esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Collection, Isolation, and Identification of Fusarium  
#### 2.1.1 Sample Collection and Fungal Isolation  
Soil samples were collected from Patras Achaia, Greece in 2019. The samples were collected from a depth of 10 cm under the surface soil layer and placed into sealed polyethylene bags after excavation. Seasonally, the samples were collected from 22 points (in total, 176 samples from April 2017 to April 2019) in two suburban green areas in the capital of the prefecture Achaia, Patras. During sampling preparation, the surface litter was removed, and the soil was dug to a depth of 10 cm with a soil core borer. 1000 g from each point were placed in plastic bags and stored at 4 \({}^{\circ}\)C, until they were transferred to the laboratory for further processing. After drying the samples with air to avoid possible entomopathogenic nematode (EPN) infestation, as suggested by [PERSON] et al. [32], they were placed on a rough cardboard on the laboratory stalls for 24 h to reduce their humidity. Afterwards, the soil was sieved (Metal, 2 mm \(\times\) 1 mm, Aggelis Equipment, Athens, Greece) and placed in Sabouraud dextrose agar (SDA) Petri dishes. Each soil sample was tested 10 times; 100 _Stiophilus granaturis_ L. (Coleoptera: Dryophilthoridae) and _Tribolium confusum_ Jacquelin du Val (Coleoptera: Tenebritidae) adults were used as insect bats and tested per soil sample. All beetles were transferred to Petri dishes with a layer of SDA. Alternatively, conidia removed from infected beetles were also cultured on the same nutrient agar. To facilitate the incubation and development of the fungi, Petri dishes were maintained at room temperature (25 \(\pm\) 1 \({}^{\circ}\)C) in complete darkness. After fungal development, one more isolation took place to prevent infestation and achieve clear cultivation. The above-mentioned process was carried out inside a laminar flow chamber (Equip Vertical Air Laminar Flow Cabinet Clean Bench, Mechanical Application LTD). The _Fusarium_ isolates were then sub-cultured several times on Petri dishes with SDA to ensure purity and monosporic cultures. SDA was amended with streptomycin sulfate and chlor-tetracycline HCl to minimize chances of any bacterial growth. The procedure of DNA sequencing was then used to identify the species.
## 4 Numerical Analysis
### Filling Treatment Effect  
As discussed earlier, the long-term stability of the sidewall cannot be ensured. Therefore, implementing a protective treatment is crucial to maintaining the integrity of the abandoned mining area. The application of mortar filling is a viable approach in this context. This method can effectively reduce deformation and improve the stress distribution and the overall stability of the rock mass. Figure 16 illustrates the corresponding numerical model used to assess the impact of the mortar filling method under various conditions, with a spalling thickness of 0.4 m. The constitutive relationship of the mortar adheres to the Mohr-Columb model, and specific parameters are detailed in Table 3.  
Figure 14: Plastic region evolvement at different wall damage states.  
Figure 15: Sloss evolvement at different wall damage states.  
The vertical stress and displacement curves at the top of the sidewall are presented in Figure 17, while Figure 18 depicts the vertical stress distribution in the surrounding rock mass under different conditions. The reinforcement provided by the mortar filling prevents a stress drop within the core compression region of the sidewall, as seen in Figure 18d, resulting in a final stress interval ranging from 16 to 20 MPa. Nevertheless, Figure 19 reveals that interconnected plastic zones still emerge when the mortar's height is below half of the sidewall's height. However, the plastic elements are constrained once the mortar's height exceeds this threshold.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Density** & **Elastic Modulus** & **Poisson’s Ratio** & **Cohesion** & **Friction Angle** \\ \hline kg/m\({}^{3}\) & GPa & / & kPa & \({}^{\circ}\) \\ \hline Filling & 2200 & 1 & 0.25 & 1 & 35 \\ \hline \end{tabular}
\end{table}
Table 3: Numerical model parameters for filling materials.  
Figure 16: Numerical model setup for filling states.  
Figure 20 shows that an inadequate mortar filling height leads to significant damage development in the rock mass at the upper area of the sidewall, with a tendency towards deeper faulting. Conversely, a sufficient filling height restricts further damage progression in the sidewall's rock mass and prevents surface softening and spalling due to groundwater seepage. Figure 20d indicates the formation of a 5 MPa principal stress within the filled mortar near the sidewall, signifying that the filler can share the load carried by the sidewall. As a result, the mortar filling substantially enhances the lateral support of the sidewall, improves the stress distribution in the damaged rock mass, and prevents sidewall instability.  
