esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
However, drought indices do not accurately reflect the water stress on vegetation, because plant water effectiveness is also influenced by factors, such as groundwater conditions and soil properties [33]. Soil moisture (SM) is a direct indicator of plant water effectiveness. However, it is a difficult task about how to accurately monitor soil moisture. In contrast, SIF is considered as a direct probe of vegetation photosynthesis, which can capture drought-induced physiological changes in vegetation in a timely manner [34]. Thus, SIF is considered to be a good indicator in monitoring drought. Therefore, the aims of this research were: (1) to design and construct an intelligent irrigation control system utilizing the IoT and (2) to evaluate the responses of SIF and NDVI to different drought conditions.
# Estimation of Resonance Frequency for Systems with Contact Using Linear Dynamics Methods
## 7 Discussion  
As presented in the Results section, the differences in resonance frequencies obtained in the tests and estimated with the presented formula were small. In particular, this could be stated compared to the values collected in the method of modal analysis, where the error was of an order higher than for the estimation method.  
Differences in the estimation method and tests may occur for several reasons. The method was prepared on one specific geometry. Several tests for other configurations of the geometry were prepared, but the common aspect was contact that allowed a clear second-mode formation in the second half-cycle. While the contact enforces vibration, that will change its shape during half-cycles, and the method will lead to significant errors or will not be applicable anymore. A similar scenario has to be applied to the system with multiple contacts that are closed during vibration. These are the limitations for the estimation method established with the presented research. More research should be completed to verify more cases to develop the field of applicability.  
Figure 14: Results of the experimental tests for satellite antenna.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Resonance Frequency (Hz)**} \\ \cline{2-4}  & **Mode 1** & **Mode 2** & **Mode 3** \\ \hline Experiment—1 st run & 2000.9 & 2102.8 & 2488.9 \\ Experiment—2 nd run & 2017.4 & 2138.0 & 2530.5 \\ Average of & 2009.2 & 2120.4 & 2509.7 \\ experiments & 2017.8 & 2145.3 & 2532.8 \\ Estimation method & 0.43\% & 1.18\% & 0.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of estimated resonance frequencies with experimental tests for the satellite antenna.  
The accuracy of the method is very good compared to the widely used techniques nowadays, which makes it better tool for resonance frequency assessment than modal only analysis.  
Despite the limitations, the method shows potential for application in systems with similar contact behavior during vibration, as was presented in the tests of the satellite antenna, which was more complex than the cantilever beam.
## 3 Experimental Research
### Step 4: GR Calculations
#### 3.4.2 Ground Resistance of Two Rod Connection  
The calculation of the GR for two and three bars developed in [25], unlike the [PERSON] equations, considers the effect of the GR of the conductor.  
The GR of two rods (see configuration 2 RP of Figure 1) is calculated by Equation (2). \(R_{1}\), \(R_{2}\) and \(R_{m}\) and Rm are obtained by Equations (3), (4) and (5), respectively.  
\[R_{g}=\frac{R_{1}R_{2}-{R_{m}}^{2}}{R_{1}+R_{2}-2R_{m}} \tag{2}\]  
\[R_{1}=\frac{\rho}{\pi d}\biggl{[}\ln\left(\frac{2d}{a^{\prime}}\right)+4k_{1}- k_{2}\biggr{]} \tag{3}\]  
Figure 1: Basic GS configurations applied in each soil.  
\[R_{2}=\frac{\rho}{4\pi L_{r}}\left[\ln\left(\frac{4L_{r}}{b}\right)-1+\frac{8k_{1} \times L_{r}}{d}\Big{(}\sqrt{2}-1\Big{)}^{2}\right] \tag{4}\]  
\[R_{m}=R_{1}-\frac{\rho}{\pi d}\left[\ln\left(\frac{L_{r}}{\sqrt{h\times 2a}} \right)-1\right] \tag{5}\]  
where:  
\(\rho\): ground resistivity, [\(\Omega\cdot\)m]  
\(L_{r}\): rod length, [m]  
\(b\): rod radius, [m]  
\(R_{1}\): resistance to earth of the conductor, [\(\Omega\)]  
\(R_{2}\): earth resistance of the rods, [\(\Omega\)]  
\(R_{m}\): mutual resistance between mesh and rods, [\(\Omega\)]  
\(R_{g}\): overall system resistance, [\(\Omega\)]  
\(d\): separation distance between two rods, \(d>L_{r}\), [m]  
\(h\): conductor burial depth, [m]  
\(a\): conductor radius, [m]  
\(a^{\prime}\): is \(a\) for the conductor on the surface of the earth, [m] or  
\(a^{\prime}\): is \(\sqrt{a\cdot 2h}\) for conductors buried at a depth \(h\), [m]  
\(k_{1}\) and \(k_{2}\): coefficients [see Figures 2 and 3, respectively]  
For the process of calculating the GR of the present study, Equations (6) and (7) are used obtained from curves B of Figures 2 and 3 for \(k_{1}\) and \(k_{2}\), respectively.  
