esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of GPS, Seismic and Integrated Waveforms
The precision of integrated displacement is significantly improved by precise dynamical information provided by seismic sensors. Figure 2: Comparison of GPS-only and tightly-integrated displacements on the collocated AKT006 (seismic) and 0183 (GPS) pair and NGN017 (seismic) and 0986 (GPS) pair during the Tohoku-oki earthquake on 11 March 2011. The sub-figures show from top to bottom the entire period of seismic shaking in north, east and up components respectively. The 1 Hz GPS-only and 100 Hz tightly-integrated displacements are shown respectively by the black crosses and the red line. Figure 4: Power spectral densities. (**a**) Power spectral density for 1 Hz GPS displacements at station 0183 (the black line), 100 Hz seismic displacements at AKT006 (the blue), and 100 Hz tightly-integrated displacement waveforms at AKT006/0183 (the red line). (**b**) Power spectral density for 5 Hz GPS displacements at P744 (the black line), 200 Hz seismic displacements at 5028 (the blue line), and 200 Hz tightly-integrated displacements at P744/5028 (the red line). Figure 3: Comparison of GPS-only and tightly-integrated displacements on the collocated 5058 (seismic) and P496 (GPS) pair and 5028 (seismic) and P744 (GPS) pair during the El Mayor–Cucapah earthquake on 4 April 2010. The sub-figures from top to bottom show the entire period of seismic shaking in north, east and up components respectively. The 5 Hz GPS-only and 200 Hz tightly-integrated displacements are shown respectively by the black cross symbols and red lines. The power spectral densities of the three kinds of displacements (GPS-only, seismic-only, and integrated displacements) at AKT006/0183 and P744/5028 pairs are also compared in Figure 4 to illustrate the frequency content of the signal. The frequency domain analysis of these waveforms shows in which frequency bands each data type is reliable. GPS performs better at lower frequencies and seismic sensor is better at higher frequencies. We can see that the power spectral densities of integrated displacements follow the GPS-only spectrum at the low frequencies and the seismic-only spectrum at the high frequencies. From the power spectral density analysis, we can also infer that the integrated waveform is more precise and accurate than the GPS-only or seismic-only waveforms. An accurate broadband waveform, which has the advantages of both sensors, has been achieved.
###### Abstract  
A bridge hybrid isolation system, integrating both active and passive control strategies, is proposed and investigated to enhance seismic performance. The system is modeled as a two-layer structure, with the upper layer subjected to active control via a Proportional-Integral-Derivative (PID) controller, and the lower layer employing a conventional passive base isolation system. The displacement response of the superstructure is minimized by the control forces generated by the PID controller, which also accounts for the reaction forces transmitted to the lower isolation layer. To optimize the controller's performance, a genetic algorithm is implemented for real-time tuning of the PID parameters. Numerical simulations, conducted using the Newmark method, are employed to assess the influence of active control on the lower isolation system. The results reveal that, while active control increases the peak displacement of the superstructure to some extent, it significantly prolongs the structural period, thus enhancing the system's overall seismic resilience and stability.  
hybrid isolation system; PID control; active control; passive isolation system; Newmark method; genetic algorithm; control force
## 4 Machine Learning Algorithms and Performance Evaluation Indices
### Hyperparameter Optimization  
To optimize the hyperparameters of each machine learning model, a grid search algorithm combined with 5-fold cross-validation was employed. Grid search systematically evaluates all possible combinations of hyperparameters within predefined search ranges, as summarized in Tables 3-7. For each candidate configuration, the training dataset was partitioned into five equally sized subsets. In each iteration, four folds were used for training and one fold for validation, cycling through all possible validation folds. This procedure allows for a robust assessment of model generalization performance across different data partitions. The average prediction performance across the five folds was calculated for each hyperparameter set, and the configuration yielding the best overall accuracy was selected as optimal. This process was applied independently for each ML algorithm, and the final selected hyperparameters, along with their corresponding search intervals and categorical options, are presented in Tables 3-7. This approach ensures unbiased model tuning and helps prevent overfitting by validating the model performance on unseen subsets of the training data during optimization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Optimal Value** & **Search Range** & **Step/Options** \\ \hline n\_estimators & 100 & 50–200 & Step = 25 \\ max\_depth & 20 & 5–30 & Step = 5 \\ min\_samples\_split & 2 & 2–10 & Step = 2 \\ min\_samples\_leaf & 1 & 1–5 & Step = 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters for the Random Forest model.
# Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics
## 3 Results and Discussion
### Classification Results and Analysis of Discriminant Models Based on Feature Wavelengths
Selecting too few spectral variables might result in the loss of useful information, thus leading to unsatisfactory classification accuracy. Moreover, the effect of different variable selection methods would be different for different applications. Different feature extraction methods influence the performance of discriminant model distinctively. It is necessary to utilize an appropriate variable selection method or directly use the full wavelengths according to the characteristics of the specific data set.
## 5 Results
### Calculation of the Geodetically Based Parameters
#### 5.1.3 Maximum Shear Strain (MaxSS)  
The Maximum Shear Strain (MaxSS) values within the broader Strymon area range between 0.7 and 2743 \(\times\)\(10^{-9}\) (Figure 6b). In particular, the maximum MaxSS values are documented along the eastern boundary of the Strymon basin, while similar values are also observed at the northern part of the corresponding western boundary (Figure 6a). In addition, much lower but considerable MaxSS values also occur at the southern part of the western boundary Strymon basin. The geostatistical analysis (Figure 6b) shows a clearer view of the MaxSS distribution, indicating the locations of the higher values along a WNW-ESE-trending imaginary axis crossing the Strymon basin from the southern tip of Lake Kerkini to the west to Nea Zichni to the east. It is noteworthy that extremely low, almost negligible values occur in all of the Drama basin. Therefore, the high shear values indicate the significant active tectonic structures along the Strymon basin boundaries (especially the eastern one).
# An In-Network Cooperative Storage Schema Based on Neighbor Offloading in a Programmable Data Plane
## 3 Proposed System Architecture
### An ID-Based Cooperative Storage Protocol
The storage service node requests the data chunk through the storage service node's device ID as the source ID and the data chunk ID as the destination ID. Then, the producer sends the data packet with the producer's device ID as the source ID, the storage service ID as the destination ID, and the data chunk ID and data contained in the payload. If the storage service node offloads the packets, the packets' destination IP is modified and the ID fields are unchanged, and are then forwarded. All IPv6-compliant hosts should be able to dynamically determine the packet length to be transmitted, for example, by using the path MTU discovery process. Briefly, when a host sends a very large IPv6 packet, the router does not segment the packet if it cannot forward such a large packet, but returns an error message to the sending host. This message tells the host that all future packets sent to the destination address will be segmented. It is far more efficient for hosts to send packets of the right size in the first place than for routers along  
Figure 3: ID combination relationship in an in-network storage service scenario. Figure 2: IDP protocol layer layout. the way to dynamically fragment each packet. Because IPv6 is an end-to-end transmission protocol and the concept of an end-to-end connection does not apply in ICN, our proposed transport protocol is connectionless. There is no handshake throughout the transmission, thus reducing the latency associated with establishing the connection [25]. Considering the optimal chunk size for efficient transmission in ICN and the large overhead that a small data chunk imposes on transmission and caching [33], the data chunk size is set to several MBs in our proposal. The designated data chunk size is larger than that of the path MTU, so the data chunk needs to be segmented. The segmentation information, including data chunk ID and the segment number, remains in the data chunk transport layer header. In addition, a new field, \"preference\", was designed, and is mapped from application-related requirements, including storage QoS, specific storing positions, caching strategies, security strategies, multipath transmission, and one-to-many transmission based on multicasting. The intermediate node may execute the corresponding action according to the \"preference\" field. Our proposed transport protocol SEADP header mainly contains \"type\", \"data chunk ID\", \"sequence\", and \"preference\" fields. The \"type\" field currently contains the data request packet and the data chunk response packet. The \"data chunk ID\" field represents the identifier of the data to be transmitted and stored. The \"sequence\" field of the packet indicates the segment number of the data chunk segmented by data source.
