esa-sceva/satcom-chunk-collection huggingface dataset:
# Inv-ReVersion: Enhanced Relation Inversion Based on Text-to-Image Diffusion Models
## 4 Experiments
### Quantitative Comparisons  
In our comparison experiments, we evaluated the Inv-ReVersion framework against Stable Diffusion 1.5, Textual Inversion, and ReVersion using average user ranking (AUR) as the evaluation metric. The results (Table 1) indicate that our method surpasses the other three in terms of entity accuracy and relation accuracy.  
Compared to Stable Diffusion 1.5: Stable Diffusion 1.5 struggles to generate images with specific relations due to its lack of high-level semantic understanding. Even with natural language descriptions that closely represent the exemplar images, it fails to capture and generate the intended relations. Additionally, since most images in the LAION training set used for Stable Diffusion contain only one entity, the model is prone to feature fusion between entities. These issues highlight the focus of our work: enhancing text-to-image diffusion models to improve their understanding of high-level semantics and address the feature fusion problem in multi-entity images.  
Compared to Textual Inversion: Textual Inversion tends to leak features of entities from exemplar images into the relation concept \(\langle R\rangle\). For example, in Figure 6, the fourth row of the Textual Inversion method, the bird exhibits features of the cat's ears from the exemplar image. Moreover, Textual Inversion performs poorly in generating relations because it lacks a module specifically designed to understand high-level image semantics, leading to a focus on entity features and subpar performance in handling complex relations.  
Compared to ReVersion: ReVersion, one of the best-performing methods in relation inversion tasks, can generate images with specific relations more accurately. However, it suffers from severe feature fusion between entities during the image generation process. For instance, when generating the scene \"tree inside a lightbul\", (Figure 6) ReVersion merges the light of the bulb with the green of the tree, and the tree trunk takes on the shape of the filament, significantly degrading the quality of the generated image.  
Overall, our Inv-ReVersion framework excels in relation inversion tasks, outperforming other methods in both entity accuracy and decoupling, as well as relation accuracy. The results of the user study (Table 1) further confirm the advantages of the Inv-ReVersion framework in these aspects, validating the effectiveness of our approach. These findings underscore the potential of Inv-ReVersion in handling complex relations and enhancing image generation quality, demonstrating its broad application prospects in future image generation tasks.
# Mechanisms of Fluoride Toxicity: From Enzymes to Underlying Integrative Networks
## 3 Mechanisms of Fluoride Toxicity on Cellular Level
### Fluoride Inhibits Enzymes through Competition with Magnesium  
Fluoride in 0.01% concentration has an appreciable effect in inhibiting the anaerobic production of energy from glucose. Of the salts examined only fluoride inhibits glycolysis in low concentrations in various tissues [14]. [PERSON] and [PERSON] have shown that this is due to the inhibition of enolase [15].  
Enolase is a dimeric metal-activated enzyme, which uses two magnesium ions (Mg\({}^{2+}\)) per subunit [16]. The inhibitory effect of fluoride on enolase through competition with Mg\({}^{2+}\) has been demonstrated in various cells and tissues such as hepatocytes and liver, muscle, red blood cells, kidney, intestine, testis and embryonic cells, brain, retina, and others [3; 16; 17]. The extent of fluoride inhibition depends on the concentration of Mg\({}^{2+}\) and phosphate.  
The mechanism of fluoride inhibition of enzymes through competition with Mg\({}^{2+}\) has been accepted as the common mechanism of fluoride toxicity since Mg\({}^{2+}\) is the activator of more than 300 enzymes in humans [18].  
Magnesium does not form stable compounds with fluorides in water at sub-molar concentration. Nevertheless, [PERSON] et al. found trifluoromagnesate (MgF\({}_{3}\)-) in kinases, mutases, phosphatases, and hydrolases with catalytic Mg\({}^{2+}\). These enzymes catalyze the transfer of phosphate [19; 20; 21]. Trifluoromagnesate is a new transition state analogue for phosphoryl transfer.
