esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
Moreover, examining the ISE values, which are the indices with the best results for both the ACO and FPA algorithms, it can be observed in Figure 6 that the ISE value for the FPA algorithm (297.83) is lower than that of the ACO algorithm (389.536), indicating better overall performance in minimizing the integral of the squared error. These results underscore the effectiveness of the FPA algorithm in achieving lower error indices, thereby enhancing the control system's stability and performance. Figure 7 illustrates the values of the respective cost functions for each algorithm as they iterate to find the optimal FOPID constants (\(K_{p}\), \(K_{i}\), \(K_{d}\)) of the control signal. The graph shows that the ACO algorithm's cost function values are consistently higher than those of the GWO and FPA algorithms. The convergence behavior of the algorithms is also noteworthy. The ACO algorithm takes four iterations to converge to its optimal solution, while the FPA algorithm requires seven iterations, and the GWO algorithm converges the fastest at three iterations. This indicates that while the ACO algorithm has higher cost function values, it converges relatively quickly, similar to the GWO algorithm, which combines lower cost values with the fastest convergence. Though the FPA algorithm has lower cost values, it takes more iterations to reach its optimal solution. These observations highlight the trade-offs between convergence speed and cost function values among the different algorithms. Figure 8 presents the values of the respective cost functions for each algorithm as they iterate to find the optimal fractional orders (\(\lambda\), \(\mu\)) of the FOPID control signal. The graph shows that the cost function values for the ACO algorithm are consistently higher compared to those for the GWO and FPA algorithms. The convergence behavior of the algorithms is also noteworthy. The ACO algorithm requires seven iterations to converge to its optimal solution, while both the FPA and GWO algorithms converge more quickly, each taking three iterations. This indicates that although the ACO algorithm has higher cost function values and requires more iterations to converge, the GWO and FPA algorithms achieve lower cost values and faster convergence. These observations highlight the efficiency of the GWO and FPA algorithms in optimizing the fractional orders of the FOPID controller. Figure 7: Convergence of cost functions for ACO, GWO, and FPA algorithms.
## 1 Introduction
### Interoperability Applied to Constrained Environments  
Unlike many environments where systems using different kinds of devices are present and physical transmission of bits can be done in a reliable way with plenty of resources, underwater communications present major issues:  
1. _Unreliable environment_. The most optimized solution for underwater transmission is acoustic waves. In this environment any kind of wireless, regular communication would be unfeasible to transmit with the existing technology. Furthermore, due to the nature of the environment where data are transmitted, information cannot be transferred in a reliable manner and chances of losing data are higher than in wireless, Over-The-Air transmissions (and even higher than in a cabled environment). Underwater acoustic communications are strongly influenced by path loss, ambient channel noise, multi-path, Doppler spread, severe attenuation, high and variable propagation delay, high bit error rates and temporary losses of connectivity. Acoustic signals  
Figure 1: Location of middleware in a scenario with AUVs.  
are much slower than radio waves, and signal propagation is affected by refraction, absorption, high reflection and scattering through the water. Also, attenuation is greater in water than in air [8; 9; 10; 11]. Therefore, mechanisms that optimize data transmissions at higher levels are required, since a Client/Server paradigm may be prone to issues not easy to solve, e.g., resending data due to transmission errors will put strain on the already constrained environment.
2. _Bandwidth constraints._ All the previously described factors determine the temporal and spatial variability of the acoustic channels, and make the available bandwidth of the underwater acoustic channel limited and dramatically dependent on both range and frequency. Long-range systems that operate over several tens of kilometers may have a bandwidth of only a few kHz, while a short-range system operating over several tens of meters may have more than a hundred kHz bandwidth. In both cases these factors lead to low bit rates [5]. This poses serious challenges for the transmission of a significant amount of bits during a reduced period of time.
3. _Hardware constraints._ AUVs are supposed to operate in harsh environments. The most critical factor here is the ambient pressure, which increases at a rate of roughly 1 bar for each 10 m. Thus, any construction that will operate in this environment must meet rigorous requirements. In addition, motion speed and battery time are affected negatively, which has a greater impact in this kind of application domain (where motion is done in three dimensions or an expensive robot can be lost if it runs out of energy) than in others. Obviously, both factors influence the communication possibilities of an AUV.
# Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module
## 5 Experimental Results  
### Performance Analysis  
We used the same transfer learning and hyper-parameter finetune method to evaluate the performance of the compared CNN models, including ResNet50 [37], DenseNet201 [38], Xception, MobileNetV1 [39], ShuffleNet [33], SE_Xception2, as well as the proposed SE_Xception and SE_miniXception. Table 1 lists the experimental results of compared models on ATLDD dataset with six types of apple leaves. As illustrated in Table 1, SE_Xception, SE_Xception2, Xception, and SE_miniXception rank among the top four in the aspect of disease classification accuracy. Among them, SE_Xception has the highest classification accuracy (99.40%) and \(F_{1}\) score (99.10%) with a relatively fast classification speed. The number of parameters of SE_Xception is 14% higher than Xception, but its classification accuracy is 1.99% higher and \(F_{1}\) score is 2.32% higher than Xception. For SE_Xception2, although there are some improvements compared to Xception, its average recognition accuracy and \(F_{1}\) score is not as high as those of SE_Xception. Its accuracy is 0.61% higher than Xception and 1.38% lower than SE_Xception, and the \(F_{1}\) score is 1.58% lower than those of SE_Xception.  
The accuracy and \(F_{1}\) score of the proposed SE_miniXception lightweight model are 97.01% and 96.29%, respectively. Its accuracy is still higher than ResNet50, DenseNet201, MobileNetV1, and ShuffleNet, but its number of parameters is only 0.61M, which is only about 3% of those of ResNet50, DenseNet201, and 18.89% of those of MobileNetV1, 31.41% of those of ShuffleNet. The number of FLOPs of SE_miniXception model is 0.13M, which is only 0.26% of SE_Xception and is the lowest in the table. The classification speed of SE_miniXception is 7 ms/image, which is only a little slower than MobileNet and faster than the rest of the compared models in Table 1.  
Results in Table 1 shows that deep fusion of SE modules into Xception network with channel weighting method significantly improves the accuracy and robustness of model classification. Moreover, when compressing the SE_Xception network, the classification performance of SE_miniXception stays at a good level, and its memory usage and number of FLOPs drops greatly compared to SE_Xception. These imply the high robustness for the SE_Xception network with the proposed SE-DEEP blocks.
## 3 Hybrid Stochastic Approach to Location Estimation
### Stochastic Hybrid Approach for Location Estimation  
In the inductive approach we assume that the signal distribution for each training sample location is known in advance. Taking a sample for all possible locations is not a realistic assumption. However, for a given location we can have several training samples near to our location. In the hybrid approach we are interested in combining the information of both previous approaches to improve the system. That is, we know the signal distribution for several training samples near our location and we know how the signal is attenuated from the location of these samples to our actual location. Without loss of generality, we can write:  
\[\Pr(o\mid l)=\sum_{i=1}^{l}\Pr(T_{i}\mid l)\Pr(o\mid l,T_{i}) \tag{5}\]  
We assume that \(\Pr(T_{i}|l)\) is uniformly distributed. Then, we are only interested in defining the second term. Using the same assumption that in equation (3), we can write:  
\[\Pr(o\mid l,T_{i})=\prod_{j=1}^{b}\Pr(o^{j}\mid l,B^{j},T_{i}) \tag{6}\]  
Now, we define the random variable (\(o^{j}|l,B^{j}\),T\({}_{i}\)) in the same manner as (\(o^{j}|l,B^{j}\)) has been defined in equation (4). But, instead of \(o_{o}\) (the signal strength measured in the reference distance \(d_{0}\)), we use \(o_{i}^{j}\) (the signal strength measured in the training sample location \(i\)).  
\[(o^{j}\mid l,B^{j},T_{i}^{j})=o_{i}^{j}+10n\log(\frac{d^{j}}{d_{i}^{j}})+\frac {{L_{w}}^{j}}{{L_{w}}^{j}} \tag{7}\]  
where \(o_{i}^{j}\) is a random variable that represent the signal strength of training sample \(i\) from base-station \(j\). \(d_{i}^{j\[{o_{i}}^{j}=o^{j}-10n\log(\frac{d^{j}}{d_{i}}^{j})+\frac{L_{w}^{j}}{L_{wi}^{j}} \tag{8}\]  
In the training phase, we have estimated p(\(o_{i}^{j}\mid l_{i}B^{j}\)). In this stage several methods such as histogram or kernel can be used. Then, using equation (8), we can write:  
\[{\rm p}(o^{j}\mid l,B^{j},T_{i}^{j})={\rm p}(o^{j}-10n\log(\frac{d^{j}}{d_{i}} ^{j})+\frac{L_{w}^{j}}{L_{wi}^{j}}\mid l,B^{j}) \tag{9}\]  
where n = 2 in free space. In order to obtain the optimal location for equation (2), the proposed algorithm is written in pseudocode in Figure 1. Its explanation is as follows: given the input signal strength the location probability for each point is evaluate using 0.5 meter greed. For each point the k nearest samples are taken. The probability of this location is calculated using equation (5), but, using only these k samples instead the all training set. Farther samples will distort the results. For each sample (of k nearest samples), first we use equation (9) to combine the deductive approach, to take into account the shift from the actual location to the sample location, and, second the inductive approach, to obtain the signal probability in a well known place.
