esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Surface Vickers hardness  
Figure 9 shows the surface Vickers hardness of untreated and treated samples with different applied loads. The surface Vickers hardness of the untreated sample with applied loads of 0.49 N and 0.98 N was 335 HV and 253 HV, respectively. With increasing boriding temperature, surface Vickers hardness increased to maximum values of 2767 HV and 2340 HV at 1150 \({}^{\circ}\)C for loads of 0.49 N and 0.98 N, respectively. A previous study [39] showed that boriding of a Ti-6 Al-4V alloy formed a layered structure mainly composed of TiB and TiB\({}_{2}\), where the surface Vickers hardness of TiB was 2351-2757 HV, and that of TiB\({}_{2}\) was about 3248 HV. The coexistence of TiB and TiB\({}_{2}\) phases on the surface of the sample may lead to a lower surface Vickers hardness. Moreover, the surface Vickers hardness of our samples was lower than that of the hardness of the boride layers measured in the previous study. This was the result of the relatively thin boride layer on our samples, where the lower hardness of the base material also contributed to the measured surface Vickers hardness. This was evident from the surface Vickers hardness decreasing with increasing applied load; smaller loads resulted in smaller penetration depths and less contribution of the softer substrate material.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{_d_ (\(\times\)**10\({}^{-6}\))(m)**} \\ \cline{2-3}
**T** (\({}^{\circ}\)C) & **TiB** & **TiB\({}_{2}\)** \\ \hline
950 & 15.2 & 2.24 \\
1050 & 30.9 & 4.48 \\
1150 & 56.8 & 8.14 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Diffusion calculation distance value with different boriding temperatures.  
Figure 9: Surface Vickers hardness of samples with different boriding temperatures.  
The hardness of the surface layer is determined by a series of factors, including grain size distribution, phase composition, the thickness of the layer, and the crystallographic orientation between the strengthening phase and the substrate. Thicker hardened layers result in relatively higher measured hardness, as observed for the sample borided at 1150 \({}^{\circ}\)C. At this processing temperature, the highest content and layer thickness of the hardest compound (TiB\({}_{2}\)) were observed, resulting in the highest measured surface Vickers hardness for this sample. It is worth noting that the surface Vickers hardness testing results here show comprehensive performance of the hardened layer on the surface, not just the hardness of the borided layer, because the borided layer generated at lower processing temperatures was too thin for surface Vickers hardness testing. The test result was inevitably affected by the Ti substrate.
## 3 Mathematical Preliminaries and System Model  
### \(\beta\)-GPP Model  
This section provides a brief overview of \(\beta\)-GPP. For the positive integer \(\chi\), we assumed \(\beta=-\dfrac{1}{\chi}\) and considered a Hilber-Schmidt operator \(K\)[27]:  
1. \(K\) is a symmetric integral operator with upper and lower bounds, whose kernel is defined as \(\mathcal{K}\);
2. The spectrum of \(K\) belongs to \([-1/\beta,1]\);
3. \(K\) is a local mapping of tracking classes.  
Then, the \(\beta\)-GPP \(\Omega\) with intensity \(\rho\) and in a viewing window \(\mathcal{B}\) is characterized by the core \(\mathcal{K}\). It is defined by  
\[\mathcal{K}(\mathbf{x},\mathbf{y})=\rho\exp\Bigl{(}\pi\rho\mathbf{x}\hat{ \mathbf{y}}-\dfrac{\pi\rho}{2}\Bigl{(}|\mathbf{x}|^{2}+|\mathbf{y}|^{2}\Bigr{)} \Bigr{)}; \tag{1}\]  
here, \(\mathbf{x},\mathbf{y}\in\mathcal{B}\). \(\mathbf{x}\), \(\mathbf{y}\) and \(\bar{\mathbf{x}},\bar{\mathbf{y}}\) illustrate the conjugate and the Euclidean 2-norm of complex scalars \(\mathbf{x}\), and \(\mathbf{x}\), respectively. \(A\subset\mathbb{R}^{2}\) and \(B\subset\mathbb{R}^{2}\) are disjoint-bounded sets. We can obtain the following equation:  
\[Cov\bigl{(}N(A),N(B)\bigr{)}=\alpha\int_{A\times B}\mid\mathcal{K}(\mathbf{x}, \mathbf{y})\mid^{2}d\mathbf{x}d\mathbf{y}, \tag{2}\]  
where \(N(A)\) and \(N(B)\) represent a random number of points in set \(\mathbb{R}^{2}\). The correlation between \(N(A)\) and \(N(B)\) becomes higher as \(\beta\longrightarrow 1\). That is, the rejection between points increases and the spatial distribution is more uniform and regular. If the points in \(\beta\longrightarrow 0\), \(\beta\)-GPP are independent of each other, approximating PPP.  
