esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulated Theoretical Smooth Profile Retrieval
### Profile Retrievals from Simulated TES Measurements  
Profile retrievals using also simulated Tropospheric Emission Spectrometer measurements onboard the Aura spacecraft following the same approach as for CrIS are conducted in this study. The \"select channel\" algorithm leaves us 956 out of 3167 channels for the same selected window (900-1090 cm\({}^{-1}\)), which can be used for an O\({}_{3}\) retrieval without using any interfering gases in the forward model. The number of selected channels is \(\sim\)5.6 times higher than that of CrIS measurement because the TES instrument is designed with \(\sim\)10 times higher spectral resolution (0.06 cm\({}^{-1}\)) than CrIS (0.625 cm\({}^{-1}\)). Effective TES measurements are generated by adding random noise at the rate of SNR = 300 on top of the simulated spectrum as specified by the noise level of TES.  
As observed in Figures 1 and 2, the retrievals of O\({}_{3}\) profiles and surface temperature from simulated TES spectrum are improved compared to those of CrIS. This is expected because of both higher SNR and higher spectral resolution of TES as compared to CrIS. The retrieval up to 25 km for TP1 can be reliable for any shape of the IG. The retrievals of TP2 and TP3 (Figure 2b) from IG1 are remarkably good. The above mentioned weak space at \(\sim\)10 km is also observed for TES solution and we need further studies of window/channel selection to improve the information at \(\sim\)10 km by additional measurements. The RMSE value of surface temperature reduces further. The most interesting observation from this study is that the solutions of three Monte-Carlo simulations are  
Figure 1: Simultaneous retrievals of O\({}_{3}\) and surface temperature using RTLS from simulated CrIS measurements; Upper-panel: O\({}_{3}\) profiles; Lower-panel: surface temperature: (**a**) retrieved O\({}_{3}\) profiles (solid red from IG1 and cyan from IG2) from two different IGs (IG1 is green and IG2 is blue) for realistic true profile (solid black) and (**b**) retrieved O\({}_{3}\) profiles (solid red) from IG1 (solid green) only for two different sinusoidal true profiles (solid black).  
almost identical as opposed to the same for CrIS. Three different noises in measurements produced three different retrieved profile shapes for CrIS. Although the solutions are convincing (especially for TES) as compared other published results, the exact solution is not achievable using these experimental setup (selection of channels). Thus, the focus of this study will be how much information can be extracted from the measurements by reduction of the IG error.
# Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems
## 4 Discussion
The complexity is also affected by the need to estimate the channels and acquire the channel statistics of all IoT devices. Another reason is that M-MMSE performance is hard to analyze mathematically, while other schemes can give more insightful closed-form spectral efficiency expressions. A third reason is that receiver combining methods are often developed for single-cell scenarios and then applied heuristically in multi-cell systems. * Additionally, as mentioned earlier, olive farming at Al Jouf, with an area of 7730 hectares and over 30 million olive trees [28], leads to a massive amount of collected data. Given this, it is essential to deploy real-time control, and data should be protected from unauthorized access or interception.
# Emotion Detection Using Facial Expression Involving Occlusions and Tilt
## 2 Related Work
It automatically detects features but requires more data sets for training. For the current study, we use extended the Cohn-Kanade (CK+) [31] and Japanese Female Facial Expression (JAFFE) [32] datasets which contain large data and are frequently used. Our main focus is to mitigate the effects that occur in images due to occlusions. We focus on human emotions such as joy, grief, surprise, fear, contempt, anger, and neutral. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Ref. ** & \begin{tabular}{c} **Face** \\ **Detection** \\ \end{tabular}  & **Preprocessing** & \ \begin{tabular}{c} **Feature** \\ **Extraction** \\ \end{tabular}  & **Classification** & **Database** & **Emotions** & **Result** \\ \hline \multirow{3}{*}{[21]} & MCT-based eyes and face detection & \multirow{3}{*}{ \begin{tabular}{c} Alignment base \\ on eye \\ \end{tabular}  } & Block discrete cosine transform & SVM (LIBSVM using an RBF. kernel.) & GEMEP-FERA dataset. & \multirow{3}{*}{5} & \multirow{3}{*}{24.7\%} \\  & & & & & & \\ \cline{1-1}  & Viola–Jones & - & LGBP and LBP & Multi-class SVM & BU3 DFE & 6 & 71.1\% \\ \hline \multirow{3}{*}{[23]} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{HOG} & \multirow{3}{*}{CNN, LSTM} & FER2013, IMFED, K, CK, CK+ & & \multirow{3}{*}{74\%} \\  & & & & &
# Analysis of the Development Status of eLoran Time Service System in China
## 6 Signal System
### Ninth Pulse Modulation Principles  
The ninth pulse technology for data transmission has a 32-ary PPM scheme. An additional pulse is inserted after the eighth pulse of the traditional LORAN pulse group.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Pulse State**} & \multicolumn{2}{c}{**Transmission Time Minus**} & \multirow{2}{*}{**Indication**} \\  & **Time of Reference Pulse (\(\upmu\)s)** & \\ \hline Advanced pulse & \(-1\) & \(-\) \\ \hline Prompt pulse & 0 & 0 \\ \hline Delayed pulse & +1 & + \\ \hline \hline \end{tabular}
\end{table}
Table 4: States of 3S-PPM modulation.  
