esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing Strategies for Isochronal-Evolution Random Matching Network
## 3 Path Planning and Routing Decision-Making in the IERMN  
### Definition and Expression of the Path  
We define the path in an IERMN as a space-time sequence of the vertices and edges that a packet travels through when it is transmitted from its source to its destination. There are two kinds of paths. The path that is determined before transmitting a packet is called the prior path, while the path that is formed during the transmission process of the packet is called the posterior path.  
The path that is from \(v_{i}\) to \(v_{j}\) and planned at slot \(k\) is  
\[P_{ijk}=\left\{(v_{i},e_{i\mu}^{[v]},v_{\mu}),\cdots,(v_{\rho},e_{\rho j}^{[ o]},v_{j})\right\} \tag{10}\]  
where \((v_{i},e_{i\mu}^{[v]},v_{\mu})\) denotes one hop of the path, \(v\) is the slot at which the packet departs from its source vertex (so \(v\) is known as the departure slot), and \(o\) is the slot at which the packet arrives at its destination vertex (so \(o\) is known as the arrival slot). For \(P_{ijk}\), slot \(k\) is known as the planning slot; its departure slot and arrival slot are denoted by \(v(P_{ijk})\) and \(o(P_{ijk})\), respectively.  
As a space-time sequence, a path has not only a spatial length but also a temporal length. The temporal length of a path is the number of slots between its departure slot and arrival slot; the spatial length is the number of hops in the path. For \(P_{ijk}\), the spatial length and temporal length are denoted by \(SL_{ijk}\) and \(TL_{ijk}\) respectively. The other meaning of the temporal length is the delay of a path that is counted by slots.
## 5 Results and Discussion
### Geometric Accuracy
To further confirm the robustness of the estimate, the L-band calibration detailed in this section was repeated several times. In each iteration, the initial phase center positions were randomly perturbed by \(\pm 1\) m in each dimension and the estimation process was repeated. The resulting phase center estimates were found to be in agreement with a standard deviation of under 2 mm. A quantitative evaluation is even more difficult for the tropospheric propagation correction \(\Delta c\), where optimization yields a value of \(6\times 10^{-5}\) for the L-band results of this section. This correction changes the refractivity \(N_{air}\) from 283, the assumed default value, to 342 parts per million. Both of these values are plausible, in that they are reproducible by stablished models for reasonable choices of temperature, pressure and relative humidity ([PERSON] et al., 2014). While there has not been a systematic effort to validate this aspect of the calibration model further, phase center position corrections are generally smaller and appear more plausible when the tropospheric propagation correction is included in the underlying model. A rigorous analysis of this calibration parameter is, however, beyond the scope of the present discussion. To do so would require additional ground truth and simulations.
# GPU@SAT DevKit: Empowering Edge Computing Development Onboard Satellites in the Space-IoT Era
## 2 Background and Related Work
Typically, this task falls upon the user, who must possess proficiency in C and OpenCL, as well as familiarity with the architecture, to instruct the host processor. Consequently, application development, particularly during the testing phase, demands a significant investment of engineering time.
# A Novel Cross-Latch Shift Register Scheme for Low Power Applications
###### Abstract  
The conventional shift register consists of master and slave (MS) latches with each latch receiving the data from the previous stage. Therefore, the same data are stored in two latches separately. It leads to consuming more electrical power and occupying more layout area, which is not satisfactory to most circuit designers. To solve this issue, a novel cross-latch shift register (CLSR) scheme is proposed. It significantly reduced the number of transistors needed for a 256-bit shifter register by 48.33% as compared with the conventional MS latch design. To further verify its functions, this CLSR was implemented by using TSMC 40 nm CMOS process standard technology. The simulation results reveal that the proposed CLSR reduced the average power consumption by 36%, cut the leakage power by 60.53%, and eliminated layout area by 34.76% at a supply voltage of 0.9 V with an operating frequency of 250 MHz, as compared with the MS latch.  
