esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Results
### Pressure Boundary Conditions
In the initial stage (0 to 0.2 s), the flow rate rises rapidly and reaches its peak at \(t\) = 2 s. The peak value is highest when the grouting port is at 30\({}^{\circ}\), reaching 0.251 m\({}^{2}\)/s, while the peak values at 90\({}^{\circ}\) and 150\({}^{\circ}\) are lower, at 0.177 m\({}^{2}\)/s and 0.185 m\({}^{2}\)/s, respectively. In the middle stage (0.2 to 1 s), the flow rate decreases rapidly. The decrease is most significant when the grouting port is at 30\({}^{\circ}\), dropping to approximately 0.160 m\({}^{2}\)/s, whereas the flow rate decline is less pronounced at 90\({}^{\circ}\) and 150\({}^{\circ}\). In the later stage (1 to 3 s), the flow rate stabilizes. When the grouting port is at 30\({}^{\circ}\) and 90\({}^{\circ}\), the flow rates are equal and remain higher than the flow rate at 150\({}^{\circ}\). This phenomenon can be explained as follows: In the initial stage, cement grout is injected into the porous medium under high pressure. Since the pores of the medium are not yet fully filled, the resistance is relatively low, leading to a rapid increase in the flow rate. As the injection progresses, the cement grout gradually fills the pores, causing an increase in permeability resistance, which results in a decrease in the flow rate until it stabilizes. When the grouting port is at \(90^{\circ}\), the flow rate exhibits relatively small variations, indicating that the permeability of the pore structure in this region is more uniform. In contrast, when the grouting port is at \(150^{\circ}\), the flow rate fluctuates more significantly, which may suggest that the pore structure near the grouting port is denser or that local clogging has occurred.
# Large Scale Implementations for Twitter Sentiment Classification
## 6 Results and Evaluation
### Our Datasets for Evaluating MapReduce versus Spark Framework
#### 6.1.4 Running Time  
In this final experiment, we compare the running time for multi-class and binary classification while also measuring the scalability of our approach. Initially, we calculate the execution time in all cases in order to detect if the Bloom filters speed up or slow down the running performance of our algorithm. The results when \(k=50\) are presented in Table 6 for MapReduce and Spark implementation. It is worth noting that, in the majority of cases, Bloom filters slightly boost the execution time performance. Especially for the multi-class hashtags and binary emoticons cases, the level of time reduction reaches 17%. Despite needing more preprocessing time to produce the features with Bloom filters, in the end, they pay off since the feature vector is smaller in size. Moreover,  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Framework** & \multicolumn{3}{c}{**MapReduce**} & \multicolumn{3}{c}{**Spark**} \\ \hline
**Setup** & \(\mathbf{k=50}\) & \(\mathbf{k=100}\) & \(\mathbf{k=150}\) & \(\mathbf{k=200}\) & \(\mathbf{k=50}\) & \(\mathbf{k=100}\) & \(\mathbf{k=150}\) & \(\mathbf{k=200}\) \\ \hline Binary Emoticons BF & 0.77 & 0.77 & 0.78 & 0.78 & 0.77 & 0.77 & 0.77 & 0.78 \\ Binary Emoticons NBF & 0.69 & 0.75 & 0.78 & 0.79 & 0.76 & 0.77 & 0.78 & 0.78 \\ Binary Hashtags BF & 0.74 & 0.75 & 0.75 & 0.75 & 0.73 & 0.73 & 0.73 & 0.74 \\ Binary Hashtags NBF & 0.53 & 0.62 & 0.68 & 0.72 & 0.71 & 0.72 & 0.73 & 0.74 \\ Multi-class Emoticons BF & 0.55 & 0.55 & 0.55 & 0.55 & 0.59 & 0.59 & 0.59 & 0.59 \\ Multi-class Emoticons NBF & 0.56 & 0.58 & 0.6 & 0.6 & 0.56 & 0.58 & 0.58 & 0.59 \\ Multi-class Hashtags BF & 0.32 & 0.32 & 0.32 & 0.32 & 0.37 & 0.37 & 0.37 & 0.38 \\ Multi-class Hashtags NBF & 0.33 & 0.35 & 0.37 & 0.37 & 0.35 & 0.36 & 0.37 & 0.38 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of \(k\) in classification performance (BF for Bloom filter and NBF for no Bloom filter).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Framework** & \multicolumn{2}{c}{**MapReduce**} & \multicolumn{2}{c}{**Spark**} \\ \hline
**Setup** & **BF** & **NBF** & **BF** & **NBF** \\ \hline Binary Emoticons & 98 & 116.76 & 1605.8 & 1651.4 \\ Binary Hashtags & 98 & 116.78 & 403.3 & 404 \\ Multi-class Emoticons & 776.45 & 913.62 & 3027.7 & 3028 \\ Multi-class Hashtags & 510.83 & 620.1 & 2338.8 & 2553 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Space compression of feature vectors (in MB).  
