esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Massive MIMO systems can have hundreds or even thousands of antenna channels in the array. Finally, in NOMA schemes multiple users can share non-orthogonal resources in a synchronous way, thus achieving a higher spectral efficiency by allowing some degree of multiple access interference at mobile receivers [24]. It becomes apparent from the above that accurate performance evaluation and radio network planning of 5G systems can be a quite challenging and computationally demanding procedure, since a considerable number of novel technologies is introduced compared to previous wireless protocols. In general, prior to the actual deployment of a wireless cellular network, it is important to estimate a number of associated parameters, such as total capacity, maximum transmission rate both in uplink and downlink, delay, latency, outage probability, etc. Due to the large number of associated parameters (i.e., the number of active users, number of transmit/receive antennas, propagation environment, requested service per user, etc. ), there are no analytical solutions for such complex wireless cellular orientations. Hence, parameter estimation can be performed only numerically, via Monte Carlo (MC) simulations [25]. Therefore, the goal of this review article is to provide all latest achievements on simulation platforms and techniques for 5G interfaces. In this context, channel modeling issues for massive MIMO systems and mmWave transmission are discussed, along with simulation and evaluation procedures. Additional issues, such as Radio Network Planning (RNP) and integration of high bandwidth zero latency applications (e.g., autonomous driving in future electrical smart grids, network recovery after physical disasters, or bandwidth on demand in crowded areas) are discussed as well. The rest of this manuscript is organized as follows: In Section 2, channel modeling issues for 5G MIMO wireless orientations are discussed, along with associated simulation procedures. In Section 3, the basic concept of system and link level simulators is described. In this context, latest achievements in 5G simulators are discussed as well. Section 4 deals with RNP issues for 5G networks, while in Section 5 additional challenging features such as Vehicle to Everything (V2X) communications in order to support bandwidth demanding applications are discussed as well. A summary of the key findings of this article in terms of simulator capabilities is provided in Section 6, while concluding remarks are provided in Section 7.
# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 3 Materials and Methods
### Semantic Segmentation Module
#### 3.1.2 Improved DeepLabv3+ Architecture  
In this paper, an improvement scheme is proposed for the original DeepLabv3+ model, which has problems such as a large number of model parameters, inconvenient portability, and poor real-time performance. The improved network structure is shown in Figure 2, and the improved lightweight real-time multi-object semantic segmentation detection network is called Multiclass MobileNetV2 ECA DeepLabv3+ (MM-ED). The improvement is mainly reflected in the following two aspects:  
1. The lightweight architecture MobileNetV2 [22] is introduced in the backbone network. This effectively reduces redundant computations and memory accesses, decreases model size, and speeds up inference.
2. The Efficient Channel Attention Network (ECANet) attention mechanism [23] is introduced at the end of the backbone network, incorporating channel attention. This greatly enhances the feature extraction capability of the model.  
Figure 1: DeepLabv3+ architecture.  
The model improvements proposed in this paper were optimized considering the limited computational capabilities and small storage spaces often found in the embedded platforms carried by gardening pruning robots devices. Reducing the size of the backbone network is conducive to operating efficiently on such platforms. Additionally, incorporating attention mechanisms can enhance the model's recognition accuracy within constrained computational resources.
# Enhanced Bit Repair IP Fast Reroute Mechanism for Rapid Network Recovery
## 2 Related Works  
In recent years, the IP Fast Reroute technology has experienced massive research interest. In the IoT and IP network areas, several existing solutions dealing with rerouting problems have been proposed [40; 41; 42]. As a result of the FRR development, there are two main groups of FRR solutions--reactive and proactive. Most of the existing FRR solutions are proactive, which means they use the calculation of an alternative FRR path in advance [17; 33; 37; 43; 44].  
The well-known proactive IP Fast Reroute mechanisms, according to our analysis done in recent years, are LFA [17; 45; 46] and its enhanced version R-LFA [17; 44; 47], Multiple Routing Configurations (MRC) [23; 48; 49], and several FRR mechanisms based on alternative trees [50; 51; 52; 53; 35]. Amongst them, the Maximally Redundant Trees (MRT) is the most used [54; 55].  
The LFA IP Fast Reroute mechanism was one of the first and is immensely popular. Several other enhanced versions of it have been proposed, such as the Directed LFA [56] and Topology-Independent LFA (TI-LFA) [56]. Only a few existing FRR solutions, such as LFA and R-LFA, are implemented in real router operating systems.  
The reactive IP Fast Reroute mechanisms include only Multicast Repair (M-REP) [57] and Enhanced M-REP [58].  
In the following two subsections, we provide a brief description of two FRR reactive mechanisms with LFA and one representative FRR reactive mechanism, M-REP.
