esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Analysis
### Packet Delivery Probability
From (7), the probabilities of \(N_{R_{1}}\) and \(N_{R_{2}}\) can be calculated as:  
\[\Pr\Bigl{\{}N_{R_{1}}=n\Bigr{\}}=p(n,2R_{T}-d)=\frac{\left(2\rho R_{T}-\rho d \right)^{n}e^{-\left(2\rho R_{T}-\rho d\right)}}{n!},n=0,1,2,\cdots. \tag{14}\]  
\[\Pr\Bigl{\{}N_{R_{2}}=m\Bigr{\}}=p(m,d)=\frac{\left(\rho d\right)^{m}e^{-\rho d }}{m!},m=0,1,2,\cdots. \tag{15}\]  
From (13)-(15), the probability \(q\) that a node that has received the packet becomes a potential helper can be calculated as:  
\[q=\sum_{n=1}^{F-1}\frac{\left(2\rho R_{T}-\rho d\right)^{n}e^{-\left(2\rho R_{ T}-\rho d\right)}}{n!}\sum_{m=0}^{F-n-1}\frac{\left(\rho d\right)^{m}e^{- \rho d}(F-n-m)}{m!(F-n)} \tag{16}\]  
From (2), the packet delivery probability \(p_{s}^{D}\) for once direct broadcasting is given by:  
\[p_{s}^{D}=p_{s}(1) \tag{17}\]  
For once cooperative broadcasting, the packet delivery probability \(p_{s}^{C}\) can be calculated as:  
\[p_{s}^{C}=p(E_{K})p_{s}(K+1)+(1-p(E_{K}))p_{s}(1) \tag{18}\]  
For the original repetition-based broadcasting (RBB) scheme, a packet is broadcasted \(V\) times by the source to its neighbors within the lifetime of the packet. Hence, the overall packet delivery probability \(PDP_{D}\) of RBB scheme is given by:  
\[PDP_{D}=\sum_{i=1}^{V}\Bigl{(}1-p_{s}^{D}\Bigr{)}^{i-1}p_{s}^{D} \tag{19}\]  
For the proposed repetition-based cooperative broadcasting scheme, the overall packet delivery probability \(PDP_{C}\) is given by:  
\[PDP_{C}=p_{s}^{D}+\Bigl{(}1-p_{s}^{D}\Bigr{)}\sum_{i=2}^{V}\Bigl{(}1-p_{s}^{C }\Bigr{)}^{i-2}p_{s}^{C},\ V\geq 2 \tag{20}\]
# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
## 5 Performance Analysis  
### Simulation Setting  
During training, the proposed methods use momentum SGD as the optimizer, whose momentum is 0.9 and initial learning rate is 0.01. The weight decay of all the optimizers is 1 \(\times\) 10\({}^{-4}\) and the step decay learning rate strategy is used as the learning rate schedule with 16 steps and a decay factor of 0.3. All models were trained for 200 epochs whose mini-batch size is 128 and tested on a computer with CPU Intel Core i7-9700K, running memory 32 GB and GPU GeForce RTX 2070 Super.
## 3 Results and Discussion
### Piezocone Test Results and Soil Classification
The depths and dissipation times are presented in detail in Table 1. As presented in the results, the excess water pressure was not recorded as more than 100 kPa, but in CPTu-03 at 5.24 m, it was identified as clay soil (Figure 5), and a pore pressure of 380 kPa was measured. Dissipation testing demonstrates that soils over 5 m have a sand behavior in concordance with the soil classification in Figure 5.  
