esa-sceva/satcom-chunk-collection huggingface dataset:
# A Joint Dual-Frequency GNSS/SINS Deep-Coupled Navigation System for Polar Navigation
## 4 Experimental Results and Discussion
### Experiment Discriptions  
#### 4.1.1 Hardware GNSS Signal Simulation  
The data collection process is shown in Figure 3. A customized HWA-GNSS-8000A hardware GNSS simulator, which is a multiple constellations and frequencies GNSS simulator, was used to simulate the dual-frequency GPS L1 C/A and L2 CM signals in polar areas. The ionospheric scintillations were generated by a scintillation simulation generator and then added to the GNSS signals. The scintillation simulation generator is based on the multiple phase screen (MPS) method introduced in [26], which is a numerical technique for simulating the propagation of radio waves through a refractive and diffractive medium and allows both amplitude and phase fluctuations to accumulate within the medium. For the detailed design of a similar scintillation simulation, the reader can refer to [14]; ours was modified for ground receivers. Besides this, we used the NT1065-based NUT4 NT [27], which is a multifrequency, multisignal satellite navigation front-end receiver board used to receive GNSS signals through the simulator and then convert the high-frequency GNSS signals down to lower intermediate-frequency signals.
## 1 Introduction
### Contributions of This Paper  
This study introduces a novel high-gain, dual-band microstrip antenna designed for operation at 28 GHz and 38 GHz, offering a compact, cost-effective, and high-performance alternative to conventional array configurations. The proposed design challenges the prevailing assumption that single-element antennas are inherently limited in performance by demonstrating that the gain and return loss values rival and, in some cases, exceed those of multi-element array systems. These findings provide new possibilities for efficient antenna solutions in 5G and beyond. The key contributions of this work are as follows:  
* The proposed antenna achieves industry-leading return loss values of \(-\)53.04 dB at 28 GHz and \(-\)83.65 dB at 38 GHz, thereby setting a new benchmark for single-element microstrip antennas. Additionally, the measured gain values of 7.82 dBi at 28 GHz and 8.98 dBi at 38 GHz surpass those of comparable designs in the literature, demonstrating the suitability of the proposed design for high-performance mmWave applications.
* This study systematically examines the impact of an SMP connector on mmWave antenna performance, providing critical insights into its effects on the return loss and gain. Unlike most studies that assume negligible connector influence, this research highlights the significance of the influence and offers practical guidelines for real-world antenna integration.
* A rigorous optimization framework that combines analytical methods with parametric analyses was used to refine the antenna geometry. The influence of each patch edge on the S\({}_{11}\) parameter was carefully evaluated to ensure minimal sensitivity to the fabrication tolerances. This approach enhances antenna reliability and manufacturability, which are critical factors for large-scale deployment.  
* With dimensions of only 33 \(\times\) 27 \(\times\) 1.6 mm\({}^{3}\), the proposed design minimizes material usage and manufacturing complexity while maintaining superior performance. The compact structure and simplified fabrication requirements make it highly viable for commercial applications where size, cost, and efficiency are paramount.
## 2 Results and Discussion
### Star Polymers with PDMAEMA Arms  
#### 2.2.1 Influence of DVB/PDMAEMA-CPTCD Ratio  
There have been a lot of research reports about star polymer preparation via arm-first method. For instance, [PERSON] _et al._[17] applied the linear macro RAFT agent to prepare star polymer via arm-first method. It was found that the formation of star polymer by arm-first method was affected by molecular weight of macro-CTA or macro-initiator, feed ratios and polymerization time in the previous literature [48]. In this work, in order to convert almost all of the linear polymer chains to the star polymers, the polymerization time on the RAFT polymerization of DVB was set to be 12 h. The effects of the ratio of DVB to PDMAEMA-CPTCD (\(M_{\mathrm{n,GPC}}=8325\) g/mol, PDI = 1.27) were studied, and the results were listed in Table 2. The corresponding GPC traces were shown in Figure 3. In general, the molecular weight of polymer stars increased with the increasing molar ratio of DVB to PDMAEMA-CPTCD. With the increase of DVB to PDMAEMA-CPTCD ratio, the first peak at lower molecular weight position belonging to the linear macro RAFT agent, PDMAEMA-CPTCD, stayed at the same elution time and its integral area decreased gradually. Then, the second peak at higher molecular weight position increased continuously, which suggests that the polymerization of DVB occurred and more and more linear polymers changed to star polymers. When DVB:PDMAEMA-CPTCD = 50:1, a small third peak appeared, which might be owing to star-star coupling reaction and too high a ratio will lead to the  
Figure 2: **(A)** Dependence of In ([M]o/[M]) on time (h); **(B)** Relationship of the molecular weights and polydispersity index (PDI) of PDMAEMA macro-chain transfer agent obtained with monomer conversion using AIBN as the radical initiator and CPTCD as the RAFT agent in toluene at 70 \({}^{\circ}\)C. DMAEMA/CPTCD/AIBN = 150:1:0.25, \(\mathrm{C_{DMAEMA,0}}=2.0\) mol/L.  
gel effect (e.g., DVB:PDMAEMA-CPTCD = 60:1). The linear macro RAFT agent, which was not attached to PDMAEMA stars, still remained throughout the polymerization process.  
