esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks
## 1 Introduction  
Mobile ad-hoc networks (MANETs) have a decentralized nature that makes them suitable for a variety of applications. The main advantage of a MANET [1] is that its nodes can communicate without any infrastructure. As a result, MANETs are usually deployed in battlefields, natural disasters, etc. MANETs differ from the long-established computer networks, as they have unique characteristics. For example, in a MANET, we observe a frequent link breakage because of node mobility, a high channel-error rate, severe link-layer contentions, and different path properties such as delay, bandwidth, and packet loss rate. Due to these characteristics, the overall performance of a MANET is disturbed in terms of packet delivery ratio, end-to-end delay, network throughput, and network overhead. By applying the principles of MANETs, a vehicular ad-hoc network (VANET) [2] can be established in an ad-hoc mode by vehicles. Using a VANET, vehicles can directly communicate among them, with no supporting infrastructure. Besides, VANETs are employed for road monitoring and infotainment applications, which constitute an integral part of the intelligent transportation system paradigm.  
This Special Issue, entitled \"Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks\", aims to expose the readership to the latest novel works in terms of solutions and techniques for MANETs and VANETs. The Special Issue is composed of five referred papers, covering interesting topics such as power-aware optimization solutions for MANETs, data dissemination in VANETs, adaptive multi-hop broadcast schemes for VANETs, multi-metric routing protocols for VANETs, and incentive mechanisms to encourage the distribution of information in VANETs. The Issue sets out to demonstrate pioneering work in these fields, investigate novel solutions and methods, and discuss future trends in these fields.
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 2 Basic Principles of FL over Wireless Networks
### Signal Transmission Models
#### 2.2.1 TDMA-Based Uplink Transmission  
In the TDMA scheme, each client is assigned a distinct, orthogonal time slot of length \(t_{k}\) to communicate with the central server (CFL) or with other clients (DFL), ensuring interference-free transmission. The achievable uplink rate for the \(k\)-th client (in bps) is given by  
\[R_{k}^{\mathrm{u}}=\frac{B_{\mathrm{u}}t_{k}}{T}\log_{2}\!\left(1+\frac{|h_{k} ^{\mathrm{u}}|^{2}p_{k}}{\sigma_{\mathrm{noise}}^{2}}\right), \tag{4}\]  
where \(\frac{|h_{k}^{\mathrm{u}}|^{2}p_{k}}{\sigma_{\mathrm{noise}}^{2}}\) represents the signal-to-noise ratio (SNR) of the \(k\)-th client during uplink transmission. \(|h_{k}^{\mathrm{u}}|^{2}\) captures the uplink channel gain between the \(k\)-th client and the central server (CFL) or other clients (DFL). \(\sigma_{\mathrm{noise}}^{2}\) represents the variance of the background noise. \(T\triangleq\sum_{k=1}^{K}t_{k}\) is the total time resource, \(B_{\mathrm{u}}\) denotes the total uplink bandwidth allocated for local model transmission.
## 1 Introduction
Likewise, axle configuration, in terms of tire spacing, has a significant effect on how stress is distributed. Greater spacing between tires minimizes overlap stresses, while closely spaced tires increase stress overlap [16; 17; 18; 19]. These challenges show the need for advanced modeling tools like PITRA PAVE. PITRA PAVE is a tool used to study pavement performance by analyzing how it responds to structural stresses induced by traffic loads. It contains a wide variety of materials, including asphalt mixtures, granular layers, and subgrade. The tool models multiple load configurations, including single, dual, and multi-axle loads, while accounting for dynamic loading conditions. Furthermore, PITRA PAVE enables for wide assessments of stress, strain, and deflection within pavement layers. This provides important information on the long-term performance of pavement infrastructure. Moreover, PITRAPAVE offers advanced functions, including dynamic loading simulations and better material analysis. These advancements enable for a more thorough understanding of pavement behavior under complex loading situations, surpassing earlier tools like KENPAVE [20; 21; 22]. In addition to PITRA PAVE, response surface methodology (RSM) is widely used to model the performance of pavements. RSM applies statistical tools to investigate correlations between variables like layer thickness and tire spacing, predicting pavement behavior under load [23,24]. RSM provides valuable insights, however, it has its limitations. It struggles to capture complex, nonlinear interactions and manage the large datasets common in real-world pavement systems [25,26]. This is where machine learning (ML) starts to assist the development of models. Unlike RSM, which relies on predetermined mathematical assumptions, ML algorithms can evaluate enormous datasets, find nonlinear patterns, and generate predictions without simplifications. Integrating ML into the modeling process provides for more precise and trustworthy pavement performance forecasts, leading to better assessments of long-term pavement behavior [27,28]. This research aims to develop models to improve stress prediction in flexible pavements under fixed traffic conditions. A homogeneous single-layer model was adopted to isolate the effects of axle configuration (single, tandem, tridem), tire spacing, and pavement thickness, avoiding the complexity of multi-layer systems. Response surface methodology (RSM) has been used to model stresses from single, tandem, and tridem axles. To further enhance the accuracy of these models, machine learning (ML) was integrated, enabling more precise predictions by analyzing complex patterns.