observe that these configurations have the biggest compaction ratio according to Table 5. According to the analysis made so far, the importance of Bloom filters in our solution is threefold. They manage to preserve a good classification performance, despite probable errors, slightly compact the storage space of the feature vectors and enhance the running performance of our algorithm.
###### Abstract
This paper takes the aberration coefficient related to the misalignment as the key factor to characterize the relationship between the sensitivity of the optical system and the field of view, proposes a desensitization function with the aberration coefficient and the field of view as the core, and establishes a design method of catadioptric optical system with low tilt sensitivity of secondary mirror. The paper mainly includes the following aspects: Section 2 introduces the image motion compensation model of secondary mirror tilt motion, the NAT of the misaligned system. Section 3 defines the tilt sensitivity, introduces the desensitization function and the desensitization design method. Section 4 takes the catadioptric optical system with a focal length of 350 mm, a field of view angle of \(\pm 3^{\circ}\) and a secondary mirror's maximum tilt angle of \(\pm 0.1^{\circ}\) as an example, and analyzes the changes of the desensitization function, the incident angle, and the relationship between the sensitivity of the aberration term and the field of view during the desensitization process, which verifies the effectiveness of the method in this paper. Section 5 summarizes the whole paper. The analytical expressions of the aberration coefficients related to misalignment when one surface of the optical system is tilted are given in the Appendix A.
## 3 Conventional Geolocation Techniques
### Time of Arrival (TOA)  
TOA is the one-way emitting time of the signal propagating between an emitter and a sensor. It means the emitter and all sensors are required to be accurately synchronized to obtain the TOA information. However, such synchronization is not necessary if the two-way of TOA is measured. Each TOA measurement corresponds to a circle centred at a sensor on which the emitter must lie in the 2D space [12; 42]. Geometrically, three or more circles were obtained without TOAs noise in a unique intersection, which is the emitter location, indicating that at least three sensors are needed for 2D geolocation, as illustrated in Figure 5.  
With the knowledge of three or more sensors, the optimization technique is used to convert the error TOAs measurement into a set of circular equatio  
Figure 4: The geometry and Performance validation of the AOA technique. In the simulation scenario, 100 sensor network geometries were considered to achieve the average MSE. The sensors have been divided into 10 groups distributed randomly and centred at \([0,0,0]^{T}\). In addition, the emitter position of each sensor was placed randomly in a larger cube centred at \([0,0,0]^{T}\) with a distance of 500. In addition, the azimuth angle error, elevation angle error and sensor position error were independent [39].  
Figure 5: Time of arrival geolocation technique.  
location can be determined. Mathematically, the distances between the emitter and sensor (\(i\)) at a time (t) are calculated as in [43].  
\[d_{i}=p_{e}-s_{i} \tag{2}\]  
where \(p_{e}\) is the emitter position, \(s_{i}\) is the sensors position and \(i=1,2,..L\)  
From that, TOAs \((\tau_{i})\) can be calculated by  
\[\tau_{i}=\frac{d_{i}}{c} \tag{3}\]  
where \(d_{i}\) is the distance between the emitter and \(i^{th}\) sensors, \(c\) is the speed of light at \(\approx\)2.998\(\times\)10\({}^{8}\) m/s.
