esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Scheme HFECS
### Packet Overhead Calculation  
In our proposed scheme, each CH acquires the integrated sensed data from its CMs and then forwards the aggregated data to BS after detecting the change in integrated data for every time period \(T\). In our paper, the hesitant fuzzy entropy balances the network load during CH rotation in successive rounds of communication when applied on MADM. Let us assume that we have \(O(N_{W})\) exchanged packets as an overhead in which \(N_{W}\) is the number of working sensor nodes in the network. If we assume that \(k\) is the number of CHs per round, then we can calculate the overhead packets as:  
* \(k\) packets used for broadcasting CH_Announce by all CHs.
* \(N_{W}-k\) packets in terms of Con_Req by working CMs.
* \(k\) packets used for broadcasting TDMA schedule to working CMs in CM_set by all CHs.
* \(N_{W}-k\) packets used for sending data to CHs by working CMs.
* \(k\) packets used for forwarding data to the BS by all CHs in case if final prediction result is changed after time period \(T\).
* \(\frac{N}{N_{C}}-1\) packets received by all CHs from their CMs for link connectivity calculation.
* \(\frac{N}{N_{C}}-1\) packets received by all CMs from their neighboring CMs and current CH for link connectivity calculation comparison.
* \(k\) packets used for the CHs rotation in case if all CHs select one of its CMs as the next CH.  
Therefore, the overhead of HFECS will be \(O(N_{W})\) as \(k\ll\frac{N}{N_{C}}\ll\,N_{W}\).
## 4 Methodology
### Logical Block
#### 4.2.1 Bitwise AND  
Bitwise AND module performs 'AND' operation for a given 16-bit input and gives an output for each bit individually. It takes two binary numbers of equal length, resulting in the operation, where each bit from the \(A\) array is compared to its counterpart in \(B\). Thus, if both bits in the compared location are 1, the bit in the resultant binary representation is 1 (1 \(\times\) 1 = 1); otherwise, the result is 0 (1 \(\times\) 0 = 0 and 0 \(\times\) 0 = 0). The bitwise AND operator can be implemented to clear certain bits (or flags) of a register, where each bit represents a distinct Boolean state. This method effectively stores many Boolean values while utilizing as little memory as feasible. For example, 0110 (decimal 6) can be considered a set of four  
Figure 13: Waveform results from the 16 \(\times\) 16 division module.  
flags, where the first and fourth flags are clear (0), and the second and third flags are set (1). The third flag can be removed by performing a bitwise AND with a pattern with just a zero in the third bit. Figures 14 and 15 show the circuit and waveform results from our bitwise AND implementation.
# Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources
## 5 Assessing Resource Sharing in the Cloud-RAN Architecture
### Sharing Computing Resources at the CU  
In the network scenario evaluated in this section, three user-space applications share resources in the CU. Firstly, vBBU1, which deploys split 7.1, is the most time-critical application. Since vBBU1 shares frequency domain samples of the signal between the DU and the CU, it is sensitive to processing interference or resource unavailability. Evidence suggests that running vBBU1 on a minimum of four CPUs guarantees stable performance [50]. On the contrary, vBBU2, which implements split 2, hosts L3 functions at the CU. L3 functions are not time-critical and, therefore, run as non-RT threads in the Linux RT-Kernel. Consequently, in this deployment, two CPUs are assigned to vBBU2. Lastly, service A runs as a best-effort application for BT packets. Thus, no more than a single CPU is assigned to LXC hosting A.  
To mitigate processing interference from collocated LXCs, we assess the CPU affinity scenario _IA-WA_ introduced in Section 3. Figure 10 shows the CPU affinity assigned to the LXCs hosted at the CU. Following the _WA_ approach, while the CPU affinity to vBBU1 is \(C_{W}^{BBU1}=\{C4,C5,C6,C7\}\), the CPU affinity to vBBU2 is defined as \(C_{W}^{BBU2}=\{C2,C3\}\). Similarly, the CPU affinity to A is \(C_{W}^{A}=\{C1\}\). In this way, by pinning LXCs to disjoint subsets of CPUs, user-space process isolation is guaranteed.  
