esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model and Algorithm
### Variant of CoSaMP for Fixed Subspace  
To tackle this special sparse recovery problem, we adopt the basic idea of CoSaMP [11]. Ordinary CoSaMP could efficiently solve the following underdetermined linear equation system if \(x\) is a \(K\)-sparse signal and the measurement operator satisfies RIP with constant \(\delta_{K}\).  
\[y=\mathcal{A}(x)+\epsilon \tag{3}\]  
Comparing the problems between Equations (1) and (3), for a fixed low-rank subspace, if we can remove the dense signal generated by the subspace, then problem Equation (1) will be converted to the classic sparse recovery problem Equation (3). We consider the greedy approach to iteratively remove the effect of the dense signal then estimate the sparse signal in the framework of CoSaMP. The main idea is as follows: we iteratively estimate the weights \(w^{k}\) which constructs the low-rank background signal \(l=Uw^{k}\) ; then form the signal proxy from the current residue \(u^{k}=v-\mathcal{A}(Uw^{k}+s^{k})\) ; then identify the support of the sparse signal just as ordinary CoSaMP; finally estimate the signal by least squares over the identified support.  
We summarize the proposed _CoSaMP_subspace_ as Algorithm 1. Here \(\mathcal{A}^{{}^{*}}\)denotes the adjoint of the operator \(\mathcal{A}\). The notation _supp(y;K)_ denotes the largest \(K\)-term support of the vector \(y\). Let \(\mathcal{A}_{{}_{T}}\) denote the restriction of the operator to the support \(T\).  
```
1: Initialize \(s\),\(w\),\(u\): \(s^{0}=0,w^{0}=0,u^{0}=0\).
2:while\(\frac{\parallel u^{k}\parallel}{\parallel v\parallel}>\varepsilon\) and \(k<maxIter\)do
3: Estimate weights \(w\): \(w^{k+1}=(\mathcal{A}(U))^{-1}(v-\mathcal{A}(s^{k}))\)
4: Form signal proxy: \(y=\mathcal{A}^{{}^{*}}(u^{k})\)
5: Support identification: \(\Omega=supp(y;2K)\)
6: Merge support: \(T=\Omega\cup supp(s^{k})\)
7: Signal estimation by least squares: \(s^{k+1}\mid_{T}=\mathcal{A}_{T}^{-1}(v-\mathcal{A}(Uw^{k+1}))\), \(s^{k+1}\mid_{T}=0\)
8: Update residue: \(u^{k+1}=v-\mathcal{A}(Uw^{k+1}+s^{k+1})\)
9: \(k=k+1\)
10:endwhile
11: \((s^{*},w^{*})=(s^{k},w^{k})\)
```  
**Algorithm 1** Given a fixed subspace \(\mathcal{S}\) spanned by the column space of an \(n\times d\) orthonormal matrix \(U\), the variant of CoSaMP solver for the sparse recovery problem Equation (1).  
It can be easily seen from Algorithm 1 that, compared with CoSaMP [11], the variant tries to estimate the weights of the dense signal in step 3 and remove its effects from the CS measurement in Step 8. The main framework follows CoSaMP. Thus, the proposed algorithm inherits the merits of CoSaMP, for example the total cost per iteration is O(\(n\)log\(n\)) [11], and the total storage is, at worst, O(\(nd\)) because of holding the fixed low-rank subspace.
## 2 Materials and Methods
### Definition and Design of the System's Components
#### 2.2.4 TE0720 SoC  
To manage and coordinate the uplink and downlink at the Tx and Rx sides, the system requires a robust and versatile processing platform. In this context, the Trenz TE0720-03-1 QFA-V1 system on a chip (SoC) module plays a pivotal role. The datasheet of the TE0720 SoC can be found in [42]. The TE0720 SoC was manufactured by Trenz and bought on [[https://www.trenz-electronic.de/en/](https://www.trenz-electronic.de/en/)]([https://www.trenz-electronic.de/en/](https://www.trenz-electronic.de/en/)) (accessed on 5 September 2024).  
The Trenz TE0720-03-1 QFA-V1 system on a chip (SoC), referred to as TE0720, includes the Xilinx Zynq XA7Z020-1 CLG484Q FPGA. This module features 1 GByte DDR3 SDRAM and 32 MBytte QSPI flash memory. The dimensions of the TE0720 are 4 \(\times\) 5 cm\({}^{2}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline R\({}_{1}\) & 100 \(\Omega\) & R\({}_{3}\) & 1 k\(\Omega\) & C\({}_{2}\) & 22 pF \\ R\({}_{2}\) & 100 \(\Omega\) & R\({}_{4}\) & 1 k\(\Omega\) & R\({}_{6}\) & 50 \(\Omega\) \\ C\({}_{1}\) & 100 nF & R\({}_{5}\) & 50 \(\Omega\) & C\({}_{3}\) & 22 pF \\ ADC & MAX1448 & Amplifier & MAX4105 & C\({}_{4}\) & 100 nF \\ Oscillator & TXO crystal 40 MHz & & & \\ \hline \end{tabular}
\end{table}
Table 4: Components of the PD driver/FPGA connection.  
