esa-sceva/satcom-chunk-collection huggingface dataset:
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 4 Adversarial Attack Generation Methods for IoT Networks
### Causative Attack Methods
#### 4.2.2 Label Flipping Attack  
Label-flipping attack (LFA) falls within the category of causative attack methods where the adversary poisons the training dataset by flipping the labels. There are two main methods to add label noise to the training dataset via LFA: random and targeted label flipping. When employing random flipping, the attacker arbitrarily picks a subset of training samples and alters their labels. In contrast, targeted label flipping involves the adversary's pursuit of the most optimal arrangement of label flips that maximizes the classification error rate on the testing data, while adhering to the predetermined number of allowed label flips.  
The LFA method was proposed by [PERSON] et al. in [59] against SVM, following which they improved the method via optimization-based poisoning attacks [58], where the authors resolved a two-level optimization problem to ascertain the best poisoning samples that maximize the hinge loss for SVM. Likewise, [PERSON] et al. [60] describe the attack strategy as a bi-level Tikhonov regularization optimization problem, followed by the application of a relaxed formulation to identify data instances with near-optimal label flip. Subsequently, these optimization-driven poisoning attacks have been carried out against various types of ML models, including neural networks [61; 62] and deep learning [63].
## Abbreviations  
DD  
Double differenced  
GNOS  
Global navigation satellite system  
Inertial Navigation System  
Pseudolite  
Ambiguity resolution  
Carrier phase-based RAIM  
Pseudorange-based RAIM  
Pseudolite based RAIM  
Horizontal protection level  
Horizontal alert limit  
Horizontal positioning error  
Misleading information  
Hazardously MI  
Detectable deviation bias  
Radio frequency  
False alert  
Missed detection  
Known point initialization  
Precise point positioning  
Real-time kinematic positioning  
Extended Kalman filter  
The sum of squares due to error  
Universal software radio peripheral  
Reference pseudolite  
Number of visible pseudolites  
Ambiguity function method  
Standard deviation  
Root mean square
# Enhancing Asphalt Binder Performance and Storage Stability with Trinidad Lake Asphalt (TLA)
## 3 Results and Discussion
### Thermal Cracking Property (Low Temperature)  
The Superpave asphalt binder specification mandates a maximum creep stiffness of 300 MPa, recognizing that lower stiffness values are advantageous for reducing low-temperature stress and preventing thermal cracking. In experiments utilizing the Bending Beam Rheometer (BBR) at \(-12\)\({}^{\circ}\)C, it was found that the addition of 10%TLA to virgin binders initially decreased the stiffness compared to the virgin binder alone. This initial reduction in stiffness is likely due to the TLA's ability to enhance the binder's flexibility and improve its stress relaxation properties.  
However, increasing the TLA content beyond 10% resulted in higher stiffness values. Notably, incorporating 30%TLA raised the stiffness above the 300 MPa threshold, with standard errors and linear exponential equations as depicted in Figure 10. This increase in stiffness with higher TLA content could be attributed to the additional TLA particles creating a denser and more rigid network within the binder, thus increasing the overall stiffness.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{Jnr 44 \({}^{\circ}\)C} \\ \hline \multicolumn{1}{c}{\multirow{3}{*}{**C**}} & \multicolumn{8}{c}{Original (\(\alpha\))} & \multicolumn{8}{c}{Top (\(\alpha\))} & \multicolumn{8}{c}{Bottom (\(\alpha\))}  
From a scientific perspective, lower creep stiffness at low temperatures indicates better relaxation properties, which can mitigate thermal cracking in pavements. The m-value is also critical; higher m-values are indicative of better stress relaxation capabilities. Therefore, binders that maintain lower stiffness and higher m-values at low temperatures are expected to enhance pavement durability and extend service life by reducing the likelihood of cracking under thermal stress. The experimental results suggest that while the moderate addition of TLA can improve binder flexibility and reduce stiffness, excessive TLA content can lead to increased stiffness, potentially compromising the pavement's performance under low-temperature conditions.