shift register; power consumption; cross-latch scheme; leakage power; layout area +
Footnote †: journal: Applied Sciences
# Tethered Balloon Cluster Deployments and Optimization for Emergency Communication Networks
## 5 Simulation Validations and Discussion
### Discussion  
In summary, this study reveals the performance characteristics of MOPSO-based high-altitude balloon networks in terms of coverage, energy efficiency, throughput, delay, and packet loss rate. The physical layer analysis demonstrates how MOPSO effectively manages the complex electromagnetic environment through optimal spatial deployment and power control. The results show that MOPSO, with a faster convergence speed, exhibits significant advantages in optimizing balloon deployment locations and power allocation and can effectively improve performance. This is particularly evident in the improved SINR distribution and reduced electromagnetic interference patterns across the network. The observed non-linear growth and fluctuations in performance reflect the complex interaction between algorithmic optimization, network interference, and adaptive power control. This dynamic equilibrium highlights the multi-objective nature of the high-altitude balloon network optimization problem and the effectiveness of MOPSO in dealing with such complex problems, providing a viable solution for building high-performance and reliable high-altitude communication systems. SA lacks a systematic search strategy and directional guidance, leading to slow convergence and lower-quality solutions, especially when dealing with high-dimensional or multi-objective optimization problems. Future research directions could focus on improving MOPSO to better handle local optimum problems, developing more efficient interference management strategies and exploring the possibility of integrating machine learning techniques into the optimization process. These improvements are expected to further enhance the performance of high-altitude balloon networks and lay the foundation for large-scale deployments.
## 6 Propagation Models Versus Measurements
It is recommended for evaluating signal propagation under NLOS conditions in Scenario 3, providing accurate path loss estimates compared to other models. In Figure 18, the path loss of different models in Scenario 5 is compared. The \"Free Space\" and \"Shadowing Path Loss\" models have the lowest path loss predictions, while the \"Egli\", \"Hata\", and \"COST 231\" models have the highest. Under non-line-of-sight  
Figure 17: Scenario 3—RSSI versus distance for NLOS conditions: Comparison between the measurement and five models. Figure 18: Scenario 5—RSSI versus distance for NLOS conditions: Comparison between the measurement and five models. conditions, the \"Free Space\" and \"Shadowing Path Loss\" models perform well, while others are unrealistic. The \"Free Space\" model is recommended for accurate signal propagation evaluation in Scenario 5, as it provides closer estimates to the actual values and better matches the measured data. In our comparison of different indoor propagation models with the field measurement data, we found that only five models closely approximate the actual measurement characteristics: the \"Free Space\", \"Shadowing Path Loss\", \"Egli\", \"Hata\", and \"COST 231\" models. All other models deviated significantly from the measured data, indicating their inefficiency for network planning in this region. However, the \"Free Space\" propagation model, after correction, showed a good fit with the measured received signal power for the required distances, as observed in Figures 15-18.
# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
## 5 JDT Algorithms Based Deep Learning
### Fusion Feature Extraction and Data Association  
The deep features retrieved from the object detection network and the deep apparent features used for data association are distinct, as shown by the study of the DBT method. Fusion can boost the neural network's ability to achieve feature fusion and reuse by detecting relevant depth features, REID features, or fusing parent features and motion features.  
The above-mentioned Tracktor++ framework is still limited in tracking performance due to the low degree of integration between functional modules. In response to this situation, Peng et al. [74] proposed the CTrack algorithm, which combines the three modules of object detection, feature extraction, and data association. The fusion is integrated into an end-to-end network structure, and CTrack is simple and fast with the help of chain structure and pairwise attention regression technology.  
Based on the analysis in the TBD framework, it can be seen that there are differences between the deep features extracted by the detection network and the deep apparent features that the data association relies on. The object detection module fuses appearance features and motion features. Wang et al. [75] proposed a JDE model based on the YOLOv3 detection algorithm. The starting point of the framework is to increase the reusability of features, adding an apparent feature extraction branch to the original classification and regression branch. Although the fusion technique increases multi-object tracking accuracy, it significantly slows down the tracking algorithm.
## 4 Experiment and Discussion
The crack extraction results of the four methods are shown in Figure 6.  
Figure 6: Fracture extraction results of different methods. (**1**) Proposed method. (**2**) Literature [4] method. (**3**) Literature [5] method. (**4**) Literature [6] method. According to Figure 6, the proposed method can completely extract the cracks in the image without information loss. The literature [4] and literature [5] methods have also extracted the non-fracture areas in the road. The literature [6] method extracts the missing details of cracks. To sum up, the proposed method has a high fracture extraction accuracy. The proposed method, literature [4] method, literature [5] method, and literature [6] method are used to test the detection of highway asphalt pavement, and four F1 methods are introduced. The detection accuracy of the F1_score index detection method is shown in Formula (29):  
\[F1\_score=\frac{2\times precision\times recall}{precision+recall} \tag{29}\]  
where _precision_ represents the accuracy rate; \(recall\) indicates the recall rate. The detection accuracy of the four methods is shown in Table 2.  
