esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this paper, we go a step further and propose the use of the developed technology to measure online distributed dark I-V curves of the PV modules in the plant. The online measurement refers to the acquisition without the need to disconnect the PV module from the string to which it is connected. It is a distributed strategy, since all the electronics required for the I-V measurement are located in the PV modules. Therefore, the proposed strategy implies the automatic acquisition of dark I-V curves by the developed electronic device, which is included in the PV modules, without the need for the onsite disconnection of any component in the plant. This is especially interesting for PV plant operators, who can obtain the dark I-V curves of the modules, which give relevant information of the individual modules, as has been outlined (characteristics of the device, to analyze various failure modes, to determine solar cell parameters, for monitoring the degradation of modules; to diagnosis or characterize certain defects and failure modes) with the advantage of online measures, significantly reducing the onsite workforce, acquisition time, and costs. The paper has been structured as follows: it starts with an introduction to the research in Section 1, followed by the materials and method presented in Section 2, the results and their discussion in Section 3, and the main conclusions in Section 4.
## 3 Results  
### Diurnal Cycle  
As illustrated earlier regarding angular effect, a canopy attenuates a larger fraction of the incident solar radiation in the morning and a much smaller share at noon. Therefore FPAR is expected to have a valley around noon.  
Figure 2 shows the normalized FPAR for each day in 2005 and 2006 from both observations and model. From the site-observed FPAR figures (Figure 2(a,b)), we can see the minimum value occurring around noon in the early-growing months (March, April and May, MAM), which is only around 30% of the maximum FPAR. It verifies the angular effects of FPAR that we illustrated earlier. Minimum value is also shown around noon in MAM in the model simulated FPAR (Figure 2(c,d)), but is around 80% of the maximum FPAR. Figure 3 shows the monthly gross primary production (GPP) estimation from site observations [55]. It shows that the vegetation at the site starts photosynthetic activities in March and peaks in July. This peak possibly indicates the fully-grown canopy (_i.e._, high LAI values). By comparing Figures 2 and 3, we notice that the diurnal cycle is more significant before the GPP peak month (usually June for this site). One possible reason is that the between-crown gaps have been filled in by growth of leaves (high LAI) when GPP peaks. This assertion might explain the inter-annual differences between 2005 and 2006. Diurnal cycles exist with a clear valley pattern around noon after the GPP peak (July) in 2005, but not in 2006. Correspondingly, the GPP peak in 2005 is around 0.5 mol/(m\({}^{2}\)-s) lower than that in 2006 (Figure 3). Since GPP is a good indicator for LAI, the lower GPP in 2005 suggests a lower LAI, and thus between-crown gaps cannot be filled by lower LAI canopies.  
The CLM4 FPAR diurnal cycle is shown in Figure 2(c,d). The model shows a much weaker diurnal cycle in MAM without a valley around noon. This suggests that CLM4 did not adequately simulate the solar angular effect, although some sun angle dependence may have been included in the model. As for the angular effect we discussed, the solar zenith angle change from 90 \({}^{\circ}\)in the morning to a minimum angle at noon should result in a large change in FPAR. In sum, we have three findings: (1) the site level FPAR diurnal cycle shows patterns that we expected when canopies are not fully grown; (2) the CLM4 land surface solar radiation partitioning scheme does not adequately reproduce the diurnal cycle in FPAR at these times; and (3) though the fractional cover of the canopies do not change, leaf growth influences between-crown gaps and as such the angular effect in FPAR.
# Numerically Optimized Fourier Transform-Based Beamforming Accelerated by Neural Networks
## 5 Replacing Optimization Algorithms by Trained ANNs
### Generating Datasets for BFNN Training for Single-Beam and Dual-Beam Cases  
Datasets consisting of 3200 and 1000 data samples at 5 GHz and 10 GHz have been analyzed for single-beam and dual-beam cases using the PMS model shown in Figure 3, respectively. The datasets are generated so that the main beam directions \((\theta_{i},\varphi_{i})\), \(i=1,\ldots L\)  
Figure 11: Block diagram and graphical representation of a Feed-Forward Neural Networks (FFNN) approach. (**a**) Connection of BFNN and control FFNN. (**b**) Internal structure of an FFNN.  