## 5 Experimental Validation and Benchmarks
### Benchmarking AI Models for Traffic Prediction  
I benchmarked several AI models, including deep learning, reinforcement learning, and decision trees, on their ability to predict network traffic. The models were evaluated based on the following metrics:  
* Accuracy: The percentage of correct predictions.
* Latency: The time required for the model to make a prediction.
* Energy Consumption: The power consumed by the system during predictions.  
The results present in Table 8 show that deep learning achieves the highest accuracy (95%) but comes with a higher latency and energy consumption compared to decision trees, which provide a good balance between performance and resource usage.
## 1 Introduction
The results showed that dust loadings peaked in the afternoon and in summer. [PERSON] et al. [32] simulated dust concentration in East Asia during summer 2010 using WRF-Chem. Additionally, [PERSON] et al. [33] used WRF-Chem to show that the direct radiative effects of airborne mineral dust reduce the surface temperature, but warm upper air layers. Additionally, [PERSON] et al. [34] used WRF-Chem to model the 120-day wind and dust storm activity pattern over Iran's Sistan Basin. Several schemes are available to simulate dust emissions depending on different input parameters. Each of these schemes parameterizes wind erosion in different ways. One of the wind erosion schemes that are widely used to predict dust particles is the Georgia Institute of Technology--Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model [19; 35]. Another widely used wind erosion scheme is the Air Force Weather Agency (AFWA) model [25; 36; 37; 38]. WRF-Chem is an extended version of the WRF model, which includes a chemistry component [23] and has been widely used to study dust impacts on local to regional scales [39; 40; 41; 42; 43; 44; 45] studies have demonstrated the suitability of the WRF-Chem model to simulate the desert dust cycle [25; 32]. However, attention must be paid to the chosen land surface model [28] model vertical resolution [30] and particulate matter distribution [46]. The global erodibility map developed by [PERSON] et al. [19] is the only one available by default for dust simulations in WRF/Chem. Such a map works well on large-scale regions (i.e., Sahara Desert, Arabian peninsula) [34]. [PERSON] et al. [43] investigated the sensitivity of the WRF-Chem simulated dust radiative forcing to dust emission and aerosol schemes. Additionally, modeling of the effect of mineral dust on the wind profile and near-surface wind speed was carried out WRF-Chem model [33]. Additionally, [PERSON] et al. [47] used the WRF-Chem model to numerically simulate extreme dust storms in eastern Iran. [PERSON] et al. [48] studied the severe dust storms in west and southwest Iran by the WRF-Chem model. Model outputs showed the good performance of WRF-Chem in simulating the spatial distribution of dust. In the present study, the most severe dust storms in Iran, which reduced horizontal visibility to less than 1000 m, were selected using data from synoptic stations of the region. Subsequently, these dust storms were evaluated using Moderate Resolution Imaging Spectroradiometer (MODIS) images and the Ackerman index. Then, using the WRF-Chem model and the GOCART and AFWA wind erosion schemes, the spatial and temporal pattern of vertical dust flux and of surface dust concentration were studied and compared with time series of visibility at the surface. The goal of this work is to accurately simulate such severe dust storms in order to determine the external and internal dust sources relevant for these events in Iran. The paper is organized as follows. Materials and methods are presented in Section 2. Results are given in Section 3, while Discussion and Conclusions are drawn in Section 4.
