esa-sceva/satcom-chunk-collection huggingface dataset:
# Backdoor Attack Against Dataset Distillation in Natural Language Processing
## 2 Related Work
### Backdoor Attacks  
The fundamental concept behind backdoor attacks is to alter the model's behavior in accordance with the attacker's objectives when a specific backdoor trigger is present in the input sample. Potential objectives include causing misclassifications ([PERSON] et al., 2019; [PERSON] et al., 2020; [PERSON] et al., 2020). The overall process of backdoor attacks in natural language processing is shown in the Figure 2. Backdoor attack methods can be divided into two primary categories: poison-label attacks and clean-label attacks ([PERSON] et al., 2020). In poison-label backdoor attack scenarios, attackers manipulate both the training data and their corresponding labels; in contrast, clean-label backdoor attacks consist of modifying training samples while keeping their original labels intact ([PERSON] et al., 2020; [PERSON] et al., 2020).  
Figure 2: Process of backdoor attack in NLP.  
In poison-label strategies, attackers may introduce irrelevant words [59] or sentences [60] into valid samples in order to generate poisoned versions. To enhance the subtlety of these contaminated samples, [PERSON] et al. [61] used syntactic structures as triggers. Furthermore, [PERSON] et al. [62] proposed a weight-poisoning technique that complicates defenses against implanted backdoors. Attackers have also taken advantage of vulnerabilities in prompt-based learning by using rare words [63], short phrases [64], and adaptive techniques [65] as triggers for contaminating the input space. Regarding clean-label backdoor assaults, an innovative approach introduced by [PERSON] et al. [66] generates poisoned samples through mimetic techniques. Additionally, genetic algorithms were employed by [PERSON] et al. [67] to produce less conspicuous poisoned examples. The stealthiness of these assaults can be further improved by utilizing prompts themselves as triggers while preserving label accuracy, as indicated in research from [PERSON] et al. [56]. A novel method presented by [PERSON] et al. [68] involved creating a malicious tokenizer for executing training-free backdoor assaults.  
Additionally, there has been growing interest in examining large model security within NLP research contexts [69]. For instance, a Trojan activation attack method was put forward by [PERSON] et al. [58] in which Trojan steering vectors are embedded within an LLM's activation layers. Research conducted by [PERSON] et al. [70] illustrated how predefined triggers can influence model behavior during the instruction and tuning phases. Similarly, [PERSON] et al. [71] used instructions as hidden vulnerabilities in large language models. [PERSON] et al. [72] integrated a reasoning step with embedded backdoors into chain-of-thought processes, with the aim of manipulating model actions. Meanwhile, [PERSON] et al. [57] incorporated vulnerabilities during the fine-tuning stage which can then activate during context-based learning scenarios.
# Discrete-Time First-Order Plus Dead-Time Model-Reference Trade-off PID Control Design
## 5 Numerical Examples
### Robust Stability  
The effectiveness of the accomplished robust stability is shown. The PID controller is designed based on the nominal model Equation (36) in Section 5.1 in the servo and regulation, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \(M_{s}^{d}\) & \(K_{p}\) & \(T_{i}\) & \(T_{d}\) & \(M_{s}\) & \(J_{s}\) & \(J_{r}\) \\ \hline \multirow{4}{*}{Servo Optimization} & 1.4 & 1.0217 & 1.3331 & 0.1048 & 1.3998 & 0.9576 & 1.2253 \\  & 1.6 & 1.3709 & 1.4633 & 0.1090 & 1.5964 & 0.7638 & 1.1531 \\  & 1.8 & 1.6359 & 1.5879 & 0.1360 & 1.7937 & 0.7064 & 1.0688 \\  & 2.0 & 1.8093 & 1.7116 & 0.1537 & 1.9936 & 0.6970 & 1.0274 \\ \hline \multirow{4}{*}{Regulator Optimization} & 1.4 & 1.0159 & 0.6876 & 0.1737 & 1.4052 & 1.3048 & 0.8667 \\  & 1.6 & 1.3430 & 0.6641 & 0.1681 & 1.5944 & 1.0673 & 0.6466 \\ \cline{1-1}  & 1.8 & 1.6065 & 0.7020 & 0.1597 & 1.7913 & 0.9705 & 0.5302 \\ \cline{1-1}  & 2.0 & 1.8217 & 0.7174 & 0.1589 & 1.9922 & 0.9458 & 0.4565 \\ \hline \end{tabular}
\end{table}
Table 4: Proportional-integral-derivative parameter, \(M_{s}\), and index values.  
