esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2010**, _10_, 3280-3304; doi:10.3390/s100403280  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**GFZ Wireless Seismic Array (GFZ-WISE), a Wireless Mesh Network of Seismic Sensors: New Perspectives for Seismic Noise Array Investigations and Site Monitoring**  
**[PERSON] \({}^{1,\ast}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{1}\)**  
* Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum, Telegrafenberg, 14473 Potsdam, Germany; E-Mails: [EMAIL_ADDRESS] (C.M.); [EMAIL_ADDRESS] (K.-H.J.); [EMAIL_ADDRESS] (S.P.); [EMAIL_ADDRESS] (J.Z.); [EMAIL_ADDRESS] (I.V.)
* Department of Informatics, Humboldt University (HU) Berlin, Germany; E-Mail: [EMAIL_ADDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +49-331-288-1299; Fax: +49-331-288-1204.  
_Received: 4 February 2010; in revised form: 12 March 2010 / Accepted: 15 March 2010 / Published: 1 April 2010_  
**Abstract:** Over the last few years, the analysis of seismic noise recorded by two dimensional arrays has been confirmed to be capable of deriving the subsoil shear-wave velocity structure down to several hundred meters depth. In fact, using just a few minutes of seismic noise recordings and combining this with the well known horizontal-to-vertical method, it has also been shown that it is possible to investigate the average one dimensional velocity structure below an array of stations in urban areas with a sufficient resolution to depths that would be prohibitive with active source array surveys, while in addition reducing the number of boreholes required to be drilled for site-effect analysis. However, the high cost of standard seismological instrumentation limits the number of sensors generally available for two-dimensional array measurements (_i.e._, of the order of 10), limiting the resolution in the estimated shear-wave velocity profiles. Therefore, new themes in site-effect estimation research by two-dimensional arrays involve the development and application of low-cost instrumentation, which potentially allows the performance of dense-array measurements, and the development of dedicated signal-analysis procedures for rapid and robust estimation of shear-wave velocity profiles. In this work, we present novel low-cost wireless instrumentation for dense two-dimensional ambient seismic noise array measurements that allows the real-time analysis of the surface-wavefield and the rapid estimation of the local shear-wave velocity structure for site response studies. We first introduce the general philosophy of the new system, as well as the hardware and software that forms the novel instrument, which we have tested in laboratory and field studies.  
sensor network; seismic array; wireless mesh network; site effects; MEMS; earthquake risk
# Inter-Data Center RDMA: Challenges, Status, and Future Directions
## 5 Future Directions
### Packet Retransmission  
Based on our previous analysis, there is currently a lack of research on packet retransmission optimization for inter-DC RDMA transmission scenarios. However, the GBN packet retransmission mechanism in RDMA not only severely delays FCT but also results in extremely low bandwidth utilization. This is why RDMA relies on the reliability of lossless networks when used within DCs, as its performance is significantly degraded when packet loss occurs. Therefore, when the application scenario extends to inter-DC, the longer packet loss feedback loop caused by long-distance interconnection undoubtedly exacerbates the impact of packet loss on performance. Consequently, a more efficient packet retransmission mechanism for inter-DC RDMA is an important research direction for the future.  
Currently, the commonly used packet retransmission schemes in intra-DC scenarios are generally divided into two types: GBN and Selective Repeat (SR). The primary advantage of the GBN scheme is its simplicity, as the logic for packet loss detection and handling is straightforward and does not rely on specialized network equipment. However, GBN results in a significant amount of redundant data transmission, leading to low bandwidth utilization and increased latency. In contrast, the SR scheme avoids unnecessary transmissions by retransmitting only the lost packets, thereby making more efficient use of bandwidth. However, the implementation of this scheme is more complex and resource-intensive due to the requirement for multiple timers and buffers to manage out-of-order packets.  
Regardless of the retransmission mechanism mentioned above, when applied to long-distance inter-DC connections, packet loss and retransmission feedback are significantly delayed, leading to notable performance degradation. Therefore, in long-distance interconnection scenarios, it is advisable to consider the in-path packet loss detection and retransmission mechanism or near-end packet loss detection and retransmission mechanism. The in-path packet loss detection and retransmission mechanism refers to the capabilities of intermediate nodes within the transmission path to detect and retransmit lost packets rather than relying solely on end-to-end detection and retransmission. However, this approach requires all intermediate nodes in the network to possess packet loss detection and retransmission capabilities, which poses compatibility challenges with existing infrastructure. In contrast, the near-end packet loss detection and retransmission mechanism requires only the deployment of specialized network devices at the DC egress to enable the designed packet loss detection and retransmission methods. Therefore, inter-DC RDMA solutions for lossy networks, based on near-end packet loss detection and retransmission mechanism, could be a highly significant research direction.
