esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Interference-Aware Power Optimization for Multi-Task Over-the-Air Federated Learning
## 3 Convergence Analysis and Problem Formulation
### Pareto Boundary Definition and Characterization  
To achieve balanced learning performance across different cells, we introduce the concept of the optimality gap region, denoted by \(\mathcal{G}\). This region represents the set of all achievable tuples \((\Delta_{1},\Delta_{2},\ldots,\Delta_{M})\), where each component \(\Delta_{m}\) corresponds to the optimality gap of cell \(m\in\mathcal{M}\) under the given system constraints such as per-device power budgets. Formally, the optimality gap region is defined as  
\[\mathcal{G}=\bigcup\left\{(\Delta_{1},\Delta_{2},\ldots,\Delta_{M})\mid\Delta_ {m}\geq\mathrm{Gap}_{m^{\prime}},\forall m\in\mathcal{M}\right\}, \tag{25}\]  
where \(\mathrm{Gap}_{m}\triangleq J_{m}L_{m}\eta_{m}^{2}\mathbb{E}\left[\Re\{\epsilon_{ m,c}\}^{2}\right]\) represents the minimal achievable optimality gap for cell \(m\).  
The Pareto boundary of region \(\mathcal{G}\) is defined as the set of all Pareto optimal points, for which it is impossible to reduce any component \(\Delta_{m}\) without increasing at least one of the others. These points characterize the optimal trade-offs between the learning performance results of different cells under shared resource constraints and provide a theoretical limit to how well system-wide performance can be balanced.  
To mathematically characterize the Pareto boundary, we adopt the rate profile method proposed in [30]. This method enables joint optimization across all APs by formulating a scalarized minimization problem, where the individual performance weights are specified by a profile vector \(\kappa=[\kappa_{1},\kappa_{2},\ldots,\kappa_{M}]\). Each weight \(\kappa_{m}\) reflects the relative importance or performance share assigned to cell \(m\), subject to the normalization constraint \(\sum_{m\in\mathcal{M}}\kappa_{m}=1\)The optimization problem for characterizing the Pareto boundary is then given by  
\[(\mathrm{PI}): \min_{\{p_{k},0<\theta_{m},0<\varsigma\}}\varsigma\] (26) s.t. \[\mathrm{Gap}_{m}\leq\kappa_{m}\varsigma,\forall m\in\mathcal{M}, \tag{27}\] \[0\leq p_{k}\leq p_{k}^{max},\forall k\in\mathcal{K}_{m},m\in \mathcal{M}, \tag{28}\]  
where \(\varsigma\) serves as an upper bound that balances the individual optimality gaps according to the profile vector \(\kappa\).  
For a given profile vector \(\kappa\), the solution \(\varsigma^{*}\) to problem \((\mathrm{P1})\) determines a Pareto optimal tuple as \(\kappa\varsigma^{*}\). Geometrically, this point lies on the intersection between the ray defined by direction \(\kappa\) and the Pareto boundary of region \(\mathcal{G}\). By varying \(\kappa\) over the unit simplex, the entire Pareto boundary can be traced as illustrated in Figure 2, thereby fully revealing the trade-off structure among competing tasks.
## 2 Materials and Methods
### Design of the Proposed Capacitive Sensors  
For the fabrication of the two proposed sensors, recycled aluminum sheets from beer cans were utilized. This material was selected due to its accessibility, reusability, and alignment with the overarching objectives of the project. It is important to note that beer or soft drink cans can be made of either steel or aluminum, depending on the application and the production region, where the availability of these materials may vary. However, in most cases, cans are made of aluminum, as in this study, due to its effectiveness in preserving beverages by protecting them from air, light, and potential contamination risks, including bacteria, viruses, hazardous chemicals, and physical agents.  
