esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Key-Value Data Placement for ZNS SSD
## 3 Motivation  
### ZenFS  
ZenFS is a file system plugin for RocksDB developed by Western Digital [12]. It is located between the RocksDB and Linux Kernel. To use the Portable Operating System Interface (POSIX)-compliant I/O function, it needs to obtain the file descriptor using the libzbd [21]. After the file descriptor is obtained, ZenFS can flush the RocksDB data to the ZNS SSD in the form of a direct I/O with POSIX-compliant functions [22]. Since data have to be recorded sequentially in each zone, it needs to change the I/O scheduler for ensuring I/O ordering [23].  
Figure 3 shows that ZenFS manages each zone, as divided into journal and data areas. The number of zones in the journal area is statically defined when it is compiled. By contrast, the number of zones in the data area is defined as the total number of zones subtracted by the number of journal area zones.  
The journal area contains the ZenFS superblock and zonefile metadata. The superblock area has ZenFS creation information. The remaining area in the journal area has zonefile metadata, such as WAL and SSTable mapping zone information. The zonefile metadata contain a file ID, file size, file name and an array of the extent metadata.  
A zonefile is an abstracted file for ZenFS for handling RocksDB files. Its metadata are uploaded to the memory and written to the journal area for recovery when the system crashes. Its data are written in the unit of extent to the data area. An extent has contiguous parts of zonefile data written in a zone. In the extent, \"start\" indicates the position of the write pointer (WP) in a zone when data are first written and \"length\" indicates the contiguously written size in a zone.  
Figure 4 depicts the allocation of a zone in ZenFS. In the figure, L3 (Level 3) data request a zone. In this case, the zone allocator checks all zones in the ZNS SSD and finds the zones that have only invalid data. If the zone allocator finds a zone that only contains invalid data, then all the found zones are erased. For example, in the figure, Zone #2 has invalid data only. Therefore, Zone #2 is selected as an erase target and erased. After erasing all zones, the zone allocator finds a zone that has the smallest level differences from the requested data. In the figure, the zone allocator finds that Zone #1 has the smallest level difference with the requested data. As a result, the zone allocator returns Zone #1 to write the requested data. Moreover, the allocation result is written to the zonefile metadata.  
Figure 3: Overall architecture of ZenFS.
## 3 Results and Discussion
### Evaluation of Hydraulic Parameters
The values of the hydraulic parameters show a dependence on the quantity of nanotubes present in the columns. In short, it was found that the contact times, the total treatable volume and the empty bed contact time increased with an increase in the quantity of nanotubes, while the value of the carbon usage rate was inversely proportional. However, it should be emphasized that these dependencies were not linearly proportional to the quantities of nanotubes, but these parameters underwent a sort of amplification, even for small increases in carbon nanotubes. This is because when the nanotubes are not dispersed, unlike what happens in the case of a batch system, but are compacted in a column, a small increase in their quantity leads as a consequence, in addition to the increase in the adsorbent material that determines a greater adsorption, to an amplification of the resistances that oppose the descent of the solution. This results in a longer contact time and EBCT. The synergistic action of the increase in the adsorbent material and in the contact times leads to a marked improvement in the adsorption capacity. For example, by comparing the data of the treatable volume in columns C1 and C2, it can be seen that a doubling of the quantities of carbon nanotubes inside the column did not lead to a doubling of the treatable volume, but rather a volume that was about 4 times greater. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Column** & \begin{tabular}{c} **M\({}_{ads}\)** \\ **[g]** \\ \end{tabular} & \begin{tabular}{c} **V\({}_{Treated}\)** \\ **[L]** \\ \end{tabular} & \begin{tabular}{c} **V\({}_{ads}\)** \\ **[m\({}^{3}\)]** \\ \end{tabular} & \begin{tabular}{c} **A** \\ **[m\({}^{2}\)]** \\ \end{tabular} & \begin{tabular}{c} **Q** \\ **[L/h]** \\ \end{tabular} & \begin{tabular}{c} **Time** \\ **[h]** \\ \end{tabular} & \begin{tabular}{c} **CUR** \\ **[g/L]** \\ \end{tabular} & \
