esa-sceva/satcom-chunk-collection huggingface dataset:
# Web-Based Toolkit for Performance Simulation and Analysis of Power Line Communication Networks
## 4 Description of the Developed Toolkit
### SimPRIME
* MTU (Maximum Transmission Unit): This field refers to the maximum payload size of the MAC layer in bytes. For example, if an S02 report is to be sent and 100 is entered in this field, 15 frames will be sent at the MAC layer with a payload of 100 bytes together with a last frame with a payload of 68 bytes. The maximum value that this field can take is 256 bytes, as indicated in the standard. * WS (Window Size): This field refers to the size of the data link layer transmission window (i.e., the number of packets that can be sent at the data link layer without needing to be acknowledged). For example, if \"WS\" is set to 1, each frame must be individually acknowledged (i.e., \"stop and wait\"). * Polling Strategy: This drop-down allows establishing the polling strategy of the smart meters, being able to select \"Sequential\" (the concentrator is requesting the reports one by one) or \"Simultaneous\" (the concentrator requests all the reports at once by means of a broadcast message). The default strategy is \"Sequential\". * keepalive: This field represents the period in which the smart meters exchange keepalive messages with the concentrator (in seconds). Low values allow a vision of the status and topology of the network very accurately and promptly but overload the network with control traffic. High values reduce the possibility that such traffic negatively affects the performance of the network but also causes the information on the status and topology of the network to be outdated. The default value of this field is 120 s.  
Therefore, the application allows simulating:  
1. Real scenarios (deployed in the field), if the maps in Shapefile format that describe them are provided, or
2. Synthetic scenarios, if S11 topology files are provided and the BER is specified between nodes at the same logical level (BER1) and between nodes at adjacent logical levels (BER2). The first option may allow evaluating the configuration of the PRIME network that best fits a specific actual scenario, whereas the second one may allow evaluating which protocol parameters (e.g., MTU, WS) are more suitable depending on the network conditions. Figure 5 shows in detail how the inputs and outputs are processed and connected to the core of SimPRIME. One of the main inputs of the core of SimPRIME is the attenuation matrix between each pair of nodes in the network. Such a matrix allows calculating, based on knowledge of the transmission power of the sending node, the power received by all the nodes of the network and, therefore, given a reference noise level, obtaining the SNR (signal-to-noise) ratio for such a packet in all network nodes. In the first option described in the previous paragraph, such an attenuation matrix is obtained by applying Transmission Line Theory to the graph that is generated from the Shapefile map (module \"Process SHAPEFILE\" in Figure 5). In the second option, such a matrix is obtained from the bit error probabilities BER1 and BER2.
# Attention Block Based on Binary Pooling
## 1 Introduction
The initial stage heavily relied on recurrent neural networks (RNNs) for implementing the attention mechanism. Subsequently, the advent of the SENet [18] marked the beginning of a new stage, which introduced a channel attention network. ECANet [19] and Convolutional Block Attention Module (CBAM) [20] were representative works in this phase. Finally, the concept of self-attention mechanism was initially introduced by Transformer [21] in natural language processing and then successfully applied to computer vision by Vision Transformer. Series of models based on Vision Transformers such as Tokens-to-Token Vision Transformer [22], Pyramid Vision Transformer [23], Swin Transformer [24], Convolutional Vision Transformer [25], Vision Outlooker [26], and CoAtNet [27] demonstrate the immense potential of attention mechanisms. GAP has played an important role in previous attention mechanisms. However, it suffers from significant information loss as it fails to preserve the spatial structure and positional information in the feature maps. Consequently, it cannot capture subtle differences and local structures in different regions of the image. Additionally, since GAP uniformly processes the entire image, it fails to differentiate between important and unimportant regions in the image. This results in the model assigning equal importance to features from all regions, without focusing on the areas that are more relevant to the classification task. Moreover, GAP leads to a single spatially invariant global feature representation lacking diversity. This limitation prevents the model from capturing multiple local feature patterns or information at different scales in the image, thus  
* To address the issue of insufficient information in GAP, a binary pooling operation is proposed. This approach effectively enhances the representational capacity of the convolutional network. * The proposed channel attention mechanism leads to improved image feature representation. * Experimental results demonstrate that the proposed method outperforms other attention mechanisms, yielding superior performance on ImageNet. The rest of the paper is structured as follows. Section 2 presents a review of related work. Section 3 illustrates how the model is constructed. In Section 4, comprehensive experiments are conducted on ImageNet to evaluate the effectiveness of the proposed method, which achieves excellent results. Finally, this work is summarized in Section 5.