become the inputs of the training model and a vector of \(M\times N\) reflection phase values is the output of the training model. We have taken 32 arbitrary beam directions distributed approximately equidistantly in the hemisphere \(z>0\) with at most \(75^{\circ}\) beam tilt angle to generate the datasets to train the BFNN. The generated datasets include 100 data points for each direction. Approximately 70% of the data are used to train the network, and the remaining 30% are utilized for validation. We have used 2240 and 700 samples to train the network in the single-beam and the dual-beam cases, respectively, and 960 and 300 samples were used for validation. Instead of using the phase values directly in the training process, we apply trigonometric sin and cos functions to the phases and use the resulting sine and cosine values in the training in order to mitigate the 2\(\pi\) phase jumps. This approach has proven to work better than any phase unwrapping. Regarding the volume of training data, it should be mentioned that, usually, the ratio between the number of samples and the number of features within each sample is more important than just the total number of samples; therefore, we consider the generated dataset volume to be sufficient for our purpose.
# Initial Positioning Assessment of BDS New Satellites and New Signals
###### Abstract  
With the official announcement of open service since the end of 2018, the BeiDou navigation satellite system (BDS) has started to provide global positioning, navigation and timing (PNT) services. Thus, it is worth assessing the positioning service of new BDS satellites and signals. In this paper, we comprehensively assess the system status and the global positioning performance of BDS regarding single point positioning (SPP) and real-time kinematic (RTK) performance. Results show that the signal in space range error (SISRE) of BDS-3 satellites is superior to that of BDS-2 satellites, showing an overall accuracy of 0.71 m versus 0.97 m, which is competitive with GPS and Galileo. With the contribution of BDS-3, the number of global average visible satellites has increased from 5.1 to 10.7, which provides a mean global position dilution of precision (PDOP) value better than 6 at 99.88% and the mean availability of basic PNT performance is also improved from 35.25% to 98.84%. One week of statistical results from 54 globally distributed international GNSS service (IGS) stations show that the root mean square (RMS) of SPP accuracy is 1.1 m in horizontal and 2.2 m in vertical, which is at the same level of GPS. The new B1c and B2a signals show a smaller observation noise than B1I, and SPP performance of B1c is similar to that of B1I. However, the positioning precision is slightly worse at the B2a frequency, which may be due to the inaccurate BDS ionosphere correction. As for short baseline RTK, baseline accuracy is also improved due to the increased number of new BDS satellites.  
BeiDou navigation satellite system; signal in space range error; position dilution of precision; single point positioning; real-time kinematic 2019 11 1320 10.3390/rs11111320
# Evaluating the Feasibility of Intelligent Blind Road Junction V2I Deployments
###### Abstract
Figure 2: Example of an FSN, with several sensors mounted at a high vantage to appropriately observe a blind junction scenario. use case. Notably, [PERSON] et al. [36] demonstrate using DSRC that direct V2V communications are likely not a feasible option given Line-of-Sight (LOS) constraints. [PERSON] et al. [37] and [PERSON] et al. [38] develop and demonstrate V2I use cases using Multi-Access Edge Computing (MEC) [39] technology, demonstrating its efficacy over cloud computing. The aim of this work is to investigate the wireless access challenges of a blind road junction V2I use case, specifically regarding the degree to which the trade-offs between network performance and implementation cost affect the feasibility of the V2I system. In particular, this work intends to study only the technical feasibility of the V2I system. Legal, standardisation, or insurance implications surrounding the security, privacy, and risk factors of this concept are not within the scope of this work. Additionally, the experimental methodology and data analysis techniques can be replicated using off-the-shelf equipment and therefore can be easily re-implemented, repeated, and augmented. The remainder of this paper is structured as follows; an overview of the chosen small-scale V2I use case is presented in Section 2. The experimental methodology used to conduct this study is outlined in Section 3. Section 4 presents the results of the measurement study and discusses the findings for each of the network architectures. Section 5 discusses the limitations of this study and future work. Finally, Section 6 concludes this paper.
