esa-sceva/satcom-chunk-collection huggingface dataset:
# First Evidences of Ionospheric Plasma Depletions Observations Using GNSS-R Data from CYGNSS
## 1 Introduction
### Using GNSS-R to Study the Ionosphere  
The goal of this study is to provide a way to improve the ionospheric models, by means of GNSS-R. This idea was firstly proposed in 1996 by [PERSON] and [PERSON] [9], but not really explored the following years because the reflection over ocean (rough) surfaces is mostly incoherent, and sensitive to the winds, as reported in an airborne experiment of the same authors [10]. It was not until 2016 that it was shown [8] that the large fluctuations in the peak of the measured Delay-Doppler Map of GNSS-R instruments around the geomagnetic equator in calm sea conditions (wind speeds \(<\) 3 m/s), where reflections are more coherent, could be due to ionospheric scintillation.  
The main purpose of GNSS-R missions is to study geophysical parameters of the Earth by studying the reflection of GNSS signals on the Earth's surface. Applications include sea altimetry, soil moisture measurement or ice detection. The signals used to apply this technique cross the ionosphere in their down-welling and up-welling paths, that is, first from the GNSS satellite to the specular reflection point on the Earth's surface, and then from this point to the GNSS-R receiver. Because of the height of the satellites involved, the down-welling path always crosses all the ionospheric layers, but the up-welling one may only cross some of them, depending on the altitude of the GNSS-R instrument.  
Furthermore, GNSS signals would only allow the study of ionospheric scintillation in the GNSS bands, usually the L1/E1/B1, L2, and L5/E5. Other bands object of interest nowadays in Earth Observation, such as P-band (to be used in ESA BIOMASS Mission) are also affected by these perturbations, but they cannot be detected and/or quantified by this technique, although it could be applied using other signals of opportunity such as those from MUOS (Mobile User Objective System) satellites [11].
## 2 Materials and Methods
### Motion Compensation
#### 2.2.3 Speed Compensation  
If the vehicle-mounted platform maintains a uniform speed in the horizontal direction, i.e., the platform has zero acceleration in all directions, then the motion speed compensation is a constant, i.e., only the effect of the vehicle's linear motion needs to be considered during the driving process. However, since it is difficult for the vehicle to maintain a uniform speed, a motion speed compensation algorithm is introduced in the actual driving process [38].  
\[V_{r}=V_{T}-V_{R}, \tag{10}\]  
where \(V_{r}\) is the radial velocity of the aerosol particles relative to the radar antenna, which is also the actual radial velocity measured by the Lidar antenna. Meanwhile, \(V_{T}\) is the radial velocity of the aerosol particles relative to the F-system, and \(V_{R}\) is the radial velocity of the A-system relative to the F-system. Obviously, the accurate acquisition of \(V_{T}\) is necessary for obtaining \(V_{R}\)'s accurate and reliable value.  
The corresponding algorithm flowchart is shown in Figure 7. Initially, the running speed of the vehicle-mounted platform is measured in the F-system using the CNS. Moreover, the azimuth and elevation of the radar antenna relative to the M-system are obtained from the observation data. The radial running speed of the vehicle platform is calculated based on the speed, azimuth angle, and elevation angle. Then, the aerosol particles' radial speed and relative distance are obtained from the observed data. Furthermore, the actual radial speed of the aerosol particle is calculated, and the actual position of the aerosol particle and the angle matrix along the three axes are calculated in the F-system by coordinate transformation. Finally, the speed of aerosol particles along three coordinate axes is obtained in the F-system.  
Figure 6: Flowchart of attitude compensation.
## 5 Overview of Existing Technique
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Security and Issues** & **Privacy** & **Key Technology Contribution** & **Ref.** \\ \hline
1- & THz & \\ \hline Authentication & attacks that can exist in the 5G.then the security requirements in the 5G & [5, 46, 48, 116, 201, 207, 208] \\ \hline Privacy & Describes these threats and sketches future research on how to mitigate the security concerns that a modern distributed control infrastructure poses. & [7, 53, 109, 111, 115] \\ \hline Malicious behavior & Privacy and confidentiality issues. & [29, 68, 75, 114] \\ \hline
2- & AI & \\ \hline Communication & vision of AI-enabled 6G system, the driving forces of introducing AI into & [8, 10, 22, 62, 112, 113, 203, 204] \\ \hline Privacy & Secure ML structure, or the correct application of ML, can protect privacy in 6G. & [9, 11, 21, 37, 61] \\ \hline Access control & Identify fascinating services and use-cases of 6G, which can not be supported by 5G appropriately.. & [49, 60] \\ \hline
3- & Quantum communication & \\ \hline Communication & Highlight the use cases and applications of the proposed 6G networks in various dimensions. & [13, 65, 118, 209] \\ \hline
4- & Blockchain & \\ \hline Communication & Challenges and canvassed the key role of blockchain. & [14, 44, 47, 66] \\ \hline
5- & Molecular communication & \\ \hline Privacy & Preserving the privacy of the users is the primary concern of mobile and service providers. & [39, 41, 42, 52, 54, 56, 57, 202] \\ \hline Encryption & Several challenges pertaining to resource allocation, task offloading, energy efficiency, latency reduction, fairness and security based 6G enabled massive IoT. & [69, 70] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overview of the main security and privacy issues in key 6G technologies.
