esa-sceva/satcom-chunk-collection huggingface dataset:
# A Linear Strong Constraint Joint Solution Method Based on Angle Information Enhancement
## 2 5G Reference Signals
### The SSB  
The 5G signal synchronization block (SSB) is used for functions such as cell search, time-frequency synchronization, and beam capture. It consists of a primary synchronization signal (PSS), a secondary synchronization signal (SSS), and a physical broadcast channel (PBCH). The SSB occupies 4 OFDM symbols in the time domain and 240 subcarriers in the frequency domain, corresponding to the numbers \(\{0,1,\ldots,239\}\). The PSS is located on the 127 subcarriers in the middle of the 0 th symbol of the SSB, while the SSS is located on the 127 subcarriers in the middle of the 2 nd symbol of the SSB, with corresponding subcarrier indices ranging from 56 to 182. The PBCH occupies all 240 subcarriers in the first and third symbols, as well as most subcarriers in the second symbol except for the SSS. The time-frequency resource allocation is shown in the Figure 1.  
3 GPP selects ZC sequence as the synchronization sequence in LTE, which can achieve good synchronization performance in the 2 GHz frequency band. In 5G NR, as the carrier frequency band increases, larger frequency offsets have a more serious impact on ZC sequences, resulting in a decrease in the peak value of correlation peaks and an increase in false detection rate. To improve the autocorrelation performance and frequency offset resistance of the synchronization sequence, 5G chooses m-sequence as the synchronization sequence to achieve better detection performance when there is a large frequency offset in the initial access.  
The PSS and the SSS are orthogonal m-sequences with a length of 127. For all signals transmitted by 5G base stations, there are 3 known possible SSS numbers, corresponding to \(N_{ID}^{(2)}=(0,1,2)\), and 336 known possible SSS numbers, corresponding to \(N_{ID}^{(1)}=\{0,1,\ldots 335\}\). Obtain the information of \(N_{ID}^{(1)}\) and \(N_{ID}^{(1)}\) and the frame header position of the received signal by detecting the PSS and the SSS respectively.  
Figure 1: SSB resource allocation diagram.  
The demodulation reference signal (DMRS) is located in the time domain of the first and third symbols of the SSB, and in the frequency domain of the \(\left(4\text{g}+\text{v}\right)^{th}\) subcarrier, g = {0, 1,..., 59}. Each symbol is configured with a total of 60 DMRSs, with adjacent DMRSs separated by 4 subcarriers, where v is the starting position of the DMRS, v\(\in\){0, 1, 2, 3}. Determining the uniform arrangement of DMRS in the frequency domain based on the cell number and transmitting antenna sequence number can better reflect the state changes in the signal during transmission.
## 2 Overview of Blockchain Technology
### Blockchain Architecture
#### 2.1.4 Blockchain Cryptography
Fault attacks, a type of side-channel attack, can be launched by an attacker to induce a fault and analyze its effects to obtain sensitive information from the encrypted data. Thus, robust error detection mechanisms must be deployed for the cryptographic techniques that are deployed in blockchains to recover from fault attacks. WAGE is a stream cipher derived from the Welch-Gong cipher, and error detection using signatures for non-linear sub-blocks of it has been effective in a FPGA-based hardware implementation with good error coverage [91]. Furthermore, an error detection framework for the Camelia block cipher considering non-linear and linear sub-blocks, using different S-box variants, where the reliability can be fine-tuned based on requirements having high error coverage has been studied in [92]. Similarly, for the symmetric key Midori cipher, a fault diagnosis scheme for the non-linear S-box layer is presented, and simulations with injected faults show that the proposed framework is reliable [93]. Likewise, a hardware-based, lightweight signature-based error detection for block cipher QARMA for 64- and 128-bit versions that can counter both permanent and transient faults has been benchmarked in an FPGA device satisfying reliability requirements with acceptable overhead [94]. Differential Power Analysis (DPA) is a passive attack where an attacker analyzes the power traces to get an inference about the internal operation of the cryptographic algorithm. DPA can be combined with Differential Fault Analysis (DFA) by an attacker to launch a more powerful attack, so there should be robust countermeasures against such attacks. Error detection is deployed against fault analysis, while masking techniques can be deployed to counter power analysis attacks in order to protect ciphers from side channel attacks [95].
