esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 3 CR-WSN Characteristics and Architecture  
### Characteristics of CR-WSNs  
The existing clustering algorithms in the literature [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28] consider homogeneous CR-WS nodes, and CHs are selected iteratively based on residual energy and other parameters. Most of these clustering methods are similar to conventional clustering for WSNs, and seldom consider the complexities of CR-WSNs.  
In CR-WSNs, the CH should continuously monitor the channel status, i.e. whether the channel is occupied by any incumbent users, also called primary users (PUs). The CH receives spectrum information from the BS and neighbor nodes in its vicinity, and negotiates and allocates available channels. Therefore, the CH requires dedicated hardware, and conventional methods of CH selection  
Figure 1: Conventional typical WSN cluster structure.  
that are applied in WSNs (based on geographic region, load, residual energy, links, etc.) do not provide enough parameters. Nevertheless, the methods applied by the conventional CH selection algorithms could be considered when choosing an ideal place to deploy a cluster head (where it can cover the maximum number of neighbor sensor nodes) or when determining the shortest distance from the base station, among other things.  
Resources in WSNs are greatly constrained in terms of power supply, processing capability, and transmission bandwidth. Different from WSNs, a CH in a CR-WSN may need to be powered by a heavy-duty battery. Additionally, processing capability, memory, etc., should be enhanced significantly.  
It is better to design a global addressing scheme, such as Internet Protocol (IP) for better protection of the license users' right to access the incumbent channels. With a global addressing scheme, it is easy for the BS to know which wireless sensor node is using which particular channel in certain geographical location. This information helps BS to allocate a channel that has less probability of PUs' arrival, which mitigates the collision probability with the PUs and protects the right of PUs to access the channel. Applying IP in WSNs is difficult, because address updating in a large-scale or dynamic WSN can result in heavy overhead. However, CR-WSNs can be designed with IP addresses, because the CH has special hardware. Table 1 summarizes the differences between clustering in WSNs and CR-WSNs.
# A Traceable Spectral Radiation Model of Radiation Thermometry
## 2 The Direct (Physical) Model of Radiation Thermometry
### Input Data of the Physical Model
#### 2.3.4 Sensor Absorption and Characteristic Function  
The combined spectral radiance travelling towards the measuring instrument is absorbed at the sensor. The actual detected (measured) spectral radiance \(M_{meas}\) to the sensor shown in Figure 1 prior to integration is the incident spectral radiance to the sensor \(M_{\text{sen}}(\lambda)\), reduced by the sensor's relative spectral sensitivity \(S(\lambda)\). Relative spectral sensitivity is used exclusively in this research. Absolute scaling is considered as accounted for in sensor calibration and does not influence the operation of the models.  
\[M_{\text{meas}}=\int M_{\text{sen}}(\lambda)S(\lambda)d\lambda \tag{12}\]  
Figure 5 presents the relative spectral sensitivity \(S(\lambda)\) of the Heitronics KT19.01 and two temperature ranges of the Heitronics Transfer Radiation Thermometer II (TRT II), digitized from Heitronics pyrometer documentation [29], and the generic spectral sensitivity of vanadium oxide (VOx)-based microbolometer detectors of FLIR thermal imagers with 45\({}^{\circ}\) field-of-view optics [30].  
Analytical solutions for the integration of [PERSON]'s law are complex, impractical, and moreover infeasible when the discretely measured spectral sensitivity of the sensor and other influence parameters are taken into account. For this reason, we have opted for entirely numerical calculations.  
A sensor's characteristic function converts from the input quantity to the output quantity. The characteristic function is usually obtained by calibration. Sensors for thermal radiation convert the radiance to an electrical response, generally obtained numerically as a sensor reading.  