## 1 Introduction
### Related Works
#### 1.1.4 Deep Learning-Based Methods  
With the rise of vision-oriented neural networks, works such as ResNet [25] and Vision-Transformer [26; 27] have also been applied to various fields [28], including infrared small target detection. Recent research has extensively explored both Transformer-based and CNN-based approaches [29; 30; 31; 32; 33]. However, the lack of neighborhood texture information for infrared small targets and the diversity of targets in complex backgrounds make it challenging to learn target features, which significantly limits the applicability of end-to-end networks.
# Back to the Metrics: Exploration of Distance Metrics in Anomaly Detection
## 2 Methodology
### The Structure of Memory Bank-Based Methods (MBBM)
#### 2.2.3 The Iterative Greedy Approximation Algorithm  
Both PatchCore and BTF utilize the iterative greedy approximation (IGA) algorithm [48] to subsample the memory bank. IGA significantly addresses the issue of overemphasis on outlier samples in both active learning and anomaly detection scenarios. This method ensures the coverage, representativeness, and information complexity of the coreset samples. However, it is worth noting that in active learning, the normalcy of outliers is not a primary concern, whereas in anomaly detection, it is important.  
The IGA algorithm tends to collect samples at the edges of sample clusters, typically high-frequency samples around low-frequency regions. Based on this characteristic of the IGA algorithm, we have the following assumptions:  
**Assumption 1**.: _If there are abnormal samples in the training set, they are likely to be captured by IGA and become outliers (considered normal) in the core set._  
**Assumption 2**.: _In low-frequency areas near high-frequency features (the boundary of the sample cluster), data points are likely to be outliers and should receive higher anomaly scores._
# Reliable Internet of Things: Challenges and Future Trends
## 5 Review of Recent Work in Literature on Reliable Data Transmission in IoT
### Reliability Metrics for IoT
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reliability Metric** & **Metric Key Idea** & **Uses** \\ \hline Packet Delivery Ratio & Ratio of packets successfully received & Resource allocation IoT \\ \hline Outage Probability & Receiver becomes out of range of transmitter & Resource allocation IoT \\ \hline Channel Busy Ratio & Measure of channel load in the network & Resource allocation IoT \\ \hline Packet Inter-arrival time & Time difference between two consecutive packets at receiver & Latency management IoT \\ \hline End-to-End Delay & Total time taken to receive a packet & Latency management IoT \\ \hline Reliability Range & Communication range at which packets are received with a probability \(\beta\)r & Vehicular Networks \\ \hline Safety Awareness Range & Vehicle safety and message reliability for finding & Vehicular Networks \\ \hline Signature verification time & Total time required to verify the signature of a message & Security IoT \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Reliability metrics used for IoT. are received at a receiver within a short time, they may have to wait in a queue before going through the signature verification process. A higher signature verification time means that overall end-to-end delay is increased. Hence, nodes may have to use another security algorithm with low encryption key sizes, or a faster processor is needed at the receiver.
## 2 Background
### Swarm Foraging  
Swarm robotics is a field of robotics in which a collection of robots collaborate with each other to complete a task [17]. It draws inspiration from animal societies and insect colonies, which have several advantages, such as flexibility, scalability, and fault tolerance [18]. The main characteristic of robots in this field is to be simple and resist communication failures; thus, they are more scalable than one complex robot. Hence, the cost of the robots is relatively low, and they can reallocate themselves.  
One of the most popular swarm intelligent tasks is foraging. Foraging behavior can be observed in insect colonies such as ants and bees. In these colonies, the insects behave intelligently to select the richest source of food or the shortest path to a food source [18]. The foraging task can be divided into different subtasks, including exploring the environment, searching for food (resources), transporting food to a safe area called the nest, and navigating the environment while bringing back food. Hence, labor division (i.e., DTA) is necessary in foraging to allocate the robots and collect as many resources as possible.  
