esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
## 7 Experiment Analysis
### Advantages and Disadvantages of Multi-Object Tracking Algorithm Based on Deep Learning  
We evaluated and analyzed the multi-target tracking method using the data set test results as an entry point in the previous section. The advantages and disadvantages of the TBD, JDT, and Transformer-based tracking algorithms are then briefly discussed.  
(1) TBD Framework  
The deep learning-based TBD algorithm continues to be the widely used research framework for multi-object tracking currently. The TBD algorithm has a straightforward layout and excellent interpretability. Modules such object identification, feature extraction, and data association can be employed by embedding deep learning modules in conventional modules. Tracking is stronger. The TBD algorithm's design is bloated, though, and it is challenging to strike a balance between accuracy and real-time performance.  
(2) JDT Framework  
The multi-module joint learning integration JDT algorithm has been created. The design structure is made simpler through the integration of several sub-modules, and an end-to-end multi-object tracking algorithm is produced, in which the detector performance is crucial to the effectiveness of the JDT algorithm. Currently, the multi-object tracking algorithm is typically moving from the TBD method to the JDT algorithm, and the algorithm's accuracy and speed are gradually balanced. In situations like frequent occlusion of numerous objects and having too many objects in the MOT dataset test, the JDT algorithm still performs inaccurately.  
(3) Transformer-based Framework  
Table 7 shows that TransTrack, TransCenter, and TransMOT, three multi-object tracking algorithms based on transformers, perform at the cutting edge on a variety of benchmark datasets (MOT16, MOT17, and MOT20). However, we think that tracking algorithms based on transformers still have a lot of drawbacks. First, the model for the transformer-based multi-object tracking algorithm is enormous and expensive to compute. For instance, TrackFormer's model size is 2.1 G. The FairMOT algorithm model, which is based on JDT, may, in contrast, achieve comparable performance with a 265 M file size. Due to this, the multi-object tracking algorithm's transformer architecture still adheres to the NLP standard transformer and has not been updated for the CV area.
## 6 Conclusions  
The platform proposed in the paper responds to the need for enabling a more flexible and customizable experimentation on a variety of MANET scenarios. The platform has a modular software architecture, which facilitates adaptations, modifications and/or extensions, and can be operated in conjunction with a variety of wireless communications technologies. Its software runs on inexpensive Raspberry Pi-based commodity hardware, using one board computer per network node, and this hardware can be attached to robotic devices in a straight-forward manner, without tight coupling, for moving the network nodes in accordance with controlled mobility patterns. Moreover, the platform provides facilities for restricting through software the communications range (thus affecting the network density) or for emulating the mobility patterns of nodes. By leveraging these features, the operation of the platform can span the range between emulated and real-world experiments, trading repeatability and control for realism, as required.  
The effectiveness and versatility of the platform were demonstrated through results from an extensive set of experiments on the comparative performance evaluation of selected routing protocols under diverse conditions. Although the experiments were conducted on a small area and employed only a few mobile nodes, they were capable of reproducing many important phenomena identified in previous results, derived in connection with idealized simulation-based setups. Moreover, the experiments provided additional insight into real-world aspects, such as the impact of inaccurate location information or of restricted mobility patterns. Overall, the results provide validation and a strong indication of the platform's potential for supporting the realization of meaningful experiments.  
Conceptualization, I.M.; methodology, I.M.; software, I.M. and D.L.; prototyping, I.M. and D.L.; validation, I.M., D.L. and K.K.; resources, I.M., D.L. and K.K.; writing--original draft preparation, I.M.; writing--review and editing, I.M., D.L. and K.K.; visualization, I.M., D.L. and K.K.; supervision, K.K. All authors have read and agreed to the published version of the manuscript.  