Figure 19: Plastic region evolvement at different filling heights.  
Figure 17: Vertical stress–vertical displacement curve at the monitoring point after filling.  
Figure 18: Vertical stress evolvement at different filling heights.
## 1 Introduction
Furthermore, fracture analysis of alloys subjected to solutionizing and aging heat treatments revealed a ductile fracture mechanism, typically characterized by dimples, as some researchers have demonstrated previously [15; 16; 17]. The mechanical properties of aluminum alloys are closely related to heat treatment parameters, as evidenced by the studies reviewed previously. In optimizing heat treatment conditions, employing specialized methodologies becomes helpful in effectively assessing the critical variables inherent in these processes. Among the techniques available, the Taguchi design of experiments (DOE) is a robust methodology devised by the Japanese engineer [PERSON]. This approach simplifies processes and elevates product quality by highlighting the factors influencing performance and variability [18]. Its efficacy becomesparticularly important in scenarios necessitating meticulous tests and measurements to comprehend and predict mechanical properties, including tensile strength. Several remarkable applications of Taguchi DOE in optimizing mechanical properties in materials have been documented. For instance, [PERSON] and [PERSON] [19] optimized the critical parameters of an aluminum heat treatment process to improve alumina/aluminum metal matrix composites, resulting in a notable increase in yield strength. Similarly, [PERSON] et al. [20] utilized the Taguchi method to optimize heat treatment parameters for enhanced mechanical properties in a 2219 aluminum alloy produced via wire-arc additive manufacturing. Their findings emphasized the significant impact of solution temperature on alloy strength. Moreover, in the study undertaken by [PERSON] et al. [21], the Taguchi DOE was employed to enhance the corrosion resistance of a 6061 aluminum alloy. By optimizing heat-treatment process parameters and investigating the effect of NaCl (sodium chloride) concentration, researchers successfully improved the alloy's resistance to corrosion. Another notable application was performed by [PERSON] et al. [22], in which the Taguchi DOE was instrumental in identifying optimal heat-treatment parameters for a forged 2219 aluminum alloy, improving tensile strength and elongation percentage. These examples highlight the versatility and efficacy of the Taguchi DOE in optimizing mechanical properties across various materials and fabrication processes. Our investigation was conducted to determine the phases present and the influence of the strengthening \(\beta\) phase on the tensile and hardness properties of a 6061-aluminum alloy. Solutionizing and artificial aging heat treatments were applied at various temperatures and for various durations based on a Taguchi experimental design. Characterization techniques such as XRD and SEM were employed to identify the phases and assess the tensile properties after the heat treatments. The Taguchi experimental design, through analysis of variance (ANOVA), facilitated the systematic selection of the heat treatment parameters, allowing for a comprehensive understanding of the relationship between the \(\beta\) phase and the mechanical properties of the alloy.
## 4 Taxonomy of the Existing Multipath Routing Protocols
### Concurrent Multipath Routing
#### 4.2.1 Multipath Routing Protocols for Reliable Data Transmission
During this phase, sensor nodes collect information regarding to the cost of data transmission though their neighboring nodes. In the second phase of this protocol, the sink node starts the route discovery process by sending a _Route-request_ message to its preferred neighbor selected by Equation (5). Intermediate nodes use Equation (5) to select the most preferred next-hop neighboring node towards the source node from their neighboring set \(N\). This process continues among the intermediate nodes until the source node receives a _Route-request_ message transmitted by the sink node:  
Figure 6:0  
where \(N_{x}\) is the neighbor set of node \(x\). \(E_{resd,y}\) and \(B_{buffer,y}\) indicate the residual battery level and available buffer size at neighbor \(y\), respectively. \(I_{interference,xy}\) is the experienced SNR over the link between node \(x\) and node \(y\). All the sensor nodes calculate the values of these parameters for their neighboring nodes during the first stage of this protocol. Besides the primary-path establishment process, the sink node also starts to construct additional paths by sending subsequent _Route-request_ messages to its next-preferred neighboring nodes. Whenever all the possible paths between a pair of source-sink nodes are discovered, a set of paths will be selected based on the probability of successful data transmission over each path. Furthermore, according to the propagation delay of the _Route-request_ messages, EQSR estimates the data transmission delay of the paths and dedicates the best \(L\) paths for real-time traffic and the remaining paths for non-real-time traffic. At the last stage of this protocol, EQSR uses a lightweight XOR-based FEC algorithm to calculate Error Correction Codes (ECC) for data packets. Finally, the source node distributes its traffic over the selected paths according to their end-to-end delay. While EQSR reduces transmission delay and improves reliability, nevertheless, the FEC mechanism which is used to compute ECCs and retrieval of the original messages, imposes high control overhead. Furthermore, like DCHT, this protocol uses a flooding strategy to estimate the experienced SNR over wireless links at the initialization phase and uses these values to discover the minimum interfering paths. However, the employed routing cost function cannot lead to the construction of interference-minimized paths. In fact, using a simple flooding strategy during the neighbor discovery phase may exaggerate the exact value of mutual interference between different paths.
