation data were then sampled and decimated at the very same frequencies as in the experiments before being used in the corresponding algorithms. To obtain statistically relevant data, the simulation experiments were executed following a Monte Carlo scheme, consisting of a set of \(b\) independent runs, where \(b\) denotes the dimension of the base parameter vector \(\beta\). For each method and each independent run, the identification process was repeated 25 times with different initial parameter vectors to investigate the sensitivity to initial conditions, eventually resulting in a total of \(M=25\cdot b=1300\) runs for each identification method and on each robot. The 25 initial parameter vectors were obtained using the reference parameters' values (i.e., previously used for collecting simulation data) distorted by a relative error of 15%, which suitably represents the tolerances obtained with computer-aided design (CAD) software. The validation experiments were performed on the real robots. In this case, the identification methods requiring an initial parameter estimate - namely IDIM-IV, DIDIM, CLOE, CLIE, ML, NKF, AdaNN, and HTRNN--were initialized using the filtered IDIM-OLS estimate.
## 3 Methodology  
In traditional remote sensing image segmentation methods, feature extraction and model establishment are achieved manually [70; 71]. However, for the segmentation of specific targets in remote sensing images, the object-relational mapping between the feature space and the segmentation target is high-dimensional and strongly nonlinear. Therefore, effectively constructing this type of model through manual methods is difficult. To this end, various learning-based remote sensing image segmentation methods can help to undertake high-dimensional, strongly nonlinear mapping through sample learning.
## 4 Estimation of Initial Temperature and Performance Evaluation  
Estimation of proper initial temperature value has a great influence on the result of tomography calculation. Inappropriate initial values lead to the calculation in the diverging direction. It is difficult to determine an initial temperature value from an unknown temperature field. Also, finding the optimum value from an arbitrary temperature value will take a lot of computation time. In this study, initial values were  
A DFB (distributed feedback) diode laser (NTT Electronics Co., NLK1E5 GAAA) of which working wavelength is 1388 nm with a scanning range of 0.6 nm was used to get H\({}_{2}\)O gas absorption spectra. In order to measure simultaneous 2-dimensional absorption spectra a 16-path cell as shown in Figure 6 has been used. Laser paths have 7 mm to 12.3 mm spacing with each other for covering 2-dimensional space. The laser beam has been separated by the use of an optical fiber splitter (OPNETI CO., SMF-28e 1310 nm SWBC \(1\times 16\)). Then, the separated 16 beams pass through 16 collimators (THORLABS Co., 50-1310-APC) to attain focused light intensity. The intensity of the transmitted light has been detected by 16 photodiodes (Hamamatsu Photonics and G8370-01), and they have scanned with 1 kHz and have been recorded onto the recorder (HIOKI E.E. Co., 8861 Memory Higchoda HD Analog16). The detected signals have been amplified using the amplifier (Stanford Research Systems, SR445A).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Combustion Fuel** & **Methane (L/min)** & **Dry Air (L/min)** & **Around Air** (L/min)** & **All Air: Dry Air + Around Air (L/min)** \\ \hline Methane-Air premixed flame & 0.74 & 3.4 & 96.6 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameters.  
Figure 5: Experimental apparatus for 2D temperature measurement in flame burner using CT-TDLAS.  
Figure 6: 16-paths CT-TDLAS measurement cell.  
For temperature comparisons, the temperatures at the same position of the laser 16-path were also measured by one thermocouple with a diameter of 100 \(\upmu\)m (ANBE SMT Co., BM-100-100-050).
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 4 Experimental Results and Analysis
### Ablation Studies  
#### 4.2.1 Module Contribution Analysis  
To demonstrate the contribution of each module to the overall performance, we conducted ablation studies based on the baseline model. The baseline model employs basic IoU matching and linear Kalman filtering for motion matching, utilizes average pooling to extract feature vectors from target regions, and calculates the cosine distance between feature vectors of consecutive frames for feature matching. Finally, it combines motion and feature matching through weighted fusion before applying the Hungarian algorithm to obtain tracking results.  
As shown in Table 1, each module contributes significantly to the overall performance. The MS-FAE module improves MOTA by 12.6%, while CFAM achieves the most substantial increase in IDF1 (+9.8%), demonstrating its crucial role in maintaining ID consistency. The DMM module further reduces ID switches by 111 instances, and the bi-modal dynamic matching decision method enhances all evaluation metrics comprehensively. Notably, although the addition of these modules slightly decreases processing speed, the complete model still maintains real-time performance at 19.6 FPS, validating our emphasis on efficiency in algorithm design.