This work and the APC was partially supported by the European Union's Horizon 2020 Framework Programme for Research and Innovation, under Grant Agreement No. 645220 (project RAWFIE--\"Road-, Air- and Water-based Future Internet Experimentation\") and the project SYNTLESIS \"Innovative Technologies and Applications based on the Internet of Things (IoT) and the Cloud Computing\", which is funded by the Operational Programme \"Competitiveness, Entrepreneurship and Innovation\" (NSRF 2014-2020) and co-financed by Greece and the EU (European Regional Development Fund) under Grant MIS 5002521.  
Not applicable.  
The authors declare no conflict of interest.
# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
## 2 Related Work
### Vision Transformers  
The transformer [29] was originally introduced for machine translation. [PERSON] et al. [30] extended its application to images, introducing the vision transformer (ViT) for image classification. Subsequently, various adaptations of transformers for vehicle re-identification have been proposed. For example, [PERSON] et al. [18] proposed a Vehicle Attribute Transformer (VAT), which incorporated color, model, and viewpoint embeddings into a unified feature for effective vehicle re-identification. [PERSON] et al. [31] innovatively designed a Graph Interactive Transformer (GiT), leveraging a combination of a graph network and a transformer to enhance cooperation between local and global features, thereby improving vehicle re-identification. [PERSON] et al. [32] introduced a Multi-Scale Knowledge-Aware Transformer (MsKAT) to eliminate state interference (e.g., camera and viewpoint) and gather attribute information (e.g., color and type) for reliable representations of vehicle images. Different from these methods, we employ a ViT to construct stripe-based features that enrich the global feature for vehicle re-identification. We additionally introduce the SaGL to optimize the model. During inference, we exclusively use the global feature, resulting in reduced computational and storage requirements.
## 2 Materials and Methods  
### Materials  
The nanoparticles used in the experiments were alpha alumina nanoparticles (\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)), manufactured by SkySpring Nanomaterials (Houston, TX, USA) with an average particle diameter of 50 nm. Limestone cores were bought from Kocurek Industries, Inc. (Caldwell, TX, USA). Each core's diameter was approximately 1 inch, and its length was approximately 2 inches. The cores consisted of 97% calcite and 3% montmorillonite. The permeabilities and porosities of the cores were measured using the PoroPerm gas permeameter apparatus manufactured by Vinci Technologies (Nanterre, France) with a measurement error of 0.001 for permeability and 0.01 for pore volume. Each used value was the average of two measurements. Porosity was then calculated as the pore volume percentage from the core's volume. The properties of the three cores used in the core flooding experiments are mentioned in Table 1. Two more limestone cores were used for contact angle measurement and the initial pore surface evaluation.  
When mixed with water, CO\({}_{2}\) dissolves and produces H\({}_{2}\)CO\({}_{3}\), which reduces the pH of the medium [24]. The same effect applies to nanofluids with water as their base fluid. Generally, the pH of formation water within limestone rocks is slightly basic [26]. Therefore, nanofluid alternating CO\({}_{2}\) injection was used to lower the pH within limestone cores. Another reason for choosing H\({}_{2}\)CO\({}_{3}\) is that it is a weak acid, and reacts slowly with calcite in comparison to strong acids, and their reaction does not produce materials that are detrimental to the stability of the Al\({}_{2}\)O\({}_{3}\) nanofluid, such as calcium chloride (CaCl\({}_{2}\)) [27].  
Figure 1: Surface charge of alumina in the aqueous solution at various pH conditions [25].