## 3 Problem and Definition
### Relevant Definitions  
**Definition 1**.: _User and POI: In LBSN data, the set \(U=\{u_{1},u_{2},...,u_{m}\}\) represents \(M\) users, and the set \(V=\{v_{1},v_{2},...,v_{n}\}\) represents \(N\) POIs. The information of each POI \(v\in V\) includes its geographical coordinates \(l_{v}\) (latitude and longitude) and the category of the POI \(c_{v}\), such as cinema, restaurant, etc._  
**Definition 2.**_Friendship relationship \(\left(u_{i},u_{j}\right)\) represents a friendship relationship between user \(u_{i}\in U\) and user \(u_{j}\in U\). If there is a friendship definition between them, it is labeled as \(y_{i,j}=1\); otherwise, \(y_{i,j}=0\). The set \(F_{ui}=\left\{u_{1},u_{2},...\right\}\) represents the friend list of user \(u_{i}\), and \(F_{U}=\left\{F_{u1},F_{u2},...,F_{um}\right\}\) represents the friend lists of all \(M\) users._  
**Definition 3.**_Check-In Activity: For each user \(u_{i}\in U\), each check-in record is denoted by \(\left(u_{i},v_{j},t_{k}\right)\), indicating that user \(u_{i}\) visited POI \(v_{j}\) at time \(t_{k}\). By combining the definition check-in record of user \(u_{i}\) with the POI's location and category, we obtain the check-in activity of user \(u_{i}\), represented as 5-tuple \(H_{u_{i}}^{t_{k}}=\left(u_{i},v_{j},t_{k},v_{j},c_{v_{j}}\right)\), where user \(u_{i}\) visited POI \(v_{j}\) at time \(t_{k}\), and the location of POI \(v_{j}\) is \(l_{v_{j}}\), with the category \(c_{v_{j}}\)._  
**Definition 4.**_User's Historical Check-In Trajectory: All check-in activities of use \(u_{i}\in U\) form their historical check-in trajectory \(H_{u_{i}}=\left\{H_{t_{1}}^{u_{i}},H_{t_{2}}^{u_{i}},...,H_{t_{3}}^{u_{i}}\right\}\). The set \(H_{U}=\left\{H_{u_{1}},H_{u_{2}},...,H_{u_{m}}\right\}\) represents the historical check-in trajectories of all \(M\) users._  
**Definition 5.**_Co-Category: if category \(c_{i}\) of POI \(v_{i}\in V\) is the same as category \(c_{j}\) of POI \(v_{j}\in V\), then there exists a co-category relationship between POI \(v_{i}\) and POI \(v_{j}\)._
# Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors
## 3 Research Method
### Fusion Method  
Both the BLE-derived locations and PDR results have pros and cons. A pedestrian's trajectory derived by the BLE-based approach may still contain inconsistency. In contrast, locations generated by PDR are more continuous. However, PDR needs absolute coordinates as the starting location, and PDR cannot yield accurate locations in a long term due to the drift of orientation. Thus, we combine the two types of approach for indoor real-time positioning. A KF is employed to fuse the two approaches and yield more accurate trajectories.  
The real-time BLE-derived locations provide the initial state for the PDR method, which can correct the drift of PDR result when the trajectory is accidentally out of the current room. Considering the linear motion in each segment of user trajectory, theoretically, the last stride length is equal to the current one. We built up the prediction equations of the Kalman filter in Equations (16) and (17).  
Figure 5: Heading orientation deviation after a turn.  
Figure 6: Detection of turns in gyroscope data.  
The matrix \(Q\) in Equation (17) stands for the covariance matrix of process noise, and \(P_{k}\) is the covariance matrix predicted at time \(k\).  
\[\hat{X}_{k}=\begin{bmatrix}x_{k}\\ y_{k}\\ d_{k}\end{bmatrix}=F_{k}\hat{X}_{k-1}=\begin{bmatrix}1&0&cos\theta_{k-1}\\ 0&1&sin\theta_{k-1}\\ 0&0&1\end{bmatrix}\begin{bmatrix}x_{k-1}\\ y_{k-1}\\ d_{k-1}\end{bmatrix} \tag{16}\]  
\[P_{k}=F_{k}P_{k-1}F_{k}^{T}+Q \tag{17}\]  
According to Kalman filtering, the update equations of this KF are presented in Equations (18)-(20). The matrix \(H_{k}\) in Equation (18) is an identity matrix, and \(Z_{k}\) refers to the BLE-derived locations at time slot \(k\). The matrix \(R\) represents the covariance matrix of measurement noise (Equation (20)). After the Kalman gain \(K^{{}^{\prime}}\) is calculated by Equation (20), the state vector \(\hat{X}_{k}^{{}^{\prime}}\) and the covariance matrix \(P_{k}^{{}^{\prime}}\) can be updated. \(\hat{X}_{k}^{{}^{\prime}}\) contains the fused coordinates \((x_{k},y_{k})\) and the stride length \(d_{k}\).  
