esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
With the development of deep-space exploration missions and Space Information Network (SIN) applications, the Ka-band and higher Q/V band channels are viewed as a primary solution to improve communication capacity [1]. Compared to commonly used X-band, Ka-band can offer 50 times higher bandwidth [2; 3]. The Mars Reconnaissance Orbiter (MRO) mission demonstrated the availability and feasibility of the Ka-band for the future exploration missions [4; 5].  
However, the Ka-band channel is much more sensitive to the weather conditions surrounding the terrestrial stations, such as rainfall, which can significantly degrade the quality of service [6; 7]. Furthermore, the space nodes in SIN only have limited communication resource, thus the optimal transmission policy should consider the trade-off between complexity and transmission performance [8; 9]. Considering the huge distance and long propagation delay in SINs, the handshake process of conventional Transmission Control Protocol/Internet Protocol (TCP/IP) is not suitable for space communication scenarios [10; 11]. Generally, the delay tolerant network protocols that Consultative Committee for Space Data Systems File Delivery Protocol (CFDP) and Licklider Transmission Protocol (LTP) are widely used in SIN communication scenarios [12; 13; 14], where the transmitter can obtain the delayed Channel State Information (CSI) from Negative Acknowledgment (NACK) feedback [15; 16].  
In previous studies [17; 18; 19], the time-varying rain attenuation at the Ka-band channel is used to model to a two-state Gilbert-Elliot (GE) channel, and several works have focused on the optimal data transmission policy. In [20], three data transmission actions were proposed to be chosen at the beginning of each time slot to maximize the expected long-term throughput.  
For the Mars-to-Earth communications over deep space time-varying channels, an optimal data transmission policy has been developed with the delayed feedback CSI in [21]. The adaptive coding schemes for deep space communications over the Ka-band channel were also studied in [22; 23]. However, little work has been done in optimizing the transmission policy for SINs, especially in the presence of highly time-varying Ka-band channels.  
In this paper, by utilizing the delayed feedback CSI, we propose an optimal transmission scheme based on the Partially Observed Markov Decision Process (POMDP), and derive the key thresholds for selecting the optimal transmission actions for the SIN communications.  
The rest of this paper is organized as follows. In Section 2, a two-state GE channel is modeled. We derive the threshold of which we should perform channel sensing before we start the transmission or not in Section 3, and we also derive the thresholds of choosing data transmission actions from two or three actions in POMDP. In Section 4, simulation results show that the proposed optimal transmission policy can increase the throughput in SIN communications. Finally, Section 5 concludes the paper.
## 5 Electrical Characterization
### Temperature Testing  
Temperature testing is vital in the comprehensive electrical characterization of PUFs. As temperature variations can significantly influence the performance and reliability of electronic devices, it is imperative to evaluate the behavior of PUFs under different thermal conditions.  
To assess the robustness and reliability of the PUFs, we conducted comprehensive tests and developed comprehensive characterizations at three different temperatures: 0 \({}^{\circ}\)C, 23 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C. To isolate the impact of temperature on the MRAM devices and exclude the influence if external measurement circuitry, we used flat flex cables and adapters during the temperature tests. This allowed us to focus solely on the temperature effects on the MRAM itself, gaining a clear understanding of its isolated thermal behavior.  
By subjecting the PUFs to different temperature ranges and analyzing their responses, we aimed to gain valuable insights into their performance characteristics. This approach allowed us to identify potential vulnerabilities, enhance overall performance, and develop effective calibration or compensation techniques that can be applied when deploying the PUFs in real-world scenarios.  
To test varying temperature conditions, we used the Associated Environmental Systems SD-501 temperature chamber. This chamber allowed us to create a wide range of temperature conditions, spanning from \(-37\)\({}^{\circ}\)C to 180 \({}^{\circ}\)C. By examining the PUFs' behavior across different temperature conditions, we could better understand the impact of temperature variations on their reliability and make informed decisions to optimize their operation and ensure their dependable performance in practical applications.
## 4 Results and Discussion
### Strengths and Limitations  
The most applicable soil loss estimation model is RUSLE. Because it can predict soil loss using little information, this model is ideal for developing countries with scarce data. The results of other models are elaborate but difficult to interpret and validate due to their complex nature. Most other models are based on empirical rules, leaving much room for error [85]. This model identifies high-risk areas, and management actions are required immediately. It provides an easy-to-use, adaptable, and physical foundation for determining the relative soil loss pattern.  
