esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Framework
### Model Reliability and Robustness  
To ensure the reliability and robustness of the CLEAR framework, we implement two key mechanisms.  
**Input validation and consistency:** Our Relation-aware Context Filter incorporates multiple validation layers to ensure input consistency. The bridge entity scoring mechanism uses four complementary metrics (direct, indirect, potential co-occurrence, and relation similarity) to provide robust sentence selection. This multi-criterion approach reduces the risk of selecting irrelevant context due to any single scoring mechanism's limitations.  
**Attention mechanism stability:** The PDWA module employs normalized distance weights to prevent attention collapse or excessive concentration on single paths. The normalization in Equation (15) ensures that \(\sum_{j=1}^{N}\alpha_{i,j}^{2}=1\), maintaining stable attention distributions across different document path configurations. Additionally, the GumbelSoftmax temperature parameter \(\tau\) is carefully tuned to balance between discrete selection and smooth gradients.  
**Cross-validation and generalization:** Beyond the standard train/dev/test splits, we perform 5-fold cross-validation on the training set to assess model consistency. The standard deviation of F1 scores across folds remains below 2.1%, indicating stable performance. Additionally, we evaluate the model on the Cross-Document Event Coreference Resolution dataset to verify its generalization capabilities.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 4 Security Hardening of Kubernetes Components
### Identity and Access Management
#### 4.4.1 Using Certificates  
The API server enables Client certificate authentication by supplying the -client-ca-file parameter. The API server configuration is in /etc/kubernetes/manifests/kube-apiserver.yaml (static pod) or /etc/kubernetes/kube-apiserver (if operating as a systemd service). The designated file must contain one or more certificate authorities to authenticate client certificates presented to the API server. Figure 7 illustrates the method for supplying the -client-ca-file parameter to the API server within the configuration file:  
Upon the provision and successful authentication of a client certificate, the subject's common name is utilized as the username for the request. Every user holds a distinct X.509 client certificate when using this method. X.509 certificates are essential for securing communication among cluster components, authenticating users, and encrypting data. Kubernetes extensively employs X.509 certificates to secure its control plane and manage cluster security via TLS encryption.  
The API server authenticates the client certificate with a specified certificate authority. Upon successful validation, the common name from the subject's details functions as the username for the request, while any organizations linked to the subject are regarded as groups. Certificates can be generated manually using the OpenSSL tool. The OpenSSL cryptographic library offers access to encryption algorithms employed in various Internet protocols and standards.  
Its functionalities include symmetric encryption, asymmetric key cryptography, key exchange protocols, certificate management, cryptographic hashing algorithms, secure pseudo-random number generators, message authentication codes (MACs), key derivation functions (KDFs), and various auxiliary tools. The certificates can be generated by adhering to the procedure outlined below:
## 2 Experimental Procedure
### Characterization Techniques  
The layers' X-ray diffraction (XRD) characterizations were performed using a Philips X'Pert MPD XRD diffractometer with monochromatic radiation Cu-K\(\alpha\) (1.51418 A), in the range of 20-80\({}^{\circ}\), with a scanning rate of 0.05\({}^{\circ}\).s\({}^{-1}\). SEM investigations were undertaken using a JSM-6510 scanning electron microscope from JEOL, Ltd., with accelerating voltage (0.5 kV to 30.0 kV) at low vacuum (10 Pa-150 Pa) and magnification up to 600,000\(\times\). The prepared samples were also characterized by a Tecnai F20 (200 kV) transmission electron microscope (TEM) from FEI. The samples' DC and AC gas sensor electrical signals were measured using a source/pico-ammeter HP4140B and Solartron 1250 impedance analyzer from L.M.M.A laboratory, respectively, at a low-bias voltage of 50 mV.