The TE0720 board is equipped with three Razor Beam High-Speed Hermaphroditic Terminal/Socket Strip connectors: one 60-pin connector (labelled JM\({}_{3}\)) and two 100-pin connectors (labelled JM\({}_{1}\) and JM\({}_{2}\)). These connectors are located on the underside of the printed circuit board (PCB), as depicted in Figure 19. Through these connectors, the TE0720 board interfaces with the internal interface and the external interface boards.
## 3 Theoretical Background
### Alarm Reduction Algorithm Based on Hierarchical Clustering
#### 3.1.2 Distance Definition  
After obtaining the generalized alarm set, in order to cluster the alarms in the original alarm set, we need to define the distance calculation rule in the clustering problem, that is, defining the distance between two alarms to judge whether they belong to the same cluster. In fact, it is easy to calculate the distance between attributes of a numeric type, but there  
Figure 1: Hierarchical tree structure of four attributes: (**a**) IP address attribute; (**b**) time attribute measured in weeks; (**c**) port number attribute; (**d**) time attribute measured in months.  
is a problem if the alarm property is a category, time, or string property using the same distance calculation method. We give the following definition to calculate the distance between two alarms in a hierarchical tree.  
**Definition 5**.: _The distance between any two nodes in the same hierarchical tree depends on the number of edges between them. If two nodes have directly linked edges, the distance between them is 1._  
**Definition 6**.: _If there is a generalization-specification relationship between two alarms, the distance between the two alarms is defined as the average distance between their attributes._  
**Definition 7**.: _The distance of an alarm set is defined as the average distance between the minimum coverage in the set and each alarm._  
Consider the alarm sets \(A_{1}\) (ip1,80,h1,11), \(A_{2}\) (DMZ,80,h0,MIDDLE), and \(A_{3}\) (DMZ, PRIVATE, WEEKEND, MIDDLE) mentioned above, where the minimum coverage in the alarm set is \(A_{3}\). The distance between \(A_{1}\) and \(A_{2}\) is \((2+0+2+1)/4=1.25\). The distance between the minimum coverage and alarm sets is \((1.5+0.75+0)/3=0.75\).
## 4 Proposed Method
### Text Character Recognition
#### 4.2.3 CTC Loss  
CTC loss [57] is used to match the input segments into the desired sequence. CTC aligns a probability output sequence with the label sequence. In text recognition systems, the transcription layer is constructed on top of the recurrent layers to carry out segmentation. The character set is represented by the notation \(\mathbb{C}^{\prime}=\mathbb{C}\cup\{blank\}\), where \(\mathbb{C}\) is a fixed set of labels and \"_blank_\" stands for no labels. Multiplying the probabilities of labels along this path yields the conditional probability of a path \(\pi\) across the lattice of output labels over all time steps for an input sequence of length T, \(x=x_{1},x_{2},\ldots,x_{T}\)  
\[p(\pi\mid x)=\prod_{t=1}^{T}p(\pi_{t},t\mid x) \tag{7}\]  
where the path \(\pi\) label at time t is represented by \(\pi_{t}\)t. A reduction procedure, represented by the letter B, extracts the label sequence from a path by first eliminating duplicate labels and then eliminating any blanks in the path (e.g., B(_hh_e_ll_ll_oo_) = B(_h_ee_ll_o) = hello). The overall probability of all the paths, where each path is converted into this label sequence by B, is the likelihood of a label sequence l from an input sequence x. It appears as follows:  
\[p(l\mid x)=\sum_{\pi:B(\pi)=l}p(\pi\mid x) \tag{8}\]  
The best label is decoded by  
\[l_{max}=B(\pi_{max});\pi_{max}^{t}=\operatorname*{argmax}_{k}\big{(}y_{k}^{t} \big{)},t=1\ldots T \tag{9}\]