Analysis of the data in Table 2 shows that the detection accuracy of the four methods decreased with the increase in the number of images. Under the same number of images, the proposed method shows the F1_score consistently above 0.85, indicating that the proposed method is suitable for large-scale crack damage detection.
## 4 Methodology
### Autoencoder Implementation
#### 4.2.1 Feature Compression Using Autoencoder  
Before engine state classification, engine datasets must be processed through the encoder-decoder-type neural network. Such a network architecture allows the conversion of various engine data sequences [55] into the compressed dataset, which is then converted into the vectors of engine parameters, which might be easily processed into engine state clusters. An autoencoder is a neural network that learns a compressed input data representation. It consists of an encoder that compresses the input data into a lower-dimensional representation and a decoder that reconstructs the input data from the compressed representation.
# A Snapshot of the Microbiome of a Portuguese Abandoned Gold Mining Area
## 2 Materials and Methods
### DNA Extraction, High-Throughput Sequencing  
The air-dried and sieved soil samples DNA was extracted using a combination of the Nucleospin Soil Kit ([PERSON], Duren, Germany), Buffer SL1 and the Enhancer SX according to the manufacturer's instructions. Samples were then prepared for Illumina Sequencing by amplification of the 16S rRNA gene V3/V4 hypervariable regions and the Internal Transcribed Spacer 2 (ITS2) sub-regions of the microbial communities. For this purpose, the hypervariable DNA regions were initially amplified with specific primers and further reamplified with a limited-cycle PCR reaction aiming to add the sequencing adapters and dual indexes. The initial PCR reactions were individually performed using the KAPA HiFi HotStart PCR Kit (according to manufacturer suggestions), 0.3 uM of each PCR primer and 2.5 uL of template DNA in a total volume of 25 uL. The primers considered consisted of the forward primer Bakt 341F \({}^{\circ}\)-CCTACGGSINGGCWGCAG-3\({}^{\circ}\) and the reverse primer Bakt 805R 5-GACTACHYGGGATCTAATCC-3\({}^{\circ}\) for bacteria [5; 53]; and a pool of forward primers ITS3 NGSI F 5\({}^{\circ}\)-CATCAGTAGAGAACCAG-3\({}^{\circ}\), ITS3 NG52 F 57-CAACGATGAGAACCAG-3\({}^{\circ}\), ITS3 NG53 F 5\({}^{\circ}\)-CATCGATGAAAGCCG-3\({}^{\circ}\), ITS3 NGS4 F5\({}^{\circ}\)-CATCGATGAAAGACTGG-3\({}^{\circ}\) and ITS3 NG510 F 5\({}^{\circ}\)-CATCGATGAAAGAGCGTG-3\({}^{\circ}\) and the reverse primer ITS4 NGS001 R 5\({}^{\circ}\)-TCCTSCCTTATTGTATGC-3\({}^{\circ}\) for fungi [54]. The conducted PCR reactions entailed a 3 min denaturation at 95 \({}^{\circ}\)C, 35 cycles of 98 \({}^{\circ}\)C for 20 s, 55 \({}^{\circ}\)C (bacterial region)/60 \({}^{\circ}\)C (fungal region) annealing for 30 s, extension at 72 \({}^{\circ}\)C for 30 s and a final extension step at 72 \({}^{\circ}\)C for 5 min. Indexes and sequencing adapters to both ends of the amplified target regions were added through a second PCR reaction according to the manufacturer's recommendations [55]. Negative PCR controls were considered for all amplification procedures. Amplification was evaluated by electrophoresis on a 1% (\(w/v\)) agarose gel and quantified by the Qubit dsDNA High Sensitivity Assay Kit (Invitrogen Life Technologies, Waltham, MA, USA) on the Qubit 2.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). The obtained PCR amplicons were then one-step purified and normalized using the Sequal-Prep 96-well plate kit (ThermoFisher Scientific, Waltham, MA, USA) [56]. This kit allows a solid phase, high-throughput amplicon purification and normalization through DNA binding, washing (to remove contaminants) and elution at predetermined concentrations (normalization). Purified amplicons were then pooled and pair-end sequenced in the Illumina MiSeq(r) sequencer with the MiSeq(r) Reagent Kit v3, according to manufacturer's instructions (Illumina, San Diego, CA, USA) at Genoinseq (Cantanhede, Portugal).
