esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 4 OFDMA in Wi-Fi 6
### Related Work
#### 4.3.1 Random Access
[45] proposed hybrid channel access to alleviate the collisions in dense networks. Firstly, they modeled the upper bound efficiency of random access concerning the number of stations, contention window, and available RUs. Next, they adjusted the OFDMA contention window for uplink OFDMA to its optimal value to produce results similar to the developed model. [PERSON] et al. [46] proposed to fairly allocate the optimal number of random and scheduled RUs to provide high throughput. The access point assigns scheduled RUs to the stations from which it received a BSR. Then, it leaves the remaining RUs for other stations to contend for the channel access. Efficient Resource Allocation (ERA) [47] classified stations according to their load. Three classes were created with a different RU width to accommodate the different traffic loads. Stations with more traffic to send are assigned to an RU with a larger bandwidth. [PERSON] et al. [48] adapted Wi-Fi 6 to implement random access in the frequency domain. The stations send tones (i.e., non-modulated signals) to compete for medium access. An ordered list of subcarriers is used to transmit the tones: only the stations that do not detect any tones transmitted through a lower subcarrier win the round. After a few rounds, the collision probability becomes very small. The authors of [49] offered a better user experience by enabling the access point to assign the stations to multiple groups. Then, it assigns the resources to each of these groups. The authors defined a utility function based on the user data rate and minimum bandwidth requirement. The access point applies K-mean clustering based on the closeness of the stations' utility functions to group the stations. Finally, it assigns RUs to these groups to have a maximum utility value per group. Conversely, we can adopt a user-centric (selfish) approach: each user can independently select its access point while trying to globally balance the load [50]. A station considers the signal strength of the neighboring access points as well as its relative capacity, achievable data rate, and the location of other stations. Since this method is user-centric, it greatly reduces the overhead, but the system may fall in a local optimum, or even worse may result in oscillations.
# Ensemble Clustering in GPS Velocities: A Case Study of Turkey
## 2 Data and Methodology
### Generation Mechanisms
#### 2.3.3 Mini Batch K-Means Clustering  
The mini batch \(k\)-means is first presented by [PERSON] [36] and is proposed as an alternative to the \(k\)-means algorithm for especially clustering large data sets. As in partitioning clustering methods, it is used to group data with similar characteristics in a given data set. The main idea underlying the mini batch \(k\)-means method is to make a large data set storable in memory by using small random batches of data of fixed size. A new random sample from the data set is retrieved in each iteration and then is used to update the clusters. Finally, this process is repeated until convergence. Unlike \(k\)-means clustering, this one does not need all the data sets in the main memory, and therefore greatly reduces the temporal and memory costs of the algorithm [37].  
For a given data set \(D=x_{1},x_{2},\ldots,x_{y}\), \(x_{i}\in\mathbb{R}^{m}\), \(x_{i}\) refers to a network record. \(m\) represents the number of records found in the data set \(D\). The optimization clustering problem is to find the set \(C\) of cluster centers \(c\in\mathbb{R}^{m}\) so as to minimize over a data set \(D\) of examples \(c\in\mathbb{R}^{m}\) the following function:  
\[min\sum_{x\in D}\left\|f(C,x)-x\right\|^{2} \tag{13}\]  
where \(f(C,x)\) returns the nearest cluster centroid \(c\in C\) to record \(x\) using the Euclidean distance. With \(|C|=k\), \(k\) is the number of clusters that want to be found [36].
## 3 Results
### CFO Estimation  
Figure 4 illustrates the Mean Absolute Error (MAE) between the normalized true CFO and the estimated CFO, considering different SNR and phase offset values. The results demonstrate a noticeable improvement in performance as the fine estimation follows the coarse estimation, leading to a reduction in the MAE. Consistent with our expectations, higher SNR values correspond to lower MAE values, indicating improved CFO estimation. Furthermore, the results confirm that phase offset has a negligible impact on CFO estimation, aligning with our prior understanding.  