On the other hand, following the _IA_ approach, we adopted the CPU affinity to the NIC. This time, the CPU affinities are allocated to the SR-IOV's VFs, as shown in Figure 10. More specifically, the RX/TX Hard-IRQ queues associated with VF1 and VF4 in vBBU1 are allocated CPU affinity \(C_{VF1}^{BBU1}=C_{VFA}^{BBU1}=C7\). Similarly, the RX/TX Hard-IRQ queues associated with VF2 and VF5 in vBBU2 are allocated CPU affinity \(C_{VF2}^{BBU2}=C_{VFS}^{BBU2}=C3\). Finally, the RX/TX Hard-IRQ queue associated with VF3 in A is allocated CPU affinity \(C_{VF3}^{A}=C1\). This way, high-rate incoming packets and their corresponding Hard-IRQs or Soft-IRQs (in NAPI) are processed by a different CPU than the ones used by the vBBUs, thus avoiding processing interference.
# Downlink Training Design for FDD Massive MIMO Systems in the Presence of Colored Noise
## 5 Asymptotic Analysis of the SINR with RZFBF Precoding
Matrix \(\bar{\bf T}\in\mathbb{C}^{N\times N}\) is given by_  
\[\bar{\bf T}={\bf T}\Bigg{(}{\bf I}_{N}+\frac{K\bar{\delta_{\rm h}}\mathbf{ \Phi}_{\hat{\rm h}}}{N(1+\delta_{\rm h})^{2}}\Bigg{)}{\bf T}, \tag{39}\]  
_where \(\bar{\delta_{\rm h}}\in\mathbb{R}\) is an auxiliary variable and is given by_  
\[\bar{\delta_{\rm h}}=\Bigg{(}1-\frac{{\rm tr}\big{(}\mathbf{\[PERSON]}_{\hat{\rm h }}{\bf T}\mathbf{\[PERSON]}_{\hat{\rm h}}{\bf T}\big{)}}{\big{(}N(1+\delta_{\rm h} )\big{)}^{2}}\Bigg{)}^{-1}\frac{1}{N}{\rm tr}\big{(}{\bf T}\mathbf{\Phi}_{ \hat{\rm h}}{\bf T}\big{)}. \tag{40}\]  
_Parameter \(\xi\in\mathbb{R}\) denotes the deterministic equivalent of RZFBF precoding normalization, which is provided by substituting matrix \({\bf T}\) in (38) and matrix \(\bar{\bf T}\) in (39) into_  
\[\bar{\xi}=\big{(}{\rm tr}({\bf T})-\zeta{\rm tr}(\bar{\bf T})\big{)}^{-1}. \tag{41}\]_The parameter \(\tilde{\mu}\in\mathbb{R}\) is determined by utilizing the dominated convergence theorem and the theorem of the continuous mapping, as provided in [10], and hence, it is obtained in (42)-(44)._  
\[\tilde{\mu}_{\rm h}=\frac{1}{N}\text{tr}\big{(}\mathbf{R}_{\rm h}\mathbf{T}^{ \prime}\big{)}-\frac{2\text{Re}\big{(}\text{tr}\big{(}\mathbf{\Phi}_{\rm\tilde{ h}}\mathbf{T}\big{)}\text{tr}\big{(}\mathbf{\Phi}_{\rm\tilde{h}}\mathbf{T}^{ \prime}\big{)}\big{)}\big{(}1+\delta_{\rm h}\big{)}-\text{tr}\big{(}\mathbf{ \Phi}_{\rm\tilde{h}}\mathbf{T}\big{)}^{2}\delta^{\prime}_{\rm h}}{\big{(}N(1+ \delta_{\rm h})\big{)}^{2}} \tag{42}\]  
\[\mathbf{T}^{\prime}=\mathbf{T}\Bigg{(}\mathbf{\[PERSON]}_{\rm\tilde{h}}+\frac{K \mathbf{\[PERSON]}_{\rm\tilde{h}}\delta^{\prime}_{\rm h}}{N(1+\delta_{\rm h})^{2}} \Bigg{)}\mathbf{T} \tag{43}\]  
\[\overline{\gamma}_{\rm h}^{\rm RZFBF}=\frac{KN\xi\delta_{\rm h}^{2}}{\sigma_{ \rm d}\big{(}1+\delta_{\rm h}\big{)}^{2}+\bar{\xi}K^{2}\hat{\mu}_{\rm h}}, \tag{35}\]0  
The SINR expression given in (35) is a deterministic approximation of the true SINR in (6) that gives a lower bound in (4). The SINR analysis of RZFBF precoding provided in (35) is valid for any spatial correlation model and DTS type. Accordingly, the achievable sum rate expression based on the random matrix theory method can be written as  
\[\overline{\gamma}_{\rm h}^{\rm RZFBF}=\frac{KN\xi\delta_{\rm h}^{2}}{\sigma_{ \rm d}\big{(}1+\delta_{\rm h}\big{)}^{2}+\bar{\xi}K^{2}\hat{\mu}_{\rm h}}, \tag{35}\]1  
The following section discusses the physical channel correlation models based on the ULA and UPA array configurations.