DTA in swarm foraging involves dividing the task into interdependent subtasks and assigning the robots to perform them in parallel [3]. Since the main task in swarm foraging is to collect resources, the subtasks are regions of the foraging area, which are divided among the robots so that they can collect resources simultaneously. To improve the overall performance in the foraging task, the allocation should be dynamic and adaptive. DTA in foraging can be divided into three types: centralized, negotiation-based, and self-organized [18]. Centralized DTA requires a centralized coordinator that communicates with all the robots and allocates the tasks depending on the information it collects. In this type of allocation, availability and robustness are major concerns, and any communication issue with the central coordinator can cause a failure in the whole system, even when all the robots are working properly. Negotiation-based DTA allows all the robots to continuously communicate and negotiate which tasks to do. Even though this type of task allocation is very robust, it requires a high rate of communication among the robots, which can be difficult to achieve when the rescue area is sparse and the connections are limited. Finally, self-organized DTA allows each robot to make its decision autonomously based on the information it collects while having limited communication with its neighboring peers. Self-organized DTA is observed in nature, where peers autonomously make decisions with limited communication and no centralized control. This type of DTA is less prone to catastrophic failures than other types and is a better approach when the environment is changing rapidly.
## 2 Materials and Experimental Methods  
### Materials and Mixture Proportions  
For the experiments, 4 \(\upmu\)m (diameter) \(\times\) 12 \(\upmu\)m (length) wollastonite fibers manufactured by NYCO Minerals Inc. (Willsboro, NY, USA) were used. A parametric study was performed by replacing cement with a percentage of wollastonite fibers by mass. Type I/II Ordinary Portland Cement (OPC) was substituted with 5%, 10%, and 15% wollastonite fibers by mass. No more than 15% of wollastonite was added due to the improper workability of the cement paste and associated deterioration of mechanical performance as evidenced by previous studies. A control sample with 0% fibers was made to compare the effects of the wollastonite fiber reinforcement. A constant mass-based water-to-cement (w/c) ratio of 0.3 was chosen for all samples. Metflux\(\copy\) 4930F superlasticizer manufactured by BASF Construction Chemicals was used in the range of 0.5-2.5% of cementitious solids by mass to compensate for the loss in workability due to the addition of vollastonite fibers. The density of the used vollastonite fiber is 2.8 g/cm\({}^{3}\), which is similar to that of hardened cement paste (2.4 g/cm\({}^{3}\)) used in this study. As a result, the addition of vollastonite did not significantly alter the density of the resulting samples. The density of the vollastonite fiber reinforced samples ranged from 2.42 to 2.49 g/cm\({}^{3}\), which was within a relatively narrow range. For each of the mixtures, 50 mm cube samples were prepared and the samples were kept in a moist environment containing 95% humidity with a temperature of 23 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C for 28 days. While some of these samples were used for uniaxial compression tests, small cubes of 4 mm sides were cut from the larger cubes for microindentation experiments. These cubes were polished to a 0.04 um colloidal silica finish.
# Low-Power Wireless Data Transfer System for Stimulation in an Intracritical Visual Prosthesis
## 3 System Architecture
### External Transmitter  
The external transmitter is essentially a binary phase shift keying (BPSK) transmitter. BPSK generally gives a good balance between power consumption and bit error rate performance [26]. To facilitate a future CMOS implementation, it comprises of an oscillator for generating the carrier frequency and a mixer for modulating the carrier signal. Since the intended carrier frequency ranges between 4 and 24 MHz to achieve sufficient data rate, the mixer can be low power, consuming about 1 mW in 180 \(\upmu\)m CMOS technology [27]. The BPSK mixer designed is shown in Figure 3. The proposed mixer is comparable to the reported mixer in [27], with the exception that the transconductance current is not converted to a voltage level, by omitting the common-mode feedback structure. Two inverters act as switch pairs, switching between signal paths to generate the BPSK signal currents. Next, these BPSK currents of the external transmitter need to be passed to the implanted receiver through a suitable wireless link.  
Figure 2: System architecture of the communication system for downlink.