Figure 6: Output and input responses obtained using the proposed method in both servo and regulator design, where the system is disturbed by a unit step disturbance after 15 s.  
The scenario is that the plant is Equation (36) from the start to 30 s and is changed to Equation (38) after 30 s as the model perturbation.  
\[P_{d}^{\prime}(z^{-1})=\frac{0.0530}{1-0.9788}z^{-21} \tag{38}\]  
The transfer function is the discrete time version of the following continuous time model with the sampling interval \(T_{s}=0.03\) s:  
\[P^{\prime}(s)=\frac{2.5}{1.4s+1}e^{-0.6s} \tag{39}\]  
The simulation results for the servo and regulator designs are shown in Figure 7, where the reference input is given by a unit step function, and the control input is disturbed by a unit step function after 15 s. The simulation results show that the effect of the model perturbation is suppressed by small \(M_{s}^{d}\). However, note that the control performance is superior before the model perturbation when the value of \(M_{s}^{d}\) is large.
## 2 Data and Methods
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Section** & & **Elements** \\ \hline TX & - & Mach-Zehnder Electric Modulator - - & Bessel filter \\  & - & Laser CW 1 MHz by FWHM - & \\  & - & C band, around 1550 nm \\ \hline \multirow{4}{*}{Chanel} & - & ITU-G652 standard - & \\  & - & SSMF-28 fiber - & \\  & - & EDFA amplifiers - & \\  & - & DCF compensation fiber \\ \hline \multirow{4}{*}{RX} & - & Sensitivity –30 dbm - & \\  & - & Bessel type electric filter - & \\ \cline{1-1}  & - & Elevated cosine optical filter. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Elements of a WDM network. 20 Gbps, 40 Gbps, or 100 Gbps, formats, such as differential phase shift keying/differential quadrature phase shift keying (DPSK/DQPSK) respond well; return-to-zero (RZ) encoding is recommended as it has a better tolerance to non-linearity than the non-return-to-zero (NRZ) format [15]. Table 1 lists the elements of a WDM network. Table 2 shows the variables to be used in the proposed model, where the heterogeneous network simulation schemes were defined to implement in the Optsim software. This article will address a heterogeneous network consisting of 4 channels of 2.5 Gbps + 2 channels of 10 Gbps + 1 of 40 Gbps, which allows a complete analysis of the parameters involved in a good use of the optical spectrum to obtain the most out of bandwidth without affecting network throughput.
# Constellation Encryption Design Based on Chaotic Sequence and the RSA Algorithm
## 1 Introduction
This paper combines the advantages of the algorithm so that it will not affect the judgment performance of the system and this further increases the interception resistance of the communication signal during transmission. The contributions made in this article are summarized below:  
The contributions of this work are summarized as follows:  
1. The parameters transmitted by the asymmetric RSA algorithm directly affect the generation of the chaotic sequence, which increases the flexibility and security of the chaotic sequence as a secret sequence. 2. The constellation encryption is directly performed using the secret sequence, so that the points on the constellation are not in the real position. In this process, the amplitude and phase of the constellation are not processed so that a better demodulation signal performance can be achieved, even under the conditions of a low signal-to-noise ratio. 3. Using the secret sequence to directly encrypt the constellation, the obtained constellation diagram has the same shape as the unencrypted constellation diagram, so the illegal receiver cannot observe the constellation encryption from the constellation diagram, which improves the interception resistance of the satellite communication. This paper proposes a constellation encryption design method, based on chaotic sequence and the RSA algorithm, that further improves the security of satellite communication. The remainder of this paper is as follows: Section 2 theoretically introduces the typical Logistic chaotic map and the RSA algorithm. Next, the design ideas and specific design steps of the proposed method are discussed in Section 3. Section 4 mainly provides a series of performance simulations on the design method of constellation encryption to verify the feasibility of the proposed design method. The last section draws conclusions from the prior discussion and experimental results.
# MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane
## 4 Mission Elements
### Space Segment
In the case of MERLIN, however, a dramatic relaxation of the required frequency knowledge can be achieved by tuning the online position close to the trough minimum of the one-way optical depth spectrum, as illustrated in Figure 5. For this frequency position, the measured DAOD is rather immune to small changes of the laser frequency, as shown by the derivative function with respect to frequency. Further details of the error budget are given in Section 5. The platform considered for MERLIN strongly benefits from the MYRIADE Evolutions Program, which is an enhancement with respect to the original MYRIADE concept of small satellite series. The new concept offers the MERLIN instrument payload to allocate more power (around 150 W) and a greater mass (around 140 kg). The bus structure is a rectangular parallelepiped with dimensions of 98 \(\times\) 102 \(\times\) 58 cm\({}^{3}\). The main improvements are a new structure compatible with a satellite mass up to 470 kg (limited to 430 kg on MERLIN due to launcher capacities); an increased solar array capacity and power supply; a more effective propulsion system; an improved Attitude and Orbital Control System (AOCS); an increased capacity for payload data storage; improved reliability figures, and; obsolescence handling and finally compatibility with low orbits (addressing atomic oxygen issues). Figure 5: (**a**) One-way optical depth (thick black line) and its first derivative (thin dashed line) for the selected R6 multiplet around a frequency of 6077 cm\({}^{-1}\). The simulation is based on Voigt-profile calculations using the HITRAN database with US Standard Atmosphere. The thin solid line corresponds to the one-way optical depth for selected on-line frequency marked by the dashed arrow. (**b**) Corresponding pressure-based weighting function for the selected online frequency. The Lidar payload is housed on the upper panel of the platform. Some platform units are located close to the instrument: two star tracker optical heads with their baffle, two solar arrays, one X-band antenna and a sun sensor notably to take into account a sun avoidance mechanism.
Rather than enforcing strict decentralization, we adopt this paradigm to enable personalized and weighted optimization across heterogeneous data distributions, specifically between high-resource (e.g., English) and low-resource (e.g., Twi) language domains. This controlled gradient reweighting ensures that the model learns balanced representations without allowing high-resource language data to dominate, which is a common pitfall in centralized multitask learning. By simulating a federated environment, our method retains the modular benefits of federated architectures while adapting them to centralized but diverse data settings, making it both effective and feasible for low-resource machine translation. Our contributions include the following:  
* A novel personalized Cross-Lingual Optimization Framework (CLOF) that dynamically adjusts gradient aggregation weights throughout training to enhance the robustness of translation models;
* A scalable framework for cross-lingual learning that efficiently utilizes high-resource language data to improve translation performance for low-resource languages;
* Empirical insights about linguistic interaction patterns in federated learning and their influence on model convergence;
* A practical application showcasing enhanced translation skills for Twi, hence augmenting NLP functionalities for African languages;
* A flexible methodology that can be modified for various low-resource languages is especially advantageous for underrepresented language communities. The remainder of the paper is organized as follows: First, we provide a comprehensive review of related research in low-resource machine translation, federated learning, and dataset aggregation techniques. Next, we present our methodology, which includes the architecture of our proposed system and the dynamic dataset aggregation processes. This is followed by detailed experimental results that validate the effectiveness of our approach. Finally, we discuss the implications of our findings and propose directions for future research in this rapidly evolving field.