Figure 3: Gel permeation chromatograph (GPC) traces of SPDMAEMA with the DVB/PDMAEMA-CPTCD = 25 (SPDMAEMA-1); 35 (SPDMAEMA-2); 40 (SPDMAEMA-3); 45(SPDMAEMA-4); 50 (SPDMAEMA-5); 55 (SPDMAEMA-6) obtained for 12 h polymerization at 100 \({}^{\circ}\)C in the presence of AIBN as initiator (molar ratio: PDMAEMA-CPTCD/AIBN = 1:0.5), PDMAEMA-CPTCD: 0.4150 g, Toluene: 8.26 mL.
## 3 Results and Discussion
### The Response of NEE and LE Fluxes to the Extreme Temperature Anomalies
#### 3.1.2 Variation in NEE and LE Flux in Cold Season  
The influence of temperature anomalies on NEE of forest ecosystems during the cold season was mainly manifested by changes in the ecosystem respiration rate depending on the air temperature oscillation. The anomalies of the LE were also closely related to the variations of the temperature and the solar radiation. The number of considered forest ecosystems was reduced to 21, because at 5 out of 26 considered stations there was no possibility to detect the cold season because the duration of the period with negative temperatures never exceeded 7 days.  
In the evergreen needleleaf forests, the strong NEE and LE anomalies (exceeding STD) occurred rather rarely during extremely cold periods; at most stations, less than 10% of extremely cold days were characterized by extreme flux anomalies (Figure 7d). This may be the result of a general weakening of the CO\({}_{2}\) and H\({}_{2}\)O fluxes during the winter months. The thaws (temperature higher than 95% quantile) mostly caused increased CO\({}_{2}\) emissions (red bars on Figure 7c) due to intensified ecosystem respiration. The results are in good agreement with recent findings of increased CO\({}_{2}\) release in northern permafrost regions during winter, which has accelerated recent Arctic warming and permafrost thawing [74; 75].  
In other forest types south of evergreen coniferous forests, extremely low temperatures mostly led to negative NEE anomalies (blue bars in Figure 10b), whereas high temperatures mostly led to positive NEE anomalies (red bars in Figure 10a). Soil respiration was expected to be a major driver of ecosystem fluxes in winter [76; 77].  
The decreased evaporation (yellow bars) was associated with more than 20% of the days with extremely low temperatures at 6 out of 21 stations, whereas at other stations, the anomalies were minimal (Figures 7d and 10b). On the contrary, the periods of winter temperature increase more often resulted in positive anomalies of LE (green bars in Figures 7c and 10a), which was the common feature for warm and cold seasons.
# Expansion Lemma--Variations and Applications to Polynomial-Time Preprocessing
## Box 23  
Almost \(t\)-Disjoint Cycle Packing Problem.  
**Input:**_An undirected graph \(G\) and an integer \(k\)._  
**Parameter:**_\(k\)._  
**Question:**_Are there at least \(k\) distinct cycles \(C_{1},\ldots,C_{k}\) in \(G\), such that every vertex of \(G\) appears in, at most, \(t\) of these cycles?_  
**Box 24**.: Pairwise \(t\)-Disjoint Cycle Packing Problem.  
Pairwise \(t\)-Disjoint Cycle Packing (\(t\)-PDCP)  
**Input:**_An undirected graph \(G\) and an integer \(k\)._  
**Parameter:**_k._  
**Question:**_Are there at least \(k\) distinct cycles \(C_{1},\dots,C_{k}\) in \(G\) such that for every \(i\
eq j\), \(|V(C_{i})\cap V(C_{j})|\leq t\)?_  
A simple application of Lemma 3 can obtain a kernelization with \(\mathcal{O}(k^{2})\) vertices. Fomin et al. [37] provided an improved kernel with \(\mathcal{O}(k^{5/3})\) vertices for 3-Path Packing. They used Lemma 3 to obtain this result. Later, Bessy et al. [53] introduced a new technique called _rainbow matching_ and improved this result into a kernel for 3-Path Packing (Box 25) with \(\mathcal{O}(k)\) vertices.  
