esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
An approach aimed at obtaining the SPP at THz frequencies is an urgent need, since it could promote the applications in biochemical spectroscopy, medical imaging, and so forth. This review summarizes the THz-ATR application in modulation, sensing, spectroscopy and imaging (as shown in Figure 1). The THz attenuated total reflection (THz-ATR) system is first introduced, including the principle and impact factors. Following this, the recent advances on THz wave modulation and THz surface plasmon sensing based on this system are demonstrated. Next, the applications in spectroscopy and imaging are reviewed and discussed. Finally, the challenges and outlook of this field are summarized. and dielectric parameters can be obtained and extracted through THz-ATR spectroscopy, including refractive index, extinction coefficient, permittivity, dielectric loss, and so forth. The methods of parameters extraction and calculation can be found in the reported research and studies [34]. Choosing the \(xz\) plane as the incident plane, the expressions of the evanescent field and the penetration depth are:  
\[\overset{\rightarrow}{E}_{2}=\overset{\rightarrow}{A}_{2}\exp[-z\frac{2\pi}{ \lambda_{1}}\sqrt{\sin^{2}\theta_{i}-\frac{n_{2}^{2}}{n_{1}^{2}}}]\exp[i(x \frac{2\pi}{\lambda_{1}}\sin\theta_{i}-\omega t)], \tag{1}\]  
\[d_{p}=\frac{n_{1}\lambda_{1}}{2\pi\sqrt{{n_{1}}^{2}\sin^{2}\theta_{i}-{n_{2}}^{ 2}}}. \tag{2}\]  
while \(\overset{\rightarrow}{A}_{2}\) is the amplitude, \(\omega\) is the angular frequency, \(t\) is the time, \(x\) is the distance in \(x\) direction, and \(z\) is the distance in \(z\) direction. \(n_{1}\) is the refractive index of the ATR crystal, \(n_{2}\) is the refractive index of the sample. Parameter \(\theta_{i}\) is the incident angle, \(\lambda_{1}\) is the wavelength of the incident wave. The amplitude of the evanescent wave attenuates exponentially in the \(z\) direction. While the penetration depth is usually equal to the order of magnitude of \(\lambda_{1}\), which plays an important role on the sensitivity of the ATR method. In the THz region, the penetration depth \(d_{P}\) of the evanescent waves ranges from several micrometers to hundreds of micrometers, which is deeper than that in the infrared region, especially at the low-frequency region. The stability of the THz-ATR system is essential for accurate data acquisition. It shows that the factors such as prism misalignment [35], delay drift [36], incidence angle change [37], total propagation efficiency [38], and thermal expansion [39] could impact the system stability. While, good thermal stability of the system will help minimize the uncertainty of the system [36].
## 8 Practical Implications  
The relevant literature heavily focused on user satisfaction and content-system quality; however, educators should consider potential technical risks, and usability features are advised to since such issues may cause risks and could provide limited functionality, which can easily disrupt the learning process. Therefore, educators should consider the proposed aspects of the study in the careful selection of mobile learning platforms for their students. The findings of the study could be used as a guideline for educators who are aiming for reliable, mobile learning environments.  
Developers of mobile learning platforms could benefit from the findings of the study by improving their products over problematic areas. Developers can significantly minimize the risks associated with fault tolerance and optimize time behavior to make platforms more attractive and usable. Maintaining a technically robust platform also reduces costs and platform failures.  
The results of the study amplify a comprehensive and holistic risk-based approach for the integration of mobile learning tools into the curriculum, which aligns with policies supporting equitable and sustainable education for policymakers. Mobile learning platforms with high technical performance should also be available and accessible to all learners, encouraging lifelong learning.
# A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs +
## 2 Proposed System  
### Overview  
The task of singing synthesis mimics the task of a singer during a studio recording, that is, interpret a musical score with lyrics to produce a singing waveform signal. The goal of our system is to model a specific singer's voice and a specific style of singing. To achieve this, we first record a singer singing a set of musical scores. From these recording, acoustic features are extracted using the analysis part of a vocoder. Additionally, the recordings are phonetically transcribed and segmented. Note level transcription and segmentation can be generally obtained from the musical scores, as long as the singer did not excessively deviate from the written score.  
