esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Section
### GlobAlbedo-Derived LAI and fAPAR
#### 2.1.3 Spatial Resolution  
GlobAlbedo-derived fAPAR and LAI data are produced at 3 separate resolutions: 1, 5 and 25 km. An important point is that the 5 km and 25 km data products are not simply aggregated versions of the 1 km products, but are produced from up-scaled albedo data. These data in turn are calculated from up-scaled bidirectional reflectance factor (BRF) data. This is done in the GlobAlbedo processing to ensure energy conservation in upscaling to each of these native resolutions. Simple aggregation cannot account for sub-pixel heterogeneity correctly (due to non-linearities in spatial scaling) and so the upscaled products are generated using underlying albedo data at the native resolution. Furthermore, simple aggregation would require the (variable) spatial covariance of the uncertainty, which is per se not available.
## 3 The Hybrid Evolutionary Optimization Method Coupling CA with GAs
### The Main Numerical Implementation of HCGA  
The main numerical implementation of HCGA is described step-by-step as follows:  
**Step 1:**: Initialization of algorithm parameters (\(N\), \(l\), \(P_{m}\), \(P_{c}\), \(T_{max}\), \(l^{\prime}\), \(u^{\prime}\), \(K\), \(\alpha\), \(\beta\), \(\gamma\), \(\mu\), \(p\)).
**Step 2:**: Initializing the population space. The initial population in the population space is generated randomly within the lower and upper bounds of the design variables, and the fitness of each individual in the initial population is evaluated. Set current generation \(T=1\).
**Step 3:**: Initializing the belief space. Situational knowledge is initialized to the best individual in the initial population. In the normative knowledge, \(U(1)\) and \(L(1)\) are initialized to \(-\infty\), and \(u(1)\) and \(l(1)\) are initialized to the upper and lower bounds of the design variables. In the historical knowledge, \(ex\) is initialized to the best individual in the initial population, while the average offset distance and the average offset direction are initialized to 0.
**Step 4:**: Updating the population space. Evaluate the fitness of each individual and update the individuals in the population space by the genetic operation (selection, crossover, mutation). Calculate the population variance and population entropy and update the degree of freedom parameter \(n\).
**Step 5:**: If the current generation \(T\) is divisible by \(K\), then go to Step 6; otherwise go to Step 8.
**Step 6:**: Acceptance operation. Individuals are selected from the population space as samples to be passed to the belief space, and the number of acceptances is determined according to Equation (15).
**Step 7:**: Updating the belief space. The update of knowledge in the belief space is performed according to Equation (2) and Equations (4)-(9).
**Step 8:**: Influence operation. According to Equations (17)-(19), the influence operation is performed to update the individuals in the population space.  
**Step 9:** Stop the algorithm if the stopping criterion is satisfied; otherwise \(T=T+1\) and go to Step 4.
# Random Access Performance of Distributed Sensors Attacked by Unknown Jammers
## 2 System Model
### Power Capture Model for Message Transmission  
When an SN receives AI successfully, it sends its MSG that occupies \(z\) access slots. Let \(t\) (\(1\leq t\leq z\)) denote a time index of the access slots used by delivered MSG. Additionally, let \(n_{p,t}\) and \(n_{m,t}\) denote the number of SNs transmitting a preamble and the number of SNs transmitting MSG at access slot \(t\), respectively. In MT, the desired MSG from an SN is received by HS through \(L\) signal paths. A collision-outage is assumed to occur if the received power, from SN-A for example, of at least one path drops below the power from other SNs that use the same code. If the collision-outage occurs, the desired MSG is corrupted by other MSGs. Recalling that \(w\) denotes the number of SNs that receive the same AI with SN-A, the probability of safe reception of MSG from SN-A without collision-outage at HS is given by:  
\[\mathcal{P}_{A}^{(\text{wco})}=\prod_{l=1}^{L}\Pr\left\{P_{A}^{(m,l)}>\max_{i =1,2,\cdots,w}P_{i}^{(m,l)}\right\}, \tag{7}\]  
where \(P_{i}^{(m,l)}\) is the received MSG power at the \(l\)-th finger from SN-\(i\). It is noted that \(P_{i}^{(m)}=\sum_{l=1}^{L}P_{i}^{(m,l)}\) for all \(i\). A closed-form expression of \(\mathcal{P}_{A}^{(\text{wco})}\) is presented in Appendix A.  
