esa-sceva/satcom-chunk-collection huggingface dataset:
# A Closer Look at Few-Shot Classification with Many Novel Classes
## 5 Experiments
### Experimental Setup
#### 5.1.2 Experimental Protocols  
To ensure fairness in experimental setups and to avoid over-engineered designs, this paper selects publicly available backbone networks pretrained on the ImageNet-1k dataset. For the backbone network selection, this paper utilizes ViT [30] and ResNet50 [31]. Additionally, the paper employs various pretraining strategies, including supervised pre-training [31] (abbreviated as Sup), DINO [32], Deit [33], and MAE [34].  
DINO, a self-supervised learning algorithm proposed by the Facebook AI Research team, trains Transformer models to learn meaningful features from unlabeled data through a label-free self-distillation process. Deit, also by Facebook AI Research, is a method for efficiently training vision Transformers by combining knowledge distillation and data augmentation techniques, allowing for vision Transformers to match the performance of convolutional networks without large-scale data or computational resources. MAE, introduced by Meta AI Research, is a novel self-supervised learning approach focusing on the pretraining of vision Transformers. Its core idea involves masking parts of an image and having the model reconstruct the original image, thus learning useful image representations, especially under data scarcity.  
In the meta-learning tests of FSL-MNC, the number of classes exponentially increases from 5 to 2560. Specifically, for novel class numbers \(N\geq 160\), 80 meta-testing few-shot tasks are conducted following the setup by [PERSON] et al. [3], whereas for \(N<160\), 600 meta-testing few-shot tasks are performed, adhering to the standard experimental protocol by [PERSON] et al. [15]. Each class has 15 query samples. Although this testing setup introduces high variance, it has been effective in enhancing experimental efficiency in practice. The paper reports average accuracy (in percentages %).  
For traditional few-shot datasets, to assess few-shot classification performance, 600 tasks are simulated from each dataset's test set. The evaluation metric is the average classification accuracy per task. For miniImageNet and CIFAR-FS, the conventional approach evaluates the 5-way-1-shot (5w1s) and 5-way-5-shot (5w5s) setups, with each task's query set size fixed at \(15\times 5\). For cross-domain few-shot learning on the Meta-Dataset, the number of classes, training samples, and testing samples are randomly sampled uniformly, except for ImageNet-1k and Omniglot, which follow specific sampling strategies based on class hierarchies.  
All experiments are conducted on a system equipped with eight GPU nodes, each containing eight NVIDIA A100 GPUs (NVIDIA, Santa Clara, CA, USA), ensuring abundant computational resources and efficient execution of experiments even with a large number of categories, thus guaranteeing the reliability and timeliness of the experimental results.
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 1 Introduction
The main contributions of the paper are as follows:  
* Theoretical key concepts required to understand the use of game theory in WSN routing are briefly discussed;
* Routing protocols are examined in relation to their operational principles and key features;
* Routing protocols are classified based on the game applied;
* Routing protocols are compared with each other in terms of major characteristics, pros, and cons;
* Certain challenging problems in the design and implementation of a routing protocol are discussed, in addition to future research directions;
* Possible applications and future improvements of the protocols are discussed. The rest of this paper is organized as follows: in the following section, game theory for wireless sensor networks, along with the most commonly used metrics for game theory-based routing protocols is introduced; in Section 3, the latest routing protocols based on game theory are presented, along with their technical strengths, weaknesses, possible improvements, and applications; in Section 4, game theory-based routing protocols are compared with each other in terms of major features, advantages, limitations, and network architecture; in Section 5, certain future research directions and challenging research concerns are discussed; finally, the paper is concluded in Section 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Survey Article** & **Practical Examples** & \begin{tabular}{c} **Classification of the** \\ **Protocols Based on** \\ **Game Theory** \\ \end{tabular} & \begin{tabular}{c} **Qualitative** \\ **Comparison of the** \\ **Protocols** \\ **Protocols** \\ \end{tabular} & \begin{tabular}{c} **Possible** \\ **Applications of the** \\ **Protocols** \\ \end{tabular} & \
\begin{tabular}{c} **Possible Future** \\ **Improvements of the** \\ **Protocols** \\ \end{tabular} &
\begin{tabular}{c} **Challenging and** \\ **Open Research** \\ **Issues** \\ \end{tabular} \\ \hline Reference [19] & No & No & No & No & No & No \\ \hline Reference [20] & No & Yes & No & No & No & No \\ \hline Reference [21] & No & Yes & No & No & No & Yes \\ \hline Reference [46] & No & No & No & No & Yes & Yes \\ \hline Reference [47] & No & No & No & No & No & Yes \\ \hline Our Survey & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of our survey with existing surveys.