During training, our model learns to produce acoustic features given phonetic and musical input sequences, including the begin and end time of each segment. However, during generation, we only have access to the note begin and end times, and phoneme sequence corresponding to each note (generally a syllable). As we do not have access to the begin and end times of each phoneme, these must be predicted using a phonetic timing model. The next step is to predict F0 from the timed musical and phonetic information, using a pitch model. The predicted phonetic timings and F0 are then used by the timbre model to generate the remaining acoustic features such as the harmonic spectral envelope, aperiodicity envelope and voiced/unvoiced (V/UV) decision. Finally, the synthesis part of the vocoder is used to generate the waveform signal from the acoustic features. An overview of the entire system is depicted in Figure 1.  
Figure 1: Diagram depicting an overview of the system with its different components. Here, V/UV is the predicted voice/unvoiced decision, and the Fill UV block fills unvoiced segments by interpolation.
# Adaptive Access Selection Algorithm for Large-Scale Satellite Networks Based on Dynamic Domain
## 4 Experimental Verifications
### Simulation Analysis  
In the scenario without insufficient resources, satellite failures, and ESLs interruption, the relationship between the \(P_{u}\) value and the access time of the DAA algorithm and the traditional access algorithm is shown in Figure 10. The vertical coordinates in the graph indicate the average value of \(P_{u}\) obtained from multiple experiments. The horizontal coordinate indicates the moment when \(ES_{1-4}\) start transmitting packets. The simulation results show that the average \(P_{u}\) values of the DAA algorithm, comprehensive weighting algorithm, longest covering time algorithm, and maximum elevation angle algorithm are 97%, 85%, 79%, and 51%. Therefore, the coverage time of the access satellite selected by the DAA algorithm is the most matched with \(t_{u}\). The \(P_{u}\) value of the DAA algorithm is the most stable because each access uses the process shown in Figure 5 to select the satellites with the most matching \(t_{u}\) as the access satellites.  
The relationship between the probability of insufficient satellite resources and switching times is shown in Figure 11. The number of switches in the figure is the average of the experimental results obtained from multiple experiments. The vertical coordinate is the sum of the number of switches required by \(ES_{1-4}\) to complete the data transmission. The horizontal coordinates are the probability of insufficient satellite resources. As the maximum elevation angle access algorithm has the minimum \(P_{u}\) value, the switching times are much more than the other three algorithms. Therefore, the experimental results of the switching times of the maximum elevation angle access algorithm are no longer displayed in Figure 11.  
Figure 10: Coverage time utilization with different access algorithms.  
Figure 9: Coverage time of satellites.  
It can be seen from Figure 11 that the average switching times of the DAA algorithm under different probabilities are less than those of the other two algorithms; this is because the \(P_{u}\) value of the DAA algorithm is larger than the traditional access algorithm. In addition, the DAA algorithm can further reduce the switching times by obtaining the resource status of some satellites in the communication domain by ACKs. In the simulation process, compared with the traditional access algorithm, the DAA algorithm does not need the interaction information between ES and the satellites before selecting the access satellite, and the maximum \(t_{u}\) value can be achieved only through the calculation of ES. The DAA algorithm saves channel resources and reduces the cost of ES and satellites.
## 5 Advantages, Limitations, and Future Research Directions of the Model
### Model Limitations and Future Research Directions
#### 5.2.1 Effects of Different Preprocessing Methods on the Model
Because we not only use the preprocessed text to extract text features but also need to use the text to generate images, the impact of the quality of the text input model on our model is growing exponentially. Therefore, if subsequent researchers want to apply our method to specific tasks, they must choose a text preprocessing method suitable for their datasets.
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding
### Damson
#### 3.3.5 Results and Conclusions
Finally, the channel characterisations obtained with the DAMSON project were very useful to define the requirements of the NATO robust HF waveforms: (a) STANAG 4415, which is also included in the (b) US Mil-Std 188-110A/B/C standards [49; 50] and (c) in NATO STANAG 4539 [51], and also supported the HF automatic radio control system standard (d) STANAG 4538.
