esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Comparative Evaluation
The factor responsible for this with respect to SCEEM can be traced to the cost of communication for synchronizing the nodes in the  
Figure 17: Latency _vs._ PU occupancy. Figure 18: Consumed energy/packet _vs._ PU occupancy. cluster and, subsequently, the intra-cluster communication. Alongside these, the cost incurred in idle listening when considered for the inter cluster communication makes the incurred energy cost exceed that of ROR. On the other hand, the hop length extension offered by ROR also decreases the energy consumption. Further, since ROR was designed to maximize the use of idle listening, the cost of idle listening is converted by the nodes to make critical network decisions without having to design separate algorithms to achieve same. Additionally, it should be noted that, unlike SCEEM in which cluster formation and maintenance are part of the routing process, the organization of the virtual clusters in ROR is fundamentally part of the network initialization stage, which is fully a part of the route request and reply stage. When compared to the standard AODV setup, the only difference is, again, the use of idle listening for nodes to decide if they are part of a virtual cluster or not. Thus, not only is ROR reliable, it is also an energy-efficient scheme that should be utilized for efficient routing in CRSNs.
# Two Medoid-Based Algorithms for Clustering Sets
## 2 Related Work
### K-Sets and K-Suaps  
K-sets is the direct adaption of k-means to sets. It initializes the \(K\) centroids (representatives) through a uniform random selection in the dataset. The initial representatives have unitary frequency for each component item.  
Then, the two basic steps, object _assignment_ and centroids _update_, are carried out as follows: In the assignment step, each data object (set) is assigned to the cluster according to the nearest centroid rule. In the update step, the representative of each cluster is redefined by calculating the new histogram (of \(m\) length) with the number of occurrences of each item in the data objects of the cluster. This operation replaces the classical centroid update of K-means because we cannot compute the _mean_ of the data objects which are variable-sized sets of elementary items. The quality of the clustering is evaluated by the \(\underline{\textbf{sum}}\) of the \(\underline{\textbf{distances}}\) to the \(\underline{\textbf{histogram}}\) (\(SDH\)):  
\[SDH=\sum_{j=1}^{K}\sum_{X\in C_{j}}d\big{(}X,h_{j}\big{)} \tag{3}\]  
The two basic steps (_assignment_ and _update_) are iterated until \(SDH\) stabilizes, that is, the difference between the current and previous value of \(SDH\) is smaller than a threshold \(TH=10^{-8}\).  
Similar modifications were also introduced in the random swap algorithm [11] that result in the so-called K-swaps algorithm [8]. It integrates K-sets in the logic of pursuing a global search strategy via centroids swaps. The initialization step is the same as in K-sets, which is immediately followed by a first partitioning. At each swap iteration, a centroid is randomly chosen among the K representatives, and replaced by a randomly chosen data object in the dataset. After the swap, two K-sets iterations are executed and the corresponding \(SDH\) value is evaluated. If the swap improves (reduces) \(SDH\), the new centroid configuration is accepted and becomes the current solution for the next swap iteration.  
Good clustering results are documented in [8], by applying K-sets and K-swaps to 15 benchmark datasets, referred to as _Sets_ data in [22]. K-swaps resulted in low \(SDH\) values and high clustering accuracy, measured in terms of the adjusted rand index (ARI) [23; 24] values in the case of all benchmark datasets, and K-sets in the case of most datasets.
## 4 Experiment and Analysis
### Data Sets  
The real data sets used in the experiment are Yoochoose, Diginetica, and Tmall. The Yoochoose data sets are from RecSys Challenge 2015, which contains user click data of e-commerce websites within 6 months. This paper uses 1/64 of the Yoochoose data sets as the data sets to prevent the memory overflow caused by the Yoochoose data sets being too large. The Diginetica data sets are from CIKM Cup 2016, and this article only uses its transaction data. The Tmall data sets are from the IJCAI-15 competition data sets, which contain anonymous user shopping data on the Tmall shopping platform. In data preprocessing, all sessions of length 1 and items that appear less than five times in the data sets are filtered out, and the data sets are reordered in chronological order. For the Yoochoose data sets, the data from the last day are used as the test set and the remaining data as the training set, while for the Diginetica data sets, the data from the last seven days are used as the test set and the remaining data as the training set. For the Tmall data sets, the last 100 s are used as the test set, and the rest of the data are used as the training set. Then, the sequences and corresponding labels by splitting the input sequence are generated [41]. For session \(S=[v_{s,1},v_{s,2},v_{s,3},\ldots v_{s,k}]\), the sequences and corresponding labels are generated by sequence preprocessing, i.e., \(([v_{s,1}],v_{s,2})\), \(([v_{s,1},v_{s,2}],v_{s,3}),\ldots,([v_{s,1},v_{s,2},\ldots,v_{s,k-1}],v_{s,k})\) is training and test sets for all three data sets. The statistics of the preprocessed data set are shown in Table 1.