\[k_{1}=-0.05\left(\frac{L_{x}}{L_{y}}\right)+1.20 \tag{6}\]  
\[k_{2}=0.10\left(\frac{L_{x}}{L_{y}}\right)+4.68 \tag{7}\]  
where:  
\(k_{1}\) and \(k_{2}\): are the coefficients  
\(L_{x}\): maximum length of braided conductors in \(x\), long, [m]  
\(L_{y}\): maximum length of mesh conductors in \(y\), width, [m]  
Figure 2: Coefficient \(k_{1}\).  
According to [25], \(x=1\) for the configurations of two linearly connected rods (Section 3.4.2), three linearly connected rods (Section 3.4.3), and three triangularly connected rods (Section 3.4.4).
# Nitrate-Polluted Waterbodies Remediation: Global Insights into Treatments for Compliance
## 6 Final Remarks  
Nitrate-related environmental problems concern all economic sectors and have a particular impact on aquatic ecosystems. It is a serious cross-border problem of global concern that must be decisively confronted because the negative effects are extremely evident in many areas of the planet, and in some of them, they are even bordering on irreversible.  
The immediate step to address the problem of nitrate-polluted waterbodies is to control the main causative process; therefore, awareness should be raised to reduce nitrogen inputs to the soil to slightly lower levels than expected to obtain the optimum yield by applying less nitrogen fertilizer and further limiting livestock density. Treatment of waterbodies that already have excessive nitrate concentrations is also necessary to regenerate damaged ecosystems and reduce associated health hazards.  
As we have described, different physical, chemical, and biological processes (ion exchange, reverse osmosis, adsorption, chemical reduction, biological denitrification, etc.) are known to be effective for denitfying surface and groundwater bodies. Table 2 summarizes the main strengths and drawbacks of all of them. Table 2 does not go into capital investment and energy consumption considerations for the different technologies. Figure 11 groups them according to these two variables.  
Each of the technologies reviewed is certainly suitable in specific situations to achieve significantly high nitrate removal, but all of them suffer from drawbacks when they do not generate by-products and concentrated waste brines, consume a lot of energy, or have a high economic cost.  
Figure 11: Categorisation of nitrate removal technologies according to capital investment and energy consumption.  
The main technologies applied commercially are the biological and ion exchange treatments. Ion exchange technology has long been the proven and reliable treatment route, with emphasis on the use of modern nitrate-selective anion exchange resins. Commercial biological treatments usually consist of fixed film bioreactors in which sand or carbonaceous particles are suspended or fluidized within the reactor by the upward flow of water through the system. The suspended medium provides a large surface area for microbial growth and allows for a high biomass density. The microorganisms in the reactor degrade all incoming nitrates under anoxic conditions. The use of membrane bioreactors and reverse osmosis plants is more focused on desalination processes of waterbodies that besides nitrates, also have a high content of other salts, either for agricultural purposes or to obtain drinking water [31; 43; 142].