## 4 Simulation Results
### Experimental Results
#### 4.2.3 Throughput  
A high throughput is essential for an efficient system. Figure 10 shows the overall throughput in terms of number of messages received at the BS from CHs, which is significantly greater in EDACP-CS against LEACH-Hetero and EECDA protocols. The reason is that EDACP-CS has three types of nodes (normal, advanced, and super) and takes into consideration the distance from the BS, because nodes that are closer to the BS and has higher residual energy than the other nodes have more chances to be selected as CHs for current round. Therefore, the super nodes become CHs more than both the advanced and normal nodes. The advanced nodes take up the role of CH more frequently than the normal nodes. Moreover, using CS would optimize energy usage to reduce storage space and energy consumption. Consequently, EDACP-CS has better network monitoring quality.
# A Novel OFDM-Based Time Domain Quadrature GSM for Visible Light Communication System
## 1 Introduction
Moreover, the ML-MRC detection can also be deployed in OFDM-based generalized MIMO systems, which have less complexity than the traditional ML algorithm [15; 19]. Nevertheless, the detection of the transmitter sequence number will seriously affect the symbol correctness in the following demodulation steps, leading to an unexpected BER performance reduction. Hence, developing an efficient detection algorithm with low complexity and satisfactory BER performance remains an urgent issue. In [20], a sparse signal reconstruction detection method combining greedy algorithm and ML was proposed for generalized space shift keying (GSSK) in a VLC system with low complexity and good BER performance. However, it cannot be directly used in VLC-GSM-based multicarrier transmission schemes. In this paper, two novel OFDM-based quadrature generalized MIMO (QG-MIMO) transmission schemes, in terms of TD quadrature GSM (TD-QGSM) and TD quadrature GSM (TD-QGSMP), are proposed in a VLC system, which can significantly improve the SE performance while achieving an excellent BER performance compared to the existing OFDM-based generalized MIMO schemes. In addition, by utilizing the spatial sparsity, the compressed sensing (CS) theory is involved and an illegal vector correction (IVC)-based orthogonal matching pursuit (OMP) detection algorithm is proposed to reconstruct the sparse received signal from its compressed observations with high probability by employing a correction criterion to adjust the estimated index vector when it fell into the illegal detection region. The simulation results show that the proposed QG-MIMO schemes can provide excellent SE and BER performance, which becomes more apparent with high modulation order. Moreover, the BER performance can be further improvedby the proposed IVC-based OMP detection algorithm as compared with the traditional detection methods. The remainder of the paper is organized as follows. Section 2 presents the system model of a general indoor VLC-MIMO system. In Section 3, two novel OFDM-based QG-MIMO schemes are proposed, and the analytical SE is also calculated. In Section 4, the CS-based detection algorithm is proposed to improve the demodulation performance and the computational complexity is also analyzed. In Section 5, the simulation results and discussions are demonstrated. In Section 6, the conclusion of this paper is provided. _Notation:_ Let \((\cdot)^{T}\) denote the transpose of a vector or matrix and \(\lfloor\cdot\rfloor\) represent the floor operator, respectively. \(\mathcal{C}(\mathcal{W},\mathcal{Z})\) is used to denote the binomial coefficient as \(\mathcal{Z}\) out of \(\mathcal{W}\) is chosen. In addition, \((\cdot)^{-1}\) and \(\left\lVert\cdot\right\rVert_{2}\) are used to denote the inverse of a matrix and the \(L_{2}\)-norm operation, respectively.