## 2 Theoretical Background
### Stochastic Computing  
Stochastic computing is a low complexity algorithm design and hardware implementation technique, where a numerical value \(a\) is represented with stochastic bit stream \(A_{i}\), \(i=1,2,...,N\). As a result, the complex operations in conventional TCS systems can be mapped into quite simple logic operations. A typical stochastic computing-based DSP system is illustrated in Figure 2.  
* For a numerical value \(a\in[0,1]\), the bipolar format can be utilized to transform it to bit stream \(A_{i}\) by comparing it with a uniform distributed random number \(R(t)\sim U(0,1)\), where \(\Pr\{A_{i}=1\}=a\). The corresponding hardware architecture of unipolar format stochastic computing is shown as \"stochastic bit stream generation\" in Figure 2.
* For a numerical value \(a\in[-1,1]\), the bipolar format bit stream generation is required [4,11], where \(\Pr\{A_{i}=1\}=(a+1)/2\). The bipolar format bit stream generation could share a similar hardware architecture with unipolar format by comparing \((a+1)/2\) with the random number \(R(t)\).  
The stochastic computing-based logic circuits could significantly reduce the hardware cost and critical path delay compared with conventional TCS circuits, illustrated as Figure 3a, where the multiplication \(P_{c}=P_{a}\cdot P_{b}\) is implemented by a single \"AND\" logic [16]. As shown in Figure 3b, a simple \"XOR\" logic is used to perform \(P_{c}=P_{a}\cdot(1-P_{b}+(1-P_{a})\cdot P_{b})\) calculation. The scaled addition \(P_{c}=P_{a}\cdot P_{s}+P_{b}\cdot(1-P_{s})\) can be realized by a multiplexer logic shown as Figure 3c, and the sum is scaled according to \(P_{s}\). Except for the linear computation, addition and multiplication, the division \(P_{c}=P_{a}/(P_{a}+P_{b})\) can be implemented by a J-K Flip-Flop illustrated as Figure 3d. The backward-transformed numerical value is shown as \"Stochastic to binary convertor\" in Figure 2.  
Figure 3: Basic calculation units of unipolar format stochastic computing [17].  
Figure 2: Typical-Stochastic-Computing-based DSP system [15].
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 4 Conclusions and Future Works  
A hybrid network is a network that integrates conventional wireless sensor nodes, sensor tags, hybrid RFID and a base station into the same network in order to provide different services to users in IoT environments. In such settings, sensors are designed to offer different services and, therefore, have different energy patterns, which may lead to network energy imbalance if not managed carefully. This paper proposed the SAC routing protocol to improve energy management in a hybrid network designed to identify objects and sense their environments by using hybrid sensor/RFID devices. The proposed routing protocol uses a centralized mechanism to address the energy imbalance problem by letting the base station select cluster heads depending on the residual energy of nodes, energy properties, the node's type and service to be delivered and the position of nodes in the network. Experiments were conducted in a hybrid emulated/simulated IoT environment to compare the efficiency of the proposed SAC algorithm to the LEACH and LEACH-C protocols and the more recent DECSA and MOCRN protocols. It was observed, from these experiments, that the SAC protocol achieved the best energy management and better traffic engineering performance, leading to the longest life span.  
Figure 16: Routing efficiency. (**a**) End-to-end delay; (**b**) Packet delivery ratio.  
Figure 15: Signaling frugality. (**a**) Energy consumption; (**b**) Radio duty cycle.  
Following the model presented in [21], experiments were conducted in parallel with those proposed in this paper to assess the relevance of using embedded boards, such as the raspberry pi, as smart gateways for the centralized clustering process proposed in this paper, as these devices are energy frugal and can be deployed pervasively and unattended with solar harvested energy in different IoT applications. Their efficient use in long-distance deployments [22] is another issue that is currently being addressed using different radio propagation models in different frequency bands, both ISM frequencies and white spaces. The experimental results related to this work are expected to be published as future work. Multipath routing [23; 24] is often listed besides clustering as another routing approach for providing quality of service (QoS) in sensor networks. A comparison between the clustering model proposed in this paper and multipath routing is another avenue for future research work.  
The three authors contribute to the design of the SAC model and wrote different parts the paper under the coordination of [PERSON]. [PERSON] conceived, designed and implemented the distributed signalling model and the IoT network management model, while [PERSON] conceived the energy models and conducted the SAC, DECSA and MOCRN experiments. The experiments of the family of LEACH protocols were conducted by [PERSON] and [PERSON].  
The authors declare no conflict of interest.
# Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis
## 5 Results and Discussion
### AUC and ROC  
The Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC) are common assessment metrics for binary classification problems. The performance of a binary classifier at various classification thresholds is shown graphically via the ROC curve. Plotting the true positive rate (TPR) vs the false positive rate (FPR) at different threshold levels is how it is made. A helpful tool for assessing the effectiveness of various classifiers is the ROC curve. Higher AUC scores indicate better separation of the two classes, making them superior classifiers. The area under the ROC curve is represented by a single-value statistic called AUC, on the other hand. Higher values correspond to greater classifier performance; the range is 0 to 1. An overall indicator of the classifier's capacity to distinguish between positive and negative classes across all potential thresholds is provided by the AUC. Both AUC and ROC are useful metrics for evaluating the performance of a binary classifier, especially when the two classes have imbalanced data distribution.
## 7 Evaluation and Results
### Localization Accuracy
#### 7.1.1 Average Localization Error
These results improved to 6.59, 6.94, 7.11, 1.52 and 1.56 m when WCWCL is used. Two interesting points here to mention. First, while increasing the movement distance showed better localization in most trials, this not always true. Increasing the maximum movement distance does not always mean that the accuracy will increase as shown in the figures. Second, the static models  
Figure 12: Average localization errors versus maximum movement in (**a**) WCL, and (**b**) WCWCL. behave differently based on the implemented localization algorithm. For example, Z-Curve shows better performance than SCAN and LMAT when WCWCL is used with 105 m.  
In the second experiment, shown in Figure 13, we conducted the same parameters with a fixed maximum distance, \(d_{max}\), of 70 m and a different standard deviation of noise, \(\sigma\). Figure 13a,b shows the average localization error for the chosen models with a different deviation od noise, \(\sigma\), when WCL and WCWCL algorithms respectively are used. In all runs and regardless of the value of the standard deviation of noise, \(\sigma\), our proposed model, FLPPL, offered the most accurate estimations, with a localization error of less than 1.5 m in both WCL and WCWCL. Indeed, FLPPL was able to achieve even lower results than 1 m when the values of 5 and 7 were used as standard deviation of noise, \(\sigma\) in WCWCL. NLA_MB showed closed results and the impact of changing the standard deviation of noise, \(\sigma\), was insignificant. Static models offered better results when \(\sigma\) increases to 5. Once more, static models with WCWCL outperformed those with WCL. To sum up this point, taking into account the strength of the node's RSSI signals, locations and distance of both the MA localization points and the localized neighbour nodes helped to achieve high accuracy values in FLPPL using both WCL and WCWCL. Moreover, in FLPPL, the localized nodes are given the opportunities to provide their neighbours with their own localization information, therefore, the unknown node can determine its own location based on the most accurate information received. NLA_MB also consider the usage of the nearby localized nodes to supply their own neighbour with their locations, which helps to increase such ratio.
## 3 Results
### 3D Reconstruction Results
However, Dixon-VIBE is the standard acquisition in PET attenuation correction and it is usually easier to have access to this type of acquisitions for the pelvis anatomy. Therefore, the type of network that is implemented does not have a great impact on the results. In this work, different ways of reconstructing pseudo-CT volumes from 3D patches were evaluated as well. According to our results, the best option would consist in using stride 16 and the inner cube of the patch. Compared to the averaging technique, the quantitative results were similar but the visual results (Figures 12 and 13) showed less artifacts and aliasing effect in the boundaries of the patches when the inner patch is used. The only reason to use a direct merge of the patches using stride 32 would be real-time applications in which a fast reconstruction of the volumes is needed. However, the stride 16 scheme took around one minute to complete a volume, which is fairly low compared to acquisition times in MRI. Finally, it is important to mention the main limitations of this study. Firstly, the architectures that were implemented are not exactly the same as the original ones and they lack some post-processing, such as the adversarial scheme. Moreover, it is hard to verify the reproducibility and robustness of the methods compared to traditional approaches. Thus, there exists a need of a specific training dataset for scans from different vendors or field strengths [40]. Likewise, a common dataset would be useful to compare novel architectures to the state-of-the-art methods. In addition, there exist some limitations during network training, which are not the most important ones. Figure 12: Comparison in the sagittal and coronal direction for the head between the 2D and 3D schemes. Figure 13: Comparison in the sagittal and coronal direction for the pelvis between the 2D and 3D schemes. training due to RAM (Random Access Memory) memory restrictions. A larger memory would enable the algorithm to admit the whole volumes as input without the need of using 2D slices or 3D patches. This way, the efficiency of the DCNN would increase and better results could be obtained. Finally, using datasets with larger cohorts could also help improve the quality of the resulting pseudo-CTs. In summary, 3D networks would be the best option if a GPU with enough memory to accommodate a whole 3D volume or at least a bigger 3D patch. A GPU with 11 GB of memory was used for this study. Nevertheless, recently, Nvidia released a new GPU with 24 GB of RAM that could help in these tasks. In addition, building a network that adds up the best qualities of the present networks is also encouraged. A U-net with residual blocks and dilated convolutions in the middle could be a good starting point. This network could exploit the progressive reduction of the feature maps and increase the value of the features using residual and dilated convolutions.