**Box 25**.: \(3\)_-Path Packing Problem._  
3-Path Packing  
**Input:**_An undirected graph \(G=(V,E)\) and an integer \(k\)._  
**Parameter:**_k_  
**Question:**_Are there \(k\) pairwise vertex disjoints inducing \(P_{3}\) in \(G\)?_
# A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling
## 4 Modeling  
### Two-Stage Optimization Model for Stand Allocation and Ground Support Vehicle Scheduling  
We propose a collaborative scheduling mechanism for stand allocation and ground support vehicle resources, as illustrated in Figure 3. In the process of solving the stand allocation problem, tractor service constraints are incorporated to prevent the departure times of flights stationed in the same tractor service area from being too concentrated. Additionally, one of the goals is to minimize the driving time of ferry vehicles. The allocation results yield the assigned parking stands for each flight and the service time windows. The ground support vehicle system does not operate independently, and the gate assignment plan may lead to vehicle scheduling plans failing to meet service demands. Therefore, we adopted the two-stage model to study the integrated scheduling of ground support vehicle resources and gate resources. This approach analyzes the coupling factors and strategies of integrated scheduling.  
The optimization scheduling of ground support vehicles can be understood as maximizing the task efficiency of support vehicles based on the optimal allocation of stands. This is achieved by utilizing the flight stands and service time windows generated from the allocation results. The vehicle routing schedule is then obtained according to the sequence of vehicle services and their collaborative relationships.  
Figure 2: Diagram of ground support vehicle working modes.
## 6 Benefits and Deployment Considerations of GLI-Split
### Benefits for Classic IPv6 Nodes in GLI-Domains  
Classic IPv6 nodes can be accommodated in GLI-domains. This is a valuable feature for incremental deployability since equipment for which upgraded GLI-networking stacks are not yet available, or legacy equipment for which GLI-networking stacks will no longer be provided, can be operated in GLI-domains.  
Internal renumbering after a provider change is simplified because classic IPv6 nodes in GLI-domains know only their local GLI-address. Hence, provider changes are invisible to them, as are nodes behind a NAT-gateway.  
Multihoming is possible. When communicating with upgraded GLI-nodes, they can perform host-based rerouting so that also classic IPv6 nodes in multihomed GLI-domains achieve better resiliency. Traffic engineering is supported for incoming traffic but not for outbound traffic.
## 2 Problem Formulation and System Model
### System Model
#### 2.2.3 Encrypted Hybrid Fingerprint Model  
We adopt the above privacy-preserving strategy that involves pixel position scrambling encryption and noisy linear mapping encryption to secure the image data. In this approach, the pixel position scrambling encryption does not alter the values of the original camera fingerprints \((c,d)\), ensuring that the hybrid fingerprint model remains unaffected. Therefore, in the encrypted environment, we focus solely on the impact of coefficient changes introduced by the noisy linear mapping encryption within our hybrid fingerprint model. Specifically, we define \(\xi_{m_{k}}\) and \(\xi_{m_{k}}\) as the noise before and after encryption of the \(k\)-th pixel in an image, respectively. The relationship between between \(\xi_{m_{k}}\) and \(\xi_{m_{k}}\) can be written as  
\[\xi_{m_{k}}=\varphi\xi_{m_{k}}, \tag{4}\]  
where \(\varphi\) is a linear coefficient. Specifically, the linear coefficient \(\varphi\) serves as a critical parameter for encrypting image noise. It is exclusively determined and maintained by official institutions and is not disclosed to any third parties, thereby ensuring the security of the encryption process. The official institution randomly sets the linear coefficients of different camera models, which can effectively change the noise characteristics of the image, and thus protect the image source. Note that the linear coefficients used in noise linear mapping encryption should be distinct depending on different camera models or devices. In addition, when identifying whether an image is from a specific camera model or device, the linear coefficient \(\varphi\) used to encrypt the image must be consistent with the linear coefficient \(\varphi\) of the corresponding camera model or device.  
Based on (4), the noise \(\xi_{m_{k}}\) of each pixel is multiplied by a linear coefficient \(\varphi\). Thus, the encrypted hybrid fingerprint model can be further written as  
\[\sigma_{m_{k}}^{2}=\varphi^{2}\sigma_{m_{k}}^{2}\overset{\triangle}{=}f(\mu_{ m_{k}};c,d)=\varphi^{2}(c\mu_{m_{k}}^{2}+d\mu_{m_{k}}+\frac{\Delta^{2}}{12}), \tag{5}\]  
where \(\sigma_{m_{k}}^{2}\) is the variance of the \(k\)-th pixel after encryption. \((c,d)\) is the encrypted camera fingerprint.