Figure 9 presents the soil's mechanical properties and unit weight estimation for the three points where the piezocone tests were conducted. Additionally, the variation of the \(I_{\text{c}}\) index is presented. These properties were estimating the equations proposed by [PERSON] [36] and [PERSON] [37]. Soil total unit weights (\(\gamma\)) are best obtained by taking relatively undisturbed samples (e.g., thin-walled Shelby tubes; piston samples) and weighing a known volume of soil. When this is not feasible (as a present study), the total unit weight can be estimated from CPT results in concordance with Equation (4):  
\[\gamma/\gamma_{w}=0.27\ [\log\ \text{Fr}]+0.36\ [\log(q_{\text{f}}/p_{a}]+1.236 \tag{4}\]  
Figure 7: Normalized Maximum shear modulus and limits of uncomented soils. where \(\gamma_{w}\) is the unit weight of water. The friction angle \(\phi^{\prime}\) is estimated as (Equation (5)):  
Figure 5.0  
Theoretical solutions have provided valuable insight into the relationship between cone resistance and undrained strength \(s_{u}\). All theories result in a relationship between cone resistance and \(s_{u}\) of the form (Equation (6)):  
Figure 5.1  
\(N_{kt}\) is a preliminary cone factor value; typically, \(N_{kt}\) varies from 10 to 18. To simplify the application of the CPTu SBT chart shown in Figure 5, the normalized cone parameters \(Q_{lm}\) and Fr can be combined into one Soil Behavior Type index, \(I_{\rm c}\), where \(I_{\rm c}\) is the radius of the essentially concentric circles that represent the boundaries between each SBT zone. \(I_{\rm c}\) can be defined as follows (Equation (7)):  
Figure 5.2  
The friction angle at the three points where the tests were conducted varied from 30 to 45 degrees, with average values of 40 degrees, typical of sandy soils. Small lenses of soils with a significant percentage of fines cross the granular layers, which can be attributed to the low estimated values for this parameter. On the other hand, undrained strength was found in some soil intervals where compressible clays varied up to 200 kPa. The unit weights ranged from 14 to 19 kN/m3. The specific weight results are consistent across the various borehole profiles, with values falling within the typical range for granular soils. The decrease in specific weight values observed at a depth of 3 m is likely determined by the significant amount of fines mixed with the sand. Finally, the \(I_{\rm c}\) index demonstrates that the soil behavior is predominantly sandy, with values greater than 1.5. Figure 5.3 Dissipation time curing CPTu testing.
# Power Control Techniques for Interference Management-
## 2 Systematic Review Method
### Identification of the Relevant Literature  
#### 2.2.1 Keywords and Database Search Filters  
We developed a search strategy for this systematic search to identify the relevant literature. This search strategy targeted journal articles published in English from 2018 to 2023. The search strings from each database, including the keywords and the operators, were as follows: \"power control OR power allocation AND resource management OR interference management AND 5g wireless network* OR fifth-generation wireless network*)\".  
After running a search, we applied various filter options available for each database to refine and focus the results yielded. Each database has limits or filters, including publication years, document types, and content types. More detailed filters can be applied using the other options available. Table 2 presents the filters applied for each database to reach the refined article count for this review.  
Scopus, WoS, and IEEE Xplore allow users to download the search results in different formats, including CSV and Excel (tab-delimited file). For each literature source, we determined its relevance using its title. From the title, if the content seemed to discuss power control modelling or techniques in 5G wireless networks, we obtained its full reference (including author(s), year, title, etc.), interference management approach(es) and technique(s), and the country. A summary of the observations from each article was made.
## 3 RL-Based Multimedia Traffic Routing Architecture
### Architecture and Components
#### 3.1.2 Control Plane  
This plane connects with the infrastructure plane through the southbound interface. It has a global view of the infrastructure plane since it utilised to gather information about network topology to obtain the environment states. It contains six modules. 1 Network awareness; maps the SDN-enabled network topology into a graph representation and stores its physical information to use them in other modules. 2 Network statistics; maintains the flow state within the network environment (SDN-enabled network) by periodically gathering statistical information of all flows. 3 Video stream classifier; classifies the network traffic flows where video flows are prioritised against the best-effort traffic generated by using Iperf testing tool [31]. 4 Flow installation; operates re-actively which means that the OpenFlow switches in the infrastructure plane are re-actively programmed to build flow entries once traffic arrives. 5 Network data processing; receives network information from the network statistics module and periodically computes network data represented by the network QoS parameters, including link delay, jitter, loss ratio and available bandwidth. 6 Network data repository; stores the four QoS metrics processed by the network data processing module. The storage module carries records that outline the source and destination nodes plus the related tuples of QoS metrics.
