esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The usage of random modulated radar signals was formally described in the year 1959 in [1], often treated as the origin of noise radar technology (NRT). The authors of [2] even trace back the concept of noise radar to an experiment performed in the year 1897. A general introduction and details on noise radar signal processing can be found in [3].  
Noise radar technology is well known for its main benefits which are its low probability of intercept (LPI) characteristics [4], its inherent robustness against mutual interference [5, 6], and its close-to ideal, thumbtack-like, ambiguity function (AF) [7]. In the latter, neither range nor Doppler ambiguities exist and range-Doppler coupling is absent. This paper adds a new feature to the list of benefits. It is shown that a very precise determination of the velocity of a target may be performed on noise radar data even when the duration of the recording is too short to perform conventional Doppler analyses of the same precision. The work is structured as follows. The introduction continues with background information on conventional Doppler analyses and motivates potential applications for the presented processing scheme. Section 2 defines a formalism to quantify the Doppler tolerance in noise radar, filling a current gap in the scientific literature. The new signal processing scheme for Doppler extraction in noise radar data is presented in Section 3 and applied to experimental data in Sections 4 and 5. Finally, a discussion of the achievements is given in Section 6.  
The investigation presented in this paper is a follow-on research based on the findings of [8], a PhD thesis completed during doctoral studies at the University Tor Vergata of Rome, Italy, in cooperation with the German research institute Fraunhofer FHR.
## 3 Discussion of Experimental Results
### Discussion of Experimental Results
#### 3.3.2 Optimization of Huff and Puff Rounds  
With the increase in the huff and puff rounds, the oil replacement efficiency will be reduced as well as the extraction efficiency, and the reasonable number of huff and puff rounds needs to be optimized. The changes in the NMR curves during the whole process of the huff and puff rounds and the extraction degree of different rounds are shown in Figures 14 and 15 and Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Huff and Puff Rounds** & **State** & **Signal** & **Cumulative Extraction Level** & **Extraction Level** \\  & & & **(\%)** & **(\%)** \\ \hline \multirow{4}{*}{1} & Stewing well 65 h & 4841.620341 & 0.00 & \\  & 23 MPa & 4743.884626 & 2.02 & \\  & 20 MPa & 4647.844035 & 4.00 & \\  & 18.5 MPa & 4545.979105 & 6.11 & \\ \hline \multirow{4}{*}{2} & 22 MPa & 4500.34 & 7.05 & \\  & 20 MPa & 4457.713783 & 7.93 & \\  & 18.5 MPa & 4395.258746 & 9.22 & \\ \hline \multirow{4}{*}{3} & 21 MPa & 4386.35 & 9.40 & \\  & 19 MPa & 4353.821 & 10.08 & \\  & 17.5 MPa & 4339.02 & 10.38 & \\ \hline \multirow{4}{*}{4} & 20 MPa & 4324.948887 & 10.67 & \\  & 16 MPa & 4316.79 & 10.84 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: NMR signal data for different rounds.  
Figure 13: Crude oil changes during well casing in different rounds of huff and puff experiments. (**a**) Change in NMR signal volume for round 1 stewed wells; (**b**) change in NMR signal volume for round 2 stewed wells; (**c**) change in NMR signal volume for round 3 stewed wells; (**d**) change in NMR signal volume for round 4 stewed wells.  
From the analysis of the extraction degree and single-round extraction degree of the four rounds of huff and puff, it can be seen that the effect of the first three rounds of huff and puff was evident, and the single-round extraction degree was 6.11%, 3.11%, 1.16%, and 0.46%, respectively, in which the first three rounds of huff and puff phase extraction degree per round accounted for 56.4%, 28.7%, and 10.7% of the extraction degree, respectively, and the optimal recommended number of rounds of huff and puff was three.
# Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models
## 4 Experiment Results
### On-OFF Model  
We set the ON/OFF mode parameters as follows: (i) the server remains in the ON and OFF states for exponentially distributed time periods \(T_{\text{ON}}=T_{\text{OFF}}=20/40/60\) s; thus, the server changes its state every 20, 40, and 60 s, respectively; (ii) messages are processed with a mean service demand \(D=0.125\) s; (iii) there is sufficient buffer capacity so that no messages are dropped; and (iv) messages arrive to the queue with a mean rate varying from 0.05 to 4 messages per sec (\(\lambda_{max}=4\) messages/s). By applying \(\lambda\) rates greater than 4 messages/sec, the system saturates. Using the above settings in our simulator, we ran the system and derived the simulated curve of the mean response time for several \(\lambda\) rates as depicted in Figure 5.
# Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression
## 7 Discussion  
Elastic resource distribution is a hotspot in cloud data center technology research. In the beginning, this paper improved online SVR to predict the load time sequence of stream data and predicted the future time window data load based on historical data load, so that resources can be allocated to reduce cloud resource distribution delay in advance. Then, using to the relationship between the number of virtual machines in the time window and the maximum throughput in the time window, it finally gave a flexible distribution algorithm according to the stream data load prediction value and MSTW performance model to guarantee the SLA of the system.  
The key point of this study is to guarantee the system SLA but fails to consider the subsequent impact of the virtual machine distribution or recovery; If the workload suddenly changes (a traffic surge), it can't allocate cloud resources as effectively as usual. In the next step, the author will consider the problem of increasing or reducing the energy consumption of the cloud data center when the virtual machine is increased or reduced, and it also will use another new algorithm to deal with the major variables.
# Positioning-Based Uplink Synchronization Method for NB-IoT in LEO Satellite Networks
## 3 Position-Aided Uplink Synchronization Method
### NPSS Detection
#### 3.1.2 Fine Estimation of Time Delay Observations  
In NB-IoT LEO satellite communication scenarios, situations such as low satellite elevation angles or signal blockage often result in low SNR at the terminal. To enhance synchronization performance under low SNR conditions, a more accurate synchronization algorithm is applied in the frequency domain based on the coarse estimation. Performing more intensive computations around the coarse synchronization point can improve estimation accuracy under low SNR conditions. Specifically, the sampling point obtained from the coarse estimation algorithm is used as the coarse synchronization point. Then, leveraging the good frequency-domain correlation properties of the ZC sequence, precise synchronization is performed in the frequency domain.  
First, a segment of 40 samples before and after the coarse synchronization point \(\hat{n}_{k}\) is selected and transformed into the frequency domain using an FFT of length \(N\) to obtain \(r(k)\):  
\[r(k)=FFT[y(n)] \tag{9}\]  
\(r(k)\) denotes the FFT result of the received signal when processing begins from some sampling point before timing synchronization is achieved.  
\(s(k)\) is the locally generated 11-element ZC sequence; its generation formula is given in Equation (1). By performing circular shifts on \(s(k)\), 11 local reference sequences \(zc_{1},zc_{2},\ldots\ldots zc_{11}\) are obtained. \(k\) is a counting variable. Each of these reference sequences is then correlated with the FFT-processed received signal to identify the peak and determine the fine timing point, resulting in 11 correlation values:  
\[u_{i,n}=\sum\limits_{k=1}^{11}r(k)\cdot zc_{i}(k),i=1,2,\ldots,11 \tag{10}\]The maximum value of \(u_{i,n}\) is selected as the metric value for the corresponding sampling point. By performing correlation in the frequency domain with the circularly shifted local sequences and selecting the maximum value, the influence of Doppler frequency offset is mitigated. This process is repeated for the sampling points near the coarse synchronization point, and the metric values of each sampling point are plotted. The point corresponding to the maximum value is designated as the fine synchronization point \(\hat{n}\):  
\[\hat{n}=\max_{n}(u_{1,n},\ u_{2,n}\ldots u_{11,n}) \tag{11}\]  
This fine synchronization algorithm effectively enhances detection accuracy in low signal-to-noise ratio environments, thereby improving the overall uplink synchronization performance of the terminal.
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Performance Evaluation Metrics
#### 4.1.1 Average Communication Cost (\(\bar{C}\))  
The average total communication cost per node per data-gathering cycle can be computed by calculating the mean of the total communication cost of all the nodes for all data-gathering cycles, as given in Equation (19). The lower value of \(\bar{C}\) indicates that the data collection method induces a lesser communication cost on average.  
\[\bar{C}\ =\frac{1}{\mathcal{K}N}\sum_{m=1}^{\mathcal{K}}\sum_{i=1}^{N}\sum_{j=1 }^{L}C_{j}P_{ij} \tag{19}\]  
In Equation (19), \(\mathcal{K}\) is the total number of data-gathering cycles, and \(L\) is the total number of communication channels used by the \(i^{th}\) node. \(C_{j}\) represents the communication cost per byte for communication using the \(j^{th}\) communication channel whereas \(P_{ij}\) is the total packet size for sending a packet from the \(i^{th}\) node using the \(j^{th}\) communication channel.