## 4 Results after Laser-Frequency Stabilization
The measurement result is given in Figure 5, which indicates that the in-phase component signal has no dependence with respect to the magnetic field strength. It should be noted that, for larger magnetic field, the increased magnetic field gradient inside the shield could distort the profile of the magnetic resonance signal, change the in-phase component, and in turn affect the performance of the laser frequency stabilization. There are also some other error sources that can affect the in-phase signal, i.e., the laser intensity fluctuations, the magnetic field gradient, the phase shift setting in the lock-in amplifier and the collision shift, in future work these issues will be addressed in more detail.
# Toward the Utilization of 3D Blading in the Cantilevered Stator from Highly Loaded Compressors
###### Abstract  
Three-dimensional blading is an efficient technique in compressor aerodynamic design, and its function mechanism in the cantilevered stator needs to be addressed. This paper focuses on the sweep and dihedral in the cantilevered stator and seeks to expose their effects through detailed flow field analysis. Results show that the forward sweep could alleviate the corner flow separation by preventing the accumulation of the secondary flow toward the corner region, resulting in stronger flow separation at the blade trailing edge; in summary, forward sweep with appropriate parameters could increase static pressure rise by 14.3%. The positive dihedral will carry the endwall flow to the upper-span sections, thereby reducing blade corner separation; hence, as much as 23.5% improvement in static pressure rise could be obtained with the appropriate dihedral. Moreover, the combination of a relatively large sweep height and a moderate sweep angle with a low dihedral height and a moderate sweep angle provides optimum aerodynamic performance; the static pressure rise coefficient sees an increment of 25.5% at the near stall point. An experiment is then performed to further validate the theory, which shows a 2% improvement in efficiency of 3D blading at small mass flow rates. However, the secondary leakage should be given attention at high mass flow coefficients, while the corner separation needs further elimination at small mass flow rates.  
cantilevered stator; 3D blading; leakage flow; secondary flow +
Footnote †: journal: Applied Sciences
# Non-Classic Atmospheric Optical Turbulence: Review
## 3 Theoretical Modeling of Optical Turbulence  
In the previous section, the motivation for the thorough analytic modeling of non-classic optical turbulence was presented. Here, we follow the well-known and recent theories capable of characterizing optical turbulence in various regimes. In particular, we first review the Obukhov-Corrsin theory, adjusting the Kolmogorov spectrum in the near-dissipation regime and then outlining the theories for non-Kolmogorov, anisotropic turbulence (in inertial range) followed by jet-stream turbulence and coherent turbulence.