# Comparison of Prediction Methods for Axial Strength of Ground Connections with Shear Keys
## 3 Comparison of Calculation Results
### Space of Shear Key  
The relationship between shear strength \(f_{\rm bk}\) calculated by different codes and the applicable space of shear key \(s\) is shown in Figure 2, where a maximum value for \(s\) is not specified by API, HSE, and DNV-GL. In this study, \(s\) does not exceed 2 m. It can be found from the figure that within the selected value range of \(s\), shear strength \(f_{\rm bk}\) calculated by HSE is the largest, followed by the calculated result of API, and the smallest is by DNV-GL. With increased \(s\), the variation trend of shear strength obtained by different specifications is consistent. In the interval where \(s\) is less than 800 mm, the value of \(s\) has a significant effect on shear strength. As \(s\) increases, the calculated value of shear strength decreases rapidly, and the curve calculated by HSE is the most sensitive. After the value of \(s\) continues to increase beyond 1000 mm, shear strength \(f_{\rm bk}\) of the grouted connection slowly decreases, and the curve calculated by each code gradually flattens. Since the DNV calculation result is fixed, it is considered that the shear strength of the grouted joint is not related to the space of the shear key, which is inconsistent with [PERSON]'s experimental phenomenon [16]. The Wimpey laboratory tested specimens with different shear key distances. The results showed that the shear strength of the grouted connections increased continuously when the space of the shear key \(s\) continued to decrease. However, as \(s\) was reduced to a certain extent, the failure mode of the grouted joint changed, and the shear strength of the grouted section decreased.  
Figure 2: Relationship between interface shear strength (\(f_{\rm bk}\)) and shear key distance (\(s\)).
# A Review of Physical Layer Security Techniques for Internet of Things: Challenges and Solutions
## 4 Promising PLS Solutions for IoT
### Noise Aggregation and Self-Encryption
Since the \"secret key\" \(X_{1}\) has already been obtained during slot 1, [PERSON] can XOR \(X_{1}\) with \(X_{2}\oplus X_{1}\oplus W_{2}\) to produce \(X_{2}\oplus W_{2}\), which is the sufficient statics for the detection of \(X_{2}\). Then, the signal detection at [PERSON] is only impaired by the noise in slot 2. In contrast, the signals received at Eve during slot 1 and slot 2 are \(X_{1}\oplus Z_{1}\) and \(X_{2}\oplus X_{1}\oplus Z_{2}\), respectively. After the XOR operation which cancels out the interference term \(X_{1}\), the derived sufficient statics to detect \(X_{2}\) will be \(X_{2}\oplus Z_{1}\oplus Z_{2}\). Note that because \(X_{1}\) and \(X_{2}\) are independent variables that are both unknown to Eve, the observation \(X_{1}\oplus Z_{1}\) is statistically independent from \(X_{2}\oplus Z_{1}\oplus Z_{2}\), which offers no information for the recovery of \(X_{2}\). Obviously, the noises within both slot 1 and slot 2 are aggregated at Eve, thus degrading the signal detection performance of the eavesdropper. Compared to the AN based approaches, the noise aggregation method does not depend on the use of artificial noise signals. Therefore, it is more energy-efficient and is thus appealing to IoT applications. On the other hand, in contrast with the traditional encryption method which relies on the exchange of dedicated secret keys, the noise aggregation approach is essentially a self-encryption technique which utilizes the previously transmitted messages to encrypt new messages. To realize the self-encryption, the transmitter only needs to perform the XOR operation, which is also advantageous in terms of implementation complexity. The noise aggregation based self-encryption approach has already been applied in several application scenarios. For instance, in [63], we developed a security enhancement scheme for video transmission, where the adoption of noise aggregation brings in approximately 1 dB SNR gain for Bob compared to Eve at the same level of frame error rate. In [64], we applied the noise aggregation technique in two-way untrusted relaying systems to realize a message-prioritization based unequal secrecy protection. Interested readers are suggested to refer to these two papers [63; 64] for more information.