## 6 Conclusions  
This multidisciplinary and interdisciplinary study contributes to the field of natural language processing (NLP) and connected vehicle (CV) technology development. The primary contribution is the development of a novel metric (the H-index) to quantify the alignment between subject matter expert (SME) classifications and topic assignments by state-of-the-art, open-source NLP and ML models. This metric, comprising a purity factor and a dispersion factor, offers a quantitative and objective understanding of the effectiveness of various NLP and ML approaches in capturing the essence of patent documents covering complex technological domains such as CVs.  
This research navigated the challenges of analyzing a vast and unstructured dataset of patent documents. The NLP pipeline included a variety of the top performing tokenizers, normalizers, vectorizers, and topic modelers to demonstrate the potential of ML models to categorize and produce insights into the intricate patterns within patent documents. The results highlighted the misalignment of these models with SME classifications by revealing areas where the models diverged, offering critical insights into their limitations and areas for improvement. The integration of manifold visualizations provided a clear and intuitive representation of the areas of misalignment. This visualization tool can help people interpret the complex relationships and patterns within the data, making the findings more accessible to both technical and non-technical stakeholders.  
In broader implications, it is evident that while there has been significant progress in the field, challenges remain. The complexity of CV technology, combined with the rapidly evolving landscape of patent information, requires continuous refinement of combined NLP and ML techniques. Future research should assess the interpretability and accuracy of these models in other domains to benchmark their performance for comparison with evolving tools such as large language models (LLMs) and their multimodal variants.  
In summary, this study is a step forward in the intersection of NLP, ML, and patent analysis. By developing a metric that bridges the gap between algorithmic efficiency and human-centric interpretability, this research contributes a useful tool for researchers and practitioners in the field. It paves the way for future innovations in NLP and ML and sets a precedent for the rigorous evaluation of these models in the analysis of patent data, particularly in specialized domains such as CV technology.  
This research was funded by the United States' Department of Transportation, grant name Mountain Plains Consortium.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The author declares no conflict of interest.
# Prediction of Curing Time/Shear Strength of Non-Conductive Adhesives Using a Neural Network Model
## 3 Results
### Case Study 2: Optimization of NCA Based on Resin, Hardener, and Catalyst  
The snap curing time of fewer than 5 s and the shear strength of over 34.33 MPa of NCA are recommended to apply for mass production in the electronic industries [4; 30; 31]. NCA formulations satisfying the requirements were derived from the ANN models. In the optimization, the resin (DGEBA) and the catalyst (HX-3941) contents were used as input variables, while the hardener (MHHPA) and coupling agent (A-174) contents were fixed at 80 phr and 0.55 phr, respectively.  
As shown in Figure 8, the optimized NCA formulations that satisfied conditions for mass production were shown using contours. The contours for curing time (s) were indicated as color, and the contours for shear strength (MPa) were indicated as a line.  
The range of the optimized NCA satisfying both the curing time of fewer than 5 s and the shear strength of over 34.33 MPa was able to be approximated as a linear dotted line. The optimized NCA formulation was suggested as 0.2137 \(\times\) (resin content in phr) + (catalyst content in phr) \(\geq\) 35.87. Increasing the resin content may reduce the required catalyst content and vice versa.  
Figure 8: The optimized NCA formulation. Curing time (s) is contoured by color, and shear strength (MPa) is contoured by line.  
Figure 7: The predicted shear strength with DGEBA (resin) and HX-3941 (catalyst) (**a**) with hardener and (**b**) without hardener.