## 4 Experiment Verification
### Leakage Identification  
In the leakage identification verification, the related parameter are as follows: false alarm rate \(\alpha=0.001\), missing alarm rate \(\beta=0.005\)[25]. The SPRT indexes are normalized to \([-1,1]\), while \"1\" indicates leakage, \"\(-1\)\" means normal, and the interval means that the result cannot be judged and the acquisition process continues. The Kalman and SPRT test models are programmed according to Figure 3 using MATLAB software. The test results are shown in Figure 6.  
Figure 6 shows that the internal and external leakage in different braking levels (three in this paper) can be detected quickly and correctly. The BC pressure is about 5 kPa higher than the normal value when the internal leakage occurs, while the BC pressure is about 5 kPa lower than the normal value when the external leakage occurs. The leakage of 5 kPa is undetectable by the existing diagnostic system. However, there is obviously a hidden danger. Increased leakage may cause the brakes to not release or to apply insufficient braking force, thereby affecting the safety and efficiency of the EMU. The method proposed in this paper can be used to predict and warn relay valve leakage.
## 2 SFCLs in a Power System  
### The Advantage of the R-SFCL to DC Distribution System  
In recent years, DC distributed energy, such as solar energy and wind energy, has been gradually connected to the power system. Considering the security of the power system, the distributed energy should be isolated quickly when the power system fails. In order to decrease the short-circuit current in the power system, the power transport from the distributed energy system to the main power system should be restricted. The application of the R-SFCL is an effective way to decrease the short-circuit current in a system; thus, the power transport from the distributed energy system can be increased significantly. In this way, the installed R-SFCL in the DC system can also promote the development of new energy sources.  
For the high voltage source converter (HVSC) DC system, generally, the resistance of the DC fault circuit is very small, which satisfies the underdamping oscillation conditions. Therefore, when the voltage at the DC side oscillates to zero, all the freewheeling diodes in the converter will conduct at the same time. This process is named = the all diodes conducting phenomenon [2]. In addition, this phenomenon will have a negative impact on the AC side.  
When the R-SFCL is applied in the DC system, it can not only limit the fault current at the DC side but also limit the overcurrent in the AC side and in the converter station [24]. This is because, after the overcurrent occurs, the R-SFCLs are quenched within 1 ms. The resistance of the R-SFCLs increases very fast to a certain value, which limits the fault current to a relatively low value. Furthermore, the circuit breaker and other power equipment in the system do not need to withstand a high fault current. Figure 1 shows the transient characteristics of the DC fault when the R-SFCLs are applied to the power system [24]. Figure 1 shows that the R-SFCL can limit the fault current to a lower level than the reactive SFCL. After 5 ms, R-SFCL limits the fault current to only around 20% of the fault current. Thus R-SFCL is more suitable for the voltage source converter (VSC) DC system than the reactive SFCL.