# Biosignals Monitoring of First Responders for Cognitive Load Estimation in Real-Time Operation
## 4 Experimental Tests
### Practice Scenario  
Using the same tools as in the in-lab scenario, the practice scenario simulates real operations in a collapsed structure environment (see Figure 6). A total of 24 FRs (3 doctors, 9 nurses, 10 technicians, and 2 firefighters) were selected (see Table 2). They performed a single exercise task to check biosignals baseline and then two different multi-objective tasks in a collapsed structure field.  
The baseline task was a walk with the PPE (helmet, boots, intervention jacket, and trousers) and technical material (see Figure 7a), crawling in a 4 m open stretch tunnel (see Figure 7b), and climbing up and down a 6 m tower (see Figure 7c). The helmet was the same for all users to avoid differences in the HoloLens' measurements. The rest of the elements were the ones used by the FRs in their daily activities. After the exercise, the volunteer performed a NASA TLX test to establish the biosignals baseline (see Figure 5b).  
The second task was related to visual attention. The FR had to remember and find different images with shapes and colors as they enter and search a crushed and tilted bus (see Figure 8). While performing the search, each time the volunteer found a specific color card, he/she had to inform the command post about the total number of cards and how many of them were of that color. The volunteer had ten minutes to complete the task. Every two minutes he/she was informed about the time left. All the communication was made through walkie talkie. After the exercise, the volunteer performed a NASA TLX test (see Figure 5b).  
Figure 6: Images of the collapsed structures for the practice scenario.  
Figure 7: (**a**) Walk, (**b**) tower structure climb, and (**c**) open tunnel crawl exercises of the practice scenario.  
The third task focused on hearing acuity and mental operations while crawling through a narrow tunnel (see Figure 9). Before the exercise, each volunteer memorized a four-digit number. The FRs were getting different instructions from the command post through the walkie talkie while they were crawling in the tunnel. In the first part of the tunnel, they were provided with a sum of two numbers of two digits each together with the result. The FR had to confirm whether or not the sum was correct. If correct, he/she continued with the exercise; if incorrect, he/she would have to go back three steps. In the second part of the tunnel, the researcher was narrating through the walkie talkie different four-digit numbers. When the number was the same as the number he/she memorized before the start, the volunteer had to say: \"everything is correct, I continue with the exercise\". After the exercise, the volunteer performed a NASA TLX test (see Figure 5b).
## 3 Simulation and Results
### Area-A
#### 3.4.1 Customized versus Standard Channel Profiles  
The effect of constructing customized CDL channel profiles is investigated. A simulation is performed on area-A, considering terrestrial APs and standard CDL and TDL channel profiles. Standard profiles A and D are used for NLoS and LoS scenarios, respectively. While the average CQI is 6 for customized CDL channel profiles, as shown in Table 8, it is found to be 9 for standard CDL channel profiles and 7 for standard TDL channel profiles. The number of SNs reporting each CQI index is illustrated in Figures 5 and 6, considering the customized and standard channel profiles. The considerable difference in the CQI indices clearly shows the advantage of customizing the CDL channel profile based on the geometry of the environment to obtain more accurate results. In terms of the average network DL throughput, it is found to be 11.51 Mbps for standard CDL channel profiles and 9.04 Mbps for standard TDL channel profiles, as compared with 7.44 Mbps for customized CDL channel profiles. Table 9 summarizes the results for different channel profiles.
## 2 Principles of the Proposed Method
### CD Estimation Principle Based on FrFT
#### 2.2.2 Simulation Analysis of Chirp Parameter Estimation  
The FrFT is used to simulate the chirp parameter estimation of the chirp signal. The chirp signal with an amplitude of 1, initial phase of \(\pi/4\), initial frequency of 30 Hz, sampling frequency of 100 Hz, and frequency modulation slope of 10 Hz/s is generated in the Matlab environment. Set the transform order from 0 to 2 in steps of 0.01, and calculatethe corresponding FrFT spectrum and the curve projected in the fractional domain through discrete sampling FrFT, as shown in Figure 2.  
In order to verify the influence of the chirp parameter and sampling points on the optimal order in FrFT, we first set the different chirp parameter as 10 Hz/s, 20 Hz/s, 40 Hz/s, and 80 Hz/s to simulate the FrFT of the chirp signal. The results are shown in Figure 3a. It can be seen from the figure that the peak position of the chirp signal is closely related to the magnitude of the chirp parameter. The estimated value of the chirp parameter can be calculated according to Equation (11), which verifies the effectiveness of FrFT in the parameter estimation of the chirp signal. Secondly, the FrFT of the chirp signal is simulated with different sampling points of 256, 512, 1024, and 2048, respectively. The results are shown in Figure 3b. When the number of sampling points changes, the optimal order will change, and the smaller the number of samples, the wider the peak, so it is easier to search the optimal order, which greatly reduces the computational complexity.  