## 3 Results and Discussion
### Automated Column Sorption System
#### Hydraulic Conductivity Considerations  
Following HMI uptake analysis, the columns are drained to sit overnight. The standard test method for permeability of granular soils (constant head) (ASTM 2434-68) [49] is adopted to determine the variance in the overall hydraulic conductivity between the sorption columns. The hydraulic coefficient of permeability is given by adapting the standard test in the following relationship:  
\[\mathrm{k_{T}}=\frac{\mathrm{V_{C}}\cdot\mathrm{L}}{\mathrm{A}\cdot\mathrm{H_ {C}}\cdot\mathrm{T}} \tag{6}\]  
where \(\mathrm{k_{T}}\) (in cm/s) is the coefficient of permeability, \(\mathrm{V_{C}}\) (in cm\({}^{3}\)) is the quantity of water that has discharged from the column and collected, \(\mathrm{L}\) (in cm) is the column height, \(\mathrm{A}\) (in cm\({}^{2}\)) is the column cross-sectional area, \(\mathrm{H_{C}}\) (in cm) is the constant head of water on the column or the vertical distance between the feed head level and the column overflow level, and \(\mathrm{T}\) (in s) is the time required to collect the \(\mathrm{V_{C}}\) volume. With a plumb tank clamp support system, the water is fed in upflow mode from its base. The collection volume (\(\mathrm{V_{C}}\)) is set to 50-mL by a graduated cylinder, with a column height (zeolite bed depth) and cross-sectional area of 30.48 cm and 5.37 cm\({}^{2}\), respectively. Based on an 18 \({}^{\circ}\)C detected water temperature, the viscosity correction factor of \(\mathrm{n_{T}}/\mathrm{n_{20}}=1.0508\) is applied to reveal the hydraulic conductivity of columns C1 and C2 as 4.08 \(\times\) 10\({}^{-4}\) m/s and 3.89 \(\times\) 10\({}^{-4}\) m/s, respectively. With a 4.84% difference to the average between the columns, this demonstrates a consistency in the overall executed compaction method.
## 3 Experiments  
### Data Preparation  
#### 3.1.1 Simulation  
The simulated datasets are generated by performing the differential fan-beam forward projection operation to images in the head and neck CT image database of The Cancer Imaging Archive (TCIA) [42, 43], as shown in Figure 2. TCIA is a large-scale open-access database that contains medical images of common tumors and the corresponding clinical information, such as magnetic resonance imaging (MRI), positron emission computed tomography (PET), and CT. While performing the differential forward projection operation, the sampling step is set to 0.5, 2, 3, 4, and 6 with complete scanning of 360\({}^{\circ}\), corresponding to 720, 180, 120, 90, and 60 views, respectively. Projection sinograms with 720 views are considered as complete and others as sparse-view. The distance between the source and detector and that between the source and object are set to 20,000 and 18,000 pixels. The offset is set to 0.600 CT images from 30 patients, and a size of 368 \(\times\) 368 pixels is used to generate the simulated datasets, where 400 CT images are used to train the network and 200 CT images to test the network. Each patient provides 20 CT images.  
Specifically, the differential forward projection as expressed in Equations (3) and (4) of the mentioned sampling factors are performed on the phantoms, where complete projection sinograms are used as the labels of the enhanced network in the projection sinogram domain. The PCCT FBP reconstruction as expressed in Equations (1) and (2) is executed on these sinograms to obtain the reconstructed images, where the images reconstructed with complete projection sinograms are used as the labels of the enhanced network in the image domain. Finally, the differential forward projection of 720 views is performed on the degraded images reconstructed with sparse-view projection sinograms, and the results are used as the input of the network. In addition, the projection sinograms used in this network have a size of \(720\times 368\) pixels and the CT images 368 \(\times\) 368 pixels.
## 3 Results and Discussion
### Fillers' Water Absorption  
Figure 5 displays the results of the flours' water absorption. The accompanying graph shows that the water absorption capacity varies for each filler. For spirulina, the water absorption was only 101%, much lower than that of the reference rye flour. Comparable values were obtained for lupine flour at 289%. The highest values were recorded for coconut flour at 528% and bamboo flour at 575%, which is consistent with studies by other researchers [52]. High water absorption made the glue consistency very dense during the application, making it more difficult to apply, which could affect the accuracy of gluing and the strength parameters.  