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 3 UAV Localization and Tracking
### UAV Tracking
#### 3.2.1 Filters-Based Tracker  
The core of the prediction problem is to solve the distribution, which is particularly challenging when only part of the data is observed. In such cases, it is necessary to solve a conditional distribution, also known as the posterior distribution. In time-shifting systems, the current posterior distribution is often referred to as a filter. For instance, in object tracking, an inference is made based on the current state of the system, which involves a filtering task. Bayesian filtering is a widely used framework for solving filtering problems, based on a recursive method. Kalman filter is a popular Bayesian filter that is based on Gaussian process modeling. It uses a recursive method to solve linear filtering problems. The small memory footprint and high speed of the Kalman filter make it well-suited for real-time applications and embedded systems, as it only needs to retain the previous state and can efficiently update the estimated state in real-time. Different types of Kalman filters are among the most important technologies for moving target tracking [103; 104; 105]. In these studies, the Kalman filter is typically used for tracking after UAV detection and recognition. For instance, in Ref. [105], the authors focus on tracking a large number of targets simultaneously using Kalman filter.  
In anti-UAV systems and UAV detection and tracking tasks, it is almost a consensus to utilize different types of sensors as they complement each other and greatly improve the detection effect. The Kalman filter is also known for its extraordinary advantages in multi-sensor data fusion, which is one of the reasons why it is widely used in UAV tracking tasks. By integrating information from multiple sensors, the Kalman filter can provide more accurate and reliable estimates of UAV states and trajectories. Therefore, the Kalman filter plays a crucial role in improving the effectiveness of multi-sensor-based anti-UAV systems.  
Discriminative Correlation Filters (DCF) is a widely used method in visual tracking tasks. The DCF-based tracking approach involves training correlation filters online over regions of interest by minimizing a least-squares loss. The trained filters are then convolved by Fast Fourier Transform (FFT) to detect objects in consecutive frames [102]. DCF has also been shown to be a highly competitive method in vision-based UAV tracking tasks [106; 107; 108]. Through the exploitation of discriminative information between the target object and the background, DCF-based methods can achieve high tracking accuracy and robustness against challenging scenarios, such as occlusion and deformation. Therefore, DCF-based methods have gained significant attention in the field of UAV tracking and have been shown to outperform many other state-of-the-art methods.
# Impact of Fiber Type on Chloride Ingers in Concrete: A MacroXRF Imaging Analysis
###### Abstract  
Chloride ion penetration is one of the most aggressive threats to reinforced concrete, as it triggers the electrochemical corrosion of steel reinforcement, compromising structural integrity and durability. Chloride ingress occurs through the porous structure of concrete, making permeability control crucial for enhancing structural longevity. Fiber-reinforced concrete (FRC) is widely used to improve durability; however, the effects of different fiber types on chloride resistance remain unclear. This study examines the influence of glass and polypropylene fibers on concrete's microstructure and chloride penetration resistance. Cylindrical specimens were prepared, including a reference mix without fibers and mixes with 0.25% and 0.50% fiber content by volume. Both fiber types were tested for chloride resistance. The accelerated non-steady-state migration method was employed to determine the resistance coefficients to chloride ion penetration, while X-ray macrofluorescence (MacroXRF) mapped the chlorine infiltration depth in the samples. Compressive strength decreased in all fiber-reinforced samples, with 0.50% glass fiber leading to a 56% reduction in strength. Nevertheless, the XRF results showed that a 0.25% fiber content significantly reduced chloride penetration, with polypropylene fibers outperforming glass fibers. These findings highlight the critical role of fiber type and volume in improving concrete durability, offering insights for designing long-lasting FRC structures in chloride-rich environments.  
fiber-reinforced concrete; macrofluorescence; chlorides 2025158495
# Methodological Framework to Collect, Process, Analyze and Visualize Cyber Threat Intelligence Data
## 6 Case Studies and Discussion
### SolarWinds SunBurst Timeline  
In early December 2020 [38], the security company FireEye announced that they were attacked by a supposedly nation state-backed attack group (probably a Russian group named Cozy Bear [39; 40]) that stole tools that were used by FireEye red-hackers team. The attackers exploited a vulnerability in Orion updates--a popular IT infrastructure management software that is distributed by SolarWinds--and infiltrated trough the FireEye network, inserting their malware on their servers and opening a backdoor to steal data.  