Moreover, aluminum is an economical and sustainable material, thanks to its high recyclability [21]. Cans are typically coated with a thin layer of paint and plastic. The plastic serves to separate the beverage from the aluminum, preventing chemical reactions between the liquid and the metal or contamination from direct contact. The most common plastics used for this type of coating include polyethylene (PE), polypropylene (PP), polystyrene (PS), polyethylene terephthalate (PET), and polyvinyl chloride (PVC). As an insulating  
Figure 1: Appearance of the capacitive sensor.  
material, plastic possesses a relative electrical permittivity, which directly influences the capacitance value of each sensor. Table 1 provides the relative electrical permittivity values for the aforementioned types of plastic [22].  
During the construction process of the MT and MNT sensors, a plastic layer (polyppropylene) was used as protection against the environment in which they will be used, establishing a value of \(\varepsilon_{r}=2.6\). In addition, this layer allowed maintaining a constant distance between the plates, avoiding direct contact between them.
## 3 Toxicity/Toxicological Effects
### Toxic Effects of PFAS in Organisms
The analysis of the impact takes into account both the exposure period and the chemical structure. [PERSON] et al. [63] showed that appropriate data on the impact on health of short-chain PFAS may be obtained using the same strategies used for in vitro human cellular models. Human cell lines from six distinct tissues were exposed to five short-chain PFASs and two legacy PFASs: colon (CaCo-2), brain (HMC-3), kidney (HEK293), lung (MRC-5), liver (HepaRG), and muscle (RMS-13). RMS-13 showed the highest decline in viability after being exposed to PFBS for 48 h, whereas HepaRG appeared to be the most susceptible to hexafluoropropylene oxide-dimer acid (HFPO-DA) exposure. For the long-chain PFAS cytotoxicity, Caco-2 and MRC-5 were less vulnerable to PFOA exposure, whereas PFOA had the largest impact on viability in HepaRG, HMC-3, and RMS-13 comparable to 6:2 FTOH for the short-chain group. An independent t-test was used to compare these data, but no differences were found. [PERSON] et al. [64] studied the relationship between high PFASs exposure and cardiovascular diseases and cardiometabolic outcomes. Blood samples of 232 male ex-workers in Europe's most important PFAS manufacturing factory between 1968 and 2018 were tested, and four out of twelve PFASs were found in at least half of the samples. The combination of four PFASs combined were positively correlated with TC, LDL-C, and SBP (markers of cardiovascular diseases). [PERSON] et al. [65] showed for the first time a link between PFAS concentrations in serum and thrombograms of a panel of young and middle-aged persons in Asia, particularly in Taiwan. Moreover, for PFOS and -PFOA, concentrations were higher than the 50 th percentile, while the average number of platelets was at its lowest.
## 4 Multi-Armed Bandit (MAB) for Optimal MPTCP Scheduling
### With Packet Cloning  
We develop _packet cloning_ inside our _LD-MPSP_ algorithm to resolve the challenges over \(802.11 ay\) networks. _If packets are cloned before transmission over multiple links, then the performance of short flows will no longer be limited by the worst link._ In fact, it will be guaranteed (opportunistically) to be at least equal to that due to the best link. Packet cloning is more implementable over wireless links than sending packets only on the best link. In practice, the best link is never known a priori (and is expensive to determine). Moreover, replication-based MPTCP will maintain the worse link(s), which will increase the robustness against possible failure of the better link(s).  
By performing such data replication inside _LD-MPSP_, we provide an opportunistic and elegant solution for short data flows. This is easy to understand by considering the extreme case of _full-cloning_ in which all the data packets are copied over all the available links. With _full-cloning_, one can solve the packet reordering problem, and it is extremely simple to implement. The simplicity of cloning will be highly effective for improving the performance of short flows that often finish before any sophisticated control algorithm could possibly take effect. However, cloning of packets will increase the data traffic on the Internet and can adversely affect the competing TCP and MPTCP flows which are not using redundancy. Prolonged TCP flows may learn link characteristics on the fly as packets are transferred, but smaller TCP flows have minimal information and guidance on the link characteristics.  