# Video Captioning Based on Channel Soft Attention and Semantic Reconstruct
## 3 Method
### Training  
Formally, the encoder-decoder module and reconstructor module are trained together. To generate the correct captions, the encoder-decoder module is trained by minimizing the negative log-likelihood. The loss of the encoder-decoder module can be defined as follows:  
\[Loss_{e-d}=\sum_{t=1}^{T}(-\log{P(y_{t}|v,y_{t-1};\theta)}) \tag{25}\]  
Figure 3: An illustration of the proposed semantic reconstructor module.  
where \(y_{t}\) denotes the word generated at time \(t\), \(v\) is the encoded video features and semantic vector, and \(\theta\) represents the parameters need to optimize. The total loss involving the encoder-decoder and reconstructor can be defined as:  
\[Loss_{total}=\lambda MSE(s,s_{r})+\sum_{t=1}^{T}(-\log P(y_{t}|v,y_{t-1};\theta)) \tag{26}\]  
where \(\lambda\) is a hyper-parameter to trade-off the encoder-decoder module and reconstructor module.
## 4 Discussion
Consequently, other applicable MeSH terms are excluded, leading to fewer usable MeSH terms for distinguishing and searching clinical trials. To better understand how both methods compare in regard to the retrieved results given the initial medical device information, and also on a content level, the overlap of the result sets and MeSH terms of retrieved publications was analyzed. The comparison of retrieved publications by both methods revealed a rather weak overlap in the first 500 results, indicating that both methods retrieve different publications while maintaining fairly high precision. This shows that both methods retrieve relevant publications, but possibly with different focus points. The MeSH-based method retrievespublications with matching MeSH terms; hence, publications of the same domain are retrieved with possibly unrelated titles and abstract text. The BERT-based method contrasts this, by retrieving publications with highly similar text, but possibly focusing on a different domain since MeSH terms are not considered. Structural differences in data quality also contribute to search differences: MeSH terms are missing in approximately 14% of publications and 33% of publications are without abstract text in Medline [56]. Consequently, the MeSH-based method cannot retrieve every publication and the BERT-based method can only compute the similarity based on the title of specific publications, possibly containing only a fraction of the important aspects of the publications as opposed to summarizing abstracts [57]. Nevertheless, despite a weak overlap of the result sets at the document level, as measured by the Jaccard index, the overlap based on MeSH terms indicates that the largest overlap with the MeSH terms rated as relevant by the SMEs lies in the overlap region of both search methods. On one hand, analyzing MeSH terms of retrieved publications allows us to compare different search methods on a content level and opens up possibilities to make the rationale of any search method, such as the black-box BERT-based search, more understandable. On the other hand, this overlap indicates the potential for improvement could exist in a synthesis of both search methods. Such synthesis could facilitate both methods to complement each other for an optimized retrieval of relevant documents for each specific use case using feedback loops while establishing transparency via MeSH terms.
# Industrial Image Anomaly Detection via Self-Supervised Learning with Feature Enhancement Assistance
## 3 Materials and Methods
### Self-Supervised Training  
In the previous section, we artificially generated pseudo-anomaly images and pseudo-labels. This section focuses on learning self-supervised representations by constructing a one-class classifier using normal images and the generated anomaly images. Our goal is to discriminatively distinguish between the feature distributions of the normal and abnormal images, rather than carefully classifying the features. In other words, our aim is for the model to learn to recognize what is normal and what is abnormal, rather than accurately identifying specific types of anomalies, due to the diversity and uncertainty of abnormalities in reality.  
Unlike some other self-supervised models, our approach, inspired by embedded methods, does not involve training the entire network from scratch but rather focuses on the feature level. Firstly, we extract features from the middle layer through the pre-trained network. Subsequently, using multi-scale feature fusion techniques, we obtain fused features with varying semantic information and resolutions. Next, these fused features serve as inputs to our model where we construct a concise encoder to project them into a latent space for discrimination and classification. This design enables our model to leverage the intrinsic structure of the data more effectively, thereby enhancing classification performance.  