## Abbreviations  
A list of the abbreviations used in this paper is given as follows:  
\begin{tabular}{l l}
**Ack** & Acknowledgment \\
**ARQ** & Automatic repeat request \\
**ASD** & Number of rounds spent when all SNs die \\
**BE** & Backoff exponent \\
**BS** & Base station \\
**CH** & Cluster-head \\
**CCA** & Clear channel assignment \\
**CMs** & Cluster members \\
**CSMA-CA** & Carrier sense multiple access/collision avoidance \\
**CTS** & Clear to send \\
**EEECA-THWSN** & Enhanced energy-efficient clustering approach for three-tier heterogeneous \\  & wireless sensor networks \\
**EEUCH** & Energy-efficient unmanned aerial vehicle (UAV) assisted clustering \\  & hierarchical \\
**Fol** & Field of interest \\
**FSD** & Number of rounds spent when first SN dies \\
**IEE-LEACH** & Improved energy-efficient LEACH \\
**IoT** & Internet of Things \\
**LEACH** & Low energy adoptive clustering hierarchy \\
**LEACH-C** & LEACH centralized \\
**LoS** & Line of sight \\
**MR** & Main radio \\
**MST** & Mode switching time \\
**NB** & Current number of backoffs \\
**PEGASIS** & Power-efficient gathering in sensor information systems \\
**RR** & Rumor routing \\
**RTS** & Request to send \\
**SEP** & Stable energy protocol \\
**SNs** & Sensor nodes \\
**TEEN** & Threshold sensitive energy effective sensor network \\
**TDMA** & Time division multiple access \\
**UAV** & Unmanned aerial vehicle \\
**WRP** & Wireless routing protocol \\
**WSN** & Wireless sensor network \\
**WuC** & Wake-up call \\
**WuR** & Wake-up radio \\
**WuRx** & Wake-up receiver \\ \end{tabular}
## 4 Experimental Results and Analysis
### Experimental Results and Analysis
7. From Table 3, it can be seen that our proposed KBDSP method has the smallest standard deviation in almost all cases, which means that KBDSP has good stability. 8. The one-way ANOVA was used to test the significant difference in the Purity of compared methods. From Figure 1, we can see that the \(p\)-values for the Purity metric is less than 0.05, showing significant difference between performance of the proposed and existing methods. In summary, the experimental results demonstrate that our method can beat others in almost all experiments. This is because our method introduces simultaneously the similarity-preserving mechanism, block diagonal representation term, and kernel self-expressing model. Experimental results prove the proposed KBDSP can not only exploit the intrinsic structure and nonlinear structure of original data, but also obtain a coefficient matrix with an optimal block diagonal structure. Therefore, KBDSP can obtain better clustering performance compared to other state-of-the-art methods.
## 5 Results
### TES Pilot Plant
#### 5.1.4 Downtime Behavior  
The temperature behavior of the slag inventory with regard to different downtimes is also investigated. Therefore, the TES is switched to downtime mode after the charging cycle. Figure 11 shows the inventory temperature profile over the storage height. Three different cases are considered: no downtime, one day and two days of downtime. A comparison of the various experimental results (solid lines) reveals high thermal losses. After the first day, the energy loss is 22%, which increases to 35% after two days. This results from the small pilot scale and the associated high surface-to-volume ratio. The higher losses at the storage ends (0 m and 1.7 m) result from radiation losses at the distributor. Comparing the simulation (dashed lines) results with the experimental ones a good agreement can be stated. The maximum deviation here is 10%.  
To sum up the experimental results with the test facility HOTREG show a good thermal performance and indicate the suitability of slags as an inventory material for solid media TES. In addition, a good agreement between simulation and experiment is achieved with a maximum deviation of 10%. Deviations can result from the small prototype scale of the test facility, which results in a higher surface-to-volume ratio compared to a full-scale plant. This results in a higher influence of boundary conditions as well as radiation losses, which may not yet exactly be integrated into the simulation model.
## 2 Related Work  
The problem of routing in LEO satellite networks with ISLs has been widely addressed in the literature, and it is now commonly accepted that adaptive routing is an absolute requirement to optimize network utilization [8]. Thus, the work in [9] presents and evaluates different adaptive routing schemes in a constellation of LEO satellites, also in comparison to static routing. The most important conclusion in this work is that, although taking network load into account can improve both network utilization and QoS, it also makes the routing architecture more complex. Another relevant conclusion of this work is that isolated traffic adaptive routing (i.e., using only locally available information) is an interesting and efficient alternative to more complex distributed routing schemes.  
However, the use of traffic adaptive routing schemes in regularly meshed networks with many alternative paths, as it is our case, may cause traffic load oscillations between alternative paths that are particularly inconvenient under heavy traffic load conditions [10]. For this reason, many works propose adaptive multipath routing mechanisms that implement load balancing [11; 12; 13; 14]. Finally, Ref. [15] surveys other advanced routing algorithms, such as virtual topology routing and virtual node routing.  
