esa-sceva/satcom-chunk-collection huggingface dataset:
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 3 Case Study: Zhongma Landslide
### Model Training  
The constructed dataset was divided into training and testing sets by the ratio of 7:3. The ResNet model was constructed for classification training. The model architecture is shown in Figure 9.  
The random search algorithm is applied to find the best optimal hyperparameters of the model. The iteration is 20 times. The result is validated by a fivefold cross-validation technique. The number of residual blocks ranges between 1 and 6. The initial learning rate is set to be one of 0.1, 0.01, 0.001 or 0.0001. The batch size selection parameters range from 16 to 128 with a step size of 2, and the optimization function selection parameters are SGD, Adagrad, RMSprop and Adam.  
By the random search algorithm, we obtained the optimal hyperparameters of the model: four residual blocks, Adam optimizer, loss function of the categorical cross-entropy, batch size of 32, the initial iteration number of 1500 and the initial learning rate of 0.01. To speed up the training, a learning rate dynamic decay strategy was employed to accelerate convergence and the EarlyStopping method was used to stop the iteration before overfitting and terminate training (i.e., stop training when the accuracy is not increasing within 100 iterations).  
Figure 8: Results of gray correlation analysis for the Zhongma landslide.  
The computer configuration for this work was Intel(R) Core (TM) i5-9400F CPU @ 2.90 GHz CPU processor, OS Windows 10 Professional, 16 GB RAM and NVIDIA GeForce GTX 1660 SUPER graphics card.  
The training results show that the accuracy, precision and recall of the algorithm are 0.981, 0.971 and 0.871, respectively. The trained ResNet algorithm was applied to the landslide deformation dataset, and the deformation state classification results are shown in Figure 10.  
The above results show that the K-means-ResNet method can identify the deformation state of landslide accurately on the whole. According to the deformation state, we can carry out landslide warning.
## 2 Experimental Methods  
### Specim Design and Preparation  
This investigation utilized sandstone specimens collected from the Mesozoic stratigraphic outcrops in Songjiahuan Tuangou Village, Junan County, Linyi City, Shandong Province, China. A multi-modal analytical approach was employed to characterize textural configurations and constituent mineral proportions, combining digital macrography with quantitative petrography. Detailed petrographic analysis through polarized light microscopy was conducted, with systematic comparison to macroscopic specimen documentation as visually documented in Figure 3a-c. The sandstone exhibited a mineral assemblage comprising 55% feldspar (Fsp), 42% quartz (Qz), 1% sericite, and 2% other minerals, classifying it as siliceous-cemented lithic feldspar sandstone (Figure 3d). To ensure test accuracy and minimize material variability, specimens were systematically obtained from a homogeneous sandstone block, which exhibited no visible cracks and had a uniform texture.  
Figure 3: Polarized microscope photographs and mineral composition of red sandstone: (**a**) naked eye view; (**b**) single polarization; (**c**) orthogonal polarization; (**d**) lithological makeup.  
The sandstone specimens used in the simulation experiments were processed into cylindrical standard specimens (\(\varphi\) 50 \(\times\) 100 mm), cubic (100 mm-edge) specimens with rectangular perforation and fissure, and cubic specimens with only rectangular perforation (Figure 4a). The rectangular perforations (30 mm edge dimension) were centrally located in the specimens. The pre-fabricated fissures were positioned 32.5 mm from the center of the rectangular hole, with angles, lengths, and widths of 0\({}^{\circ}\), 20 mm, and 2 mm, respectively. The fissure penetration levels were 25%, 50%, and 100% (Figure 4b-d). For convenience, the specimens are labeled as \"S-0-50\", where \"S\" represents the specimen, \"0\" indicates the angle between the fissure and the bottom edge of the rectangular perforation (Y-direction), and \"50\" represents the fissure penetration level in the Z-direction (i.e., 50% penetration). The specimen machining precision followed ISRM (International Society for Rock Mechanics) standards, [44] maintaining 0.0175 mm general and 0.025 mm perpendicularity tolerances. The physical and mechanical properties of the rock materials were measured during the experiment, as shown in Table 1.  