# Multi-Object Tracking with Grayscale Spatial-Temporal Features
###### Abstract  
In recent multiple object tracking (MOT) research, there have not been many traditional methods and optimizations for matching. Most of today's popular tracking methods are implemented using deep learning. But many monitoring devices do not have high computing power, so real-time tracking via neural networks is difficult. Furthermore, matching takes less time than detection and embedding, but it still takes some time, especially for many targets in a scene. Therefore, in order to solve these problems, we propose a new method by using grayscale maps to obtain spatial-temporal features based on traditional methods. Using this method allows us to directly find the position and region in previous frames of the target and significantly reduce the number of IDs that the target needs to match. At the same time, compared to some end-to-end paradigms, our method can quickly obtain spatial-temporal features using traditional methods, which reduces some calculations. Further, we joined embedding and matching to further reduce the time spent on tracking. Our method reduces the calculations in feature extraction and reduces unnecessary matching in the matching stage. Our method was evaluated on benchmark dataset MOT16, and it achieved great performance; the tracking accuracy metric MOTA reached 46.7%. The tracking FPS reached 17.6, and it ran only on a CPU without GPU acceleration.  
multiple object tracking; tracking-by-detection; grayscale; spatial-temporal features +
Footnote †: journal: Applied  
doi: 10.3390/  
**Citation:** [PERSON]; [PERSON] Multi-Object Tracking with Grayscale Spatial-Temporal Features. _Appl. Sci._**2024**, _14_, 5900. [[https://doi.org/10.3390/app1435900](https://doi.org/10.3390/app1435900)]([https://doi.org/10.3390/app1435900](https://doi.org/10.3390/app1435900))  
Academic Editors: [PERSON] and [PERSON]  
Received: 14 June 2024  
Revised: 2 July 2024  
Accepted: 5 July 2024  
Published: 5 July 2024
## 3 Dynamic Response Analysis of the Test Model
### Distribution of Peak Ground Acceleration (PGA) Amplification Factor of the Slope  
According to the test results of the horizontal accelerometers in the test model, the peak acceleration in the time-history curve was extracted and divided by the peak table acceleration to obtain the PGA amplification factor [22], which was combined with the horizontal and vertical coordinates of the monitoring section to draw a two-dimensional equipotential map by conversion into the form of a digital matrix by using the Renka Cline random matrix generation method.  
Figure 15: Distribution of bending moment. (**a**) Back-row anti-slide pile; (**b**) back-row pile of bridge foundation; (**c**) front-row pile of bridge foundation; (**d**) front-row anti-slide pile.  
Figure 16 shows that the PGA amplification factor of the slope had a vertical amplification effect. As the height increased, the PGA amplification factor tended to increase and had a layered distribution. The sliding section of the slope and the steep section in front of the pier had relatively poor stability and a strong tendency for sliding failure, and the PGA amplification factor of the corresponding area was large. Because the back-row anti-slide piles withstood most of the landslide thrust, the horizontal inertial force of the soil mass behind the pier was limited. Therefore, the PGA amplification factor in the area between the pier foundation and the back-row anti-slide piles was relatively small, and the back-row anti-slide piles played their role in seismic reinforcement. The bedrock was artificially filled and compacted, resulting in a large elastic modulus and a weak PGA amplification effect. Because there were only three accelerometer points in the bedrock, there was a certain error in plotting the equipotential lines according to the digital matrix, leading to a larger PGA amplification factor in the upper area of the bedrock. As the peak acceleration of the seismic wave increased from 0.1 g to 0.6 g, the PGA amplification factor of the slope generally showed a decreasing trend. As the shear modulus of the soil decreased and its damping ratio increased, the frictional energy dissipation between the soil particles increased, and the energy loss of the upwardly propagating seismic wave increased, resulting in a reduction in the PGA amplification factor. When the peak acceleration of the seismic wave reached 0.6 g, the slope completely failed, and the gravel soil accumulated substantially behind the back-row anti-slide piles. The compactness of soil in this region increased to some extent, resulting in a weakened dynamic response and a smaller PGA amplification factor.  
Figure 16: _Cont._  
Figure 16: Peak ground acceleration (PGA) amplification factor distribution equipotential maps of slope with different seismic inputs. **(a)** 0.1 g; **(b)** 0.2 g; **(c)** 0.3 g; **(d)** 0.4 g; **(e)** 0.5 g; **(f)** 0.6 g.
