esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background Information  
### Kernel Extreme Learning Machine (KELM)  
In this section, a brief description of KELM is provided. KELM is an improved version of ELM, proposed by [PERSON] et al. [3], which combines the kernel function into ELM, ensuring that the network has good generalization performance and greatly improves the forward nerve network learning speed and avoids many problems of gradient descent training methods represented by back propagation neural networks, such as their easily falling into local optimal, infinite iterations, etc. KELM not only has the multi-dominance of the ELM algorithm, but also combines the kernel function to map the linear inseparable pattern non-linearly to the high-dimensional feature space in order to achieve linear separability and further improve the accuracy.  
Using \(L\) hidden nodes in the output layer, the output function of the single-hidden-layer feed-forward neural networks (SLFNs) can be expressed as:  
\[f(x)=h(x)\beta \tag{1}\]  
where \(\beta=[\beta_{1},\ \beta_{2},\ \cdots,\ \beta_{L}]^{\mathrm{T}}\) is the output weight vector between the hidden layer with \(L\) neurons and the output neurons, \(h(x)=[h_{1}(x),\ h_{2}(x),\ \cdots,\ h_{L}(x)]\) is the output vector relative to the hidden layer of input \(x\), which maps the data from the input space to the ELM feature space.  
In the newly-developed KELM, the introduction of a positive coefficient into the learning system makes the ELM more stable. If it is non-singular, the coefficient C can be added to the diagonal of \(\mathrm{HH}^{\mathrm{T}}\) when calculating the output weight \(\beta\). Thus, the corresponding output function of the regularized ELM can be expressed as follows:  
\[f(x)=h(x)\beta=h(x)\mathrm{H}^{\mathrm{T}}\bigg{(}\frac{\mathrm{I}}{\mathrm{C }}+\mathrm{HH}^{\mathrm{T}}\bigg{)}^{-1}\mathrm{T} \tag{2}\]  
It has been shown that ELM with a kernel matrix can be defined as follows.  
\[\Omega_{KELM}=\mathrm{HH}^{\mathrm{T}}:\ \Omega_{KELMi,j}=h(x_{i})\,\cdot\,h(x_{j})= K(x_{i},\ x_{j}) \tag{3}\]The output function can be written as:  
\[f(x)=h(x)\mathrm{H}^{\mathrm{T}}\left(\frac{\mathrm{I}}{\mathrm{\bar{C}}}+\mathrm{ HH}^{\mathrm{T}}\right)^{-1}\mathrm{T}=\left[\begin{array}{c}K(x,x_{1})\\ \ldots\\ K(x,x_{N})\end{array}\right]^{\mathrm{T}}\left(\frac{\mathrm{I}}{\mathrm{\bar{C} }}+\Omega_{KELM}\right)^{-1}\mathrm{T} \tag{4}\]  
In this case, we do not need to know the hidden layer feature map \(h(x)\) because we can replace it with the corresponding kernel function \(K(u,v)\).
## 2 Data Collection  
In order to comprehensively understand the characteristics of raw GNSS observations from Xiaomi Mi 8 and assess its navigation performance under different environments, static and kinematic experiments were carried out. It should be noted that, during GNSS data collection, the Wi-Fi and Bluetooth of Xiaomi Mi 8 were turned off to avoid disturbances from these sensors, and only the location permission was accessed. The system of the Xiaomi Mi 8 smartphone used in the experiments was Android 8.0, whose battery saving mechanism for the GNSS chip, 'duty cycle', was off by default.
# A Novel Methodology for Planning Urban Road Safety Interventions
## 3 Novel Methodology  
The methodology proposed in this paper integrates reactive and proactive approaches to support decision-making for urban road safety improvements. This structured process identifies critical areas (black spots) and evaluates risks, enabling optimal resource allocation and prioritization of interventions. The methodological phases include the following:  
* The reactive analysis, in which historical accident data are collected, validated, and geolocated to identify high-frequency accident zones--black spots and spatial and statistical analyses provide insights into incident patterns and user behavior (see Figure 2a);
* The proactive assessment based on the iRAP method, which evaluates intrinsic road risks based on geometry, traffic flow, and speed independent of past accident data. This step validates and complements reactive findings, identifying high-risk segments requiring urgent attention (see Figure 2a);
* The intervention design and cost-benefit analysis, in which specific safety solutions are proposed, and their potential impact on reducing fatalities, severe injuries, and social costs is quantified. Implementation costs are estimated, and a benefit-cost ratio is calculated to rank interventions within and across identified sites (see Figure 2b).  