## 2 Model and Method
### Model of DES Optimization
#### 2.4.3 Device Model  
The output thermal efficiency and electrical efficiency of the gas turbine are determined by the load rate (\(x_{i}\)) [35]. The load rate is determined by the operational strategy. To accurately calculate the hourly operational efficiency of the equipment, the polynomial fitting method is used to fit the scattered data of existing gas turbines. The fitting function equations are as follows:  
\[\eta_{gt.E}(x_{i})=\sigma_{1}\cdot{x_{i}}^{4}+\sigma_{2}\cdot{x_{i}}^{3}+ \sigma_{3}\cdot{x_{i}}^{2}+\sigma_{4}\cdot{x_{i}}\;+\sigma_{5} \tag{11}\]  
\[\eta_{gt.E}(x_{i})=\sigma_{1}\cdot{x_{i}}^{4}+\sigma_{2}\cdot{x_{i}}^{3}+ \sigma_{3}\cdot{x_{i}}^{2}+\sigma_{4}\cdot{x_{i}}\;+\sigma_{5} \tag{12}\]  
where \(\eta_{gt.E}\) and \(\eta_{gt.h}\) are the power and heat generation efficiency of the gas turbine, respectively; and \(\sigma\) and \(\tau\) are the coefficients of the fitting function.  
The operational efficiency and the gas consumption of the gas turbine in time \(i\) can be calculated by the above gas turbine load rate (\(x_{i}\)) and the thermoelectric efficiency fitting function. The equations are as follows:  
\[E_{gt,i}=\frac{\eta_{gt,E}(x_{i})\cdot V_{gt,E}}{\eta_{gt,E}^{max}} \tag{13}\]  
\[H_{gt,i}=\frac{\eta_{gt,H}(x_{i})\cdot V_{gt,H}}{\eta_{gt,H}^{max}} \tag{14}\]  
\[G_{gt,i}=\frac{E_{gt,i}}{\eta_{gt,E}(x_{i})\cdot Q_{gas}} \tag{15}\]  
where \(E_{gt,i}\) and \(H_{gt,i}\) are the power and heat generation of the gas turbine, respectively; \(G_{gt,i}\) is the natural gas consumption of gas turbine; and \(Q_{gas}\) is the calorific value of natural gas.  
According to the research results of other researchers [13; 24], the models of the heat recovery steam generator, the gas-fired boiler, the water chiller, and the absorption refrigeration unit are as follows:  
\[H_{hrsg,i}=H_{gt,i}\cdot\eta_{hrsg} \tag{16}\]  
\[H_{gb,i}=G_{gb,i}\cdot\eta_{gb} \tag{17}\]  
\[C_{\sigma,i}=E_{\sigma,i}\cdot\eta_{\sigma} \tag{18}\]  
\[C_{\sigma,i}=(H_{gb,i}+H_{hrsg,i})\cdot\eta_{ar} \tag{19}\]  
where \(H_{hrsg,i}\) and \(H_{gb,i}\) are the heat generation of the heat recovery steam generator and gas-fired boiler, respectively, at time \(I\); \(G_{gb,i}\) is the natural gas consumption of the gas-fired boiler at time \(i\); and \(\eta_{\sigma}\) and \(\eta_{ar}\) are the refrigerating output efficiency of the electric refrigeration unit and the absorption refrigeration unit, respectively.
## 3 Results and Discussions
### Incident S Wave  
Here, only _FS_ and _TS_ reflecting shear strain are discussed because \(FV_{2}\), \(FV_{4}\) and \(TV\) reflecting areal strain are zero for the incident S wave. Figure 6 shows the comparison of amplitude results of the two-ring model and the empty model for incident S wave. It can be seen that the frequency response characteristics of the incident S wave are similar to that of the incident P wave. The existence of the grout causes the decrease of the peak value and the increase of the corresponding frequency of peak value. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{\(\mathbf{\theta_{1}}\)(\({}^{\circ}\))} & \multicolumn{3}{c}{\(\mathbf{E_{2}}\) = 30 GPa, \(\mathbf{\
u_{2}}\) = 0.25, \(\mathbf{\rho_{2}}\) = 2.4 g/cm\({}^{3}\)} \\  & & **Zero** & **Peak Value** & **\(\mathbf{f}\) for Peak Value** & **abs(max)** \\  & & **Frequency Gain** & **(\(\mathbf{\
u_{2}}\)-) \(\mathbf{\
u_{2}}\) (\(\mathbf{\
u_{2}}\)+)** & **(\(\mathbf{\
u_{2}}\)-) \(\mathbf{\
u_{2}}\) (\(\mathbf{\
u_{2}}\)+)** & **\(\mathbf{\
u_{2}}\)-(\(\mathbf{\
u_  
Table 5 shows the characteristic parameters for the incident S wave. As can be seen from the table, the peak value, attenuation rate, 1% bandwidth and amplitude of the two-ring are smaller than in the empty borehole model, while the corresponding frequency of peak value and the corresponding frequency of 1% bandwidth are larger than in the empty borehole model. It shows that the expansive grout will cause the peak value to decrease, and cause the overall change trend to slow down. \begin{table}
\begin{tabular}{c c c c c c} \hline  & & & \(E_{2}\) = 30 GPa, \(\
