esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
###### Abstract  
Although millimeter-wave (mmWave) and massive multiple input multiple output (mMIMO) can be considered as promising technologies for future mobile communications (beyond 5G or 6G), some hardware limitations limit their applicability. The hybrid analog-digital architecture has been introduced as a possible solution to avoid such issues. In this paper, we propose a two-step hybrid multi-user (MU) equalizer combined with low complexity hybrid precoder for wideband mmWave mMIMO systems, as well as a semi-analytical approach to evaluate its performance. The new digital non-orthogonal multi carrier modulation scheme generalized frequency division multiplexing (GFDM) is considered owing to its efficient performance in terms of achieving higher spectral efficiency, better control of out-of-band (OOB) emissions, and lower peak to average power ratio (PAPR) when compared with the orthogonal frequency division multiplexing (OFDM) access technique. First, a low complexity analog precoder is applied on the transmitter side. Then, at the base station (BS), the analog coefficients of the hybrid equalizer are obtained by minimizing the mean square error (MSE) between the hybrid approach and the full digital counterpart. For the digital part, zero-forcing (ZF) is used to cancel the MU interference not mitigated by the analog part. The performance results show that the performance gap of the proposed hybrid scheme to the full digital counterpart reduces as the number of radio frequency (RF) chains increases. Moreover, the theoretical curves almost overlap with the simulated ones, which show that the semi-analytical approach is quite accurate.  
Keywords:massive MIMO; millimeter-wave communications; GFDM; hybrid analog-digital architectures +
Footnote †: journal:  
_et electronics_
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 1 Introduction
### Related Work  
Physical-layer-based security techniques have been widely studied for wireless technologies, including WiFi, ZigBee, Bluetooth. These techniques can be mainly categorized into key generation and device authentication [25]. Key generation exploits the unique and reciprocal characteristics of wireless channels as cryptographic keys. A comprehensive review has been given in [26] and it has been widely studied for WiFi [27], ZigBee [28] and Bluetooth [29]. Physical-layer-based device authentication relies on unique and stable hardware impairments and exploits their features as device identifiers [30]. Any wireless devices have such impairments, which result from the manufacturing process. Similar to key generation, there have been many research efforts on leveraging hardware impairments as fingerprints for WiFi [31] ZigBee [32], and Bluetooth [33].  
During the past few years, several review papers have been published on LoRaWAN security, of which many concentrate on basic security features and attack/defense methods. For instance, in [9], the authors identify potential threats and countermeasures and deliver recommendations for system hardening. In a similar vein, ref. [34] focused on applied LoRaWAN security, where attacks and protection methods were identified from the per spective of a real-world agriculture use case. Further, the authors of [35] compared security features and issues between the LoRaWAN specifications 1.0 and 1.1, and subsequently highlighted security needs for specification 1.1. The works aiming at more systematic threat analysis include, firstly, [36], where an analysis on confidentiality, availability and integrity along with relevant state-of-the-art works was given. Secondly, ref. [37] presented a comprehensive threat catalogue for LoRaWAN specification 1.1, which becomes useful while, e.g., performing risk analysis of a LoRaWAN use case. Finally, a review of authentication methods concentrating on light-weight techniques suitable for LoRaWAN devices was presented in [38].