\begin{tabular}{c} **J** \\ **[m\({}^{3}\)/m\({}^{2}\).h]** \\ \end{tabular} &
\begin{tabular}{c} **EBCT** \\ **[min]** \\ \end{tabular} \\ \hline C1 & 2.5 & 3.8 & 14.14 \(\times\) 10\({}^{-6}\) & 3.14 \(\times\) 10\({}^{-4}\) & 0.0194 & 195.7 & 0.66 & 0.066 & 43.687 \\ C2 & 1.25 & 0.9 & 7.07 \(\times\) 10\({}^{-6}\) & 3.14 \(\times\) 10\({}^{-4}\) & 0.0947 & 9.5 & 1.39 & 0.301 & 4.477 \\ C3 & 0.75 & 0.6 & 4.40 \(\times\) 10\({}^{-6}\) & 3.14 \(\times\) 10\({}^{-4}\) & 0.0869 & 6.9 & 1.25 & 0.278 & 3.036 \\ C4 & 0.50 & 0.1 & 3.14 \(\times\) 10\({}^{-6}\) & 3.14 \(\times\) 10\({}^{-4}\) & 0.5 & 0.2 & 5.00 & 1.591 & 0.377 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Carbon usage rate (CUR), flux (J), and empty bed contact time (EBCT) for the different adsorption columns.
# Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction
## 1 Introduction
Due to the burning of natural gas during the heating season, and the fact that winter 2016 was a warm winter without strong winds in Beijing, the air quality continued to worsen, so the PM2.5 value slowly increased. Moreover, the periodicity within 24 h was very obvious between day and night. From the changes in each day, we can conclude that human and industrial activities during the day such as car trips and factory production will contribute to the PM2.5 increase. In contrast, at night, such activities are relatively reduced, so the PM2.5 concentration will decrease. In this paper, we propose an integrated predictor of decomposing PM2.5 into three components such as the trend, period, and residual component, and give different predictors for each sub-sequence (i.e., the ARIMA model for the trend component and two GRU networks for period component and residual, respectively). The rest of the paper is organized as follows. Section 2 proposes the algorithmic framework for the time series prediction, especially the decomposition and the prediction model. To further explore and explain the applicability of the proposed model, experiments on the datasets of  
Figure 1: Decomposition of PM2.5 time series in Beijing, China. Beijing PM2.5 are illustrated in Section 3. Section 4 discusses the ability of deep learning networks to model linear time-series data, and analyzes the reasons that the prediction performance of deep learning networks will decrease for the trend component. Finally, Section 5 summarizes and concludes the paper.
## 4 Analysis of Security and Performance
### Security Analysis
#### 4.1.1 Masquerading Attacks  
In the proposed scheme, one vehicle (\(V_{i}\)) uses the information of anonymous identity included (\(ID_{Ra}\), \(T_{X}\), \(ID_{Vi}\), \(DS_{Vi}\)), to say \"hello\", and it uses \(PRS_{Vi}\) to generate a pairing value for message authentication or communication. \(ID_{Vi}\), which is one \(IDS_{Vi}\), can be used only in \(T_{t}\). For any \(ID_{Vix}\) in \(IDS_{Vi}\), the public key and private key are as follows.  
\[PK_{Vlx}=H(ID_{Ra}\parallel ID_{Vix}\ /\ T_{t})\ P \tag{3}\]  
\[PR_{Vlx}=SP_{Rt}\ (d_{Vlx})\ PK_{Vlx} \tag{4}\]\(V_{i}\) knows \(ID_{Vix}\), \(d_{Vix}\) and \(PR_{Vix}\) but it cannot retrieve \(SP_{Rt}\) (\(d_{Vix}\)) because the ECDLP (Elliptic Curve Discrete Logarithm Problem) is a hard problem. Therefore, \(V_{i}\) cannot masquerade as having another anonymous ID without information of the secret pool. In message authentication and the construction of a set of neighbors, the only exposed information is \(ID_{Vi}\), \(DS_{Vi}\), \(F(x)\) and \(HMAC(M\parallel T_{S})_{K}\), so an attacker cannot retrieve any private information about the private keys. Therefore, masquerading attacks are impossible.