# Stimuli-Responsive Polymeric Nanosystems for Controlled Drug Delivery
## 2 Typical Polymers for Controlled Drug Delivery
### Natural Polymers  
The other class of polymers that have been applied in drug delivery are natural occurring ones such as albumin, collagen, hyaluronic acid (HA), chitosan, dextran, and cyclodext due to their good biocompatibility and abundance in nature [86]. Compared with synthetic polymers, the natural ones are more easily degraded in the physiological conditions and more biocompatible, while their programmability are limited. Among these natural polymers, the HA and chitosan are the two most widely used materials in drug delivery applications. Specifically, chitosan is derived from chitin that is naturally found in crustacean exoskeleton [87]. Chitosan has been widely employed to develop nanosystems for oral drug delivery [88]. It is broken down by lysozyme and chitosan polymers with low degrees of acetylation and can remain in vivo for several months. It has been used for DNA delivery and pulmonary drug delivery due to its positive charge and uncoadhesive properties [89; 90]. Additionally, the ease of the side group modification and integration with many other polymeric materials making chitosan a versatile building block for development of a variety of polymeric nanocarriers for controlled drug release [91; 92; 93]. HA is the other extensively studied natural polysaccharide polymer that is composed of \(N\)-acetyl-D-glucosamine disaccharide and dglucuron acid [94]. HA has been widely utilized in biomedical applications including tissue engineering and drug delivery due to its favorable properties such as good biocompatibility and low immunogenicity [95; 96; 97]. The conjugation of HA with other hydrophobic therapeutics can improve the drug solubility and blood circulation time. Owing to its highly hydrophilicity and capability to expand the volume (up to 1000-fold) after absorbing water, HA has been widely used to develop hydrogel-based drug delivery systems [98; 99].  
To make it clearer, the above-mentioned polymers, their applications, advantages, and drawbacks are summarized in the Table 1 bellow.
## 3 Results
### Image-Based Features  
Overall, CTRCD+ patients showed lower \(SUV_{max}\) values compared with CTRCD\(-\) patients (Figure 1a), both in the baseline, intermediate, and EOT imaging studies, with statistically significant differences in the \(SUV_{max}\) values between groups only present in the intermediate PET (CTRCD+: 8.28 \(\pm\) 0.40 vs. CTRCD\(-\): 15.26 \(\pm\) 0.40, \(p\) = 0.009).  
Similarly, CTRCD+ patients showed lower overall \(SUV_{mean}\) values compared with CTRCD\(-\) patients (Figure 1b), with statistically significant differences in the \(SUV_{mean}\) values between groups only observed in the intermediate PET study (CTRCD+, 4.30 \(\pm\) 0.32 vs. CTRCD\(-\), 7.10 \(\pm\) 0.39, \(p\) = 0.025).  
Radiomic features were also obtained to assess the differences between CTRCD groups (Supplementary Table S10). A total of 34 radiomic features showed statistically significant differences between the CTRCD+ and CTRCD\(-\) groups in the intermediate image studies: 14 first-order features (including \(SUV_{max}\) and \(SUV_{mean}\)), 6 GLCM features, 4 GDLM features, 8 GLRLM features, and 2 GLSZM features. No statistically significant differences between CTRCD groups were observed in the radiomic features in baseline and EOT image studies.  
The \(H_{Rad}\) index distribution is shown in Figure 2. In the CTRCD\(-\) group, an overall increased heterogeneity of the metabolic uptake in the intermediate PET image was observed compared to the CTRCD+ group. Statistically significant differences in the \(H_{Rad}\) index values were found in the intermediate PET between groups (CTRCD+: 7.15 \(\pm\) 0.45 vs. CTRCD\(-\): 13.18 \(\pm\) 0.40, \(p\) = 0.025).  
Figure 1: Comparison of value distribution and change over time (baseline, intermediate, and EOT) in the mean [18]FDG PET/CT scan SUV values between the CTRCD+ and CTRCD\(-\) groups. The vertical axis is expressed in log scale to evidence the differences between groups. Statistically significant differences between CTRCD groups after multiple-testing correction are indicated with their respective \(p\)-values. (**a**) \(SUV_{max}\). (**b**) \(SUV_{mean}\). SUV: standardized uptake value; EOT: end-of-treatment.
# Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
## 3 Applications
### Research Potential for Applications
#### 3.3.3 Smart Intersection Management Systems  
According to many studies [118; 119], intersections are accountable for a large number of dangerous accidents in addition to high traffic delays in urban cities. As a result, intersections have a serious impact on the efficiency of traffic management systems [120]. Therefore, CV data such as real-time heading, speed, and position are considered significant elements in assisting to optimize the traffic management systems. This will pertain to safety and operational cases, where controlling traffic signals is a necessity when different traffic streams coexist. This could be achieved with priority rules, which include stop signs, roundabouts, and right-of-way rules along with different traffic-signal approaches, such as fixed, actuated, and adaptive approaches. For instance, studies have addressed adaptive traffic control algorithms, where predictive models are integrated with the adaptive traffic control systems to have an estimate on the number of arriving vehicles and the queue length at the intersections. Even though these control strategies are able to accommodate variations in traffic demand, there exist some limitations regarding the maximization and minimization of the green-time periods. This allows for more potential efforts in enhancing the traffic operation efficiency at intersections to accommodate the variations in traffic demands effectively, and hence decreasing traffic delays [121].  
Even with the existence of many studies in both signalized and non-signalized intersections [122; 123; 124; 125], intersection traffic management remains a challenging research area in intelligent transportation systems.
# LaRaWAN Metering Infrastructure Planning in Smart Cities
## 3 Materials and Methods
### Radio Transmission Range Modeling
The model combines empirical and deterministic approaches to estimate path losses in urban areas in the frequency range from 800 MHz to 2000 MHz [34] and incorporates the results of experimental measurements carried out in many cities throughout Europe. For both the reasons mentioned above, as well as its popularity in the literature, it seems reasonable to apply it to studies using LoRaWAN, although the literature related to this radio technology also indicates the validity of the variations of the [PERSON] [35] or Okumura model [36]. The model is expressed by the following formula [34]:  
\[P_{r}=(46.3+33.9 log_{10}(f_{c}))-13.82 log_{10}(h_{b})-C_{h}+\\ +(44.9-6.55 log_{10}(h_{b}))log_{10}(d)+C, \tag{2}\]  
where:  
\[C_{h}=h_{m}(1.1 log_{10}(f_{c})-0.7)-(1.56 log_
## 1 Introduction
### Contributions  
The major contributions of this paper are summarized as follows:  
1. We present a novel feature learning method for plant leaf representation, which can exploit pretrained neural network features without time-consuming end-to-end retraining.
2. We recalibrate each leaf feature map by using spatial weighting and channel weighting, which is able to capture salient information and squash non-salient information.
3. We propose to leverage the feature maps of multiple pretrained CNNs and multi-layers; this strategy not only can combine the features from different networks, but also can explore the complementary elements between low-level and high-level features from different layers.
4. Extensive plant leaf recognition and retrieval experiments are conducted on eight popular and complicated datasets; the mean accuracies and mean average precisions can corroborate the effectiveness and feature discrimination of our method.  
The remainder of this paper is arranged as follows. Four kinds of related works are presented in Section 2. The three procedures of our proposed method are detailed in Section 3. Plant recognition experiments on eight datasets are provided in Section 4, as well as plant leaf retrieval experiments. Finally, the conclusions are presented in Section 5.  
Figure 1: Our learned feature distribution of the 9 species belonging to the same genus Acer from MEW2012 dataset. The t-SNE technology is applied for feature distribution display. Images of the same class are marked with the same color and marker. A total of 600 images are used.
# Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels
## 2 Actions for Collision Avoidance
### Methods of Collision Avoidance
#### 2.2.2 Reduction of Speed  
When altering course cannot effectively avoid a collision, such as when there is insufficient sea-room, vessels must decrease their speeds to avoid collision or allow more time to assess the situation.  
As illustrated in Figure 3a,b, when a ship sails at the same period, but in (b) the ship creates ample space by reducing its speed to successfully avoid collision.