## 2 Related Works
### Interference Coordination Strategies in Heterogeneous Networks
The study [34] presents a novel heterogeneous framework to combine GEO and LEO systems to enhance uplink spectral efficiency. This framework uses two satellites simultaneously to receive signals from ground users and employs non-orthogonal multiple access techniques for frequency resource multiplexing. The reference [35] proposes a dynamic spectrum-sharing method, which continuously optimizes power allocation to ensure that the services of low Earth orbit satellites do not affect the service quality of GEO beams. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Key Strength** & **Main Limitation** \\ \hline \multicolumn{3}{c}{Part 2.1: Resource Allocation Strategies} \\ \hline
[10] & Improves energy efficiency and reduces traffic load & Lacks interference handling \\ \hline
[11] & Reduces latency with caching and MEC integration & GEO–LEO scalability not addressed \\ \hline
[12] & Enhances QoE with adaptive video streaming & Focused on terrestrial systems \\ \hline
[13] & Reduces delay using caching techniques & Limited satellite network considerations \\ \hline
[14] & Enhances multimedia resource allocation performance & Lacks multi-layer satellite focus \\ \hline
[15] & Balances resource scheduling between layers & Limited to static scheduling \\ \hline
[16] & Minimizes delay and offloading time using MEC & Assumes ideal network conditions \\ \hline
[17] & Improves spectral efficiency with NOMA integration & Limited to NOMA-based systems \\ \hline
[18] & Extends device battery life in IoT networks & Focused only on IoT traffic \\ \hline
[19] & Optimizes caching and computation offloading & Lacks satellite-specific integration \\ \hline
[20] & Simplifies hybrid SDN network management & Limited to flow table control \\ \hline
[21] & Integrates network, caching, and computing resources & Limited real-world validation \\ \hline
[22] & Dynamically improves QoE for real-time services & Focuses on terrestrial RAN \\ \hline
[23] & Balances energy use and QoE for video streaming & Targets only video traffic scenarios \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of References in Section 2. Although the ITU and some scholars have studied the interference between LEO and GEO satellites, most research is based on isolated interference scenarios without incorporating specific business contexts, highlighting the need for further study.
###### Abstract  
Wear of the journal bearings in a diesel engine is usually caused by asperity contact. Increased contact potential is caused by the asperity contact between the journal bearing and the shell. This paper analyzes the relationship between the contact potential and asperity contact and presents a method based on contact potential to monitor the bearing wear caused by asperity contact. A thermo-elastic hydrodynamic lubrication (THL) model of the journal bearing on the test bench was established and was verified by measuring its axis orbit. The asperity contact proportion was calculated based on this THL model, and its relationship with the measured contact potential was determined. The main contribution of this paper is to present a new method for monitoring the lubrication conditions of journal bearings in a diesel engine based on contact potential. The results showed that (a) when the minimum oil film thickness was less than 5 \(\upmu\)m, asperity contact occurred between the bearing shell and the journal, which led to a sharp increase in contact pressure and a rapid increase in friction power consumption. Further, (b) there was a positive correlation between contact potential and asperity contact. The contact potential was greater than 0.75 mv when asperity contact occurred. These results proved that asperity contact could be accurately monitored using the contact potential, and the feasibility of using the contact potential to monitor the lubrication condition of a bearing was verified.  
diesel engine; bearing wear; monitoring; contact potential +
Footnote †: journal: Applied Physics Letters B
## 3 Medical Justification  
A substantial problem of lung cancer is represented by the stage of the disease at the moment of diagnosis, as most lung tumors remain undiagnosed due to non-specific, easy-to-ignore signs [33]. That is why when symptoms occur, for the patients that are being directed to the thoracic surgery units, the disease is most probably diagnosed locally and/or in the systemic advanced stage [33; 34; 35]. There have been many attempts to develop screening for lung malignancies. The United States Preventive Services Task Force (USPSTF) and CMS recommend annual low-dose CT lung screening (CTLS) for current and former smokers at high risk [36; 37]. This desiderate is not reachable in poorly developed countries, where the patient's access to a computer tomography is limited and thus is not applicable. Moreover, most patients that reach a CT scanner have a higher stage at diagnosis. In Romania alone, an eastern European 20 million people country, in 2020 only, there were almost 100,000 new cases, while the death toll by this disease was 54,486 cases [38]. These numbers show two things: first, the high number of deaths associated with this disease, and the second, most important, that these deaths occurred due to the high frequency of advanced stages at diagnosis.  
Figure 2: Exemplification of input data (the columns entitled ‘semo011’ and ‘sem012’ correspond to halo sign—referring to part solid ground-glass opacity—and feeding blood vessel sign, respectively).  
Another known issue with lung tumors is with clinical-stage 1A diseased patients. Those patients are fully asymptomatic; most of them discovered the lesion at a routine chest X-ray taken as required for employment, marriage, etc. These patients usually present a unique less than 1-2 cm lung nodule with non-specific radiologic signs. This entity is also known as solitary lung nodule (SPN) and requires special attention. If the nodule is less than 8 mm, the indication is to follow up the patient with a CT scan. Otherwise, if malignancy is suspected, the patient must perform a PET/CT scan or a biopsy [39]. If these procedures are not accessible and/or the patient opts out of performing a biopsy, then the SPM must be monitored by using another CT scan 6 months later [39; 40].  