u_{2}\) = 0.25, \(\rho_{2}\) = 2.4 g/cm\({}^{3}\) & \\  & \(\theta_{1}\)(\({}^{\circ}\)) & **Zero** & **Peak Value** & **\(f\) for Peak Value** & **abs(max)** \\  & & **Frequency Gain** & **(\(\rho_{2}\)-)**\(\rho_{2}\) (\(\rho_{2}\)+)** & **(\(\rho_{2}\)-)**\(\rho_{2}\) (\(\rho_{2}\)+)** & **\(\rho_{2}\)-(\(\rho_{2}\)+)** \\ \hline  & 0  
Figures 7-9 describe the influence of the [PERSON]'s modulus, [PERSON]'s ratio and density of the expansive grout on the theoretical amplitude spectrum of gauge combination _TS_ for the incident S wave, respectively. The changes of the [PERSON]'s modulus and Poisson's ratio cause large changes in the amplitude of _FS_ at small incident wave frequency \(f\), while there is almost no change with the different densities of the expansive grout. From Tables 6-8, we see that the influence of the [PERSON]'s modulus, [PERSON]'s ratio and density of the expansive grout on _FS_ and _TS_ for the incident S wave are the same as that of the incident P wave, except for the influence of the Poisson's ratio on the corresponding frequency of peak value. Figure 6: Theoretical amplitude spectrum of gauge combination _TS_ for the incident S wave.
## 3 Path Planning Problem  
For mobile robots, the aim of path planning is to find a feasible path in a specific environment. This path begins at the starting point (\(S\)) and ends at the target point (\(T\)). We explain the aspects of the PP problem in the following text. They include the environmental modeling, the path form, and the definitions of objectives.  
In this article, the workspace is considered to be a continuous two-dimensional space. In this continuous workspace, obstacles are assumed to be static polygons, and the robot is assumed to be a single point. If the robot is a polygon, the Minkowski sums can be adopted for the two sets: obstacles and robot. Then, the polygon robot is assumed to be a point. In regard to the field of computational geometry, this article has gives approximation algorithms [42].  
A path consists of successive segments. By summing all the segments of this path, the length of the entire path can be obtained. We assume that path \(p=[S=p_{0},p_{1},p_{2},...,p_{n},p_{n+1}=T]\) contains a starting point (\(S\)), target point (\(T\)), and \(n\) consecutive points. The character (\(n\)) is the number of intermediate points or rotation points (RPs). Thus, a segment of path is generated by two consecutive points in the path. Figure 1 graphically represents the path.
## 3 Results and Discussions
### Packets Drop Comparison  
Figure 8 illustrates the packets drop of the EMBLR, MP-OLSRV2, and MEQSA-OLSRv2 routing approaches with different speeds of network devices. It can be observed that the EMBLR routing approach outperforms the other routing approaches. The MP-OLSRv2 and MEQSA-OLSRv2 routing approaches exclude the channel condition awareness for data transmission; due to this, there is a collision of the data packets at the MAC layer. Whereas the EMBLR utilizes the HETX metric before the data transmission to the link that minimizes the collision of the data packets, as well as the dropped packets. As the devices' speed elevates, the number of data packets dropped in the network also elevates. The MP-OLSRv2 packet drop (27.43%), and MEQSA-OLSRv2 packet drop (14.15%) is higher than the EMBLR routing approach at the highest 60 m/s device speed.  
Figure 7: Packet delivery ratio vs. various devices speed.
# Smart Healthcare Network Management: A Comprehensive Review
## 5 Reference Architecture for Smart Healthcare System Network Management
### Resource Management Layer
#### 5.4.1 Fault Management  
Fault Management is a set of functions, which enables the detection, localization, isolation, and correction of abnormal operations of the telecommunication network and its environment. Various types of faults can arise in IoT-based healthcare networks, affecting data integrity, system performance, and network reliability. These faults can be categorized into data-centric, system-centric, and network-centric issues, each with distinct causes,  
Figure 6: TMN model for managing open systems.  
durations, and impacts. A detailed classification and analysis of these fault types are provided in Table 4, highlighting their characteristics and potential consequences for system operations [101].