## 3 Results and Discussion
### Effect of Layer Height on the Dynamic Property  
Figure 8 shows the effect of different layer height (0.1, 0.15 and 0.2 mm) on the frequency response of ME plates, with no filtering implemented. The results are detailed in Table 5. It could be seen that Sample L\({}_{0.1}\) had the highest values of resonant response (occurring due to the impulse excitation), meaning that the anti-vibration capability was worse than Sample L\({}_{0.15}\) and L\({}_{0.2}\). Therefore, the sample's anti-vibration capability would be improved if the layer height was increased.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Samples**} & \multicolumn{3}{c}{**Order**} \\ \cline{3-5}  & & **1 st** & **2 nd** & **3 rd** \\ \hline \multirow{3}{*}{Average measured natural frequency \(A\) (Hz)} & E\({}_{0.4}\) & 18.0 & 154.9 & 384.8 \\  & E\({}_{0.3}\) & 17.1 & 145.6 & 371.1 \\  & E\({}_{0.2}\) & 16.1 & 134.2 & 346.0 \\ \hline \multirow{3}{*}{Average predicted natural frequency \(B\) (Hz)} & E\({}_{0.4}\) & 19.3 & 170.1 & 396.4 \\  & E\({}_{0.3}\) & 18.5 & 161.6 & 373.6 \\  & E\({}_{0.2}\) & 17.0 & 147.6 & 334.4 \\ \hline \multirow{3}{*}{Error (\%)} & E\({}_{0.4}\) & 7.2 & 9.8 & 3.0 \\  & E\({}_{0.3}\) & 8.2 & 11.0 & 0.7 \\ \cline{1-1}  & E\({}_{0.2}\) & 5.6 & 10.0 & 3.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The effect of different extrusion width on the samples’ inherent characteristics.  
Figure 8: The average measured frequency response of the samples fabricated with different layer height.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Average Resonant Response (m/s\({}^{2}\) N)**} \\ \cline{2-4}  & **1 st Order** & **2 nd Order** & **3 rd Order** \\ \hline L\({}_{0.1}\) & 4.8 & 5.0 & 5.1 \\ L\({}_{0.15}\) & 4.3 & 4.4 & 3.0 \\ L\({}_{0.2}\) & 4.0 & 3.5 & 2.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The resonant response values of the samples fabricated with different layer heights.  
The corresponding dynamic inherent characteristics are detailed in Table 6. It could be seen that the predicted natural frequencies and mode shapes agreed well with the measured data. The influencing trend of different layer heights was predicted precisely. The discrepancy of natural frequency was between 0.9% and 13.3%, the predicted mode shapes were the same as the measured ones. These confirmed the reliability of the theoretical modeling. Besides, different layer height caused the changes of elastic modulus, shear modulus and density of the samples, and eventually influenced the natural frequency. Since Sample L\({}_{0.2}\) had the largest values of material property (shown in Table 2), the corresponding natural frequency was the highest. The first three measured natural frequencies were 20.1 Hz, 167.1 Hz and 392.3 Hz, with the predicted values being 21.3 Hz, 189.3 Hz and 434.3 Hz. Since different extrusion widths did not change the structural characteristics of the samples, the mode shapes were kept the same.
## 3 Micro-Accelerator-in-the-Loop Framework
### MA Peripheral Mixed Emulation  
The MA defined in this paper is placed on-chip to act as a peripheral connected to the MCU's core bus. MA is used to accelerate TinyML software running on MCU. The on-chip peripheral is configured and triggered by software embedded in MCU flash memory. Similarly, the MA also accesses the necessary data for calculations by setting registers from instruction executions in the on-chip flash memory. However, it is laborious to integrate an MA designed in RTL into the already existing commercial MCU for evaluation. Currently, most of the cores, target of the TinyML application, protect internal RTL code as confidential for commercial purpose. Even if we implement a full MCU chip in RTL and attach MA peripheral, there will be overhead in full RTL simulation for reflecting interaction between software. A custom MA designed in RTL requires cycle-accurate simulation, while accessing or initializing the remaining peripherals is sufficient for high-abstraction-level functional emulation.  
The existing project emulates the software execution of a genera-l-purpose MCU, partially replicating memory access by instructions, generalpurpose I/O (GPIO), and some interrupts. We propose the MAIL framework by modifying the emulator to emulate the process of initializing the hardware in the MCU system emulator and accessing memory-mapped registers from the TinyML firmware to the custom MA peripheral. The MAIL emulates the MA peripheral attached to the core peripheral bus for Arm core-based MCU. MCU manufacturers using Arm cores provide memory-mapped register address regions and register bitfield information for all peripherals present in the MCU with the system view description (SVD) format. The MAIL adopted a QEMU structure that parses SVD and automatically generates a template for each peripheral that converts firmware-level register access to an emulator program. Therefore, we added the MA peripheral register information to the SVD file and enabled MA access with the TinyML firmware instruction in the emulator, as shown in Figure 2.