## Appendix C The Construction of the B-Spline Time-Varying Filter  
**Step 1**: The Hilbert transform is used to calculate the instantaneous amplitude \(A(t)\) and instantaneous frequency \(\varphi^{\prime}(t)\) of the input signal \(x(t)\).  
\[A(t)\ =\ \sqrt{x^{2}+\hat{x}(t)^{2}} \tag{1}\]  
\[\varphi^{\prime}(t)\ =\ d(\arctan[\hat{x}(t)/x(t)])/dt\  
Then, Equations (A7) and (A8) can be obtained by substituting Equation (A6).  
\[A(t_{\rm min})\ =\ \big{|}a_{1}(t_{\rm min})-a_{2}(t_{\rm min})\big{|}\] (A7) \[\varphi\prime(t_{\rm min})A^{2}(t_{\rm min}) =\varphi_{1}\prime(t_{\rm min}){a_{1}}^{2}(t_{\rm min})-\varphi_{ 1}\prime(t_{\rm min})a_{1}(t_{\rm min})a_{2}(t_{\rm min})\] (A8) \[\ \ \ \ +\varphi_{2}\prime(t_{\rm min})\Big{[}{a_{2}}^{2}(t_{\rm min })-a_{1}(t_{\rm min})a_{2}(t_{\rm min})\Big{]}\]  
Simultaneously, \(A(t_{\rm min})\) denotes a local minimum of \(A(t)\), let \(A^{\prime}(t_{\rm min})\ =\ 0\), the Equation (A9) can be acquired.  
\[a_{1}\prime(t_{\rm min})-a_{2}\prime(t_{\rm min})\ =\ 0\] (A9)  
Thus, the minimum value of \(A(t)\) can be obtained by solving Equations (A4)-(A9). Similarly, the maximum value of \(A(t)\) can be determined, too.  
**Step 3**: Calculate \(a_{1}(t)\) and let,  
\[\begin{array}{l}\beta_{1}(t)\ =\ \big{|}a_{1}(t)-a_{2}(t)\big{|}\\ \beta_{2}(t)\ =\ a_{1}(t)+a_{2}(t)\end{array}\] (A10)  
Thus, the Equation (A11) can be obtained from Equation (A5).  
\[\begin{array}{l}\beta_{1}(t_{\rm min})\ =\ A(t_{\rm min})\ =\ \big{|}a_{1}(t_{\rm min})-a_{2}(t_{\rm min})\big{|}\\ \beta_{2}(t_{\rm max})\ =\ A(t_{\rm max})\ =\ a_{1}(t_{\rm max})+a_{2}(t_{\rm max}) \end{array}\] (A11)  
Because \(a_{1}(t)\) and \(a_{2}(t)\) change slowly, \(\beta_{1}(t)\) and \(\beta_{2}(t)\) can be acquired by interpolation in point set \(A(\{t_{\rm min}\})\) and \(A(\{t_{\rm max}\})\) respectively. \(a_{1}(t)\) and \(a_{2}(t)\) can be gained by Equation (A11).  
\[\begin{array}{l}a_{1}(t)\ =\ \big{[}\beta_{1}(t)+\beta_{2}(t)\big{]}/2\\ a_{2}(t)\ =\ \big{[}\beta_{2}(t)-\beta_{1}(t)\big{]}/2\end{array}\] (A12)  
**Step 4**: Calculate \(\varphi_{1}\prime(t)\) and \(\varphi_{2}\prime(t)\), let,  
\[\begin{array}{l}\eta_{1}(t)\ =\ \varphi_{1}\prime(t)\Big{[}{a_{1}}^{2}(t)-a_{1}(t)a _{2}(t)\Big{]}+\varphi_{2}\prime(t)\Big{[}{a_{2}}^{2}(t)-a_{1}(t)a_{2}(t)\Big{]} \\ \eta_{2}(t)\ =\ \varphi_{1}\prime(t)\Big{[}{a_{1}}^{2}(t)+a_{1}(t)a_{2}(t)\Big{]}+ \varphi_{2}\prime(t)\Big{[}{a_{2}}^{2}(t)+a_{1}(t)a_{2}(t)\Big{]}\end{array}\] (A13)  
From Equation (A5), we have  
\[\begin{array}{l}\eta_{1}(t_{\rm min})\ \ =\ \varphi\prime(t_{\rm min})A^{2}(t_{\rm min})\\ \  
**Step 5**: Calculate the local cut-off frequency \(\varphi_{bis^{\prime}}(t)\) as follows:  
\[\varphi_{bis^{\prime}}(t)\ =\ \frac{\varphi_{1^{\prime}}(t)+\varphi_{2^{\prime}}(t)}{2 }\ =\ \frac{\eta_{2}(t)-\eta_{1}(t)}{4a_{1}(t)a_{2}(t)} \tag{10}\]  