Figure 4: CFO estimation results. (**a**) Mean Absolute Error (MAE) between the true CFO and estimated CFO for \([0,5]\) dB Signal-to-Noise Ratio (SNR). (**b**) MAE between the true CFO and estimated CFO for \([10,20]\) dB SNR.
## 2 Related Work
### PS-Based Distributed Training  
In PS-based distributed training, a dedicated parameter server (PS) manages the global parameters by aggregating the training results of workers. In bulk synchronous parallel (BSP) [12; 13], a synchronous centralized algorithm, the parameters of all workers are synchronized via PS that aggregates the gradients from all workers at once. Thus, all workers train the model in a consistent direction based on the same parameter values. However, the synchronization overhead can be significant, especially in a heterogeneous cluster, where some workers have relatively slow training speeds. To reduce the synchronization  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **PS-Based.** & **P2P-Based.** \\ \hline
**Synchronous.** & Bulk Synchronous Parallel (BSP) [12] & AllReduce-SGD (AR-SGD) [27] \\ \hline \multirow{4}{*}{**Asynchronous.**} & Asynchronous Parallel (ASP) [6] & Gossip-SGD (GoSGD) [20] \\  & Stale Synchronous Parallel (SSP) [7] & Decentralized Parallel SGD (D-PSGD) [21] \\ \cline{1-1}  & Elastic Averaging SGD (EASGD) [8] & Asynchronous D-PSGD (AD-PSGD) [22] \\ \cline{1-1}  & Dynamic SSP (DSSP) [9] & Stochstic Gradient Push (SGP) [23] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Existing data-parallel distributed training algorithms.  
overhead, many asynchronous distributed training algorithms [6; 7; 8; 9; 18; 28; 29] have been studied. In asynchronous parallel (ASP) [6], PS aggregates the gradients from workers in an asynchronous manner: PS does not wait for all workers; instead, PS processes the gradients from each worker individually. However, the parameter variance among workers could be large if there are workers with different training speeds, causing global model convergence delayed. Stale synchronous parallel (SSP) [7], positioned in the middle of BSP and ASP, relaxes the parameter synchronization by allowing workers to train with different parameter versions. However, SSP controls the difference among the versions to be always less than the predefined threshold. Dynamic SSP (DSSP) [9] further improves SSP by varying the threshold dynamically as training progresses. Elastic averaging SGD (EASGD) [8] reduces the communication overhead via periodic communication between PS and workers.
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
###### Abstract  
Homomorphic encryption is a powerful mechanism that allows sensitive data, such as biometric data, to be compared in a protected way, revealing only the comparison result when the private key is known. This is very useful for non-device-centric authentication architectures with clients that provide protected data and external servers that authenticate them. While many reported solutions do not follow standards and are not resistant to quantum computer attacks, this work proposes a secure biometric authentication scheme that applies homomorphic encryption based on the Classic McEliece public-key encryption algorithm, which is a round 4 candidate of the NIST post-quantum standardization process. The scheme applies specific steps to transform the features extracted from biometric samples. Its use is proposed in a non-device-centric biometric authentication architecture that ensures user privacy. Irreversibility, revocability and unlinkability are satisfied and the scheme is robust to stolen-device, False-Acceptance Rate (FAR) and similarity-based attacks as well as to honest-but-curious servers. In addition to the security achieved by the McEliece system, which remains stable over 40 years of attacks, the proposal allows for very reduced storage and communication overheads as well as low computational cost. A practical implementation of a non-device-centric facial authentication system is illustrated based on the generation and comparison of protected FaceNet embeddings. Experimental results with public databases show that the proposed scheme improves the accuracy and the False-Acceptance Rate of the unprotected scheme, maintaining the False-Rejection Rate, allows real-time execution in clients and servers for Classic McEliece security parameter sets of 128 and 256 bits (mecliece348864 and mecliece6688128, respectively), and reduces storage requirements in more than 90.5% compared to the most reduced-size homomorphic encryption-based schemes with post-quantum security reported in the literature.  