## 2 Materials and Methods  
### The Framework of the Proposed Network  
The framework of our proposed network is shown in Figure 1. The network consists of a Raman spectrum enhancement module (RSEM), a Raman spectrum denoising module (RSDM), and a distributed learning classification module (DLCM). First, we use a Raman spectrometer to obtain spectral data from the sample and use relevant algorithms to preprocess the data to eliminate the impact of the spectrometer itself or environmental factors on the data. Subsequently, the preprocessed data is input into the RSEM for training, thereby amplifying the sample dataset. The blue box represents the RSEM module, and the core of the module is generative-adversarial. As shown in the upper part of the blue box, the generative adversarial network achieves data generation by training the generator network and discriminator network. Specifically, the type of generative adversarial network is the WGAN; due to its excellent performance in generating images, it has been improved and utilized as the network for Raman spectroscopy data amplification in this experiment. At the same time, in order to enable the WGAN to train normally on datasets with small amounts of data, we used a large microbiological Raman dataset for pre-training and then froze and transferred the trained parameters. In addition, experiments have proven that using transfer learning in an RSEM can also speed up network training and improve the quality of generated data. After waiting for the RSEM to complete training, the data generated in the module will be input into an RSDM for further processing. In this module, we use the improved UNET network to denoise the generated data. As shown in the orange box, the network can be viewed as an encoder and decoder. The encoder compresses the dimensions of the data, while the decoder is used to restore the dimensions of the original data. The network uses a loss function to adjust network parameters by minimizing the error between input and decoded data, thereby achieving the goal of noise reduction. The network can reduce the noise generated by WGAN training, thereby improving the classification accuracy of the downstream classification model. The green box displays the general framework of the DLCM module, which includes a server and multiple clients. The server receives the parameters trained by the client, generates global parameters through algorithms, and sends them to the client. In the client, we use residual networks to avoid gradient vanishing during the iteration process, effectively improving the accuracy of classification.
## 3 Statistical Analysis
### Hourly Values
#### 3.1.8 Modeling Radiation Indices  
The relationship between the direct normal transmittance (\(k_{n}\)), the diffuse fraction (\(k_{d}\)), the direct fraction (\(k_{b}\)), the beam clearness index (\(k_{bt}\)) with the clearness index (\(k_{t}\)) and DBR with the beam clearness index (\(k_{bt}\)), are illustrated in Figure 22. The fourth graph of the layout shows the relationship between the direct fraction and the direct normal transmittance which is expressed as a quadratic equation:  
\[k_{b}=0.023+1.993*k_{n}-1.155*k_{n}^{2}\ R^{2}=0.968 \tag{34}\]  
The correlation between the diffuse to beam ratio and the beam clearness index (Figure 22f) is given as:  
\[DBR=0.316*k_{bt}^{-0.937} \tag{35}\]  
Almost similar statistical values were obtained by [PERSON] [2] in UK.  
The generic multiple predictor logistic model developed by [PERSON] et al. [23] known as BRL model (Boland-Ridley-Lauret) was used to estimate the diffuse irradiance. The model  
Figure 21: Monthly boxplots of: (**a**) clearness index (\(k_{t}\)); (**b**) clearness index of direct normal irradiance (\(k_{n}\)); (**c**) direct horizontal fraction (\(k_{b}\)); (**d**) direct clearness index (\(k_{bt}\)); (**e**) diffuse fraction (\(k_{d}\)); and (**f**) diffuse to beam ratio (\(DBR=D/B\)).  