# Planar Array Diagnostic Tool for Millimeter-Wave Wireless Communication Systems
## 6 Antenna Array Diagnosis from Measured Data
### Number of Measurement Points versus Noisy Measured Data  
Measurement points affect field reconstruction fidelity and, hence, the scheme of diagnosis. The proposed BCS-based procedure performed well despite significantly reduced measurement data due to added sparse information. According to the study and experiments performed by [PERSON] et al. [14], total variation (TV), mixed \(\ell_{1}/\ell_{2}\) norm, and minimization of \(\ell_{1}\) techniques require 64 measurement points for accurate reconstruction and diagnostics, compared to the BCS technique that requires 30 or less measurement points. The proposed method significantly reduces the number of measurements needed for diagnosis as compared to those three approaches. Since the speed of diagnosis is inversely related to the required number of measurement points, the BCS approach enables a faster diagnosis of antenna arrays.  
Also, according to the diagnostic procedures proposed by [PERSON] et al. [14], total variation (TV), mixed \(\ell_{1}/\ell_{2}\) norm, and minimization of \(\ell_{1}\) techniques can only accommodate measured data with the lowest SNR of 40 dB. A lower SNR results in bad diagnostics for the three procedures. However, there are practical measurements that exhibit SNRs lower than 40 dB which require diagnosis. To this advantage, the proposed BCS approach was theoretically and practically used to diagnose antenna arrays from measurement data with 20 dB SNR. It can equally adapt to measured data with lower SNR. The comparison is summarized in Table 1. The BCS approach requires a few seconds more computational time; however, this is very small with respect to the measurement time cost. Therefore,  
Figure 20: Reconstructed excitation error in dB by 30 random noisy measurement points.  
the proposed method shows its robustness to noisy measured data, and a reliable diagnosis was obtained for low SNRs.  
Figure 21: Experimental performance assessment of the proposed BCS-based diagnostic procedure. (**a**) Reconstruction error versus number of measurements, (**b**) Reconstruction error against SNR, (**c**) Reconstruction error with different failure rate
## 4 Derivation of Spectograms  
For SHM purposes, it is necessary to trace the evolution of the eigenvalues of the pylon as its attached mass varies with time from tracing the transient displacement \(u(x,t)\) at a given station \(x\). In reference to the pylon described in Section 2, the dynamic characteristics of the combined pylon-attached mass system vary with time and the resulting transient response is characterized from a stochastic viewpoint as non-stationary. This requires subjecting the transient signal to a series of continuous transformations to the frequency domain by implementing the short time Fourier transformation (STFT). For sufficiently small-time steps, the signal recorded within a given time interval can be considered as stationary. To this purpose, we employ the Fourier transform [22] in the following form:  
\[F_{u}(f,t)=\int\limits_{-\infty}^{+\infty}u(x=L,\tau)e^{-i(2\pi f)\tau}W(\tau -t)\;d\tau \tag{26}\]  
In the above, \(F_{u}(u,t)\) is a Fourier transform of the pylon displacement at \(x=L\), see Equation (5), and is known as a spectrogram. Moreover, \(f=\omega/2\pi\) is the frequency (in Hz) and function \(W\) is the Hanning window within a time interval \(\Delta t=\tau-t\):  
\[W(n)=0.5-0.5\cos(2\pi j/J),\;\;0\leq j\leq J \tag{27}\]  
The width of the Hanning window is \(H=J+1\) and should cover at least one cycle of vibration. Fixing the window's width, as well as the placement of two consecutive windows, is a trial-and-error procedure for achieving optimal results.  
The derivation of a spectrogram is now given for both cases where the mass attachment on the pylon is either full of a liquid that is allowed to drain, or empty and allowed to fill. At first, Figure 6 plots the transient response at the top of the pylon following a modal analysis based on the results of the eigenvalue extraction problem. Note that when alternative numerical methods such as the Runge-Kutta method are used for computing  
Figure 5: pylon-mass system eigenfrequency \(f_{i},\;\delta_{i}\;,\;i=1,2,3,4,5\) evolution with time for the case of (**a**) decreasing fluid mass \((\mu>0)\) and (**b**) increasing fluid mass \((\mu<0)\).  
the transient displacements, then the time evolution of the eigenvalues cannot be recovered directly, but require additional processing by various transformation techniques. The input is a time harmonic ground motion with an excitation frequency of \(f=10\) Hz. As the attached mass decreases with time, the structural system becomes stiffer and the response of the pylon slowly decreases by about 18% at the end of the time interval of about 0.85 s required to completely remove the mass, see Figure 6a. At the same time, the corresponding spectrogram, which reproduces the analytically computed time evolution of the combined system eigenfrequencies that was previously obtained, shows a 30% change as the system becomes stiffer due to the decreasing top mass. Finally, the white line at the bottom of the plot corresponds to the forcing frequency of 10 Hz. The exact opposite behavior is shown in Figure 6b, with the response increasing and the eigenfrequencies dropping as the system becomes more flexible with time.