From the analysis of the above simulation results for the chirp signal, it can be seen that in the process of parameter estimation, in addition to the change of sampling points  
Figure 3: Schematic diagram of the FrFT optimal order of the chirp signal under different (**a**) chirp parameters and (**b**) sampling points.  
Figure 2: (**a**) FrFT spectrum of the chirp signal and (**b**) schematic diagram of the transform projection in the order domain.  
affecting the optimal order, the setting of a transform order search interval will also affect the selection of the optimal order until it further affects the parameter estimation accuracy. In this regard, we conduct a parameter estimation simulation for different sampling points (256, 512, 1024, and 2048, respectively) and a transform order search interval of 0.01 and 0.001, respectively, when the chirp parameter is 40 Hz/s. We thereby obtain the corresponding optimal transform order value, estimation error, and average error rate under each simulation environment. The detailed simulation results are shown in Table 1.  
It can be seen from Table 1 that under the same number of sampling points and with the reduction of the search interval, the estimated error value of the chirp parameter and the average error rate show a decreasing trend, but it will bring about the disadvantage of increasing computational complexity, and the value of sampling points will also have a great impact on the estimation accuracy and computational complexity.
# Fabrication of Magnesium-Aluminum Composites under High-Pressure Torsion: Atomistic Simulation
## 1 Introduction  
Magnesium (Mg) alloys are of considerable interest nowadays in the automotive and aerospace industry since they have lightweight properties[1; 2]. Moreover, magnesium-based alloys can be biocompatible and biodegradable. However, magnesium has quite low strength, elastic modulus, creep resistance, and formability [3], which considerably limits its applications. Thus, the search for new possibilities to fabricate composites based on Mg and other metals that can demonstrate improved mechanical properties is of high importance. For example, aluminum (Al) can prevent the corrosion process of Mg alloy and possess better properties for both Mg and Al [4; 5; 6].  
One of the possible ways to obtain metallic composites is high-pressure torsion (HPT), during which high compressive and shear stresses result in the formation of the composite structures. To date, several types of composite materials were obtained by HPT from metallic plates: Al-Cu [7; 8; 9; 10; 11; 12; 13], Al-Mg [14; 15; 16], Al-Nb [17], and Al-Ti [18]. Since severe plastic deformation can increase the diffusion in the materials [19; 20], in situ composites can be obtained by HPT through the bonding of different metals, which can lead to the formation of new intermetallic phases. According to the phase diagram, several intermetallic Al-Mg phases can be obtained, which are AlMg, Al\({}_{3}\)Mg\({}_{2}\)-\(\beta\), Al\({}_{30}\)Mg\({}_{23}\)-\(\epsilon\), and Al\({}_{12}\)Mg\({}_{17}\)-\(\gamma\)[21]. The intermetallic Al\({}_{12}\)Mg\({}_{17}\) can significantly influence the corrosion and mechanical properties of the Al-Mg structure.  
Fundamental aspects of deformation behavior between Al and Mg and the formation of intermetallic compounds can be effectively studied by molecular dynamics (MD). Dueto some limitations of experimental methods, MD is widely used to study phase transformations and to determine structural properties of different materials [22; 23; 24; 25]. Moreover, it allows scientists to visualize the structure on the atomistic level, analyze the distribution of atoms during different processing stages, and calculate physical or mechanical properties. MD was used for studying of effect of grain boundary segregation on the deformation mechanisms and mechanical properties of Al-Mg alloys [26; 27; 28], reduction of tensile strength for Al matrix with Mg inclusion [29], strengthening effects of basal stacking faults in Mg [30], etc.  
In this work, the interactions between Al and Mg on Al-Mg interface under compression combined with shear load using the MD simulation method is studied. This combination of compression and shear is the attempt to realize HPT which was successfully used to obtain in situ composites. The present work is the continuation of the previous work by authors of [31], in which preliminary results were published. To study the strength of obtained mixed structure on the Al-Mg interface, a tensile strain is applied to the sample.
## 7 Limitations and Future Work  
Although our proposed method demonstrates significant improvements in model compression and performance, several limitations remain. One key challenge lies in the trade-off between the compression ratio and accuracy. Although our sensitivity-based rank selection method helps mitigate performance loss, certain architectures, especially those with deeper layers, experience a slight drop in accuracy after aggressive compression. The balance between reduced model size and the maintenance of high performance is a complex issue that warrants further exploration.  
Another limitation is the computational overhead introduced by some tensor decomposition methods. Techniques like CP and Tucker decomposition, although effective in reducing model size, can introduce complexity in implementation and require additional computation during both training and inference. Optimizing these decomposition methods to further reduce computational cost, especially in low-power edge devices, remains a critical area for future research.