Considering that PET/CT scans are still a poorly accessible procedure due to low availability and high costs, we look for an alternate method of increasing diagnostic accuracy and estimating the nature of the lung nodule (e.g., malignant or benign) using the standard CT scan of the patients. This helps the thoracic surgeon to establish the optimal surgery moment in case of a thoracic cancer is suggested.  
This decision is usually taken by the surgeon after examining a set of clinical parameters and their evolution in time. Therefore, it is sometimes subjective, it depends heavily on the previous experience of the surgeon and/or the radiologist interpreting the images, and it might not be confirmed by the pathological examination.
## 6 Experimental Results
### Results of Proposed Q-Learning Algorithm
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Instances**} & \multicolumn{2}{c}{**Optimal Solution**} & \multicolumn{2}{c}{**DDQN**} & \multicolumn{3}{c}{**Proposed Q-Learning**} \\ \cline{2-9}  & **Distance** & **Execution** & **Distance** & **Execution** & **GAP** & **Distance** & **Execution** & **GAP** \\  & **(m)** & **Time (s)** & **(m)** & **Time (s)** & **(m)** & **Time (s)** & **(m)** & **Time (s)** & **GAP** \\ \hline ESOGU\_C5 & 3956.00 & 0.50 & 4203 & 0.2212 & 0.06\% & 3989.19 & 0.66 & 0.008\% \\ ESOGU\_R5 & 4977.00 & 0.53 & 5322 & 0.2368 & 0.06\% & 5064.28 & 0.67 & 0.01\% \\ ESOGU\_RC5 & 4944.00 & 0.41 & 5352 & 0.2404 & 0.08\% & 4944.00 & 0.71 & 0.0\% \\ ESOGU\_C10 & 5646.00 & 124.33 & 6705 & 0.3920 & 0.18\% & 5871.79 & 1.89 & 0.03\% \\ ESOGU\_R10 & 4889.00 & 389.74 & 5675 & 0.3960 & 0.16\% & 4898.02 & 2.48 & 0.001\% \\ ESOGU\_RC10 & 5843.00 & 345.63 & 6815 & 0.4011 & 0.16\% & 5851.72 & 2.26 & 0.001\% \\ ESOGU\_C20 & 9125.00 & 11,160.69 & 12,878 & 0.6067 & 0.41\% & 10,203.54 & 5.12 & 0.11\% \\ ESOGU\_R20 & 9974.00 & 11,134.64 & 13,373 & 0.5582 & 0.34\% & 10,292.60 & 5.15 & 0.03\% \\ ESOGU\_RC20 & 9161.00 & 11,113.25 & 11,977 & 0.6027 & 0.30\% & 10,082.14 & 4.76 & 0.10\% \\ ESOGU\_C40 & 16,102.00 & 10,800.05 & 22,483 & 2.5354 & 0.39\% & 17,358.23 & 17.26 & 0.07\% \\ ESOGU\_R40 & 18,276.00 & 10,800.02 & 24,882 & 2.0652 & 0.36\% & 20,906.46 & 12.74 & 0.14\% \\ ESOGU\_RC40 & 17,053.00 & 10,800.02 & 24,563 & 2.0283 & 0.44\% & 19,293.44 & 15.01 & 0.13\% \\ ESOGU\_C60 & 26,773.00 & 10,800.02 & 37,532 & 3.2772 & 0.40\% & 30,392.72 & 26.19 & 0.13\% \\ ESOGU\_R60 & 26,159.00 & 10,800.03 & 37,601 & 2.9562 & 0.43\% & 29,459.82 & 29.88 & 0.12\% \\ ESOGU\_RC60 & 27,905.00 & 10,800.02 & 38,699 & 3.0054 & 0.38\% & 30,143.03 & 33.31 & 0.08\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons of proposed Q-learning solutions with optimal solutions and DDQN. Within the scope of last-mile delivery, algorithms capable of producing high-quality solutions in a short time are critical for companies. Overall, the comparative analysis highlights that the proposed Q-learning not only approximates optimal solutions with high accuracy but also surpasses the DDQN in distance efficiency while maintaining superior execution speed relative to traditional mathematical models. These attributes make Q-learning a highly promising approach for solving the CEVRP in operational settings, offering a balanced trade-off between solution quality and computational efficiency. This study demonstrates that Q-learning can achieve near-optimal solutions in a significantly reduced execution time.