## 3 Methodology
### Type-2 Fuzzy Sets (T2 FS)  
Treating normalized rail section attributes and its distances to the ideal solutions by means of T2 FS yields more freedom for capturing inherent vagueness related to data collection and measurement when compared to type 1 fuzzy sets [33]. This happens because T2 FS can reflect the uncertainty of inaccurate data by means of primary and secondary membership functions related, respectively, to normalized attributes and their distances to the ideal solutions [34]. As regards the application to Brazilian rail sections, while distances to the ideal solutions are usually computed at each normalized attribute level, very often identifying an unbiased global ideal solution is difficult due to multicollinearity issues among attributes. This being the case, extensive pairwise comparisons may assist in developing unbiased evaluation alternatives [35].  
T2 FS are defined by primary and secondary membership functions to represent uncertain information more effectively [36]. In this section, basic concepts and arithmetic operations of T2 FS are summarized from [PERSON] and [PERSON] (2010) [37] and [PERSON] et al. (2006) [38], and are given below.  
**Definition 1**.: _A T2 FS \(\overset{\approx}{A}\) in the universe of discourse \(X\) can be represented by a type-2 membership function \(\mu\overset{\approx}{A}\), as shown next [38]:_  
\[\overset{\approx}{A}=\Big{\{}\Big{(}(x,u),\mu_{\overset{\approx}{A}}(x,u) \Big{)}\Big{|}\forall_{X}\in X,\forall_{u}\in J_{x}\subseteq[0,1],\;0\leq\mu_{ \overset{\approx}{A}}(x,u)\leq 1\Big{\}} \tag{1}\]  
_where \(J_{x}\) denotes an interval in \([0,\;1]\) which represents the support for the secondary membership function. Moreover, the T2 FS \(\overset{\approx}{A}\) also can be represented as follows [38]:_  
\[\overset{\approx}{A}=\iint_{x\in X}\mu_{\overset{\approx}{A}}(x,u)/(x,u), \tag{2}\]  
_where \(J_{x}\subseteq[0,1]\) and the double integral denotes union over all admissible \(x\) and \(u\)._  
**Definition 2**.: _The concept of hesitant T2 FS (HT2 FS) is introduced to represent a particular subset of the original T2 FS and can be used to explore alternative membership possibilities with respect to \(x\) and \(u\)[36]._  
**Definition 3**.: _Let \(\overset{\approx}{A}\) be a fixed set. An HT2 FS on \(\overset{\approx}{A}\) is expressed in terms of a function that returns a type-2 fuzzy subset when applied to each (x,u) pair in \(\overset{\approx}{A}\)._  
To make it easily understood, this HT2 FS is represented by \(E=\Big{\{}<(x,u),\widetilde{h}_{E}(x,u)>\Big{|}\)\(x\in X,u\in J_{x}\Big{\}}\), where \(\widetilde{h}_{E}(x,u)\) denotes the possible membership degrees for each pair \((x,u)\) with respect to the subset \(E\).
# IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities
## 8 Lessons Learned  
The review of the state of the art of gas-sensing technologies has demonstrated the need for more research into ameliorating the sensing characteristics of the transducers to achieve better levels of reliability. The use of these devices can grant better results in numerous fields, as well as grant security to people.  
Many researchers mistake metal oxide semiconductors for electrochemical and catalytic sensors; the principle of operation and their sensing characteristics are similar, although the fabrication techniques, sensing methods, as well as the types of materials to construct the transducers differ in quite a few aspects.  
As presented above, ZigBee is chosen by many authors due to its easy deployment characteristics and lower power consumption, compared to Wi-Fi and Bluetooth. Nevertheless, the standard IEEE 802.15.4 with the 6 LoWPAN should be considered as a relevant alternative in future solutions given its promising characteristics.  