However, in radiation thermometry, radiance is measured by integration of spectral radiance, according to Equation (13). Calibrations are usually limited to black bodies and therefore the input quantity of a sensor characteristic function is usually extended from  
Figure 5: Spectral sensitivity of Heitronics KT19.01 and the high and the low temperature range of Heitronics Transfer Radiation Thermometer II (TRT II), digitized from Heitronics pyrometer documentation [29], and generic spectral sensitivity of vanadium oxide (VOx)-based microbolometer detectors of FLIR thermal imagers with 45\({}^{\circ}\) field-of-view optics [30].  
radiant power to the temperature of a black body radiator. This assumption utilizes [PERSON]'s law (1) for the missing relationship between the spectral radiance and the temperature of a black body.  
\[M_{\text{BBS}}(T)=\int M_{\text{BB}}(T,\lambda)S(\lambda)d\lambda \tag{13}\]  
For the purpose of this research, the output quantity of the sensor is considered correctly calibrated as the radiance of a black body \(M_{\text{BBS}}(T)\), detected at the sensor in W sr\({}^{-1}\) m\({}^{-2}\). The sensor characteristic function is presented in Figure 6.
## 3 Control Algorithm Description
### Reference Trajectories Assignment Problem
\right)\quad\}N\]  
The first constraint \(\vec{\mathbf{A}}\vec{\mathbf{x}}=\mathbf{b}\) in (28) corresponds to the requirement that only one reference trajectory can be assigned to each satellite, and the second constraint \(\mathbf{e_{1}}\leq\vec{\mathbf{x}}\leq\mathbf{e_{2}}\) is the requirement that the vector \(\vec{\mathbf{x}}\) can contain only 0 or 1. The solution vector \(\vec{\mathbf{x}}\) can be found numerically by any of the linear programming solver methods [31]. It should be noted that the computational cost of the numerical solution of the assignment problem is high, and it is possible that this problem cannot be solved in orbit with onboard computers. Thus, it is assumed that the problem (28) is to be solved on the ground before the image construction and each time before the reconfiguration. After the assignment of a particular reference trajectory to each satellite, new reassignment is not recalculated during the convergence, even if the sum of the cost functions (26) on the trajectory could eventually be less than the initial value.
## 3 Results and Discussion
### Antimicrobial Activity  
Table 7 depicts the results from the antimicrobial assay of 2,4-dithiouracil and its complexes. The addition of Cu(II) to the ligand slightly improved the antimicrobial effect on _Staphylococcus aureus_ ATCC 25923, _Escherichia coli_ ATCC 8739, and _Salmonella enterica_ ssp. _enterica_ ser. Enretridis ATCC 13076, while _Pseudomonas aeruginosa_ ATCC 9027 and _Saccharomyces cerevisiae_ were more resistant to the activity of the Cu complex in comparison with the ligand. The rest of the test microorganisms did not exhibit differences in their sensitivity between the ligand and the Cu complex.  
On the other hand, the 2,4-dithiouracil complex with Au(III) showed a stronger antimicrobial effect against all of the test microorganisms, except _Escherichia coli_ ATCC 8739, where no change in activity was observed. The most significant increase in activity was determined against _Staphylococcus aureus_ ATCC 25923, _Eterococcus faecalis_ ATCC 19433, and _Klebsiella pneumoniae_ ATCC 13883. The antimicrobial activity of Au(III) is well documented [44; 45] and similarly to our previous study on the addition of Au(III) to 2-thiouracil [31], the complex exhibited increase antimicrobial activity in comparison with the ligand.
# Typhoon Track Prediction Based on Deep Learning
## 3 Experiment Results and Analysis
### Comparison Experiments Level
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Structure** & \multicolumn{2}{c}{**Average Surface Distance Error (km)**} \\ \hline  & Training set & 19.795 \\ C-LSTM + MSE + Adam & Test set & 20.651 \\  & Validation set & 20.232 \\ \hline  & Training set & **14.215** \\ C-LSTM + cross-entropy loss function + Adam & Test set & **14.291** \\  & Validation set & **15.487** \\ \hline  & Training set & 20.716 \\ C-LSTM + cross-entropy loss function + SGD & Test set & 21.623 \\  & Validation set & 21.303 \\ \hline  & Training set & 23.999 \\ LSTM + MSE + Adam & Test set & 24.15 \\  & Validation set & 23.546 \\ \hline  & Training set & 22.69 \\ LSTM + cross-entropy loss function + Adam & Test set & 23.981 \\  & Validation set & 22.011 \\ \hline  & Training set & 37.0865 \\ LSTM + cross-entropy loss function + SGD & Test set & 37.289 \\  & Validation set & 37.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average surface distance error between predicted and real paths with different parameters of the C-LSTM model and LSTM model (unit: km).