# Survey on Implementations of Generative Adversarial Networks for Semi-Supervised Learning
## 5 Discussion
### Quantitative Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Citation** & **Proposed Model** & **Datasets Evaluated On** & **Results** \\ \hline \multirow{4}{*}{[36]} & \multirow{4}{*}{SGAN (Structured)} & \multirow{4}{*}{MNIST, SVHN, CIFAR-10} & MNIST: 0.89\% error rate w/100 labeled, outperforms all but \\  & & & equal as Ladder; SVHN: 5.73\% error rate w/1000 labeled, outperforms all; CIFAR-10: 17.26\% error rate w/4000 labeled, outperforms all \\ \hline \multirow{4}{*}{[27]} & \multirow{4}{*}{marginGAN} & \multirow{4}{*}{MNIST} & 2.06\% error rate w/3000 labels, outperformed all \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & \\  & & & & \\  & & & \\  & & & & \\  & & & & \\  &A natural progression can be seen where encoder-based architectures such as ALI outperformed CatGAN, and this, in turn, was outperformed by TripleGAN and its derivatives. The more recent manifold regularization-based approaches also outperformed TripleGAN. However, the most recent manifold regularization-based paper [44] reported a 4.54% error rate on SVHN using 1000 labeled samples and a 14.27% error rate on CIFAR-10 using 4000 labeled samples. This model was outperformed by the most recent TripleGAN-based approach [28], which reported a 2.79% error rate on SVHN and 6.69% error rate on CIFAR for the same amount of labeled samples. Therefore, it is reasonable to claim that the R\({}^{3}\)-CGAN architecture holds the current state of the art as none of the other papers surveyed had a similar evaluation process or a comparison to this model. A number of interesting aspects of the R\({}^{3}\)-CGAN that could have contributed to its success. While the underlying architecture was based on TripleGAN, a Random Regional Replacement regularization was applied by making use of the CutMix mix-sample augmentation technique [51]. This technique has been implemented in non-generative semi-supervised learning techniques in order to achieve consistency regularization with good results. Therefore, its success in a generative architecture suggests adaptation of other semi-supervised learning techniques into GANs as well. It is interesting to note that while R\({}^{3}\)-CGAN is seemingly the best performing GAN-based technique currently available, it fades in comparison to non-GAN state of the art SSL techniques such as FixMatch [8], which reported error rates of 4.26% on CIFAR-10 with 4000 labeled samples and 2.28% on SVHN with 1000 labeled samples, in addition to showing a good performance of 11.39% error for CIFAR-10 with only 40 labeled samples and 3.96% for SVHN with 40 labeled samples. Therefore, the gap between GAN-based SSL and other state of the art techniques is apparent, and so it would be interesting to attempt to apply some of the techniques used in other SSL algorithms to GANs in order to unify the enhanced performance seen in the state of the art SSL algorithms with the generative aspect that GANs are known for.
# Frequency-Dependent Schroeder Allpass Filters
## 2 Schroeder Allpass Filters
### [PERSON]'s Allpass  
Whereas [PERSON]'s brief contribution [5] introduced a multitude of generalizations to the allpass filter, here, we focus on the filtered feedforward/-back gains. Figure 2 shows [PERSON]'s formulation of the Schroeder allpass.  