## 3 Distributed Estimation of Laplacian Spectrum  
Given an initial input-output pair \(\{\mathbf{x}(0),\tilde{\mathbf{x}}\}\), with \(\tilde{\mathbf{x}}=\frac{1}{N}\mathbf{1}\mathbf{1}^{T}\mathbf{x}(0)\), the matrix factorization problem (3) is equivalent to minimize the cost function \(E(\mathbf{W})=\left\|\mathbf{x}(D)-\tilde{\mathbf{x}}\right\|^{2}\) that can also be rewritten as follows:  
\[E(\mathbf{W})=\left\|\prod_{t=D}^{1}\mathbf{W}_{t}\mathbf{x}(0)-\tilde{ \mathbf{x}}\right\|^{2}, \tag{4}\]  
where \(D\) is the number of steps before reaching average consensus and \(\mathbf{W}_{t}=\mathbf{I}_{N}-\boldsymbol{\alpha}_{t}\mathbf{L}\).  
Note that there is no need for a central node to set the initial input-output pair. Indeed, such a pair can be obtained after running a standard average consensus algorithm. Each node keeps in memory its own initial value and the consensus value.  
Solving this factorization problem consists in finding the sequence of stepsize \(\{\boldsymbol{\alpha}_{t}\}_{t=1,\cdots,D}\). It is obvious that \(\boldsymbol{\alpha}_{t}\) are global parameters. To relax these constraints, define the factor matrices as \(\mathbf{W}_{t}=\mathbf{I}_{N}-\boldsymbol{\Lambda}_{t}\mathbf{L}\), where \(\boldsymbol{\Lambda}_{t}=diag(\boldsymbol{\alpha}_{t})\), \(\boldsymbol{\alpha}_{t}=[\alpha_{t,1},\alpha_{t,2},\ldots,\alpha_{t,N}]\), \(t=1,2,\ldots,D\). The problem above can be reformulated as a constrained consensus problem, that is to compute the sequence of stepsize \(\{\boldsymbol{\alpha}_{t}\}\) so that \(\alpha_{t,1}=\alpha_{t,2}=\ldots=\alpha_{t,N}\). Moreover, in Section 2.1, \(D\) is denoted as number of non-zero distinct Laplacian eigenvalues. However, in this work, Laplacian matrix is assumed to be not-known in-a-priori. Therefore, the authors have assigned \(D\) as \(h=N-1\) since \(N\) can be estimated in the configuration step through the Random Walk Algorithm proposed in [20]. Furthermore, the Laplacian Spectrum estimation procedure is divided in following stages:  
* _Stage 1_: Distributed estimation of the set of non-zero Laplacian eigenvalues \(\mathcal{S}_{1}=\{\lambda_{1},\lambda_{2},\ldots,\lambda_{h}\}\), composing of the set of \(D\) non-zero distinct Laplacian eigenvalues \(\mathcal{S}_{2}=\{\lambda_{1},\lambda_{2},\ldots,\lambda_{D}\}\).
* _Stage 2_: Eliminating the wrong eigenvalues in the set \(\mathcal{S}_{1}\) to obtain the set \(\mathcal{S}_{2}\)
* _Stage 3_: Estimating the multiplicities \(\mathbf{m}\) corresponding to each eigenvalues in the set \(\mathcal{S}_{2}\) to achieve the whole Laplacian spectrum \(sp(\mathbf{L})\).
## 2 Literature Review
### Application of Machine Learning and Big Data Analysis in Construction  
Machine learning is crucial for making construction \"smart,\" enabling site supervision, automatic detection, and intelligent maintenance [33]. The main benefit of machine learning is its ability to identify complex patterns and relationships within data sets that may be overlooked by humans [34]. By applying machine learning algorithms in construction project management, it becomes possible to reveal these patterns and correlations, allowing managers to enhance their decision-making and anticipate potential challenges [35; 36]. Previous studies [37; 38; 39; 40] illustrate that machine learning can provide crucial insights into various aspects of project management. These insights can assist in predicting project duration, anticipating cost overruns, optimizing resource use, and identifying key risk elements that could significantly affect project outcomes. This predictive capability empowers project managers to proactively address challenges and implement changes to enhance overall project performance.  