For a function \(q:\mathbb{R}^{2}\mapsto[0,\infty]\) and an \(\beta\)-GPP \(\Omega\), the Laplace transform is given by  
\[\mathbb{E}[\exp(-\Sigma_{\mathbf{x}\in\Omega}q(\mathbf{x}))]=Det\bigl{(} \mathbf{I}+\beta\mathcal{K}_{q}\bigr{)}^{-\dfrac{1}{\beta}}, \tag{3}\]  
where \(\mathcal{K}_{q}(\mathbf{x},\mathbf{y})=\sqrt{1-\exp(-q(\mathbf{x}))}\mathcal{ K}(\mathbf{x},\mathbf{y})\sqrt{1-\exp(-q(\mathbf{y}))}\), and \(Det(\mathbf{I}+X)\) denotes the Fredholm determinant and \(\mathbf{I}\) denotes the identity operator [28].  
According to [27], for a point process \(\Omega\sim\) DPP with kernel \(\mathcal{K}\), the reduced Plam distribution coincides with another DPP, which has the kernel,  
\[\mathcal{K}_{x_{q}}(\mathbf{x},\mathbf{y})=\dfrac{1}{K(\mathbf{x}_{o},\mathbf{ x}_{o})}\Big{|}\begin{array}{cc}K(\mathbf{x},\mathbf{y})&K(\mathbf{x}_{o}, \mathbf{x})\\ K(\mathbf{x}_{o},\mathbf{y})&K(\mathbf{x}_{o},\mathbf{x}_{o})\end{array}\Big{|}. \tag{4}\]  
Performing simple algebraic manipulations, the intensity of the points under the reduced palm distribution \(\beta\)-GPP is given by  
\[\mathcal{K}(\mathbf{x},\mathbf{x})=\dfrac{c}{\pi}\Bigl{(}1-\exp\Bigl{(}- \dfrac{c}{\beta}\Bigl{(}|\mathbf{x}-\mathbf{x}_{o}|^{2}\Bigr{)}\Bigr{)}\Bigr{)}. \tag{5}\]
# Prompt-Based Graph Convolution Adversarial Meta-Learning for Few-Shot Text Classification
## 3 Methodology
### Pgca
#### 3.2.3 GCN-Based Meta-Knowledge Extractor  
We constructed the meta-knowledge extractor using a graph convolutional neural network (GCN). For a given \(N\)-way \(K\)-shot task, we constructed a graph \(G=(V,\,E,\,A)\) to represent the relationships between instances, where \(V\) and \(E\) denote the set of nodes and the set of edges, respectively. Each instance is treated as a node \(v_{i}\in V\), and the edges \(e_{i,\,j}\in E\) correspond to the connectivity of neighboring nodes \(v_{i}\) and \(v_{j}\). \(A=R^{n\times n}\) denotes the adjacency matrix, where each element represents the connectivity between nodes and \(n=N\times K\). Let \(\mathbf{H}\in R^{n\times d}\) be the matrix containing all \(n\) nodes and their features. \(d\) is the dimension of the feature vector, and each row \(\mathbf{h}_{i}\in R^{d}\) is the feature vector of node \(v_{i}\). GCN can capture information about their direct neighbors through one layer of convolution, and when multiple GCN layers are stacked, information about larger neighbors can be obtained.  