With _partial-cloning_, one will be able to improve the performance of longer flows that are amenable to control by providing just the right amount of redundancy over multiple heterogeneous links (in a dynamic manner according) to the changing conditions of the wireless links. Therefore, we exploit loss-based adaptive cloning in our _LD-MPSP_, in which \(w\) MTCP packets are cloned randomly over all links with probability:  
\[c_{p}=\min\{k,p_{e}(t)/(1-p_{e}(t))\},0.5\leq k\leq 1 \tag{23}\]  
such that the total number of MPTCP packets (including the cloned packets) is given by  
\[w^{\prime}=\min\{(1+k)w,\,[w/(1-p_{e}(t))]\} \tag{24}\]  
where \(p_{e}(t):=\max_{i}\{p^{i}_{e}(t)\}\), \(p^{i}_{e}(t)\) is the packet loss probability due to wireless channel errors along link \(t\).
## 2 Materials and Methods
### Theoretical Considerations
#### 2.2.2 Determining the IFD for Emission Classes A3X  
If the modulation signal is represented as \(x(t)=X_{m}f(t)\) in which \(X_{m}\)--is the amplitude of the modulation signal, \(f(t)\)--represents the normalized modulation signal with zero mean value (\(\overline{f(t)}=0\)), and \(m\) represents the degree or index of modulation, the amplitude-modulated signal \(s_{A3X}(t)\) with double sideband can be described by Equation (4):  
\[s_{A3X}(t)=A_{p}[1+m\cdot f(t)]\cos\omega_{p}t, \tag{4}\]  
where \(A_{p}\) is the carrier amplitude, and \(\omega_{p}\) is the carrier angular frequency. The signal power \(P_{\textit{Simp}}\) at the demodulator input is given in Equation (5):  
\[P_{\textit{Simp}}=\overline{s^{2}{}_{A3X}(t)}=\frac{1}{2}A_{p}{}^{2}\overline {[1+m\overline{f(t)}]}^{2}=\frac{1}{2}A_{p}{}^{2}\Big{[}1+\overline{m^{2}f(t) }^{2}\Big{]}, \tag{5}\]  
Considering that the efficiency of the detector (demodulator) is \(\eta_{d}\), the following signal in Equation (6) is obtained at its output:  
\[s_{out}(t)=\eta_{d}\big{[}A_{p}m\cdot f(t)+n_{1}(t)\big{]}, \tag{6}\]  
Figure 2: Equivalent scheme for demodulation analysis in the presence of noise.  
At the output, the signal power, \(P_{Sout}\), and the noise power, \(P_{Zout}\), are expressed as in Equations (7) and (8):  
\[P_{Sout}=\eta_{d}^{2}A_{p}^{2}m^{2}\cdot\overline{f^{2}(t)}, \tag{7}\]  
\[P_{Zout}=\eta_{d}^{2}\cdot\overline{n_{1}^{2}(t)}=\eta_{d}^{2}N_{0}B, \tag{8}\]  
It turns out that the IFD is computed with Equation (9)  
\[\rho_{A3X}=\frac{P_{Sout}/P_{Zout}}{P_{Sinp}/P_{Zinp}}=\frac{A_{p}^{2}m^{2} \cdot\overline{f^{2}(t)}/N_{0}B}{{A_{p}}^{2}\Big{[}1+\overline{m^{2}f(t)^{2}} \Big{]}/2 NB_{0}}=\frac{2m^{2}\cdot\overline{f^{2}(t)}}{1+m^{2}\cdot\overline{f ^{2}(t)}}, \tag{9}\]
# Photovoltaic Cell and Module I-V Characteristic Approximation Using Bezier Curves
## Nomenclature  
_Main Symbols_  
\(a\) & Diode ideality factor  
\(G\) & Actual irradiance  
_Gref_ & Reference irradiance, 1000 W/m\({}^{2}\)  
\(I\) & Output current  
_Imp_ & Output current at maximum power point  
_Isc_ & Short circuit current  
_Isc,ref_ & Short circuit current 25 \({}^{\circ}\)C  
\(k\) & Boltzmann constant  
\(k\) & Current temperature coefficient, A/K  
_kV_ & Voltage temperature coefficient, V/K  
_kR,ba_ & _Rsh_ temperature exponent  
_n_s & Number of series cells  
_Pmp_ & _Vmp_ & _Imp_ & Maximum output power  
\(q\) & Electron charge  
_R_s & Series resistance  
_R_s,ref & Series resistance at 25 \({}^{\circ}\)C  
_R_s0 & Series resistance based on I-V characteristic slope close to _Voc_  
_Rsh_ & Parallel (shunt) resistance  