Figure 4: The examples of the birch specimens with different fillers after the shear strength test (15 pbw filler content).  
Studies on plywood using soy flour as a filler confirmed that soy flour increases water absorption and thickness swelling after 2 and 24 h of soaking in water. That suggests soy flour has a high water absorption capacity [2].  
The differences in water absorption capacities between various flours and spirulina are primarily due to their distinct compositions. Each type contains varying levels of fiber, proteins, and other components, such as fats or polysaccharides, influencing how much water they can absorb. Rye flour is rich in soluble fiber and absorbs water effectively due to the high content of arabinoxylans, which bind water molecules [53]. Lupine flour contains high amounts of protein and fiber, contributing to its water retention capacity [19]. Bamboo flour has a higher cellulose content, which leads to moderate water absorption compared to other flours [54]. Spirulina is a protein-dense algae, and its water absorption is influenced by polysaccharide content [55]. Coconut flour is highly absorbent due to its high fiber and fat content, which allows it to hold significant water [56].
## 7 Performance Evaluation and Simulation Results
#### 7.1.2 Very Low Social Media Interaction  
Node's Density = 200 Nodes/km\({}^{2}\)  
In this scenario with a higher nodes' density with very low social interaction, the forwarding paths established from source to destination shows a variable \(\overline{TS}\) around 0.3, measured in the simulations when we vary \(w_{TS}\) from 0.2 to 0.8, see the blue line in Figure 6. For \(w_{TS}=0\) (i.e., \(w_{QoS}=1\) according to Equation (10)), the selection of the best forwarding path is done considering only QoS parameters, and consequently the percentage of packet losses is low, around 15% (see red line in Figure 6). For \(w_{TS}=1\) (i.e., \(w_{QoS}=0\)), the selection of the best forwarding path is done considering only the social TS parameter, and consequently the percentage of packet losses is the highest, around 25%.  
The average end-to-end packet delay is around 0.9 s for all \(w_{TS}\) values, slightly higher than for the scenario with 100 nodes/km\({}^{2}\). Also, the percentage of packet losses is slightly lower in this case, around 19%, see Figure 6. The reason is that for a higher nodes' density of 200 nodes/km\({}^{2}\) the network connectivity improves compared to the case of 100 nodes/km\({}^{2}\), and consequently packet losses decreases. Thus, packet delay increases as more packets that took longer routes made it to their destination.  
Figure 4: Percentage of packet losses and average tie strength of the forwarding path. Scenario with very low social media interaction (tie strength (TS) normal distribution with \(\mu=1\) and \(\sigma=1\)). Nodes’ density = 100 nodes/km\({}^{2}\).  
Results for a Low Social Media Interaction. TS Normal Distribution with Mean \(\mu=2\), Standard Deviation \(\sigma=1\)  
For a scenario with a low social media interaction between users, the tie strength (TS) between consecutive nodes in the forwarding nodes from source to destination is going to increase its importance in the forwarding decision and will produce higher values in the \(\overline{TS}\) measured in the simulation.
# Exploring a Moderate Fallow Scale of Cultivated Land in China from the Perspective of Food Security
###### Abstract  
Food security remains a primary concern because of the large population and scarce land resources in China, and it is a core task to determine the appropriate proportion and scale of fallowing for fallowing systems. The aim of this study was to systematically estimate the grain production potential (GPP) of existing and unexcavated cultivated land due to land use change from 1990 to 2017 and calculate the theoretical fallowing scale by using a population carrying capacity model. The reserved GPP from cultivated land to be excavated was \(7470\times 10^{4}\) t in China, and the GPP stored by the change in grain yield per unit, multiple crop index (MCI) decline, and agricultural structure adjustment were \(921\times 10^{4}\) t, \(4321\times 10^{4}\) t, and \(7760\times 10^{4}\) t, respectively, and the lost GPP caused by construction land expansion was \(5287\times 10^{4}\) t. The population carrying capacity of cultivated land in China was estimated to be 1.469 to 1.515 billion in 2017 on the basis of the national average living standard. The proportion of the population that could be fed more was between 6.28% and 9.54% depending on the number of people included, which provided an opportunity to implement the fallowing system in China. Meanwhile the proportions of the theoretical fallow scale were 6.28% and 9.54%, and the fallow scale ranged from \(850\times 10^{4}\) hm\({}^{2}\) to \(1296\times 10^{4}\) hm\({}^{2}\) under the premise of fully tapping the potential of cultivated land. In addition, taking the decline in MCI as an example, the grain yield reduction was equivalent to the grain yield of \(829\times 10^{4}\) hm\({}^{2}\) of newly reclaimed cultivated land over the past 30 years, which saved 621.48 billion yuan. The costs and benefits when formulating relevant policies of land utilization should be considered, and exploiting the productive capacity of cultivated land that exists is better than reclaiming new cultivated land.  
