esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Compressive Strength
The 7 day and 28 day compressive strength of high-performance self-compacting concrete incorporating polypropylene fibres could be estimated by the below quadratic equations:  
\[f^{\prime}_{c7}=-330.19V_{PP}^{2}+78.96V_{PP}+38.73 \tag{1}\]  
\[f^{\prime}_{c28}=-224.8V_{PP}^{2}+38.6V_{PP}+70.22 \tag{2}\]  
where \(f_{c7}\) and \(f^{\prime}_{c28}\) are the 7 day and 28 day compressive strength of cubic specimens (MPa), and \(V_{PP}\) is the polypropylene fibre volume content (%). The correlation coefficient \(R^{2}\) reached values of 0.283 and 0.533 for 7 day and 28 day compressive strength, respectively. The variabilities of these proposed relationships are large. Therefore, the reliability is bad. The scatter of data points may be due to the decrease in workability of HPSCC with the increase of PP fibre fraction and the agglomeration of these fibres. Following the test results of the compressive strength, the 28 day compressive strength of HPSCC was predicted using the 7 day compressive strength and is expressed as a linear relation:  
\[f^{\prime}_{c28}=0.57f^{\prime}_{c7}+46.88 \tag{3}\]  
The 28 day compressive strength prediction using Equation (3) agreed quite weakly with the test results illustrated in Figure 8. The linear trend was characterised by a weak correlation coefficient R\({}^{2}\) = 0.53 and relatively high errors in the intercept. The greatest dispersion of results was observed in the HPSCC groups with 0.05% PP fibres and the highest content of PP fibres equal to 0.25%. The derived equation should not be applied to predict the relationship between the 7 day and 28 day compressive strength of the HPSCC cube specimens studied herein.
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 3 Results
### Computational Performance  
The computational time required by the forward wave modeling (RT and FDFD) for each scenario is shown in Table 1. It was measured running the codes in a conventional laptop with 16 GB of RAM and Intel Core i7-6700 HQ. As expected, RT is much faster than FDFD. It must be noticed that the pathlength is computed only once for each ray, since it is independent of the frequency. Furthermore, RT can be parallelized, which would reduce the computational time even more.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Multimonostatic** & **Multistatic** & **Multibistatic** \\ \hline RT & 6.1 s & 0.5 s & 9.2 s \\ FDFD & 28.5 h & 32.2 min & 32.7 h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of computational time.  
Figure 11: SAR image from RT (**a**) and FDFD (**b**) simulations: multibistatic scenario with a dielectric target.  
Figure 12: SAR image assuming free-space propagation from RT simulations: multimonostatic scenario with a metallic target (**a**); and multibistatic scenario with a dielectric target (**b**).  
Regarding the inversion step, the SAR algorithm takes 5.1 s to run. It must be notice that it can also be easily parallelized to reduce the computational time.
## 2 Related Work
### Existing Single-Server Highly Concurrent Task Traffic-Shaping Methods  
Recent advancements in single-server high-concurrency traffic shaping for GIS platforms have focused on optimizing resource allocation under dynamic workloads. A foundational approach is the hierarchical token bucket (HTB) algorithm, which prioritizes traffic classes and dynamically allocates bandwidth. [PERSON] and [PERSON] [26] applied HTB in university networks to manage bandwidth distribution, reducing packet loss by 90% and improving throughput consistency. However, HTB's performance depends on static parameter configurations, limiting its adaptability to fluctuating task demands.  
To address this, priority-based queueing methods have been proposed. [PERSON] et al. [27] introduced a Priority Queue Token Bucket Algorithm (PQTBA) for IoT networks, categorizing traffic into preemptive/non-preemptive groups based on real-time requirements. PQTBA achieved a 20% improvement in throughput compared to traditional token bucket approaches, but it requires predefined priority rules, which may not align with the fluid task hierarchies in GIS systems.  
To solve traffic collisions in wired networks, [PERSON] and [PERSON] [28] employed OPNET Modeler to determine the optimal token bucket size for traffic shaping, significantly reducing network latency and minimizing response times during high-concurrency application requests. However, this optimization remains fundamentally static, relying on predefined variable adjustments that fail to adapt to dynamically changing traffic demands.  