Figure 4: Schematic diagram of sandstone specimens and penetration: (**a**) sandstone specimens; (**b**) “S-0-25”; (**c**) “S-0-50”; (**d**) “S-0-100”.
# An Artificial Intelligence-Based Algorithm for the Assessment of Substitution Voicing
## 2 Materials and Methods  
### Groups  
Speech samples were collected from 379 male participants (100 after laryngeal oncosurgery and 279 healthy) assessed at the Lithuanian University of Health Sciences Department of Otorhinolaryngology in Kaunas, Lithuania. The ages of the subjects ranged from 18 to 80 years. The study included 100 male patients (mean age 63.1; SD 22.8 years) who were surgically treated for histologically confirmed laryngeal cancer. Patients with endolaryngal cordectomy type III/IV, partial vertical laryngectomy, or total laryngectomy with tracheoesophageal prosthesis implantation were specifically selected for this investigation, since they communicate via speech created by either a single vocal fold or none [50; 51]. This sort of speech creation is known as substitution voicing (SV) [6]. Patients were divided into groups based on the number of residual vibrating laryngopharyngeal structures employed for SV production. The single vocal fold group (endolaryngeal cordectomy and partial laryngectomy) included 70 patients, whereas the total laryngectomy group had 30 individuals. Patient voice recordings were gathered at least 6 months following surgery to guarantee that healing and speech rehabilitation programs could be completed.  
The control group included 279 healthy male volunteers with normal speech (mean age 38.1; standard deviation (SD) 12.7 years). They had no current or previous speech, neurological, hearing, or laryngeal abnormalities. At the time of speech recording, the control group was clear of a common cold or upper respiratory illness. Individuals with any pathological laryngeal changes discovered after laryngeal endoscopy were excluded from the study.  
For the cordectomy, partial laryngectomy, and control groups, laryngeal endoscopy was conducted without topical anesthesia using the XION EndoSTROB DX instrument (XION GmbH, Berlin, and Germany) with a 70\({}^{\circ}\) rigid endoscope. Before recording, the tracheoesophageal prosthesis was examined for signs of leakage or infection and, if necessary, replaced.  
The collected speech recordings from control and patients' groups were randomized. Then, the SV recordings were split into two subgroups, i.e., cordectomy and partial laryngectomy (phonation with single vocal fold) and total laryngectomy (phonation without vocal folds). Speech recordings of 309 subjects were used to train the algorithm. The additional speech recordings of 70 subjects were reserved to evaluate how the algorithm performs with recordings unknown to it. The arrangement of the study groups is presented in Table 1.
## 3 Integrity Monitoring Method of Space-Ground Integration
### Ground verification  
Despite the development of A-SAIM, ground monitoring stations still play an important role in ensuring the integrity of the BeiDou satellites. Ground monitoring stations receive the signals from the BeiDou satellites and the raw observation data are sent to the ground integrity processing center. The ground integrity processing center will determine whether there is possibly a fault in the satellites based on these observation data and the known coordinates of the stations.  
In the procedure above, the second step can be implemented in two different ways. As shown in Figure 5, the first approach is to establish communication between LEO satellites and BeiDou satellites with the BeiDou short-message communication capability. Figure 6 demonstrates the second approach. First, LEO satellites send the raw observations to the ground integrity processing center; second, the ground integrity processing center generates integrity monitoring results for the MEO/IGSO/GEO satellites based on (a) the observations from space monitoring stations and ground monitoring stations and (b) the data from inter-satellite links; finally, the integrity monitoring results are uploaded to MEO/IGSO/GEO satellites with ground uplink stations.  
Figure 5: Communication between LEO and BeiDou satellites with Beidou short messages.  
Figure 6: Communication between LEO and BeiDou satellites based on space–ground connections.