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Feature Attribution and Game Theory
#### 6.6.1 Technical and Methodological Insights
[60] advance beyond superficial Bag-of-Words token attribution (e.g., highlighting \"/\" counts) by employing context-aware RoBERTa embeddings, revealing semantic drift in cyber-threats--such as the decline in SQLi tokens (\"DROP TABLE\") and the rise in URL-encoded macros in later datasets. This progression from syntactic to semantic explanation underscores how attribution methods themselves evolve to capture deeper conceptual shifts. Similarly, [PERSON] et al. [100] spatially map drift through in-model attention layers and post hoc Grad-CAM, wherea heatmap fixating on scanner tables instead of lung tissue makes data bias viscerally apparent to clinicians, prompting targeted retraining. Notably, several papers position attribution geometry as the core adaptation trigger. The SCAL framework introduced by [PERSON] et al. projects SHAP vectors into an \"explanation space,\" using cluster dynamics--silhouette scores and noise emergence--to autonomously guide hyperparameter adjustments [59]. A multiplying cluster count during COVID-19 signals diversified consumption behavior, prompting recalibration that improves accuracy. [PERSON]'s OEP-TT clusters TreeSHAP [144] lag-importance profiles, making ensemble updates interpretable: replacing a \"Lag-15\" model with a \"Lag-3\" representative concretely narrates the shift from long-term seasonality to recent spikes. [PERSON] and [PERSON] [111] further demonstrate proactivity, using the Jeffrey divergence between successive SHAP-derived ranking-preference lists to trigger retraining before accuracy drops, while actively querying samples whose local attributions diverge from global rankings. This contrasts with the forensic use by [PERSON] et al. of gradient-based attribution to retrospectively dissect why a static malware detector failed--revealing, for instance, that new fraud families leveraged \"googletagmanager.com\" strings unseen in training data [33]. Collectively, these works illustrate a paradigm shift: feature attribution transcends post hoc analysis to become an embedded, self-updating ledger of concept evolution. By preserving axiomatic properties (e.g., conservation laws) while streaming interpretable outputs--rankings, rules, or spatial maps--the approaches of [PERSON], [PERSON], [PERSON], [PERSON], and others ensure that the very values justifying a prediction also chronicle the model's changing rationale. This intrinsic coupling allows stakeholders--clinicians, engineers, fraud analysts--to read drift directly from evolving attribution landscapes, transforming what was once a detection challenge into a continuous, operational narrative of adaptive intelligence. Therefore, the explanatory process is diagnostic and applied after observation, fitting this class into the XAI group.
# Iterative QR-Based SFSIC Detection and Decoder Algorithm for a Reed-Muller Space-Time Turbo System
## 1 Introduction
In the first place, it forms the sufficient statistics for the MMSE estimate according to QR decomposition-based soft feedback successive interference cancellation, which saves computation. Second, the signal that originated from the symbols of the reliable segment is iteratively cancelled with the QRSFSIC, which guarantees the optimal detection sorting in case of the risk of EP and, thus, leads the proposed scheme to approaching the upper bound of the diversity gain. Finally, RM-turbo codes with minimal trellis make it feasible for an iterative trellis-based MAP RM turbo decoder, resulting in the global optimum, corresponding to the transmitted data, with lower complexity. Throughout this paper we adopt the following notational conventions. Boldface capitals and lower-case letters symbolize matrices and vectors, respectively. Furthermore, \((\ )^{T}\), \((\ )^{H}\), and \((\ )^{-1}\) represent the transpose, Hermitian transpose, and inverse, respectively. Finally, indices \([m,l]\) are used to mean the \(l\)th bit position of the constellation symbol for the \(m\)th transmit antenna while \(\mathbf{x}_{k+1}\) and \(\mathbf{x}_{k-1}\) denote \([x_{k+1},\cdots,x_{N_{l}}]^{T}\) and \([x_{1},\cdots,x_{k-1}]^{T}\), respectively, and the subscript \(k!\) means that the range of index is from \(k\) to \(l\).
# Tunable RF Filters Based on Liquid Crystal for Space Applications
## 3 Qualification Tests and Results
### Test Results  
RF performance measurements for all steps except for the power handling test are shown in Section 3.2.1. Power handling measurement results are reported in Section 3.2.2.