## 3 Digital Implementation
### Network-On-Chip (NoC) Architecture  
The digital structure of the proposed hippocampal neural network is based on the NoC architecture that can enable a scalable and cost-efficient digital neuromorphic system. It is so indispensable that it determines the hardware performance of the proposed system. In the proposed study, the torus structure is used because it can avoid the node closure on the edge of toroidal topology; thus, the system can show better performance. Figure 2 shows the detailed implementation of the proposed \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Excitatory Neuron** & **Inhibitory Neuron** \\ \hline \(a\) & 0.04 & 0.04 \\ \(b\) & 5 & 5 \\ \(c\) & 140 & 140 \\ \(\tau_{u}\) & \(43\pm 4.3\) & 100 \(\pm\) 0 \\ \(k_{u}\) & \(0.24\pm 0.02\) & 0.25 \(\pm\) 0 \\ \(V_{0}\) & \(-65\pm 6.5\) & \(-65\pm 0\) \\ \(\Delta U\) & \(10\pm 1\) & \(1\pm 0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter values of the neuron models. NoC-based SNN. The top-level NoC structure is shown in Figure 2a, which contains 64 nucleus processors (NPs). Address event routing (AER) is essential to data communication between NPs determined by routers. The specific structure is shown in Figure 2b: Each NP contains a neuron unit, a router, a silicon synapse unit, and a configuration unit. The routers can transmit the data flow via the east, south, west, or north ports. The configuration unit is used to configure the routers, which determines the data transmission within an NP. The neuron unit is used to compute the hippocampal neurons, and the synaptic current is calculated in the silicon synapse unit. In the router module, the data flow is determined by hippocampal information processing (HIP) scheduling as shown in Figure 2c. The NoC structure proposed in this paper uses a torus topology and requires a router with a dedicated routing algorithm to carry out correct data transmission, which is quite different from the conventional network structure. In the case of inter-chip data communication, the router receives external events from four adjacent NPs and sends data streams based on programmed routing rules from the configuration unit. The Algorithm 1 shows the routing algorithm of the proposed system. The AER spike information will be firstly routed along the X direction. After it reaches the location along the X direction checked by the embedded router, the information will be then routed along  
Figure 2: Digital implementation of the proposed hippocampal neural network. (**a**) Digital implementation of the network-on-chip (NoC) architecture; (**b**) Digital implementation of the nucleus processor (NP); (**c**) Digital implementation of the router. the Y direction to the destination node according to the routing table. A judgement is executed during the following routing algorithm to determine the routing direction for the shorter routing path planning. The AER data in the packet transmitted through the router is synaptic information rather than peak information, which is different from the traditional AER-based implementation of SNN.
## 2 Results and Discussion
### Characterization of Fresh VOOs
Regarding the volatile composition, Table 2 shows the concentration of the identified volatile compounds of the fresh oils before starting the storage experiment, their odor thresholds and their sensory attributes. These volatile concentrations provide useful information about their oxidation state or the presence of some oxidative/ferrantative defects [1; 30; 31; 31] before the storage. Table 2 shows the high content of C6 aliphatic compounds, such as hexanal, (_E_)-2-hexenal, hexyl acetate, hexanol, (_E_)-3-hexen-1-ol, (_E_)-3-hexen-1-ol, (_E_)-2-hexen-1-ol, (_E_)-2-hexen-1-ol and (_Z_)-3-hexenyl acetate, which derived from linoleic and linolenic acids through the lipoxygenase (LOX) pathway [1; 32; 33]. The total concentration for this group of compounds, which provide pleasant notes to the oil, represented more than 20% of the total concentration of volatiles in all samples. Thus, the highest concentration for C6 lipoxygenase products was found in VOO1-0m, representing 33% of its total volatile compounds, with a value of 18.26 mg/kg. Whereas, the percentages and concentrations values for the rest of samples were 25.33% and 11.13 mg/kg for VOO2-0m, 24.41% and 15.45 mg/kg for VOO3-0m and 25.90% and 11.09 mg/kg for VOO4-0m. (_E_)-2-hexanal is one of the most abundant compounds in all fresh samples, with a concentration value that ranged from 4.53 to 5.81 mg/kg. Hexanal and hexanol showed high concentration values as well, in the range of 1.08-3.83 mg/kg. These three compounds strongly contributed to the aroma of all fresh samples since their concentrations exceeded their odor threshold value (Table 2). The high amount of (_Z_)-3-hexen-1-ol in VOO1 (1.10 mg/kg) and (_Z_)-3-hexenyl acetate in VOO3 (1.73 mg/kg) are also remarkable. These compounds are characterized by ripe fruity, bitter and green sensory attributes.