## 3 Mechanical Analysis and Numerical Simulation of Roadway Wall Rock
### Numerical Simulation of Roadway Wall Rock Based on FLAC 3D
After the roof had been supported, the overall displacement trend of the roof was not obvious, the overall displacement of the roof was controlled, and the displacement control effect in the middle area of the roof was most obvious. Figure 5: Vertical displacement distribution of roadway roof. (a) Before support, (b) after support. 2. Stress distribution characteristics of roadway roof  
The stress distribution characteristics of the roadway roof before and after temporary support are shown in Figure 7. The wall rock in the middle of the roof mainly bore tensile stress, while the wall rock in other areas mainly bore compressive stress. According to the rock properties, the wall rock in the middle of the roof was most likely to be deformed and damaged. By comparing (a) and (b), it can be seen that the maximum horizontal stress of the roof before the support was 0.56 MPa, and the maximum horizontal stress of the roof after the support was 0.35 MPa. The maximum stress of the roof was 36.5% lower than before, indicating that the stress of the roof was controlled under the support of the temporary support equipment. The stress curve of the roadway roof before and after temporary support is shown in Figure 8. The wall rocks on both sides of the roadway mainly bore compressive stress, and the wall rocks in the empty roof area mainly bore tensile stress. The closer the roof was to the roadway, the greater the compressive stress was. At x = 7.75 m and x = 12.25 m, that is, at the side wall of the roadway, the stress trend of the roof changed, the compressive stress decreased, and the compressive stress changed to a tensile stress. After the support, the roof stress was reduced, and the stress control effect of wall rock in the empty roof area was the best. According to the physical characteristics of rock with a strong compressive capacity but poor tensile capacity, the tensile stress of the roof was reduced after support,  
Figure 6: Vertical displacement curve of roadway roof. (**a**) Before support, (**b**) after support. Figure 7: Roadway roof stress distribution. (**a**) Before support, (**b**) after support. which indicated that temporary support equipment played a certain role in protecting the roof wall rock. (3) Plastic failure characteristics of roadway roof  
The plastic failure characteristics before and after roadway roof support are shown in Figure 9.  
After the roadway is excavated, the internal stress of the wall rock is released, and the stress release process will cause the plastic failure of the roadway roof, floor, and two sides to varying degrees. The comparative analysis of (a) and (b) shows that the application of temporary support reduced the plastic failure area of the roof, indicating that the plastic failure trend of the roof was controlled to a certain extent, preventing the continuous expansion of the plastic circle of the wall rock; it showed that the temporary support could prevent further damage of the roof wall rock.