Figure 1: Integrated road safety management.  
By combining reactive data insights with proactive risk evaluation, this methodology provides a comprehensive framework for urban road safety planning, as demonstrated in Figure 2. It equips decision-makers with objective criteria to prioritize actions and achieve sustainable improvements in traffic safety.  
While the iRAP star rating methodology is a valuable tool for proactive risk assessment, it has limitations, particularly in complex urban environments. The model is primarily designed for highway and arterial road assessments, which can make it less precise for dense city networks with frequent intersections, varying traffic conditions, and mixed road users. Additionally, iRAP does not account for real-time traffic flow variations or signal timing effects, which are crucial in urban safety assessments. To mitigate these limitations, field observations and complementary GIS analyses were incorporated to refine risk evaluations.
# Polynomial Expressions for the Thermal Efficiency of the Parabolic Trough Solar Collector
###### Abstract  
The parabolic trough solar collector (PTC) is the most mature solar concentrating technology, and this technology is applied in numerous thermal applications. Usually, the thermal efficiency of the PTC is expressed with the aid of polynomial expressions. However, there is not a universal expression that is applied in all cases with high accuracy. Many studies use expressions with the first-degree polynomial, second-degree, or fourth-degree polynomial expressions. In this direction, this work is a study that investigates different expressions about the thermal efficiency of a PTC with a systematic approach. The LS-2 PTC module is examined with a developed numerical model in the Engineering Equation Solver for different operating temperatures and solar beam irradiation levels. This model is validated using experimental literature data. The found data are approximated with various polynomial expressions with up to six unknown parameters in every case. In every case, the mean absolute percentage error and the R\({}^{2}\) are calculated. According to the final results, the use of the third power term leads to the best fitting results, as well as the use of the temperature difference term (\(\Delta T\)), something that is new according to the existing literature. More specifically, the final suggested formula has the following format: \"\(\eta_{col}=a_{0}+a_{3}\cdot\Delta T^{3}/G_{b}+b\cdot\Delta T^{\prime\prime}\). The results of this work can be used by the scientists for the optimum fitting of the PTC efficiency curves and for applying the best formulas in performance determination studies.  
parabolic trough collector; thermal efficiency; approximation formula; concentrating solar power; polynomial solar efficiency Article
## 5 Multiple Scatterer DRFM
### Multiple Scatterer DRFM Implementation  
To resolve the issue of multiple scatterer replication in DRFM systems, a new architecture is needed that can replicate Equation (6). That is to say, create not just a single return signal but a superimposed set of jammer signals, which vary with angle-of-arrival, frequency, and polarization.  
A version of this multiple scatterer DRFM was developed and implemented in System Generator and tested using a Kintex-7 FPGA, as shown in Figure 19. However, for simplicity of illustration, the complexity was limited to four scatterers with the signal only varying with the angle of arrival. Expanding on the architecture to replicate \(N\)-scatterers that vary with the azimuth, elevation, frequency, and polarization while not trivial, only requires scaling the number of RCS components for each  
Figure 18: The radar cross section of 10 randomly distributed cylinders with a 0.5 m radius and height of 1 m.  
scatterer and switching to a four dimensional look-up table to store the values for every possible combination of variables.  
The DRFM shown in Figure 19 adapted the I/Q DRFM system shown in Section 3.3, with several modifications. The received signal is stored in the two-port RAM and replayed with the appropriate modification to generate the desired EA (such as range-gate pull-off, inverse-gain jamming, cover pulses, etc.). However, after the DRFM generates the waveform to be transmitted, it splits into a series of subsystems to replicate the delay, amplitude, and frequency shift of each individual scatterer. The modifications for each scatterer are determined from a look-up table based on previous measurements. Finally, the waveform generated from each synthetic scatterer in the DRFM system is superimposed and sent to the transmitter.