Since a code jammer behaves similarly to legitimate SNs, it causes further interference additional to the interfering powers in the right-hand side of (7). With code jamming, the above collision-outage probability then becomes:  
\[\mathcal{P}_{A}^{(\text{wco},c)}=\prod_{l=1}^{L}\Pr\left\{P_{A}^{(m,l)}>\max \left(\max_{i=1,2,\cdots,w}\left(P_{i}^{(m,l)}\right),P_{j}^{(m,l)}\right) \right\}, \tag{8}\]where \(\mathcal{P}_{I}^{(m,l)}\) is the received power of the \(l\)-th finger from the code jammer that transmits a fake message. If the jammer cannot receive the AI, then it is assumed to transmit a fake preamble and hence works like a power jammer. A closed-form expression of \(\mathcal{P}_{A}^{(\text{wco,c})}\) is presented in Appendix B.  
Let \(P_{i,t}^{(p)}\) and \(P_{j,t}^{(m)}\) be the total received power from SN-\(i\) transmitting a preamble and SN-\(j\) transmitting MSG (using the different access codes from SN-A) at the \(t\)-th (\(1\leq t\leq z\)) access slot, respectively. Let \(n_{p,t}\) and \(n_{m,t}\) be the corresponding numbers of SNs transmitting a preamble and MSG at the \(t\)-th access slot, respectively. Then, the SINR of MSG signals from SN-A, \(\gamma_{A}^{m}\), is defined by:  
\[\gamma_{A}^{(m)}\overset{(\text{def})}{=}\frac{zP_{A}^{(m)}}{\sum_{t=1}^{z} \left(\sum_{i=1}^{n_{p,t}}P_{i,t}^{(p)}+\sum_{j=1}^{n_{m,t}}P_{j,t}^{(m)} \right)+z\left(\sum_{i=1}^{zv}P_{i}^{(m)}+n_{I}P_{I}\right)+z\eta}. \tag{9}\]  
Thus, \(\gamma_{A}^{(m)}\geq\beta_{m}\) is the condition for correct reception of MSG sent by SN-A, where \(\beta_{m}\) is the SINR requirement. In this paper, since we model that condition \(\gamma_{A}^{(m)}\geq\beta_{m}\) is applied only when collision-outage is free, \(\gamma_{A}^{(m)}\) is equally obtained for power and code jamming.
## 3 Problem Formulation
### The Partitioning and Scheduling Problem  
The partitioning and scheduling problem is essentially an NP-hard problem [26]. Partitioning and scheduling are two aspects that are closely related with each other. The problem of resource partitioning and application scheduling consists of two parts, i.e., resource partitioning and scheduling. The resource partitioning is to partition the reconfigurable resources of FPGA into multiple reconfigurable regions. We need to determine the number of reconfigurable regions and the size of each reconfiguration region. Application scheduling is to determine the reconfiguration order of all tasks included in the application, and the mapping and scheduling relationship between tasks and reconfiguration regions.
# Hybrid MU-MIMO Precoding Based on K-Means User Clustering
## 2 Precoding Schemes
### Complexity Evaluation  
The complexity can be evaluated by counting the number of Floating Point Operations (FLOPs) required for multiplications and additions of complex values. A multiplication and an addition requires six and two FLOPs, respectively. The computational complexity of the SVD decompositions required by the BD-THP precoder is neglected, being computed less when relying on statistical CSI. The complexity of the QR decomposition of channel matrix \(\mathbf{H}\) is \(8N_{T}K^{2}-8K^{3}/3\). Generating the matrix \(\mathbf{H}\mathbf{H}^{H}\) for the RZF precoder uses \(K(K+1)(4N_{T}-1)\) FLOPs due to the Hermitian property of the Gram matrix. More details on how to evaluate the complexity calculations can be found in [13].  
The complexity of various precoding schemes is represented in Table 1[3; 13], for \(K_{g}=K/G\) users in each group.  
In Figure 4, the equations above are plotted for \(N_{T}=16\) transmit antennas and \(K=16\) single-antenna users grouped into \(G=4\) groups. Although the BD-THP scheme is a good tradeoff between the THP and the RZF schemes in terms of the sum rate (as will be seen in the following sections), its complexity is below both of the previous schemes. The computation complexity required to generate \(T\) precoded data vectors can change the ranking of schemes (for low values of \(T\)); however, the BD-THP precoder will always show the lowest complexity among them.