## 5 Simulation Results
Simulation results that are not shown here give an illustration of the performance of the solutions being provided for a number of different modulation possibilities, including QPSK, 16-QAM, and 64-QAM. The results indicate that the aforementioned procedures are not reliant on the particular modulation scheme that the source terminal uses. The theoretical conclusions presented in Section 4 are validated by this observation, hence this is a good fit. Figure 7: The efficacy of the proposed strategy in comparison to current solutions. The proposed algorithm 1 is represented by the solid blue line, the proposed algorithm 2 is indicated by the dashed blue line, and the algorithms of [29; 30; 40] are shown by the solid red, green, and black lines, respectively. Until now, we have assumed that the destination has ideal estimates and compensating capabilities for clock sampling and frequency imbalances. When a rectangular waveform is maintained, the clock-sampling imbalance, \(0\leq\gamma<1\), is recorded as a two-tap linkage at the matched filter's outcome. Here, \(\gamma\) is normalized to the data rate. Figure 9 depicts the influence of \(\gamma\) on the categorization performance. The investigations reveal that \(\gamma\) does not have a discernible impact on the accuracy of the categorization that is produced. Using an SNR of 10 dB as an example, the average probability of proper identification changes from 0.905 to 0.91 when \(\gamma\) goes from 0 to 1. Figure 10 displays the influence of the frequency offset, \(\epsilon\), on the categorization performance. Here, \(\epsilon\) is normalized to the bandwidth. The findings demonstrate that \(\epsilon\) has a serious impact on Algorithm 1 while it has no substantial effect on the categorization accuracy achieved by Algorithm 2. For clarification, at SNR = 10 dB, when the frequency offset varies from \(10^{-4}\) to \(10^{-2}\), the average probability of correct identification of the second algorithm drops from 0.91 to 0.16. However, the performance of the first algorithm remains fixed at a value of 0.91. This is because, unlike Algorithms 1 and 2 does not need precise knowledge of where the peaks are. In this case, we take into consideration the fact that \(\epsilon\) shifts the positions of the peaks. Figure 8: The impact of the number of collected symbols, \(M\), on the proposed solutions. **Algorithm 2:** The second proposed algorithm. If \(q_{i}^{(3)}-q_{i+1}^{(3)}=N/8\), for all \(i=0,\cdots,6\), then \(\alpha=c_{3}\) is declared else if \(q_{i}^{(2)}-q_{i+1}^{(2)}=N/4\), for all \(i=0,\cdots,2\), then \(\alpha=c_{2}\) is declared else if \(q_{1}^{(1)}-q_{2}^{(1)}=N/2\), then \(\alpha=c_{1}\) is declared else \(\alpha=c_{0}\) is declared. Figure 10: The impact of the frequency imbalance, \(\epsilon\), on the offered solutions. Figure 9: The effect of the clock-sampling, \(\gamma\), on the offered solutions.
## 3 Results
### Integration Strategies for Nanosensors in Bridge Maintenance  
Nanosensor integration in bridge maintenance encapsulates methodologies that not only concern the effective placement of sensors but also necessitate robust strategies for connectivity, data management, power supply, and system durability under various environmental conditions. The multidisciplinary nature of these integration strategies ensures a symbiotic relationship between the sensor technology and structural health monitoring (SHM) effectiveness.
# YOLO-RRL: A Lightweight Algorithm for PCB Surface Defect Detection
## 4 Experimental Procedure Design
### Defect Dataset Selection and Enhancement  
In this study, the PCB defect dataset (PKU-Market-PCB) [27], released by the Intelligent Robotics Open Lab of Peking University, is employed as the basis for the experimental dataset. The dataset comprises 693 high-resolution images of printed circuit board (PCB) defects, with an average resolution of 2777 by 2138 pixels. The dataset includes a comprehensive range of defect types, with each image labeled in detail. These encompass open circuits, shorts, missing holes, mouse bites, spurs, and spurious copper. The location and type of defects in each image are also labeled in detail, providing a substantial corpus for training and testing purposes. Figure 7 depicts the images of the six defect types. The images are in JPEG format and of high resolution, thus enabling the clear display of the intricate details of the PCB board. The dataset encompasses a diverse range of PCB types, including single-layer, multi-layer, rigid, flexible, and other unique PCB designs.  