# Hybrid Beamforming for Reduction of Inter-Beam Interference in Millimeter-Wave Cellular Systems
## 2 Hybrid Beamforming Techniques for a Multicell Multiuser Environment
### Channel Estimation
The \(MS\) estimates the effective network channel using the correlation property of the CBRS. The highest peak occurs when the reference BID \((i^{\prime})\) is matched with the transmitted BID \((i)\). The correlator output becomes zero when the reference BID is not matched with the transmitted BID. The correlator output becomes a small value (\(\frac{1}{\sqrt{N}}\)) when the reference CID and BID \((c^{\prime},\ i^{\prime})\) is not matched with the transmitted CID and BID \((c,i)\). Using these properties, the \(MS\) can estimate all effective channels in neighboring cells by correlating the received signal with all possible reference CIDs and BIDs. If the total number of beams to be generated in neighboring \(BS\)s is smaller than the number of BIDs \((N_{I})\) generated by the CBRS, it will be advantageous to allocate the same CID to the neighboring \(BS\)s. In this case, no IBI will occur owing to the ideal correlation property, which allows us to estimate all channels accurately. However, if the total number of beams to be generated in neighboring \(BS\)s is larger than the number of BIDs \((N_{I})\), it will be advantageous to allocate the same CID to the \(BS\)s located near the serving \(BS\), and to allocate different CIDs to the \(BS\)s located far way. In this case, the interference term (\(\frac{1}{\sqrt{N}}\)), which is generated by the \(BS\) located far way, will be reduced further owing to the path loss.
# Application of Image Recognition Methods to Determine Land Use Classes
###### Abstract
A post-processing approach that utilizes confidence maps to refine classification results and reduce misclassification errors was proposed. 4. The results of experiments performed on the territory of Lithuania demonstrated a substantial enhancement in classification accuracy, taking into account the diverse land use classes, seasonal variations, and frequent cloud cover present in the region. The remainder of the paper is organized as follows. Section 2 (Related Works) provides a detailed review of recent studies employing ML algorithms for land cover classification, emphasizing the effectiveness of the Random Forest algorithm in various scenarios. Section 3 (Methodology) describes the hybrid approach proposed for classifying land use with Sentinel-2 imagery. This section is subdivided into data acquisition, preprocessing, classification, and post-processing phases, detailing each step in the pipeline. Section 4 (Experimental Results) presents the outcomes of applying the proposed methodology to Sentinel-2 data covering Lithuania. Section 5 (Discussion) analyses the results, discussing the effectiveness of the approach and its limitations. Section 6 (Conclusions and Future Works) summarizes the key findings and contributions of the study.
## 2 Materials and Methods
### Evaluation of Virtual Reality as a Technological Tool
#### 2.7.2 Content Validation: Content Validity Index (CVI)  
The item content validity index (I-CVI) was applied to appraise the questionnaire's suitability and specificity [35]. This process entails comprehensively analyzing the questions to ascertain their suitability in assessing the intended concept. Content validity pertains to the extent to which the questions in a questionnaire accurately reflect the aims and goals of the VR training module. The following steps were utilized to perform the content validation and additionally calculate the content validity index:  
* Define the construct clearly to measure.
* Select experts and identify a panel of experts knowledgeable in VR.
* Item generation creates questions for the measuring tool based on the construct.
* Content review by experts panel evaluation of the questions to ensure they are clear, relevant, and indicative of the VR training module goals.
* Gather expert advice on rephrasing, adding, or eliminating items to improve questionnaire validity.  
* Content validity index (CVI) calculation: CVI is calculated separately for each item and then averaged across all items, using the item content validity index (I-CVI). \[\text{I}-\text{CVI}=\frac{\text{Number of experts agreeing that the item is the content }-\text{valid}}{\text{Total number of experts}}\] (4) In general, an I-CVI of 0.78 or greater is deemed acceptable.  
Comprehensive Evaluation of the Effectiveness of VR-Assisted Teaching in Enhancing Students' Learning Experiences  
Prior to the students undertaking the video and VR training, a 5 min multi-choice pre-test was used to measure the students' learning on completion of reading the study notes. The test questions were specifically aligned to the manufacture and quality control of \({}^{99\text{m}}\)Tc-MDP radiopharmaceutical in compliance with radiation protection rules and protocols. After the student groups had completed the video and VR training, the students were given the same 5 min test. To evaluate student knowledge retention, a 5 min test was given to the students 1 month after the training. Furthermore, the students took the same test 3 months after the training to evaluate their long-term knowledge retention. Moreover, VR has been shown to enhance memory retention by creating immersive, context-rich, interactive, emotionally engaging, and personalized learning experiences that facilitate deeper cognitive processing and recall [38; 39; 40; 41].