## 3 Geometrical Model of Reader Antenna Deployment  
According to our proposed model there two or more reader antennas are installed at the toll gate, for simplification we optimized the signal strength of single reader antenna in this section. This is an open unbounded environment with very less probability of having multipath reflection from any scattering object. Therefore, line of sight (LoS) and ground reflection paths are the significant parameters to characterize the propagation model in the reader-tag channel. Hence, two-ray model has been implemented without any loss of generality. Further, both the reader and tag antennas are considered to be polarization matched for the sake of simplicity. Figure 2 illustrates a geometrical model of reader antenna placement in relation to road based on the two-ray ground reflection phenomenon. An evaluation of the effective signal received by the tag on the car is based on the line-of-sight signal (direct wave) from the reader antenna to the car and the reflected wave from the ground. Using [PERSON] transmission equation, the power transmitted from the reader to the tag antenna is given as follows [26]. \[P_{rT}=P_{tR}G_{tR}G_{rT}(\lambda/4\pi d)^{2} \tag{1}\]where, \(P_{IR}\), \(G_{IR}\) are the transmitted power and gain of the reader antenna, respectively. \(P_{rT}\) is the received power by the tag antenna and \(G_{rT}\) is the gain of the tag antenna, respectively. The distance between reader and tag is \(d\) and wavelength of the signal is \(\lambda\). From (1) it is noted that received power is varies with \(\frac{1}{d^{2}}\). Referring to Figure 1, the distances of the path travelled by the direct and reflected waves are \(d1\) and \(d2\), respectively. These distances are obtained as follow [3]. \[d1 =\sqrt{d^{2}+\left(H_{t}-H_{r}\right)^{2}} \tag{2}\] \[d2 =\sqrt{d^{2}+\left(H_{t}+H_{r}\right)^{2}} \tag{3}\]  
where \(H_{t}\) is the height of the reader antenna from the ground, and \(H_{r}\) is highest vehicle height. Considering both the direct and reflected waves, the total received power \(P_{Total}\) is given as follows [26]. \[P_{Total}=P_{IR}G_{IR}G_{rT}(\lambda/4\pi)^{2}\left|\frac{e^{-j\frac{2\pi}{ \lambda}d1}}{d1}+\Gamma\frac{e^{-j\frac{2\pi}{\lambda}d2}}{d2}\right|^{2} \tag{4}\]  
where \(\Gamma\) is the reflection coefficient of the road which is assumed to be made of asphalt concrete. The value of \(\Gamma\) can be computed from the following equations [27]. \[\Gamma=\frac{\eta_{2}-\eta_{1}}{\eta_{2}+\eta_{1}} \tag{5}\]  
\[\Gamma=\frac{1-\sqrt{\epsilon_{2}}}{1+\sqrt{\epsilon_{2}}} \tag{6}\]  
where \(\eta_{2}\) is the wave impedance of the ground and \(\eta_{i}\) is the wave impedance of the air. \(\eta_{i}=377\sqrt{\frac{\mu_{i}}{\epsilon_{i}}}\), where \(\mu_{i}\) and \(\epsilon_{i}\) are the relative permeability and relative permittivity of the \(i\)th medium and \(377\)\(\Omega\) is the impedance of free space. In this scenario, \(\mu_{i}\) is taken as  
Figure 2: Layout of the proposed two-ray ground model. unity. The relative permittivity of the Asphaltic concrete material is 3.8 [28]. Therefore, Equation (4) can be written as follows.
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 4 Results  
### Evaluation of Model Performance  
Tables 3 and 4 present the MAE, MSE, and RMSE values of different machine learning models under various training and testing split strategies (i.e., 7:3 and 8:2). The results indicate that the LSTM model consistently achieves the lowest errors under both split ratios, outperforming other models. Specifically, with a 7:3 split, the LSTM model's MAE is 0.58, its MSE is 0.44, and its RMSE is 0.67. When the split ratio was adjusted to 8:2, the model's performance improved further, with MAE dropping to 0.48, MSE reducing to 0.30, and RMSE decreasing to 0.55. Therefore, the LSTM model is selected as the optimal model. Considering that more training data generally enhances a model's generalization ability, the 8:2 split ratio is ultimately chosen for model training and testing.
## 2 Algorithm  
We shall now describe our system setup followed by our proposed solution, which is a fully distributed and absolute localization solution specifically designed for both one-hop and multi-hop WSNs. Our considered WSN consists of \(N_{s}\) number of sensors randomly placed onto a map of predefined size with \(N_{b}\) number of beacons. Let \(\mathbb{S}\) and \(\mathbb{B}\) be the sets describing all sensors and beacons respectively, where each sensor is noted as \(Sensor_{i},i\in\mathbb{S}\) and each beacon is noted as \(Beacon_{j},j\in\mathbb{B}\). Each node either a sensor or a beacon is noted as \(Node_{p},p\in\mathbb{S}\cup\mathbb{B}\), and vector \(\overrightarrow{V_{p}}\) is used to represent the coordinate of \(Node_{p}\). Beacons are placed onto the map with fixed coordinates \(\overrightarrow{V_{j}}\), where \(j\in\mathbb{B}\). We assume that each beacon is aware of its own absolute location. Whereas each sensor is unaware of its own location, and is configured with an initial guess of location unrelated to its actual deployed location. The two-dimensional (2-D) localization problem is the estimation of \(N_{s}\) unknown-location coordinates \(\overrightarrow{V_{i}}\), where \(i\in\mathbb{S}\).  