# Performance Estimation of Intensity Accumulation Display by Computer-Generated Holograms
## 1 Introduction  
The use of a computer-generated hologram (CGH) is a promising technique for naked-eye three-dimensional (3D) display without the issue of vergence-accommodation conflict [1]. To realize the display, the CGHs are applied to address a spatial light modulator (SLM), generating the light field of a 3D scene. Usually, the SLM is a phase-modulation (PM) SLM for the high diffraction efficiency. Alternatively, the 3D display can also be realized by using a binary SLM; i.e., the modulation is 0, \(\pi\) in phase or 0, 1 in amplitude [2; 3; 4; 5; 6]. The display quality of a binary SLM is usually low because of the limited degrees of freedom. Although optimization algorithms, such as binary search [7; 8; 9] or simulated annealing [10], can be applied to improve the quality of binary CGH display, the quality of a single binary CGH is still limited. Error diffusion [11; 12; 13] can generate a binary CGH with good quality only when the object distance, the normalization factor, and the weighting factors are optimized. Super-pixel display schemes [14; 15; 16] can achieve high-quality display by binary CGHs. However, its optical filtering setup significantly increases the form factor, thereby limiting its application potential. On the other hand, it is easier to drive a binary SLM, and thus the display frame rate of a binary SLM can be ten to a thousand times faster than an eight-bit PM SLM. Therefore, it is capable of quickly displaying different CGHs of the same scene, which is called intensity accumulation (IA) [17; 18; 19; 20]. In this way, the speckle noise can be averaged out and thus the quality of a reconstructed image can be significantly improved. However, there is no simple method to predict the dependence of averagenoise and accumulation number, and we can only know the results after finishing the full computation. In this paper, our goal is to find a suitable model to describe the dependence between the average noise and the accumulation number. This model is confirmed to fit the properties of binary CGHs generated by the random phase (RP) method, the modified iterative Fresnel algorithm (MIFA), and the localized random down-sampling (LRDS) method. With the help of the noise model, we can optimize the design strategy for various application circumstances.
## 5 Results
### Effects of Users' Prior Experience on Agreement
To further understand these differences, we computed between-group coagreement rates for each task. For example, coagreement for the task \"Long press on the target\" was \(CR_{b}=0.207\), showing that only 20.7% of all pairs of users across the two groups were in agreement regarding how to press the selected target in a HAR application, i.e., by holding button three. The reason why the other participants disagreed was that while the users with prior HAR experience preferred to press on the selected target by holding button three (equivalent of performing hold gesture on the smartphone), the users with no prior HAR experience elicited more variations, such as by tapping button one and holding button two. All of the elicited gestures from users who had never used HAR applications before showed a clear influence from previous experience acquired from touchscreen devices--this difference was statistically significant (\(p=0.003\)) for the \"Long press on the target\" and exposed the largest effect size (\(V_{b}=370.806\)) among all other tasks. A similar effect was observed for the \"Take a photo\" task as well, but this time, from another perspective: although the agreement rates of the two groups were similar (0.190 vs. 0.210) and the difference was not significant \(V_{b(2,N=18)}=3.096\)), the coagreement rate displayed different gesture preferences for two groups (\(CR_{b}=0.167\)).
## 4 Experiments and Results
### Visualization  
We compared the improved model with the baseline network in terms of visual effects. Figures 7 and 8 present partial visualization results of validation images from the WiderPerson dataset and the CityPersons [72] dataset, respectively. The first row displays the detection results of the baseline model (YOLOv8-L), where red bounding boxes indicate pedestrians detected by the baseline model. The second row presents the detection results of the state-of-the-art model (LEL-YOLO), where red bounding boxes indicate pedestrians detected by the baseline model, green bounding boxes denote pedestrians missed by  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **AP** & **MR\({}^{-2}\)** & **GFLOPS** \\ \hline YOLOv7 & 86.8 & 43.9 & 104.7 \\ YOLOv9-M & 88.7 & 42.7 & 76.3 \\ YOLOv10-M & 89.5 & 42.1 & 59.1 \\ YOLOv11-M & 90.2 & 41.3 & 68.0 \\ Dense-YOLOv8-M (Ours) & 92.7 & 39.6 & 82.1 \\ \hline RF-DETR-B & 92.8 & 39.9 & 98.6 \\ RF-DETR-L & 93.1 & 39.5 & 435.2 \\ YOLOE-v8-L (no promt) & 91.4 & 39.9 & 165.2 \\ YOLOE-11-L (no promt) & 91.7 & 39.7 & 86.9 \\ YOLOv6-L & 86.3 & 43.7 & 150.7 \\ YOLOv7-X & 87.5 & 42.9 & 189.9 \\ YOLOv9-C & 89.5 & 41.9 & 102.1 \\ YOLOv10-L & 90.1 & 40.8 & 120.3 \\ YOLOv11-L & 91.0 & 39.9 & 86.9 \\ Dense YOLOv8-L (Ours) & 93.3 & 38.7 & 169.3 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of different YOLO versions and RF-DETRon CrowdHuman (AP% and MR\({}^{-2}\)% and GFLOPS).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **AP** & **MR\({}^{-2}\)** \\ \hline Faster R-CNN & - & 46.1 \\ PS-RCNN [65] & 90.0 & - \\ R2 NMS [66] & 85.5 & 52.2 \\ PedHunter [67] & - & 39.5 \\ IterDet [68] & 90.9 & 42.3 \\ Double Mask R-CNN [69] & 86.8 & 39.1 \\ Reference [70] & 91.3 & 40.4 \\ Reference [71] & 92.9 & 38.4 \\ Dense-YOLOv8(ours) & 93.1 & 37.9 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of AP and MR\({}^{-2}\) with existing methods on WiderPerson (%).