# A Temperature Fiber Sensor Based on Tapered Fiber Bragg Grating Fabricated by Femtosecond Laser
## 5 Conclusions  
In conclusion, a tapered temperature fiber sensor based on FBG fabricated by femtosecond laser has been proposed and realized with good reproducibility. By arc-discharge elongation using two standard single-mode fibers, the fiber taper with 25 \(\upmu\)m diameter and 1000 \(\upmu\)m length was fabricated. A reference FBG was inscribed 10 mm away from the fiber taper for comparison, and then the tapered-FBG was inscribed in the aforementioned taper. Both FBGs are first-order, point-by-point direct-written, and each grating length is 1000 \(\upmu\)m. The grating pitches of the tapered FBG and the reference FBG are 535 nm and 550 nm, respectively. The characterized wavelengths of the tapered FBG and reference FBG are 1540.25 nm and 1583.62 nm, respectively. The temperature experimental results show that in the range of 30-350 \({}^{\circ}\)C, the temperature sensitivity of the tapered FBG has increased from 11.0 pm/\({}^{\circ}\)C to 12.3 pm/\({}^{\circ}\)C. In future, a greater improvement in temperature sensitivity could be achieved by optimizing the taper profile as well as the femtosecond laser fabrication technique, which  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**FBG Index**} & **Grating** & **Grating** & **Bragg** & \multirow{2}{*}{**
\begin{tabular}{} \end{tabular}**} & \multicolumn{2}{c}{**Temperature Sensitivity**} \\  & **Pitch (nm)** & **Length (nm)** & **Wavelength (nm)** & **Heating Process** & **Coiling Process** & **Mean Result** \\  & & & & **(pm/\({}^{\circ}\)C)** & **(pm/\({}^{\circ}\)C)** & **(pm/\({}^{\circ}\)C)** \\ \hline
1 & 535 & 1000 & 1540.25 & 12.32 & 12.27 & 12.30 \\
2 & 535 & 1000 & 1540.25 & 12.17 & 12.10 & 12.14 \\
3 & 535 & 1000 & 1540.24 & 12.34 & 12.07 & 12.2 may further promote this proposal to be successfully implemented for sensing other parameters in physical, chemical, and biomedical applications.  
Conceptualization, W.Z.; Data curation, X.L.; Methodology, L.Z. and M.D.; Software, F.L.; Validation, W.Z.; Writing--original draft, W.Z.  
This research was funded by Changjiang Scholars and Innovative Research Team in University (No. IRT_16R07), Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (No. IDHT20170510), National Natural Science Foundation of China (No. 61801030), and General Project of the Science and Technology Program of Beijing Municipal Education Commission (No. KM201811232008).  
The authors would like to acknowledge the research and technical support given by Beijing Laboratory of Optical Fiber Sensing and System, and Overseas Expertise Introduction Center for Discipline Innovation (\"111 Center\").  
The authors declare no conflict of interest.
## 2 Composition of Resource Scheduling Module
### Priority Assignment Module and Dwell Task Model  
The priority assignment module computes the overall priority of each task through a weighted summation based on factors such as the importance of different task types and the threat level of tracking targets. Specifically, it considers a combination of each task's execution deadline (reflecting task urgency), the inherent priority of its operation mode (e.g., confirmation tasks have higher mode priority than tracking tasks), and the threat level of the associated target. Currently, a common method for determining threat level is the analytic hierarchy process (AHP), which calculates threat scores by weighting parameters such as relative radial distance, velocity, angle, and altitude of the target, as illustrated in reference [19]. Alternatively, threat levels may also be provided via an external system. However, since threat level evaluation is treated as an external input to the scheduling algorithm and does not affect the timing design or complexity analysis of the scheduling model itself, this paper does not discuss the specific method for determining threat level in detail. After the priority assignment is completed, the task model input into the task scheduling module.  
Figure 1: Module composition of phased array radar resource scheduling system.  
\[\mathrm{T}=\{P,t_{e},w,\Delta T,p_{x}\}, \tag{1}\]  
In (1), \(P\) denotes the task priority, \(t_{e}\) is the expected execution time, \(w\) is the time window length, \(\Delta T\) is the dwell length, \(p_{x}\) is the transmission power. When a task is executed, its actual execution time should satisfy the condition: \(t_{e}-w\leq t_{s}\leq t_{e}+w\). The actual execution time is determined by the scheduling results of the task scheduling module. The mapping relationship from the task request list to the actual dwell tasks is shown in Figure 2.
## 2 Project Overview  
### Project Background and Construction Method  
The project is located in the urban area of Beijing. The plane dimension of the foundation pit is 350 m long, the width 120-190 m, and the depth of the foundation pit is 32.6-36.9 m. The excavation scale is far beyond an ordinary foundation pit. The construction scheme of the foundation pit is mainly divided into two kinds: the construction of the range of elevation from 0 m to 20.5 m is adopted in the straight way, and the construction of the range of elevation from 0 m to the bottom of the foundation pit is adopted in the reverse way. On the north side of the foundation pit, the two underground layers are supported by reverse soil compression and side span inversion, and the three underground layers are supported by full inversion. On the south side of the foundation pit, a three-axis cement mixing pile water stop wall is added outside the 30 m area of the foundation pit, and the interior adopts the ground connecting wall, the underground three-layer reverse work, and the anchor cable enclosure system. On the north side of the foundation pit, the plan of the first underground layer is adopted, the slope is 8 m, and the engineering piles, ground connecting walls, and bored piles are installed on the platform. The rest of the enclosure is in the form of an underground continuous wall, two layers of reverse slab, and two concrete supports. The internal structure of foundation pit, including the underground diaphragm wall, engineering pile, reverse pile, steel pipe column, slope protection pile, etc., are all operated at the absolute elevation of 13.0 m. The 3-3 profile of the foundation pit is shown in Figure 1. The thickness of the underground continuous wall inside the foundation pit is between 0.8 m and 1.2 m, and the depth is about 52 m, of which the stressed section is 45 m, and the section below 7 m is the extended section. In addition, distributed fixed-point optical cable is used to monitor soil settlement.