_Rsh,ref_ & Parallel (shunt) resistance, at 25 \({}^{\circ}\)C  
_Rsh0_ & Parallel (shunt) resistance based on I-V characteristic slope close to _Isc_  
\(T\) & Internal temperature, (K)  
_Tref_ & Reference temperature 298.15 K  
\(T\) & Temperature difference  
\(V\) & Output voltage  
_Voc_ & Open circuit voltage  
_Voc,ref_ & Open circuit reference voltage at 25 \({}^{\circ}\)C  
_Voc,cell_ & Solar cell open circuit voltage  
_Voc,cell,ref_ & Solar cell open circuit reference voltage at 25 \({}^{\circ}\)C  
_Vmp_ & Output voltage at maximum power point  
_Abbreviations_  
AM & Air Mass  
MPPT & Maximum Power Point Tracking  
PV & Photovoltaic  
SAS & Solar Array Simulator  
STC & Standard Test Conditions (cell temp. 25 \({}^{\circ}\)C; irradiance 1000 W/m\({}^{2}\); air mass 1.5)  
_GREs_ & Series resistance temperature coefficient (linear law)
## 2 Acoustic Emission Based Evaluation of Embrittlement Temperatures
### Testing Asphalt Binders
In Table 1 and in Figure 8, the term Rolling Thin-Film Oven (RTFO) indicates the binder was submitted to short-term aging, the Term Pressure Aging Vessel (PAV) indicates the binder was submitted to long-term aging (or a 7 to 10 year period), and TANK indicates virgin, i.e., unaged binder. Figure 6: Typical acoustic emission event waveform (**a**) and corresponding spectral content (**b**). Figure 8: Correlation between AE embrittlement temperature and BBR-based cracking temperature, illustrating the conservative nature of the BBR-based cracking temperatures, see Table 1.  
Figure 7: Typical experimental plot of cumulative events and energy versus temperature for asphalt binder test samples. Results also indicate that AE-based embrittlement temperatures are lower than the corresponding BBR-based critical cracking temperatures. This is not surprising, mainly because the AE-based embrittlement temperatures denote the measured values, while the BBR-base critical temperatures are based upon the binder's rheological material properties and include an inherent factor of safety to avoid low-temperature pavement cracking. For additional information, the readers are referred to References [3; 4; 36]. The developed AE-based approach was successfully employed to estimate the low-temperature performance grade of virgin, short-term, and long term-aged asphalt binders. In addition to the initial transverse cracks shown in Figure 9, which divide the binder test sample into several blocks, Figure 9 also shows three-dimensional spiral cracks, which became visible only after removal of the top material from the test sample [38]. These spiral cracks are a result of the three-dimensional state-of-stress field that develops in each block (created by the transverse cracks) by the constraints imposed by the granite block. These spiral cracks were modeled as three-dimensional logarithm spirals using three parameters, (a spiral tightness parameter \"b\", an apparent length scale parameter \"A\", and the pitch angle \"\(\varphi\)\") that control how tightly and in which direction the spiral is wrapped. In addition to observing that the spiral pattern represents the crack trajectory with maximum energy release, it was also observed that the AE obtained embrittlement temperatures and fracture energy (obtained using indirect tension tests) are related to the spiral crack tightness parameter [38]. For additional information regarding modeling spiral cracks as the mode of failure in asphalt materials, the readers are referred to [PERSON] et al. [38].