The output of the graph convolution operation for the \(L\)-th layer is defined as:  
\[\mathbf{H}^{(L+1)}=\mathbf{\sigma}\bigg{(}\overset{-\frac{1}{2}}{\mathbf{D}}\overset{ \sim\sim-\frac{1}{2}}{\mathbf{A}}\overset{\sim-\frac{1}{2}}{\mathbf{H}}^{(L)}\mathbf{W}_{ \text{G}}^{(L)}\bigg{)} \tag{4}\]  
where \(\mathbf{H}^{(L)}\in R^{n\times d}\) is the node feature matrix of this layer, representing the meta-knowledge contained in the sentence obtained by the meta-knowledge extractor, \(\mathbf{W}_{\text{G}}^{(L)}\) is the learnable weight matrix of the graph convolution of this layer, and \(\mathbf{\sigma}\) is the activation function. In the masked language model, the node feature matrix \(\mathbf{H}^{(0)}\) at layer 0-th is the label prediction output \(\mathbf{X}\) of \(\mathbf{x}_{prompt}\). \(\overset{\sim}{A}=A+\mathbf{I}_{n}\), where \(\mathbf{I}_{n}\) is the unit matrix. The adjacency matrix is constructed as shown in Figure 2, and the adjacency matrix is constructed after experimental verification to obtain the optimal structure. \(\overset{\sim}{D}_{ii}=\sum\limits_{j}\overset{\sim}{A}_{ij}\) is the degree matrix of the adjacency matrix, which is used to normalize the node features. GCN convolves the features of neighboring nodes and propagates their embedded vectors to their nearest neighbors, learning global features through the information interaction of neighboring nodes, fully mining the useful information carried in small-sample instances, and obtaining a better representation of sample embeddings.  
Figure 2: Visual heat map of the adjacency matrix, with orange indicating directed connections of vertical axis nodes to horizontal axis nodes and yellow indicating no connections. (**a**) is the adjacency matrix for the GCN in the 5-way 1-shot task, and (**b**) is the adjacency matrix for the GCN in the 5-way 5-shot task.
## 2 Results and Discussion
### VapBC-COG2886-PIN Composite  
VapBC (virulence associated protein) is the most widespread TA family in bacteria and archaea [94], but the least well-described [95; 96]. The VapBC family is composed of a protein harboring a PIN domain as the toxin, and a DNA-binding domain as the antitoxin [95].  
The PIN (PilT N-terminus) domain, is a type of pili protein [95] associated with the ribonuclease activity of the VapC toxin, which has a bacteriostatic effect [94; 97]. The PIN-like domain-harboring toxin could associate with any protein harboring a DNA-binding domain as an antitoxin [95]. DifferentTA composites have been reported [22; 96], one of which is the COG2886-PIN composite [69]. COG2886 proteins belong to the UPF0175 (uncharacterized protein family 0175) protein superfamily, according to the NCBI CDD analysis tool, and represent putative DNA-binding antitoxins [75; 90]. Only two of the four _Geobacillus_ strains have a putative VapBC family, the COG2886-PIN composite, as discussed below.  
VapBC of _G. kaustophilus_ HTA426 and _G. thermolevorans_ Gts  
The genes with the locus tags GK1949 and *_-22480 in HTA426 and Gts, respectively, code for a putative COG2405 domain-harboring protein, belonging to the DUF3368 protein family (Table 1). The COG2405 is a PIN-like domain, and thus the protein is a putative VapC toxin [90]. The aa sequences of these two putative toxins are identical between the two strains (Table S1; global alignment in Figure S9a). In both strains, we noticed that there are differences in the aa sequences of the putative toxins between the original and RefSeq genome annotations. Nevertheless, in both records, the protein harbors the same domain that identifies it as a putative VapC toxin. For our analysis, we used the putative toxin annotated in the RefSeq genome record of each strain (Table S1).  