## 1 Introduction
(2018) evaluated the performance of FA- and SG-based GPC columns and reported that most of the columns failed around the mid-height of specimens, and steel fibers can be incorporated to increase the loading capacity and ductility of columns [33]. GPC, therefore, can be considered as an alternative to conventional concrete construction due to the reduction in energy consumption, CO\({}_{2}\) emissions, low cost, and effective consumption of waste materials. FA and SG have been enormously used in the research to produce GPC mixes. However, shortages in the supply of FA have been faced in some parts of the world due to strict by-laws around coal-fired power stations. Similarly, the annual global supply of SG is fairly limited in comparison to the annual requirement of concrete for the global construction activities. It is important, therefore, to find alternatives for FA and SG to be used as precursors in the production of GPC, to promote commercialization of GPC and lessen the costs, as well as to meet the increasing infrastructural demand. Consequently, all the existing alternative raw material options should be explored in pursuing sustainable alternatives to conventional concrete construction. This study is planned to study the effect of the incorporation of QRD as a replacement for SG in ambient-cured GPC columns. Regardless of the numerous environmental benefits of GPC, its use in construction practice is significantly limited. This is primarily due to comparatively fewer studies reporting the structural behavior of GPC structural elements. GPC is still a relatively new material in terms of field application and has yet to secure international acceptance as a construction material. The major portion of research into GPCs has focused on mix design and quantification of mechanical behavior in comparison to the studies that consider the behavior of GPC structural elements [34]. More studies on the full-scale structural elements should be undertaken to ensure its feasibility in practice. This will help to expand this relatively new material technology to field and site applications. To date, a few studies are available which are devoted to exploring the behavior of steel-fiber-reinforced GPCcolumns [33; 39; 40]. Moreover, there is a lack of data demonstrating the behavior of quarry-rock-dust wastes (dolomite, silica powders, and limestone) blended steel-fiber-reinforced GPC columns. The objective of this study, therefore, is to develop a thorough understanding about the behavior of ambient-cured, quarry-rock-dust-incorporated, steel-fiber-reinforced GPC square columns under concentric and eccentric loading.
## 4 Proposed Scheme
### Secure Domain Key Calculation Phase  
Once TA calculates the domain key for 5G-enabled vehicular networks, TA multicasts it to the domain of the vehicles via 5G-BS in the following steps.  
* TA sets the randomly selected value \(s_{d}\in Z_{q}^{*}\) as an updated domain key and then calculates its corresponding public key \(\gamma_{d}=s_{d}\times\mu\);
* TA assigns \(\gamma_{d}\) and \(ET_{i}\) utilizing its private key \(sk_{TA}\) as \(SIG_{sk_{TA}}(\gamma_{d}||ET_{i})\), where \(ET_{i}\) denotes the valid period of this domain key \(s_{d}\);
* TA calculates \(P_{pub}^{d}=s_{d}P\) and broadcasts the tuple \(\{\gamma_{d},P_{pub}^{d},SIG_{sk_{TA}}(\gamma_{d}||ET_{i})\}\) to all 5G-BS and vehicles in \(D_{y}\);
* Once the authorized vehicle receives \(\gamma_{d}\) from the TA side, it can obtain an updated domain key \(s_{d}\) via a one modulo division operation \(gamma_{d}\) mod \(sk_{i}=s_{d}\).  
Due to \(s_{d}<q<sk_{i}<p\) and \(\mu\) mod \(sk_{i}=1\), \(s_{d}\) obtained via the above steps should equal the number of \(s_{d}\) computed in the first step of this phase. Once \"i\" holds to n, TAperforms the system setup process to calculate \(\zeta_{g}\), \(vbs_{i}\) and \(\mu\) for \"\(m\)\" users value, where \(m=n\times\zeta\), where \(\zeta\) is a constant that fulfills \(\zeta<5\).