# Robustness of Cyber-Physical Supply Networks in Cascading Failures
## 3 The Theoretical Model of Cyber-Physical Supply Networks
### Physical Supply Network
#### 3.2.2 Load and Capacity of the Physical Supply Network  
The material flows can be treated as the loads in the physical supply network. Specifically, material flows describe the transport of material, components, or products [14; 29]. Three methods are used to signify the node's initial load, which include the node degree centrality [30], node betweenness centrality [31], or the node-outdegree centrality [15]. As the operation of firms in the physical supply network is related to both upstream firms and downstream firms, the initial load \(L_{v_{i}^{p}}(0)\) for \(v_{i}^{p}\) is defined as a function of the degree of \(v_{i}^{p}\):  
\[L_{v_{i}^{p}}(0)=(k_{v_{i}^{p}})^{\alpha}, \tag{6}\]  
where \(\alpha\) is the tunable parameter used to adjust the initial load.  
The physical supply network often transfers some loads, where the most massive load that a node can deal with is named the capacity. A node's capacity is limited due to confined cost. For example, the supply capacity and manufacturing capacity of a firm are restricted by the firm's scale. In other words, each node has a specific upper-bound capacity, which is linearly correlated with its initial load. The upper node capacity \(C_{v_{i}^{p}}(max)\) is  
\[C_{v_{i}^{p}}(max)=\beta L_{v_{i}^{p}}(0), \tag{7}\]  
where \(\beta(\beta>1)\) is the upper-bound parameter of the node.  
Furthermore, the physical supply network works to provide products and services to customers, and the goal of each firm is to obtain revenue. If a firm's product demand or raw material supply is below a certain level, the company will not operate normally and eventually close down due to unprofitability. Therefore, the load to maintain the firm's regular operation must be higher than a specific limit. The lower-bound capacity \(C_{v_{i}^{p}}(min)\) for \(v_{i}^{p}\) is presented as follows:  
\[C_{v_{i}^{p}}(min)=\gamma L_{v_{i}^{p}}(0), \tag{8}\]  
where \(\gamma(0<\gamma<1)\) is the lower-bound parameter of the node.
# Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding
## 4 Analysis of the Combination Approach
\(\mu_{L}\) is its mean and \(\sigma_{L}\) is its variance respectively. Combining Equations (5)-(8), we get the model of the intensity distribution for the image with reflected laser lines. \[f(x,y)=\left\{ \begin{array}{ll}w\mu(x,y)+\frac{C_{a}(T)}{\left[\left(x-x_{0 }\right)^{2}+\left(y-y_{0}\right)^{2}+d^{2}\right]^{3/2}};&(x,y)\in A\\ \frac{C_{a}(T)}{\left[\left(x-x_{0}\right)^{2}+\left(y-y_{0}\right)^{2}+d^{2} \right]^{3/2}};&(x,y)\
otin A\end{array} \right.. \tag{9}\]  
From the above model, we see that the fuzziness caused by \(\mu(x,y)\) should be reduced effectively to obtain high segmentation accuracy. However, the moving average filter can only reduce the Gaussian noise instead of removing the fuzziness. Thus, a new enhancement method is required. After the image fuzziness has been added in the derived image model (Equation (9)), we need to theoretically find the reasons that why the fuzziness causes parts of the segmented laser lines missing. To this end, the mathematical explanation of how the gradient detection filter works for segmentingthe objects from the uneven background is described at first. According to Equation (9), the gradient value at the position \((x,y)\) that is caused by the arc light background can be formulated as:  
\[g^{a}=\frac{C_{c}(T)}{\left[\left(\frac{N-1}{N_{2}}+x-x_{0}\right)^{2}+\left( \frac{N-1}{N^{2}}+y-y_{0}\right)^{2}+d^{2}\right]^{3/2}}-\frac{C_{d}(T)}{\left[ \left(x-y_{0}-\frac{N_{2}}{N^{2}}\right)^{2}+\left(y-y_{0}-\frac{N_{2}}{N^{2} }\right)^{2}+d^{2}\right]^{\frac{3}{2}}}. \tag{10}\]  
The gradient values caused by the laser line can be formulated as:  
\[g^{l}=\left\{ \begin{array}{ll}w\mu(x,y)+g^{a};&\quad if\ top\ part\ of\ K_{g}\
otin A\\ -w\mu(x,y)+g^{a}&\quad if\ bottom\ part\ of\ K_{g}\
otin A\\ g^{a};&\quad if\ whole\ part\ of\ K_{g}\in A\end{array} \right.. \tag{11}\]  