\[\hat{X}_{k}^{{}^{\prime}}=\hat{X}_{k}+K^{{}^{\prime}}(Z_{k}-H_{k}\hat{X}_{k}) \tag{18}\]  
\[P_{k}^{{}^{\prime}}=P_{k}-K^{{}^{\prime}}H_{k}P_{k} \tag{19}\]  
\[K^{{}^{\prime}}=\frac{P_{k}H_{k}^{T}}{H_{k}P_{k}H_{k}^{T}+R} \tag{20}\]  
The BLE-based approach may involve a lower output frequency than PDR due to the RSSI data scanning, and thus locations generated by this fusion algorithm is periodically used to correct the drifting error of PDR (e.g., every 10 s). In addition, we introduce a simple geometric restriction to correct location estimates, i.e., the boundary of space/room. This correction would be triggered if the location estimate is unreasonably outside of the room. To ensure the proposed algorithm can be applied to general navigation scenarios, we do not use any map matching means in this fusion algorithm.
# Effect of Load Transfer Section to Toughness for Steel Fiber-Reinforced Concrete
## 4 Analysis and Discussion
### Load Transfer Types by Fiber Content and Material Properties  
As shown in Figure 6, three different behavioral types of the load displacement curve of the steel fiber-reinforced concrete were identified from the analysis of the flexural behavior characteristics, from which some principles among the fiber contents, material properties, and the load displacement curve were derived. First, the smaller the fiber content and the lower the tensile strength of the fiber, the wider and more drastic the load reduction that was detected in the section where the load was reduced after the first peak load was reached (type A). Second, the greater the increase in the fiber content and the higher the tensile strength of the fiber, the longer the load displacement curve increased over the first peak load after a slight load decline (type B). Finally, after the cracking of the concrete matrix, the load transfer to the fiber increased nonlinearly, without any load reduction (type C).  
Figure 5: Overall toughness by specimen (0–2 mm).
# Analysing Near-Miss Incidents in Construction: A Systematic Literature Review
## 4 Results
### Key aspects of near-miss investigations in the construction industry
#### 4.4.3 Time Series of Near Misses  
Unsafe events at construction sites occur at discrete points in time and form a stochastic process [92]. They represent a measurable representation of safety system variability over time [93]. To effectively manage accident risk, the analysis of time series of near misses has become a significant tool for better understanding the dynamics and evolution of safety situations over time. However, in most studies on the temporal nature of near misses, stochastic statistical models have been utilized, assuming random event frequencies [16].  
[PERSON] et al. [93] demonstrated the existence of temporal characteristics of near-miss events using complex network theory and visibility graph algorithms. This feature was discovered using data from events occurring during the construction of the metro in Wuhan, China, between 2011 and 2015. It was found that the time series of near misses exhibit scale-free properties, clustered in hierarchical structures, and demonstrate small-world characteristics.  
The trend line of the time series of hazardous events is influenced by various factors associated with construction processes. These include variables such as the scale of construction production, fluctuating workforce numbers, the level of safety culture and climate within the company, and, primarily, human factors--specifically, workers' attitudes towards reporting near misses and their understanding of the issue's importance [94].  
The method of detecting anomalies in time series was also applied in a study aimed at developing a method for detecting fall-related hazardous events [17]. Inertial Measurement Units (IMUs) were used to collect data on typical movements, postures, and near-miss accidents caused by workers' falls. This enabled the quantification and real-time detection of near misses during classified worker postures/movements. On the construction site, workers' postures and movements were recognized using supervised classification algorithms.