Keywords:fallow scale; fallow system; food security; land-use change; population carrying capacity model; China +
Footnote †: journal: International Journal of Environmental Research and Public Health
## 1 Introduction  
Augmented Reality (AR) and Mixed Reality (MR) have spread rapidly to become familiar technologies. Thanks to the emergence of more powerful devices (both mobile and wearable ones) and evolved Software Development Kits (SDKs) and frameworks that facilitate development of AR and MR applications, its future seems brighter than ever. This is such that some sources indicate that, for example, 100 million customers will use AR both in physical stores and when making on-line purchases [1]. Much of the potential of AR and MR is shown in collaborative scenarios. Collaboration in AR and MR is not a new issue and quite a few systems have already been developed mainly to provide solutions for specific applications, thus lacking generalization capabilities.  
This paper introduces SARA, a microservice-based architecture to facilitate the deployment of Shared-Augmented Reality experiences and Applications. SARA has three main strengths. First, it allows building AR-based collaborative applications regardless of the end-devices (mobile or wearable ones). SARA is also able to automatically orchestrate the rules for the collaboration policy needed for the target application. To do so, we defined a set of collaboration models which implement the most frequent collaboration strategies, such as turn-based, layer-based and hierarchy-based ones. Finally, the architecture facilitates the adaptation of non-collaborative applications to make them collaborative, and even to turn those non-AR applications into AR ones. SARA architecture is tested in this paper through a case-based methodology--technology empirical evaluation--to complete a proof-of-concept validation. The case under study is the development of a voxel-based collaborative game, which is implemented to demonstrate the development life-cycle and the management of unconstrained and turn-based collaboration models.  
This paper is structured as follows. First, in Section 2 we provide an overview of the current state of the art regarding AR collaborative systems as well as collaborative Augmented Reality architectures. In Section 3 we present an analysis of the collaboration over AR, analyzing its main characteristics and functional features. To do so we divide the collaboration schema into three different layers (visualization, interaction and collaboration management) and we evaluate the workings of some examples of collaboration models such as turn or layer-based ones, among others. Next, in Section 4 we present the main characteristics of SARA, the architecture itself and its components. In Section 5 the data model is introduced, with the main entities handled within the architecture. Then, in Section 6, we present the process of building an application based on a specific and illustrative case. Finally, Section 7 concludes the work and summarizes future research lines.
## 2 Foundations of Peer-to-Peer
### Architectures
#### 2.2.1 Ring  
Ring based architectures as, e.g., Chord distribute the node-IDs on a circular ID-space as shown in Figure 1(a). In Chord, every object with an object-ID \(o\) is stored at the node with the next higher node-ID \(n\) (\(n>o\)). The lookup is based on the node-ID and is done by forwarding the request clockwise on the circular ID-space. To offer shortcuts to nodes located far away in the ring topology, Chord uses a finger table that stores the required mapping of IP addresses to distant node IDs (the fingers are represented by arrows in Figure 1(a)). The finger table of node \(n\) is structured in \(i\) rows where each row provides the address of the node with the ID \(nodeID_{i}=nodeID_{n}+2^{i-1}\). This results in lookup algorithm that is scalable to the network size regarding the routing table size, as only a small part of the networks nodes are stored at the finger table, and to the hop length of the request due to the structure of the finger table.