## 5 Results and Discussion
Finally, the results for receiver sensitivity are shown in Figure 10 where BER performance of SISO and the two approaches for MIMO are shown against the received optical power without considering any interference signal. Figure 5: Estimated channel response for the LoS signal and strongly reflected (non-LOS) version of the same signal. Figure 6: BER performance using SISO transmission using a single LED unit for the cases when receiving (i) LOS signal; (ii) LOS signal with a reflected version; (iii) LOS, reflected signal and an interference signal. reflection or interference signal for simplicity. The transmission distance is fixed to 6 m, and the effects of atmospheric attenuation and FOV are included to compare the performance of each case in terms of received power. The proposed MISO processing results in improved receiver sensitivity due to the enhancement in SNR and thus improves the power efficiency of the overall system as the photodetector requires a reduced amount of power to provide the same BER performance, as in the case of MISO with a conventional receiver. Even in the presence of strong interference signal, the proposed framework shows successful transmission of much higher data rates as compared to [27], and three times longer transmission distance as compared to the previously reported MIMO-based VLC transmission [30]. This shows the superiority of the proposed MISO receiver in the VLC-based ITS, which is evident from the provision of higher data rates, longer transmission distances and improved receiver sensitivity than recently proposed solutions [31]. Figure 8: BER performance using 4 \(\times\) 1 MISO processing for the cases when the receiver detects MIMO-encoded (i) LOS signal; (ii) LOS signal with a reflected version; (iii) LOS, reflected signal and an interference signal. Figure 7: Improved SNR for the LED array using array gain from MISO transmission.
# CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention
## 3 Method
### Channel Fusion Attention Mechanism (CFAM)
#### 3.3.3 The Backend: Calculate the World Coordinates of the Keypoints  
Through the activation above, the network can recalibrate features and learn to use global information to selectively emphasize informative features and suppress less useful ones. The output feature maps from channel attention block \(O_{k}\) are concatenated with the information to determine whether the hand is a left or right hand and is processed further using two fully connected layers. Then, the two parallel streams are fed directly into fully connected layers to estimate the camera coordinate \(W^{camera}\) and the camera rotation angle \(R\). Both estimations are combined with an estimation of the world coordinate \(W^{world}\). The final process is as follows, where \(FC_{k}\) is the operation of the full connection:  
\[W^{camera}=O_{1}*FC_{1}, \tag{8}\]  
\[R=O_{2}*FC_{2}, \tag{9}\]\[W^{world}=W^{camera}R. \tag{10}\]
## 4 Enabling Technologies for FSO Communication
### Adaptive Detection Thresholds
#### 4.10.4 Implementation Challenges  
Despite their advantages, implementing ADTs in FSO systems comes with challenges:  
1. Complexity: The algorithms required for real-time threshold adjustment can be complex and computationally intensive, necessitating advanced processing capabilities.
2. Latency: Real-time adjustments must be swift to be effective. Any latency in the feedback loop or threshold adjustment process can degrade performance.
3. Calibration: Adaptive systems require initial calibration and ongoing tuning to ensure they respond appropriately to changing conditions.