Our algorithm uses RSS as a measure for ranging between a sensor and its one-hop neighbors, which can be either sensors or beacons. Precisely, each \(Node_{p}\) advertises its own location \(\overrightarrow{V_{p}}\) to its one-hop neighbors, where \(p\in\mathbb{S}\cup\mathbb{B}\). Using this advertised transmission, each \(Sensor_{i}\) measures the RSS \(s_{i,p}\) that emitted from the advertising \(Node_{p}\). According to \(s_{i,p}\), the distance \(d_{i,p}\) in between is estimated using the path loss model [18]. Using all collected \(\overrightarrow{V_{p}}\) and estimated \(d_{i,p}\), \(Sensor_{i}\) executes our proposed localization solution based on spring-relaxation technique to achieve accurate estimation of its location. Our proposed localization solution consists of two phases both using the spring-relaxation technique. In the first phase, sensors perform localization based on the location advertisements only from visible beacons, whereas in the second phase, sensors perform localization using the location advertisements transmitted from all neighbors including beacons and sensors. In the following, we first explain the spring-relaxation technique for localization, and then describe the algorithms for the two phases for localization.
## 2 Materials and Methods
### Sample Processing  
#### 2.5.1 Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy  
The morphology of microplastic (MPs) particles isolated from stabilized sewage sludge was initially examined using a PerkinElmer Frontier Spotlight 400 FTIR microscope. For this analysis, all residues on the filter were transferred onto a microscope slide. During examination, the MPs were counted and categorized based on their color and shape, distinguishing between fibers and fragments [13]. Fourier transform infrared (FTIR) spectroscopy was employed to record the IR transmittance spectra of the MPs and to identify the characteristic functional groups. A PerkinElmer Frontier attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) spectrometer, equipped with a mid-infrared (MIR) range, a DTGS detector, and an additional wide-range MCT detector, was used. The FTIR spectra were recorded within the wavenumber range of 530-4000 cm\({}^{-1}\), averaging 64 scans per spectrum at room temperature [27].
## 2 State of the Art
In addition, the Flex PON architecture proposed in [23] defines a full-service Software-Defined Solution to provide a flexible and a programmable service provisioning in VPONs. In the article, authors experimentally demonstrate flex link using Digital Signal Processing (DSP) technologies. Therefore, it can be stated that the integration of SDN technologies in PONs could provide great advantages to network operators, since they can optimize the network operation and reduce the network complexity. However, some tasks regarding online service and network configuration or dynamic bandwidth allocation are difficult to move to external SDN controllers since they are time-critical operations, although some of these policies can be very interesting to be managed by SDN controllers to provide a more efficient control in PON architectures. As a consequence, we propose and experimentally test an SDN solution over legacy GPONs, which allows moving some global service configuration policies to an SDN controller by means of programming an external SDN management layer that dynamically adjusts these policies according to the stipulated dynamic QoS requirements of residential users. This proposal can enable new business models for network operators and providers, so the viability of our solution is demonstrated proposing a new possible business scenario for users and operators. However, other business models can be applied with our strategy to exploit the possibilities it offers. Finally, our proposal also demonstrates the feasibility of moving some QoS services and network configurations out of the PON network to an external SDN controller. To the best of our knowledge, this is the first time that a real-time reconfiguration service policy is experimentally tested in a legacy GPON using SDN techniques apart from the approach described in [17]. However, in the architecture presented in [17] they build a new OpenFlow-enabled OLT node with an embedded OpenFlow agent. To control this network architecture, authors propose extending the existing OpenFlow protocol to interact with the main characteristics of PONs (such as guard time, bandwidth, time slot, ONU identification) and the architecture permits dealing with the DBA algorithm inside the OLT. In contrast, in our proposal, we use the real OpenFlow standard that does not allow the control of PON parameters in its flow rules. Moreover, legacy GPON equipment does not permit modifying the DBA algorithm, since it is integrated in a chipset inside the OLT. Therefore, we propose a different solution using existing technologies to provide a realistic network scenario with legacy and commercial GPON equipment. Experimental OpenFlow Proposal over Legacy GPONs to Allow Real-Time Service Reconfiguration Policies
## 4 Results and Discussion
### Debris Mixed Stratum Model  
The results of Debris Mixed Stratum as shown in Figure 17. If there were multiple materials in the stratum, such as debris mixed stratum, the materials of Layer 1 could be identified clearly. The materials after Layer 2 were fuzzified and presented in zones. When the medium had relatively high resistance, the resistivity of the first layer of the medium was lower than the set value, and the resistivity decreased as the difference between the R1 and R2 values increased. When the background soil layer had relatively high resistance, the profile variation was more obvious as the difference between the R1 and R2 values increased, as shown in Figure 17a. When the covering depth increased, the deep medium was completely unidentifiable, as shown in Figure 17b. When the mesh area was enlarged, the resolution capability of the first layer of the medium was better, but the medium after Layer 1 was unidentifiable, as shown in Figure 17c. When the mesh spacing increased, the resolution capability of the first layer of the medium was better, but the medium after Layer 1 was unidentifiable, as shown in Figure 17d. When there was noise interference in the detection process, the resistivity profile had a random jump; the random jump worsened as the noise increased. The first layer of the medium was still identifiable, but the profile of the medium after Layer 1 was distorted, as shown in Figure 17e.