# Life Cycle Analysis Challenges through Building Rating Schemes within the European Framework
## 1 Introduction
### The European Standardisation and Level(s) Framework  
While local voluntary initiatives and best practices have historically led sustainability efforts within the sector, European policies are now taking over. At the heart of the circular economy and Renovation Wave strategies, the European Joint Research Centre (JRC) hascreated the Level(s) framework [23] geared towards transforming the sector [24]. Level(s) is a voluntary reporting framework to improve the sustainability of buildings. Using existing standards, it provides a common EU language and approach to assess performance in the built environment. The first version was published in 2017 and was tested in 83 buildings across Europe in 2019. The second version appeared at the start of the Renovation Wave plan in October 2020 [25]. It focuses on six \"hotspots\" through the whole building life cycle: GHG emissions, resource efficiency, water use, health and comfort, resilience and adaptation to climate change, and cost and value. Its 16 indicators can be used at the design, construction, and operational levels, giving the framework its name and bringing usability to various stakeholders. Relevantly here, Level(s) is the only framework covering all LCA stages [26], as well as the IEQ related factors (thermal, visual and acoustic comfort, plus indoor air quality).  
One of the many activities for the penetration of Level(s) into the EU building market [27] is to take advantage of the fittest and most innovative building rating systems (BRS) in the EU. In this study, five BRS have been chosen to run against Level(s). Both BRS and Level(s) had to share a voluntary basis, a set of tools and datasets where life cycle thinking (LCT) is central. As an eligibility criteria, they are second generation BRS and fully integrated in their national sector. They represent a variety of European building cultures. They have been active in the development of Level(s). Other researchers have studied BRS but excluding Level(s) and with different purposes and eligibility criteria [28; 29]. Research about Level(s) within BRS has just begun and only on specific issues [30].
# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 5 Experiment
### Experiment Results  
#### 5.2.1 Performance Comparison  
We conducted experiments on three real datasets and compared the results with baseline modes. The reported results include Acc@1, Acc@5, Acc@10, and MRR, as shown in Table 2.  
From the experiment results, it is evident that the model proposed in this paper outperforms the benchmark model across all datasets, except for the Acc@1 metric on the TKY dataset. Among all the results, our model shows the most significant improvement in NYC, particularly in the Acc@5 and Acc@10 metrics, with improvements of 13.63% and 12.73%, respectively. This may mainly be attributed to the similar sequences as they can provide necessary information for the current prediction. Nearly all the models perform worse on the CA dataset compared to the other two datasets, which may be inferred from two perspectives. First, the average check-ins for each user and POI are lower than the other two datasets. This may increase the difficulties to accurately capture the user-POI interaction relation, leading the insufficient information for other relations and affecting the model's ability to capture users' preference. Moreover, the wider coverage area may result in sparser users' check-in activities and makes it more challenging to learn user behavior patterns on different POIs.
## 4 Discussion
This research provides valuable insights into integrating thermocycling tests to evaluate the occurrence, quantity, length, and orientation of EMCs, thereby enhancing the field given its restricted accessibility and significance. On the other hand, the limitations inherent in this in vitro study stem from its experimental design. Moreover, the sample size is relatively small, potentially limiting the generalizability of the findings. Furthermore, the short-term evaluation and absence of clinical correlation restrict the extrapolation of results to real-world orthodontic practice. Additionally, the study did not account for potential confounding factors that could influence the outcomes. Therefore, continued investigation is warranted, encompassing larger sample sizes, long-term assessments, and the incorporation of potential confounding variables to yield more definitive conclusions. The comparison of various methods for examining enamel surface is also recommended for future research.
