esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems
###### Abstract  
Pilot contamination is the reuse of pilot signals, which is a bottleneck in massive multi-input multi-output (MIMO) systems as it varies directly with the numerous antennas, which are utilized by massive MIMO. This adversely impacts the channel state information (CSI) due to too large pilot overhead outdated feedback CSI. To solve this problem, a compressed sensing scheme is used. The existing algorithms based on compressed sensing require that the channel sparsity should be known, which in the real channel environment is not the case. To deal with the unknown channel sparsity of the massive MIMO channel, this paper proposes a structured sparse adaptive coding sampling matching pursuit (SSA-CoSaMP) algorithm that utilizes the space-time common sparsity specific to massive MIMO channels and improves the CoSaMP algorithm from the perspective of dynamic sparsity adaptive and structural sparsity aspects. It has a unique feature of threshold-based iteration control, which in turn depends on the SNR level. This approach enables us to determine the sparsity in an indirect manner. The proposed algorithm not only optimizes the channel estimation performance but also reduces the pilot overhead, which saves the spectrum and energy resources. Simulation results show that the proposed algorithm has improved channel performance compared with the existing algorithm, in both low SNR and low pilot overhead.  
**Keywords:** massive MIMO; complexity; signal detection; feedback overhead; pilot signal +
Footnote †: journal:  
_Article_
# Influence of Girder Flaring on Load Effect in Girders of Composite Steel Bridges
## 6 Conclusions and Recommendations
### Conclusions About Shear Load Effect  
* The tributary width concept is a good approach for finding the shear dead load effect in splayed interior and exterior girders. As in the case of flexure, superimposed dead load distribution among the girders is not shared equally, as permitted by AASHTO, since exterior girders collect higher loads from the parapet weight than interior girders.
* Increasing the splayedness ratio causes a negligible effect on the GDF for shear in both interior and exterior girders. On the other hand, the increase in the smaller-to-larger girder spacing ratio, while keeping the splayedness ratio constant, results in a significant increase in the GDF for shear. As in the case of flexure, both the splayedness angle and the girder spacing ratios cannot be used as standalone indices in a splayed girder bridge without specifying the magnitude of the (smaller or larger) girder spacing.
* The effect of slab thickness on the shear GDF for splayed girder bridges is moderate in the interior girders and negligible in the exterior girders. Girder stiffness and the number of girders (if above five) have little influence on the shear GDF for both interior and exterior girders.
* Adding cross-bracing to splayed girder bridges reduces the GDF for shear in the interior girders, particularly if the cross-bracing spacing is small. The effect of usingcross-bracing in splayed girder bridges on the exterior girders shear GDF is not significant since the bracing impacts these girders from one side only.
* The use of the lever rule to predict shear in interior or exterior splayed girders yields reliable results compared to the finite element outcome. Girder distribution values from the lever rule yielded results that deviated from the finite element findings by \(-4.9\%\) to \(9.5\%\) for the critical interior girder and by \(-0.8\%\) to \(9.4\%\) for the exterior girder. On the other hand, the AASHTO GDF for shear could not reasonably predict the shear live load effect in the girders, as the outcome from this approach yielded overly conservative values.
# Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Reschargeable Sensor Networks
## 5 Energy-Aware Traffic Scheduling Algorithm
In Algorithm 3, we choose one optimal path from the optional paths according to the following sequences: (1) We preferentially choose the path with the minimum \(count\), which obviously allows more nodes to be turned into sleeping state. (2) If there are several paths having the same minimum \(count\), we arrange these paths in descending order according to \(\overline{r}\) and \(restsize\) and choose the first path with a positive \(restsize\). When some optional paths have the same \(count\), the optional path with the maximum \(\overline{r}\) may have a negative \(restsize\), which means that some parts of the path cannot transmit more data. At the same time, the \(\overline{r}\) of this optional path is high and then the amount of flows that are influenced is relevantly large. Consequently, there are more flows that should be allocated on the paths with a higher \(count\), and this is not good to save energy. Choosing the first path with a positive \(restsize\) can not only guarantee the path has a \(\overline{r}\) as large as possible, which means that the path may have a higher possibility to accommodate more flows, but also make sure the nodes on the path could be used in the following allocation. (3) If all optional paths have a negative _restsize_, we choose a path with the minimum \(\overline{r}\) so as to affect less paths. According to the rules above, we select an optimal path and allocate the corresponding flow. The nodes in the selected path should be on working state. The previously selected paths will influence the selection in the later. After allocating a flow on a selected path, the \(count\), \(\overline{r}\) and _restsize_ for other optional paths should be recalculated, until each flow is allocated with a proper path. ```
0: The set of optional paths \(P_{f}\) of all flows \(F\)
0: A selected optional path \(p\) and its corresponding flow \(f\)
1:for each \(p\in P\)do
2: Calculate the \(count\), _restsize_, and \(cap\) of \(p\);
3:endfor
4: Arrange \(P\) in ascending order according to \(count\);
5: Initialize \(i\gets 0\), a temporary set of optional paths \(P_{t}\leftarrow\emptyset\);
6:for\(i=0\); \(i\leq|P|\); \(i++\)do
7:if\(count[i]\)==count[0] then
8:\(P_{t}\gets P_{t}+P[i]\);
9:endif
10:endfor
11: Arrange \(P_{t}\) in descending order according to \(\overline{r}\) and _restsize_;
12:for\(i=0\); \(i\leq|P_{t}|\); \(i++\)do
13:if\(restsize[i]>0\)then
14: break;
15:endif
16:endfor
17:return\(p\gets P[i]\) and the corresponding flow \(f\). ```  
**Algorithm 3** Optional Path Selection Function
## 2 Materials and Methods
### Statistical Analysis  
In order to identify the dynamics of the results and the differences between the initial and final tests, we used the statistical method using SPSS-IMB 24 software. In the present study, in which we followed the impact of the implementation of an innovative technological device on the improvement process and the effectiveness of free throws, we considered the following statistical-mathematical parameters: arithmetic mean of the scores (X); standard deviation (SD); variation, median, Skewness parameter, difference of scores between tests (\(\Delta\)X); Student's \(t\)-test (\(t\)); confidence interval (95% CI) including two levels of appreciation, low (lower) and high (upper); effectiveness (%) which was measured in percentages represents the ratio of the number of successes to the total number of free throws made; progress (%) which was the percentage difference between the efficiencies recorded in the final test compared to the initial test; and p, the probability level. The mean difference between the three independent age groups was analyzed using analysis of variance (ANOVA) with the mean square, F-test (F), and degrees of freedom (df). For the research, we chose a statistical probability level of \(p<0.05\). 6.25%; and between the initial and the final test in the U14 category, the group improved its results by 0.562 points, representing a progress of 4.68% (Table 1). In the FRB test, the effectiveness that was recorded by the male experimental teams in all age categories were higher in the final tests compared to the initial tests. The highest progress was recorded by the male experimental group in the U18 category and the lowest by the U14, which was determined by the level of athletic training, athletic experience, and the peculiarities of age and physical development. For the male control groups, in this standardized FRB free throw evaluation test, the results in the final tests were better than in the initial tests. Thus, between the initial and final tests in the U18 category, the group improved its results by 0.642 points, representing a 5.35% improvement; between the initial and the final test in the U16 category, the group improved its results by 0.312 points, representing an improvement of 2.60%; and between the initial and the final test in the U14 category, the group improved its results by 0.125 points, representing an improvement of 1.04%. The differences in the arithmetic means between the final and the initial tests recorded by the male control group in the FRB Test were only partially significant for the U18 sample, otherwise in the other two groups U16 and U14 the athletes' performance in making free throws did not improve significantly. The variance values ranged between 0.264 and 0.841, the spread of the results being normal. The median of the male control groups ranged between 4 and 5.50 points. The values of the statistical parameter Skewness ranged from \(-\)0.542 to 1.174, reflecting a relatively normal distribution of the data (Table 1).