**Step 6**: Rearrange \(\varphi_{bis^{\prime}}(t)\) to solve the problem of signal intermittence.
# Prediction of Blade Tip Timing Sensor Waveforms Based on Radial Basis Function Neural Network
## 3 Neural Network Model
### Constructing RBF Neural Network Models for Data Prediction  
By utilizing the RBF NN interpolation algorithm, which predicts the collected static calibration experimental data, the prediction accuracy of the neural network model is initially analyzed for different data points.  
Establish five groups of sample data for different scenarios and predict them using the RBF model, where Figure 4a-e represent the prediction models under ten, eight, six, five, and three sets of sample points, respectively. Figure 4a illustrates the prediction model for the full data point, where the prediction accuracy of the prediction model at this data point is theoretically closest to the experimentally measured true value. Figure 4b demonstrates the static calibration data at 0.55 mm, 0.45 mm, 0.4 mm, 0.35 mm, 0.3 mm, 0.25 mm, 0.2 mm, and 0.1 mm to compare the effect on the prediction accuracy of the RBF neural network model with reduced sample points. Figure 4c,d show the experimental data measured in the absence of 0.1 mm to observe the effect of the experimental data on the absence of proximity on the prediction ability of the waveform in the range of the BTC. Figure 4e presents the experimental data with only 0.1 mm, 0.4 mm, and 0.55 mm retained to compare the prediction ability of the RBF model in conditions where only the ends of the BTC measurement range and the middle are retained. Figure 4f shows the forecasting data with the Kriging forecasting model to compare the accuracy of the RBF forecasting model under six sets of sample points.  
Figure 4: Comparative plots of different groups of sample points with RBF-based prediction models. (**a**) RBF prediction model under ten sample sets of points. (**b**) RBF prediction model under eight sample sets of points. (**c**) RBF prediction model under six sample sets of points. (**d**) RBF prediction model under five sample sets of points. (**e**) RBF prediction model under three sample sets of points. (**f**) Kriging prediction model under six sample sets of points.  
Table 2 provides a comparative analysis of MSE under four groups of different sample data, intercepting representative data from each group of data points as shown in the table, when the number of neurons increases, the value of MSE becomes smaller, the difference between the predicted value and the true value of the RBF model is smaller, with a better fitting of the model. Meanwhile, the MSE of RBF model is lower than that of Kriging and its accuracy is higher than the Kriging model, moreover, in the two models with a different number of groups of samples, the more data points there are and the smaller the value of MSE, the higher the prediction accuracy is. From the table, with fewer data points, the MSE value of the Kriging model instead becomes smaller for fewer data points, resulting in a difference in the degree of fitting compared to before.
## 4 Results and Discussion
### Rockfall Hazard Assessment  
Rockfall hazard along the KKH from Besham to Dasu is shown in Figure 8. From the figure, it is evident that rockfall hazard is acute in 57% and moderate in 18%, whereas it is low to very low in only 25% of the area along the entire road section at various places. High and very high hazard zones are not a continuous path along the highway; rather, they are concentrated around various places such as Jijal, Pattan, Leo, Mandraza, and the surrounding area of Dasu. The Besham to Dubair section falls into the low to moderate hazard zone, and is associated with comparatively gentler slopes and a lower population. Rockfall hazard is high surrounding the Jijal area. From Jijal to Pattan, rockfall hazard is high to very high due to active high-frequency debris flow gullies, shallow landslides, and steep slope coupled with narrow highway and intense human and animal activity at higher altitudes due to settlements. The danger of rockfall hazard from Pattan to Dasu also fluctuates between moderate to high and very-high owing to 35\({}^{\circ}\) to 79\({}^{\circ}\) slope variation and the dominance of suspended mass, favoring toppling failure. Rockfalls and debris slides routinely occur after every rainfall in the area, with the KKH being blocked for a few hours or days. Generally, the rainfall activity is more intense from March to October. Therefore, rockfall and debris slides are expected to occur more frequently from March to October, during the rainy spells of the year, with the exception of ground shaking due to earthquakes.  