## 3 Results
### Publication Type  
Original research articles accounted for over 80% of the dataset. Review papers and patents were also included, primarily from MDPI and Google Scholar.  
The type of publication in which a study appears often reflects its level of maturity, methodological depth, and intended audience. For this reason, the articles included in this review were classified as original research articles, review papers, or patents. Figure 4 (see Section 3.3) and the corresponding distribution table provide a breakdown by publication type across the three source categories.  
Of the one hundred eighty-five total articles  
* One hundred fifty (81.1%) were classified as original research articles,
* Thirty-three (17.8%) were categorized as review papers,
* Two (1.1%) were patents or patent-related literature.  
Original research articles were dominant across all sources, but particularly within Elsevier, which showed a strong bias toward full-length studies involving case validation, simulation modeling, and detailed algorithmic implementation. These papers often followed a conventional structure--introduction, methodology, results, and discussion--and typically included quantitative performance evaluations, making them valuable for meta-analytical or benchmarking efforts.  
MDPI exhibited a higher proportion of review papers compared to the other sources. These reviews frequently provided taxonomies of existing algorithms, frameworks, or case applications, serving as entry points for researchers seeking to understand the current landscape of fault diagnosis in power systems. While these reviews varied in comprehenseness, many offered useful classifications of methods and application domains (e.g., thermal plants, smart grids, or renewable generation units).  
Google Scholar, which aggregates articles from preprint servers, institutional repositories, and journals of varying impact, included a small number of non-traditional sources, such as conference proceedings and technical white papers. While some of these lacked peer-review certification, they contributed additional diversity and occasionally introduced novel or exploratory ideas not yet published in mainstream journals.  
The scarcity of patents (only two identified) suggests that although there is significant academic interest in fault detection technologies, and commercial adoption and intellectual property protection remain under-represented in the scientific literature. This discrepancy may be due to confidentiality in industrial applications, delays in patent publication, or the use of proprietary methods not disclosed in full in academic contexts.  
Understanding the distribution of publication types is important not only for assessing the methodological rigor of the literature but also for contextualizing its relevance to academic, industrial, and regulatory stakeholders. While original research dominates, review papers and emerging sources contribute to a more nuanced and inclusive understanding of the field.
# Hybrid Modulation Strategy to Eliminate Current Distortion for PV Grid-Tied H6 Inverter
## 3 Hybrid Modulation Mode under Non-Unity Power Factor
### Hybrid Modulation Mode with H5 and HERIC  
Apart from the H6 topology a number of other topologies have been developed which also allow for maintaining the common-mode voltage constant. These include the H5 topology from SMA Corporation [10] and the HERIC topology developed by the Sunways Company [16]. Similar to the H6-type inverter, both topologies can maintain a constant common-mode voltage with no common-mode leakage current for a single general modulation mode. The HMM technique presented here can also be extended to the H5 and HERIC topologies. As shown in Figure 7, utilizing the HMM, a loop for the freewheeling current can be formed for the non-unity power factor operation of both the H5 and HERIC converters thus eliminating the current zero-crossing distortion. The proposed HMM not only suits these three topologies, but also can be adopted in other single-phase topologies which need the freewheeling path and is required to eliminate the current zero-crossing distortion and provide reactive current.  
Figure 6: The novel hybrid modulation mode and operation mode 5. (**a**) The new Hybrid Modulation Mode (HMM), (**b**) operation mode 5.  
Figure 7: Freewheeling mode in the positive half period with the new hybrid modulation mode (HMM) for both unitary and non-unity power factor. (**a**) H5 grid-tied inverter. (**b**) HEIRC grid-tied inverter.