For the designed filter \(K_{g}(N,\theta)\) in this research work, the following conditions need to be met. \[w\mu(x,y)\gg g^{a}. \tag{12}\]  
Since \(g^{a}\) is the gradient of a small part of the background with a range of \(N\), its value is much reduced compared to the variation of the total background. Thus, the condition of Equation (12) can be easily satisfied if \(\mu\) is a constant to make the laser line gradient distinguished from the background variation. However, \(\mu\) is the membership function and its value is random between 0 and 1. It is impossible to make Equation (12) true for every point. Hence, if the local fuzzy membership \(\mu(x,y)\) could be fitted as a global function for the whole image to remove the randomness, Equation (12) could be easily satisfied. A two-dimensional spline function is an ideal global function to the image and thus it is used. The fitting process is implemented by minimizing the following energy function to get an enhanced image, \(s(x,y)\) from the original image \(f(x,y)\). \[E=\frac{1}{2}\iint(s(x,y)-f(x,y))^{2}dxdy+\frac{1}{2}\iint\left|\frac{d^{2}s( x,y)}{dxdy}\right|^{2}dxdy. \tag{13}\]
# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
## 2 Related Work
In [35], the authors introduce a novel approach to generating super-cumulants by fusing the traditional mathematical method of linearly computing combinations of cumulants with a genetic algorithm (GA). The super-cumulants allow the K-nearest neighbor technique to classify five digital modulation schemes when applied to fading channels. The authors deployed a multi-task learning framework based on deep neural networks to perform modulation and signal classification tasks concurrently while considering diverse wireless signals in the electromagnetic spectrum, such as radar and communication waveforms. The proposed design uses the tight link between the two objectives to increase the classification accuracy and learning efficiency by employing a lightweight neural network model [36]. In addition to experimental evaluations of the model using samples from the air, the authors provided first-hand knowledge of model compression and a deep learning pipeline for deployment on resource-constrained edge devices. Using the proposed paradigm, they demonstrated considerable computational, memory, and accuracy improvements over two reference designs. In addition to simulating a lightweight MTL model appropriate for resource-constrained embedded radio systems, the authors modeled a lightweight one. The authors in [37] presented the classification of modulation formats using random forest. The classification algorithm's performance depends on the extracted features, even though some of the collected characteristics are noisy, irrelevant, and redundant. Inappropriate or noisy features will have a negative impact on the profile performance. Increasing the number of retrieved features would directly or greatly increase the training time of various pattern recognition algorithms. Various deep-learning-based classifications are proposed in [38; 39; 40; 41]. A detailed review of the classification of modulation formats using deep learning with their datasets and challenges is presented in [42]. In addition, the authors conducted extensive tests to assess the state-of-the-art models for single-input, single-output (SISO) systems in terms of accuracy and complexity. They suggested using DL-AMR in a novel multiple-input multiple-output (MIMO) scenario with precoding. Finally, existing obstacles and potential future avenues of study are highlighted.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 3 Proposed Methodology
### Training Procedure  
The training process follows a two-step optimization strategy. The critic is first updated to estimate the Wasserstein distance while incorporating SNNL to improve feature space alignment. Once the critic stabilizes, the generator is updated to minimize both the WGAN loss and SNNL regularization. The complete procedure is outlined in Algorithm 1.  
