esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Modification of the Measured BRIR
#### 2.2.2 Modification of the Reverberant Part in the BRIR  
To investigate the influence of removing the reverberation in contralateral and ipsilateral ear signals on perceived externalization, the reverberant part was \"maximally\" removed in (i) BRIRs of the left ear (condition \"RL\"), (ii) BRIRs of the right ear (condition \"RR\"), and (iii) BRIRs of both ears (condition \"RB\"). The removal of reverberant parts in BRIRs was achieved by applying a 2.5 ms-long time window with a 0.5 ms-long half raised-cosine fall time (only the direct parts of BRIRs remained). All truncated BRIRs were zero-padded to a length of 260 ms. The binaural signals rendered by using non-processed BRIRs were applied as references (condition \"RN\"). Figure 5 shows the modified BRIRs at 60\({}^{\circ}\) for the \"RN\" (left top), \"RL\" (right top), \"RR\" (left bottom), and \"RB\" (right bottom) conditions in the time domain. To better visualize the BRIR modification, only the first 30 ms impulse responses are plotted, and an offset (amplitude of 1.5) was added to the left ear BRIRs for each condition. The resulting magnitude spectra for modified BRIRs at 60\({}^{\circ}\) are displayed in Figure 6. The magnitude spectra shown in each panel were normalized to the maximum magnitude level of the right ear BRIR. Note that the \"RN\" and \"SN\" conditions were the same, both of them denoting non-processed BRIRs.  
Figure 4: Examples of magnitude spectra for the BRIR at 60\({}^{\circ}\) for the “SN” (left top), “SL” (right top), “SR” (left bottom), and “SB” (right bottom) conditions. The solid and dashed curves denote the magnitude spectra for the BRIR of left and right ears, respectively.  
Figure 5: Examples of BRIRs with modified reverberant parts at 60\({}^{\circ}\) for the “RN” (left top), “RL” (right top), “RR” (left bottom), and “RB”(right bottom) conditions in the time domain. The solid and dashed curves denote the modified BRIRs of left and right ears, respectively (the left ear BRIR was offset by 1.5 for better visibility).  
Figure 6: Examples of magnitude spectra for BRIRs with modified reverberant parts at 60\({}^{\circ}\) for the “RN” (left top), “RL” (right top), “RR” (left bottom), and “RB” (right bottom) conditions in the frequency domain. The solid and dashed curves denote the magnitude spectra for modified BRIRs of left and right ears, respectively.
# Analysis and Design of Rateless Two-way Relay Networks Based on a Multiply-and-Forward Scheme
## 2 System Model
Then, the received signals at nodes A and B can be represented as  
\[\mathbf{y}_{\text{RA}}=h_{\text{A,R}}\sqrt{P}\mathbf{x}_{\text{R}}+\mathbf{n }_{\text{RA}} \tag{9}\]  
and  
\[\mathbf{x}_{\rm B}=1-2\mathbf{c}_{\rm B} \tag{2}\]0  
Finally, the received signals are processed at each source node to recover source bits from the opposite one. Note that all elements in \(\mathbf{n}_{\text{iR}}\) and \(\mathbf{n}_{\text{R}i}\) are i.i.d (independent and identically distributed). Gaussian variables following \(\mathcal{N}\left(0,\sigma_{\text{n}}^{2}\right)\), \(i\in\{\text{A,B}\}\). In addition, the duration of each exchange round is variant since SLT codes are rateless, i.e., each round comes to an end when both transmitters receive acknowledge from the corresponding destinations.
# Damage Characteristics of Thermally Deteriorated Carbonate Rocks: A Review
## 2 Thermal Dilatancy and Alteration in Rock Fabric  
Carbonate rocks obtained from various sources can differ significantly in their texture, depositional environment, chemical composition, crystal structure, and mineral geometry [19]. Limestones contain more than 50% calcite and a trace amount of a variety of minerals, including quartz, feldspar, pyrite, siderite, micrite, clay minerals, and other materials. On the other hand, dolostone is composed of the dolomitization process in which calcite transforms into magnesium-rich calcium carbonate. It contains dolomite as a primary mineral and a trivial amount of quartz, mica, iron oxide, and clay minerals [20]. They are chemically reactive substances and show large variations in their chemical reactions. Microstructure patterns and impurities, such as silica, iron, magnesium, manganese, sodium, potassium, etc., considerably affect their chemical reactivity [19]. The calcite and dolomite minerals belong to the hexagonal-rhombohedral crystal system. In this crystal system, the hexagonal unit cell is placed over the rhombohedral unit cell [21]. Calcite mineral has ordered planes of Ca\({}^{2+}\) attached with the CO\({}_{3}\)\({}^{2-}\) groups orthogonal to the c-axis. Whereas, dolomite mineral exhibits a well-defined order of alternating planes of Ca\({}^{2+}\) and Mg\({}^{2+}\) bonded with the CO\({}_{3}\)\({}^{2-}\) groups perpendicular to the c-axis [22].