## 4 Experimental Results
### Hard Exudates (HaEx) Segmentation Module Results  
The HaEx segmentation module, employing the ConvUNeXt model, demonstrated effective performance in segmenting lesions from RGB fundus images. The evaluation was conducted using the Messidor dataset, described in the methodology section.  
In Figure 6, we can observe examples of test results evaluating [PERSON]'s original images with the ConvUNeXt-trained segmentation model.  
Figure 6: Messidor dataset results after training ConvUNeXt.  
The results of both preprocessing methods, Contrast Enhancement (CE) and CIELAB, improve identifying areas with hard exudates as seen in the first row of Figure 7, while in images without exudates, such as those shown in the second row, there are no highlighted areas in yellow or blue depending on the preprocessing applied.  
The metric values of the three models are shown in Table 4. Both preprocessing methods, CE and CIELAB, improve the IoU, indicating enhanced capability to detect affected areas.
# Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems
## 2 Method
### Sparse Coding in Training  
To describe our method formally, let us suppose that we train a deep neural network with \(n\) examples, \(\{(x_{i},y_{i})\}_{i=1}^{n}\), where \(x_{i}\in\mathbb{R}^{d}\) is an input vector and \(y_{i}\in\mathbb{R}\) (regression) or \(y_{i}\in\{1,2,\ldots,K\}\) (classification) is the corresponding outcome. In training, we obtain the optimal weight \(w^{*}\in\mathbb{R}^{p}\) by solving the following optimization problem:  
\[w^{*}\in\operatorname*{arg\,min}_{w\in\mathbb{R}^{p}}\ \ \ \frac{1}{n}\sum_{i=1}^{n} \ell(w;x_{i},y_{i}).\]Here, \(\ell(w;x_{i},y_{i})\) is the loss function describing the gap between the prediction made by the neural net and the desired outcome. In sparse coding, we add a regularizer \(\Psi(w)\) to the objective function and solve a modified training problem:  
\[w^{*}\in\operatorname*{arg\,min}_{w\in\mathbb{R}^{p}}\ \ \frac{1}{n}\sum_{i=1}^{n} \ell(w;x_{i},y_{i})+\Psi(w). \tag{1}\]  
We use the \(\ell_{1}\)-norm \(\Psi(w)=\lambda\|w\|_{1}\) for our method in particular, where \(\lambda>0\) is a hyperparameter that controls the rate of model compression (the larger \(\lambda\) is, the resulting model will be more compressed as more weight values will set to the zero value).  
Note that the use of \(\ell_{1}\)-regularizer by itself does not automatically lead to sparse coding. We need an explicit mechanism that sets weight values to the zero value while solving Equation (1). The mechanism is called a proximal operator.
## 2 Introduction to Adsorption Separation Technology
### Shape Selective Adsorption Materials  
The most important representative of shape-selective adsorption materials is a molecular sieve and porous materials. The so-called shape-selective catalysis refers to the selective adsorption performance of molecular sieves, that is, molecular sieving is carried out according to the size and shape of molecules in the adsorption process. Different types of porous materials have different pore sizes, which can be matched with molecules of different sizes. Only the molecules matching the pore size of porous materials can be adsorbed and separated. We can select porous materials with an appropriate pore size as adsorbents to meet the needs of different types of reactions according to the kinetic diameter of the molecules to be adsorbed.  