# A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems
## 4 Virtual World Architecture Inspection  
Existing virtual world system architectures can be classified in client/server (C/S) architectures, peer-to-peer (P2P) architectures, and hybrid architectures. Figure 8 illustrates their conceptual models. This section introduces the existing virtual world architectures and conducts a qualitative study on their design requirement satisfaction and limitations. Table 1 lists the representative solutions to the design issues. In this paper, only the issues related to emerging requirements are elaborated.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Requirement** & **Design Issue** & **C/S** & **Hybrid-I** & **P2P** & **Hybrid-II** \\ \hline \multirow{3}{*}{Sufficiency} & Energy & \multirow{3}{*}{Energy trade-off [67]} & \multirow{3}{*}{} & \multirow{3}{*}{} & \\  & Efficiency & & & & \\ \cline{2-5} \cline{5-6}  & QoS/QoE & & & & \\ \hline \multirow{2}{*}{Reliability} & Reliability & Database & Database & Database replication [88] & \multirow{2}{*}{} & Backup virtual \\  & & replication [88] & & & node [90] \\ \hline \multirow{4}{*}{Persistency} & State & \multirow{2}{*}{} & Centralized control [91] & \multirow{2}{*}{} & State Action \\  & Persistency & & & & Manager [90] \\ \cline{2-5} \cline{5-6}  & Content & & & & \\  & Persistency & & & & \\ \hline \multirow{4}{*}{Credibility} & Secure Access & Policy-based access control [93] & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\  & Regulation & Data compliance [95] & & & \\ \cline{2-5} \cline{5-6}  & Ownership & User representative [96] & & & \\ \cline{2-5} \cline{5-6}  & DRM & License Server [97] & DRMChain [98] & \\ \cline{2-5} \cline{5-6}  & Content & \multirow{2}{*}{Content} & \multirow{2}{*}{Content approval or social accountability [86]} & \\  & Moderation & & & & \\ \hline \multirow{2}{*}{Consistency} & Causality & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\  & Perceptive & & & & \\  & Consistency & & & & \\ \hline \multirow{4}{*}{Scalability} & World & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\  & Scalability & Cloud gaming [99] & Cloudlet [100] & Red-Black [101] & Cloud \\ \cline{1-1} \cline{2-5} \cline{5-6}  & Hotspot & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\  & Scalability & KIWANO [102] & Peer-assisted [91] & AOI-cast [103] & \multirow{2}{*}{} \\ \hline \multirow{4}{*}{Security} & Cheating & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\  & Prevention & & & \\ \cline{1-1} \cline{2-5} \cline{5-6}  & Cheating & Behavior & & & \\ \cline{1-1} \cline{2-5} \cline{5-6}  & Detection & monitoring [105] & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Representative solution of the design issues.
# Multi-Layer Problems and Solutions in VANETs: A Review
## 2 Multi-Layer Challenges in VANETs  
The Physical (PHY) and MAC Layer challenges presented in [1] explains in depth the Channel Estimation at PHY layer and behavior of Carrier Sense Multiple Access (CSMA) in high node density and potential solutions to the congestion control along with the Multi-Channel Operations in IEEE 1609.4. The performance using Single and Multiple Radios has been discussed along with some simulation results. Moreover, the paper also highlights the evolution of the DSRC from the PHY and MAC Layer perspective. [PERSON], et al. [2] give a very clear picture of the Multi-Channel Communications in VANETs. It gives an overview of the WAVE Stack, the Multi-Channel Operation; its issues and the countermeasures, some open issues which are not addressed yet, and like [1] uses both the Single and Multiple Radios approach to explaining the problem. The Self Organized Time Division Multiple Access (STDMA) has been used to solve the scalability problem in VANETs and has  
Figure 1: Dedicated Short Range Communication (DSRC) Architecture. RSU: Road Side Units; V2I: Vehicle to Infrastructure.  
shown promising results, in terms of scalability and reliability, compared with CSMA [3]. Popular research issue, analysis, and trends in VANETs are presented in [4]. This paper also gives a layered view of the various underlying challenges in VANETs. But the work has been limited to the Routing, Security, and Application Layers. Perhaps the most detailed performance analysis of V2V Communication has been performed in [5]. The author has performed detailed analysis along with simulation scenarios to prove, yet again, that STDMA outperforms CSMA in high density networks.  
In less dense VANETs, several performance issues are ignored as the currently available standards and protocols offer reliable performance; however, as the network size increases, many new challenges appear. The inefficiency of certain protocols becomes evident and several trade-offs exist. In this section we will see several factors, starting from the Physical Layer and moving up to the Network Layer in the communication protocol stack. The main focus will be on the WAVE Protocols, features of the protocols, and the limitations of the currently used standards in commercial vehicular networks. The security layer has been left out intentionally as the scope of this paper is mainly the layers involved in the communication of data.