Figure 8: Schematic diagram of modulated pulse.  
Figure 9: 3S-PPM modulation.  
The thirty-two state PPM is used to represent the time delay from the zero symbol offset. Each signal position is defined by a five bit symbol, the first two signals are roughly delayed, and the last three digits indicate a small delay. In this way, the data transmission rate is 5 bits/GRI.  
The zero sign offset of the additional pulse is 1000 us. The delay time of the remaining 31 symbols relative to the zero symbol is shown in microseconds. The ideal delays are given by the formula:  
\[d_{i}=1.25 mod(i,8)+50.625 floor(i/8) \tag{1}\]  
The actual delays are the ideal values shifted to coincide with the ticks of a 5 MHz clock [17].  
Figure 10 shows the ninth pulse modulation given in Ref. [30]. All the modulation messages and consist of three components: 4 bit for the type, 41 bit for the payload, and 75 bit for the parity component. The message sending rate is 5 bits/GRI. The time length of the messages is 24 GRI (maximum of approximately 2.4 s).
## 1 Introduction
The combination of RHEED with PLD enhances the reliability and reproducibility of the deposition process and expands the range of materials and applications accessible from oxide electronics to spintronics and advanced photonic devices. An emerging area of interest in thin films and their application is the detachment of the films from their original substrates for transfer onto alternative materials. This step is essential for integrating films with flexible, lightweight, or unconventional substrates that are incompatible with the high temperatures and oxygen partial pressures (10\({}^{-3}\) to 1 mbar) typically required during deposition. Techniques such as sacrificial layer etching have been developed to facilitate this transfer, broadening the functionality and applicability of PLD-grown films. The nature of the substrate plays a crucial role in determining the quality of PLD-produced films. For epitaxial thin films free of cracks and grain boundaries, single-crystal substrates with lattice parameters closely matching those of the deposited film are generally required. However, the deposition conditions--temperatures ranging from 400 \({}^{\circ}\)C to 800 \({}^{\circ}\)C--often preclude direct deposition onto substrates such as integrated circuits. To overcome this limitation, films are commonly transferred to alternative substrates more suitable for subsequent applications. PLD remains an indispensable technique for exploring novel materials and advancing next-generation technologies, especially in fields demanding precise control of thin-film properties and the ability to integrate films onto diverse substrates. SAO has been shown considerable interest due to its favorable physical and chemical properties that make it an ideal sacrificial material. One of the primary advantages of SAO is its high solubility in water, allowing an easy removal process that is gentle on SDC and GDC films [12]. Upon water immersion, SAO rapidly dissolves without leaving residues, which simplifies the fabrication of defect-free, free-standing oxide membranes. Additionally, SAO possesses high thermal stability and is compatible with elevated temperatures used during the deposition of ceramic films. This stability ensures that the sacrificial layer can endure the high-temperature processing required for oxide film sintering and densification without significant decomposition or degradation. Another critical factor in the suitability of SAOas a sacrificial layer is its minimal chemical interaction with doped ceria films. Unlike other potential sacrificial materials that might introduce unwanted side reactions, impurities, or residual stresses, SAO remains chemically inert concerning both SDC and GDC during high-temperature processing. This inertness helps preserve the microstructural integrity and ionic conductivity of the ceria films, both of which are essential for their performance in electrochemical applications.
# Evaluating the Effectiveness of COVID-19 Bluetooth-Based Smartphone Contact Tracing Applications
## 5 Evaluation
### Centralized vs. Decentralized Solutions  
We now evaluate the efficiency of the centralized and decentralized approaches, which rely mainly on the tracing time (\(TT=1/\tau_{T}\)) and the ratio of users checking for exposure (\(RC\)). The latter parameter has already been evaluated in the previous subsection. In a centralized approach, \(RC\) will be 1, and so the results in Figure 4 clearly show a greater efficiency when compared with lower values of \(RC\) associated with the decentralized approach.  