The pore steric hindrance effect of molecular sieves can be well used for shape-selective screening of alkanes, cycloalkanes, or aromatics in oil products. For example, the commonly used FAU, BEA, and LTL molecular sieves are widely used in petrochemical catalysis because of their suitable solid acidity and texture structure [18]. [PERSON] et al. [19] used different types of molecular sieves, such as mordenite, \(\beta\), and Y zeolite to perform the adsorption and separation of benzene in a benzene/propylene mixture. The research results of adsorption selectivity showed that \(\beta\), MCM22, and MOR zeolites have high selectivity for benzene, and the adsorption performance has good stability. While Y zeolites show low benzene adsorption at low temperature and low pressure, indicating that the molecular sieves with different texture types have different selectivity for hydrocarbon molecules.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using On/Off-Policy
#### 3.3.2 Safety Control  
In [85], the authors present a framework that ensure robot operates safely. The authors emphasize the need for robust safety measures to prevent accidents or damage during the learning process. The authors identify three key criteria for a general safe learning framework: high assurance in the system's safety, adaptability to work with various learning algorithms, and minimal interference to avoid disrupting the learning process. The framework treats safety as a differential interaction between the controller and potential turbulence. Analysis methods such as Hamilton-Jacobi reachability and Bayesian inference were used to ensure safety and refine the analysis as the robot collected new data. A Gaussian process was used to model uncertainty in how the system behaved, adjusting disturbance limits as new data were gathered.  
Figure 16: Simulated learning environment using OpenAI Gym, ROS, and Gazebo. For more information, please refer to Table 4.  
The authors validated their framework in \"_tested on the Stanford-Berkeley Testbed of Autonomous Rotocraft for Multi-Agent Control (STARMAC) using Ascending Technologies Pelican and Hummingbird quadrotors_\" [85] to track a trajectory close to the ground. In the experiments, the authors used the PPO to show that the system could safely learn a control policy from random initialization, adjust to wrong safety set estimates, and react to disturbances. A key innovation of the framework is the concept of online safety guarantee validation. This involves continuously monitoring the system's confidence in the model-based safety guarantees and intervening early if the confidence drops below a certain threshold. They describe two different ways to do online validation: a global analysis that finds the safety function level sets where the model stays correct and without use of local analysis that focuses on how reliable the model performing. For more information on the hardware and software used, please see Table 4.  
The authors' work significantly advances the field of safe learning in robotic applications by combining robust control-theoretical analysis with data-driven Bayesian learning. The framework's versatility and real-time validation capabilities make it a promising choice for various applications where robots need to learn and operate safely in challenging and unpredictable settings. This integrated approach may enhance the robotic systems in several fields, especially in complex settings.
## 3 Finite Element Simulation of the CRCP Segment
### Elements Used in FE Simulation  
The upper and exterior edge faces of the CRCP segment as shown in Figure 5 which are directly exposed to the external environment, are modeled using a four-node isoparametric quadrilateral element (BQ4 HT), which is specially used to define the convective interface boundaries in 3D heat flow analysis. The concrete is discretized by using a 20-node isoparametric solid brick element (CHX60). The interface between the concrete and ground base is modeled by an eight-node element (CQ481) with interface elements of zero-thickness. The accuracy of the FE analysis depends on the size and characteristics of the element in the mesh. The size of the element is taken as 30 mm based on the mesh sensitivity analysis.
# Implementing GDPR-Compliant Surveys Using Blockchain
## 4 System Design  
### Challenges  
The main challenges when storing personal data in a blockchain relate to GDPR's right to rectification (article 16) and right to erasure (article 17). These entitlements conflict with the principal characteristic of blockchains, i.e., maintaining permanent and immutable records.  
However, compliance is possible without significant security compromises by storing personal data off-chain while keeping only validation information (such as hash codes) in the blockchain.  
Processing sensitive data is not always necessary; however, it is indispensable in some cases. For example, to confirm that all ethnicities are treated equally or that there is no discrimination based on sexual orientation. Processing of sensitive information must be performed with GDPR constraints in mind and confirmed with a GDPR specialist.  
Despite GDPR mandates, answering highly personal and sensitive questions raises other concerns. To receive the respondents' consent, the surveys must ensure anonymization and erasure of a person's answer if required. To achieve this, we decided to segregate the data provided on registration from the survey answers. This separation enables the deletion and modification of any of the datasets independently. This design choice also enforces anonymity since the identification data are only stored in the registration file, not together with the answers to the survey.  
It is also essential that only people registered in the system can answer the surveys and that a person can only answer each survey once.
## 3 Results and Discussion
### Performance of Short-Reach MCF-Based System Employing ML Techniques  
In this subsection, the BER obtained with and without using ML techniques is evaluated by simulation. The performance improvement enabled by shallow FNNs, \(k-\)means or KNN techniques are identified and insight about the design of ML-assisted short-reach MCF\(-\)based systems is provided.  