The augmentation of the dataset to generate a more diverse range of training data enables the model to gain a deeper understanding of and greater resilience to PCB defects in a variety of contexts. This, in turn, has the potential to enhance the performance of the PCB defect detection model significantly and to facilitate its deployment in a wider range of practical application scenarios. The images were enhanced through the use of techniques such as mirroring, brightness adjustment, cropping, noise addition, and rotation. This resulted in an enriched dataset comprising 3465 images, which were divided into three subsets: a training set (70%), a validation set (20%) and a test set (10%). Figure 8 illustrates the number of defects per category in the dataset, both before and after data enhancement. The orange bars represent the number of defects in the original dataset, while the blue bars indicate the number of defects in the enhanced dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Configuration** \\ \hline CPU & AMD Ryzen 9 7945 HX \\ GPU & NVIDIA GeForce RTX 4060 8G \\ RAM & 16G \\ Operation System & Windows 11 \\ Framework & PyTorch 2.0.0 \\ Programming environment & Python 3.9 \\ CUDA & 11.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental environment configuration.  
Figure 7: PCB Defect Type Image. The blue boxes show specific defects: (**a**) missing hole; (**b**) mouse bite; (**c**) open circuit; (**d**) short; (**e**) spur; (**f**) spurious copper.
## 5 Discussion
### Limitations and Prospects  
Despite the results, our research still has some limitations. Based on the landscape index, the regional land use conflict is weighted and summed in accordance with three perspectives, complexity, fragility, and stability. The operation method is relatively simple, and it effectively reflects the spatial dynamic change characteristics of the land use conflict in urban agglomerations. During the index construction, however, the spatial measurement is only carried out from the perspective of landscape ecology. Factors such as economy and society are not considered. Since land use conflict is not only a spatial conflict, but also a complex social, ecological, and economical system, constructing the evaluation criterion with multiple factors by referring to various mathematical models needs to be further improved [8; 54; 55; 56; 57]. Another limitation is that the influence of a sudden government policy on land use conflicts is not taken into consideration. As policies are the catalyst of land use conflicts, we should explore the ways in which best to include policy factors in our models. At the same time, our research expounds on land use conflict from the perspective of spatial statistical analysis. As it is difficult to quantify social and economic data reasonably, our study lacks in major factors that affect land use. In future studies, aim to extensively collect socio-economic variables and geographical characteristics such as the NPP, precipitation, and the average daily temperature at urban and even county levels in order to better reflect the multi-level driving mechanism of the land use conflict.
## 3 Results  
As a result of the set of experiments, technological regimes for producing waveguides with better surface and profile qualities were determined and are summarized in Table 2. We note that the work is ongoing and further minor adjustments to the finally selected technological regime are possible. The development of the RIE etching process was done in two iterations: first, modes similar to those published in the articles (Table 1) were tested. Recipe #13 provided the most acceptable profile quality in the first iteration. Second, recipes #14-#17 were tried in an attempt to improve the quality of the results that recipe #13 gave and, finally, recipes #14-#17 showed improved structure anisotropy.  
The etching depths for the given recipes varied from 0.23 to 0.60 \(\upmu\)m, the mask selectivity varied from 1:4 to 1:8 and the best value of the slope angle of the waveguide sidewall obtained in this work was 70-75\({}^{\circ}\) (depending on the recipe).  
A detailed description of the waveguide profiles formed with various etching recipes is presented below. The SEM images of two formed waveguides are shown in Figure 5.  
An SEM image of the waveguide profile etched with recipe #13 is shown in Figure 5a.  
The sidewall angle of the waveguide formed with recipe #13 was \(\sim\)62\({}^{\circ}\) and the surface in the near-waveguide region was smooth. The LER value of the formed waveguide after RIE etching was equal to the LER value of the hard mask because the sidewall roughness of the waveguide was inherited from the hard mask.  
The SEM image of the waveguide profile etched with recipe #14 is shown in Figure 5b, where the slope angle of the waveguide sidewalls was \(\sim\)75\({}^{\circ}\).  