## 3 Methodology
### Evaluation Parameters  
Deployment and self-healing algorithms in an MWSN can be evaluated in terms of coverage and network life. These two factors are going to be analyzed in terms of the following parameters, which we originally proposed in [28]:  
* Blanket coverage: percentage of the deployment region \(A\) covered by at least one sensor. Coverage \(C\) is the ratio between the union \(\cup\) of all \(A_{i}\) and \(A\), \(A_{i}\) being the round area covered by node \(i\). For \(N\) sensors: \[C=\frac{\cup_{i=1,..N}A_{i}}{A}\] (1) If we assume that cell \(i\) has a probability \(p_{i}\) of detecting an event on the cell, we can model Equation (1) with a probabilistic grid of \(M\) cells [40]. Any event at cell \(i\) can be detected by several nodes, i.e., node \(j\) may detect an event at cell \(i\) with a probability \(p_{ij}\). Hence, \(p_{i}\) can be calculated from the probability of an event going undetected at cell \(i\) (\(p_{i}\)): \[p_{i}=1-\bar{p}_{i}=1-\prod_{N}(1-p_{ij})\] (2) Finally, coverage \(C\) is obtained using Equation (3): \[C=\sum_{i=1}^{M}\frac{p_{i}}{M}\] (3)
* Energetic efficiency: The cost of deployment and self-healing depend on distance \(d\) traveled by a node to its current location; and time \(t\) to reach its current location [41].
* Energy cost when nodes are not moving depends on the uniformity \(U\) of the deployment topology. In a network of \(N\) nodes: \[U=\frac{1}{N}\sum_{i=1}U_{i}\] (4)  
Figure 4: Hierarchical MWSN: **(a)** with all clustering forces; **(b)** without clustering force L1 vs. the sink node (L2); **(c)** without clustering force L0 vs. L1 (figures are scaled 1:10 m).  
\[U_{i}=(\frac{1}{K_{i}}{\sum_{j=1}^{K_{i}}{(r_{i,j}-\bar{r}_{i})^{2}}})^{1/2}\] (5) \(K_{i,j}\) being the number of neighbors of node \(i\), \(r_{i,j}\) being the distance between nodes \(i\) and \(j\) and \(\bar{r}_{i}\) being the average distance between node \(i\) and its neighbors.
* The average power that nodes require to send a message to the network \(\bar{P}\): \[\bar{P}=\frac{1}{N}{\sum_{i=1}^{N}{\bar{P}_{i}}}\] (6) \(\bar{P}_{i}\) being the average power that node \(i\) needs to send a message to the network. \(\bar{P}_{i}\) has an impact on the network lifetime, and it can be obtained as: \[\bar{P}_{i}=\frac{1}{N}{\sum_{i=1}^{N-1}{P_{ij}}}\] (7) \(P_{ij}\) being the power needed to send a message from node \(i\) to \(j\). This power depends on the physical features of the RF chipset the network is using. If messages need to hop through \(k\) nodes, it is necessary to add the involved transmission power between each of the two nodes: \[P_{ij}=P_{11}+...+P_{ik}\] (8)
* Networks are unbalanced when some nodes consistently transmit more packets than others. In the non-balanced situation, the life time of loaded nodes is significantly shorter that the rest. Failure in some critical nodes may lead to disconnection of large areas of the network. Unbalance can be analyzed by the evaluation deviation in the number of routed packets per sent message in the network (\(\sigma Ms_{Tx}\)). If \(\sigma Ms_{Tx}\) is high, some nodes are routing far more traffic than the rest.
# Cascaded Dual-Inpainting Network for Scene Text
## 3 Methodology  
A cascaded dual-inpainting network for scene text (CDINST) is proposed to reconstruct text structures and enhance recognition accuracy in downstream recognition tasks. The proposed framework consists of two cascaded modules: the SGM and the SRM, as depicted in Figure 1. The SGM generates the preliminary text structure. It is directly fed into the SRM as the structure-guided condition to refine text structure reconstruction. The two modules are independently designed, sequentially cascaded, and jointly trained.
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 2 Related Work
### Delay Tolerant Networking (DTN) Routing Protocols
#### 2.1.4 Prophet  
To improve the delivery probability and reducing the consumption of network resources, another DTN routing was introduced called Probabilistic Routing Protocol using History of Encounters and Transitivity (PROPHET) [18].  
PROPHET, unlike Epidemic, uses information which is derived from the past node encounters such as number of packet copies, mobility pattern, location, contact time and social behavior of nodes to optimize the packet delivery [19]. In PROPHET, each node uses a probabilistic metric named delivery predictability, _P(a,b)_\(\in\) [0, 1] which is established at every node, \(a\), for each known destination \(b\), indicating the probability of the relay node for delivering packets to the final destination [18]. By using this metric, reliable node choices for the next message hop can be estimated.  