# Versatile Video Coding-Post Processing Feature Fusion:
## 4 Experimental Evaluations and Discussion  
### Testing Environment Configuration  
To assess the performance of the proposed method, we selected the JVET NNVC CTC sequences for evaluation, which were not part of the training dataset. These 22 sequences, categorized into classes A1, A2, B, C, D, and E, were used to evaluate the RA and LD configurations according to the JVET NNVC CTC guidelines. Specifically, we tested RA and LD using classes A1, A2, B, C, and D and classes B, C, D, and E, respectively. The test QP values for all configurations were 22, 27, 32, 37, and 42.
## 5 Results and Discussion
### Comparison with State-of-the-Art Methods  
Our model outperforms other lightweight and unsupervised methods (e.g., Zero-DCE, EnlightenGAN) and approaches the quality of fully supervised large-scale networks (e.g., SID) with a fraction of the complexity. It is among the few models achieving  
* High perceptual quality (low LPIPS);
* Real-time performance;
* No dependency on extensive paired datasets.  
To further assess the practicality of our approach for real-time applications, Figure 5 presents a comparative analysis of runtime performance versus model complexity across several state-of-the-art low-light enhancement methods.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 4 State of the Art Detection and Mitigation Strategies
### Performance Evaluation  
Despite the wide range of detection and mitigation strategies surveyed, a consistent limitation in the literature is the lack of standardized benchmarking frameworks. Most studies evaluate their proposed methods in isolated environments, using various metrics, traffic patterns, and network configurations. As a result, direct comparisons of detection accuracy, latency, and scalability remain largely qualitative. For example, while ML-based approaches frequently report high true positive rates (above 95%) [43], these evaluations are often based on synthetic datasets or restricted simulations, limiting generalizability. Similarly, blockchain-enabled strategies highlight trust and decentralization benefits but seldom quantify the impact on packet forwarding delay or computational overhead [47]. PIT management techniques, such as ChoKIFA+ [22], report effective state cleanup but do not continuously evaluate performance in real-time or mobile scenarios.  
This inconsistency in performance evaluation presents a barrier to identifying the most practical defenses for specific deployment contexts, particularly in resource-constrainedIoT or edge computing environments. Future work should prioritize the creation of a reproducible benchmarking environment, preferably with shared datasets, simulation configurations, and agreed-upon evaluation metrics, to enable head-to-head comparisons across mitigation strategies. Until such standards are established, the absence of quantitative parity between studies remains a critical limitation in evaluating the operational trade-offs of IFA countermeasures [8; 33].
# Fiber Bragg Grating-Based Optical Signal Processing: Review and Survey
## 2 Review of Design Methods for Fiber Bragg Gratings
### Approximate Methods
#### 2.1.1 First-Order Born Approximation  
The first-order Born approximation has been used as a grating design tool to either determine the grating amplitude and phase profiles required to obtain a target reflective spectral response \(H_{R}(f)\), or to anticipate the \(H_{R}(f)\) that will produce a certain grating profile. In particular, this approximation establishes that under weak-coupling conditions (i.e., when the maximum reflectivity is \(\max[R(f)]=R_{\max}\ll 1\)), an estimation of the reflective impulse response of an FBG, \(h_{R,est}(t)\), is directly related to the spatial profile of the refractive index perturbation, \(n_{FBG}(z)\), by [26]:  
\[h_{R,est}(t)=\left.\frac{-\frac{dn_{FBG}(z)}{dz}}{2\cdot n_{FBG}(z)}\right|_{z =\frac{z}{2}\frac{-c}{\pi\rho(z)}};\ n_{av}(z)=\int_{0}^{z}n\big{(}z^{\prime} \big{)}dz^{\prime} \tag{4}\]where \(n_{az}(z)\) is the average refractive index from the grating input to the location \(z\), and \(c\) is the speed of light in a vacuum. Under the Born approximation, the estimation of the reflection impulse response, \(h_{R,est}(t)\), is proportional to the spatial index-modulation profile of the grating in amplitude and phase. Therefore, the estimation for the reflection frequency response is directly obtained by:  
\[H_{R,est}(f)=\Im[h_{R,est}(t)] \tag{5}\]  
This simple approach can be readily employed to implement the analysis and synthesis of a wide variety of FBGs operating on waveforms with picosecond resolutions. However, this approximation starts to fail for strong-coupling gratings. The reason is that the Born approximation is strictly valid when \(|\kappa|L\ll 1\), where \(\kappa\) is the coupling coefficient, which is proportional to the index modulation amplitude, i.e., \(|\kappa|=\pi\cdot\Delta n/\lambda\) (\(\lambda\) is the wavelength). The parameter \(\kappa\) provides the relative amount of power coupled between two modes per unit length. Longer gratings are associated to weak \(\Delta n\), since the coupling coefficient needs to be sufficiently low so that the electromagnetic field maintains certain magnitude to penetrate the full grating length.