Therefore, in this subsection, we focus on the tracing time. We increase this value (\(TT=1/\tau_{T}\)) from 1 day to 2 and 4 days in order to evaluate its impact. We repeated the experiments in Figure 4c,d, which had a tracing time of one day. The results are shown in Figure 6. Increasing the tracing time to 2 days (Figure 6a,b) evidences a substantial increase in the percentage of individuals infected for similar utilization levels, requiring also more people to be quarantined. The results for a tracing time of 4 days confirm the significant impacts on the control and spread of the epidemics. Figure 6c not only shows an increased proportion of people infected, but also that only with an application utilization near to 100% can the outbreak be controlled (compare these results with the one of Figure 4c where for an utilization of about 50% the outbreak can be controlled with practically no quarantine). These results confirm the necessity of a fast tracing of the previous contacts in order to get an effective quarantine.  
Summing up, a centralized approach is clearly more effective than the decentralized one. It becomes easy to compare the difference between the obtained results for the centralized approach  
Figure 6: Efficiency of the contact tracing application considering the tracing time (all plots are for \(R_{0}=2\)): (**a,b**) percentage of population infected and quarantined when tracing time is 2 days (\(TT=2\)); (**c,d**) percentage of population infected and quarantined considering when tracing time is 4 days (\(TT=4\)).  
(curves \(RC=1\) for \(TT=1\) in Figure 4c,d) and the decentralized approach (curves with \(RC<1\) in Figure 6).
# Research on Short Video Hotspot Classification Based on LDA Feature Fusion and Improved BiLSTM
## 4 Experiments and Results
### Experimental Results
#### 4.6.2 Impact of Feature Fusion on the Model  
In order to accurately characterize the features of short text data and further improve the performance of the BiLSTM-Self-Attention model, the LDA topic model with excellent performance was integrated into feature extraction. The topic extension features were obtained by merging the document-topic distribution and word-topic distribution of the LDA topic model with the Word2 Vec word vector. The BiLSTM-Self-Attention model was compared with the LBSA model after feature fusion and the confusion matrix was drawn, as shown in Figure 7:  
In Figure 7, (a) is the confusion matrix diagram of the BiLSTM-Self-Attention model test set results and (b) is the confusion matrix diagram of the LBSA model test set results after feature fusion. As can be seen from Figure 7, the classification effect of the LBSA model has been improved compared with the BiLSTM-Self-Attention model as a whole. For topic 0, the improved model reduced the number of comments that incorrectly classify topic 0 as topic 2, 3, and 6. For topic 4, the improved model has 125 correct classifications, 6 more than those in the BiLSTM-Self-Attention model, and the number of wrongly classified comments for topic 2 had changed from 6 to 3. Experiments demonstrate that the fusion of extended features based on the LDA topic model reduces the error rate caused by the inconspicuous feature boundaries, making it easier for the BiLSTM-Self-Attention model to detect important features of the text and better classification results.
# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 5 Deep Learning Approaches
### Automotive Radar  
Table 5 presents a collection of odometry methods tailored for automotive radar and evaluated on the same set of the View-of-Delft dataset. Automotive radar data, known for their sparsity, are typically utilized for object detection and ego-localization rather than odometry. Given the inherent challenges of sparse radar point clouds, the focus in this context is on odometry estimation, particularly leveraging the 4D nature of radar point clouds in the View-of-Delft dataset. Notably, all models in Table 5 underwent evaluation on a single NVIDIA 2080 Ti GPU, ensuring a more reliable inference comparison than the previous comparison.  
Among the radar-only setups, RaFlow [89] and 4 DRO-Net [90] demonstrate competitive performance. Interestingly, CMFlow [106], which integrates additional information such as camera, LiDAR, and IMU data in its training setup, showcases comparable results. Notably, the radar-only methods, especially 4 DRO-Net [90], exhibit noteworthy performance in ATE and inference speed even when compared to the CMFlow setup. Moreover, the deep learning methods consistently outperform the classical NDT transform, indicating significant advancements in performance despite the computational efficiency of the classic approach, which operates without the need for a GPU during odometry prediction.