For this analysis, we used IOC data acquired from FireEye GitHub repository [41]. IOCs were not formatted on STIX format; thus, they were manually added to the timeline. This example shows that the cybersecurity engineer will not always find threat feeds formatted in STIX/TAXII standards, but that, with this tool, he will still be able to create a timeline with any information found on websites and documents that contain useful indicators for a more complete analysis of the threat. Figure 11 shows part of the timeline created for SunBurst trojan.  
The data breach occurred on 8 December 2020. By analyzing the timeline, it is possible to observe that \"strange\" domain traffic flows were observed on FireEye network logs. These domains were not related to the attack because they probably did not realize the increasing frequency of unknown domain connections inside their network traffic.  
With the timeline above, a security engineer can perceive a traffic pattern and then open a case to study that traffic and might be able to identify exploit attempts early. Another important advantage is to observe how the attackers are behaving, i.e., if they are only mapping their targets' infrastructure/systems or if they have already started their attack campaign against their target, for example.  
Thereby, both Pegasus and Amnesty analysis examples provide cyber analysts with a very valuable point of view since it brings a human friendly approach for visualizing threat history and behavior.  
Figure 10: Pegasus timeline.
## 4 Deployment of Telemedicine Services Stage
### Political Sustainability  
The telecommunications sector usually has regulation designed to manage a scarce resource such as the radio spectrum, while ensuring that the service to end users offers an adequate quality. To deploy the Napo network, PUCP and URJC decided to use equipment that works in the ISM band (Industrial, Scientific and Medical), where it is not necessary to have a license to transmit telecommunications signals. Avoiding payment for these licenses greatly reduced deployment costs and contributed to ensuring the long-term economic and legal sustainability of the network. However, when working in these bands, the teams are susceptible to receiving interference from third parties that hinder or even impede communication. For this reason, this solution is appropriate only in rural environments where low population density means that there are few emissions in these frequency bands. This limitation prevents the scaling of this solution to urban or semi-urban environments but is adequate for the case of the Napo River in the short and medium term.
## 2 Materials and Methods
### LC-ESI+MS Identification and HPLC-DAD Quantification of Individual Phenolics  
Volumes of 0.3 mL from each methanolic extract, prepared as described above, were injected in an HPLC Agilent 1200 system equipped with a photodiode array detector (DAD) and coupled with a mass spectrometer (MS) single quadrupole Agilent model 6110 (Agilent Technologies, Santa Clara, CA, USA) was used for phenolics qualitative and quantitative analysis. The phenolics' separation was done on an Eclipse XDB C18 column 5 \(\upmu\)m (4.6 \(\times\) 150 mm), (Agilent Technologies, Santa Clara, CA, USA) and a two mobile-phase gradient was applied for 30 min, 25 \({}^{\circ}\)C, at a flow of 0.5 mL/min. Solvent A was 0.1% Ac acetic in water:ACN (99:1) and solvent B was 0.1% Ac acetic in acetonitrile. The gradient: 0 min, 5% B, 0-2 min, 5% B, 2-18 min, 5%-40% B, 18-20 min, 40%-90% B, 20-24 min, 90% B, 24-25 min, 90%-5% B, 25-30 min, 5% B. For the accurate identification of each molecule, the MS used the ESI+ ionization, with a capillary voltage of 300 V, at 300 \({}^{\circ}\)C, nitrogen flow 7 L/min, for a \(m/z\) range from 100 to 1200 Da. The data acquisition and processing were done using the ChemStation Agilent software. For quantitative determinations, different calibration curves were done (Figure S1) using the LC-DAD system and different pure standards: gallic acid and vanillin (for hydroxybenzoic acid derivatives, recorded at at 280 nm), chlorogenic and ferulic acids (for hydroxycinamic derivatives, recorded at 280 nm), oleuproein (for tyrosols, recorded at 280 nm), luteolin (for flavones, recorded at 340 nm), quercetin (for flavonoids, recorded at 340 nm) and pinoresinol (for lignans, recorded at 280 nm). The total polyphenols (TP-LC) were calculated afterwards in each case from the sum of each phenolic concentration, considering the calibrations and each peak area in the LC-DAD.
