esa-sceva/satcom-chunk-collection huggingface dataset:
# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 3 The Proposed Congestion Control Mechanism
### Modified Trickle Timer Algorithm  
The backlog information should be distributed through the DIO message in a timely manner in order to detect and react quickly to congestion. As mentioned in Section 1, the standard RPL uses the Trickle timer to control the transmission interval of DIO messages. As long as the network is consistent, the DIO message interval is doubled up to a certain maximum value. The timer is reset to a minimum value when inconsistency is detected [10]. However, when the network is consistent, the long DIO interval may cause the nodes to have inaccurate and outdated congestion information, hence fail to achieve load balancing.  
To address this issue, we propose a modification to the Trickle timer in order to distribute the backlog information in a timely manner while keeping the control message overhead to a minimum. The flowchart of the modified Trickle timer algorithm is shown in Figure 4 and described as follows.  
Figure 4: Modified Trickle timer algorithm.  
The basic idea is to reset the Trickle timer interval when the node suffers a certain number of consecutive queue losses \(Q_{L}(n_{i})\). The intention behind using \(Q_{L}(n_{i})\) is that LLN nodes have small queues that may fill up temporarily even when there is no congestion in the network. This temporary situation may trigger a false congestion that leads to unnecessary overhead, which can be avoided if we reset the Trickle timer after detecting a particular number of consecutive queue losses. The Trickle timer is reset to its minimum interval \(I_{min}\)[10] when \(BF(n_{i})\) exceeds \(\delta\) and \(Q_{L}(n_{i})\) exceeds a certain limit \(\beta\). Once a queue loss occurs, a timer \(X\) is triggered. It is used as a timeout period, that is if no queue losses are detected within this period, the parameters \(Q_{L}(n_{i})\) and \(\beta\) are reinitialized. When the Trickle timer is reset to \(I_{min}\), the value of \(\beta\) is increased by a minimum value \(\beta_{0}\) to decrease strictly the number of times the Trickle timer is reset, i.e., minimize the overhead. Therefore, the proposed reset strategy allows the nodes to acquire the queue backlog information through the DIO messages in a timely fashion to act hastily to congestion events, while keeping the DIO messages overhead to a minimum.  
The proposed modification in Trickle timer adds insignificant computational complexity. In terms of memory resources, each node needs to store \(Q_{L}(n_{i})\), \(BF(n_{i})\), and \(\beta\), whose values are not too demanding for storage. In terms of the number of elementary operations, a single sum operation is added, which is executed upon Trickle timer reset, a single increment operation that is executed upon packet loss, and a single subtract operation that is executed when the timer \(X\) is fired. That is, the computational complexity of the modified reset strategy is \(O(1)\).
## 2 Methodology
### Modeling Approaches
#### 2.3.2. RF Model  
The RF model is commonly used in LHA studies [3; 20; 22; 35; 36]. Proposed by [PERSON] (2001) for multi-class classification and prediction tasks, this ensemble model is composed of a battery of tree classifiers [37; 38; 39]. It combines a random subspace with bagging ensemble learning, which can effectively reduce the overfitting risk of a single tree and significantly improve the model's accuracy and generalization ability [21]. The principal process of the RF model is carried out by the following steps [37; 40]: (1) adopt the Pearson correlation coefficient to analyze the multicollinearity of the data and utilize stratified sampling to generate the training set and test set; (2) use bootstrap sampling to randomly select samples with replacement from the original training set to generate multiple training subsets; (3) randomly select some features to construct decision trees and assign multiple decision trees to different computing nodes for parallel training; (4) obtain the prediction results by the majority voting method. During this process, the accuracy of the model is calculated using the test set, and parameters such as the number of trees and the maximum depth are optimized through cross-validation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \(n\) & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 \\ \hline \(RI\) & 0 & 0 & 0.52 & 0.89 & 1.12 & 1.26 & 1.36 & 1.41 & 1.46 & 1.49 & 1.52 & 1.54 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(RI\) values under the variation in the total number of selected factors.
## 5 Numerical Results
### Accuracy of the New Methodology to Determine Energy Consumption  
As mentioned in Section 1, the new proposed methodology to determine the average energy consumed by an SN per cycle has proved to be more accurate and systematic than those previously proposed. In this subsection, we compare the accuracy of the new methodology and the accuracy of a previous one [23] to determine the average energy consumed by an SN per cycle in the _data_ period \(E_{d,c}=E_{tx,s}^{d,c}+E_{tx,f}^{d,c}+E_{oh}^{d,c}\), \(c=\left\{1,2\right\}.\) Unfortunately, for the previous methodology, the average energy data consumed by an SN per cycle in the _sleep_ mode and in _awake_ cycles are not available.  