###### Abstract  
Food wastes represent one third of all food produced worldwide. It is crucial to both prevent the production of food waste and recover the wasted fraction with the aim to valorizing it. In this context, the conversion of the organic fraction of municipal solid wastes (OFMSW) into \(\text{bioH}_{2}\) by dark fermentation (DF) is an important technology to valorize these wastes into renewable fuel. Nevertheless, the DF of OFMSW needs to be optimized for critical operational parameters. The main purposes of this study were to investigate (i) the effect of HRT during continuous \(\text{bioH}_{2}\) production through DF and (ii) the effect of organic loading rate (OLR) ruled by HRT. In this work, three HRTs (4, 5, and 6 d) were tested in a mesophilic continuous stirred-tank reactor (CSTR). The HRTs of 4, 5, and 6 days, corresponding to OLRs of 23.6, 18.0, and 10.6 g volatile solids (VS):\(\text{L}^{-1}\)-\(\text{d}^{-1}\), respectively, showed \(\text{bioH}_{2}\) yields of 8.48, 18.2, and 1.64 \(\text{L}\)-\(\text{kg}^{-1}\)\(\text{VS}_{\text{\small Influent}}\) with an \(\text{H}_{2}\) content of approximately 25, 32, and 5% \(v/v\), respectively. An accumulation of volatile fatty acids (VFAs) was registered with the decrease in HRT, causing a decrease in \(\text{bioH}_{2}\) production. The 5 d HRT was the most favorable condition.  
BioH\({}_{2}\) dark fermentation; hydraulic retention time; microbial consortium; organic fraction of municipal solid wastes; organic loading rate +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
Based on the reversal cyclic tests used to examine the flexural and shear performances of the beam elements of exterior beam-column connections made using high-strength materials, including the longitudinal GFRP bars, the following conclusions were drawn:  
1. The specimen prematurely failed by the pull-out of GFRP bars exhibited no diagonal cracks in the column element. Meanwhile, the specimen accompanied by tensile rupture of GFRP bars showed a similar trend to the crack propagation and concrete crushing observed in the beams reinforced with longitudinal steel bars.
2. The beams with GFRP bars possessed lower diagonal cracking strengths and shear capacities than the companion beams with steel bars. However, the shear capacity of the beams with GFRP bars could be conservatively predicted using the design equation of the ACI 440.1R-15 provision.
3. The specimens with GFRP bars possessed lower values of the work damage indicator at each loading cycle when compared with the companion specimens with steel bars, indicating that the lack of anchorage length of GFRP bars in the exterior beam-column connections significantly reduced the flexural ductility of the beam elements.
4. The ACI 318-14 equations tended to overestimate the variation in the effective moment of inertia (\(l_{e}\)) of all the beams, irrespective of the type of longitudinal bars and and/or compressive strength of concrete.
5. The flexural capacity of the beam with the poor anchorage length of GFRP bars was lower than the nominal moment capacity predicted using the ACI 440.1R-15 procedure.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Specimen**} & \multicolumn{2}{c}{**Shear Capacity (kN)**} & \multicolumn{2}{c}{**Comparison**} \\ \cline{2-4}  & \(V_{n(EXP)}\) & \(V_{n(PRE)}\) & \(V_{n(EXP)}/V_{n(PRE)}\) \\ \hline S-H-C & 471.5 & 394.1 & 1.20 \\ S-H-G & 276.6 & 225.1 & 1.23 \\ \hline \end{tabular} Notes: \(V_{n(EXP)}\) is the measured shear capacity; \(V_{n(PRE)}\) is the shear capacity calculated using equations specified in ACI 318–14 for beams with steel bars and in ACI 440.1R-15 for beams with GFRP bars.  
\end{table}
Table 6: Comparisons of measured shear capacity and predictions.  
**Funding:** This research funded by Ministry of Science, ICT & Future Planning of the Korean government.  
**Acknowledgments:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2015R1A5A1037548).  
**Conflicts of Interest:** The author declares that there is no conflict of interests regarding the publication of this paper.