is a function of clearness index (\(k_{l}\)), the apparent solar time (AST), the solar altitude (\(\alpha_{s}\)), the daily clearness index (\(K_{T}\)) and the persistence \(\psi\) which is an average of both a lag and lead of the clearness index for each time interval of the day. The model was developed based on the one-year data set and has the following form:  
\[k_{d}=1/(1+\exp(-5.732+5.368*k_{l}+0.031*AST-0.011*a_{s}+3.166*K_{T}+2.051*\psi)) \tag{36}\]  
Almost similar values are obtained by [PERSON] et al. [23] at Adelaide of Australia, i.e., a location with similar climatic conditions with Cyprus. Testing the above equation needs an independent (validation) data set. Then, the estimated direct normal irradiance (\(B_{n^{-}est}\)) is calculated from the following equation:  
\[B_{n-est}=(G-k_{d}*G)/\cos\theta_{z} \tag{37}\]  
Figure 23 shows the BRL diffuse fraction model fit (Equation (36)) to the clearness index (\(k_{l}\)) for the given data set. As shown, the fitting of the spread of the data is much better than a simple line.
## 3 Power Allocation of SCO\(-\)AN
### Complexity Analysis  
In this section, we assert the superiority of ISQP by comparing the complexity of the three algorithms: ISQP, SQP, BPA and COCOA [31]. ISQP and SQP have been introduced in detail in previous sections. The BPA algorithm is a traversal algorithm which searches all directions in each iteration and then selects the best direction. The complexity of BPA algorithm is high and the search direction is greatly affected by the step size. BPA is also likely to search in the wrong direction. The complexity of ISQP, SQP, and BPA are shown as follows. The change of angle for BPA is set as \(5^{\circ}\) so 36 rounds of calculation are needed for just one iteration. The entries in the tables indicate the calculated amount required for one iteration.  
In Tables 2-5, \(N_{H1}\) denotes derivative of \(g_{1}(x,y)\), and \(N_{H2}\) denotes derivative of \(g_{2}(x,y)\). The amounts of computation for derivative of \(g_{1}(x,y)\) in SISO and MIMO are different, so we use \(N_{H1}\) in place of the amounts of computation for derivative of \(g_{1}(x,y)\). Similarly, \(N_{f1}\) denotes derivative of the objective function, and \(N_{f2}\) denotes second derivative of the objective function. Four times of calculation are needed for the second second derivative of the objective function. The objective function is a composite function, so \(N_{f2}\) is much larger than \(N_{f1}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Complexity for Each Iteration** \\ \hline ISQP & \(16(N_{H1}+N_{H2})\)+\(N_{f1}+137\) \\ SQP & \(122+16(N_{H1}+N_{H2})\)+\(N_{f1}+N_{f2}\) \\ BPA & \(36\,N_{f2}\)+144 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Complexity Analysis for SISO under Perfect Channel Estimation.
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 3 Key Challenges in AI for Constrained Devices
### Energy Efficiency and Thermal Management  
Energy efficiency is paramount in many edge deployments, especially in battery-powered or energy-harvesting scenarios. Executing computationally heavy AI tasks often leads to rapid battery drain, which not only shortens device lifespan but can also introduce thermal management challenges if components overheat [54]. The interplay between computation and power usage is complex: while simpler models require fewer multiply-accumulate operations, they might necessitate more frequent inference cycles to achieve real-time responsiveness. Designers must carefully weigh these trade-offs when deciding on an AI architecture or inference schedule [55].  
Furthermore, certain embedded use cases demand continuous operation to provide always-on monitoring. This requirement can conflict with the desire to conserve power through extended sleep modes [56]. Hence, techniques such as dynamic voltage and frequency scaling, scheduling AI workloads to periods of ample power availability, and advanced power gating for idle subsystems are being explored [57]. These solutions aim to strike a balance between responsiveness and energy consumption, ensuring that the device can sustain its tasks for the desired operational duration. The need for efficient thermal design is also non-trivial, as high-performance AI tasks can generate heat in tightly enclosed form factors, prompting consideration of lightweight heat sinks or self-regulating workloads to avoid temperature-induced failures [19].
# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
###### Abstract  
Mapping water bodies with a high accuracy is necessary for water resource assessment, and mapping them rapidly is necessary for flood monitoring. Poyang Lake is the largest freshwater lake in China, and its wetland is one of the most important in the world. Poyang Lake is affected by floods from the Yangtze River basin every year, and the fluctuation of the water area and water level directly or indirectly affects the ecological environment of Poyang Lake. Synthetic Aperture Radar (SAR) is particularly suitable for large-scale water body mapping, as SAR allows data acquisition regardless of illumination and weather conditions. The two-satellite Sentinel-1 constellation, providing C-Band SAR data, passes over the Poyang Lake about five times a month. With its high temporal-spatial resolution, the Sentinel-1 SAR data can be used to accurately monitor the water body. After acquiring all the Sentinel-1 (IA and 1B) SAR data, to ensure the consistency of data processing, we propose the use of a Python and SeNtinel Application Platform (SNAP)-based engine (SARProcMod) to process the data and construct a Poyang Lake Sentinel-1 SAR dataset with a 10 m resolution. To extract water body information from Sentinel-1 SAR data, we propose an automatic classification engine based on a modified U-Net convolutional neural network (WaterUNet), which classifies all data using artificial sample datasets with a high validation accuracy. The results show that the maximum and minimum water areas in our study area were 2714.08 km\({}^{2}\) on 20 July 2020, and 634.44 km\({}^{2}\) on 4 January 2020. Compared to the water level data from the Poyang gauging station, the water area was highly correlated with the water level, with the correlation coefficient being up to 0.92 and the \(R^{2}\) from quadratic polynomial fitting up to 0.88; thus, the resulting relationship results can be used to estimate the water area or water level of Poyang Lake. According to the results, we can conclude that Sentinel-1 SAR and WaterUNet are very suitable for water body monitoring as well as emergency flood mapping.  
Poyang Lake; Sentinel-1; SAR; U-Net; water area; water level +
Footnote †: journal: water  
0022
# Indoor Signal Strength Evaluation of the Orbcomm Low Earth Orbit Satellite Constellation +
## 4 Results
### Indoor Measurements
#### 4.2.2 First Floor  
The outer wall of the first floor consists of a layer of bricks, a layer of hollow clay bricks, and insulation in total measuring 30 cm. The slabs between the attic and first floor consist of reinforced concrete measuring 30 cm. Additionally, two small windows were present in this room. The signal strength in this room was the lowest of all above-ground rooms, reaching a maximum value of 55.6 dB-Hz in a total of 13 satellite passes. Nonetheless, all satellite passes were received. Surprisingly, in Figure 4 the satellite pass with the lower elevation angle was received more strongly than the satellite pass with the higher maximum elevation angle.
## 3 Results
### Substituted Benzenes
#### 3.3.7 Benzoic Acids  
Experimental and GC model results are shown in Table 14. To obtain a good description, we introduced an additional substitution parameter of a magnitude of +12 kJ/mol for the COOH to benzene ring correction. In addition, the previously defined additional terms for the methoxy Group (\(-\)152 kJ/mol), the amino Group (\(-\)7 kJ/mol) attached to a benzene ring, and t-butyl attached to a benzene ring (+13.5 kJ/mol) are involved in the benzoic acid series considered here. Once more, as GC methods do not tend to account for steric effects, e.g., neighbour steric overlap, GC methods predict identical or almost the same heat of formation for 2-, 3-, and 4- alkyl-substituted species. The agreement between experimental values and GC model values for 3- and 4-methylbenzoic acid is within chemical accuracy, but for 2-methylbenzoic acid, the experimental value is less negative than the GC value, suggesting steric effects being responsible. We see the same behaviour for ethylbenzoic acid. The difference between experimental results for 2- and 4-ethylbenzoic acid of 15 kJ/mol is to be compared with the B3 LYP calculated energy difference of 14 kJ/mol. The combination of these results is important with respect to a proper GC parametrization, as other GC approaches such Marrero-Gani and Constantin-Gani (implementation in the ICAS software suite [41]) reveal a GC value in good agreement with 2-ethylbenzoic acid and not in good agreement with 4-ethoxybenzoic acid, whereas it should be the other way around, because 2-ethylbenzoic acid is subject to steric effects not accounted for by the GC methods. The same behaviour is observed for the three mono-methoxybenzoic acids, with 2-meythoxybenzoic acid being significantly less stable due to steric effects as confirmed by B3 LYP relative energy calculations. And also the same behaviour is seen for the three tert-butylbenzoic acids. We also observe that agreement between experimental and model