## 4 Experimental Results
### Performance of SOC Encoding  
After switching the index value between clusters during embedding, the marked index table is sent to SOC encoding. Since the switching for embedding intends to disrupt the continuity between adjacent blocks for reversibility, such operation reduces the applicability of SOC encoding. In our experiment, the assigned length of search order code is four bits. Based on this parameter setting, the number of SOC compressible and uncompressible blocks for the test images are listed in Table 3. The compressible percentage ranges from 25.5% for Baboon to 88.3% for Bridge and is highly dependent on the feature of cover image. An index in a smooth image is more likely to find the same value in its neighbors and be compressed by SOC. On the other hand, an index in a complex image is frequently uncompressible. The total file size in bytes and image bit rate in bits per pixel are also listed in the table. The resulting file size is larger for an image with more uncompressible blocks. The embedding capacity in bits for different layers of the proposed scheme are listed in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Images** & **PSNR (dB)** & **Images** & **PSNR (dB)** & **Images** & **PSNR (dB)** \\ \hline Tank & 31.75 & [PERSON] & 30.66 & [PERSON] & 29.89 \\ [PERSON] & 31.86 & [PERSON] & 30.49 & [PERSON] & 26.45 \\ [PERSON] & 30.69 & [PERSON] & 30.16 & [PERSON] & 25.59 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PSNR values of the VQ images produced with our codebook.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Images** & **Abnormal Blocks** & **Images** & **Abnormal Blocks** & **Images** & **Abnormal Blocks** \\ \hline Tank & 7 & [PERSON] & 147 & [PERSON] & 91 \\ [PERSON] & 1 & [PERSON] & 128 & [PERSON] & 180 \\ [PERSON] & 85 & [PERSON] & 142 & [PERSON] & 373 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of abnormal blocks detected by side-match evaluation.
# Backscatter Assisted NOMA-PLNC Based Wireless Networks
## 2 System Model
### OMAC Phase  
In first time slot, near node pair send the data to the AP. The time domain received signal \(z_{r,1}(t)\) over OMAC link at the AP (i.e., relay) is expressed as  
\[z_{r,1}(t)\,=\,\sqrt{P_{t}}x_{1}(t)h_{1}(t)\,+\,\sqrt{P_{t}}x_{2}(t)h_{2}(t)+n_{ 1}(t), \tag{1}\]  
where \(P_{t}\) is the transmit power of the node \(n_{1},n_{2},n_{3}\) and \(n_{4}\), \(x_{1}(t)\) and \(x_{2}(t)\) are the binary phase shift keying (BPSK) modulated signals send by the node 1 and node 2, respectively. The average values of the transmit signals are \(\mathbb{E}(|x_{1}(t)|^{2})=\mathbb{E}(|x_{2}(t)|^{2})=1\) and \(h_{1}(t)\), \(h_{2}(t)\) are the channel coefficients between node 1 to AP and node 2 to AP whose magnitudes follows the exponential distribution with mean zero and variances \(\eta_{1}\) and \(\eta_{2}\) respectively [18], \(n_{1}\) is the independent identically distributed \((iid)\) complex additive white Gaussian noise (AWGN) with mean \(\mu\) and the variance \(\sigma^{2}\), denoted by \(n_{1}(t)\sim\mathcal{CN}(\mu,\,\sigma^{2})\). Similarly, the far node pair send the data to the AP in the second time slot. The time domain received signal \(z_{r,2}(t)\) from the far node pair is expressed as  
\[z_{r,2}(t)\,=\,\sqrt{P_{t}}x_{3}(t)h_{3}(t)+\sqrt{P_{t}}x_{4}(t)h_{4}(t)+n_{2}( t), \tag{2}\]  
where \(x_{3}(t)\) and \(x_{4}(t)\) are the BPSK signals transmitted from the node 3 and node 4 of the far node pair, \(h_{3}(t)\), \(h_{4}(t)\) are the channel coefficients between node 3 to AP and node 4 to  
Figure 1: A two pairs NOMA-PLNC-based wireless network with ambient backscattering.  
Figure 2: (**a**) Time slot management (**b**) Relay signal detection (**c**) SIC operation at far node.  
AP whose magnitudes follows the exponential distribution with mean zero and variances \(\eta_{3}\) and \(\eta_{4}\) respectively, \(n_{2}(t)\) is the \(iid\) Gaussian noise.