A substantial part of the accuracy improvement of the new methodology with respect to previous ones [21, 27, 32, 33, 34] comes from the fact that previous energy computation methodologies required the definition of intermediate parameters, while in the new methodology, the energy computation is solely based on the stationary probability distribution \(\pi_{c}(i,k)\), \(c=\left\{1,2\right\}\).  
As an example, Figure 13 shows the relative error of the average energy consumed by an SN per cycle in the _data_ period in the scenario defined in Table 1. Relative values are obtained as \(|x-y|/y\), where \(x\) is the value obtained by the analytical model, and \(y\) is the value obtained by simulation. Please note that P\({}_{\text{C1}}\) and P\({}_{\text{C2}}\) refer to the relative errors obtained using the previous methodology [23], while N\({}_{\text{C1}}\) and N\({}_{\text{C2}}\) refer to the relative errors obtained using the new methodology when measuring \(E_{d,c}\), \(c=\left\{1,2\right\}\), for C1 and C2 SNs, respectively.  
Observe in Figure 13 that the relative errors represented for N\({}_{\text{C1}}\) and N\({}_{\text{C2}}\) are almost identical at each of the load points studied. Also, the relative errors represented for N\({}_{\text{C1}}\) and N\({}_{\text{C2}}\) have an homogeneous behavior with the load. Finally, we highlight that the relative errors of the new methodology are mostly below 1%, except for \(\lambda_{2}=1\), where it is 1.5%,  
However, the relative errors represented for P\({}_{\text{C1}}\) and P\({}_{\text{C2}}\) tend to have a less homogeneous behavior with the load than the relative errors obtained by the new methodology, and for \(\lambda_{2}=1.5\), P\({}_{\text{C2}}\) achieves a relative error larger than 4.5%.  
Figure 12: Average packet delay for both SNs classes.  
Figure 13: Average relative errors of the current and previous (Pre-method) energy computation methodologies.
## 5 Two-Way Measurement Method in CCFD
### System Model of Two-Way Measurement Method in CCFD
For the \"dynamic compensation\" term, since the maximum inter-satellite communication distance can reach 70,000 km, the maximum inter-satellite transmission delay is approximately 0.23 s, which means as follows:  
\[ \begin{array}{l}t^{r1}-t^{s1}\leq 0.23\text{s}\\ t^{r2}-t^{s2}\leq 0.23\text{s}\end{array}  \tag{42}\]  
According to Equation (39), the \"dynamic compensation\" term depends on the receiving satellite's velocity (which can be calculated from ephemeris data with an accuracy of 0.1 mm/s), the inter-satellite unit direction vector, and the transmission time interval. Since the maximum inter-satellite communication duration is approximately 0.23 s and the inter-satellite distance can extend to tens of thousands of kilometers, the unit direction vector between satellites remains effectively constant during a single transmission slot. Consequently, the \"dynamic compensation\" term can be corrected to an order of \(10^{-2}\) mm, rendering it negligible in practice. For the clock bias correction term, since the frequency stability of onboard atomic clocks is generally better than \(10^{-13}\) s/s [25], and the difference between the two-way transmission/reception times and the reckoning time is no more than 0.5 s in CCFD, the maximum value of the clock bias correction term is only on the order of \(10^{-14}\) s, corresponding to a distance on the order of \(10^{-3}\) mm, which is negligible. In summary, the analysis shows that the error introduced by timestamp reckoning in CCFD is primarily caused by the \"dynamic compensation\" term in the distance correction. After ephemeris and almanac correction, the precision can reach the sub-millimeter level. Compared to the traditional TDHD system, the correction precision is significantly improved, meeting the requirements for high-precision inter-satellite measurement.
## 5 Results  
We demonstrate the computational model described in Section 4, and make a comparison with the exact analytical Equations (13), for the case of a rectangular waveguide with dimensions \(6\times 3\) cm, but this can be re-scaled to study other frequency ranges, and changing the waveguide cross-section can be achieved with minor adjustments. The propagating wave is the lowest TE mode, TE\({}_{10}\). The results are presented as the power reflection \(R_{P}\)power transmission \(T_{P}\), and power absorption \(C_{\text{abs}}\) over the thin dielectric layer of width \(2z_{0}\). First, the model is tested for constant and complex permittivites, similar to those found in [21] for TM\({}_{11}\). Then, frequency-dependent complex permittivites are demonstrated using the dielectric functions described in Section 3.