# A Python-Based Indoor Channel Model with Multi-Wavelength Propagation for Color Shift Keying
## 5 Results
### Recursive Algorithm Validation  
In [17], the authors simulated four configurations (A, B, C, and D) by varying the parameters of the room, the transmitter, and the detector. They reported the received power of the LoS and the first three reflection orders for configurations A and B. For configurations C and D, only the total received power was reported. Our Python-based model was used to simulate these same four scenarios; however, we kept the number of cells used to compute the recursive equations equal for all reflection orders. The results are presented in Table 1 together with the results from [17]. Table 2 shows the relative error between the results of the two models. The low relative error validates the correct operation of our recursive algorithm. Furthermore, because the Python-based model can compute the received powerwithout exponentially increasing the amount of memory used, the received power of configurations A and B were simulated for ten reflections. Table 3 illustrates these results.
## 4 Methods
### Pseudocode of the Simulated Annealing Algorithm  
The simulated annealing algorithm with an adaptive step size control and different cooling strategies was implemented in a MATLAB environment using Algorithm 1, which was developed based on the aforementioned articles and Equations (5)-(22).  
```
1. (Initialization) Select an initial construction \(\mathbf{d}_{0}\in\varOmega\) an initial temperature \(T_{0}\); a number of trials per temperature \(N\); a cooling function \(\varLambda(t)\); an initial step size \(\rho_{0}\); a step size adaptation factor \(\beta\); the parameters of the convergence criterion \(m_{C}\) and \(\varepsilon=(\varepsilon_{\mathbf{d}},\varepsilon_{E})\) Set the counter of the objective function call \(m=0\), the accepted moves \(k=0\) and the cooling cycle \(t=1\) Set the variables of \(\mathbf{d}_{k}=\mathbf{d}_{0}\), \(\mathbf{d}_{opt}=\mathbf{d}_{0}\)\(\rho_{t}=\rho_{0}\), \(T=T_{0}\)
2. (Generate a New Candidate) do generate a random step \(\mathbf{a}_{m}(\mathbf{\rho}_{t})\in\mathbb{R}^{n}\); \(\mathbf{d}_{m+1}=\mathbf{d}_{k}+\mathbf{a}_{m}\) while\(\mathbf{d}_{m+1}\in\varOmega\) \(\mathbf{d}_{new}=\mathbf{d}_{m+1}\); \((m=m+1)\)
3. (Metropolis Criterion) calculate \(\Delta E=E(\mathbf{d}_{new})-E(\mathbf{d}_{k})\) if\(\Delta E<0\) \(\mathbf{d}_{k+1}=\mathbf{d}_{new}\); \((k=k+1)\) if\(\mathbf{d}_{new}<\mathbf{d}_{opt}\), \(\mathbf{d}_{opt}=\mathbf{d}_{new}\) else generate a uniformly distributed random number \((r)\) in the interval \((0,\,1)\) if\(r<P_{t}=exp(-\Delta E/T_{t})\); \(\mathbf{d}_{k+1}=\mathbf{d}_{new}\), \((k=k+1)\) end
4. (Cooling Schedule, Step Size Adaptation) if\(m\%N=0\) \(T_{t}\to T_{t+1}\), call the cooling function \(\varLambda(t)\); Calculate \(\chi_{10N}\); then, \(\rho_{t}\rightarrow\rho_{t+1}\), call the adaptive step size control; \((t=t+1)\) end
5. (Stopping and Convergence Criteria) if\(m>m_{C}\) if\(C(cc_{1},cc_{2},sc)=1\), check for stopping/convergence criterion stop the search with results \(\mathbf{d}_{opt}\), \(E(\mathbf{d}_{opt})\), \(m\) else go to step 2 end  end
```  