In PROPHET, when two nodes encounter each other, they exchange their summary vectors including the delivery predictabilities for all destinations known by every node and update the interval delivery predictability. After updating the information, nodes decide which messages should be exchanged based on the forwarding strategy. If node \(a\) communicates with node \(c\), a carried message with destination \(b\) is forwarded from node a to c if _P(a,b) < P(c,b)_.  
PROPHET relies on the calculation of probabilistic metrics to estimate the probability of a node delivering the message to the destination which is calculated in three parts [20].  
* When two nodes meet each other, they exchange and update their delivery predictability, so the node, which is encountered frequently, has a higher delivery predictability than other encounters. This probabilistic metric is calculated as: \[P(a,b)=P(a,b)_{old}+(1-P(a,b)_{old})\times P_{encounter}\] (1) where \(P_{encounter}\) is an initialization constant.
* When two nodes do not encounter each other, their delivery predictability should be low. Equation (2) is the aging equation for a node where \(\gamma(gamma)\in[0,1]\) is the aging constant and \(k\) is the elapsed time since the last aging time update. \[P(a,b)=P(a,b)_{old}\times\gamma^{k}\] (2)  
Another part of the calculation of delivery probability is the transitive property. If node \(a\) frequently meets node \(b\) and node \(b\) frequently meets node \(c\) then node \(c\) is a good candidate for forwarding messages intended for node \(a\). Equation (3) indicates the effect of transitivity on the delivery predictability where \(\beta\in[0,1]\) is a scaling constant of the effect which the transitivity should have on the delivery predictability.  
\[P(a,c)=P(a,c)_{old}+(1-P(a,c)_{old})\times P(a,b)\times P(b,c)\times\beta \tag{3}\]
## 3 Optimization Framework
### Transformations  
The constraint (17) contains a mixed-integer quadratic constraint. Here this constraint is transformed into mixed-integer linear form, by considering \(ae_{g,d,t}\leq\alpha_{g,d,t}p_{g,d,t}\) we have the following constraints by employing the proposed techniques in [54]:  
\[ae_{g,d,t}\leq\overline{p}_{g,d,t}\alpha_{g,d,t} \tag{26}\]  
\[ae_{g,d,t}\leq p_{g,d,t} \tag{27}\]  
\[ae_{g,d,t}\geq p_{g,d,t}-\overline{p}_{g,d,t}(1-\alpha_{g,d,t}) \tag{28}\]  
where \(\overline{p}_{g,d,t}=\max(p_{g,d,t})\). The same transformation is applied for \(P_{f,t}v_{f,t}\). So we replace the constraint (17) with the above constraints.  
Another transformation is related to the operation of ESS. The ESS operation modes can be described by the following constraints:  
\[if\ P_{q,t}^{bus}\geq P_{q,t}^{req}\to b_{g,d,t}^{C}=1 \tag{29}\]  
\[if\ P_{q,t}^{bus}\leq P_{q,t}^{req}\to b_{g,d,t}^{D}=1 \tag{30}\]  
The above constraints contain logic constraints. In this paper, we replace these constraints with their K.K.T optimality condition [55]. The above constraints can be rewritten as follows:  
\[1=\left\{\begin{array}{ll}b_{g,d,t}^{C}&P_{q,t}^{bus}-P_{q,t}^{req}\geq 0 \\ b_{g,d,t}^{D}&P_{q,t}^{bus}-P_{q,t}^{req}<0\end{array}\right. \tag{31}\]  
By considering \(\mu_{1,t}\) and \(\mu_{2,t}\) as the Lagrangian multipliers of the constraint (31), the K.K.T optimality condition can be obtained:  
\[(P_{q,t}^{bus}-P_{q,t}^{req})-\mu_{1,t}+\mu_{2,t}=0 \tag{32}\]  
\[\mu_{1,t}(1-b_{g,t}^{D})=0 \tag{33}\]  
\[\mu_{2,t}(1-b_{g,t}^{C})=0 \tag{34}\]  
\[\mu_{1,t},\mu_{2,t}\geq 0 \tag{35}\]if \(P_{q,t}^{bus}-P_{q,t}^{req}\geq 0\), according to (31), \(b_{b,q,t}^{C}\) is equal to 1. So, the second term of (33) is not zero and its first term is zero. i.e., \(\mu_{1,t}=0\) and \(\mu_{2,t}\) is obtained as follows:  
\[\mu_{2,t}=-P_{q,t}^{bus}+P_{q,t}^{req} \tag{36}\]  
Plugging (36) in (34), the equivalent of the first term of (31) can be rewritten as follows:  
\[(P_{q,t}^{bus}-P_{q,t}^{req})(1-b_{b,t}^{C})\leq 0 \tag{37}\]  
In the same manner, the equivalent of the second term of (31) can be rewritten as follows:  
\[(P_{q,t}^{bus}-P_{q,t}^{req})(1-b_{b,t}^{C})\geq 0 \tag{38}\]  
It is worth mentioning that the non-equality constraints are preferable in the commercial solver [56]. So, the constraints (37) and (38) have been written in the form of non-equality. The scaled PEMFCS hydrogen power in Equation (3) is approximated by a first-order equation to prevent the problem from being concave.