## 4 Design Optimization
### Sensitivity Analysis of Design Parameters  
Using the established kriging metamodel, we conducted Gaussian Process Regression Variance Analysis (GPVA) to quantitatively analyze the influence of design variables on contact pressure and equivalent stress, identifying optimal parameter ranges for design optimization.  
GPVA quantifies input variables' contribution to output variability by analyzing the variance structure of the stochastic process \(Z(x)\) in Equation (13). Following standard variance analysis methodologies in metamodeling, the influence coefficient (\(\eta\)) of each design variable is calculated as [14]:  
\[\eta_{k}=\frac{\sigma_{k}^{2}}{\sum_{i}\sigma_{i}^{2}} \tag{16}\]  
where \({\sigma_{k}}^{2}\) represents the variance contribution of the kth design variable, and \(\Sigma_{i}{\sigma_{i}}^{2}\) is the sum of all variance contributions. Table 7 summarizes these results.  
\(R_{p}\) demonstrated the most significant influence on both contact pressure (63.5%) and equivalent stress (52.8%). This substantial contribution highlights \(R_{p}\)'s critical role in determining contact area and stress distribution characteristics.  
\(R_{s}\) showed moderate impact, contributing 24.3% to contact pressure and 32.4% to equivalent stress. The nonlinear influence suggests complex interactions within the mechanical system.  
Eccentricity e had the least influence, accounting for 12.2% of contact pressure and 14.8% of equivalent stress variations. Despite its minimal contribution, variations in \(e\) consistently affected both contact pressure and equivalent stress.
## 2 Related Work
The authors of [19] propose a method that employs iterative clustering on the raw log entries. This results in coherent in-between representations that are used to compress the data. Compared to gzip, the compressed data takes up 40% less space. In [20] CLC (Comprehensive Log Compression) is introduced. Short representations are assigned to frequent patterns to reduce the size. This method could reach coverage of up to 95.8% and found the templates. In this paper, we enhance our method produced in [21]. Even though LenMa reached the highest compression rate with 67.4% in our previous research, due to its high execution time, which is 8.5 times more in the case of the \"Big\" dataset, we encouraged the use of IPLoM or MoLFI, both of which are considerably faster and have an average compression rate of 57.8% and 60.8%. To further improve our algorithm, we initially examine the frequency of the templates; smaller IDs (with the least possible characters) are assigned to frequent templates. Previously, only one dictionary was created for the templates, while, in this paper, there is also one created for the variable tokens. Similar to templates, each variable gets assigned to an ID and only the ID is stored. This results in log entries that only contain IDs (numbers) separated by spaces. In the end, these entries are encoded via Huffman coding. A more detailed description is introduced in Section 4. The basic method applies template miners such as IPLoM and MoLFI to determine the event types that occur in the log file. A dictionary is then created where a new unique ID gets assigned to each of the templates. We then encoded each log entry with the use of this dictionary, so each line would consist of an ID and the parameters of that individual line. To further increase the compression rate, we take the frequency of the templates into account, encode the parameters, and apply Huffman coding. A more detailed explanation can be found in Section 4. This paper uses the following structure. Section 3 introduces the concept of log parsing. A brief summary of the used template miners, IPLoM, and MoLFI can also be found here. That section also contains a high-level overview of the used general compressing methods: LZMA, Bzip2, and PPMd. Prediction by Partial Matching (PPM) is an adaptive statistical data compression technique that has several implementations, denoted by the letters (a, b, c, d, e, f), which mainly indicate the amount of memory usage; in our case version d was used. A detailed description of the enhanced algorithm and statistical analysis of the examined data can be found in Section 4. An explanation of the concluded experiments, that assesses the compression rate and the time taken by the enhanced method and the general compressors on log files that were generated by real-world networking equipment, is presented in Section 5. The conclusion of the paper and the possible future works are listed in Section 6.