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 5 Result Analysis and Performance Evaluation
### Under Jellyfish Reordering Attacks
#### 5.3.3 Throughput  
A comparison of performance based on different numbers of reordering attack nodes is demonstrated in Figure 18. The throughput in Jellyfish reordering attacks drops as the number of malicious nodes increases. The outcomes indicate that SecE-V2X has a greater throughput value than that of other algorithms for different numbers of malicious nodes. Our protocol outperforms [37], 2017 (453.76 packets/s), and [20], 2018 (559.42 packets/s), for three malicious nodes by 612.82 packets per second. When there are  
Figure 16: Packet delivery ratio under Jellyfish reordering attacks [20; 37].  
Figure 17: End-to-end delay under Jellyfish reordering attacks [20; 37].  
nine attacker nodes, ref. [37], 2017's throughput severely drops to 290.37 packets/s. In contrast, ref. [20], 2018's throughput was 415.635 packets/s, and our protocol maintains the highest throughput of 441.145 packets/s. According to the results, SecE-V2X outperforms the competing approaches in terms of throughput, confirming that our proposed protocol is indeed effective against Jellyfish reordering attacks.
# An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features
## 5 Experimental Evaluation
### Example 1: Classification Performance  
The average classification performance of each feature selection approach is recorded in Table 2 and the pairwise \(t\)-tests at 5% significance level were conducted to validate the statistical significance. In addition to the traditional precision and AUC metrics, hamming loss penalizes incorrect the recognitions of instances to each target label, ranking loss penalizes the misordered labels in pairs, and one-error penalizes the instances whose top-ranked predicted labels are not in the ground-truth label set. Five metrics evaluated the multi-label classification performance from different aspects. A single metric is insufficient to illustrate the general classification performance on a dataset. For example, the overall performance of ML-KNN classifier [43] on birds is worse than that on enron under the precision metric, while it shows a better performance on birds than on enron under the AUC metric. Therefore, we extensively used five metrics to compare the performances of the compared approaches. As shown in Table 2, ELC outperforms MIFS, CMFS, and LLSF under various metrics. This superiority is attributed to two reasons. That is, ELC can effectively eliminate noisy features from the candidate feature subsets and maximally embed label correlation information into its selection process. The first term rules out the selection disturbance in the feature space, and the second term promises the proper guiding information extracted from the label space. By seamlessly fusing these two terms, ELC is able to find discriminative features for the downstream learning tasks. This point will be further validated in Sections 5.2 and 5.3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Set** & \(\sharp\)**Features** & \(\sharp\)**Instances** & \(\sharp\)**Labels** & **Domain** \\ \hline emotions & 72 & 539 & 6 & music \\ yeast & 103 & 2417 & 14 & biology \\ birds & 260 & 645 & 19 & audio \\ enron & 1001 & 1702 & 53 & text \\ genbase & 1186 & 662 & 27 & biology \\ business & 21,924 & 11,214 & 30 & text \\ arts & 23146 & 7484 & 26 & text \\ education & 27,534 & 12,030 & 33 & text \\ reaction & 30,324 & 12,828 & 22 & text \\ health & 30,605 & 9205 & 32 & text \\ computers & 34,096 & 12,444 & 33 & text \\ science & 37,187 & 6428 & 40 & text \\ reference & 39,679 & 8027 & 33 & text \\ society & 49,060 & 14,512 & 22 & text \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Benchmarks for multi-label feature selection. \begin{table}