## 4 Factors Affecting Mechanical Properties of Porous Materials
### Circular Pores
#### 4.1.2 Number of Pores  
On the basis of a given material and boundary conditions, the porosity of each group is controlled at 10%. Models containing 4, 6, 8, 10, 15, and 20 pores were generated and calculated in each group. As shown in Figure 8, the following results are obtained:  
From Figure 9 and Table 2, the results show that the values of Poisson's ratio and equivalent modulus basically do not change with the number of pores. The change in the number of pores has no effect on the calculation results of Poisson's ratio and equivalent modulus. As the number of pores increases, however, the variance of the two results decreases, meaning the simulation results become increasingly focused on the expected value. It can be seen from Figure 9c and Table 2 that the value of the first principal stress does not change significantly with the increase in the number of pores. The variance decreases gradually and then tends to be stable. It shows that the simulation results become increasingly stable when the number of pores increases. The above shows that the increase or decrease in the number of pores within a certain range has little impact on the mechanical properties of the pore material and can be ignored.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dispersion** & **Equivalent** & **Variance of** & & & & **Variance of** & **Variance of the** \\
**Degree of** & **Modulus** & **Equivalent** & **Poisson’s Ratio** & **Poisson’s Ratio** & **Poisson’s Ratio** & **Stress (MPa)** & **First Principal** \\
**Pores** & **(_MPa_)** & **Modulus** & & & & **Stress (MPa)** & **Stress** \\ \hline
18\% & 19320.06 & 166.51 & 0.221 & 0.006 & 10.87 & 1.37 \\
30\% & 19529.37 & 97.81 & 0.218 & 0.004 & 7.88 & 0.48 \\
44\% & 19547.45 & 61.10 & 0.217 & 0.003 & 7.18 & 0.26 \\
56\% & 19543.74 & 49.15 & 0.217 & 0.002 & 6.98 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of mechanical properties of porous materials with different degrees of dispersion.  
Figure 8: Models of different pore numbers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of** & **Equivalent** & **Variance of** & & & **Variance of** & **First Principal** & **Variance of the** \\
**Pores** & **Modulus** & **Equivalent** & **Poisson’s Ratio** & **Poisson’s Ratio** & **Stress (MPa)** & **Stress (MPa)** \\ \hline
4 & 17129.54 & 231.00 & 0.231 & 0.009 & 6.88 & 0.70 \\
6 & 17063.64 & 184.34 & 0.233 & 0.007 & 7.13 & 0.61 \\
8 & 17066.14 & 145.11
## 2 Pump Model and Numerical Setting
### Numerical Scheme
#### 2.2.2 Turbulence Model  
Sand particles influence the turbulent characteristics of water. Alternatively, turbulent fluctuations are related to the solid volume fraction and particle size. The influence of sand particles on turbulence can be modeled by adding terms in the momentum equations, and their impact on the turbulent kinetic energy is thereby represented. The interaction between turbulence and sand particles can lead to the accumulation of particles in highly turbulent regions and can affect the overall flow behaviors. For the treatment of the solid-liquid two-phase flow in the impeller pump, the \(k\)-\(\epsilon\) turbulence model exhibits three merits over the other two-equation turbulence models: (1) neglecting the molecular viscosity and emphasizing macro-level flow behaviors, (2) adapting to rough grids under high-\(Re\) conditions, and (3) accomplishing high numerical accuracy in the presence of large blade curvature. With the standard \(k\)-\(\epsilon\) turbulence model, the turbulent quantities are calculated for the mixture. The density and viscosity of the mixture are denoted by \(\rho_{m}\) and \(\mu_{m}\), respectively. By solving the transport equations of the turbulent kinetic energy, \(k\), and the turbulent kinetic energy dissipation rate, \(\epsilon\), the turbulent viscosity \(\mu_{t,m}\) of the mixture can be obtained as follows:  
\[\mu_{t,m}=0.09\rho_{m}\frac{k^{2}}{\epsilon} \tag{12}\]  
Then, the turbulent viscosity of phase \(i\) can be obtained through \(\mu_{t,I}=(\rho_{i}/\rho_{m})\mu_{t,m}\).