# NetAP: Adaptive Polling Technique for Network Packet Processing in Virtualized Environments
###### Abstract  
In cloud systems, computing resources, such as the CPU, memory, network, and storage devices, are virtualized and shared by multiple users. In recent decades, methods to virtualize these resources efficiently have been intensively studied. Nevertheless, the current virtualization techniques cannot achieve effective I/O virtualization when packets are transferred between a virtual machine and a host system. For example, VirtIO, which is a network device driver for KVM-based virtualization, adopts an interrupt-based packet-delivery mechanism, and incurs frequent switch overheads between the virtual machine and the host system. Therefore, VirtIO wastes valuable CPU resources and decreases network performance. To address this limitation, this paper proposes an adaptive polling-based network I/O processing technique, called NetAP, for virtualized environments. NetAP processes network requests via a periodical polling-based mechanism. For this purpose, NetAP adopts the golden-section search algorithm to determine the near-optimal polling interval for various workloads with different characteristics. We implement NetAP in a Linux kernel and evaluated it with up to six virtual machines. The evaluation results show that NetAP can improve the network performance of virtual machines by up to 31.16%, while only using 32.92% of the host CPU time used by VirtIO for packet processing.  
cloud computing; virtualization; I/O processing; adaptive polling +
Footnote †: journal: Applied Physics Letters
# Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models
## 5 Results
### Trade-Off Comparison  
The trade-off comparison among developed ML algorithms is depicted in Figure 13a,b, where balanced accuracy and efficiency are shown. In this way, the goal is to determine which algorithm has the best compromise, which means a high balanced accuracy with high efficiency (Mbps/LUT).  
\begin{table}
\begin{tabular}{p{42.7 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt}} \hline \hline
The difference between the highest balanced accuracy (achieved by the Decision Tree with a depth of 6 and 95.26%) and the lowest (achieved by the SVM 92.06% is 3.19%) demonstrates that the developed algorithms had high performance. However, the efficiency difference between the highest (DT5 with 9.83) and the lowest (MLP with 0.0067) is 9.82; we consider this difference due to each algorithm's type of implementation. On the one hand, Decision Trees architectures are based on comparators, avoiding adders and multipliers, which are expensive in terms of hardware. On the other hand, MLP, Logistic Regression, and SVM involve adders and multipliers. MLP especially requires 15 multipliers and 15 adders for every neuron, for a total of 165 multipliers and 165 adders, in addition to the activation functions (ReLU and Sigmoid). An ReLU is the activation function for the first two layers and a sigmoid function at the output layer. For these reasons, the efficiency is low for these three algorithms. Even if the Decision Tree with a depth of six has the highest balanced accuracy, the difference with the Decision Tree with a depth of five is minimal (only 0.2446%). If both efficiencies are compared, the Decision Tree with a depth of five is higher. Therefore, the Decision Tree with a depth of five has the best trade-off between balanced accuracy and efficiency.
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 4 Conclusions
### Summary of Findings  
In our analysis of domain adaptation techniques for computer and robotic vision, it is evident that deep learning-based methods, including Domain Adversarial Neural Networks (DANN) and CycleGAN, consistently exhibit superior performance when contrasted with conventional methodologies like Transfer Component Analysis (TCA) and Maximum Mean Discrepancy (MMD). In several real-world contexts, deep learning-based techniques regularly beat conventional approaches with regard to accuracy, recall, precision, and F1-score, among other performance parameters. Additionally, the introduction of approaches like importance reweighting, multi-task learning, and maximum mean discrepancy loss enhances model accuracy, but increases complexity. Generative models like CoGAN and MUNIT show promise for visual translation and adaptation. Models like DANN, while capable of handling significant domain shifts, exhibit sensitivity to hyperparameters. Furthermore, diverse techniques, including adversarial learning, generative adversarial networks, meta-learning, and self-supervised learning, consistently improve domain adaptation performance (Table 8).  