# Multi-Objective Decision-Making Meets Dynamic Shortest Path: Challenges and Prospects +
## 5 Multi-Objective Dynamic Shortest Path
### Problem Definition
Furthermore, this procedure updates only the paths that cross the vertex \(v\), taking advantage of the previous solution not affected by the update;
* pf_sp \((x,y)\): a procedure that returns the approximate Pareto front of optimal shortest paths from vertex \(x\) to vertex \(y\), if any;
* cost_sp \((x,y,\mathbf{w}^{\prime})\): a query procedure that receives as the input the source vertex \(x\), the destination vertex \(y\), and the decision variables'vector \(\mathbf{w}^{\prime}\), returning optimal cost vector \(\mathbf{c}_{xy}\) from the optimal shortest path, if any;
* optimal_sp \((x,y,\mathbf{w}^{\prime})\): This query procedure uses the inputs' source vertex \(x\), destination vertex \(y\), and the decision variables' vector \(\mathbf{w}^{\prime}\) to return the optimal shortest path \(\pi_{xy}\) considering the best trade-off between the decision variables' vector \(\mathbf{w}^{\prime}\) and the cost vector \(\mathbf{c}_{xy}\), if any. Bringing Figure 2 to the MODSP context, we can have several possible solutions considering the trade-off between the objectives for only one desired path \(\pi_{xy}\in G\). Figure 4 shows the same example of the Pareto front of Figure 2, in which we changed the solutions' nomenclature to possible paths to facilitate the understanding of the proposed problem. Next, we rewrite the description given in Section 3 for the context of the MODSP problem. In Figure 4, when comparing the solutions \(c_{\pi_{xy}}(\mathbf{w})\) and \(c_{\pi_{xy}}(\mathbf{w}^{\prime})\), considering only the objective \(c_{\pi_{xy}1}\), we have the solution \(c_{\pi_{xy}1}(\mathbf{w})\) as dominant, \(c_{\pi_{xy}1}(\mathbf{w})\prec c_{\pi_{xy}1}(\mathbf{w}^{\prime})\). However, when considering the goal \(c_{\pi_{xy}2}\), both solutions have the same value, \(c_{\pi_{xy}2}(\mathbf{w})=c_{\pi_{xy}2}(\mathbf{w}^{\prime})\). Figure 3: Multi-objective dynamic shortest path graph example where the dashed lines represent vertices and edges added at an unpredictable moment. The vertices are named by the letters \(x,a,u,v,b,f,g\), and \(y\), being \(x\) the source vertex and y the destination vertex. Vector \(\mathbf{c}\) over the edge represents the edge traverse multi-objective cost between two vertices. Thus, the solution \(c_{\pi_{xy}}(\mathbf{w})\) weakly dominates the solution \(c_{\pi_{xy}}(\mathbf{w}^{\prime})\), and both solutions are sub-optimal due to being out of the Pareto front. Finally, the \(c_{\pi_{xy}}(\mathbf{w}^{\prime\prime})\) solution is an example of a non-dominated solution present on the Pareto front for a specific trade-off defined for \(\mathbf{w}\). The set of non-dominated solutions of a path \(\pi_{xy}\in G\) contains the best trade-offs for the multiple objectives. Thus, the MODSP solution regards maintaining the sets of non-dominated shortest paths up to date after each vertex update operation, taking advantage of previously computed solutions. In this way, the decision-making process can proceed with the query procedures.
## 4 Results and Discussion
### Detector-Level Experiment Results
#### 4.2.3 Independent Detector Responsivity Temperature Dependence Experiment Results
The comparison of the responsivity temperature dependence results is shown in Figure 16. The difference between the two sets of results was less than 0.05% after performing temperature correction on the filter transmittance. These results suggest that the condition control measures adopted in the detector-level experiment were effective and reduced the influences of various factors on the experimental results. In particular, based on the dark reference provided by the chopper dark reference, the background radiation response at different heat sink temperatures was reduced. Thus, the accuracy of the experimental results was improved and provided a good basis for obtaining responsivity temperature dependence results, ensuring the substitutability of the detector-level experiment.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 4 Proposed Method PDTR
### The Probabilistic Routing Table Set-Up Phase  
This phase is achieved according to three selection distribution: the hop-count distribution, the sender node chooses the neighbor with lower hop-count to relay the sensed data. In the transmission distance distribution, the sender node selects the nearest neighbor to be the next hop (forwarder). Finally, in the residual energy distribution, the node with the greater residual energy has more chance to be chosen as the next-hop, if its energy has not fallen below threshold energy.
## 4 LEACH Protocol Improvement Based on the Differential Evolution Algorithm
### Collecting Status Information of Nodes inside Clusters by Auxiliary Cluster Heads  
Neighbor nodes inside the cluster send status information of their own locations and energy to auxiliary cluster heads. Now, the location \(P(p_{1},\,p_{2},\,\ldots,p_{n})\) and energy information \(E(e_{1},\,e_{2},\,\ldots,e_{n})\) of the neighbor nodes are stored in the auxiliary cluster heads, where n is the number of nodes in the cluster \(p_{i}\) is the location value of node \(i\), and \(e_{i}\) is the energy value of node \(i\).