# Risk of Tree Fall on High-Traffic Roads: A Case Study of the S6 in Poland
## 2 Materials and Methods
### Characteristics of the Data and Test Object  
The study used free measurement data from airborne laser scanning, obtained from geoportal.gov.pl [35], which acts as the central node of the Spatial Information Infrastructure, mediating access to spatial data and related services in Poland. It ensures the quality, timeliness, and reliability of the data. LiDAR data are recorded in LAS format and contain information about the class of a given point (Table 1) and the intensity of the signal reflection, and may have RGB values assigned. It is common to compress these data into LAZ versions because of the large file sizes. In Poland, ALS data are available, covering the entire country, where point densities vary from 4 pts/m\({}^{2}\) to 40 pts/m\({}^{2}\).  
The study area concerns a section of the S6 highway between Kolobrzeg-Goleniow in the West Pomeranian Voivodeship (Figure 2). For data acquisition, the Web Feature Service (WFS) was used to retrieve LiDAR indexes (PL-EVRF2007-NH) in the study area, and a total of 115 LiDAR data sheets were downloaded.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Class** & **Description** \\ \hline
0 & points processed but not classified, \\
2 & points lying on the ground \\
3 & points representing low vegetation, i.e., in the range of 0–0.40 m \\
4 & points representing average vegetation, i.e., in the range of 0.40–2.00 m \\
5 & points representing high vegetation, i.e., in the range above 2.00 m \\
6 & points representing buildings, constructions, and engineering structures \\
7 & noise \\
8 & points representing water areas \\
12 & points from multiple coverage areas \\ \hline \hline \end{tabular}
\end{table}
Table 1: Point classes [35; 36].  
Figure 2: The range of the study and downloaded LIDAR indexes (PL-EVRF2007-NH).  
The key element for carrying out the following study was to obtain a vector representation of the S6 road axis from the Topographic Object Database, which contained the geometry of the road route and its attributes. The length of the route was approximately 95 km.  
For complete analysis, it was necessary to collect data on the winds occurring in the area. This information was obtained from the Institute of Meteorology and Water Management for 2021 and 2022. Four meteorological stations that were closest to the study area (Szczecin, Koszalin, Kolobrzeg, and Smolsko) were selected, and the information regarding the direction and speed of wind gusts was used.
# Novel Faulted-Section Location Method for Active Distribution Networks of New-Type Power Systems
## 5 Simulation Analysis
### Test Case 1: Single Fault  
A single fault is simulated in the active distribution network shown in Figure 7, assuming that \(L_{24}\) is faulty. The master station receives the protection \(e_{12}\) action information, and the protection action region includes sections \(L_{24}\) and \(L_{28}\). The equipment status information is \(I=[1\ 1\ 1\ 1\ 0\ 1\ 1\ 1\ 1\ 0\ 0\ 0\ 1\ 0\ 1\ 1\ 1\ -1\ 1\ 0\ 1\ -1\ 1\ 0\ 1\ -1\ -1\ -1\ 0\ 0\ 0\ 0\ -1\ -1]\). Firstly, the suspicious fault region is located, and the boundary nodes are \(S_{1}\), \(S_{33}\), and \(S_{34}\). Secondly, it is judged that the suspicious fault region does not contain the entire protection action region. Therefore, node \(S_{28}\) farthest from the power supply in the protection action region is the boundary node. Finally, the fault region is obtained according to all boundary nodes, including 23 switching nodes and 21 feeder sections.  
The schematic diagram of the fault region is shown in Figure 8. The fault node status list is shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Node**} & **Upstream** & **Downstream** & **Equipment** & \multirow{2}{*}{**DG**} & **\multirow{2}{*}{**Node**} & **Upstream** & **Downstream** & **Equipment** & \multirow{2}{*}{**DG**} \\  & **Node** & & **Node** & & & **Node** & **Node** & **Status** & \\ \hline
1 & 0 & 2 & 0 & 0 & 18 & 17 & 0 & 0 & 0 \\
2 & 1 & 3 & 0 & 0 & 19 & 17 & 20 & 0 & 0 \\
3 & 2 & 4 & 0 & 0 & 20 & 19  
Multiple simulation experiments are carried out for multiple faults location in non-T-connected sections. The results in Table 9 show that the improved matrix algorithm can accurately locate multiple faults in non-T-connected sections.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### Interference Mitigation in HetNets
#### 4.2.2 Load Balancing and Cell Range Expansion  
3 GPP supports load-aware handovers and offloading mechanisms via CRE and mobility biasing [113]. These strategies aim to balance user distribution across tiers while coordinated resource partitioning (e.g., ABS) ensures that expanded cells do not become interference limited.
# Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
## 7 Data Analysis with Applied OMA Methods  
In this section, a portion of the sampled data is analyzed using the three OMA methods. The system's own frequencies are chosen as the modal reference parameters to be studied.  
The following datasets are analyzed:  
* 72-h dataset: both rods in nominal condition. For both Beam 1 and Beam 2, this dataset is called: \"Nominal Conditions\".
* 12-h dataset: Beam 1 with an additional mass in midspan equal to 1% of its nominal mass and Beam 2 in nominal condition. This dataset is called \"Mass 1%\" for Beam 1 and \"Set 1\" for Beam 2.
* 12-h dataset: Beam 1 with additional mass in midspan equal to 3% of the nominal mass of the beam and Beam 2 in nominal condition. A disturbance coming from a fatigue test carried out in the same lab affects this dataset. This dataset is called \"Mass 3% a\" for Beam 1 and \"Set 3 a\" for Beam 2.
* 12-h dataset: Beam 1 with additional mass in midspan equal to 3% of the nominal mass of the beam and Beam 2 in nominal condition. This dataset is called \"Mass 3% b\" for Beam 1 and \"Set 3 b\" for Beam 2.
* 12-h dataset: Beam 1 with additional mass at _L/10_ equal to 5% of the nominal mass and Beam 2 in nominal condition. This dataset is called \"Mass 5%\" for Beam 1 and \"Set 5\" for Beam 2.  
In each of the twelve-hour datasets, a mass is fixed on Beam 1 to decrease the natural frequencies of the rod in order to simulate the effect of a reduction of cross-section without effectively damaging the rods. The presence of a mass generates an effect similar to the reduction of the cross section: the bigger the mass introduced, the more severe the virtual damage is. The magnitude of the added masses is very small compared with the mass of the beam; therefore, the effect of the simulated damage is small and difficult to  
Figure 4: Modal shapes of the first five modes of vibration. (**a**) Simply supported beam. (**b**) Fully restrained beam.  
investigate because of thermal actions and ambient noise. This choice is aimed to evaluate the sensitivity of the different OMA methods applied.  
The data sets are discretized in intervals of constant length of 5 and 10 min, both granting an adequate spectral resolution. Five- and ten-minute intervals are chosen to evaluate the effect of the record length on the effectiveness of the adopted algorithms. All data analysis has been carried out in MatLab [26].  
Cov-SSI and Poly-Reference Least Squares Complex Frequency Method imply a high computational burden; therefore, the only modal parameter chosen as an indicator of damage at this stage are the frequencies of the system vibrational modes. Obviously, this approach has limitations [8], but considering the simplicity of the structural system analyzed, it provides good results.
# Joint Interference and Phase Alignment among Data Streams in Multicell MIMO Broadcasting
## 7 Simulation Results and Analysis  
We have used MATLAB for simulation and analysis. In the case of ideal CSI and delay error CSI, the algorithm of this paper is combined with MAX-SINR-SCEK [17], MIN-IL [17], and the literature [11], and the system capacity, bit error rate (BER), and convergence are simulated for comparison.  
Consider the system configuration \([G,\ K,\ d,\ M,\ N]\), that is, \(G\) cell, \(K\) users per cell, the number of data streams (degrees of freedom) sent by each user is \(d\), the number of transmit antennas is \(M\), and the number of receive antennas is \(N\). Assume that all channels are flat fading channels and satisfy the complex Gaussian distribution with a mean of 0 and a variance of 1. When comparing the time delay error CSI, assume that the symbol duration is 0.5 ms, and consider the delay is 1 symbol time; the carrier frequency band is 2 GHz, the user receiving end moves at 20 km/h, and the correlation coefficient \(\rho=0.9966\). The variance \(\sigma_{\mathbf{e}}^{2}\) of the channel error is taken as 0.001. Table 1 shows the simulation parameters which are used for performance evaluation of the proposed algorithm.