[PERSON] et al. [29] proposed a token bucket-based dynamic batching (TBDB) algorithm to determine workloads while considering data concurrency and frequency. This algorithm dynamically adjusts the maximum batch size (MBS) that triggers the next inference process, aiming to maintain throughput while reducing latency and improving device utilization, especially under high request volumes. However, the algorithm still relies on manually configured parameters such as token generation rate and bucket capacity, limiting its adaptability to diverse hardware platforms and scenarios. Additionally, TBDB solely depends on token bucket thresholds and time limits without a comprehensive consideration of system resource states, which may introduce latency due to waiting for a token generation under high concurrency.
# Sensory Perception Systems and Machine Learning Methods for Pesticide Detection in Fruits
## 4 Discussion
Overall, both devices achieved up to 100% classification accuracy in detecting pesticides in some fruits, but the E-tongue showed better precision in most cases, particularly for fruits like plums, apples, and cape gooseberries treated with various pesticides, indicating a robust capability to correctly identify both organic and pesticide-treated fruits. It is also evident that in other metrics (sensitivity, accuracy, and F1 score), the E-tongue demonstrated better efficiency than the E-nose, as the latter showed more variability in the data, especially for strawberries and cape gooseberries. This could suggest that the E-nose is more sensitive to variations in the fruit matrix or pesticide characteristics. Additionally, different pesticide concentration ranges were evaluated to assess the ability of these devices to detect and classify samples within categories, generally achieving accuracy greater than 80% and reaching 100% in detecting Preza and Bricol for cape gooseberries, depending on the classifier implemented. Similarly, the E-tongueshowed better performance than the E-nose in discriminating pesticide concentrations in fruits. This is attributed to its higher precision, sensitivity, accuracy, and specificity, as well as its more effective capability to distinguish between different pesticide concentrations in various fruits. Finally, test samples (whose condition was previously unknown) were used with the model created in the first stage of this study to determine the type of pesticide contained in the test samples, where successful classification results were achieved. Although good results have been obtained, it is necessary to validate the operation of the sensory equipment based on the previously created model and trained with the learning methods, where it is also convenient to use commercial analytical equipment such as GC-MS, since it allows for identifying the active ingredients of pesticides, and in this way verify the results obtained previously. Therefore, the detection of the active ingredients of pesticides employing this equipment would help us to confirm the information provided by farmers and by the responses of sensory perception systems. Based on the results obtained in this study, we achieved high accuracies, with some classifications reaching 100%. These outcomes demonstrate the effectiveness of our approach and make our study competitive when compared to previous research in the field of pesticide classification.
## 2 Security Requirement and S-Box Design Methods
### Random Generation  
CHAIN [35] block ciphers generate their S-box using random generation while ANUBIS [36] block ciphers generate theirs using a pseudo-random number generator with consideration of the involution S-box as a result. An involution S-box is where the S-box has an inverse onto itself. Therefore, the same S-box can be used in encryption and decryption processes. The S-box used in KHAZAD [37] block ciphers is an improvement of ANUBIS's S-box that uses linear shuffling. The same linear shuffling for S-box construction is also adopted in the ICEBERG [38] block cipher.  
KAMKAR [39] block ciphers use a key-dependent S-box. KAMKAR uses two different generators of a quasi-random sequence of numbers to alternately trigger based on the scanned cipher key. Then, the generators are used to generate two random numbers and derive two integers between 0 and 255 to be used for swapping the location from the initial S-box. In KAMKAR, the initial S-box is fixed with a linear power function (i.e., \(x\) over \(\mathbb{F}_{28}\)). The iteration continues until all bytes are scanned and swapped.