Despite the predictable improvement in the etching anisotropy (increasing the slope angle of the waveguide side wall) associated with decreasing the process pressure during  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Recipe** & **ICP (W)** & **TCP (W)** & **P (mbar)** & **SF6 Flow** & **Ar Flow** & **Sidewall** & **LER (nm)** & **Surface Roughness** \\ \hline
13 & 500 & 100 & 0.03 & 10 & 30 & \(\sim\)61 & 20–30 & Smooth \\ \hline
14 & 500 & 100 & 0.007 & 10 & 30 & \(\sim\)75 & 20–30 & High roughness \\ \hline
15 & 500 & 200 & 0.03 & 10 & 30 & \(\sim\)72 & 20–30 & Relatively smooth \\ \hline
16 & 500 & 100 & 0.03 & 10 & 50 & \(\sim\)59 & 20–30 & Relatively smooth \\ \hline
17 & 350 & 200 & 0.03 & 10 & 50 & \(\sim\)58 & 20–30 & Relatively smooth \\ \hline \hline \end{tabular}
\end{table}
Table 2: Etching recipes with the best-quality waveguide profiles.  
the etching with recipe #14 in comparison to the process with recipe #13, the surface roughness in the near-waveguide region increased drastically after etching with recipe #14.
## 3 Methodology
### Model Architecture  
Figure 2 compares our ENN model against the conventional single models. First, Figure 2a describes the existing approach method of injecting preprocessed input data through a network of a single structure. The neural network performs an examination operation for a given product image through a sufficiently learned single network and finally outputs the similarity for all classes. Figure 2b has a similar structure to Figure 2a, except that it uses distributed backbone models and an ensemble layer that makes a final selection by combining the intermediate results from the backbone models. Looking closer, the input data are partitioned into \(N\) groups. Each group passes through \(M\) sub-backbone neural network models. Subsequently, the output of the sub-models is concatenated and passed through the ensemble layer following a deep neural network (DNN) structure. We refer to this ensemble layer as a master or a parent layer. In the last step, the similarity for all final design rights is returned by an overlay function that takes the product of the ensemble model result and the weighted tensor. The ENN model proceeds through the following five steps to learn the design rights of a given product image.  
1. First, the ENN receives an image and performs augmentation, including input size adjustment and normalization.
2. The ENN outputs a distributed weighted output through a distributed backbone neural network.
3. The ENN concatenates the distributed weighted outputs of the individual sub-neural networks and converts them into [PERSON]'s tensor to be passed to the master layer.
4. With [PERSON]'s tensor as input, the ENN computes the order similarity coefficient for each backbone model  
Figure 2: Comparison of the structure of the previous model and ENN model. \(N\): Total number of design rights; \(M\): Number of sub-models; \(C\): Number of design rights on each model; \(K\): Number of additional design rights.  
The ENN multiplies the weighted input tensor and the similarity coefficient tensor to output the final closeness of an input image to every design right.  
Figure 3 represents the relearning operation that should run due to introducing new data. Previous studies, such as the one shown in Figure 3a require transfer learning of the entire model when a set of design rights is added. For the ENN model we proposed, as shown in Figure 3b, only the backbone model designated for the new dataset goes through the training phase. The DNN model at the master layer picks up the result from the new backbone model and goes through the partial transfer learning. Training time can be significantly reduced since the previous backbone models remain unchanged.