# Automatic Configurable Hardware Code Generation for Software-Defined Radios+
## 9 Related Work
### LabView  
LabView [32] offers a graphical programming approach that helps to easily develop and visualize the multi-domain applications such as data acquisition, instrument control, industrial automation, SDR, etc. IP integration requires the IP synthesis files, such as vhd files, Xilinx IP configuration files, or netlist files to meet the requirements for predefined rules for integrating the hardware models. LabView supports FPGA code generation from SDF models and other SDF-extended models which include Homogeneous Synchronous Dataflow (HSDF) [12], Cyclo-Static Dataflow (CSDF) [33], Parameterized Static Dataflow (PSDF) [34] and Parameterized Cyclo-Static Dataflow (PCSDF) [35]. Furthermore, LabView supports FPGA design using an SDF model with access patterns (SDF-AP). The LabView hardware implementation prototypes using the SDF-AP model are discussed in [10; 13; 14; 36] where the experimental results indicate that SDF-AP model can reduce the buffer size requirements by about 63%. However, generating the FPGA code from these supported dataflow models relies on _correct-by-design_ approach which does not provide conformance analysis of the hardware design. Furthermore, using LabView requires a licensing (i.e., uses closed source-code that is not shared with the public), and the FPGA design caters well for National Instruments and compatible systems. As a result, these limitations make it very difficult for experimentation by the open-source community.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Features** & \begin{tabular}{c} **Matlab +** \\ **Simulink** \\ \end{tabular} & \begin{tabular}{c} **Vivado** \\ **HLS** \\ \end{tabular} & \
\begin{tabular}{c} **LabView** \\ **DSL** [28] \\ \end{tabular} &
\begin{tabular}{c} **Frame-Based** \\ **Framework** \\ \end{tabular} \\ \hline Domain-Specific to SDR & ✗ & ✗ & ✓ & ✓ \\ Uses a dataflow-model of computation & ✗ & ✗ & ✓ & ✓ \\ Dataflow supports access patterns & ✗ & ✗ & ✓ & ✗ & ✓ \\ Low-level hardware conformance to high-level design constraints & ✗ & ✗ & ✗ & ✗ & ✓ \\ Supports design re-use & ✓ & ✓ & ✓ & ✗ & ✓ \\ Automates optimizations & ✓ & ✓ & ✗ & ✓ \\ Open-source and available to public & ✗ & ✗ & ✗ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of features for different SDR prototyping tools.
## 3 Methodology
### Static Feature Samples Construction  
MFELCM integrates dynamic and static features of AIS data for ship classification. To realize MFELCM, the static feature dataset \(SF\) and the dynamic feature datasets, i.e., \(DFD\) (dynamic feature distribution dataset), \(TS\) (time-series dataset), and \(TSF\) (time-series feature dataset) are as follows.  
For the static data \(s^{in}\), five features, i.e., ship length, ship width, aspect ratio (ldivw), area, and  
As ship static information can be entered artificially, we should remove the unreasonable data before training classifiers. The distribution of static data is variable between the different types of ships, which makes it difficult to identify unreasonable data by a uniform standard for all categories. To filter the outliers, we first calculate the upper quartile (\(Q_{u}\)), the lower quartile (\(Q_{l}\)), and the interquartile (\(IQR\)) of a certain type of ship (e.g., passenger ships). If a feature in \(s^{in}\) (e.g., \(s^{in}\) belongs to a passenger ship) is outside \([Q_{l}-3 IQR,\ Q_{u}+3 IQR]\), then \(s^{i=}\) is recognized as an outlier and should be removed. We use this approach because it has no mandatory requirements on data distribution and is robust to outlier identification. A violin plot is the combination of box plot and KDE (kernel density estimation), and it can show the distribution of the variables. To illustrate the changes in the static features before and after removing outliers, we use a violin plot to visualize the static data, as shown in Figure 6. It should be noted that the data in Figure 6 is the static data after being standardized in accordance with the whole data set. Figure 6a,b are the distribution of original static data and the data having removed outliers of passenger ships, respectively. Figure 6c,d are the distribution of original static data and the data having removed outliers of four types of ships. Take Figure 6a as an example: the green part inside the red rectangle reflects part of the probability density function (PDF) of feature A, which takes the maximum value near the value where standardized A takes \(-2\). The black part inside the blue rectangle represents the potential outliers judged by feature ldivw; the more the data is biased to both ends of the feature (i.e., ldivd) value, the more likely it is to be an outlier. By comparing Figure 6a-d, some obvious outliers are removed effectively.  