Figure 8: Rockfall Hazard map along the Karakoram highway from Besham to Dasu.
# Graphene-Based Functional Hybrid Membranes for Antimicrobial Applications: A Review
## 3 Fabrication Techniques of Graphene-Based Membranes
### Vacuum Filtration-Assisted Method  
Vacuum-assisted filtration methods are the most common and straightforward methods for the large-scale fabrication of free-standing, GO-based nanofiltration (NF), micro-filtration, and ultrafiltration membranes [48]. Generally, GO membranes are prepared by filtering GO suspensions in water through porous supports, and the selection of porous supports can optimize the roughness and wettability of the membrane surface to obtain uniform GO membranes [46]. [PERSON] et al. studied the interaction between GO laminates and salt solutions. The neatly arranged graphene oxide laminates are distributed on the top surface of the polyvinylidene fluoride-polyacrylic acid (PVDF-PAA) microporous layer by vacuum filtration. The PVDF-PAA layer was cast on the non-woven scaffold and formed by immersion in a water coagulation bath. The interaction of graphene oxide with salts plays an important role in tuning the properties of graphene oxide films (Figure 2b) [49]. [PERSON] et al. used the layer filtration construction technique to prepare GO-based membranes with improved separation efficiency and permeability [50]. The ultrathin GO framework layer was successfully deposited on the modified Torlon hollow fiber scaffold using the construction method, resulting in a composite membrane with excellent NF performance. [PERSON] et al. reported improved the antibacterial properties of rGO by preparing rGO-CuO nanocomposite films [51]. The rGO-CuO nanocomposite was synthesized using a simple hydrothermal method, and the nanocomposite membrane was prepared by filtration through a polytetrafluoroethylene (PTFE) filter, with the aid of a vacuum filtration device. The nanocomposite membrane exhibited good bacterial inactivation effect on _Pseudomonas aeruginosa (PAO1)_. The antibacterial properties are due to the nanocomposite membrane's ability to transfer electrons from bacterial cells, resulting in bacterial cell inactivation. During the preparation of rGO-CuO nanocomposites, the hydrothermal treatment reduces graphene oxide and generates CuO on the reduced graphene oxide. Therefore, rGO-CuO aggregates to form a rod-like/sphere-like structure. Furthermore, the formed nanorods were able to penetrate bacterial cells, thereby enhancing the antibacterial properties of the nanocomposites.
# Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment
## 4 Channel Sum-Capacity in LoS and Favorable Propagation Conditions
### Instantaneous Sum-Capacity PDF In High-SNR Regime and Favorable Propagation Condition  
The instantaneous sum-capacity in the high-SNR regime and favorable propagation condition is given by  
\[C_{\text{inst.}}=\sum_{k=1}^{K}\log_{2}\left(1+\frac{\rho M\eta}{d_{k}^{2}}\right)\] (30) \[\stackrel{{\frac{\rho\text{dist.}}{d_{k}^{2}}>1}}{{ \approx}}\sum_{k=1}^{K}\log_{2}\left(\frac{\rho M
# Microstructure of Mortar with Ballast Waste as a Cement Replacement
## 3 Results and Discussion
### Initial Samples
#### 3.1.3 Pozzolanicity  
For both samples, B-1 and B-2, the pozzolanic activity was assessed to determine the activation capacity of this ballast waste. The accelerated method was used over 1, 7, 14, 28, 90, 180, and 360 days, measuring the calcium concentration--expressed as calcium oxide or fixed lime--according to the standard [48].  