biometric template protection; homomorphic encryption; post-quantum security
## 2 Materials and Methods
### Photocatalytic Degradation Study  
In order to analyze the photocatalytic efficiency of ZnBi\({}_{2}\)O\({}_{4}\), Cefixime was picked as a model pharmaceutical pollutant. The photocatalytic degradation of CFX was carried out after optimizing the parameters such as pH and dose of the catalyst by sets of experiments. The optimal photocatalytic reaction conditions were found to be pH 6 and 1 g/L catalyst dose. Then, the photocatalytic removal tests under different light sources were performed by mixing a quantity of ZBO (0.1 g) in a CFX mixture of about 100 mL with an initial concentration of 10 mg/L. In a reactor in dark conditions, the received suspension was stirred for 120 min to balance adsorption and desorption before irradiation. As light sources, UV (Ultraviolet light UVA 24 W (Philips PL/L), 270 nm, 20 mW/cm\({}^{2}\)), LED (LEDs 50 W, 420 to 700 nm, 10.5 mW/cm\({}^{2}\)) and solar light (coordinates 36\({}^{\circ}\)46\({}^{\prime}\)34\({}^{\prime\prime}\) Nord, 3\({}^{\circ}\)03\({}^{\prime}\)36\({}^{\prime\prime}\) est, time from 9 to 12 AM with intensity 98 mW/cm\({}^{-2}\)) were applied separately to test the optical properties of ZBO. 3 mL samples were regularly removed from the blend and the precipitates were centrifuged for 10 min at a speed of 5000 rpm, then they were analyzed by UV-Visible spectrometry (OPTIZEN, UV-3220 UV) with 1-cm matched quartz cells. The contaminant degradation was indicated by the band's loss relating to CFX at the highest absorption wavelength (\(\lambda\) = 288 nm), as illustrated in Figure S2 in the Supplementary Data.  
The method of spectrophotometric determination of CFX was followed as mentioned in a previous paper [40]. The degradation was calculated with the following equation:  
\[\text{Photodegradation }\%=\frac{\left(\text{C}_{\text{Ad}}-\text{C}(\text{t}) \right)}{\text{C}_{\text{Ad}}}\times 100 \tag{1}\]  
where \(\text{C}_{\text{Ad}}\) (mg/L) and \(\text{C}(\text{t})\) (mg/L) are the initial concentration after adsorption and the concentration at time t, respectively.
## 5 aPDT Performance in Clinical Studies
### The Conjugates of PS and Liposomes  
Liposomes are potential PSs carriers due to their biocompatibility [101]. They are lipid-based systems that can encapsulate hydrophobic and hydrophilic therapeutic agents within their hydrophobic bilayers to control their release and protect them from aggregation or degradation [101]. As most of the PSs are hydrophobic, using liposomes as carriers is advantageous [102]. Several investigations were conducted to implement liposomes in aPDT in dermatology and oncology [103; 104]. Only in vitro investigations were conducted in dentistry to validate liposome integration with aPDT to target oral pathogens [105]. Liposomes functionalized into zinc phthalocyanine significantly reduced the growth of _P. gingivalis_[106]. Irradiating the _E. faecalis_ biofilms, using 10 and 30 \(\upmu\)M of 5,10,15,20-tetra(m-hydroxyphenyl)chlorin functionalized into liposomes resulted in around 5 to 7-log reduction, while using 50 \(\upmu\)M of the same conjugation completely eradicated the _E. faecalis_ biofilms [107]. 5,10,15,20-tetra(m-hydroxyphenyl)chlorin functionalized into liposomes was found effective in killing _E. faecalis_ biofilms up to 300 \(\upmu\)m inside the dentimal tubules of root canal systems [108; 109]. Future investigations may translate such a conjugation to a clinical model to evaluate its effectiveness, compared to conventional aPDT.  
Figure 6: Schematic drawing of the promising conjugation of nanodiamonds with a photosensitizer to enhance aPDT disinfection. Nanodiamonds can act as photosensizers by themselves, increasing the generation of reactive oxygen species and killing efficiency against biofilms.