# An Integrated Platform for the Internet of Things Based on an Open Source Ecosystem
## 2 Open Source Ecosystem for the IoT
### Application Layer Transmission Requirements
#### 2.3.1 Modbus Protocol  
Modbus technology [37] is an application layer protocol that implements client/server communication between devices connected to different buses or networks, enabling communication between multiple devices. The protocol is widely used in industrial environments and can operate on networks such as TCP, Ethernet, Serial, and RS-485. At present, there are many sensors using the Modbus protocol. Open source software that supports the Modbus protocol includes a variety of development language versions, such as Java, C, Node.js, Python, and Go.
## 4 Discussion
Furthermore, their MAE remained high at 36.11, highlighting the superior predictive accuracy of our model, even while working with a more focused selection of pollutants. Although the detailed prediction horizon was not explicitly outlined in their work, it appeared to encompass several hours over multiple time steps; however, a more in-depth review of their methodology would be necessary for definitive validation. Finally, a recent short-term study [72] explored hourly Air Quality Index (AQI) predictions using a CNN-Bi-LSTM model. Their research utilized a dataset that included NO\({}_{2}\), CO, O\({}_{3}\), SO\({}_{2}\), PM\({}_{10}\), and PM\({}_{2.5}\), as well as demonstrated strong performance over limited timeframes. Their study setup was similar to our work's 64 \(\rightarrow\) 2 h configuration, which predicts the AQI over the next two hours. Although the CNN-Bi-LSTM model performed well on its dataset (1036A), with a root mean square error (RMSE) of 38.9324, our hybrid GRU-NN model achieved comparable or even improved predictive performance, with an RMSE of 0.456 (27.07 when destandardized). Additionally, our model features a simpler architecture and requires fewer computational resources. The proposed models' applicability for predicting AQI values using meteorological and particle matter concentrations may differ only in the quantity and placement of meteorological stations monitoring the microclimate and particle concentrations that occur due to ground topography, meteorological patterns, population density, and pollutant type sources [73,74]. For example, Ioannina City is a basin and is not overcrowded. Therefore, a 10 km grid or microclimate monitoring station equipped with particle sensors can adequately cover such an area. On the other hand, big cities, like Athens, require a much denser monitoring grid to address all the issues mentioned previously. Therefore, the transferability of air quality prediction models between cities, their accuracy limitations, and their practical use for decision making depend strictly on the grid of stations used to deliver more accurate local predictions of uniform characteristics in bounded environments in terms of pollutants and environmental conditions. In a densely sensing monitoring network, if focusing on localized area predictions, the proposed model can be effectively transferred if local area data are used to train the model setup for either edge device or cloud use.
# Research on an Index System for the Earthquake Disaster Resistance Capability of Highway Tunnels
## 2 Analysis of Seismic Damage Scenes in Highway Tunnels
### Analysis of the Influencing Factors of Earthquake Disasters in Portal Sections of Highway Tunnels
#### 3.3.5 Lining Seepage  
Lining seepage, which is usually accompanied by lining damage, is highly prone to occur during earthquake disasters in highway tunnels. It mainly occurs in the vault and the sidewall. During the Wenchuan Earthquake, the damage to Longxi Tunnel was the most severe. The lining structure was destroyed, the waterproof board was exposed and a large amount of water was poured into the rock mass. Shenjiashan Tunnel is rich in groundwater, and lining seepage occurred after the earthquake.  
The above cases show that the main reason for the lining seepage is that there is a large amount of groundwater around the tunnel, which penetrated into the tunnel along the cracks generated by the lining after the earthquake, which was influenced by engineering geological factors.  
In summary, the causes of seismic damage in the tunnel body section of highway tunnels can be summarized, and the engineering geological factors are the characteristics of the surrounding rock. The tunnel structural factors include the supporting structure, section conditions and seismic measures. The operation management factor is the technical condition of the civil engineering structure. The FTA model of the seismic damage of highway tunnels is established, as shown in Figure 6.  