## 2 Materials and Methods
### Agricultural Plastic Surfaces (APS) Analysis
#### 2.2.1 Satellite APS Localization
Indeed, the RPGI (Equation (1)) was calculated for areas where Normalized Difference Vegetation Index (Equation (2)) is less than 0.73 (Figure 3) as reported by the authors [33] and in other studies [28]. The values B2, B3, B4, and B8 of Equations (1) and (2) have been previously stated. \[RPGI=\frac{B2}{1-mean(B4+B3+B8)} \tag{1}\]  
\[NDVI=\frac{B8-B4}{B8+B4} \tag{2}\]  
The RPGI index provides a good detection of greenhouse plastic covers after having identified graphically the most functional threshold. This index is widely used, although limitations do not allow exclusive use [30; 34]. Indeed, it provides very low or negative values when the greenhouses are colored white during the summer. Therefore, the RPGI index was applied to both images in order to provide a wider time span to the analysis. The final result was combined in a single remote-sensed dataset that was used as the basis for subsequent operations. To reduce errors, all non-agricultural areas in the index were excluded. This was possible by using an ancillary dataset, which is that of the 2013 Nature Map [35] that provides an accurate classification of the territory. Figure 3: Shown on the left is the result of applying the RPGI index and on the right only the true RGB co-lens representation of the 26 July 2017 satellite image. This visual comparison shows limitations and potential in the application of this index. In fact, one can see the correctly classified APSs, the incorrect ones, and the unclassified ones. In this paper, the RPGI index was used to verify its effectiveness in an initial expeditious assessment of APSs. In the end, for a general assessment of accuracy, it was compared to the final result obtained from the integrative mapping performed through the methodology applied in Section 2.2.2. In this way, it was possible to compare the classification obtained with the RPGI index and the real APS detection from the visual interpretation of orthophotos and to obtain a percentage value of accuracy. In addition to the overall accuracy in percentage, errors due to false detection were also calculated.
# Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion
## 5 Results  
The proposed truck rerouting framework was applied to three case studies using suitable alternative route selection criteria. The selected truck routes helped to reduce delays and satisfy truck maneuverability restrictions while maintaining satisfactory road conditions on the selected route. The scenario sites were determined by investigating high-crash trends and statistically analyzing incident clearance results. A stretch of I-75 was identified as a study site for applying the developed framework for each case.
## 2 Seismic Retrofitting of Reinforced RC Structures Using CFRP Sheets
Although using carbon fiber-reinforced polymer (CFRP) sheets in retrofitting existing RC frames with slabs presents specific challenges that impact the performance of the retrofitted structure. The primary challenge when using CFRP is achieving a strong and durable bond between the CFRP sheets and the existing concrete surface to transfer the load. Proper surface preparation of the concrete is highly effective in ensuring a strong bond between CFRP and the existing concrete. Inappropriate adhesive selection and inadequate surface finishing can lead to delamination of CFRP sheets during seismic events, compromising the effectiveness of the retrofitting. Furthermore, the type of adhesive and its thickness are highly effective in bonding CFRP to the concrete and consequently in the strength of the RC beam-column joint. Implementing the appropriate adhesive is able to influence the durability of the bonding and the load-bearing capacity significantly. Special types of adhesives are formulated to provide high-strength bonding; other types provide flexible bonding but are more resistant against environmental threats. Similarly, adhesive thickness is highly impactful on the quality of bonding and the strength and performance of the retrofitted RC joint using CFRP. An insufficient adhesive layer may lead to a reduction in the integrity of CFRP sheets with the concrete surface, while excess thickness can create voids or uneven stress distribution, potentially compromising the structural integrity. Therefore, implementing the appropriate adhesive type and suitable thickness can be highly contributed to achieving a reliable binding between CFRP sheets and concrete to perform together to enhance the strength and functionality of the retrofitted RC joint. This bonding is especially critical for RC frames with slabs due to the limited accessibility for creating a full loop of CFRP layers around the beams. Proper anchorage and confinement can be achieved by applying CFRP sheets to the surrounding structural elements, which is not feasible for beams in frames with slabs due to their geometry. The beam-column joint plays a crucial role in the integrity of reinforced concrete structures when subjected to lateral loads, such as earthquakes. Due to the rigidity of these connections in RC structures, the first plastic hinges often appear in the vicinity of these joints as the lateral load is transferred from the beams to the columns. This can result in a reduction in the strength of the beam-column joint, leading to instability and compromising the overall structural integrity. Therefore, the aim of this research is to propose a retrofitting scheme using CFRP sheets to relocate the occurrence of plastic hinges in the RC beam-column connections from near the column face to further within the beam. This approach is designed to enhance the safety of the joint, providing stability and preserving the structural integrity when exposed to severe earthquake excitations.