The genes with the locus tags GK1950 and *_-22490 code for a putative antitoxin harboring the domain COG2886 (Table 1). The aa sequences of these two putative antitoxins are identical between the two strains (Table S1; global alignment in Figure S9b). Each of the putative coding genes is adjacent to and upstream of the toxin-coding gene. The coding genes are also annotated differently in the RefSeq and the original genome records of each strain. Nevertheless, in both records, the protein product harbors the same putative antitoxin-associated domain, the COG2886. For our analysis, we used the antitoxin annotated in the RefSeq genome record (Table S1). The two genes coding for each of the putative TA pairs potentially share the operon in each of the strains (Table 1), and overlap by eight nt.  
Neither _G. thermodenitrificans_ NG80-2 nor _Geobacillus_ sp. ZGt-1 has VapBC TA system.
## 8 Introduction to Proprietary and Standards-based Solutions
### Standards-Based Technologies
#### 8.2.3 Narrowband IoT  
Narrowband IoT (NB-IoT) is a 3 GPP Release 13 LPWAN technology offering the flexibility of deployment by allowing the use of a small portion of the available spectrum in the long term evolution (LTE) bands. As a 3 GPP technology, NB-IoT can coexist with global system for mobile communications (GSM) and LTE in licensed frequency bands of 700 MHz, 800 MHz, and 900 MHz. It supports bi-directional communication where orthogonal frequency division multiple access (OFDMA) is used for downlink, and single carrier frequency division multiple access (SC-FDMA) is used for uplink [32]. It connects up to 50K devices per cell and requires a minimum of 180 kHz of bandwidth to establish communication. NB-IoT can also be deployed as a standalone carrier with a spectrum of more than 180 kHz within the LTE physical resource block. NB-IoT is designed by optimizing and reducing the functionalities of LTE so that it can be used for infrequent data transmissions and with low power requirements [9]. The data rate supported is 200 kbps for downlink and 20 kbps for uplink. The maximum payload size for each message is 1600 Bytes.
## 3 Results and Discussions
### Experimental Verification  
In order to further validate the above statistical results besides the physical explanations, experiments are performed. Experimental samples with an a-C layer thickness of 12 nm are annealed at 1020 \({}^{\circ}\)C, and the annealing time is varied across multiple levels from 5 to 50 min.  
Figure 10 shows the plots of the \(I_{\rm D}/I_{\rm G}\) and \(I_{\rm 2D}/I_{\rm G}\) peak intensity ratios vs. annealing time, for samples with thin a-C layer, annealed at a high annealing temperature.  
The plots in Figure 10 clearly show that the \(I_{\rm 2D}/I_{\rm G}\) peak intensity ratio is larger for shorter annealing times (depicted by a dotted circle), whereas the \(I_{\rm D}/I_{\rm G}\) peak intensity ratio is lower for longer annealing times (depicted by a dashed oval), when the experiments are conducted at a high annealing temperature for samples with a thin a-C layer. This result is in clear agreement with Table 5.  
Figure 10: Plots of the \(I_{\rm D}/I_{\rm G}\) and \(I_{\rm 2D}/I_{\rm G}\) peak intensity ratios vs. annealing time, for samples with a thin a-C layer annealed at 1020 \({}^{\circ}\)C. The dotted circle shows the region with a larger \(I_{\rm 2D}/I_{\rm G}\) peak intensity ratio at a shorter annealing times. Dotted rectangles show the region with a smaller \(I_{\rm 2D}/I_{\rm G}\) peak intensity ratio, and the dashed oval shows the region with a smaller \(I_{\rm D}/I_{\rm G}\) peak intensity ratio, both at longer annealing times.  
Therefore, in order to achieve a large \(I_{\rm 2D}/I_{\rm G}\) peak intensity ratio and a small \(I_{\rm D}/I_{\rm G}\) peak intensity ratio simultaneously, the annealing time has to be optimized.  