# Reflections on the Limited Pervasiveness of Augmented Reality in Industrial Sectors
## 2 Investigating the Causes of the Lack of Pervasiveness  
As briefly mentioned in the introduction, besides the great potential widely recognized by experts, business analysts, and researchers, AR still encounters difficulties in being fully deployed and adopted in process industries and operations.  
For verifying the suspected main reasons behind this slow-pace implementation that have been previously discussed, an empirical semiquantitative approach has been adopted in this research. Surveys in three different industrial sectors have been conducted, after testing AR applications in automotive industry, manufacturing industry, and railway industry. The three different applications have been created for digitally supporting and guiding the users in assembling operations in the automotive and manufacturing industry. Specifically, the chosen tasks were the assembly and disassembly of braking pads and disks of a vehicle for the automotive sector and the assembly and disassembly of a component of a high-speed packaging machine for the manufacturing sector. Differently, in the railway industry, the focus of the application was on supporting the train driver in solving possible software failures during the starting-up phase of the train from the cockpit. In all three cases, the Microsoft HoloLens has been used as AR device. Figure 2 shows three screenshots of the applications. Those in details, the technical prerequisite for their development and their creation in terms of technical choices and issues (still present in several projects [39]) faced during the process are being discussed in a different paper. This work, as already said, focuses on connecting impressions and feelings generated by the tests to the possible causes of the lack of AR pervasiveness.
# STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction
## 4 Results and Discussion  
### Data Pre-Processing  
For the two datasets, we collected traffic parameters into time windows of 5 min and normalized them. We used 60% of the data as the training set, 20% as the validation set,  
Figure 5: Spatial module structure.  
and 20% as the test set. We use the threshold Gaussian method to establish an adjacency matrix and calculated the weights A between each detector according to Equation (9).  
\[w_{ij}=\left\{\begin{matrix}\exp\left(-\frac{d_{ij}^{2}}{\sigma^{2}}\right),&i \
eq j\text{ and }\exp\left(-\frac{d_{ij}^{2}}{\sigma^{2}}\right)\geq\epsilon\\ 0,&\text{otherwise.}\end{matrix}\right. \tag{9}\]  
where \(d_{ij}\) represents the distance between detector \(i\) and detector \(j\). The \(\sigma^{2}\) and \(w\), setting 10 and 0.5 respectively, represent the sparsity and distribution.
# Source/Load-Pull Noise Measurements at K\({}_{\text{a}}\) [PERSON]
## 3 Results
The resulting noise parameters are shown in Figure 3:0 A noticeable difference is found in particular among the extracted traces of \(R_{n}\) and \(NF_{min}\), with a clear trend towards higher values for drain currents from 34.2 mA up. The first two values of drain current lead to very similar results, indicating that the optimum operation must lie somewhere in between. \begin{table}
\begin{tabular}{c c c c c c c} \hline \(B_{L}\) [GHz] & Configurations & Failure Rate [\%] & \(f_{0}\) [GHz] & \(NF\) [dB] & \(R_{n}\) [\(\Omega\)] & \(|\Gamma_{opt}|\) & \(\angle\Gamma_{opt}\) [rad] \\ \hline
2.5 & FWD + RVS & 0.67 & 35 & 1.41 & 7.78 & 0.48 & 1.88 \\
0.5 & FWD + RVS & 2.68 & 35 & 1.36 & 7.09 & 0.45 & 1.99 \\
0 & FWD + RVS & 19.46 & 35 & 1.34 & 6.09 & 0.43 & 2.08 \\
2.5 & FWD & 42.95 & 35 & 1.40 & 7.50 & 0.53 & 1.79 \\
0.5 & FWD & 42.28 & 35.5 & 1.16 & 5.25 & 0.60 & 1.87 \\
0 & FWD & 32.21 & 35.1 & 1.50 & 16.01 & 0.23 & 1.58 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison among different extraction settings. Figure 6: Extracted noise parameters over frequency at different current values. Finally, the black-box characterization of the active device was inserted in the simulation of a mixed-technology low-noise amplifier already described in [22]. Whereas the last three stages of the amplifier are based on 100 nm devices, the first stage features a 60 nm transistor with the same geometry considered above, i.e., \(4\times 35\) um. As can be seen from Figure 7, the noise figure simulated in this manner compares well with the actual measurement performed on the realized amplifier. However, the latter is a classical Y-factor measurement (not corrected for the imperfect match of the source termination), whereas the simulation is referred to a perfect 50 \(\Omega\) resistance.