In the case of NDN, although there are many works about routing in NDN networks [16; 17; 18], to the best of our knowledge, there are only a few works addressing the problems related to NDN routing in LEO satellite networks. In particular, we have found only two works about this type of networks, and they are mainly focused on mobility management due to satellite handovers [19; 20].  
In this paper, we present a multipath location-based forwarding strategy that benefits from some NDN features to completely avoid the need of interchanging information between nodes, as is the case with conventional adaptive routing protocols in large LEO satellite networks (and so the scalability and load oscillation problems related to them). In particular, our forwarding strategy takes advantage of the knowledge of the relative positions of the satellites and the grid structure formed by the ISLs to choose the next-hop along a minimum-hop path. When several minimum-hop paths are possible at an NDN node, the next-hop is locally chosen randomly in order to provide the desired load balancing.  
The work in [21] also proposes a location-based mechanism for IP-based LEO satellite networks that uses the geographical position of the destination to search for the satellite node serving it. However, this algorithm just chooses a single path to minimize the physical distance between the source and the destination, instead of choosing any path minimizing the number of hops between the edge nodes, as in our proposal. In relation to this, it is very important to note that a very recent work [22] demonstrated that all the shortest-distance paths belong to the set of minimum-hop paths in LEO constellations as long as the inclination is not too large (i.e., less than 68\({}^{\circ}\)) or, alternatively, the phasing offset is large enough.
# Bioremediation Methods for the Recovery of Lead-Contaminated Soils: A Review
## 4 Bioremediation via Fungi and Bacteria
### Bioremediation by Bacteria  
The bioremediation treatments with bacteria exploit mechanisms very similar to those already described for fungi. The strategies tackled by bacteria to survive in the presence of heavy metals involve the precipitation of metals as phosphates, carbonates, or sulfides, intracellular accumulation, binding with siderophores, and biosorption on the cell surface or on extracellular polymeric substances [75; 76] (Figure 2). _Rhodobacter sphaeroides_ bacterium is capable of changing the speciation of lead by transforming it into inert forms, so making it less bioavailable. This seems to be due to the formation of inert compounds such as lead sulphate (SO42- present in culture medium) and lead sulfide (S2- produced by the bacterium through desulfhydrase) [77].  
[PERSON] et al. [78] tried to elucidate the phenomena regulating the lead absorption by _Leclercia adecarboxylata_. This microorganism reacts to the stress caused by the presence of lead through the production of extracellular polymeric substances (EPS), mainly formed with proteins capable of binding the lead, so keeping lead outside the cell.  
_Kocuria flava_, on the other hand, is able to reduce the bioavailability of lead through its chelation by calcite, which is formed by the large amount of urease, a key enzyme in this process [79].
# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Classification Based on Instrument-Assisted Method
#### 5.1.2 Taas
Moreover, the SNR is assumed to be uniform and constant in the monitoring region, which is also invalid due to the interference in the multi-path environment, especially in the ocean environment. Compared with [55], the ambiguity is assumed to have been removed and the accurate bearing information can be acquired in [56]. It is worth highlighting in this paper that the problem of unobservability is eliminated by an innovative means, using two arrays cooperatively obtaining bearings from the target. However, the algorithm has the limitation of detecting accuracy when the noise of measurements exceeds three degrees. What's more, the communication channel is assumed ideal during the period of simulation, which is difficult to meet in a practical implementation. The authors in [38] are aimed at investigating the autonomous decisions for moving tracking platform to improve tracking performance. The simulation results show that the algorithm significantly minimizes the estimation error of the target position and provides a method of maintaining the track. Different from previous papers, the authors in [58] using MFP for tracking focus on overcoming the problem of tracking error incurred by environment uncertainty; however, this algorithm is only appropriate for tracking the target emitting signals with a certain strength, which restricts the application of it in underwater target tracking.
## 5 OFDM Signal Identification Based on Inherent Features  
This sub-section presents an overview of OFDM signal detection, analysis, and modulation identification techniques. Initially, high-order cumulants are computed from the sampled data of LEO satellite signals, leveraging their statistical properties for OFDM signal detection. Subsequently, the correlation properties of the cyclic prefix inherent in OFDM signals are utilized to analyze and estimate signal parameters. Finally, the multidimensional endogenous features of each subcarrier within the OFDM signal are extracted to achieve modulation identification across the respective subcarriers.
# Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
## 3 Results
### The Impact of Different Attention Mechanisms on the Model  
In this study, we tried to replace SE in bottleneck with ECA, CA [32], and CBAM [33], and designed four groups of experiments to test the effects of the four attention mechanisms. Table 2 presents the impact of different attention mechanisms on the model. On the accuracy metric, the ECA model performed best at 0.9496, while the source SE model performed relatively poorly at 0.932. Across the board, the ECA model performed the best of the four, while the source ES model performed the worst. In terms of training time, the ECA model had the shortest training time of 1850 s, while the source SE model had the longest training time of 2847 s. From the perspective of the number of parameters, the source ES model required the most parameters, which was 6.16 M, while the parameters of the other three models were all below 5 M. Therefore, in pursuit of the best indicator performance and the need to consider the training time and model size, the ECA model is a good choice.  
In addition, we drew the heat map of the model after the four attention modules using Grad-CAM [34]. Grad-CAM is a visualization method that generates a heat map showing which regions of the input picture are most relevant for the prediction of that category, based on the output category of the network. As shown in Figure 12, CA tended to focus on the edges of the picture, mistaking some backgrounds as part of the disease. SE paid less  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attention** & **Accuracy (\%)** & **Precision(\%)** & **Recall (\%)** & **F1 Score (\%)** & **Time (s)** & **Param (M)** \\ \hline ES & 93.20 & 92.96 & 92.97 & 92.96 & 2847 & 6.16 \\ CA & 94.16 & 94.09 & 94.06 & 94.07 & 1888 & 5.22 \\ CBAM & 94.24 & 94.07 & 94.00 & 94.04 & 1970 & 5.03 \\ ECA & 94.96 & 94.98 & 94.83 & 94.91 & 1850 & 4.65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of attentional mechanisms on the model.  
Figure 11: Accuracy in training.  
attention to the characteristics of the disease. CBAM lost most of the disease characteristics and also considered some backgrounds as diseases. ECA paid the highest attention to diseases and could detect almost all disease characteristics. After comparing the four sets of experiments, we replaced the attention mechanism SE in the source model with ECA.
## 5 Analytical Result
### Major and Trace Elements
We have also compiled the geochemical data of Neogene-Quaternary samples from the Taitao Island and southern Patagonian plateaus at the Chile Triple Junction. These samples were believed to be members of the igneous rocks of the magmatic arc formed by the Chile Ridge. The shaded fields in Figure 7 represent the ophiolitic and volcanic suites generated by Neogene to Quaternary ridge subduction and near-trench magmatism at the Chile Triple Junction, as well as the basaltic rocks of the Southern Patagonian plateau, which resulted from arc and back-arc magmatism linked to the Chile Ridge subduction. Upon comparison, it is evident that all samples presented in this research point within the compositional ranges of rocks formed by ridge subduction at the Chile Triple Junction (Figure 7). This indicates that the aforementioned rocks and their characteristics align perfectly with those of the rocks formed by the Chile mid-ocean ridge subduction. The trace element compositions of the volcanic rocks are presented in Table S3. In a primitive mantle-normalized spider diagram (Figure 8a), both alkaline and subalkaline series exhibit enrichment in large-ion lithophile elements (LILEs, e.g., Rb, Ba, U, and Pb) and depletion in high-field-strength elements (HFSEs, e.g., Nb, Ta, and Ti). These patterns likely reflect the influence of subducting slab fluids and/or melt influx, as well as the metasomatism of the mantle source [23]. The depletion of Nb, Ta, and Ti suggests the presence of residual minerals such as hornblende and/or Fe-Ti oxides (e.g., rutile and ilmenite). The trends of most samples closely resemble those of the island arc basalts (IABs), characterized by enrichment in light rare earth elements (LREEs) and depletion in heavy rare earth elements (HREEs). The \(\Sigma\)LREE values range from 41.54 to 297.46 \(\times\) 10\({}^{-6}\), while \(\Sigma\)HREE values range from 8.93 to 42.09 \(\times\) 10\({}^{-6}\), with (La/Yb)\({}_{\rm N}\) ratios between 1.79 and 8.49. The \(\delta\)Eu values range from 0.85 to 1.10. When plotted on chondrite-normalized rare earth element patterns (Figure 8b), the samples show slight positive or negative Eu anomalies, with a positive Sr anomaly (Figure 8a) indicating minimal plagioclase fractionation during crystallization.