So far, we have obtained the static feature sample \(s^{in}\). Let the set of all static feature samples be the static dataset \(SF\), which is defined as Equation (6)  
\[SF=\left\{s^{i_{1}},s^{i_{2}},\ldots,s^{in}\right\} \tag{6}\]
## 3 Results and Discussion
### Evaluation of Button Sensor Antenna: Simulated Results  
Several measurements and simulations were conducted to examine the performance of the wearable button antenna. These included examinations for robustness, durability, bending, and crumpling impact. A layered cubic phantom containing skin, fat, muscle, and bone with the proper electrical properties of human tissues was created. Similarly, a cylindrical arm phantom with a radius of (R) is introduced for bending investigations. These models were made in CST MWS to study the BSA performance in on-body conditions. Our three-layer standard body model has a volume of 200 \(\times\) 200 \(\times\) 50 mm\({}^{3}\) (skin = 4, fat = 8, muscle = 30, and air layer under the tissue layers = 8 (in mm)) and the four-layer phantom model has the volume of 200 \(\times\) 200 \(\times\) 88 mm\({}^{3}\) (skin = 2, fat = 8, muscle = 40, bone = 30, and air layer under the tissue layers = 8 (in mm)). The performance of the designed BSA device was evaluated for different tissue layer morphologies as well as spacing from the phantom model, according to the standard methods discussed in [51; 52; 53; 54; 55].
## 3 Proposed Protocol: Co-DNAR
### Neighbours Identification  
After random deployment of the nodes in the network, initially, all these nodes were unaware of each other. The source node broadcasts a hello packet in order to identify the neighbors. The acknowledgment in response to the hello packet is received from those nodes which are in the transmission range of the source node. A hello packet consists of sender node ID, depth and a specific predefined bit pattern. This bit pattern is known to every node, so that the changed bits in the pattern are detected and information about the link noise from the source to the node that receives the hello packet is obtained. Figure 8 shows the bit pattern and the changed bits, which are an indicative measure of the noise over the sender-receiver link. The reciprocity of the channel is assumed, which means the behavior of the channel from a sender to a receiver is the same as when the same receiver replies back to the original sender.  
Figure 6: Transmission lobes in environments with water and other real parameters.  
The size of a hello packet is 8 bytes [46]. The hello packets exchange is accomplished by all nodes so that every node can obtain an estimation of the link conditions from itself to its neighbors. Every neighbor node responds to the source node. In this response, the neighbor node informs the source node about its own depth, ID and any change in the bit pattern. This information is now enough for the source node to decide the relay and destination nodes. The selection of these nodes is accomplished by using a weighting function as below:  
\[Function(f)=\frac{1}{D_{i}\times N_{i}}, \tag{29}\]  
Figure 8: Bit pattern.  
Figure 7: The cooperative depth and noise-aware routing (Co-DNAR) network architecture.  
where \(D_{i}\) represents the depth value of the node of interest (a relay or a destination node) for the source node. The variable \(N_{i}\) is expressive of the extent of the noise over the link from source to the node of interest. The first neighbor of the source node with the highest value of the weighting function is chosen as a destination node. The relay nodes are selected based on the same criterion after the destination node is selected by the source node. That is, neighbor nodes of a source node having the second and third highest values of the weighting function are selected as relay nodes. If two or more nodes have the same values of the weighting function, the source node can opt any one of them as the node of interest. Broadcasting of the hello packets is accomplished by all nodes. If a broadcasting node obtains no response to its hello packet, it identifies himself as neighborless when a predefined time-threshold expires.