## 3 Results
### Signal Propagation Parameters during LEP Event  
Lightning-induced electron precipitation, i.e., LEP, is triggered by an extremely low frequency (VLF and ELF) portion of EM energy released by lightning discharges that manage to reach the Earth's magnetosphere and by propagating as whistler mode wave interactions with electrons from radiation belts forcing them to precipitate to lower altitudes of 60-120 km, causing an increase in electron density. Such localised and transient electron density enhancements in the D-region altitude range can produce significant amplitude and phase delay perturbations on narrowband VLF signals (with change in amplitude level up to 6 dB and in phase delay of about 20 degrees, typically with rise times up to 1 s and signal decay below 100 s) propagating through or near-by the disturbed region (e.g., [18,48,54] and references therein). An LEP event occurred on 16 November 2004 and its impact on VLF signal traces NAA/24.0 kHz and GQD/22.1 kHz (signal with codename GQD emitted from Anthorn (52.40\({}^{\circ}\) N, 1.20\({}^{\circ}\) W), GB) was selected for analysis. The presented LEP event is one of the typical LEPs recorded by the Belgrade VLF station, from those during the period 2008-2010 [48,55], similar in absolute perturbation amplitude change of up to 5 dB and phase delay of up to a few tens of degrees with maximal duration of up to two minutes. Amplitude (solid lines) and phase delay (dotted lines) registrations, with resolution 0.1 s on NAA and GQD signals, before, during and after the LEP event occurred on 16 November 2004 and are presented in Figure 4a,b, respectively. Moments at which amplitude and phase delay values for unperturbed (regular) and perturbed (irregular) ionospheric conditions in waveguide were read, are marked with red and blue arrows.  
Figure 3: X-ray flux measured by the GOES satellite (**a**), and evaluated electron density profiles (**b**) during occurrence of X1.3 class SF on 7 September 2017.
## 2 Circuit Topology of RF-Energy-Harvesting Integrated Circuit
### Low-Voltage Low-Dropout Regulator
The first stage had a p-channel differential input pair with an n-channel current mirror active load for a high gain \(A_{V1}\). The second stage is generally configured as a simple common source stage to allow maximum output swings with gain \(A_{V2}\)[25]. Bias currents were copied through the current source by controlling the resistor \(R_{3}\)  
Figure 6: Complete circuit of the adopted CMOS reference voltage. The voltage swing at V\({}_{\text{err}}\) was equal to V\({}_{\text{dc}}-|\text{V}_{\text{OD,MP6}}|-\text{V}_{\text{OD,MN6}}\) with overdrive voltages, V\({}_{\text{OD,MP6}}\) and V\({}_{\text{OD,MN6}}\), of MP6 and MN6, respectively. The overall voltage gain A\({}_{\text{V}}\) can be derived as follows:  
\[\left\{ \begin{array}{c}A_{v1}=-g_{m,MP7}\Big{(}r_{ds,MP8}\Big{|}r_{ds,MN5} \Big{)}\\ A_{v2}=-g_{m,MN6}\Big{(}r_{ds,MP6}\Big{|}r_{ds,MN6}\Big{)}\\ A_{v}=A_{v1}\times A_{v2}\end{array} \right. \tag{15}\]  
where \(g_{m,MPi}\) and \(r_{ds,MPi}\) are the transconductance and conduction resistance of the \(i^{\text{th}}\) PMOS, respectively. \(g_{m,MNi}\) and \(r_{ds,MNi}\) are the transconductance and conduction resistance of the \(i^{\text{th}}\) NMOS, respectively. Zero frequency \(\omega_{z}\) can be modified by placing a resistor \(R_{z}\) in series with the compensation capacitor \(C_{Z}\)[26]. If \(R_{z}\geq(g_{m,MN6})^{-1}\), then \(\omega_{z}\leq 0\). Zero may be moved into the left-half plane for cancelling the first nondominant pole \(\omega_{\text{p2}}\). The compensation resistance \(R_{Z}\) is then given as follows:  
\[R_{z}=\frac{1}{g_{m,MN6}}\Big{(}1+\frac{C_{1}+C_{2}}{C_{Z}}\Big{)}, \tag{16}\]  
where \(C_{1}\) and \(C_{2}\) are total capacitances at node E before \(C_{Z}\) was added and that at node V\({}_{\text{err}}\), respectively. Moreover, the increasing \(C_{Z}\) moves the dominant pole to a lower frequency without affecting the second pole. This effect ensures that the designed amplifier is more stable [25]. The simulated results indicate that DC gain, phase margin, and unit-gain bandwidth were 50 dB, 60\({}^{\circ}\), and 1.57 MHz, respectively, at a supplied voltage of 1.7 V and quiescent current of 223.22 nA. The feedback network was a voltage-trimming network and comprised four diode-connected PMOSs (M\({}_{\text{P10}}\)-M\({}_{\text{P13}}\)) and an adjustable voltage, \(V_{A}\), which is generally connected to a voltage of 1.4 V. Three PMOSs were designed to complete coarse adjustment, whereas the NMOS M\({}_{\text{P10}}\) was used to perform fine-tuning by controlling the adjustable voltage \(V_{A}\). To stabilize the LDO regulator, phase characteristics were adjusted such that a phase shift was less than 180\({}^{\circ}\) at gain crossover. Frequency compensation is completed by connecting a resistor \(R_{F}\) in series with a filter capacitor \(C_{F}\). The loop gain was zero at \(s_{z}=-(R_{F}C_{F})^{-1}\). Figure 7: Complete circuit of the low-voltage LDO regulator.