When abundant labeled data are present in the target domain, deep learning proves effective, demanding substantial computational resources. However, traditional methods, like TCA and MMD, are pragmatic when target domain data are scarce or interpretability is vital. The choice between these methods hinges on factors like data availability, computational resources, and the need for interpretability.  
Specific models, such as refs. [318], [319] and [320], exemplify the potential of these techniques, while combining various approaches, like ensemble learning and self-supervised learning, further enhances performance (Table 9).  
The investigation emphasizes the critical role that domain adaptation plays in helping computer and robotic vision systems adapt to shifting settings and deliver reliable performance under difficult circumstances. Vision systems become more dependable and flexible in practical applications because of domain adaptation, which enables the smooth adaptation to changes in illumination, weather, camera views, and object textures. Furthermore, we discover that hybrid methods that combine the advantages of conventional and deep learning-based techniques as well as domain-specific modifications show substantial potential. Accepting domain-specific adjustments enables the creation of specialized solutions that address the special difficulties and traits of application areas. On the other hand, hybrid methods make use of the complementary qualities of many methodologies, offering chances for additional performance enhancements.
## 3 Results and Discussion
### Thermal Stability Test  
The thermal stability and decomposition pattern of individual components (APP, chitosan, melamine, boric acid, epoxy resin, hardener mixture, and zirconia) and formulations (F0, F1, F2, and F3) were established through TGA under nitrogen gas atmosphere. TGA analysis was performed in the inert gas atmosphere for the determination of the thermal degradation of the sample, because using an  
Figure 10: Time to ignition values of the flame retardant samples.  
Figure 9: Peak heat release rate curves of the flame retardant samples.  
air or oxygen atmosphere may cause the sample to undergo thermal oxidation. Figures 11 and 12 and Table 4 represent the TGA and differential thermogravimetric (DTG) results of individual components and F0, F1, F2, and F3 formulations. From these data, the F0 showed thermal decomposition starting at \(\approx\)241.2 \({}^{\circ}\)C and 18.1% char residue at 800 \({}^{\circ}\)C. After 250 \({}^{\circ}\)C, the IFR system F0 sample was involved in the intunescent mechanism. At this temperature, chitosan ammonium polyphosphate and melamine borate starts to decompose and release mineral acids such as phosphoric acid and meta-phosphoric acid, a polyhydric alcohol and polyhydric phosphates, boron trioxide, and melon, which removes the water content and NH\({}_{3}\) gas from the IFR system by an esterification process; this finally leads to the formation of a protective phosphor-carbonaceous char layer that prevents the oxygen flow and heat transfer. APP and chitosan can easily participate in the phosphor-esterification reaction because chitosan has an abundant, amount of OH groups.  
The differential thermal analysis (DTA) curve (Figure 12a) clearly shows that the thermal decomposition of APP, boric acid, chitosan, epoxy resin + hardener, and melamine started at 363.8, 154.4, 303.8, 373.4 and 364.5 \({}^{\circ}\)C, respectively. The decomposition temperature was changed by adding Zr NPs to formulations (F1, F2, and F3). For formulation samples F1, F2, and F3, thermal decomposition started at 337.0, 340.6, and 348.5 \({}^{\circ}\)C (Figure 12b), respectively. At these temperatures, weight loss began for F1, F2, and F3 formulations samples could be due to the release of H\({}_{2}\)O and CO\({}_{2}\) from epoxy resin; the formation of phosphoric acid, polyphosphoric acid, and met phosphoric acid with decomposition of APP [49; 50]; the formation of polyhydrated alcohols from chitosan [51]; the formation of NH\({}_{3}\) gas from melamine and decomposed boric acid; or the release of boron trioxide. The char residues obtained at 800 \({}^{\circ}\)C of F1, F2, and F3 samples were 32.4%, 35.1% and 37.5%, respectively. Among the investigated composites, the F3 sample showed higher char % at 800 \({}^{\circ}\)C. This may be because the char layer formed and inhibited the emission of the pyrolysis gases due to the enhanced heat-shielding effect, and because Zr NPs have a superior surface-to-volume ratio but are incredibly difficult to distribute evenly. Thus, the Zr NP mass percentage performs a significant role in improving the intumescent flame retardant coating.