```
1:Input: initialized generator \(G\); initialized critic \(C\); critic update frequency \(freq\).
2:Output: well-trained generator \(G\); well-trained critic \(C\).
3:for each training iteration do
4: Sample a mini-batch of real data \(x\sim p_{\text{real}}\).
5: Generate synthetic samples \(\hat{x}=G(z)\), where \(z\sim p(z)\).
6: Compute critic loss \(\mathcal{L}_{\text{WGAN}}\).
7: Extract feature representations \(f(x)\) and \(f(\hat{x})\).
8: Compute SNNL regularization \(\mathcal{L}_{\text{SNNL}}\).
9: Update critic \(C\) using \(\mathcal{L}_{\text{WGAN+SNNL}}\).
10:if iteration \(\mod\freq=0\)then
11: Update generator \(G\) using gradients from \(\mathcal{L}_{\text{WGAN+SNNL}}\).
12:endif
13: Apply weight clipping to critic \(C\).
14:endfor
```  
**Algorithm 1** Training procedure for WGAN+SNNL
## 3 Methodology  
### PCES-YOLO Network  
The overall architecture of the PCES-YOLO model is illustrated in Figure 1. The model enhances PCB defect detection through three key innovations: feature extraction, feature fusion, and loss function optimization.  
Figure 1: The overall architecture of the PCES-YOLO model consists of four modules: C3k2-PRFEmodule, ConvNeXtBlock module, EFAN module, and Shape-IoU loss function.  
For feature extraction, the backbone network integrates the novel C3k2-PRFE module with ConvNeXt architectural elements. This design expands the feature map's receptive field, thereby deepening its understanding of input data and mitigating background interference. Employing lightweight SCDown modules, the EFAN is developed for feature fusion, which preserves critical detail information while reducing parameter quantity, effectively bridging low-level features from the backbone network and high-level semantic information from the neck network to improve small-target detection performance. In the loss function optimization aspect, a geometry-aware loss function named Shape-IoU is implemented. By considering the bounding box absolute dimensions, it enables more precise boundary regression for defect targets.
## 3 Numerical Examples
### Influences of Various Construction Factors of the Unit Cell on the Band Structures
#### 3.4.2 Effect of the Material Parameters of Constituent Beams on the Band Structures  
The material parameters involved include the Young's modulus, the shear modulus and the mass density. The Young's modulus and the shear modulus are not independent, and are related by the Poisson's ratio. To inspect the effect of the modulus, we fixed the [PERSON]'s ratio and let both the Young's modulus and the shear modulus change synchronously. The specified ratios are \(\gamma_{E}=\gamma_{G}=0.25\), \(\gamma_{E}=\gamma_{G}=1\) and \(\gamma_{E}=\gamma_{G}=2\), and the band structures of the corresponding  
Figure 10: Effect of the inertia moment of constituent beam on the band structures of the bi-coupled periodic beam with Ternary-I unit cell.  
Figure 9: Effect of the cross-sectional area of constituent beam on the band structures of the bi-coupled periodic beam with Ternary-I unit cell.  
periodic beams are depicted in Figure 11. To observe the effects of the Poisson's ratio, the Young's modulus remains fixed and the shear modulus changes, because the variation of the Poisson's ratio is equivalent to changing the shear modulus as the Young's modulus fixes. Three cases of the shear modulus are considered, i.e., \(\gamma_{G}=0.9\), \(\gamma_{G}=1\) and \(\gamma_{G}=1.2\), with the corresponding band structures provided in Figure 12. For the sake of investigating the effect of mass density on the band structures, the ratio of mass density \(\gamma_{\rho}\) is assumed as \(\gamma_{\rho}=0.25\), \(\gamma_{\rho}=1\) and \(\gamma_{\rho}=4\) with the corresponding band structures shown in Figure 13.  