## 3 Potential Security Implications of IoT in 6G Environment
### 6G Usage Scenario-Related Vulnerabilities
#### 3.2.2 Hyper Reliable and Low-Latency Communication (HRLLC)  
As wireless technology moves from Ultra-Reliable Low-Latency Communications (URRLCs) of 5G to HRRLC in 6G, it poses several security challenges for IoT devices, particularly due to the stringent time constraints required for real-time applications like  
Figure 2: The 6 usage scenarios for 6G (source: ITU-R M.2160 [6]).  
industrial automation and telemedicine. These stringent time constraints necessitate the prompt transmission and processing of data, which can leave systems vulnerable to security breaches that exploit the limited opportunities for security validation. For instance, malicious actors may inject harmful data during critical operational phases when delay is not an option [45]. Moreover, the focus on reliability in these applications can inadvertently lead to compromises in security measures, resulting in weak encryption protocols or insufficient access control mechanisms. The demand for precise positioning in critical applications, such as machine control and healthcare monitoring, further increases the susceptibility of IoT devices to spoofing attacks and tracking manipulations. Additionally, the high density of interconnected IoT devices in operational settings expands the potential attack surface, significantly elevating the risk of exploitation [45].
## 2 Materials and Method
### Loss Function  
The Loss function design in this work follows the InfoNCE Loss design from SimCLR [45], using all sample pairs within a batch except for the self-sample pair as negative samples. However, as a multimodal contrastive learning task, this work adopts the mean of the mutual InfoNCE Loss between the two modalities as the final Loss of the model, a form consistent with the final Loss design of CLIP [32]:  
\[\mathcal{L}_{\text{SimCLR}}=-\frac{1}{2N}\sum_{i=1}^{2N}\log\frac{\exp\bigg{(} \frac{z_{i}^{\gamma}z_{i}}{\tau\|z_{i}\|z_{j}\|}\bigg{)}}{\sum_{k=1}^{2N}1_{k \
eq i}\exp\bigg{(}\frac{z_{i}^{\gamma}z_{k}}{\tau\|z_{i}\|z_{k}\|}\bigg{)}} \tag{4}\]  
where in Equation (4), \(\mathcal{L}\) represents the Loss function for this task; \(z_{i}\), \(z_{j}\) are feature vectors of the same original sample after different data augmentations; \(\tau\) is the temperature parameter; \(N\) is the batch size, and \(1_{k\
eq i}\) is a flag to exclude self-comparison.  
\[\mathcal{L}_{\text{EACNN}} =\frac{1}{2}\Big{(}\mathcal{L}_{\text{crystal}\to\text{xrd}}+ \mathcal{L}_{\text{xrd}\to\text{crystal}}\Big{)} \tag{5}\] \[\mathcal{L}_{\text{crystal}\to\text{xrd}} =-\frac{1}{B}\sum_{i=1}^{B}\log\frac{\exp\bigg{(}\frac{\text{C}_{i} ^{\gamma}X_{i}}{\lambda\|\text{C}_{i}\|\|X_{i}\|}\bigg{)}}{\sum_{j=1}^{B}\exp \bigg{(}\frac{\text{C}_{i}^{\gamma}X_{j}}{\lambda\|\text{C}_{i}\|\|X_{j}\|} \bigg{)}} \tag{6}\]\[\mathcal{L}_{\text{xrd}\rightarrow\text{crystal}}=-\frac{1}{B}\sum_{i=1}^{B}\log \frac{\exp\left(\frac{X_{i}^{\gamma}C_{i}}{\lambda\|X_{i}\|\|C_{i}\|}\right)}{ \sum_{j=1}^{B}\exp\left(\frac{X_{i}^{\gamma}C_{j}}{\lambda\|X_{i}\|\|C_{j}\|} \right)} \tag{7}\]  
where in Equations (6) and (7), \(C_{i}\), \(X_{i}\) are the \(i\)-th crystal structure and xrd pattern embeddings in the batch, \(\lambda\) is a trainable temperature parameter initially set to 0.07, and \(B\) is the batch size.