Analysis of the Factors Influencing the Seismic Damage of Highway Tunnel Sections Passing through the Fault Crushing Belt
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 4 Security Issues of UWSNs
### Security Mechanisms
#### 4.2.1 Key Management  
Cryptographic and key management play very important roles in ensuring confidentiality, authentication, integrity, and non-repudiation. Cryptography enables sensitive information to be stored or delivered in unsecure networks such as the underwater acoustic channel so that it cannot be read or modified by unauthorized users. Due to the particularities and constraints of UWSNs discussed in Section 2, the encryption and key management mechanisms for WSNs are unsuitable for UWSNs. In these traditional public key cryptography (PKC) schemes, a public key infrastructure (PKI)-based method needs the certificate authority (CA) to manage the certificate and key distribution, which result in high communication and computation cost. It is not affordable for resource-constrained UWSNs [59; 60]. In identity-based encryption (IBE) schemes, there is no need for PKI, and the public keys are computed according to the node ID. Hence, an IBE scheme avoids the resource cost for storing and granting certificate, which makes it a feasible scheme for public key encryption in WSN. However, distribution of a private key by PKG will cause high communication costs, which is a heavy burden for UWSNs.  
Figure 10: Security architecture of UWSNs.  
Related research has been presented for UWSNs in recent years [61; 62; 63; 64; 65; 66; 67; 68]. Unfortunately, the existing cryptography and key management mechanisms are suffering from some problems, including cipher text expansion and computational complexity.  
Message padding and codes increase the length of messages after applying cryptography and cause more energy consumption on transmission and computation. For instance, with the standardized AES encryption, the block size is 128 bits, and the message expansion due to padding is around 18% for a typical UWSN message of 720 bits [69].  
A digital signature is usually used for message authentication. A digest is appended to an authenticated message that will cause expansion and communication overhead. For example, the size of a digest produced by SHA-256 is 256 bits, which can cause an overhead up to around 35% of an average UWAN message [70].
# Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm
## 4 Modified Differential Evolution Algorithm
### Algorithm Flow  
For task planning of space-robot cluster, the overall planning process is shown in Figure 2.  
1. First, generating the coordinates of the maintenance node, the maintenance level and the revenue and determine the number of space robots and the origin coordinates;
2. Cluster the maintenance nodes to obtain the coordinates of the cluster center point, the revenue within the cluster and the number of target points within the cluster;
3. Generate the energy loss matrix between clusters and the distance energy loss matrix from the space robot to each cluster center;
4. Modified differential evolution algorithm. 1. Initialize the multi-population parameters; 2. Randomized population; 3. Adaptive differential evolution operator \(F=F_{0}\cdot 2^{\kappa}\), \(\kappa=e^{1-\frac{gMax}{gMax+1-\kappa}}\); 4. The reward subpopulation is assigned to the \(k^{\prime}th\) population, set \(pop_{k}=pop_{k}+pop_{4}\) and \(NP_{k}=NP_{k}+NP_{4}\), where \(k=\{1,2,3\}\); 5. \(pop_{1}\),\(pop_{2}\), \(pop_{3}\) carry out their respective strategy differential evolution operations;(6) Population combination, judge whether the end condition is satisfied and output the robot cluster optimization result if it is satisfied, otherwise repeat steps (3)-(5).
# An SSI-Based Solution to Support Lawful Interception
## 2 Background
### Lawful Interception  
Lawful interception is a procedure in which a network operator provides a law enforcement agency with intercepted communications of individuals. In the following, we refer to the standard ETSI TS 101 671 V3.15.1 [10] adopted in many countries: anyway, the results emerging from this research apply also when considering other standards such as CALEA [7] (adopted in the U.S.) or 3 GPP TS 33.108 [8] (used for mobile networks).  
The LI procedure involves three actors: the LEA, the LEMF, and the NO.  
* The LEA is the Law Enforcement Agency that is involved in a certain investigation about one or more individuals;
* The LEMF (Law Enforcement Monitoring Facility) is the transmission destination for the results of interception;
* The NO is the telecommunication operator offering the network platform to the individuals under investigation.  
LI deals with two types of data: Intercepted Related Information (IRI) and Content of Communications (CC). IRI refers to the metadata of the communications, such as the source and destination of a call, whereas CC refers to the real content of the communications (video, messages, and so on). According to [10], in a LI procedure, there are three logical interfaces used for the communication between the actors, called handover interfaces, which are HI1, HI2, and HI3. The HI1 interface is used between the LEA and the NO to exchange useful information about the activation and deactivation of the LI procedure regarding a certain target. The HI2 interface is used for the transport of the IRI from the NO to the LEMF. The HI3 interface is used for the transport of the CC from the NO to the LEMF.  
We conclude this section by observing that there is no standard interface or protocol to handle communication between LEA and LEMF. This observation represents the starting point of our study.