# Caching Transient Contents in Vehicular Named Data Networking: A Performance Analysis
## 2 NDN Basics  
The NDN architecture was originally conceived for named content dissemination in the Internet [10], but today it is considered to be an enabling networking technology in different application domains, such as Wireless Ad Hoc Networks [11], IoT [12], and Edge/Fog Computing [13; 14; 15]. NDN is based on two packet types that carry hierarchical content names: the Interest, used by consumers for requesting contents, and the Data, used for carrying the content.  
Data packets are originated by a producer/source node, which also signs them to allow per-packet authentication and integrity checks. Any node receiving Data packets can cache them to satisfy further requests. In the following, we refer to as content provider, or simply _provider_, any node in the network that acts as producer or cache.  
Each NDN node maintains three data structures: (_i_) _Content Store_ (CS), used for caching incoming Data packets, (_ii_) _Pending Interest Table (PIT)_, used for recording Interests that were not yet satisfied, and (_iii_) _Forwarding Information Base (FIB)_, used to forward the Interests.  
As shown in Figure 1, at the Interest reception, each node first looks for a matching in the CS. In case of failure, it checks in the PIT for the same pending request. If a matching is found, the Interest is discarded. Otherwise, it looks in the FIB for an outgoing interface (or multiple ones) over which sending the request. Data packets follow the PIT entries back to the consumer(s); they can be cached by on-path nodes according to their storage space. The NDN reference caching implementation is Cache Everything Everywhere (CEE), where nodes cache indiscriminately every incoming Data packet. CEE is usually coupled with the Least Recently Used (LRU) replacement policy.  
Compared to traditional caching systems, such as web caching or content delivery networks, NDN caching shows some distinct features: it is performed on a per-packet basis and at line speed, during the forwarding process. Therefore, cache decision policies that require complex calculations or multiple interactions between network nodes are not affordable, since they would slow down the content delivery [16].
## 4 Discussions of the Results
### Passkey Retrieval Performance  
The passkey retrieval task results, as shown in Tables 3 and 4, further validate the effectiveness of SBA-RoPE in managing extended context windows. The high accuracy of SBA-RoPE in this task across all evaluated context windows, especially with the scaling factor \(s=4\), demonstrates its capability to attend to tokens over long distances without significant loss of performance. This is indicative of SBA-RoPE's efficient handling of the extended context, combining the advantages of PI's non-exceeding bounds and NTK's distribution of interpolation stress across dimensions.  
Notably, the performance of SBA-RoPE in the previously unseen 5 k to 10 k window range with high retrieval accuracy is a testament to its robust extrapolation capabilities. This suggests that SBA-RoPE not only effectively extends the context window, but also ensures that the model can maintain functional coherence and understanding over these longer spans, a critical requirement for tasks involving detailed comprehension and retention of information across large text bodies.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Method** & **1024** & **2048** & **3072** & **4096** & **5120** & **6144** & **7168** & **8192** & **9216** & **10240** \\ \hline Original & **1.0** & **1.0** & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ PI & 0.95 & **1.0** & **0.90** & **0.95** & **0.95** & **0.95** & 0.95 & **1.0** & 0.00 & 0.00 \\ NTK & 0.85 & 0.85 & 0.70 & 0.60 & 0.90 & 0.00 & 0.00 & 0.00 & 0.00 \\ YaRN & 0.80 & 0.65 & 0.85 & 0.80 & 0.75 & 0.55 & 0.60 & 0.00 & 0.00 & 0.00 \\ SBA-RoPE & 0.95 & 0.95 & **0.90** & **0.95** & **0.95** & **1.0** & **1.0** & **0.05** & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of passkey retrieval across different context window sizes for the original Pythia-2.8b model and its variants fine-tuned with a scaling factor of \(s=4\). The highest and second-highest accuracy values at each length are highlighted in **bold** for the highest and underlined for the second-highest.