Despite the potential of some sensing techniques, no further investigations on these topics were found. Commercial solutions are more focused on using the most researched and antique technologies such as the metal oxide semiconductors and electrochemical transducers, even knowing that these sensors can lead to mistakes due to interference from other gases. Decreasing the final prices of these transducers could help to make gas sensors ubiquitous systems.
# WIG-Net: Wavelet-Based Defocus Deblurring with IFA and GCN
## 5 Conclusions  
In this work, a deep-learning-based image deblurring algorithm was proposed that employs a forward wavelet transform to replace downsampling and an inverse wavelet transform for upsampling. This method increased the network's receptive field and minimized information loss during transmission. To recover the lost image information in the wavelet domain, a graph neural network was employed that utilizes pre-defined graph  
Figure 8: Comparison of deblurring performance on the custom-made dataset’s test set.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **PSNR** & **SSIM** & **MAE** \\ \hline Blurry image & 21.05 & 0.632 & 0.513 \\ \hline Ours & 23.46 & 0.708 & 0.435 \\ \hline \end{tabular}
\end{table}
Table 3: Quantitative evaluation on the custom-made dataset’s test set.  
structures to improve image restoration performance, while keeping the computational cost low. An IFA module can handle spatially varying defocus blur by predicting a separable deblurring filter for each pixel. Our experimental results, using the DPDD test set and the custom-made dataset's test set, demonstrated that the proposed method could produce images with better visual quality and stronger robustness in various scenarios. Our network is capable of handling large areas of defocus blurring and can be applied to medical images, traffic monitoring, and other fields.  
Our proposed network performed well in addressing defocus blur for smaller objects, as indicated in the labeled section of Figure 8e, but it still exhibited certain limitations. We plan to address this shortfall in future research by extending the dataset and enhancing the network's ability to extract detailed information.  
Conceptualization, Y.L.; Methodology, Y.L.; Software, Y.L.; Formal analysis, Y.L.; Investigation, Y.L.; Data curation, Y.L.; Writing--original draft, Y.L.; Writing--review & editing, N.W., J.L. and Y.Z.; Supervision, N.W. and Y.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the Sichuan Province Science and Technology Support Program, grant number 2021 ZYZFFGY06; the National Natural Science Foundation of China, grant number 62104202; the Sichuan Province Science and Technology Support Program, grants numbers 2022 YFG0217 and 2021 ZYZFGY06; the Guang'an Science and Technology Innovation Project, grant number 2022 CC04. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data that support this study are proprietary. The authors declare no conflict of interest.
## 2 Second Messengers in Cyanobacteria
### Guanosine-3', 5'-(bis) Pyrophosphate, (p)ppGpp
#### 2.2.2 Cyanophage and (p)ppGpp  
A fascinating aspect of (p)ppGpp regulation in cyanobacteria was observed when _Synechococcus_ sp. PCC 6301 cells were infected with the AS-1 cyanophage (a cyanomyovirus). Cyanophage-infected _Synechococcus_ sp. PCC 6301 cells failed to exhibit significantly increased intracellular (p)ppGpp levels under amino acid or energy deficiency as did uninfected cells, suggesting that infected cells did not perceive starvation [59]. One way for a cyanomyovirus to maintain low levels of (p)ppGpp is to express the protein MazG in the host; MazG is a protein found in all cyanomyovirus isolates [60]. In _E. coli_, MazG can hydrolyse (p)ppGpp [61], and if this is also true for MazG in cyanomyovirus-infected cyanobacteria, expression of MazG could allow the cyanomyoviruses to induce host cells to maintain basal levels of (p)ppGpp. Such a virus-induced response would support a normal cellular growth rate of the host, where the physiological state is expected to be optimal for production of progeny phage [62].
## 3 Proposed SDN Architecture for Deep Learning-Based Traffic Classification  
Our proposed network architecture for traffic classification is configured on an SDN architecture. The SDN separates the data plane from the control plane. The data plane remains only in the network equipment and focuses only on packet transmission, while the control plane determines the packet routing path or QoS policy in a central external controller. It can use the OpenFlow protocol for communication between the two separated planes. In addition, the use of open source APIs reducesthe dependence on network equipment companies and enables the development and use of various network software.  