# Physical Properties of _Jatropha curcas_ L. Fruits and Seeds with Respect to Their Maturity Stage
## 1 Introduction  
Biodiesel is a renewable energy source produced from oil-bearing crops, which is gaining attention due to low emission profiles and environmental benefits [1]. With regard to the topic of \"food vs. fuel\", biodiesel production from non-edible oil crops such as _Pongamia pimata_, _Azadirachtra indica_, and _Jatropha curcas_ L (_J. curcas_) has been primarily promoted [2; 3]. Among all the crops, biodiesel production from _J. curcas_ oil is rapidly growing, due to the high oil yield and long productive period of the crop [4; 5]. As a drought-resistant crop with the adaptability to grow on marginal soils, the cultivation is becoming favorable in developing countries due to its contribution to rural development [4; 6; 7]. However, postharvest technology of _J. curcas_ still requires a vast amount of research [8].  
Hulling of the fruits [9] and shelling of the seeds [10] are considered essential mechanical postharvest processes to provide kernels (shelled seeds) as the main product [11], along with fruit hulls and seed shells as byproducts [7]. The byproducts are also a valuable oil-free biomass with a great potential for use as solid biofuels [12; 13]. Physical properties of _J. curcas_ fruits and seeds are a crucial baseline information to design, construct, and develop the adequate machineries. Subsequently, an efficient postharvest process can be accomplished by processing the appropriate fruits and seeds [8; 14].  
Several studies have been conducted regarding the physical properties of _J. curcas_. [PERSON], et al. [15] examined the changes of physical properties in _J. curcas_ fruits at different moisture contents. Physical properties of _J. curcas_ vary considerably depending on biological and environmental factors [14]. [PERSON] [16] reported that the fully matured seeds can generate a 2.5% higher oil yield with better quality than those not fully matured. Also, the oil quality is strongly depending on the maturity stage [17]. Maturity stage of _J. curcas_ fruits can be determined based on the color change of their pericarp from green via yellow to black [18]. It is to be expected that moisture content, geometric characteristics, bulk, and mechanical properties, as well as sliding friction characteristics are naturally altered during maturing. To the best of our knowledge, no studies have been published that explicitly investigated the effect of maturity stage on the physical properties of _J. curcas_ fruits and seeds, in relation to engineering aspects. Therefore, this study aims to contribute to closing this knowledge gap, in order to enable the development of suitable machines for fruit and seed processing.
# Survey of Network Coding Based P2P File Sharing in Large Scale Networks
## 4 Network Coding Based P2P File Sharing Systems
### Sparse Network Coding  
In sparse coding scheme, instead of encoding all the pieces, only some pieces are selected in random to be encoded aiming to alleviate encoding and decoding computational complexities. [PERSON] et al. [56] proposed a sparse network coding scheme based on stochastic formulas; the probability of the number of pieces that will be encoded by the seeder, which has all the pieces \((n)\) is set as in (7). For a non-source peer which has \(k\) pieces, the encoding probability is set as in (8):  
\[p=(logn+d)/n \tag{7}\]  
\[p=(logk+d)/n \tag{8}\]  
where \(d\) is a nonnegative constant \((d=6)\). The study also focuses on how to choose coefficients such that no linear dependency appears. The Chord overlay network [1] that is used as a network topology and the encoding interval, which is a window to limit selecting pieces for encoding according to their expected innovation, is used to minimize the likelihood of transmitting dependent pieces. The authors argue that using the aforementioned formulas, (7) and (8), for encoding probability ensures that all of the encoded pieces generated by the same peer are linear independent with high probability (99.991%), taking into account that the used finite field is \(GF(2^{8})\). Moreover, a dependency test at the receiver nodes is conducted to prevent receiving more dependent pieces which works as follows: let \(H\) and \(G\) be two connected peers via a link, if no less than 3% of the pieces from \(H\) to \(G\) are dependent, \(H\) will stop requesting \(G\) for pieces for 30 s. If the dependency rate is greater than 5%, the link between the two peers will be torn down. We believe that the dependency rate is not increasable since the coefficients are randomly selected and the finite field size is large enough. Empirical results indicate that the sparse network coding outperforms the full coding in terms of encoding/decoding rate by 10% and slightly outperforms the baseline BitTorrent with a rarest-first scheduling policy in terms of download and distribution times. The definition of total download time does not include the decoding time. We believe that it would be fairer if the robustness to churn test was conducted between the sparse and the full network coding.