By generating simulated anomalies, we obtain normal image, texture-class anomalous image, and structure class anomalous image. These are processed through a pre-trained feature extractor to get multi-scale fusion features, which are then input into an encoder to  
Figure 3: The Pipeline of the Multi-scale Feature Aggregation Module.  
generate potential vectors. Finally, these vectors are fed into a classifier for classification, using the cross-entropy loss function for training. The training objectives are as follows:  
\[\mathcal{L}=\mathbb{E}\big{\{}\mathbb{CE}(g(\Phi(f_{agg}(x))),0)+\mathbb{CE}(g( \Phi(f_{agg}(x_{Tex}))),1)+\mathbb{CE}(g(\Phi(f_{agg}(x_{Str}))),2)\big{\}} \tag{8}\]  
In Equation (8), \(x\in\mathcal{X}_{N}\) denotes the input normal image, and \(x_{Tex},x_{Str}\in\mathcal{X}_{A}\) denote the generated texture-class and structure-class anomalies, respectively. Furthermore, 0, 1, and 2 denote the corresponding pseudo-labels, respectively. \(f_{agg}(\cdot)\) denotes multi-scale aggregated features in Equation (7), \(\Phi\) and \(g\) denote the encoder and classifier, respectively, and \(\mathbb{CE}(\cdot,\cdot)\) denotes the cross-entropy loss function. The structures of the encoder and classifier are shown in Table 1, where the encoder consists of three convolutional blocks and corresponding pooling layers, each containing a convolutional layer, BatchNorm layer, and ReLU layer. The classifier consists of a multi-layer perceptron (MLP) and fully connected (FC) layers.
# Formal Verification and Analysis of 5G AKA Protocol Using Mixed Strand Space Model
## Abbreviations
\begin{tabular}{l l} \(CA\) & the amount of calculation of the 5G AKA protocol \\ \(CK\) & a cipher key \\ \(ECDH\) & the generation and verification of an ECDH exchange \\ \(EK\) & an encryption key \\ \(f_{1}(),f_{1}^{*}(),f_{2}()\) & three message authentication functions \\ \(f_{3}(),f_{4}(),f_{5}(),f_{5}^{*}()\) & four key generating functions \\ \(F\) & the generation and verification of a key function or a key \\ \(FN\) & the number of fields (among the UE, the SN and the HN) \\  & of the 5G AKA protocol \\ \(HMAC()\) & a hash function for computing MAC \\ \(H_{1},H_{2},H_{3},H_{4}\) & four messages that are not inspected by the SN \\ \(HN\) & the HN \\ \(HRES^{*}\) & a hashing response from \(RES^{*}\) \\ \(HXRES^{*}\) & a hashing expected response from \(XRES^{*}\) \\ \(ICB\) & an initial counter block \\ \(IK\) & an integrity key \\ \(K\) & a long-term key between the UE and the HN \\ \(K_{AMF}\) & a key between the UE and the access and mobility management function \\ \(K_{AUSF}\) & a key derived from \(CK\) and \(IK\) \\ \(K_{P}\) & the key set of the penetrator \\ \(K_{SEAF}\) & a key derived from \(K_{AUSF}\) \\ \(K_{SN,HN}\) & the session key between the SN and the HN \\ \(KDF()\) & a key derivation function \\ \(MAC\) & a MAC of the HN \\ \(MACf\) & the \"MAC failure\" indication \\ \(MAC_{UE}\) & a MAC of the UE \\ \(MK\) & a MAC key \\ \(MN\) & the number of messages (among the UE, the SN \\  & and the HN) of the 5G AKA protocol \\ \(ngKS1\) & identifying the \(K_{AMF}\) and the partial native security context \\ \(PED\) & the encryption and decryption based on a public and private key pair \\ \(RAND\) & an unpredictable challenge of the HN \\ \(RAND_{SN,\,r_{SEAF}}\) & two unpredictable challenges of the SEAF \\ \(RES\) & a response \\ \(RES^{*}\) & a response from \(RES\) \\ \(Result\) & the authentication result \\ \(r_{UE}\) & an unpredictable challenge of the UE \\ \(SHA256()\) & a hash function \\ \(SN\) & the SN \\ \(SNN\) & the serving network name of the SN \\ \(SQN\) & a fresh sequence number generated by the HN \\ \(SQN_{UE}\) & the highest sequence number the USIM has accepted \\ \(SUC1\) & a SUCI of the UE \\ \(SUPI\) & a SUPI of the UE \\ \(Syncf\) & the \"Synchronization failure\" indication \\ \(UE\) & the UE \\ \(x\) & an ephemeral private key of the UE for Diffie-Hellman exchange \\ \(x\cdot G\) & an ephemeral public key of the UE for Diffie-Hellman exchange \\ \(XMAC\) & a MAC locally computed by the UE \\ \(XRES\) & an expected response \\ \(XRES^{*}\) & an expected response from \(XRES\) \\ \(y\) & an ephemeral private key of the HN for Diffie-Hellman exchange \\ \(y\cdot G\) & an ephemeral public key of the HN for Diffie-Hellman exchange \\ \end{tabular}