Despite its numerous advantages, the RUSLE model has certain limitations. This equation was established via regression analysis with little field data of similar nature regarding rain patterns, soil types, land cover, and topography [86]. In addition, since it was developed on datasets of the US, its application on a catchment in Pakistan can be questioned. Additionally, to use it for location outside the datasets on which the model was calibrated requires long-term data to calibrate and calculate its coefficients. Moreover, it is also known that gully or stream-channel erosion is not considered while calculating soil loss which is the governing factor in soil erosion. Finally, calculating soil erosion by merely multiplying various factors cannot achieve full accuracy because factors such as sediment deposition, routing, and sediment yield at the catchment outlet are not considered, resulting in inaccurate estimations [87].
# Fiber Bragg Grating-Based Optical Signal Processing: Review and Survey
## 2 Review of Design Methods for Fiber Bragg Gratings
Researchers have intensively worked on the development of algorithms that predict the reflective and transmissive spectral responses, \(H_{R}(f)\) and \(H_{T}(f)\), from the refractive index profile \(n_{FBG}(z)\) of the grating, and vice versa, i.e., algorithms that determine the \(n_{FBG}(z)\) that would lead to a desired \(H_{R}(f)\) or \(H_{T}(f)\). These algorithms are known as analysis [25, 26, 27, 28, 29, 30, 31, 32, 33] and synthesis algorithms [34, 35, 36, 37, 38, 39, 40, 41, 42], respectively. For the development of the FBG design algorithms, two hypotheses are to be considered. First, the refractive index modulation, \(n_{FBG}(z)\), is regarded as a one-dimensional parameter. Secondly, the electric fields propagating along the grating, \(E^{+}(z,f)\) and \(E^{-}(z,f)\), are assumed to be ideal monochromatic plane waves. In this Section, we briefly describe the existing algorithms with special emphasis in the more precise solutions, which are nowadays widely employed in the design of efficient FBG-based optical signal processors.
## 5 Results and Analysis
### Overall Performance Comparison  
We first compare our proposed full model, denoted as ours (Hyena + \(\Delta\)-LoRA + DANN + MTL), against several baseline models on the test sets of the core FOMC dataset (1977-2024) and the ECB dataset (1999-2024) used for evaluating cross-institutional generalization. The baselines include the following: logistic regression + TF-IDF: a classical econometric baseline using TF-IDF features with logistic regression to demonstrate the value-add of deep learning approaches. Transformer-XL + Full FT: a strong Transformer-based long sequence model with full parameter fine-tuning. Longformer + LoRA: a Transformer variant optimized for long texts, combined with standard LoRA for parameter-efficient fine-tuning. GPT-2 + AdapterFusion: a GPT-2-based model using AdapterFusion for multi-task and multi-domain adaptation. Ours (Hyena + LoRA): a variant of our model using standard LoRA instead of Delta-LoRA to assess the gain from Delta-LoRA. Ours (Hyena + \(\Delta\)-LoRA): our base model, removing DANN and multi-task components.  
Table 3 shows that our proposed full model significantly outperforms all baseline models on all main metrics across both FOMC and ECB datasets. Notably, compared to the classical econometric baseline (logistic regression + TF-IDF), our full model demonstrates substantial improvements of 14.1 percentage points in macro F1 on FOMC (0.862 vs. 0.721) and 9.5 percentage points on ECB (0.780 vs. 0.685), clearly justifying the adoption of sophisticated deep learning architectures for this task. Compared to Longformer + LoRA, our full model improves macro F1 by 6.0 percentage points (pp) on FOMC (0.862 vs. 0.802) and 5.7 pp on ECB (0.780 vs. 0.723). Diebold-Mariano tests confirmed that these improvements are statistically significant with DM statistics of 3.42 (\(p<0.001\)) for FOMC and 2.89 (\(p<0.004\)) for ECB comparisons against Longformer + LoRA. The reported 95% confidence intervals were computed using bootstrap resampling with 1000 iterations, ensuring  
robust uncertainty quantification. Diebold-Mariano tests confirmed these improvements are statistically significant (\(p<0.05\)). Comparing ours (Hyena + \(\Delta\)-LoRA) with Longformer + LoRA, even without DANN and multi-task learning, the Hyena and Delta-LoRA combination alone achieved a 4.8 pp macro F1 improvement (0.850 vs. 0.802) on the FOMC dataset. This strongly supports our hypothesis that the Hyena and Delta-LoRA-based method outperforms Transformer-based approaches with standard LoRA for the single-domain long-text task on FOMC data. Our model also demonstrates similar advantages in accuracy and AUC. These results indicate that the combination of Hyena Hierarchy's long convolution mechanism and Delta-LoRA's efficient fine-tuning strategy provides a superior foundation architecture for processing ultra-long financial policy texts.