## 3 Contributions and Scope of the Survey
#### Guiding Research Questions  
This survey is anchored by a central inquiry into the evolving role of large language models (LLMs) in recommender systems. Specifically, it seeks to understand how these models can be leveraged to enable more personalized, scalable, and context-aware recommendations across a range of application domains and data modalities.  
Primary Research Question:  
How can large language models (LLMs) be effectively integrated into recommender systems to enhance personalization, ensure scalability, and support context-sensitive decision making across diverse domains and input types?  
To support this goal, the following sub-questions are explored:  
* Sub Q1: In what ways are LLM architectures--such as BERT, GPT, and T5--adapted, fine-tuned, or prompt-engineered to handle core recommendation tasks including sequential prediction, relevance ranking, and content generation?
* Sub Q2: What are the major deployment scenarios and use cases for LLM4 Rec systems in sectors like e-commerce, healthcare, education, and social media? What domain-specific constraints (e.g., multilingual input, ethical considerations, regulatory frameworks) influence their design and performance?
* Sub Q3: Which technical limitations persist in current LLM4 Rec pipelines--particularly regarding computational efficiency, fairness, data sparsity, cold-start issues, and user privacy--and how are these being addressed in state-of-the-art research?
* Sub Q4: How do LLM-based recommender models compare to traditional or hybrid systems in terms of key performance indicators such as NDCG, Recall@K, diversity, latency, and real-time responsiveness?  
These questions guide the structure and analysis throughout the survey, informing both the taxonomy of existing models and the identification of open research challenges.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 4 Techniques of VLC-Based Indoor Positioning Systems
### Positioning Techniques
#### 4.2.8 Hybrid Schemes  
Before the end of this subsection, Table 4 shows a comparison of various VLP systems.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Receiver** & **Algorithm** & **Complexity** & **Multiple** & **Operating** & **Accuracy (cm)** & **Assumptions** \\  & & & **(Evaluation)** & **Access** & **Range (m)** & & \\ \hline
[54] PD & RSSR & Simple (P) & TDMA & \(1\times 1\times 1.3\) & Max 10.29, mean & Distortionless, only directed light, LEDs and receiver are parallel \\ \hline
[55] PD & RSS & Simple (P) & FDMA & equilateral triangle of 0.6 & 2.4 with adjustment process & Distortionless, transmitters should be fixed at the same height \\ \hline
[51] PD & RSS & Simple (S) & TDMA & \(3\times 3\times 3\) & \(<\)0.05 & Distortionless, only directed light, LEDs and receiver are parallel \\ \hline
[14] PD + IMU & RSS & Simple (P) & Random channel hopping & Conference room, cubic angle, corridor & Medium 30 & LEDs and receiver are parallel \\ \hline
[87] PD & RSS correlation & Simple, needs training stage & CDMA & \(5\times 5\times 6\) & \(<\)10 & Optical channels are line-of-sight (LOS) links \\ \hline
[52] Multiple PDs & RSS & Complex (S) & No need & \(2\times 2\times 2\) & \(<\)1.5 & There is only one transmitter, LEDs and receiver are parallel \\ \hline
[88] PD & AOA & Complex (S) & FDM & \(5\times 5\times 3\) & Max 1.4 & Distortionless, only directed light \\ \hline
[61] PD & TDOA & Complex (S) & FDM & \(5\times 5\times 3\) & \(<\)2 & The noise of system is considered as AWGN \\ \hline
[89] PD & TDOA & Complex (S) & TDMA & \(5\times 5\times 3\) & Mean 3.9 & The transmitter IDs of received signals are unknown. But, the number of transmitters is known \\ \hline