## 2 Material and Methods
### Extraction and Quantification of DON  
DON extraction was based on the method described by [PERSON] et al. [16] with modification. Treated barley grains (0.5 g) were transferred to 8 mL glass culture tubes and then 2 mL ACN-water in a proportion of 84:16 was added. The mixture was vortexed at 3000 rpm for 10 min. After thorough mixing of the samples with extraction solvent, 1 mL of the supernatant of the extract was passed through a 0.2-\(\upmu\)m PTFE syringe filter, and transferred into glass culture tubes and dried under a constant flow of N\({}_{2}\) gas. The residue was redissolved in 1 mL water, with acetonitrile (85:15 v/v) used as the mobile phase, vortexed for 1 min, and injected (25 \(\upmu\)L) into the HPLC for analysis.  
The concentration of mycotoxin was determined by HPLC (Shimadzu Scientific Instruments, Inc. MD, USA). The chromatography was performed on a reverse phased Agilent Zorbax SB-C18 250 mm \(\times\) 3 mm, 5 \(\upmu\)m column in isocratic elution. The injection volume was 25 \(\upmu\)L at the flow rate of 0.5 mL/min, using water and ACN (85:15 v/v) as the mobile phase. DON was determined at the wavelength of 218 nm, using a photodiode array (PDA) detector. The limit of HPLC detection of DON was 0.1 \(\upmu\)g/mL. The recovery of the whole procedure was 76.6% and the precision was within +/- 5%.
## 3 Results and Discussion
### Microwave Cure in the Presence of CNTs
The post-cure of 2 h 10 min was arbitrarily selected so that the total reaction/cure time (microwave + convection oven) equals 2 h 30 min. These results show that a great decrease in heating time, from 6 h, for the convection oven-only procedure, to 2 h 30 min total, for the microwave assisted step, is indeed successfully achieved. When one considers the final 2 h 10 min post-cure step as being common to both processes, the decrease in cure time is even more significant--3 h 50 min in the convection oven _versus_ 20 min in the microwave. This significant decrease in cure time represents a great advantage for industrial applications where high throughput and shorter down times almost always translate into energy saving and process efficiency. In Figure 7, a decrease of the exothermic peak at approximately 230 \({}^{\circ}\)C can be seen as the CNT loading is increased from 0.02 wt % to 0.25 wt %. This phenomenon is consistent with the results in Table 1, where increasing concentrations of CNT result in higher heating rates. Indeed, when the same cure schedule is applied to tung oil-based composites of different CNT loadings, higher temperatures are achieved, during the microwave stage of the curing process, for the samples with higher CNT loadings. The higher temperatures ensure a faster polymerization rate for the system, and according to the DSC curves presented in Figure 7, it is possible to obtain a fully cured sample, with the optimized cure schedule, by introducing 0.25 wt % of CNTs. It is important to note that a higher temperature is observed for the exothermic peak of post-cured samples (Figure 7A-C) in comparison to a non-post-cured sample (Figure 6A). This occurs because, for partially cured samples, post-curing in the convection oven enables an increase of the crosslink density. Therefore, post-cured samples require higher temperatures to resume the polymerization, which translates into an exothermic peak at a higher temperature during the DSC analysis.
## 6 The Design and Application of MESs  
MESs have been extensively studied from a bioelectrochemical, microbiological and engineering perspective [103]. This has meant that, over time, MES platform designs have been refined, depending on their final application, in an endeavour to increase outputs and efficiency. This has resulted in a number of different MES architectures (Figure 3); however, MESs are divided into two main groups. Firstly, power-generating MFCs, which include two-chamber and single-chamber MFCs, and stacked MFCs; subsets of this group, relying on redox differentials within sediments, include benthic and sedimentary MFCs. Secondly, product/biofuel-producing MECs that include biofuel-producing MECs, chemical commodity producing MECs, and microbial desalination cells (MDCs), in addition to microbial solar cells (plant, algae and photosynthetic bacteria) capable of enhancing biofuel production in addition to generating biomass. Typically, an MES contains the same basic components: an anode and a cathode in separate chambers except in a single-chamber MFC (SCMFC) where the cathode is exposed to air, a proton exchange membrane (PEM), external load/resistance, a carbon source, electron donor and an electron acceptor [15; 104]. This section details some of the most commonly used architectures and types of MESs used in both power-producing and product-producing applications.