# Similarity-Based Decision Support for Improving Agricultural Practices and Plant Growth
## 3 Results
### Sensitivity Analysis of Input Variables
#### 3.1.1 Benefits  
The predictive models developed in this study directly support farmers by offering tailored insights derived from peers facing similar environmental and operational conditions. For example, a farmer experiencing reduced yield during spring planting can use the system to identify others with comparable soil, climate, and humidity profiles who achieved better results. By analysing what fertilisers or pest management strategies those peers used, the farmer can make more informed decisions for future crop cycles. Similarly, recommendations for optimal planting periods or seed suppliers can be extracted by identifying consistent patterns in successful neighbouring profiles. These applications demonstrate the model's role in enabling data-driven, context-aware decisions on the ground.  
Both KNN and ANN-LSH models enable the provision of personalised recommendations for farmers sharing similar climatic, soil, and crop conditions. The system can suggest agricultural practices associated with successful harvests or inputs (such as fertilisers or pest control methods) that have contributed to healthier crops. These data-driven recommendations can lead to improved productivity, higher-quality yields, and increased income.  
KNN offers a straightforward approach that is easy to implement and does not require intensive training phases. It is particularly effective for basic similarity-based searches where transparency and interpretability are key.  
On the other hand, ANN-LSH provides scalability and efficiency for real-time applications. It excels in scenarios where large datasets must be queried quickly, enabling the rapid identification of similar farmers without sacrificing too much accuracy. This makes it ideal for operational environments with continuous data updates.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **KNN Model** & **ANN-LSH Model** \\ \hline
1 & Water Temperature & Humidity Level \\
2 & Soil Type & Pest Management \\
3 & Humidity Level & Water Temperature \\ \hline \hline \end{tabular}
\end{table}
Table 6: Top influential variables identified through sensitivity analysis.  
In addition to supporting decision-making, the approach fosters knowledge sharing among farming communities. By building a network of similar profiles and outcomes, the system promotes the diffusion of best practices across farmers facing comparable challenges, ultimately leading to improved agricultural resilience and innovation.
## 2 Materials and Methods
### Model Application
#### 2.2.3 Model Calibration  
After the model set up and initialization with measured soil data, the HydroPSO--a model independent R package based on particle swarm optimization algorithm developed by [PERSON] and [PERSON] [48]--was used for the calibration. The HydroPSO can be coupled with any hydrological model. This latter was also used to carry the sensitivity  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**ID**} & **Surface** \\ \cline{2-3}  & **(km\({}^{2}\))** \\ \hline
1 & Artificial Laricio pine forests with Turkey Oak in subcanopy layer & 0.3872 \\
2 & Low density of artificial Laricio pine forests & 0.0444 \\
3 & Artificial Laricio pine degraded forests & 0.1108 \\
4 & Natural and artificial Laricio pine forests (low density) & 0.258 \\
5 & Natural Laricio pine forests (high density) & 0.08 \\
6 & Artificial Laricio pine forests crossed by fire & 0.0256 \\
7 & Chestnut coppice, Alder and poplar riparian forests, Artificial chestnut forests and mixed Laricio & 0.3748 \\
8 & pine and Chestnut forests & 0.0232 \\
9 & bare soil and outcropping rocks & 0.0772 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different land covers and corresponding surface area of Bonis catchment.  
Figure 3: Localization of the Bonis basin with stations.  
analysis of the model. In order to couple the HydroPSO with the FEST-WB, minimal modeling efforts were required to exchange inputs and outputs of the simulations.  
Three text files were prepared. The first contains the list of parameters, to be calibrated and their locations in the model input files); the second defines the range of variability of parameters and the last text file contains the observations (hourly runoff discharges for this study). The interface between the HydroPSO and the FEST-WB, requires two R wrapper functions to read the model output and the observations required for the calculation of the goodness of fit function (GoF). Several GoFs are implemented in the HydroPSO such as: Root Mean Square Error (RMSE), Normalized RMS (NRMSE), Pearson correlation coefficient (r), coefficient of determination (R2), modified NSE (mNSE), index of agreement (d), coefficient of persistence (cp), percent bias (pbias), and Kling-Gupta Efficiency (KGE) [48]. Details about all the functionalities implemented in the HydroPSO, are reported in the package documentation. The number of particles (swarm size) was set to 20 and as goodness of fit function the efficiency NSE was selected. NSE was proposed by [PERSON] and [PERSON] [49] varying between \(-\infty\) and 1.  
The work was subdivided in two parts:  
* The hydrological part: sensitivity analysis and calibration using measured runoff data.
* The forest growth part: sensitivity analysis and calibration using dendrological measurements.