## 1 Introduction
### Research Objectives and Problem Statement
There will be a large amount of data streams in the network, which is likely to cause network congestion and increase the delay and energy consumption of application processing. In this case, MEC cannot solve the problem of cloud computing, as network performance will be damaged due to poor dynamic resource allocation. Therefore, in MEC, a dynamic, intelligent and fast wireless resource allocation strategy is an important prerequisite for excellent network performance. As a popular and practical technology in recent years, machine learning has brought significant economic benefits to the production process of many industrial departments [18]. It can make decisions about resource allocation efficiently. At present, relevant research mainly includes studies on learning mechanisms and the use of big data [19]. MEC networks contain a large amount of data from devices; thus, machine learning is very helpful for resource allocation in MEC networks. Generally, we can analyze the data in the wireless network, optimize the network and even provide instructions for the devices. Machine learning can greatly improve the QoS by abundant training. In the field of optimization, one of the most popular machine learning methods is reinforcement learning (RL) [20].
# A Time-Varying Filter for Doppler Compensation Applied to Underwater Acoustic OFDM
## 4 Validation Using Real Data
### Short-Range Ultra-Sonic Transmission
The horizontal lines in (**b**) are used to identify the average measured BER. repetition coding. Note that the 95% confidence interval for the measured BERs of the coded and uncoded system are in the ranges of \((0.153,0.172)\) and \((0.082,0.112)\), respectively. For Config. #2 with and without repetition coding, the bit rates are 776 bps and 2328 bps, respectively. As shown in Figure 9(a), the simulated BERs at an SNR equal to 5.5 dB after Doppler compensation with and without coding are 0.041 and 0.084, respectively. According to Figure 9(b), the measured respective BERs are 0.035 and 0.086, which are very close to the simulated values. More specifically, the BER was measured with a 95% confidence interval to be \((0.029,0.041)\) and \((0.082,0.091)\) with and without coding, respectively. The comparison between the measurements and simulation for Config. #2 are even closer in comparison to those of Config. #1. This is attributed to the fact that the channel conditions are more stable during this measurement.
# A Compact, Low-Profile, Broadband Quasi-Isotropic Antenna for Non-Line-of-Sight Communications
## 4 Measurements with the Built Prototype
To verify the CP performance of the proposed antenna, the measured ARs are also plotted for the frequencies of 2.0, 2.6, 3.2, and 3.6 GHz within the target band, as can be seen in Figure 15. The measured ARs of 26.8% and 28.8% with less than 3 dB of coverage are shown at 3.2 and 3.6 GHz, respectively, and they are smaller than the computed expectations shown in Figure 10. This difference might be caused by the sensitive nature of the unshielded feeding cable of the chamber during the measurement. Nevertheless, the linear polarization characteristic along the azimuth plane and the overall tendency of the CP coverage are well verified. To highlight the novelty and the merits of the proposed antenna design, the electrical size _ka_ and the radiation performance, including the operating bandwidth, gain deviation, and polarization, are compared with those in the previously reported works on quasi-isotropic antennas in Table 2. All of the listed values in the table are from the measurements. While the values in most of the works [23; 24], including refs. [26; 27], are not electrically  
Figure 12.0 Simulated and measured peak gain and efficiency. Figure 12.1 Measurement results of CP performance: (**a**) 2.0 GHz, (**b**) 2.6 GHz, (**c**) 3.2 GHz, and (**d**) 3.6 GHz. small with a _ka_ value larger than 1, the proposed antenna has a _ka_ value of 0.94 with an extremely thin profile of 0.0015\(\lambda\). It also shows the broadest impedance bandwidth while providing the desired quasi-isotropic radiation characteristic within most of the matched frequencies. Compared with [24; 25], the gain deviation is a bit higher, but still within the acceptable range of less than 10 dB, with a much broader operating bandwidth. The antenna in [23] has an electrically smaller size but shows a narrower bandwidth than that of the proposed antenna. Not much information about antenna radiation performance is given for this work. The proposed antenna shows broader and better gain deviation properties than the antenna designs in [26; 27] with a simple and compact structure. Among the several designs of quasi-isotropic antennas, refs. [26; 30] show the CP performance at narrowband frequencies. In [42], the elliptical loop-based quasi-isotropic antenna shows a broader bandwidth than that of the proposed antenna due to its larger size. Overall, the proposed antenna is highly efficient and shows a broader impedance and CP bandwidth than those in [26; 30] with a quasi-isotropic radiation pattern.