## 3 Results  
### Distribution and Sick Leave Days of MSDs According to Occupation-Body Region-MSD Type  
Table 1 presents the distribution and sick leave days of the MSDs according to occupation-body region-MSD type. 'Electrician/plumber-low back-herniated disc' (6.9%) was the form of MSDs with the highest frequency among total MSDs, followed by 'welder-low back-herniated disc' (6.4%), 'fitter-shoulder-rupture' (6.3%), and 'electrician/plumber-shoulder-rupture' (6.2%).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Occupation**} & \multirow{2}{*}{**Body Region**} & \multirow{2}{*}{**MSD Type**} & \multicolumn{2}{c}{**Frequency**} & \multicolumn{2}{c}{**Sick Leave Days**} \\ \cline{5-8}  & & & **N** & **\%** & **Mean** & **SD** \\ \hline \multirow{8}{*}{Welder} & Neck & Herniated disc & 985 & 100\% & 318.9 & 222.1 \\  & Low back & Herniated disc & 18 & 1.8\% & 416.6 & 388.3 \\  & Low back & Herniated disc & 63 & 6.4\% & 319.7 & 131.4 \\  & Low back & Sprain & 10 & 1.0\% & 105.1 & 60.1 \\  & Knee & Rupture & 21 & 2.1\% & 397.7 & 475.5 \\  & Shoulder & Rupture & 47 & 4.8\% & 365.2 & 173.1 \\  & Elbow & Sprain & 10 & 1.0\% & 317.7 & 106.9 \\  & Neck & Herniated disc & 17 & 1.7\% & 307.2 & 141.0 \\  & Low back & Herniated disc & 46 & 4.7\% & 305.1 & 146.5 \\  & Knee & Rupture & 32 & 3.2\% & 281.1 & 115.3 \\  & Shoulder & Rupture & 62 & 6.3\% & 315.7 & 127.0 \\  & Shoulder & Sprain & 12 & 1.2\% & 287.5 & 142.4 \\  & Neck & Herniated disc & 34 & 3.5\% & 404.3 & 265.3 \\  & Low back & Herniated disc & 68 & 6.9\% & 314.0 & 178.1 \\  & Low back & Sprain & 21 & 2.1\% & 107.8 & 99.4 \\  & Knee & Rupture & 31 & 3.1\% & 365.8 & 274.5 \\  & Shoulder & Rupture & 61 & 6.2\% & 350.6 & 209.1 \\  & Shoulder & Sprain & 12 & 1.2\% & 329.4 & 163.9 \\  & Elbow & Sprain & 22 & 2.2\% & 266.7 & 160.7 \\ Painter & Neck & Herniated disc & 13 & 1.3\% & 368.1 & 252.7 \\  & Low back & Herniated disc & 17 & 1.7\% & 352.0 & 165.1 \\  & Knee & Rupture & 12 & 1.2\% & 367.2 & 166.6 \\  & Shoulder & Rupture & 30 & 3.0\% & 308.3 & 105.8 \\  & Elbow & Sprain & 12 & 1.2\% & 193.5 & 131.7 \\ Grinding operator & Knee & Rupture & 10 & 1.0\% & 298.0 & 175.3 \\  & Shoulder & Rupture & 12 & 1.2\% & 332.2 & 157.9 \\  & Neck & Herniated disc & 19 & 1.9\% & 500.5 & 334.3 \\  & Low back & Herniated disc & 12 & 1.2\% & 310.8 & 163.1 \\ Installer & Neck & Herniated disc & 15 & 1.5\% & 337.4 & 169.4 \\  & Low back & Herniated disc & 46 & 4.7\% & 351.1 & 229.2 \\  & Low back & Sprain & 19 & 1.9\% & 72.7 & 44.3 \\  & Knee & Rupture & 24 & 2.4\% & 252.7 & 129.8 \\  & Shoulder & Rupture & 31 & 3.1\% & 301.8 & 113.9 \\  & Elbow & Sprain & 13 & 1.3\% & 252.0 & 104.9 \\ \hline \hline \end{tabular} \% = N/985, SD = standard deviation, Note: Items with % of less than 1% are ignored.  
\end{table}
Table 1: Distribution and sick leave days of the MSDs according to occupation–body region–MSD type.