On the other hand, longer annealing times with the same parameters for the annealing temperature and a-C thickness will result in multi-layer graphene with low defects (depicted by dotted rectangles in Figure 10), in agreement with Table 5. Therefore, we can see that the type of desired graphene with respect to the quality and number of layers can be adjusted by choosing suitable levels obtained from the DoE analysis.
## 4 Applicability on Performance-Constrained Platforms
### Blockchain Technology
This is substantially higher than the current block gas limit of 12 million units in Ethereum. Another paper [77] presented a compelling argument for replacing W-OTS, the current signature scheme in IOTA--a distributed ledger technology (DLT) tailored for internet of things (IoT) and internet of vehicles (IoV) applications--with the post-quantum signature scheme Dilithium. The findings indicated that Dilithium offers significant advantages over W-OTS in multiple dimensions. Firstly, it accelerates transaction processing and reduces signature space, thereby cutting down latency, which is critical in fast-paced IoV networks. Secondly, while W-OTS necessitates the repetitive generation of public and private keys, Dilithium requires only a one-time generation, offering a more efficient operational model. Furthermore, Dilithium decreases the network's bandwidth requirement by 7% due to its smaller cumulative size for cryptographic elements. The execution time for the initial use case scenario, involving the generation of public and private keys, was also dramatically reduced by 87%. Given these benefits, Dilithium emerges as a more efficient and scalable choice for enhancing the security and performance of IOTA-based networks.
## 4 Discussion
### Removal of the Ether Extract, Phenols, Chlorides and Sulfates from Sewage in Municipal WWTPs
[36] and [PERSON] et al. [39]. The research of [PERSON] et al. [36] revealed a high diversity and abundance of microbial communities, including sulfate-reducing bacteria, in anaerobic sludge. [PERSON] et al. [39] studied the kinetics of sulfate reduction under alternating aerobic and anaerobic conditions to which sulfate-reducing bacteria were subjected. They observed an almost immediate reduction of sulfate in freshly aerated activated sludge after anaerobic incubation. The authors found a high capacity for reducing sulfates in sludge stored anaerobically in settling tanks for several days before dewatering. The consequence of the activity of sulfate-reducing bacteria is the production of sulfides, the presence of which is associated with unpleasant odours, toxicity and corrosion. In their research, [PERSON] et al. [40] demonstrated the effectiveness of adding a concentrated calcium nitrate to sewage at the works inlet, achieving a rapid decrease in sulfides, both in the air and the water phase. According to [PERSON] et al. [41], the best technology for reliable sulfate removal is reverse osmosis membrane treatment. Unfortunately, however, this technology is associated with very high costs of construction, maintenance and operation (capital costs, energy costs, equipment which is difficult to maintain, and qualified WWTP operators needed). Reverse osmosis is unprofitable for all municipal WWTPs. Reverse osmosis membrane treatment, while considered the most reliable technology for sulfate removal [41], suffers from prohibitively high construction, maintenance and operating costs, rendering it economically infeasible for many municipal WWTPs. Seeking more cost-effective solutions for removing orthophosphate, nitrate and sulfate ions, [PERSON] et al. [42] proposed using adsorption on dried, crushed _Carpobrotus edulis_ plant particles. This inexpensive, local and abundant biomaterial demonstrated a 73% reduction in sulfates, with the potential for integration with the activated sludge process. Studying sulfate and sulfide removal in constructed wetlands, [PERSON] et al. [43] observed that increased sulfate loading correlated with a higher rate of sulfate removal. However, this also led to a greater proportion of discharged sulfides relative to removed sulfates (increasing from approximately 5.5% to 47%). A strong positive correlation (R\({}^{2}\) = 0.983) was found between discharged sulfide share and sulfate load, indicating that constructed wetlands can effectively mitigate sulfide discharge alongside sulfate removal.