## 3 Results and Discussion  
### Preliminary Analysis  
The correlation between the rainfall values of different stations may be either linear or nonlinear. Therefore, to capture any type of correlations, the Spearman correlation coefficients were calculated (refer to Figure 4).  
The correlation between all the considered sub-stations and the target station Ratnapura and among sub-stations are significant at 5% significance level (i.e., \(p\) value < 0.05). A strong correlation (>0.6) can be seen between Ratnapura and sub-stations Keragala, Moralioya, and Elston. The results indicate that all six sub-stations are important in modeling rainfall at Ratnapura station. Thus, it confirms the need for imputing missing values of the reference stations.
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 4 Planning Models in Penetration Path Planning
### Planning Domain Model  
Planning domain models [43; 44] are utilized in intelligent planning systems to formally describe planning problems. In general, representing planning domain models requires specific languages or frameworks such as the Planning Domain Definition Language (PDDL) [45], STRIPS [46], and ADL [47]. Among these, PDDL is currently the most widely used planning domain modeling language. It provides a standardized way to describe planning problems and has been adopted by international planning competitions. Moreover, PDDL offers excellent extensibility. For instance, [PERSON] [48] combined hierarchical planning with PDDL and proposed the Hierarchical Domain Definition Language. In a subsequent study, [PERSON] [49] developed MyPDDL, which significantly simplifies the modeling process for domain models. A typical planning domain model comprises two principal components: the \"domain description\" and the \"problem description\".  
Definition 1 (domain): The domain description includes domain knowledge such as the domain name, predicates, actions, and other elements. It enhances the model's descriptive capability with conditional effects, constraints, and numerical expressions. This comprehensive framework allows for detailed modeling of the environment and actions within it.  
Definition 2 (problem): The problem description specifies a particular planning task by defining entities, initial states, and goal states. This flexibility allows for the reuse and modification of the problem description. By providing a standardized framework, the planning domain model enables the efficient construction and solution of various planning problems.
# GDPR and Large Language Models: Technical and Legal Obstacles
## 2 Detailed Analysis of GDPR Articles Challenging for LLMs
### Article 16--Right to Rectification  
Rectifying inaccuracies within LLMs poses significant technical challenges. Once a model is trained, correcting specific inaccuracies tied to personal data is difficult because the data is deeply embedded in the model's parameters. The complex interdependencies among data points mean that isolating and updating a specific piece of information can inadvertently affect other aspects of the model's performance. Fine-tuning the model with corrected data may help, but it often fails to completely remove the influence of the inaccurate data, as the model tends to retain learned representations from the initial training [37; 39; 64].  
Furthermore, updating large-scale models is computationally expensive and may not be feasible in real-time. Even after retraining or fine-tuning, LLMs might exhibit residual memory effects due to overfitting, making it challenging to verify that inaccuracies have been fully corrected through exhaustive testing [65]. Various approaches have beenproposed, including targeted data editing techniques (such as Rank-One Model Editing, or ROME), model auditing to assess the extent of inaccuracies, and the development of continuous learning frameworks. However, each of these solutions faces limitations in scalability and practicality. In many cases, methods like retraining, fine-tuning, or modular updates remain resource-intensive and may not fully address the persistence of incorrect data. Consequently, ongoing research is essential to develop more effective techniques for enabling compliance with GDPR's Right to Rectification in the context of LLMs.
###### Abstract  
Farmed fish disease diagnosis is an important problem in the fish farming industry, affecting quality of production and financial losses. In this paper, we present a web-based intelligent system that tackles the problem of fish disease diagnosis. To this end, it uses multiple knowledge representation and reasoning methods: rule-based, case-based, weight-based, and voting. Knowledge, which concerns the diagnosis of sea bass diseases, was acquired from experts in the field and represented in the form of decision trees. The diagnostic process is performed in two stages: a general one and a specialized one. In the general stage, a level-based diagnosis is performed, where environmental parameters, external signs, and internal signs are successively examined, and the three most probable diseases are identified. In the specialized stage, which is optional, a specialized expert system is used for each of the resulting diseases, where additional parameters concerning laboratory tests (microbiological, microscopic, molecular, and chemical) are considered. The general stage is the most useful, given that it can be performed on-site in real-time, whereas the specialized one requires time-consuming lab tests. The system also provides explanations for its decisions. Evaluation of the general-stage diagnostic process showed a top-3 accuracy of 78.79% on expert test cases and 94% on an artificial dataset.  
footnote: email: [EMAIL_ADDRESS]  
fish disease diagnosis; expert system; intelligent system; hybrid reasoning; rule-based reasoning; case-based reasoning; certainty factors; voting
## 2 Control Algorithm
### Alternative from of the Controller  
In this paper, we assume that all states are attainable via state observers, but do not touch the design of the observer. The original design of the controller \(\bar{u}\) in (12) may require additional estimation of \(e^{(n)}\), which may need to employ new gain(s).  