Figure 1 shows the proposed SDN architecture for traffic classification. In the proposed architecture, the new two sub-systems, 'labeled packet collector' and 'classifier,' are located in the data plane and the control place, respectively. On the data plane, the labeled packet collectors collect and label the network packets generated by the application program run on end-hosts. In addition, the labeled packet collector sends to the classifier the bundled flow dataset in which the packets with the same five attributes, source IP, source Port, destination IP, destination Port, and protocol, are bundled into the same flow, and a label is also attached into the flow. In the classifier, the training dataset is generated through the feature selector and data preprocessing process. The generated dataset is also used for the learning process of multi-layer LSTM or CNN + LSTM model as well as parameter tuning of the models.  
The learned model is used to classify network traffic in the flow classifier. In Section 8, we describe in more detail how the flow classifier classifies network traffic using the learned model. Then, appropriate flow rules can be generated according to the QoS or scheduling policy of each application by using the classification results. Such flow rules are applied into OpenFlow switches (e.g., Open vSwitches) of data plane. Afterward, the Open Switches forward network traffic based on new flow rules. Overall, SDN-based architecture proposed in this section can classify traffic using a deep learning model and generate appropriate flow rules, according to QoS or scheduling policy of each application.
# Remote Spectral Identification in the THz Band with Reflection Spectroscopy in an Open Atmosphere
## 4 Results  
### Transmission Results  
In Figures 5 and 6, selected results of the transmission measurements are presented for tartaric acid and PABA, respectively. Samples were selected due to the occurrence of multiple spectral features originating from the technical properties of the experimental setup. Selection was based on the previous research [43], where a wider selection of the preliminary selection can be found. The measurements were conducted in an upgraded setup with an atmosphere with about 40% RH. The measurements were set with the reference data, and the water vapor spectra were added to the figures. Data gathered during transmission measurements presented low noise; hence, only a moving average was applied to smooth the curves.  
In Figure 6, it is shown that the sample of PABA was measured in two series. This is where the value of the used attenuators was lowered, and new reference measurements and sample measurements were needed. This was conducted to extend the dynamic range of the measurement above the 21 dB offered by the detector. In Figures 5 and 6, around 1.1-1.2 and 1.4 THz minor absorption features in the form of holes in the transmission spectra were noticed. They appeared on the frequencies correlated with the water vapor absorption lines. Considering the fact that the reference measurement and sample measurement were taken in comparable conditions, only the water lines with higher value had a noticeable impact. After investigation, we concluded that we could observe the effects of water lines  
Figure 4: Examples of the pulses measured at five different frequencies.  
with attenuation multiplied by the distance in an open atmosphere that was higher than 21 dB, which was strictly correlated with the detector parameters. This factor had a higher impact on the reflection measurements, where the optical path was 2-4 times longer.
## 5 Conclusions  
This study firstly establishes the assimilation system of both satellite and radar data based on POD-4 DEnVar method, instead of assimilating them separately. The performance of the system is investigated on continuous summer rainfall, which occurred from June to July in 2016 and 2017. The RMSE against ERA-5 reanalysis shows that assimilation with POD-4 DEnVar has a neutral or positive impact on the reduction of RMSE from CON. Quantitative verification using ETS indicate that although the benefits of assimilation are not universal guaranteed, the potential of POD-4 DEnVar assimilation can still be found. Particularly in 2017, the increase of ETS on heavy rain shows the ability to make short-term heavy rainfall forecast.  
The rainstorm on 30 June 2017 help better understand the contribution of assimilating satellite and radar data. Results of simulated reflectivity show that POD yields stronger development of convection than CON, which can also be supported by the precipitation forecast and agree more with observations. The increments on the initial field are also discussed in this paper, revealing that moisture, temperature and wind field in the precipitation area are changed at the initial time with assimilation. The increase of moisture over the precipitation area promotes the water vapor content. Positive increments of temperature increase the convective condensation latent heat. The strengthen of horizontal wind convergence, and vertical velocity enhances the development of convection. In addition, POD strengthens the hydrometeor intensity since the forecast begins. All the contributions prove that POD-4 DEnVar assimilation of satellite and radar data has the ability to improve the performance of NWP, which can be further applied in the operational weather forecast.  
Conceptualization, L.Z. and J.W.; formal analysis, J.W.; writing--original draft preparation, J.W.; writing--review and editing, L.Z., J.G. and M.Z.; project administration, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (41975066), the Beijing Open Research Fund for Nanjing Joint Center of Atmosphere Research (NJCAR2018 ZD03), and the National Natural Science Foundation of China (41775123). The authors declare no conflict of interest.