**Algorithm 1**Simulated annealing algorithm with adaptive step size control
## 1 Introduction
[24] extended the RCA technique to higher-frequency radars and range-height indicator (RHI) scans, and called it the eRCA technique. The studies mentioned above were commonly limited to a single radar or research radar operated for the field campaign. Therefore, an investigation is required to apply the RCA technique to an operational radar network. Recently, an integrated method was designed to complement the disadvantages of each technique. [PERSON] et al. [25] developed an online software to monitor the stability of radar calibration using SC, intercalibration, GC calibration, and solar calibration techniques. [PERSON] et al. [26] proposed an integrated method using GC monitoring, comparisons with satellite radar, and SC. [PERSON] and [PERSON] [27] compared ground radar and spaceborne radar observations to calibrate ground radars, and then used ground radar overlaps to verify the consistency between two ground radars. In this study, GC monitoring, SC, and intercomparison were combined to monitor the calibration bias in \(Z_{\rm H}\) for 10 operational S-band dual-polarization radars operated by the Korea Meteorological Administration (KMA) in real time. The common characteristic of the methods we selected is that all the methods use only radar polarimetric observations, and it has an advantage in terms of operational environment because the three methods only require radar data across the network. Section 2 describes the KMA nationwide radar network, while Section 3 introduces the concept of the combined method and principles, development, and pros and cons of each technique. In Section 4, \(Z_{\rm H}\) monitoring based on GC monitoring and SC is described. In addition, the potential of estimating calibration bias from GC monitoring was examined. The homogeneity of \(Z_{\rm H}\) over the radar network was also examined by calculating the mean bias between adjacent radars. A summary and conclusion are presented in Section 5.
## 4 Two Improved Decision-Level Fusion Criterions for MUD
### The Chairman Arbitrating Decision-Level Fusion Criterion (CA-DFC)  
As Figure 1 shows, the preliminary decisions (let \(X_{1}\), \(X_{2}\), and \(X_{3}\)) of SIC, DEC, and MMSE detectors are transmitted to the committee synchronously. Note that it is a feature of the committee that there is a chairman in it, whose role is to evaluate these preliminary decisions and make the global decision [47]. The different ways to acquire this global decision correspond to the different DFCs. So here, we consider a situation where the chairman can make his arbitration among these sub-optimal or preliminary decisions with its own arbitrating rule, to select which one is the best. This method is called the chairman arbitrating DFC (CA-DFC) in this paper and discussed below.  
Taking the objective function of OMD in Equation (6) into account, we define an approving degree function for the chairman to make his arbitration:  
\[D_{i}=2\,b^{T}\,A\,y-X_{i}^{T}\,A\,R\,A\,X_{i} \tag{27}\]  
where \(i=1,\,2,\,\ldots,\,n\). Then the CA-DFC can be derived:  
If the term  
\[D_{j}=\max_{i=1,2,\,\ldots,n}D_{i} \tag{28}\]  
satisfies, the preliminary decision \(\mathbf{X_{j}}\) is arbitrated as the global decision by the chairman. In our model, \(n\) equals 3 for SIC, DEC, and MMSE detectors are employed, and this can be generalized into the case that has more than three detectors without difficulties.  