## 3 Results and Discussion
### Thermal Characteristics of Films  
A thermogravimetric analysis was carried out to characterise the structural differences and thermal stability of films produced from ozone-treated FFSs containing WPC. The thermal degradation temperatures and weight loss (%) of gelatine-based biopolymer films from ozone-treated FFSs are shown in Table 4. The onset degradation temperature of samples ranged from 152.05 to 158.12 \({}^{\circ}\)C. The control sample had the lowest onset temperature, whereas O-30 had the highest one. Ozone treatment increased the degradation starting temperature depending on the increasing ozonation time. The degradation of the ozone-treated samples at higher temperatures was lower as compared to the control. The highest maximum degradation temperature (238.50 \({}^{\circ}\)C) was observed in the control. The maximum degradation temperature of the ozone-treated samples was lower than the control. However, ozone treatment increased the maximum degradation temperature among the ozone-treated samples. According to the results, a slightly higher temperature was necessary to decompose the control sample. The formation of covalent cross-links and hydrophobic interactions between different protein components induced by ozone treatment may be the main contributor to this increase in thermal stability [55]. The highest weight loss (93.56%) was determined in the control film. The weight loss of films from the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{4}{c}{**Colour**} & \multirow{2}{*}{**Oacity**} & \multirow{2}{*}{**Photograph**} \\ \cline{2-2} \cline{6-6}  & \(L^{*}\) & & \(a^{*}\) & \(b^{*}\) & \(\Delta E^{*}\) & \\ \hline Control & 80.10 \(\pm\) 0.14 c & 2.01 \(\pm\) 0.02 a & 7.58 \(\pm\) 0.11 b & 0 & 0.990 \(\pm\) 0.014 c \\ O-5 & 81.28 \(\pm\) 0.40 c & 1.88 \(\pm\) 0.03 b & 7.96 \(\pm\) 0.09 b & 1.25 \(\pm\) 0.24 c & 0.107 \(\pm\) 0.005 d \\ O-10 & 83.90 \(\pm\) 0.42 b & 1.87 \(\pm\) 0.05 b & 8.46 \(\pm\) 0.01 a & 3.91 \(\pm\) 0.57 b & 1.120 \(\pm\) 0.007 a \\ O-30 & 89.77 \(\pm\) 0.45 a & 1.77 \(\pm\) 0.02 b & 8.70 \(\pm\) 0.13 a & 9.74 \(\pm\) 0.59 a & 1.027 \(\pm\) 0.005 b \\ \hline \hline \end{tabular} Data are the mean \(\pm\) standard deviation (\(\mu=3\)). Different letters within the same column show the significant differences between the samples (\(p<0.05\)). Control: biopolymer film from non-cozonated FFS; O-5: biopolymer film from ozone-treated FFS for 5 min; O-10: biopolymer film from ozone-treated FFS for 10 min; O-30: biopolymer film from ozone-treated FFS for 30 min.  
\end{table}
Table 3: Colour characteristics and opacity parameter of biopolymer films produced from the ozone-treated film-forming solutions (FFSs).  
ozone-treated FFSs was between 89.78 and 92.15%. The ozone treatment of the FFSs decreased the weight loss of the film samples. This is thought to be beneficial for biopolymer film technologies. Previous studies have reported similar values for weight loss [5; 51].