# SCHC over LoRaWAN Efficiency: Evaluation and Experimental Performance of Packet Fragmentation
## 7 Discussion  
The previous section's results show the relationship between the channel occupancy efficiency and three communication parameters: the error probability of a SCHC message, the LoRa spreading factor, and the SCHC window size. Figure 7 shows an expected and intuitive behavior of the channel occupancy efficiency and error probability: there is a lower efficiency when the error probability of a SCHC message increases. The efficiency is calculated both experimentally and theoretically from the message transmission periods in a SCHC window using Equation (1). The message flow in Figure 5 is based on the SCHC standard defined in [1,2]. Using Equation (4), the only difference between the efficiency calculation using the model and the experimental data is related to the transmission periods of the SCHC Fragments and the SCHC ACK request.  
The relationship between the channel occupancy efficiency and the spreading factor was not observed to be linear. The efficiency increases when the spreading factor increases, but an exception occurs for the spreading factor 10. This can be verified by looking at  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & \multirow{2}{*}{**SF**} & \multicolumn{2}{c}{**Maximum**} & \multirow{2}{*}{**Physical Bit**} & \multicolumn{2}{c}{**Total ToA**} & \multirow{2}{*}{**Maximum Efficiency**} \\
**Rate** & & & & **Size (Bytes)** & & \\ \hline
0 & 12 & 51 & 250 & 141,970 & 15\% \\
1 & 11 & 51 & 440 & 125,197 & 10\% \\
2 & 10 & 51 & 980 & 113,493 & 5\% \\
3 & 9 & 115 & 1760 & 59,125 & 6\% \\
4 & 8 & 222 & 3125 & 36,030 & 6\% \\
5 & 7 & 222 & 5470 & 35,153 & 3\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Maximum theoretical channel occupancy efficiency in AU915 band (with process time).  
Figure 9: Maximum theoretical channel occupancy efficiency in function of SCHC window size and spreading factor.  
Tables 6 and 7. In this table, we have extracted the maximum payload size and physical bit rates from [12]. The time on air was calculated from [13]. Thus, the maximum efficiency only depends on the frame duration, calculated from Table 5 and multiplied by the number of messages of each type, as defined in Table 4.  
The final parameter studied was the SCHC window size. When focusing on the maximum theoretical channel occupancy efficiency, i.e., the efficiency when the error probability is zero, we can deduce that the maximum theoretical channel occupancy efficiency does not depend on the SCHC window size. This is interesting, as the IETF standard uses a window size of 10 bytes; however, our analysis showed that the maximum channel occupancy efficiency is constant regardless of the window size. Figure 9 shows this behaviour.
## 5 DTLI Control Features
### Decoupled Modulation Control  
As previously mentioned, the output voltage of a DTLI is obtained as the difference between the voltages generated by the two individual inverters.  
Under decoupled modulation, the reference voltage \(V_{ref}\) is decomposed into two components, namely, \(V_{ref,Master}\) and \(V_{ref,Slave}\), which are individually supplied to the master and slave inverters, respectively.  
This decomposition process is mathematically represented by Equation (13).  
\[\mathrm{V_{ref}}^{\mathrm{i}\theta_{DTLI}}=\mathrm{V_{ref,Prine}}^{\mathrm{i} \theta_{Master}}-\mathrm{V_{ref,Aux}}\mathrm{e}^{\mathrm{i}\theta_{Slave}} \tag{13}\]  
The reference vector synthesis diagram is illustrated in Figure 15. Based on Equation (13) and the diagram, it is evident that there are four user-defined parameters, which provide substantial control flexibility that can be strategically exploited.  
For instance, the authors in [69] adjusted the parameters \(\theta_{\mathrm{Master}}\) and \(\theta_{\mathrm{Slave}}\) to enhance the output voltage profile of DTLIs.  
To minimize output current fluctuations, the authors of [96] conducted an analytical study and subsequently proposed a control strategy grounded in decoupled modulation.  
Researchers in [111; 119; 120; 121; 122; 123; 124; 125; 126; 128; 129; 130; 131] focused on capacitor voltage regulation for TI-DCB configuration and the DCB-FC configuration of DTLIs, respectively.  
Nevertheless, despite its high flexibility, decoupled modulation introduces undesired voltage steps at the inverter output [119], which can deteriorate the inverter's output performance.  
Figure 15: DTLI decoupled modulation principle.