## 3 Guard Interval  
The novelty of the proposed eOFDM scheme is signified by the unique way of linking the guard interval to random user data. This section provides necessary conditions for selecting pre-defined sets of PN sequences for transmission. The set of sequences for guard interval are generated using maximum-length linear shift registers with the length  
\[m=2^{L}-1.\]  
The generator polynomial, in general, can be expressed as  
\[P_{j}(Z)=Z^{q_{R}}+\ldots+Z^{q_{2}}+Z^{q_{1}}+1, j=1,\ldots,2^{G} \tag{4}\]  
where  
\[q_{i}=\begin{cases}i\times b_{i}&,i=1,2,\ldots,R-1\\ R&,i=R\end{cases}\]  
and \(b_{i}\in\{0,1\}\), while \(R\) denotes the degree of the polynomial. The initial state vector can be represented as  
\[\mathbf{t}=[t_{R},t_{R-1},\ldots,t_{0}]\]  
where \(t_{i}\in\{0,1\}\), with the condition that \(\mathbf{t}\) is a non-zero vector. Let \(\mathbf{y}_{d}=[y_{0},y_{1},\ldots,y_{m-1}]\) be the received PN sequence. Its cross-correlation with each of the sequences \(\{\mathbf{x}_{d}\}\) is defined as  
\[C_{i}=\sum_{k=0}^{m-1}y_{k+i}x_{d_{k}}, i=1,\ldots,m \tag{5}\]  
Figure 3: Block structure of eOFDM system.  
Furthermore, the cross-correlation \(C_{i}\) for the noiseless case is defined as [36]  
\[C_{i}=\begin{cases}m&,\text{iff }\mathbf{y}_{d}=\mathbf{x}_{j}\\ \leq 0&,\text{iff }\mathbf{y}_{d}\
eq\mathbf{x}_{j}\end{cases} \tag{6}\]  
Each of the sequences in \(\{\mathbf{x}_{d}\}\) are generated using \(P_{j}(Z),j=1,\ldots,2^{C}\), which fulfills the necessary condition mentioned in (6). The probability of the correct detection of \(\mathbf{y}_{d}\) approaches unity at SNR \(\geq-15\) dB, as mentioned in [36].
## 4 Discussion
### Some Features of the Largest Concentrations
Using one of the rake angles of the moment tensors solution, the focal mechanism has been classified as \"reverse\" (or \"thrust\") if \(+20^{\circ}<\) rake \(<+160^{\circ}\), \"normal\" if \(-160^{\circ}<\) rake \(<-20^{\circ}\), or \"strike-slip\" if \(-20^{\circ}\leq\) rake \(\leq+20^{\circ}\) or if \(\mid\) rake \(\mid\geq 160^{\circ}\), following Cronin [49]. The classification for the focal mechanism and the sea/land localization of the epicenters has been reported in the last column of Table 3 and for all the earthquakes investigated in this study. Further, it needs to be taken as a reference to search for any deviations from these percentages. We report in green (positive) or red (negative) the deviation percentage with respect to the reference, and in bold, we underline the significant difference for the focal mechanisms. Faster signals (2-10 s) seem more likely (+10.5% with respect to their natural distribution) to be produced by reverse focal mechanism earthquakes at the expense of strike-slip ones (\(-10.1\%\)). The signals with a period in the range 10-25 s seem less likely (\(-15.4\%\) with respect to their standard distribution) to be produced by normal fault earthquakes, and for the slower signals, the deviation percentages are not greater than 5%, so they are not really significant. Unfortunately, the detected deviations are not statistically significant, and deeper research in the future will be necessary to understand whether the focal mechanism has no influence on the possible ionospheric electromagnetic earthquake disturbances or if our study is too limited to have a clear detection of such dependence. On the other side, land earthquakes are more likely to show lower frequency signals (from faster to slower frequencies, they are \(-32.2\%\), \(+3.6\%\), and \(+21.5\%\)), and, complementarily, the sea earthquakes seem likely to produce faster signals (from faster to slower frequencies, they are \(+7.2\%\), \(-0.7\%\), and \(-4.4\%\)), as the percentage shows a clear trend with significant deviations for the highest and lowest frequency signals. Finally, we find that only two earthquakes (above 146 with anomalies in 2-10 s, 137 in 10-25 s, and 146 in 25-50 s) are common events to all of these concentrations, so the results of this analysis are related to different earthquakes, and it is not clear if the frequency of the eventual electromagnetic wave produced by a single earthquake changes with the time, while the focal mechanism seems to have a slight or null influence on the signal frequency, and the localization in sea or land seems to play an important role for the frequency of the anomalies.