## Materials and Methods
### Apparatus  
Scanning electron microscopy (SEM, Nova NanoSEM 230, FEI, Hillsboro, OR, USA), contact angle goniometry (model 100 SB, Smatek Instruments Co., Ltd, Taipei city, Taiwan), and attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR, NICOLET 6700, Thermo Fisher Scientific, Waltham, MA, USA) were used to characterize electrode materials. DA measurements were performed using an electrochemical workstation (CHI400, CH Instruments, Inc., Austin, TX, USA). Tyrosine/chitosan/rGO/SPCE was used as a working electrode, and SPCE was used as both reference and counter electrodes. A fresh DA solution was prepared in PBS (0.01 M, pH 7.0) for all measurements.
###### Abstract  
To explore the distribution characteristics and degree of pollution of heavy metals in the farmland soil around the Dachang tin polymetallic mining area in Guangxi, a total of 140 soil samples were collected around the mining area in this study. The total amount and various forms of seven heavy metals (Cu, Pb, Cd, Cr, Zn, As, and Ni) were analyzed by inductively coupled plasma mass spectrometry, and the improved continuous extraction method of heavy metal speciation analysis in the soil, the potential ecological risk index (\(R\)I), the and Nemerow evaluation index (\(P_{N}\)) were used to evaluate pollution characteristics of the soil and the bioavailability of heavy metals. Corresponding remediation suggestions were given according to the pollution degree. The results show that the whole soil in the study area is acidic, reducing, and the content of organic matter is low. The average content of heavy metal elements is higher than the background value of Guangxi, among which Cd, Pb, and As exceed the control value and are the main elements of pollution. The speciation analysis of heavy metals in soil shows that Cd is dominated by the ion exchange form; Cu is mainly residual and in a humic acid combined form; and the rest of the elements are mainly in residual form. Among the seven heavy metals, Cd has the strongest mobility, biological toxicity, and ecological risk, followed by As, Ni, and Zn. The overall pollution level of the soil in the study area is heavily polluted (\(P_{N}\) = 39.6), which is a very strong ecological risk level (\(RI\) = 2196.9), and the main pollutants are Pb, As, and Cd; Cd pollution is the most serious. Correlation (CA) and principal component analysis (PCA) indicate that the pollution sources of Pb, Cu, Zn, Cd, and As among the seven elements were mainly controlled by tailings accumulation, mining, and transportation, and the sources of Cr and Ni were controlled by soil-forming parent materials. Furthermore, according to the actual situation of the farmland around the mining area, two remediation suggestions are put forward: (1) use stabilization remediation technology to clean up the pollution source, such as calcium dihydrogen phosphate to reduce the bioavailability of the most polluted elements, Cd and Pb, in the soil; (2) under the concept of green environmental protection, use _Typha orientalis_ Presl to repair the industrial and mining wasteland and some unused land.  
heavy metal; existing form; bioavailability; risk assessment; Dachang mining area 2020
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 5 Proposed Scheme-II (Key Recovery System Using Proxy Re-Encryption)
### Key Pair Generation Phase  
In this phase, \(Part_{i}\) receives a partial private key from \(KGC\) and uses it to generate full private key \(sk_{i}\) and public key \(pk_{i}\).  
* **Step 1:**\(KGC\) generates parameters \(x_{i}\in\mathbb{Z}_{q}^{*}\) for participant \(Part_{i}\) through the following operation and sends them to \(Part_{i}\) through a secure channel. \[X_{i}=g^{x_{i}}\] (30) \[d_{i}=x_{i}+sH_{1}(ID_{i},X_{i})\text{ mod }q\] (31)
* **Step 2:**\(Part_{i}\) who receives \(X_{i},d_{i}\) from KGC, selects Random numbers \(y_{i},z_{i}\in\mathbb{Z}_{q}^{*}\) and sets \(Part_{i}\)'s private key \(sk_{i}\). \[sk_{i}=(d_{i},y_{i},z_{i})\] (32)
* **Step 3:**\(Part_{i}\) generates \(Y_{i},Z_{i}\) and sets public key \(pk_{i}\). \[Y_{i}=g^{y_{i}}\] (33) \[Z_{i}=g^{z_{i}}\] (34) \[pk_{i}=(X_{i},Y_{i},Z_{i})\] (35) After that, \(Part_{i}\) publishes public key \(pk_{i}\).