# On the Performance of a UAV-Aided Wireless Network Based on NB-IoT
## 3 The Narrowband-IoT Technology
### Energy Consumption  
Clearly, the exchange of these signalling messages will affect the energy consumed by the nodes. Therefore, NB-IoT has to introduce appropriate techniques to power savings while keeping the synchronization with the cellular network. To this purpose, NB-IoT introduces predefined intervals for discontinuous reception (DRX), enhanced DRX (eDRX) and power saving mode (PSM) to reduce at minimum the energy consumption when its demand is fulfilled. In our case, considering all the different power saving techniques is not desirable. In fact, the UAB is a mobile BS, and therefore spends a limited time interval in which grants connectivity to a group of NB-IoT nodes. Therefore, if a node has a packet (or a queue of them) ready to be transmitted, we assume attempts the RA procedure as soon as it gets under the UAB coverage, to go in PSM immediately after.
## 6 Evaluation
### Evaluation Method  
* In addition to the four proposed methods, we experimented with a method in which all nodes have the relay function enabled (hereafter, \"all nodes\").
* For Eval. 1 and Eval. 2, we created a simulator in C++ to experiment on the relay node selection by each method. Eval. 3 and Eval. 4 use the OLSR-based method for route construction. Using the NS-3 network simulator [59] version 3.30, the OLSR simulation software was modified to add OLSR-based signal processing and an information repository, and simulation experiments were conducted.
* Simulation experiments were carried out using the node arrangement model based on a regional city as shown in Figure 9. A population-dense region is located in the center, surrounded by sparsely populated regions. A total of 30 nodes were placed in population-dense region and 20 nodes were placed in each of the 8 blocks in sparsely populated regions.  
* The simulation randomly places nodes in each block with the relay node densities shown in Table 1.  
Figure 9: Random node arrangement model.  
* The radio propagation range of each node is 1.5 km, and no nodes are added, deleted, or moved.
* Eval. 3 randomly selects source and destination nodes from sparsely populated regions. Eval. 4 randomly selects source and destination nodes from both population-dense regions and sparsely populated regions.
* We repeat the relay node selection and the route construction 100 times for each method and calculate the mean and standard deviation for each evaluation perspective.
## 2 Materials and Methods
### Determination of the Chemical Composition  
The chemical composition of the liquid hydrolysate, enzymatic hydrolysis solution, and the raw material (SSB) were determined using the Laboratory Analytical Procedure of NREL [29; 30]. Furthermore, additional acid hydrolysis using 4% sulfuric acid solution was conducted to convert all kinds of carbohydrates in the liquid hydrolysate to monomeric sugars. The amount of monomeric sugars was quantified by high-performance liquid chromatography (HPLC) (Agilent 1200 Infinity, Agilent Technologies Korea Inc., Seoul, Korea). HPLC was equipped with an HPX-87H (300 \(\times\) 7.8 mm) column (Bio-Rad, Hercules, CA, USA) and a refractive index detector (RID-6A, Bio-Rad, Hercules, CA, USA). Twenty microliters of sample was injected for the HPLC analysis, and the oven temperature was set at 50 \({}^{\circ}\)C. The mobile phase was 5 mM sulfuric acid solution and eluted at a 0.6 mL/min flow rate. For quantification, the calibration curves were made using standard materials (glucose, xylose, and arabinose), which were purchased from Sigma-Aldrich Korea Co. (Yongin, Korea). The nitrogen content of SSB was determined with an elemental analyzer (Flash EA 2000, Thermo Electron Corporation, Waltham, MA, USA), and crude protein content was calculated using nitrogen content and conversion factor (6.25) [31].  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Composition 1** & **Sweet Sorghum Bagasse (SSB)** \\ \hline Lignin & 17.2 \\ Acid-insoluble & 15.1 \(\pm\) 0.1 \\ Acid-soluble & 2.1 \(\pm\) 0.1 \\ \hline Ash & 1.1 \(\pm\) 0.0 \\ \hline Crude protein & 6.5 \(\pm\) 0.0 \\ \hline Total & 101.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._The XOS to total sugar ratio was calculated by following formula:  
\[\text{XOS to total sugar ratio }(\%)\ =\ \frac{\text{XOS in the liquid hydrolysate a fter autohydrolysis }(g)}{\text{Total sugars in the liquid hydrolysate a fter autohydrolysis }(g)}\ \times 100 \tag{2}\]