Differing from O-DFC and MV-DFC, this CA-DFC doesn't require the assumption of statistical independence between these sub-optimal detectors, as well as the accurate BERs of them. Furthermore, it can be speculated that the BER performance of this CA-DFC based MUD method is better than that of any of these sub-optimal detectors, due to the operation of Equation (28). That is, its BER achieves  
\[P_{eem\_C\_4}<\min\left\{P_{e1m},P_{e2\_m},P_{e3m}\right\} \tag{29}\]
## 3 Results and Discussion
### Fatty Acid Analysis  
Table S4 showed the fatty acid composition and relative contents of egg yolk under different ozone treatment times, and 17 fatty acids were identified. Among them, oleic acid and palmitic acid were the major fatty acids in the egg yolk, accounting for 41.47% and 26.89% of the total fatty acids in the untreated egg yolk, respectively [31]. Myristic acid, myristic brain acid, _cis_-10-heptadecenoic acid, \(\gamma\)-linolenic acid, \(\alpha\)-linolenic acid, _cis_-11-eicosatrienoic acid, _cis_-8,11,14-eicosatrienoic acid, carnisc acid and docosahexaenoicenoic acid were the relatively small amounts of fatty acids in egg yolk, accounting for 0.34%, 0.09%, 0.08%, 0.11%, 0.27%, 0.14%, 0.14%, 0.63%, and 0.56% of the total fatty acids in the egg yolk, respectively. No significant changes were found in the types of yolk fatty acids after ozone treatment, but the relative contents of the components differed significantly (\(p<0.05\)). Notably, _cis_-11,14-eicosadienoic acid was not detected in untreated egg yolks but was detected in all ozone-treated egg yolks. In addition, there was a significant difference in the relative content of palmitic acids in the treated group compared to the control group. This might be due to the oxidation of ozone that disrupted the long-chain fatty acids in the yolk and thus part of the carbon chain breaks, causing an increase in the content of these fatty acids [32]. Oleic acid, the major fatty acid in egg yolk, underwent a significant decrease after 40 min of ozone treatment.  
The relative content of linoleic acid in egg yolk increased significantly (\(p<0.05\)) with the increase in ozone treatment time. The first unsaturated double bond of oleic acid was located on the penultimate ninth carbon atom at the end of the carbon chain (methyl ester end), which was an \(\omega\)-9 type polyunsaturated fatty acid. The first unsaturated double bond of linoleic acid was located at the penultimate sixth carbon atom at the end of the carbon chain (methyl ester end), which was an \(\omega\)-6-type polyunsaturated fatty acid. In addition, linoleic acid was more easily absorbed by the body than oleic acid [33].
## 2 Related Work
[PERSON] et al. 's Transformer architecture, powered by self-attention, fundamentally changed NLP [28]. [PERSON] et al. conducted an extensive analysis of attention mechanisms, providing theoretical foundations [29]. Applied specifically to documents, [PERSON] et al. proposed hierarchical attentional hybrid neural networks for effective document classification [30], [PERSON] et al. demonstrated the effectiveness of combining embeddings with attention for sentiment analysis [31], and [PERSON] et al. developed attention-based features for urgent post-classification [32]. These works powerfully model intra-document relationships and feature importance. However, they primarily focus on understanding individual texts and lack explicit mechanisms for modeling inter-document relationships and dependencies across a corpus of related documents within a workflow context. This limitation, stemming from the traditional independent treatment of documents, prevents capturing the full contextual interplay vital for tasks like flow-based classification. Our AM-HDGAT module directly builds upon the power of attention [28; 29] but critically extends it to capture mutual relationships between documents through graph attention networks, enabling a holistic view crucial for document flow management. Synthesis: Bridging the Gaps with AttenFlow. The reviewed literature demonstrates substantial progress in core areas relevant to document processing. Nevertheless, a critical, unifying limitation underpins much of the prior work: traditional methods typically treat documents independently [21; 22; 23; 30; 31; 32], failing to capture the complex relationships and dependencies that exist within document corpora. This omission is particularly detrimental to tasks requiring contextual understanding across multiple documents, such as nuanced classification and precise retrieval within dynamic flows. Additionally, state-of-the-art retrieval fusion methods remain static [13; 14], ignoring consensus patterns among retrievers and lacking confidence estimates, limiting their robustness and adaptability to varying agreement scenarios. Our work directly targets these fundamental gaps:  
RCCF Method: Revolutionizes retrieval fusion by dynamically evaluating consensus among retrievers and providing confidence estimates for results, overcoming the static nature and lack of confidence assessment in methods like RRF [14]. AM-HDGAT Module: Leverages graph neural networks and mutual-attention mechanisms [28] to explicitly model intricate inter-document relationships and dependencies, directly countering the isolated document view prevalent in prior extraction, classification, and clustering work. AttenFlow Framework: Integrates RCCF and AM-HDGAT synergistically within a unified architecture. This integration enables a comprehensive approach that jointly enhances retrieval effectiveness through consensus-aware fusion and boosts classification robustness by understanding document interdependencies within the workflow, offering a significant step beyond the capabilities of existing isolated solutions.