## 4 Results
These results indicate that our model is better than the intercept-only model. The model explains more than 50% of the variability based on the Nagelkerke R-square with no evidence of poor fit; for example, Pearson Chi-Square = 13.580, \(p=0.481\), Deviance Chi-Square = 12.744, \(p=0.547\) (see Tables 4 and 5). Table 5 provides various pseudo-R-squared metrics used to assess the model's explanatory power. The Nagelkerke R\({}^{2}\) value of 0.513 indicates that the model has decent explanatory power as it explains more than half of the variability in the dependent variable. This means that the model can predict the purpose of ridesharing well enough based on the variables analyzed. The Cox and Snell R\({}^{2}\) and Nagelkerke R\({}^{2}\) values are relatively high, which supports the strength of the model. A lower McFadden R\({}^{2}\) value is expected but still indicates a good level of explanatory power of the model. Table 6 shows the statistically significant variables estimating the dependent variable. All these inputs, such as status, bike-sharing services, and average driving distance, are statistically significant variables (\(p<0.05\)). In other words, we reject all null hypotheses and accept alternative hypotheses. \begin{table}
\begin{tabular}{c c c c} \hline  & **Chi-Square** & **df** & **Sig.** \\ \hline Pearson & 13.580 & 14 & 0.481 \\ Deviance & 12.744 & 14 & 0.547 \\ \hline \end{tabular}
\end{table} 
Table 4: Goodness-of-fit. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\(-2\) **Log-Likelihood**} & \multicolumn{3}{c}{**Likelihood Ratio Tests**} \\  & & **Chi-Square** & **df** & **Sig.** \\ \hline Intercept-only & 154.572 & & & \\ Final & 56.162 & 98.410 & 8 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model fitting information. \begin{table}
\begin{tabular}{c c} \hline \hline Cox and Snell & 0.455 \\ Nagelkerke & 0.513 \\ McFadden & 0.278 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Pseudo-R-square. First, Table 7 shows that employees are 4.5 times more likely compared to students to use shared bikes for leisure rather than commuting to school. In addition, two bike-sharing companies, A and B, are used several times more compared to C for relaxation than commuting to school. Finally, we find that the average ride distance of up to 3 km is lower by more than 70% than the average ride distance of more than 3 km when using a shared bike for leisure as a commute to school. In other words, employees use A and B for leisure time with an average driving distance of over 3 km. Second, we find that employees commute 37 times more than students when comparing commutes to work and school. In addition, A is used almost 4 times more than C for commuting to work rather than school. Finally, the results show that the average driving distance of up to 3 km is lower by more than 80% than the average driving distance of more than 3 km when comparing commuting to work versus school. These results demonstrate that employees use A to commute to work, especially with a distance of more than 3 km.
## 2 Experimental Works
### Statistical Model  
The effect of the independent variables on modeling UCS was studied using the optimization of the response surface methodology [39; 40]. Additionally, a quadratic model was developed and fitted to the experimental results in order to study the UCS of the limestone when varying vp, n, and psat.  
From the uniaxial compressive strength tests, Minitab 18 software was used to investigate the linear effects, interactions, and quadratic effects of the independent variables on UCS. Experimental results were fitted firstly to a simple regression model (\(v_{\rm P}\) dependent). They were then fitted to a multiple regression analysis [41] (adding n and \(\rho_{\rm sat}\)) using a quadratic model, considering only those variables that can explain the variability of the model and, therefore, those variables with a large statistical significance. The general expression of the experimental model is shown in Equations (15) and (16):  
\[\rm Y=(overall\ constant)+(linear\ effects)+(interaction\ and\ curvature\ effects), \tag{15}\]\[Y=b_{0}+\sum_{i=1}^{n}b_{i}x_{i}+\sum_{i=1}^{n}\sum_{j=1}^{n}b_{ij}x_{i}x_{j}, \tag{16}\]  
where \(x_{i}\) are the independent variables \(v_{p}\), \(\rho_{sat}\), and \(n\); \(b_{i}\) and \(b_{ij}\) are the coefficients; and the result \(Y\) is the UCS. The experimental data used to design the model are shown in Table 3.  
\(R^{2}\), \(p\)-values, and Mallows' \(C_{P}\) statistics, as well as the indicators of goodness of fit, indicate if a model is adequate in order to describe the dependent variable on the pertinent domain. The coefficient of determination (\(R^{2}\)) measures the total variability proportion of the dependent variable related to its average value obtained by the regression model. \(p\)-values represent statistical significance and they indicate if there is a significant relation between the response of the model and the experimental results. Mallows' \(C_{P}\) is a measure of the precision of the model as well as the bias on the actual parameters of the regression analysis [42; 43].
## 3 Results and Discussion  
The alloy studied in this work has been produced by arc melting followed by EBM-PDME processing. The composition of the alloy was analyzed by the EDX method (Table 1).  
It is confirmed that the chemical composition of the resulting alloy is close to the initially specified nominal composition.