# New Method for Simultaneous Arsenic and Selenium Speciation Analysis in Seafood and Onion Samples
## 3 Materials and Methods
### Analytical Instruments  
An ICPMS-2030 mass spectrometer (Shimadzu, Tokyo, Japan), directly coupled with a Prominence LC 20 Ai inert system, was used for creating the new speciation method. The LC inert system allowed us to eliminate the possibility of metal background leaching from the mobile phase and enabled us to obtain the lowest possible detection limit, which is required for speciation analysis. The ICP-MS operated at 1.2 kW with 8.0 L\(\cdot\)min\({}^{-1}\) Ar plasma gas flow, 0.7 L\(\cdot\)min\({}^{-1}\) nebulizer Ar gas flow, and 1.1 L\(\cdot\)min\({}^{-1}\) auxiliary Ar gas flow. The concentric (MicroMist) nebulizer with 0.6 mL\(\cdot\)min\({}^{-1}\) (carrier) argon gas flow was used for nebulizing the HPLCuate. The sampling depth was set to 5.0 mm. The spray chamber temperature was set to 3 \({}^{\circ}\)C. Optimized conditions of the collision cell were \(-\)90 V of cell gas voltage, 7.0 V of energy filter voltage, and a 9.0 mL\(\cdot\)min\({}^{-1}\) cell gas (He) flow rate. The HPLC was equipped with binary pumps LC 20 Ai, an autosampler (SIL 20 AC), a vacuum degasser (DGU20A3R), a heated column compartment (CTO 20 AC), and a controller (CBM 20A) (Shimadzu, Japan). Polypropylene autosampler vials were used, because of the possibility of contamination of the samples with arsenate if using glass vials. The vials were cleaned with dilute nitric acid and thoroughly rinsed with ultrapure deionized water (UPW) (Merck, Kenilworth, NJ, USA) [55].
## 3 On-Chip Circuit Implementation  
### CMOS Rectifier and Voltage Protection  
Power is coupled from the primary coil to the secondary coil, and AC voltage is developed between \(\mathrm{V_{AC1}}\) and \(\mathrm{V_{AC2}}\), the two terminals across the secondary LC tank. A rectifier plus voltage doubler as shown in Figure 4a is used to convert the AC voltage to a DC voltage to power up the internal implant unit. The transistors \(\mathrm{M_{N1}}\) and \(\mathrm{M_{P1}}\) with low threshold voltages of 0.25 V are diode-connected; the transistors \(\mathrm{M_{N2}}\), \(\mathrm{M_{N3}}\) and \(\mathrm{M_{P2}}\) are connected as metal oxide semiconductor (MOS) capacitors, and \(\mathrm{C_{1}}\) is composed of both metal-insulator-metal (MIM) and MOM capacitors to maximize the capacitance density. The diode \(\mathrm{M_{P1}}\) and the capacitor \(\mathrm{M_{P2}}\) constitute the first rectifier for \(\mathrm{V_{AC1}}>\mathrm{V_{AC2}}\); and the diode \(\mathrm{M_{N1}}\) and the capacitor \(\mathrm{M_{N2}}\) constitute the second rectifier for \(\mathrm{V_{AC2}}>\mathrm{V_{AC1}}\); and the two rectifiers are stacked up as a voltage doubler with filtering capacitors \(\mathrm{M_{N3}}\) and \(\mathrm{C_{1}}\). The doubler improves thevoltage gain from the PA supply voltage to the rectifier output and relaxes the design of the coupling coils and the supply voltage requirement of the external control unit. In the steady state, V\({}_{\text{AC2}}\) is a DC voltage of V\({}_{\text{DC}}\)/2, while V\({}_{\text{AC1}}\) swings between around \(-\)0.3 V and V\({}_{\text{DC}}\) + 0.3 V. In one cycle, V\({}_{\text{AC1}}\) charges the first and the second rectifier in the positive and the negative phase, respectively, and the ripple frequency of V\({}_{\text{DC}}\) is 81.36 MHz that is double of the power carrier frequency. Figure 4e shows the simulated V\({}_{\text{DC}}\) vs. rectifier loading current (I\({}_{\text{rec}}\)).  