The corresponding transfer function is  
\[H_{\text{Gerzon}}(z)=g(z)+\frac{\Big{(}\frac{g(z)}{g^{*}(z)}-g^{2}(z)\Big{)}z ^{-m}}{1+g(z)z^{-m}}. \tag{15}\]  
While for a scalar gain \(g\), (13) and (15) are identical, they are subtly different for frequency-dependent gain \(g(z)\). The original formulation (13) is only directly realizable for scalar gains because \(g^{*}(z)\) is  
Figure 1: Classic Schroeder allpass filter [1], i.e., a feedforward–feedback delay filter.  
Figure 2: [PERSON]’s allpass filter with feedforward/-back gain \(g(z)\)[5].  
non-causal for FIR filters or unstable for IIR filters. In contrast, [PERSON]'s formulation (15) is realizable as long as the allpass filter \(\frac{g(z)}{g^{*}(z)}\) is realizable. Due to (7), this allpass  
\[\frac{g(z)}{g^{*}(z)}=\frac{g(z)}{z^{l_{b}-l_{a}\overset{\rightharpoonup}{ \gtrdot}}(z)}=z^{l_{a}-l_{b}}\frac{b(z)\overset{\rightharpoonup}{\gtrdot}(z)}{ a(z)\overset{\rightharpoonup}{\gtrdot}(z)} \tag{16}\]  
is stable if \(g(z)\) is stable and the zeros of \(g(z)\) (and the zeros of \(b(z)\), respectively) are outside the unit circle. In other words, \(g(z)\) needs to be a maximum phase IIR filter. While this is a step forward, also the computational cost increased: the Gerzon formulation (15) requires about three times of the operations of the Schroeder formulation (13). A further drawback with the IIR filter formulation is that the filter \(g(z)\) itself introduces new poles that are independent of the filter response design.
## 2 Materials and Methods
### Extracting Data and Synthesising Results  
The remaining peer-reviewed studies (137) were found to be highly pertinent to remote sensing in peatlands. After filtering and evaluating them, possible effective scaling options were revealed. Subsequently, we identified five primary clusters--papers that relied on (1) satellite-, (2) airborne-, (3) UAV-, or (4) in situ/ground-based remote sensing measurements in peatlands, and papers that used (5) multiple remote sensing platforms in peatland studies.  
The classification of the five primary clusters was further refined by their corresponding subclusters, which follow the electromagnetic spectrum in ascending order of frequency. The subclusters commence with passive remote sensing--specifically, optical remote sensing--and continue with active remote sensing techniques, including Light Detection and Ranging (LiDAR). Finally, the subclusters culminate with Radio Detection and Ranging (Radar) remote sensing. It should be emphasised that this classification scheme is predicated on frequency, with lower-frequency domains being represented earlier in the sequence. For the synthesising of results, the following elements of information were extracted from each original research publication that met the inclusion criteria: (1) the author(s), (2) the year of publication, (3) the journal, (4) the country of study site, (5) the peatland type, (6) the dominant vegetation, (7) remote sensing platforms, (8) sensors used, (9) study focus and concern, (10) the study result, and (11) future work and limitations.
## 2 Topology and Modelling of the Studied System
### Electrochemical Storage Modelling  
In this paper, the electrochemical storage is a system characterized by 300 V voltage and 300 Ah capacity. The storage system modelling equations are expressed by Equation (8), where \(v_{S}^{\prime}\) and \(i_{S}^{\prime}\) represent the voltage and current of the storage next to the DC bus, \(v_{S}\) and \(i_{S}\) are the voltage and current of the storage before the converter, \(m_{S}\) is the storage control variable, and \(L_{S}\) is the storage inductor.  
\[\begin{array}{l}\frac{di_{S}}{dt}=\frac{v_{S}^{\prime}-v_{S}}{L_{S}}\\ \left[\begin{array}{c}v_{S}^{\prime}\\ i_{S}^{\prime}\end{array}\right]=f_{S}\left[\begin{array}{c}v_{C}\\ i_{S}^{\prime}\end{array}\right]\Rightarrow\left[\begin{array}{c}v_{S}^{ \prime}\\ i_{S}^{\prime}\end{array}\right]=m_{S}\left[\begin{array}{c}v_{C}\\ i_{S}\end{array}\right]\\ m_{S}=\frac{1}{T}\int_{0}^{T}f_{S}dt\text{ with }m_{S}\in\left[0;1\right]\end{array} \tag{8}\]  
Figure 3: Electrical scheme of the DC microgrid power architecture.