## 3 Numerical Simulation and Experimental Analysis
### Rotating Speed
#### 3.4.2 Experimental Analysis  
Experiments are performed in the rig according to the conditions listed in Table 2. The uniform shaft possessing the same mass with the non-uniform shaft is also studied. The shafts are set to rotate at different speeds. The propagation characteristics will be obtained by comparing the spectrum of the signals. The results of the experiments are shown in Figure 13.  
Figure 13: Experimental propagation characteristics derived from different rotating speeds, where (**a**) is for 0 Hz, (**b**) is for 10 Hz, (**c**) is for 20 Hz, (**d**) is for 30 Hz and (**e**) is for 40 Hz.  
Figure 13 shows the experimental stopbands derived at different rotating speeds. Similarly, the time histories of the acceleration signals from both the uniform and non-uniform shafts are truncated to 0.01 s. The real lines and the dotted lines stand for the uniform shaft and the non-uniform shaft, separately. As for the case of 50 Hz, the experimental results have been shown in Figure 7. All the experimental results are listed in Table 8.  
From Table 8, it can be found that the experimental results match well with the numerical ones. The central frequencies of all the three stopbands will be higher and higher with the increase of the rotating speed, while the bandwidth of which remain the same.  
There is a similarity in the change of the central frequencies between the case of crack occurring and the case of rotating speed varying, and the difference lies at the bandwidths of the three stopbands. In a real practice, if only the central frequencies of the three stopbands vary, it might be caused by the changing in rotating speed, while if the bandwidths change at the same time, it might be interpreted as the occurrence of a transverse crack.
## 2 Materials and Methods
### Methodology
#### 2.2.3 Hydrological Modelling  
HEC-HMS is a well-known rainfall-runoff model developed by the US Army Corps of Engineers. It is widely used in hydrological modelling, as it requires fewer input parameters than some other physically based models [54]. The relatively modest data requirements allow for HEC-HMS to be applied in many scenarios and case studies worldwide [55; 56; 57].  
HEC-HMS is selected for this study due to a large literature base of different applications of the software and for its ability to quantify changes in runoff volume and infiltration volumes, as well as peak discharge [40; 54; 58]. Simulating the implementation of different NbS can be performed by changing the CN parameter and impervious % [39; 40] or by implementing sinks or reservoirs [38].  
Figure 2: Project methodology, including the data used, the pre-processing steps taken and the modelling conducted.  
Ten precipitation events and corresponding discharge and soil moisture data were selected to represent a range of precipitation volumes and intensities. The moisture level of the soil determines how much water the soil can absorb. It is used to categorise the soil's antecedent moisture condition (AMC) as wet, dry or at an intermediate level.  
The Snyder method was used to determine the lag time (t\({}_{\text{p}}\)) parameter, based on the following Formula (1) [59]:  
\[\text{t}_{\text{p}}=0.75*\text{C}_{\text{t}}\big{(}\text{LL}_{\text{c}}\big{)}^{0.3} \tag{1}\]  
where t\({}_{\text{p}}\) = lag time (hrs); C\({}_{\text{t}}\) = catchment coefficient; L = length of the main stream (km); L\({}_{\text{c}}\) = length from the nearest point on the stream to the centroid to the outflow point (km). Both L and L\({}_{\text{c}}\) were calculated using GIS from the outputs of the stream definition from the delineation. The centroid was calculated using \"calculate geometry\" in the attribute table of the watershed polygon for L\({}_{\text{c}}\). C\({}_{\text{t}}\) was estimated to be 3.5.  