## 3 Key Modules
### Channel Estimation and Compensation  
In recent years, many studies have shown that UWA channels have sparse characteristics in many situations, that is to say, most channel taps are zero or very small and the number of channel taps is far less than the maximum channel delay. Due to the sparse characteristics, using the compressive sensing (CS) method to do channel estimation is very suitable for an underwater acoustic channel. On the one hand, the minimum mean square error (MMSE) method which has superior equalization performance should be adopted. The MMSE takes noise effects into account. As compared with the zero forcing (ZF) method, the MMSE can improve system performance. Also, the MMSE method can get higher diversity gain than the ZF method.  
In the UWAOSDM/PN system, channel estimation is conducted in the time domain and equalization is calculated in the frequency domain. We assume that the UWA channel is sufficiently stable to allow prediction. After synchronizing the received signal using Doppler estimation and compensation, channel estimation is conducted according to the above principle. Figure 5 shows the channel estimation and equalization process. First, the PN sequence is used as the initial vector and the Toeplitz format is employed to construct the observation matrix. Because of the pseudo randomness of the PN sequence, the constructed matrix meets the RIP principle [21]. Then, the orthogonal matching pursuit (OMP) method is used as the recovery method to estimate the sparse underwater acoustic channel in the time domain, and then, transforming the time domain \(h\) to the frequency domain by FFT. Last, calculating the channel equalization coefficients \(C_{n}\) according to the MMSE criterion.
# Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording
## 3 Filter Design for Obtaining Time Domain Spherical Coefficients  
In Section 2, we have presented a method to obtain \(b_{nm}(\
u,R_{Q})\) from recorded sound pressure \(S(\mathbf{x}_{q},\
u)\) with a spherical microphone array. In this section, we design a series of FIR filters to obtain \(a_{nm}(\
u)\) from given \(b_{nm}(\
u,R_{Q})\).
## 3 Results and Discussion
### Evaluation Under Simulated Operating Conditions
Although minor overestimation was observed toward the later steps, the predicted CR maintained good consistency with both the coupon measurements and the readings from the commercial LPR sensor at the final stage (Coupon #5). The ability to provide reliable predictions across multiple scenarios emphasizes the practical value of the regression model. By integrating the effects of temperature and flow velocity, the model offers a powerful tool for continuous corrosion monitoring in dynamic environments. The study shows that accurate prediction of CRs can help operators anticipate equipment degradation and implement preventive actions proactively, reducing the risk of unexpected failures.
# Noise-Free Security Assessment of Eviction Set Construction Algorithms with Randomized Caches
## 4 Eviction Set Construction Algorithms
### Candidate Set  
Some eviction set construction algorithms require an initial set of virtual addresses, which is called a _candidate set_. A candidate set is a set of virtual addresses that has a good chance of falling into the target cache set.  
An unprivileged process does not have full control over the cache placement, especially in a large last-level cache. Indeed, as we show in Figure 4, the upper address bits are used to determine the set and the slice that are selected by the operating system through its page allocation mechanism. On Linux, it has been shown that the buddy allocator can be tricked into allocating continuous chunks of memory [54], but page allocation can usually be viewed as a random oracle. Therefore, a candidate set can be built by fixing the controlled bits of the set index to the correct value and selecting many multiples of this address. The use of huge pages (2 MB or 1 GB), either with privileges or through transparent huge pages [55], can increase the number of controlled bits in the address (In general, huge pages can only be requested by privileged processes).  
Suppose that the process has no control over virtual addresses (e.g., the address mapping is randomized); in that case, addresses have to be selected randomly. For a candidate set to be used as an eviction set, the collection of \(n\) virtual addresses must satisfy Definition 2. Let \(X\) be a random variable representing the number of congruent addresses found in a set of \(n\) virtual addresses. According to [48], with \(p=\frac{1}{5}\) (where \(S\) is the number of sets), the probability of having at least \(a\) congruent addresses in a candidate set of size \(n\) is given by:  
\[\mathrm{P}(X\geq a)=1-\sum_{i=0}^{a-1}\left(\begin{array}{c}n\\ i\end{array}\right)p^{i}(1-p)^{n-i} \tag{1}\]