# A Survey of Graphical Page Object Detection with Deep Neural Networks
## 6 Discussion and Conclusions
### Difficulties and Challenges  
After reviewing several methods in the field of graphical page object detection, we have noticed some key issues that deserve to be addressed. These are as follows:  
* First and foremost is that the current state-of-the-art performs better when the network is trained for a single type of graphical object, i.e., only for table or only for formula. The performance of a graphical page object degrades when it is trained to detect multiple graphical page objects in document images [5; 32].
* The second critical challenge is low inter-class (between different classes) and high intra-class (within the same class) variation. Due to low inter-class variance, tables without ruling lines can easily be misclassified with algorithms or mathematical formulas and vice-versa [27; 55]. Similarly, a figure can be falsely predicted as a table and vice-versa on account of low intra-class variance.
* The datasets differ significantly from each other. At present, several datasets only focus on a single graphical page object [38; 46; 52]. Therefore, there is a growing need for large-scale datasets that provide annotations for multiple page objects like figures, formulas, and tables [5; 13].
* The recent two-stage object detection networks are generally gigantic in size [56; 57]. It is not easy to process the images at their original resolution with limited computational resources. Therefore, some important features are compromised during the downsampling process in the case of detecting smaller graphical page objects such as embedded formulas [58].
* Most of the current state-of-the-art methods rely on some post-processing to obtain reliable results [28]. Therefore, more generic deep learning-based solutions are required that can detect distinctive graphical page objects in a diverse environment.  
Because of the challenges mentioned above, we can conclude that standardization is required with diversity to tune the methods towards generic graphical object detection in document images. Moreover, the development of methods tailored only for graphical page object detection in document images can significantly improve the performance. This work is one effort to unify the performance of the deep neural network architectures for most renowned datasets.
## 2 Set-Up and Hardware Description
### Data Collection Campaign  
The data collection campaign carried out during the FANTASTIC project was necessary and very useful for the development of the proposed WE function in Section 3. Specifically, the data captured includes the following recordings:  
* **Benign**: Static recording at the open sky scenario shown in Figure 5a. The antenna was placed on a tripod on a farmer's field with dry soil and no objects anywhere nearby. This test is attempted to minimize multipath and to be the reference file for calibration purposes.
* **Foliage**: Static recording under dense tree canopy to capture multipath and/or diffraction, shown in Figure 5b.
* **Urban**: Static recording between two buildings (see Figure 5c). This test is attempted to capture multipath and NLOS conditions.
* **Dynamic**: Dynamic recording with a moving car in a mixed environment including open sky, forests and deep urban scenarios. Figure 5d shows the truth trajectory of the moving car around Leuven, Belgium.  
Figure 4: Software modifications included in the AsteRx-U receiver.  
Figure 5: Test scenarios used for the data collection campaign carried out by Septentrio (SSN). (**a**) Static recording attempted to minimize multipath. (**b**) Static recording under trees. (**c**) Static recording between two buildings. (**d**) Truth (pink) and computed (blue) trajectory.  
Each recording lasted for about 2 h. The measurements were done during the same time slot at consecutive days. This results in a nearly identical GPS constellation behavior, because of the periodicity of GPS.  
The goal of these data was to assess the ability of a 2 pol antenna system to detect and mitigate multipath. The focus was to evaluate the behavior of the signal strength measured from the LHCP and RHCP components with the elevation angle and multipath environment. To show the capabilities of the 2 pol antenna to sense the multipath environment, Figure 6 shows the ratio of the desired RHCP signal component to the spurious LHCP component in a polar azimuth-elevation plot. This was done in the benign environment (see Figure 6a) and the urban environment (see Figure 6b). The antenna has excellent cross-polar ratio in open sky environments and clearly detects signals which have been corrupted by diffraction and reflections. This is indicated with a large positive ratio between RHCP and LHCP components (RLR) (blue color) in the benign scenario and the small and negative RLR (red color) in the harsh environment. We show the results for GPS L1/L2 and GLONASS L1, showing similar results for all the constellations and frequencies. This confirms the good performance of the 2 pol antenna designed within the FANTASTIC project, providing similar results for all the frequency bands under analysis. Formally, the RLR is defined as  
\[\mathrm{RLR}_{k}(T)\doteq\frac{P_{\mathrm{R},k}(T)}{P_{\mathrm{L},k}(T)}, \tag{2}\]  
with \(P_{\mathrm{R},k}(T)\) and \(P_{\mathrm{L},k}(T)\) the RHCP and LHCP correlation output for satellite \(k\) at epoch \(T\), respectively, given by (1).  
Figure 6: _Cont._