## 3 Results and Discussion
### Sensory Analysis  
The sensory analysis revealed that the final beverage achieved a score of 80.83 \(\pm\) 0.39, classifying it as a specialty coffee according to SCA metrics (Figure 7). Descriptors noted flavors of butter, caramel, cocoa, malt, dark chocolate, and herbal undertones in the coffee beverage. However, it exhibited low citric acidity, a medium body lacking in cleanliness, and imbalance. The limited sensory complexity may be attributed to the absence of yeasts and LAB during the fermentation process, typically responsible for esters, aldehydes, and lactic acid, crucial for shaping the beverage's sensory profile [38]. Another hypothesis is that the 24 h fermentation period might have been too short for microbial compounds to fully permeate the beans [17]. [PERSON] et al. [7] assessed the sensory quality of semi-dry-processed coffee in similar humid subtropical climates. Semi-dry processing involves mechanically depulping coffee fruits and subjecting them directly to sun drying. The authors noted a coffee beverage with more pronounced sugar and caramel-like perceptions, characteristics more commonly associated with dry-processed coffees. This underscores the role of processing methods and microbial activity in shaping the sensory profile of coffee beverages.  
Figure 6: Network map based on the relationships between the main microbiological groups and metabolites generated during the coffee fermentation in the subtropical region. The size of each node is determined by its degree, representing the number of connections it has with other nodes. The thickness of the edges is defined by the weight of the correlation, based on [PERSON]’s correlation values.
## 2 Literature Review
### Single Tasking Sensor  
In the current literature, the general problem formation of target tracking is reformed to be a sensor selection problem with the uniform sampling interval and without incorporating the target dynamics, _i.e._, the information-driven sensor query (IDSQ) approach [6] and the entropy approach based on sensor selection [7]. In contrast, the authors in [8, 9] propose adaptive scheduling strategies to choose the single tasking sensor and determine the sampling interval simultaneously. In [8], the sensors are scheduled in two tracking modes: (1) the fast tracking approaching mode with the unsatisfactory predicted tracking accuracy; and (2) the tracking maintenance mode with the satisfactory predicted tracking accuracy.  
The approach employs an Extended Kalman Filter (EKF) based estimation technique to predict the tracking accuracy, and adopts a linear energy model to predict the energy consumption. In [9], the proposed algorithm applies the interactive multiple model (IMM) filter to estimate and predict the target's dynamic state and select the tasking sensor node and sampling interval for each time step based on both the tracking accuracy and the energy cost. The simulation results show that the proposed approach outperforms the popular extended Kalman filter (EKF) based tracking scheme for maneuvering target in terms of tracking accuracy and energy efficiency. In [10], a small region is specified in order to select the single tasking sensor for achieving energy conservation. The distributed IMM filter is employed to estimate target position and velocity. A novel dynamic grouping idea is proposed to schedule next tasking node. However, the IMM filter has to face the problem of high complexity, especially with the operation of dynamic grouping.  
In general, the localization problem can be solved by the joint time-of-arrival (TOA)/angle-of-arrival (AOA) positioning scheme using a single seed (_i.e._, a sensor node with a known position). However, in the case of poor observations, more TOA/AOA measurements (_i.e._, multiple tasking sensors) may be applied to complement the measurements of the environment.