In the steady state, the output voltage of the rectifier V\({}_{\text{DC}}\) is regulated to around 3.5 V during the power-regulation phase. However, during startup, V\({}_{\text{DC}}\) could even be higher than the breakdown voltage of the process. Therefore, a protection circuit of 7 diodes in series (Figure 4b) is added at the output of the rectifier to clamp V\({}_{\text{DC}}\) to within around 4.5 V. When V\({}_{\text{DC}}\) is higher than 4.5 V, M\({}_{\text{P3}}\) is turned on, which then turns on M\({}_{\text{N4}}\) to discharge the filtering capacitors across V\({}_{\text{DC}}\) to prevent it from getting higher.
# Efficient Massive MIMO Detection for M-QAM Symbols
## 4 Simulation Results
### Discussion  
The MMSE detectors provide a good soft decision for MIMO detection; however, they are difficult to implement in large MIMO systems. In addition, the box-constrained-based detectors can achieve significantly better performance than the MMSE detector in a large MIMO system [19]. Iterated MMSE detectors avoid the BER floor while requiring a large complexity of \((V-1)K_{t}^{3}\), which is not implemention friendly in massive MIMO systems. The INL-BOXDCD algorithm uses iterated BOXDCD algorithms with deregularization to solve normal equations. For a fixed \(N_{u}\), the BOXDCD exhibits a BER floor at a high SNR. Increasing \(N_{u}\) reduces the BER floor. The performance and computational complexity of INL-BOXDCD mainly depend on the number of bits \(M_{b}\), updates \(N_{u}\) and iterations \(V\). Using lower \(N_{u}\) and iterated BOXDCD algorithms, the INL-BOXDCD detector could achieve a good tradeoff between performance and complexity compared to only applying the BOXDCD detector. According to the BOXDCD algorithm FPGA design in [20], the method requires 184, 210 and 223 slices for the system sizes of \(16\times 16\), \(64\times 64\) and \(128\times 128\) in Xilinx Virtex-6 FPGA (XC6 VSX475T-2 FF1759). To achieve the same performance, the INL-BOXDCD algorithm can be performed by using a single BOXDCD block with less processing time. It might be an alternative way to solve a fully loaded MIMO system.
# Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
## 3 Deep Learning-Based Channel Estimation
### Motivations
Additionally, since it may be difficult to obtain the exact distribution of channel impulse responses in general [38], the performance of the LMMSE estimation cannot always be guaranteed.
## 6 Results and Discussion
### Sentiment Analysis
#### 6.2.2 FastText Models  
For the FastText models, most of the models performed well in SA, with stable performance across different models. The Wiki models had the best accuracy scores, as illustrated in Figure 8.  
Moreover, for the Watan models, as shown in Figure 10b, the CBOW models showed steady performance, where the small window sizes provided the best scores within the same vector size. However, both the CBOW and FastText models presented their best performances with a vector size of 300.  
On the other hand, by increasing the corpus size, the FastText models seemed to have a more stable performance. As is shown for the Books corpus models in Figure 10d, most of the skip-gram models outperformed the CBOW models. Models with a window size of 7 for both the skip-gram and CBOW models had the best scores. Overall, the skip-gram model with a vector size of 400 presented a score of 96% accuracy.  
Among all models, the FastText models with the Wiki corpus presented outstanding scores. All the models scored above 90% accuracy. The CBOW models, in particular, performed the best, with a score of 99% accuracy.  
It is worth noting that for all corpora, the increase in the vector size had a negative effect on the accuracy, with the models scoring the best results at a vector size of 200. There was no clear pattern when using different window sizes. On the other hand, the CBOW models outperformed the skip-gram models, as shown in Figure 10f. The Watan models presented the worst results among all models, likely due to the fact that they were trained using a smaller vocabulary size.  
Overall, the models presented good performances for word-level SA, with FastText providing the best results. Additionally, using a small vector size and a high vocabulary size corpus could lead to improved results. In general, our Word2 vec models scored 91%, while the FastText models achieved 99% accuracy. As illustrated in Table 8, we also applied the test to the Aravec [11] model since it is available. Our models outperformed the Aravec [11] model, which achieved 90% accuracy. Unfortunately, the [PERSON] et al. [9] model is not available; thus, we could not perform our test on it in order to make a fair comparison.