## 4 Results and Discussion
### Quantitative Comparison  
In the following experiment, we compare the proposed method with the aforementioned methods using several evaluation indexes to conduct a quantitative analysis. Here, three evaluation indicators, namely the mean absolute error (MAE), the error rate (ER), and the detection rate (DR), are employed for quantitative comparison. The MAE, ER, and DR can be expressed by the following equations:  
\[\text{MAE}=\frac{1}{m\times n}\sum_{x=1}^{m}\sum_{y=1}^{n}\Bigl{|}Det_{(x,y)}- gt_{(x,y)}\Bigr{|}, \tag{23}\]  
\[\text{ER}=\frac{1}{m\times n}\sum_{x=1}^{m}\sum_{y=1}^{n}\Bigl{|}Det_{(x,y)}- gt_{(x,y)}\Bigr{|}\Big{/}\frac{1}{m\times n}\sum_{x=1}^{m}\sum_{y=1}^{m}Det_{(x,y)} \cdot gt_{(x,y)}, \tag{24}\]  
\[\text{DR}=\frac{1}{m\times n}\sum_{x=1}^{m}\sum_{y=1}^{m}Det_{(x,y)}\cdot gt_{ (x,y)}/\frac{1}{m\times n}\sum_{x=1}^{m}\sum_{y=1}^{m}\Bigl{(}Det_{(x,y)}+gt_{( x,y)}\Bigr{)}, \tag{25}\]  
where \(m\) and \(n\) represent the length and width of the image, \(Det\) is the result of image segmentation, and \(gt\) is the hand-crafted ground truth. So, \(Det_{(x,y)}\cdot gt_{(x,y)}\) represents the contour that is accurately extracted by the model and \(Det_{(x,y)}+gt_{(x,y)}Det_{(x,y)}+gt_{(x,y)}\) represents the union of the image segmentation result and the ground truth. The larger the \(Det_{(x,y)}\cdot gt_{(x,y)}\), the more contour that is correctly extracted. \(Det_{(x,y)}-gt_{(x,y)}\) represents the pixels that are incorrectly extracted, so the larger \(Det_{(x,y)}-gt_{(x,y)}\) is, the more pixels are incorrectly extracted. So, the smaller the value of ER, the more accurate the result of contour extraction. The smaller the value of MAE, the more accurate the contour extraction result. Moreover, a large value of DR can indicate that the contour extraction result of the model is accurate. The evaluation results of the aforementioned 5 methods are shown in Tables 3-5.  
Figure 7: Comparison of our method with LBF, LGIF, LIF, RMPCM. (**a**–**e**) Results of LBF, LGIF, LIF, RMPCM, and our method. The upper rows of (1), (2), and (3) are the segmentation results of the saliency images, and the lower rows are the segmentation results of the corresponding original images.  
A smaller value of MAE represents a higher contour extraction accuracy. According to Table 3, the contour extracted by the proposed model obtained the smallest MAE value, which shows that the proposed model can extract target contours more accurately than the other 4 models. Table 4 shows the error rate (ER) of the 5 methods. The ER values between the target contour extracted by the proposed method and ground truth are the smallest, so the proposed method has the highest accuracy. Table 5 shows the detection rates of the above five methods. The detection rate represents how many contour pixels are correctly extracted. Therefore, our model with the highest detection rate can extract the target contour more accurately.
## 3 Metrics for Jamming Attack Detection
### Bad Packet Ratio (BPR)  
BPR is same as that PDR which is to be measured at the receiver-end, as suggested by [PERSON] _et al._[1]. However, [PERSON] _et al._[3] call it BPR and define BPR as the ratio of the number of bad packets received by a node (_i.e._, the number of received packets which have not passed the Cyclic Redundancy Check (CRC) carried out by the node) to the total number of packets received by the node over a given period of time. We find BPR to be a very effective metric which can indicate all types of jamming, is easily calculable, and is fit for WSN where the system of acknowledgements is not required. The CRC is a normal procedure which nodes have to do under most of the existing protocols to check whether a received packet is correct or erroneous. If the packet is correct (good packet), it is received or queued for further transmission, and if the packet is erroneous (bad packet), it is dropped and their count is maintained. Therefore, both values, the number of bad packets and the number of total received packets, are readily available for computing the BPR without imposing any significant burden on the system. Also, there is no sampling or fixing of thresholds involved here. We find this metric suitable for our system.
## 3 Construction of the DT Model
### Mechanism Modeling: PV Cell and Electric Drive System  
#### 3.2.1 Photovoltaic Cell Model  
The output characteristics of photovoltaic (PV) cells are similar to those of semiconductor diodes, where the current varies exponentially with voltage when light is available. The photogenerated current is regarded as a constant current source. The material body resistance and the P-N junction cross boundary area carrier load and contact resistance are equivalent to the series resistance _Rs_. The leakage current at the edge is equivalent to the shunt resistance _Rsh_, and the equivalent circuit of a single photovoltaic cell is shown in Figure 4.  
Figure 3: Overall modeling framework diagram.  