# AR Displays: Next-Generation Technologies to Solve the Vergence-Accommodation Conflict
## 5 Multifocal Approach by LightSpace Technologies
### Implementation of the Core Volumetric Technology into an AR Device  
Based on our testing results of the VR proof-of-concept prototype system, further steps involve implementation of the proposed multifocal display architecture within an augmented reality wearable display, which will allow real-world multifocal display testing to be performed from human performance and practicality points of view. Our preliminary evaluation indicates the ability to yield a compact footprint headset using a conventional beam-splitter optical image combiner and custom design parts. The attainable footprint can be evaluated from Figure 10, which shows a computer aided design (CAD) model that is based on realistic hardware parameters. As we primarily are concerned with near-work performance, optimization is aimed towards improved image resolution at the expense of horizontal field-of-view.  
Overall it must be said that, regardless of type of the next-generation display technology, performance of current spatial light modulators is an important limiting factor constraining image performance. For a wider field-of-view, not just optics provide limits, but very much resolution of spatial light modulators as well. On one hand, there are microdisplays (LCD and OLED type), which can yield high image resolutions but still their footprint, image refresh rate, and brightness are not well suited for the next-generation AR displays. A solution, possibly facilitating emergence of discussed display technologies, might be solid-state LED microdisplays, promising a compact footprint, very high brightness, and a competitive image refresh rate. Nevertheless, \(\mu\)LED displays are also only maturing and will not become readily available for at least several years. With this in mind, reflective-type SLMs, such as DMDs and LCoS (Liquid Crystal on Silicon), are best suited for next-generation displays. Moreover, DMD performance has not reached fundamental limit, but rather is related to driving electronics, meaning that increased interest in wearable display technologies could be a good motivation for emergence of new, faster, more integrated, compact footprint reflective SLMs, opening a whole new world for VAC-corrected displays.
## 2 Design of DSCT Specimens
### Design of Tower Specimens  
Based on the joint design results, four types of modular tower specimens were constructed: the single-body specimen (SP-0), specimen using Type-S joint (SP-S), specimen with H-connectors (SP-H), and specimen using Type-W joint (SP-W). There were no commercial steel tubes with the same diameter as the designed tubes; therefore, fabricated SS400 steel tubes, which had 245 MPa yield strength and 400 MPa ultimate strength, were used to construct the specimens. All the other steel parts of the specimens were also made of SS400 materials. Figure 15 shows each specimen under construction. For SP-H, the modules and H-connectors were planned to be coupled by bolting both the upper and lower modules; however, the upper side was welded and the lower side was bolted for convenience in construction. Ordinary Portland cement with a maximum aggregate of 25 mm and a 120 mm slump was used for the modules of the tower specimens. The nominal strength of concrete was 24 MPa. For the footing and the loading area located at the top of the tower specimens, high strength concrete having a 40 MPa nominal strength was used. Concrete molds were made when placing the concrete in the specimens; they were tested after seven days and during the start of the modularized column specimen testing. The average concrete strength was 20.57 MPa after seven days and 29.5 MPa after 28 days. Table 2 lists the compressive strengths of the concrete from the mold tests, and Figure 16 shows the tested concrete molds.  
Figure 14: Design of tower specimen.  
Figure 16: Tested concrete molds.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specimen No.** & **Compressive Strength (MPa)** \\ \hline
1 & 30.70 \\
2 & 28.23 \\
3 & 29.58 \\ Average & 29.50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: 28-day compressive strength of concrete.  
Figure 15: Three types of specimen modules (**a**) Type-S specimen module, (**b**) Type-H specimen module, (**c**) Type-W specimen module.  
Before the experiment, the behavior of the designed single-body specimen (SP-0) applying the measured concrete strength was analyzed using CoWiTA [15]. This analysis is based on the nonlinear concrete model from [PERSON] et al. [16] and the nonlinear steel model by [PERSON] et al. [17] with consideration of the confining effect of concrete. The axial load-bending moment (P-M) interaction curves in Figure 17 show the results for the case considering the confinement effect and the case that did not apply the confinement effect. The maximum moment was 876.67 kN-m, which could be converted into a maximum lateral force of 427.64 kN in the case considering the confinement effect. For the case without considering the confinement effect, the maximum moment was 634.27 kN-m, and the maximum lateral force could be calculated as 209.4