# Research on Slope Stability Prediction Based on MC-BKA-MLP Mixed Model
## 2 M-C Criterion and BKA-MLP Model Principle
### BKA Algorithm  
BKA is a new metaheuristic optimization algorithm inspired by the predation behavior of kite hawks (black kites) in nature [25]. The design concept of the BKA algorithm is based on the behavior characteristics of kite hawks that hover in the air, locate targets, and hunt together. In the BKA, each candidate solution is treated as a member of a group of kite hawks who search by mimicking their flight patterns. Compared with other traditional optimization algorithms, the BKA algorithm maintains the dynamic balance of exploration and development mechanism on the basis of powerful global search ability. It has a relatively simple parameter setting, strong adaptability and stability, and a fast convergence rate of the BKA algorithm, which are certain advantages in slope stability prediction applications. The algorithm mainly defines three behavior patterns to guide the search process.  
1. Exploration Phase  
This stage simulates the behavior of kite hawks searching for prey in a wide airspace. The algorithm increases the diversity of solutions by moving randomly, ensuring that the search space can be covered as large as possible in the search for potential high-quality solutions. First, the position \(X_{i}(t)\) of a group of kite hawks should be randomly initialized in the solution space, the mathematical expression is shown in the formula, and then the kite hawks search for potential food sources through random walks; the process is described as follows [26]:  
\[X_{i}(t)=L+rand\times(U-L) \tag{4}\]  
where \(i=1,2,\ldots\); \(t\) is the number of iterations; \(L\) and \(U\) are the lower and upper limits of the variable, respectively, and rand is a random number between [0, 1].  
\[X_{i}(t+1)=X_{i}(t)+\alpha\cdot\sin\beta\cdot\left|X_{gb}(t)-X_{i}(t)\right| \tag{5}\]  
where \(X_{gb}(t)\) is the best position of the current iteration; and \(\alpha\) and \(\beta\) are the parameters controlling the exploration intensity. Usually set \(\alpha=2\), \(\beta\) is usually randomly selected from the uniform distribution U(0, \(2\pi\)).  
Exploitation Phase  
After finding a promising area, the kite hawk will focus on a detailed search of the area in order to find the best prey, updating the rules as follows [27]:  
\[X_{i}(t+1)=X_{gb}(t)+\gamma\text{-}\cos\delta\cdot\left|X_{i}(t)-X_{gb}(t)\right| \tag{6}\]  
where \(\gamma\) and \(\delta\) are the parameters controlling the development degree; \(\gamma\) is usually set to 2; and \(\delta\) is usually randomly selected from the uniform distribution U(0, 2\(\pi\)).  
There is an exchange of information between kite hawks, and they learn and adjust their strategies by observing the behavior of their peers. The social learning mechanism can be expressed by the following formula [28]:  
\[X_{i}(t+1)=X_{i}(t)+\eta\cdot\left(X_{gb}(t)-X_{i}(t)\right) \tag{7}\]  
where \(\eta\) is the learning rate used to adjust the learning speed.
# Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition
## 2 Materials and Methods
### Evaluation Metrics  
For quantitative evaluation, we selected the following Image Quality Assessment (IQA) metrics: Information Entropy (IE), SSIM, Feature Similarity Index (FSIM) [31], and correlation. These metrics allow us to comprehensively assess various aspects of image quality, including detail richness, similarity, detail similarity, and correlation to reference images.  
1. IE evaluates the average entropy of an image. A high entropy indicates distinct structure and detail richness [32]. The definition of IE for any given image is as follows: \[IE=-\Sigma p_{i}\text{log}_{2}\,p_{i},\] (15) where \(p_{i}\) is the probability of a particular gray level in the image.
2. SSIM  
The SSIM was utilized to assess the similarity between the processed images and their references, which may be the ground truth or the original images, depending on the available dataset. This metric, previously detailed, focused on measuring the visual impact of three characteristics of an image: luminance, contrast, and structure, thereby providing a comprehensive evaluation of image quality across different types of comparisons.  
1. FSIM  
The FSIM is defined as follows: \[FSIM=\frac{\Sigma S_{L}*PC_{m}}{\Sigma PC_{m}},\] (16) where \(S_{L}\) stands for similarity, and \(PC_{m}\) is the phase congruency. This value gives the low-level features of an image.  
1. Correlation  
The correlation of two images can be calculated by  
\[Corr=\frac{\Sigma(I-\mu_{I})(K-\mu_{K})}{\sqrt{\Sigma(I-\mu_{I})\Sigma(K-\mu_{ k})}}, \tag{17}\]  
This value reflects the correlation between two images. The higher the value, the more related the two images are.