## 3 Results and Discussion
### National Differences Based on USLE Input Parameters
Hence, the CZ and BY methods are more sensitive to the size of a chosen catchment where erosion should be modelled with an average \(C\) factor. The \(LS\) factor, which is calculated after a differently parametrized approach of [PERSON] and [PERSON] [36] in all tested modelling approaches, showed only minor differences when comparing catchment means (\(\Delta\)Max = 11%; Table 3). However, for individual fields, differences are much larger, which especially in case of the AT method results from not using field blocks if small field roads, hedges, etc. are located between fields. Following the descriptions of deriving the \(K\) factor in the different countries (see methods) indicates that this also has a large potential to introduce differences between modelled erosion. In the Czech Republic, the \(K\) factor is estimated based on information of the national bony map [28] which doesnot contain the data used to calculate the \(K\) factor following the original approach (rock fragment cover [16]; silt content, very fine sand content, clay content, organic matter content, an aggregation index, a permeability index [32]). Therefore, other \(K\) factor approaches cannot be used. Determination of \(K\) factor values for Czech Republic is not clearly documented and available. The official conversion table between Soil Bonity Units (see above) and \(K\) factor values was presented by official nationally used methodology [22]. In Austria and Bavaria, the Wischmeier and [PERSON] [16] approach as implemented in [PERSON] et al. [30; 31] is used. Overall, harmonized soil data containing the inputs to use the original \(K\) factor calculation seemed to be one of the, if not the, most challenging parts of harmonizing the different USLE approaches. Last but not least, the relatively large difference in mean annual erosion for all sites between the BY and the other methods (BY approx. 57% smaller) partly obviously results from a generally 15% lower \(P\) factor (Table 3). However, it cannot be proven if the assumption that some soil protection measures are always implemented is a better assumption than assuming no soil protection measures at all.
# Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals
###### Abstract  
In this paper, we study via simulation the performance of irregular repetition slotted ALOHA under multi-packet detection and different patterns of the load process. On the one hand, we model the arrival process with a version of the M/G/\(\infty\) process able to exhibit a correlation structure decaying slowly in time. Given the independence among frames in frame-synchronous coded-slotted ALOHA (CSA), this variation should only take effect on frame-asynchronous CSA. On the other hand, we vary the marginal distribution of the arrival process using discrete versions of the Lognormal and Pareto distributions, with the objective of investigating the influence of the right tail. In this case, both techniques should be affected by the change, albeit to a different degree. Our results confirm these hypotheses and show that these factors must be taken into account when designing and analyzing these systems. In frameless operations, both the shape of the packet arrivals tail distribution and the existence of short-range and long-range correlations strongly impact the packet loss ratio and the average delay. Nevertheless, these effects emerge only weakly in the case of frame-aligned operations, because this enforces the system to introduce a delay in the newly arrived packets (until the beginning of the next frame), and implies that the backlog of accumulated packets is the key quantity for calculating the performance.  
coded ALOHA; multi-user detection; correlation; heavy tails +
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00 2023 by the authors.  
Liconse MDPI, Basel, Switzerland.  
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