## 4 Results and Discussion
### Consensus on Challenges
#### 4.1.3 Sun Brightness  
Sun brightness was another challenge identified by ten papers. Implemented in a construction environment, users expressed concerns about the clarity of vision due to sun glare. In addition, even in controlled environments, researchers mentioned that the display platform of the AR virtual interface provided by head-mounted devices needed ambient to low lighting for the display to be more effective and that the use of an interactive hologram in outdoor environments could be challenging [29]. However, some studies introduced low-cost solutions using crafted tinted films mask, to be installed on the front visor of the device to mitigate the limitations during daylight [38]. Therefore, it seems that trials to overcome this challenge have been introduced and future research has the potential to address it. This challenge is related to the type of device in use since it is related only to head-mounted hardware supporting AR applications and may not be applicable to cellphones, tablets, and or similar hardware supporting AR applications.
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 4 TSAER: Protocol Description
### TSAER: Operational Steps
#### 4.3.3 Data Transmission Process  
The source node sends the data to the selected forwarding node for transmission and repeats the above steps until the packet is transmitted to the sink. If the emergency data have the same forwarding node selected for normal data, the emergency data are forwarded first based on the priority principle.  
```
Input: Data Type DT, Temperature T, Residual Energy E\({}_{\text{res}}\), Distance from Sink D Output: Benefit function BF(i) Start 1. Obtain the temperature information of node and divide the temperature status interval. 2. Calculate the data receiving probability of node under different temperature states. 3. If T = Normal temperature interval then 4. P1 = P2 = 15. else 6. If T = Heating interval then 7. P1 = (t2 - 2 T\({}_{\text{th}}\)t - T\({}_{\text{th}}\)2)/(T\({}_{\text{1}}\) - T\({}_{\text{th}}\))2. 8. P2 = 19. else 10. P1 = P2 = 011. End if 12. End if 13. Calculate BF(i) 14. If Data = Critical data then 15. BF(i) = (E\({}_{\text{res}}\)(i)/D(i, Sink)) \(\times\) P1 16. else 17. BF(i) = (E\({}_{\text{res}}\)(i)/D(i, Sink)) \(\times\) P2 18. End if End
```  
**Algorithm 2** Routing benefit calculation for different types of data  
```
Input: Source node S, Target node Sink, Maximum benefit function BF(i), Neighbor table NT\({}_{\text{i}}\) Output: Best forwarding node Process: 1. For S has packets to transmit with Sink then 2. Select the best forwarder node from NT\({}_{\text{i}}\); 3. For each record in NT\({}_{\text{i}}\) do 4. Calculate BF(i); 5. If Data = Critical data then 6. BF(i) = (E\({}_{\text{res}}\)(i)/D(i, Sink)) \(\times\) P1 7. else 8. BF(i) = (E\({}_{\text{res}}\)(i)/D(i, Sink)) \(\times\) P2 9. End if 10. Forwarder Node = arg Max[BF(i)] 11. End
```  
**Algorithm 3** Routing process
# Optoelectronic Instrumentation and Measurement Strategies for Optical Chemical (Bio)Sensing
## 3 Spectroscopic Techniques  
The combination of optical fibers, spectroscopy techniques, and new materials for sensor immobilization has improved the development of optical sensors [33; 34; 35]. All of these techniques rely on the interactions of light with matter. Such interactions produce a series of optical phenomena that can be used to quantify the characteristics of matter, resulting in optical sensors. The wavelength range covered by the optical methods applied in fiber optic sensors can be between the UV region (above 200 nm) and the near-infrared region (below 3 \(\upmu\)m). The most significant types of interaction of light with matter are absorption, reflection, scattering, and fluorescence, as shown in Figure 6. If the matter is excited with light of a certain wavelength, \(\lambda_{\text{exc}}\), one part of the light is absorbed, another part is scattered, and another part is transmitted with the original wavelength because there are no changes in the energy levels of the electrons. On the other hand, the absorbed light can produce changes in the electron levels of certain molecules, causing a new emission of light (luminescence) of lower energy and of greater wavelength, \(\lambda_{\text{emi}}\), than the original one.