Several studies have been carried out in the construction field that use machine learning for predictive modeling. For example, ref. [41] merges BIM, AI, and operational data for proactive planning. A case study of a 35,000 m\({}^{2}\) historical renovation in Rome shows that integrating BIM with AI enables predictive risk mitigation, cutting delays by 3% and optimizing resource efficiency. Results highlight improved time/cost control compared to traditional methods, showcasing the value of digital workflows in complex projects. Ref. [42] developed a hybrid model utilizing a deep forest algorithm designed to enhance HR recruitment in intelligent manufacturing companies. In a more recent study, [PERSON] et al. [43] employed a combination of hybrid feature selection, machine learning, and particle swarm optimization to boost productivity in construction labor. They utilized four different predictive models alongside random forest techniques to evaluate labor performance effectively, aiming to optimize productivity and derive the maximum value from key influencing factors. A study developed by [44] used PSO and Machine Learning to reduce construction project management costs and create predictive models. Several algorithms, such as linear regression, Decision Trees, Support Vector Machines (SVM), Gradient Boosting, Random forests, K-nearest neighbors (KNN), and CNN Regression, were tested for project cost prediction accuracy. The regression ensemble outperformed individual algorithms in predicting accuracy.
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 5 Challenges and Open Research Topics
### Open Research Topics
#### 5.2.3 Comparative Analysis of Passive and Active RISs  
Passive and active RIS technologies affect EMF exposure differently. Existing comparisons emphasize performance metrics [107], but further studies must evaluate their impact under EMF constraints to guide technology adoption.
## 3 Results and Discussions  
### Theoretical Uncertainty Analysis  
The total impact of model and input uncertainties, including uncertainties in surface emissivity, NWP and sensor calibration, measured as the root mean square difference (RMSD) of retrieved LST _versus_ the \"true\" surface temperature in the database, is presented in Figure 2 for different values of VZA and TCWV. RMSD and bias obtained for the validation database are shown in Table 2.  
The 2 K target accuracy (RMSD) of the LSA SAF LST dataset is reached for the majority of angles and TCWV classes for PMW and SMW, degrading into larger errors for very moist atmospheres with high angles, _i.e._, for very large optical paths. The slopes of the lines in Figure 2 suggest that TCWV errors are most relevant for low-to-moderate view angles. For very moist atmospheres (TCWV \(>\) 50 mm) and high viewing angles (VZA \(>\) 55 mm), the SMW performed slightly better than the PMW. We hypothesize that this reflects the implicit sensitivity of the PMW to the NWP input: [PERSON] _et al._[12] showed that uncertainties in atmospheric profiles can have a strong impact on LST retrievals. While SMWs only require TCWV as input, PMWs require atmospheric temperature and water vapor profiles, which can introduce additional uncertainties, especially for very moist atmospheres. We found that LST errors associated with emissivity uncertainties are expected to be within 1.0 K and 2.8 K in 90% of the  
Figure 2: Theoretical uncertainty for MSG/SEVIRI LST estimates. **(a)** Physical radiative transfer-based mono-window model (PMW); **(b)** statistical mono-window model (SMW). The values are the root mean square differences, assuming model and input uncertainties. VZA, satellite viewing angle.  
estimates obtained with the SWM and PWM model, respectively. However, the impact of emissivity in both models is much smaller under moist atmospheres.  
The PMW and SMW uncertainties we simulated for TCWVs \(\leq\) 45 mm (RMSD of 1.6 K) more or less correspond to the uncertainties reported by [PERSON] _et al._[12] for GSW. For moister atmospheres (TCWVs \(>\) 45 mm) the PMW and SMW uncertainties (RMSD of up to 10 K and 6 K, respectively) significantly exceed the simulated GSW uncertainties (max. error about 4.5 K; [PERSON] _et al._[12]), particularly for high VZAs. This very likely demonstrates the different sensitivity of the single-channel and GSW models to uncertainties related to inaccurate NWP input. Single-channel models rely entirely on NWP data to estimate the atmospheric state, while the two split-window channels provide additional information about the atmospheric absorption for the GSW model [8].