In order to avoid this drawback, an alternative design of \(\bar{u}\) is proposed by reducing the order of \(e^{(n)}\). (3) leads to  
\[e^{(n)} = \bar{s}^{(n)}-x_{d}^{(n)} \tag{21}\] \[= \hat{f}+\Delta f+\bar{u}-x_{d}^{(n)}.\]  
To have \(\hat{V}<0\), the control \(\bar{u}\) was originally designed as  
\[\bar{u} = -\hat{f}-k\text{sat}(s,\Phi)-\hat{w} \tag{22}\] \[= -\hat{f}-k\text{sat}(s,\Phi)-(-x_{d}^{(n)}+\hat{w}^{\prime}+e^{(n )})\] \[= -\hat{f}-k\text{sat}(s,\Phi)+x_{d}^{(n)}-\hat{w}^{\prime}-\hat{f} -\Delta f-\bar{u}+x_{d}^{(n)},\]  
where \(\hat{w}=-x_{d}^{(n)}+\hat{w}^{\prime}+e^{(n)}\), and  
\[\hat{w}^{\prime} = \left[\sum_{m=0}^{n-2}\left(\begin{array}{c}n-1\\ m\end{array}\right)e^{(m+1)}\lambda^{n-m-1}\right] \tag{23}\] \[+\left[\sum_{m=0}^{n-2}\left(\begin{array}{c}n-1\\ m\end{array}\right)e^{(m+1)}\alpha^{n-m-1}\right]\] \[+\alpha\left[\sum_{m=0}^{n-1}\left(\begin{array}{c}n-1\\ m\end{array}\right)e^{(m)}\alpha^{n-m-1}\right].\]  
As a result, (22) can be rewritten as  
\[2\bar{u} = -2\hat{f}+2x_{d}^{(n)}-\hat{w}^{\prime}-\Delta f-k\text{sat}(s, \Phi), \tag{24}\]  
or,  
\[\bar{u} = -\hat{f}+x_{d}^{(n)}-\frac{1}{2}(\hat{w}^{\prime}+\Delta f+k\text {sat}(s,\Phi)), \tag{25}\]  
To eliminate \(\Delta f\), we here re-design \(\bar{u}\) as  
\[\bar{u} = \left[-\hat{f}+x_{d}^{(n)}-\frac{1}{2}\hat{w}^{\prime}\right]-k \text{sat}(s,\Phi). \tag{26}\]  
That is, replace the original \(\hat{w}\) by letting  
\[\hat{w} = -x_{d}^{(n)}+\frac{1}{2}\hat{w}^{\prime}. \tag{27}\]  
We claim that this controller works, and this controller does not need to measure \(e^{(n)}\).  
The proof is given as follows:  
\[\dot{V} = (\hat{f}+\Delta f+\hat{w}+\hat{u})s \tag{28}\] \[= \left(\hat{f}+\Delta f-x_{d}^{(n)}+\hat{w}^{\prime}+e^{(n)}-\hat{f} +x_{d}^{(n)}-k\text{sat}(s,\Phi)-\frac{1}{2}\hat{w}^{\prime}\right)s\] \[= \left(\Delta f+e^{(n)}-k\text{sat}(s,\Phi)+\frac{1}{2}\hat{w}^{ \prime}\right)s\] \[= \left(\Delta f+\hat{f}+\Delta f+\bar{u}-x_{d}^{(n)}-k\text{sat}( s,\Phi)+\frac{1}{2}\hat{w}^{\prime}\right)s\] \[= \left(2\Delta f+\hat{f}-\hat{f}+x_{d}^{(n)}-k\text{sat}(s,\Phi)- \frac{1}{2}\hat{w}^{\prime}-x_{d}^{(n)}-k\text{sat}(s,\Phi)+\frac{1}{2}\hat{w }^{\prime}\right)s\] \[= \left(2\Delta f-2k\text{sat}(s,\Phi)\right)s\] \[\leq -2\eta\text{sat}(s,\Phi)s.\]
## 4 Application to Rolling Bearings Fault Diagnosis  
In this section, the proposed multi-fault diagnosis method is applied to the rolling bearing experiments under different operational conditions and is also compared with some existing methods.