The PV cell output current expression is given by the following equation:  
\[I=Iph-I_{0}\bigg{\{}exp\bigg{[}\frac{q(U+IRs)}{AKT}\bigg{]}-1\bigg{\}}-\frac{U+ IRs}{Rsh} \tag{1}\]  
where _lph_ is the photogenerated current; \(I_{0}\) denotes the saturation dark current (reverse saturation current) of the PV cell in the absence of light; \(q\) is the electronic charge, 1.6 \(\times\) 10\({}^{-19}\) C; \(U\) is the voltage on the load resistance, V; \(Rsh\) is the shunt resistance, \(\Omega\); \(Rs\) is the series resistance, \(\Omega\); \(k\) is the Boltzmann constant, 1.38 \(\times\) 10\({}^{-23}\) J/K; \(T\) is the cell temperature, K; \(A\) is the ideality factor of the diode, and takes the value of 1.2. The photogenerated and reverse saturation currents are solved by the following equations:  
\[\left\{\begin{array}{c}Iph=\frac{\alpha\bullet(T-T_{T})+Isc}{Iro}\bullet Ir \\ I_{0}=Irs\Big{(}\frac{T}{T_{T}}\Big{)}^{3}exp[\frac{qEg}{Ak}(\frac{1}{T_{T}}- \frac{1}{T})]\end{array}\right. \tag{2}\]  
where \(\alpha\) is the short-circuit current temperature coefficient and takes the value of 1.06 \(\times\) 10\({}^{-5}\) A/K; \(T\) is the ideal reference operating temperature, 298.15 K; \(Isc\) is the solar panel short-circuit current, and \(Ir\) is the actual light intensity; \(Iro\) is the ideal light intensity, 1000 W/m\({}^{2}\); \(Irs\) is the saturation current; \(Eg\) is the forbidden band width of the semiconductor material, taken as 1.1.  
In the actual PV cell, the \(Rsh\) resistance value tends to infinity, so the third term on the right side of Equation (1) can be equated to 0, while the PV module is a multi-piece series-parallel package. The mathematical model of the PV cell array is as follows:  
\[I=mIph-mI_{0}\bigg{\{}exp\bigg{[}\frac{q(U+IRs)}{nAKT}\bigg{]}-1\bigg{\}} \tag{3}\]  
where \(m\) is the number of cells in parallel in the module and \(n\) is the number of cells in series in the module.
# A Selectable Sloppy Heap
## 4 Conclusions  
### Technical Remarks  
1. An argument due to [PERSON] [36] shows that executing a sequence of \(n\) operations (from among Insert and Delete\(i\), where \(1\leq i\leq k\)) requires \(\Omega(n\log k)\) time in the worst case, regardless of the implementation of the data structure. The argument relies on the information theory lower bound ([26], Ch. 5.3.1). (For completeness, it is included in the Appendix A.)
2. In addition to the two operations provided Insert and Delete\(i\), the following operation can be also accommodated at no increase in cost: Read\(i\): this returns some element \(x\) which belongs to the \(i\)th quantile group of the current set, where \(1\leq i\leq k\); the element remains part of the data structure.
3. An alternative solution (to that outlined in Section 3) achieving \(O(\log k)\) worst-case time per operation was conceived by [PERSON] [36] (after the author of the current paper had communicated him the main result). (i) His solution avoids the need to merge small buckets (in order to keep the number of buckets under control) by maintaining two running copies of the data structure and performing periodic tree reorganizations that create uniform-sized buckets. Buckets that become large are split using a mechanism similar to that devised here in Section 3. Without going into the details, we point out that the decision to avoid merging small buckets comes at a high price. The main reasons are the need to maintain multiple running copies of the data structure (with two copies under permanent construction, etc), handling subtle consistency issues generally hard to satisfy, and the likely reduced speed caused by the above items. (ii) Another cumbersome approach claimed to be a simplification can be found in his subsequent arXiv posting [38]. As such, neither alternative solution--(i) or (ii) above--matches in simplicity the one given here.
4. As mentioned earlier, the \(O(\log 1/\varepsilon)\) amortized complexity of the soft heap is optimal. However, this does not hold for the worst-case complexity in regard to its Insert, Delete and Findmin operations. In contrast, the \(O(\log 1/\varepsilon)\) worst-case complexity per operation of the selectable sloppy heap is optimal (where \(\varepsilon\sim 1/k\)).
5. Although the 2 nd variant in Section 2 has a suboptimal dependence in \(k\), this variant may provide an attractive solution for implementation, particularly for small values of \(k\).