# Adhesion Evaluation of Asphalt-Aggregate Interface Using Surface Free Energy Method
###### Abstract  
The influence of organic additives (Sasobit and RH) and water on the adhesion of the asphalt-aggregate interface was studied according to the surface free energy theory. Two asphalt binders (SK-70 and SK-90), and two aggregate types (limestone and basalt) were used in this study. The sessile drop method was employed to test surface free energy components of asphalt, organic additives and aggregates. The adhesion models of the asphalt-aggregate interface in dry and wet conditions were established, and the adhesion work was calculated subsequently. The energy ratios were built to evaluate the effect of organic additives and water on the adhesiveness of the asphalt-aggregate interface. The results indicate that the addition of organic additives can enhance the adhesion of the asphalt-aggregate interface in dry conditions, because organic additives reduced the surface free energy of asphalt. However, the organic additives have hydrophobic characteristics and are sensitive to water. As a result, the adhesiveness of the asphalt-aggregate interface of the asphalt containing organic additives in wet conditions sharply decreased due to water damage to asphalt and organic additives. Furthermore, the compatibility of asphalt, aggregate with organic additive was noted and discussed.  
surface free energy; adhesion; asphalt-aggregate interface; organic additive +
Footnote †: journal: Applied Sciences
## 4 Drug Resistance Mechanisms in TB
### Intrinsic Drug-Resistance of Mtb
#### 4.3.3 Other Mechanisms  
The most important mechanisms of the intrinsic drug resistance of Mtb are considered to be the lipid-rich cell wall and the EP, but other systems are known to neutralize toxic chemicals and antibiotics, including drug inactivation or modification, and target modification.  
Among drug inactivating enzymes, Mtb \(\beta\)-lactamases are less effective than those of other bacteria to hydrolyse \(\beta\)-lactams, but their activity, together with slow penetration across the cell wall and low affinity for penicillin-binding proteins, is good enough to render Mtb intrinsically resistant to most \(\beta\)-lactams [18; 20]. The most important Mtb \(\beta\)-lactamase (BlaC) is thought to localize in the PS, and shows broad substrate specificity, including carbapenems, which are usually resistant to the \(\beta\)-lactamases of other bacteria. BlaC is inhibited by CLV that, as mentioned above, must be added to IPM- or MPM-containing prescriptions, as salvage WHO regimens for treatment of MDR/XDR-TB [8].  
As to aminoglycos (KM, AK) and cyclic peptides (CP), Mtb is able to inactivate them by acetylation performed by the enhanced intracellular survival protein encoded by _eis_, whose expression is upregulated by the MDR transcription regulator WhiB7 [20]. Promoter mutations lead to an overexpression of _eis_, resulting in low-level resistance to KM, but not AK [68].  
_M. tuberculosis_ is naturally resistant to macrolides (e.g., clarithromycin and azithromycin) because of the inducible em(37), a ribosomal RNA methyltransferase which alters ribosomes by methylating the 235 rRNA [69; 70]. Other _erm_ genes conferring inducible resistance to FQs were found in non-tuberculous mycobacteria [71]. Intrinsic resistance to FQ is also attributed to a pentapeptide repeat protein called MfpA, which mimic the size, shape and surface charge of duplex DNA by resembling the 3D structure of a DNA double helix [20; 72].  
As to resistance to the broad-spectrum agent fosfomycin, Mtb is intrinsically resistant to this agent, which inhibits the MurA enzyme, involved in the biosynthesis of PG, because a cysteine residue in the active site of Mtb MurA is changed into aspartic acid [73].