Figure 8 shows the BER of the received signal before and after using ML algorithms, considering a ICXT level of \(-13\) dB. The results obtained show that \(k-\)means and KNN algorithms do not provide performance improvement when compared with the performance obtained without using ML. This occurs because the ICXT is random and the simplest redesign of the decision boundaries does not allow to better identify the different clusters, as inferred from the constellations shown in Figure 9a-c. In contrast, Figure 8 shows that the FNN enables the mitigation of the ICXT-induced BER degradation. This is confirmed by the comparison between the constellations obtained without ML and after employing the FNN, shown in Figure 9a,d, respectively. For instance, if we consider a BER threshold of \(10^{-1.8}\) to define the system outage [6], then the BER before applying ML techniques presents 100 occurrences (out of 626) above the threshold (the ones shown in Figure 8). By applying the proposed FNN, the BER always stood below the threshold. We chose to represent only  
Figure 7: (**a**) Normalized STAXT as a function of time fractions. (**b**) BER as a function of time fraction.  
the BER occurrences above the threshold in Figure 8, as the goal is to evaluate the system performance improvement provided by FNN in extreme degradation scenarios.  
In order to conclude the analysis of the performance improvement provided by the FNN, we also evaluated the mean BER as a function of the received optical power (ROP). Figure 10 shows the mean BER as a function of the ROP considering an ICXT level of \(-15\) dB. The BER is averaged over 500 time fractions to obtain stabilized mean BER estimates. The results show that, for the BER = \(10^{-1.8}\) (corresponding to the 20% FEC  
Figure 8: BER as a function of time fractions for: (**a**) without the use of ML (crosses), \(k-\)means clustering (line); (**b**) KNN (dashed line), FNN (line).  
Figure 9: Constellation of the \(16-\)QAM signal before and after using ML techniques.  
threshold), the FNN provides an additional ROP tolerance of almost 3 dB compared with the case in which ML is not employed. It is also shown that the BER = \(10^{-1.8}\) is attained for ROP = \(-10\) dBm. If we consider the typical power levels launched into the optical fiber (between 0 and 10 dBm), this ROP level means a link budget between 10 and 20 dB. This link budget enables us to use recently fabricated MCFs where fiber loss does not exceed 0.2 dB/km and fan/in fan/out insertion losses are typically bellow 1 dB [23; 24]. For ROPs below \(-14\) dBm, the FNN does not provide performance improvement as the system is limited by the thermal noise.
## 5 Conclusions  
This study was aimed at establishing the new fire damage diagnosis system (FDDS), which can comprehensively consider the field inspection data in a systematic manner based on fuzzy theory and thus objectively evaluate the damage levels of RC members exposed to fire. In addition, the FDDS was applied to an actual fire-damaged case reported by the Architectural Institute of Japan [2], and the resulting fire damage grades of the RC members were compared to those by inspection professionals. From this study, the following conclusions can be derived:  
1. The existing fire damage evaluation methods highly rely on the experience and knowledge of the inspector, and thus, it is hard to ensure the objectiveness and reliability of his/her evaluation results. The fire damage diagnosis system (FDDS) proposed in this study, however, can provide consistent and objective evaluation results because it can ensure comprehensive considerations of the field inspection data in a systematic manner based on fuzzy theory.
2. The FDDS evaluation results for the fire-damaged case examined in this study were the same or very close to those estimated by the fire inspection professionals based on the AIJ methods. As the FDDS provides very similar fire damage grades to the inspection method widely used in current practices, it can be now straightly adopted to the field inspection.
3. The application of the FDDS can improve the reliability of the fire damage grades of RC members; it can be also very useful to establish appropriate post-fire actions, such as repairing or strengthening of the fire-damaged RC members.
4. The fire damage diagnosis system proposed in this study provides the fire damage grades of individual RC members only, but it cannot give the overall fire damage grade of an RC structure at a structural level. Thus, additional research is yet necessary to extend its application to a building-level grade evaluation.  
This research was supported by the Mid-career Researcher Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2016R1A2B2010277). All of the authors participated in developing and verifying the fire damage diagnosis system introduced in this manuscript, and the [PERSON] supervised this study. While [PERSON] and [PERSON] took the lead in writing of this paper, the second, third, fourth and fifth contributed significantly to revising and improving it. The authors declare no conflict of interest.