The pozzolanicity test results for both study samples are presented in Figure 3. The fixed lime measurements are very similar, with slightly lower values in sample B-1 at early ages (indicating a slightly slower lime fixation). However, after more than 90 days of pozzolanic reaction, the trend reverses. When comparing this type of addition--ballast waste--with other materials used in the cement industry, such as silica fume, fly ash, or even non-standardized additions like sanitary ceramic waste or slag from the steel or copper industry [50] a similar behavior was initially seen, with slow pozzolanic activity. However, by 28 days--the age considered in the regulations--a greater lime fixation capacity was achieved compared with those reported in the literature.  
\begin{table}
\begin{tabular}{c c c} \hline
**Oxides (\%)** & **B-1 Sample** & **B-2 Sample** \\ \hline Na\({}_{2}\)O & 2.97 & 4.78 \\ MgO & 1.72 & 0.95 \\ Al\({}_{2}\)O\({}_{3}\) & 13.79 & 19.60 \\ K\({}_{2}\)O & 2.85 & 4.06 \\ CaO & 2.77 & 3.26 \\ MnO & 0.11 & 0.03 \\ TiO\({}_{2}\) & 0.80 & 0.37 \\ Fe\({}_{2}\)O\({}_{3}\) & 6.72 & 3.49 \\ SiO\({}_{2}\) & 68.27 & 63.49 \\ LOI & 0.67 & 1.22 \\ \hline \end{tabular}
\end{table}
Table 2: Chemical analysis of the major elements (LOI = loss in ignition).
## 3 Multi-MAF: A Hybrid Adaptive Algorithm for Routing in WSNs
### Routing Mechanisms  
The proactive and reactive components of Multi-MAF are classical approaches for WSNs, so they are not this paper's contribution. Our goal is to show how a hybrid solution can be built having in mind the benefits of choosing a proactive or a reactive routing strategy, based on the current traffic profile. In addition, an integrated forwarding rule is also presented for hybrid operation.  
Proactive Component: EF-Tree  
A simple and efficient structure for data dissemination is a routing tree [12], such as One-Phase Pull Diffusion [13]. Generally, the tree structure is created and maintained by a sink node that periodically rebuilds that tree to support eventual topological changes, link problems due to interference or traffic variations, node failures, etc. Our implementation, called EF-Tree (Earliest-First Tree) [22], is presented in Figure 2, which is described as follows:  
* The sink node starts the process by broadcasting a building (control) message to all neighbors, as shown in Figure 2(a).
* When a node receives the building message, it sets the first sender as its parent and broadcasts the building message to all its neighbors, discarding building messages from other neighbors. Other parent selection strategies may be used, such as the node that belongs to the path of greater residual energy. An interesting practical approach is presented by [PERSON] _et al._[12], who used the radio link quality to choose the node's parent. We intend to evaluate other parent selection strategies in future work.  
* Whenever a node has a data packet to be transmitted (sensed or forwarded by another node), it sends that packet directly to its parent as shown in Figure 2(b).
* The building process is periodically repeated, so the network adapts itself to eventual topological changes, such as failures, node movements, and node additions. This periodicity depends on how frequent topological chances occur. The more dynamic the network, the shorter the rebuilding period.
# Coordinated Control Strategy of CU-MTDC under Abnormal Conditions Considering Power Supply Security
## 2 Basic System Structure of CU-MTDC
### Control Structure of CU-MTDC
#### 2.2.2 Calculation Layer of Controller Parameters  
In this layer, the controller parameters of converters should be calculated according to the calculated value from the upper layer. Meanwhile, DC network parameters, coordination mechanism of primary control are also considered during the calculation procedure.