## 3 Results and Discussion
### Background-Temperature Effect on Enhanced Biological Phosphorus Removal (EBPR) without Cu(II)
#### 3.1.2 Aerobic Metabolisms  
Figure 1b shows the aerobic PAO metabolisms at different temperatures. Apparently, PAO metabolisms in the aerobic stage were much more influenced by temperature variations than at the anaerobic stage (by comparing Figure 1a,b). Both of the specific phosphorus uptake rate (SPUR) and PHA degradation rate were significantly increased with increasing temperature. These results were the same as the results reported by [PERSON] et al. [31] in a studied range of 5-30 \({}^{\circ}\)C, but they were contrary to a long-term effect study reported by [PERSON] et al. [23]. Of note, the aerobic PHA degradation rate was negative at 10 \({}^{\circ}\)C, indicating PHA production rate was greater than PHA utilization rate in this condition. One possible reason was too much acetate remained at the end of the previous anaerobic stage because of low SSUR, resulting in PAOs that proceeded to synthesize PHAs under abundant carbon circumstances in the subsequent aerobic condition. Another possible reason was PAOs consumed less PHA because a 10 \({}^{\circ}\)C environment was unfavorable to the aerobic metabolisms of PAOs. The latter reason could be enhanced by lower SPUR and glycogen synthesis rates in a 10 \({}^{\circ}\)C environment compared to those in 20 and 30 \({}^{\circ}\)C conditions.  
The glycogen synthesis rates in Figure 1b also increased with an increasing temperature, indicating the glycogen replenishment of PAOs was significantly promoted by increasing temperature in aerobic conditions. Interestingly, both rates of PAO phosphate uptake and glycogen synthesis were apparently low for the 10 \({}^{\circ}\)C trial, also indicating that a low temperature environment was unfavorable to the aerobic metabolisms of PAOs.  
The amounts of phosphate uptake and glycogen synthesis per unit of PHA consumption are compared in Figure 1d. It could be used to evaluate the effects of temperature on PAO energy utilization, which were supplied by degrading PHAs, to take up phosphates and replenish glycogens in the aerobic stage. The results showed PAOs took up more phosphates and replenished more glycogens at 20 \({}^{\circ}\)C than in 10 and 30 \({}^{\circ}\)C environments, indicating 20 \({}^{\circ}\)C was beneficial for the aerobic metabolism of PAOs.  
Temperature Influences on Anaerobic Metabolisms of Phosphorus Accumulating Organisms (PAOs) in Conjunction with Cu(II) Presence
## 4 Equalizer Design Techniques
### Homomorphic Filtering  
Homomorphic filtering was already proposed for minimum phase equalization in the seminal paper of [PERSON] and [PERSON] [5], but many other authors introduced modified versions of the homomorphic technique [56; 57; 58]. In homomorphic filtering, the minimum phase part of the room response is extracted from the causal part of the complex cepstrum. A stable infinite impulse response (IIR) equalizer is then obtained by direct inversion of the minimum-phase part. Since the excess-phase part of the RTF was found to carry most of the reverberant energy [59], in [6; 56] the homomorphic technique was also used for mixed-phase equalization. In particular, the minimum-phase equalizer was complemented with an excess-phase equalizer, designed with a least-squares technique. Another possibility for implementing an excess-phase equalizer is to use a matched filter--i.e., a filter having an impulse response that is the time-reversal of the impulse response of the excess-phase system [57]. However, mixed-phase equalization based on homomorphic technique was found to be oversensitive to errors in the initial homomorphic decomposition of the room response [56; 60]. Improvements to the homomorphic technique were reported in [57] and [58]. In [57], an iterative homomorphic technique is proposed by iteratively flattening the RTF magnitude response. The technique overcomes potential numerical problems and _\"provides more insight into subjective aspects of magnitude and phase equalization in the reduction of acoustic reverberation\"_[57]. In [58], some of the low-frequency dominant poles of the filter transfer function are replaced by new ones with smaller magnitude before computing the inverse filter. The technique allows the extent of oscillations associated with these poles to be reduced. The main disadvantage of the homomorphic technique is the large length of the all-zero (finite impulse response) model of the room response and the high sensitivity of the model to _\"changes in source/receiver placement\"_[61]. From this point of view, the LPC analysis provides more robust results [61].
## 2 Dataset and Methodology
### Dataset and Criteria
#### 2.3.8 Wind Speed  
Wind speed can both positively and negatively impact the performance of PV panels. Moderate wind speeds are desirable to keep panels cool and improve efficiency, but excessive winds can cause structural damage, requiring careful consideration of wind patterns. Wind speed data were obtained from meteorological records; wind speeds between 0 and 3 m/s were rated as very suitable, as they provide sufficient cooling without compromising structural stability. Wind speeds exceeding 10 m/s were classified as unsuitable due to the increased risks of wear and tear on the infrastructure [25; 54].