## 4 The 3D Arbitrary Baseline Algorithm
### The Solving Ambiguous Method Based on Rotation Comparison  
For the principle of 3D arbitrary baseline algorithm, a group solution can be obtained from any four antennas placed in the space. Assuming there are \(n\) antennas, they should have \(C_{4}^{n}\) combinations. The real 2D-DOA should be contained in each combination. Comparing the results with the multi-values of solving the equation constructed by each combination, the minimum error value among each combination would be defined as the real incident direction of the incident signal.  
In order to clarify the principle of solving ambiguous problems intuitively, two combinations are used for direction finding. The result of the two combinations should be expressed as \(\left[\begin{array}{cc}\alpha_{1i}&\beta_{1i}\end{array}\right]\) and \(\left[\begin{array}{cc}\alpha_{2j}&\beta_{2j}\end{array}\right]\) respectively. The error value between two measurement results is defined as  
\[\Delta_{i,j}=\sqrt{\left(\alpha_{1i}-\alpha_{2j}\right)^{2}+\left(\beta_{1i}- \beta_{2j}\right)^{2}} \tag{21}\]  
Figure 7: Positions of the virtual elements. The black points represent the actual element (E, F and M); and the shadow points represent the virtual elements(G, H and N).  
where \(i,j=1,2,3\cdot\cdot\cdot\), which the \(i\)th (or \(j\)th) ambiguous value respectively. The real 2D-DOA \(\alpha\) and \(\beta\) are obtained by minimizing \(\Delta_{i,j}\).  
\(\gamma_{1}\) and \(\gamma_{2}\) of Equation (16) or (17) are judged by positive or negative, the direction of the incident signal can be determined and thus the mirrored ambiguity is solved.
# Improved Scheduling Algorithm for Synchronous Data Flow Graphs on a Homogeneous Multi-Core Systems
## 4 SDAG Model and HSDFG Scheduling
### SDAG Computational Model
If \(B_{1}\) is dependent on many FIFO queues, then the input port state of \(B_{1}\) is updated whenever a FIFO queue satisfies Equation (9). When all input port states of \(B_{1}\) are available, \(B_{1}\) is added to the ready queue. All instances in the ready queue can be dispatched directly to the physical core for execution.
## 4 Experimental Design
### Research Results Analysis
#### 4.3.5 Quantitative Validation of Temporal Dynamics  
To complement the narrative-based segmentation outlined in Sections 4.3.5-4.3.12, we conducted a quantitative validation of temporal dynamics surrounding utterance production. Specifically, we aligned eye-tracking data to the onset of eight semantically categorized utterances and computed time-series metrics across a \(\pm\)1000 ms window. The analysis was performed on three key gaze parameters: saccade rate, fixation stability (inverse variance of gaze coordinates), and gaze dispersion (mean deviation from centroid).  
Figure 14 presents the average trajectory of these metrics across all utterances. A consistent three-phase structure emerges:  
1. Pre-speech convergence (\(-\)1000 ms to \(-\)200 ms): Saccade rate decreases, fixation stability increases, and gaze dispersion narrows--indicating attentional focusing before speech.
2. Speech initiation (\(-\)200 ms to \(+\)200 ms): All parameters show sharp transitions--saccade rate peaks, fixation stability dips, and dispersion expands--suggesting strong visual-cognitive engagement.
3. Post-speech decay (\(+\)200 ms to \(+\)1000 ms): A clear recovery pattern occurs, with exponential normalization of all gaze metrics over approximately 800 ms, reflecting cognitive reset and re-stabilization.  
This structured modulation pattern validates our phase segmentation with empirical gaze signatures. Notably, affective utterances consistently produced the strongest modulation, supporting the hypothesis that emotionally expressive language enhances multimodal coupling. These results confirm that verbal production is temporally synchronized with systematic visual reorganization, and that the segmentation used in our study reflects robust underlying attentional dynamics.