## 6 Proposed Scheme
### RSU Registration  
TA generates secret numbers \(x_{r}\in Z_{q}^{*}\) and calculates the \(P_{pub}=x_{r}\cdot P\) as the corresponding public keys. TA chooses \(ID_{r}\) as the identity of RSU and computes \(PSID_{r}=h(ID_{r}\|K)\). Through a secure channel, TA uploads these parameters \(\{x_{r},ID_{r},PSID_{r}\}\) into RSU. RSU randomly chooses challenge \(C_{r}\) to generate their corresponding response \(R_{r}=PUF(C_{r})\). Then, RSU selects a random number \(t_{r}\) and calculates \(sk_{r}=x_{r}\oplus h(R_{r}\|t_{r})\), \(PD_{r}=PSID_{r}\oplus h(t_{r}\|R_{r})\) and stores \(<C_{r},sk_{r},ID_{r},PD_{r},t_{r}>\).  
**Remark 4**.: _The reason for deploying PUF in RSUs and protecting their secret values with PUF is to resist RSU captured attacks._
# An Asymmetric Bargaining Model for Natural-Gas Distribution
## 2 The Natural-Gas Scenario in Pakistan  
Pakistan has witnessed a significant rise in energy demand in the last two decades. The supply-production of energy has failed to cope with the demands, resulting in an increase in the supply-demand gap. As per the latest population census of 2020, Pakistan's population has reached 200 million [41]. This energy crisis has also affected the country's economic growth [42; 43]. Pakistan largely depends on gas and oil resources to meet its escalating energy demands. The natural-gas industry of Pakistan, with an offshore area of 827,268 square kilometers, is equipped with numerous exploration and production companies. Since its operations in 1952, the gas industry of Pakistan has discovered a significant gas field at Sui in the province of Baluchistan with a reserve of 0.31 trillion cubic meters (Tcm) [44]. In those days, a 16-inch transmission line used to bring gas from the province of Baluchistan to Karachi, Sindh, over a total length of 559 km. By 1960, the gas industry in Pakistan matured very quickly, and various other gas transmission and distribution lines were designed and installed by indigenous sources. The gas industry of Pakistan is the most developed industry in Pakistan. It has the earliest transmission system in the country, with more than 50 years of operational experience [44].  
The natural-gas reserves of Pakistan are less than those of Russia and Iran. Pakistan, having a gas potential of 0.5 Tcm, is currently ranked at 29 th position for its gas reserves in the world. The gas reserves in Pakistan account for almost 0.2% of the world's gas reserves [45]. The GoP is further enhancing the gas reserves by investments in the development and drilling of existing gas-supply systems and the discovery of new gas reservoirs. In the coming years, the demand for natural gas will surge. Therefore, the supply-demand gap will also increase. The main gas fields of the country include Sui, Uch, Zamzama, Badin, Manzalai, Mari, Kandhkot, Bhit, Sawan, and Qadirpur. Out of the four provinces of Pakistan, Sindh is the highest gas supplier with 64% of the total gas, followed by Baluchistan with 17%. In contrast, Khyber Pakhtunkhwa (KPK) and Punjab supply 9 and 3%, respectively [46].