## 3 Results and Discussion
### Validation of the Optimization Method  
By comparing the simulated torques of the center bucket for the original (B = 26 [cm], L = 23 [cm]) and optimized (B = 24 [cm], L = 24 [cm]) geometries, the optimization method's validity was tested. The optimized geometry torque (16,611 [Nm]) is 2.57% more than the original one (16,095 [Nm]) and is struck by the high-speed water jet earlier in Figure 17, where both the original and optimized torque measurements are displayed. The link between the calculations for torque and efficiency means that as the percentage of the maximum simulated torque increases, efficiency rises as well.  
The efficiency was calculated by the expression:  
\[\eta_{Trub}=\frac{T_{max}\times N}{\rho*g*H*Q}. \tag{7}\]  
where \(T_{max}\) is the maximum torque, \(N\) is the rotational speed of the runner, \(\rho\) is the density of the water, \(H\) the height, and \(Q\) the water flow. The efficiency of both the original and optimized buckets was calculated using the maximum simulated torque and is summarized in Table 8.  
Figure 16: Pressure distribution on the optimized bucket surface at different time instants.  
Figure 17: Simulated torque vs. time of the optimized and original geometries.  
As mentioned above, the optimized geometry displayed a maximum pressure 5% lower than the one obtained with the original geometry distributed in a greater area, which is explained by the smoothness of the bucket surface and the decreasing of the angles \(\mathbf{\beta_{1}}\) and \(\mathbf{\beta_{2}}\). The present study showed that with \(\frac{\mathbf{\beta}}{L}=\mathbf{1}\) and reducing the inlet and exit angles to their lowest possible value, an efficiency increase is achieved, which agrees with the studies carried out by [16, 19].
## 4 Semantic Understanding-Based Task Slot Structure through Visual and Auditory Channels
### Extracting Semantic Texts for Hand Posture- and Speech-based Commands Using Semantic Understanding  
Our leg-arm hexapod robot is multifunctional, and can perform many types of actions. A semantic understanding module is designed to facilitate this. By analyzing characteristics of the commands used to control the robot, a semantic understanding algorithm is proposed to covert commands in Chinese into those in the structural language. Commands conveyed by hand postures and speech are converted into their respective semantic texts, as shown in Figure 3.  
Data on the hand postures and speech are first obtained through the visual and auditory channels, respectively, and the relevant posture and speech are recognized. Based on the predefined map of hand postures, the results of recognition are converted into text conveying the relevant command in Chinese. Following this, semantic texts of the commands pertaining to the hand posture and speech are extracted.  
Figure 3: Flowchart for the extraction of semantic texts from hand postures and speech.
# Automatic Bridge Crack Detection Using a Convolutional Neural Network
## 3 Experimental Results and Discussion
### Experimental Results
#### 3.1.4 Comparison of Different Atrous Rates in ASPP Module  
In order to verify how to set the atrous rate in the ASPP module for an optimal performance, we conducted several experiments, setting the atrous rates in the ASPP module to [2; 4; 6], [2; 4; 8], and [3; 6; 9] respectively, and the test results are shown in Table 4.  
When the atrous rate was set to [2; 4; 8], the detection accuracy was much higher than that of the atrous rate set to [2; 4; 6] and [3; 6; 9]. The reasons are as follows: (1) compared with the module with an atrous rate set to [2; 4; 6], the module set to [2; 4; 8] can obtain a larger receptive field in the last layer, so as to obtain more contextual information, thereby improving the detection accuracy. (2) According to [29], when a \(3\times 3\) atrous convolution with a large atrous rate is applied, long range information cannot be captured due to image boundary effects, thus degrading into a simple \(1\times 1\) convolution. Therefore, when the ASPP module sets the atrous rate [3; 6; 9], the consequent degradation phenomenon causes the detection accuracy to decrease due to the large atrous rate.