[63] PD with extra equipment & TDOA & Complex (S) & FDM & \(5\times 5\times 3\) & Mean 0.02 & Distortionless, only directed light, transmitters should be fixed at the same height \\ \hline
[65] Tilted multiple PDs & AOA+RSS & Complex, need training stage & No need & \(2\times 2\times 2.5\) & \(<\)6 & Azimuth angles and polar angles are fixed and known \\ \hline
[90] Differential photo sensor & AOA & Complex (S) & FDM & Not mentioned & RMS 3.7 & Photo sensor is fixed and position of optical beacon changes \\ \hline
[82] PD + IMU & AOA & Complex (S) & TDMA & \(5\times 3\times 3\) & \(<\)25 & Transmitters should be fixed at the same height \\ \hline
[41] PDs + IMU & AOA & Complex (P) & TDMA & \(5\times 4\times 3\) & \(<\)6 (at 1.3 m/s) & No special assumption \\ \hline
[91] PD & RSS & Simple (P) & FDMA & \(0.75\times 0.75\times 2\) & Positioning error in the area of 1 m \(\times\) 1 m \(\times\) 2 m \(\times\) reach within a few centimeters & Less light interference and reflection \\ \hline
[75] PD & Fingerprinting Complex (S) & CDMA & \(30\times 30\times 30\) & RMS 81 & There is the possibility of obstructions \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different VLC-based indoor positioning systems. S and P refer to simulation and practical experiment, respectively.
## 4 Conclusions  
To evaluate the flavor characteristics of Chinese bayberry alcoholic beverages, fermented bayberry wine (FBW) and integrated bayberry wine (IBW) were investigated for their volatile and soluble profiles using HS-SPME GC-MS and UHPLC Q-TOF and analyzed with multidimensional statistical analysis, including PCA and OPLS-DA.  
The volatile compounds 1-pentanol, 1-caryophyllene, and isopentanol were only detected in IBW. \(\beta\)-caryophyllene, the key flavor component of bayberry, was found to be the most abundant volatile compound in IBW (25.89%), and was 3.73 times more abundant in IBW than in FBW. The levels of ethyl octanoate, ethyl nonanoate, and ethyl decanoate were also several times higher in IBW than in FBW. These compounds contributed to the strong bayberry aroma and better fruity flavor of IBW. On the other hand, high levels of ethyl acetate and octanoic acid in FBW, representing pineapple/overripe or sweat odor, were key contributors to the fermented flavor of FBW.  
Soluble sugars, such as sucrose, D-glucose, and D-tagatose, as well as amino acids, such as L-glutamate and L-aspartate, had much higher levels in IBW. The anthocyanin pigment cyanidin 3-glucoside, which generates red color, was also higher in concentration in IBW. On the other hand, most of the differentially expressed alcohols, acids, amino acids, purines/pyrimidines, and esters were present in higher concentrations in FBW compared to IBW. This demonstrated that IBW has a much sweeter and more savory taste, as well as a better color generated by more anthocyanins, while FBW presents a more acidic and drier taste, as well as a complex formation of alcohols and esters.  
Further efforts are needed to deepen our understanding of the difference between IBW and FBW due to the limitations of our instrumental environment and experimental experience. The study also prompts the need for further research on the flavor profiles of IBW and its potential application and market value.  
Conceptualization and methodology, Y.M. and G.H.; formal analysis and investigation, Y.M., X.S., Y.L. and H.H.; writing--review and editing, Y.M.; Supervision, Y.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC), grant number 31301582, and the Zhejiang Provincial Natural Science Foundation, grant number LGN20C200014.  
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.  
All of the authors declare no conflicts of interest.