Figure 11: Effect of the modulus of constituent beam as its Poisson’s ratio remains on the band structures in the bi-coupled periodic beam with Ternary-I unit cell.  
Figure 12: Effect of the Poisson’s ratio of constituent beam as its Young’s modulus remains on the band structures in the bi-coupled periodic beam with Ternary-I unit cell.  
In general, Figures 11-13 indicate that the decreasing of modulus, the decreasing of Poisson's ratio and the increasing of density have similar effects on the variation of band structures of bending waves in bi-coupled periodic multi-component beams, which are also similar to the increasing of cross-sectional area and the decreasing of cross-sectional inertia moment as these figures are compared to Figures 9 and 10. In the cases of these changes, the first pass-bands all appear at higher frequency and the phase spectra all become sparser in a specified frequency range. From the attenuation constant spectra, it is indicated that not only the central frequencies and widths of the stop-bands but also the attenuation values in the stop-bands are influenced by these changes.
# MIMO Self-Heterodyne OFDM Using Band Selection Technique
## 1 Introduction
Thus, the technique in [22] uses the bandwidth more efficiently and hence increases the data rate compared with technique in [12]. However, the technique in [22] increases data rate and enhance BER performance but at the expense of the computational complexity. The authors in [12; 22] used STBC and FSTBC, respectively, for a MIMO self-het OFDM system. However, the proposed techniques in our paper utilize the band selection (BS) approach with STBC and FSTBC for MIMO self-het OFDM system. In this paper, two techniques are proposed to enhance the performance for MIMO self-het OFDM system in terms of data rate and BER. The main idea of the proposed techniques is to use the BS approach once with STBC [12] for the first proposed technique, BS-STBC, and once again with FSTBC [22] for the second proposed technique, BS-FSTBC. The use of the BS approach with STBC and FSTBC techniques help to select the optimum channel band to transmit the data over the MIMO channel and avoid the channel fading effects. The used of the optimum sub band with higher gain for transmission reduces the probability of error and consequently improve the BER performance. Moreover, maximum likelihood combiner is used at the receiver to separate the transmitted symbols for the proposed techniques. The simulation results show that the proposed techniques BS-STBC and BS-FSTBC for the MIMO self-heterodyne OFDM system, have superiority over the conventional techniques [12; 22] in terms of BER performance. Hence, by using the BS technique, the performance of the STBC [12] and FSTBC [22] techniques are improved in term of BER. Moreover, the simulation results show that the first proposed technique BS-FSTBC outperform the second proposed technique BS-STBC in term of BER performance. The rest of this paper is organized as follows; in Section 2, the system model of the (FSTBC) MIMO self-het OFDM system is presented. Section 3 presents the BS proposed technique in the STBC MIMO self-het OFDM system and the FSTBC MIMO self-het OFDM system. Section 4 presents and discuss the simulation results of the proposed approaches. Finally, the paper is concluded in Section 5. Throughout this paper, the following notation is adopted: matrices are denoted by boldface capital letters, column vectors are represented by boldface lowercase letters, symbols are denoted in italics, H\({}_{ra}\) is the channel gain between the \(a\)th transmitting antenna and \(r\)th receiving antennas, \(\alpha_{c}\) and, \(\beta_{c}\) are the sum of channel gain to the 1 st and 2 nd receiving antenna respectively, \(\mathcal{R}\{\cdot\}\) represents the real component of a complex number, (\(\ast\)) is the conjugate operation, \(\left(\cdot\right)^{-1}\) inverse operation, \(\left(\cdot\right)^{+}\) the pseudo-inverse operation, \(\left(\cdot\right)^{H}\) is the hermition matrix, \(\left|\cdot\right|\) is absolute operator.