The peaking coefficient (C\({}_{\text{p}}\)) was then estimated by calculating the peak discharge (q\({}_{\text{p}}\)) from the lag time using Equations (2) and (3):  
\[\text{q}_{\text{p}}=4.1515*\text{t}_{\text{p}}^{-0.92} \tag{2}\]  
\[\text{C}_{\text{p}}=\frac{\text{q}_{\text{p}}*\text{t}_{\text{p}}}{6.992} \tag{3}\]  
The curve number (CN) is an important parameter for hydrological design. CN was calculated using the LULC and soil type. Field visits were conducted to refine LULC classifications. ArcGIS Pro was used to intersect the LULC with the soil classes, and the CN for each combination was chosen based on CN tables from SCS TR-55 [60]. The average CN for each sub-catchment was then calculated and adjusted for the AMC of each event. In addition, the impervious % was also calculated by digitalising the buildings and paved surfaces in the catchment from satellite imagery.
## 2 The CCD-Based MOTS with Traditional Control Methods  
The basic configuration of the MOTS is shown in Figure 1. The light from the target could pass through the reflective surface of the rotating mirror, which would then be detected by CCD to calculate the boresight error. After receiving the boresight error, the controller would drive the voice motors to make the mirror rotate accordingly, thereby tracking the target and resisting the impact of the external disturbances. To enhance the ADA, a FOG was mounted on the lens barrel to measure the angle velocity. In addition, the velocity closed loop would increase the stiffness of the object and make it easier to control the platform.
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Deposition-Associated Issues
#### 3.2.7 Shifted Layers or Leaning Prints  
Layer shifting is a result of improper axis movement, causing the extruder to misalign during mid-print (Figure 10). This includes a movement being blocked on the axis, a loose pulley causing the layer shift, and/or due to a geometry that is difficult to print. Solutions include the following:  
1. Check the tightness of X and Y belts. When plucked, the belts should sound like a low bass note. The back of the X-carriage should not be loose. Check and tighten all screws.
2. Ensure that there is no obstruction in the path of the bearing, or any possible waste stuck around the belt.
3. Check the tightness of X and Y pulleys. Ensure that the pulley on the motor shaft is secure and the idler pulley can move freely on the opposite end.
4. Design according to the guidelines and rules for ME to avoid geometry that is difficult to print, particularly for models with large overhangs that tend to warp during mid-print.
## 4 The Seismic Performance Parameters of RCS Composite Joints Under Floor Effect Are Studied
### The Influence of Floor Thickness on Seismic Performance of Nodes
#### 4.2.3 Energy Consumption and Stiffness Degradation  
The energy dissipation capability of the nodes under different slab thicknesses initially increases linearly. As cracks develop, the energy dissipation slope slows down and then rises again as damage approaches. The energy dissipation coefficient ranges from 3.05 to 3.10, with an equivalent viscous coefficient of 0.485 to 0.493, both meeting the standards, indicating the good energy dissipation performance of the nodes. Increasing the slab thickness by 20 mm (node FJD-6) only results in a slight increase of about 1.64 in energy dissipation, with minimal impact.  
The stiffness degradation shows that with the load, the stiffness of the node deteriorates rapidly, and the relaxation change trend tends to be consistent. The thicker the floor plate is, the higher the stiffness of the node (such as FJD-6). This is because the thicker floor plate increases the effective height of the beam, enhances its bending resistance, and improves its deformation resistance (Figure 20).  
Figure 19: (**a**) Comparison of nodal hysteresis curves for different floor thicknesses; (**b**) Comparison of node skeleton curves with different floor thicknesses.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
\begin{tabular}{c} 8.11 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Characteristic values of node skeleton curves for different floor thicknesses.
## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.13. QDTR [36]  
In [36], the authors proposed the first Q-learning-based energy-efficient routing protocol. It was designed for the delay-tolerant network for underwater sensors, hence the name QDRT. When the system is ready to deliver a packet from the sensor nodes to the forwarding node, it looks into the Q-table and takes the result with the best reward to forward the packet. The Q-table gets the results from previous actions. The reward function has three criteria: (a) less distance from the sink node, (b) the density of nodes, and (c) the residual energy. First, the node checks the distance between the next node and the sink. If the forwarder chooses the node closer to the sink, it receives the maximum reward. If the forwarder chooses a node with high density, it receives less reward than the first action, and it receives a lesser reward if it chooses a node with higher residual energy. The author set a deadline within which the packet will be forwarded. The simulation was performed using NS2, and their data shows that QDTR has a higher delivery ratio while using less energy.