esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Space-Time Transport Performance Measure (_STTP_)
### Calculation of _Sttp_
Conversely, the Euclidean distance solely depends on the land-use system (i.e., the amount and location of opportunities in space) therefore it is used to weight _IDTC_ and point out the role of the transport system in accessibility differences. Figure 2 summarises the calculation process for _STA_ (in red) and _STTP_ (in blue) based on _STPA_ (in black), which is the common element between them. \[STTP=\frac{IDTC}{IDD} \tag{1}\]  
\[IDTC=\sum_{a_{1}}^{a_{n}}ITC_{a,a+1}^{k}\ \ \ \text{with}\ \ \ ITC_{a,a+1}^{k}=Cm_{a,a+1}^{k}+Civtt_{a,a+1}^{k}+Covtt_{a,a+1}^{k} \tag{2}\]  
\[IDD=\sum_{a_{1}}^{a_{n}}D_{a,a+1} \tag{3}\]  
where: \(a_{1-n}\) are the fixed activities performed by an individual on a daily basis, \(k\) is the mode(s) of transport used by an individual between each couple of subsequent _as_, _IDTC_ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Main** _STPAs_ & **Fixed Activity \(a_{1}\)** & **Fixed Activity \(a_{2}\)** & **Fixed Activity \(a_{3}\)** & **Fixed Activity \(a_{4}\)** & **Fixed Activity \(a_{5}\)** \\ \hline
**Activity category:** & Home-stay & Other & Work & Other & Home-stay \\ \hline
**Activity location:** & Street 1 & Street 2 & Street 3 & Street 2 & Street 1 \\ \hline
**Activity timing:** & 00:00–07:30 & 07:50–08:00 & 09:00–17:00 & 17:30–18:00 & 19:00–24:00 \\ \hline
**Activity fixity degree:** & 4 & 5 & 5 & 3 & 4 \\ \hline \hline
* & Car driver & Car driver & Car phsenger \\ \hline \hline \end{tabular}  
* **Notes:** the transport mode used to reach the activity.  
\end{table} 
Table 2: Exemplificative _STPA_ to be set up for the calculation of _STTP_. is the Individual Daily Travel Cost incurred by an individual on a daily basis, _IDD_ is the Individual Daily Distance between the _as_ performed by an individual on a daily basis, \(ITC_{a,a+1}^{k}\) is the individual transport cost by mode \(k\) between each couple of subsequent _as_, \(\mathit{Cm}^{k}_{a,a+1}\) is the monetary cost of transport by mode \(k\) between each couple of subsequent _as_, \(\mathit{Civitt}^{k}_{a,a+1}\) is the cost of in-vehicle travel time by mode \(k\) between each couple of subsequent _as_, \(\mathit{Covitt}^{k}_{a,a+1}\) is the cost of out-of-vehicle travel time by mode \(k\) between each couple of subsequent _as_, and \(D_{a,a+1}\) is the Euclidean distance between each couple of subsequent _as_.
## 3 Results
### Technological Features and Affordances
#### 3.3.2 Computing Devices  
In professional training, data in AR systems were processed by three main types of devices: the desktop or laptop, mobile devices (including tablets, phones, and other handheld devices), and wearable devices. Figure 5 shows a decreasing trend in desktop and an increase in mobile devices, consistent with technological advancements. In the first 15 years in the 21 st century, the desktop/laptop had an overwhelming advantage over other computing devices, owing to the function of performing complex mathematical operations, satisfying the needs of simulation and calculation of AR systems. Meanwhile, in the last five years, portable devices, such as mobile or wearable devices, gradually replaced desktops and were used in some creative AR programs. For instance, [PERSON] and [PERSON] (2010) introduced an AR system in architectural education. For the convenience of virtual architecture representation, a set of wearable devices was designed for learners so that they could freely move in the scenario, which was combined with virtual images and real outdoor scenes, so as to help learners deeply understand the structure of the architecture and accept training [34].
# Incorporating Grey Total Influence into Tolerance Rough Sets for Classification Problems
## 3 Grey-Total-Influence-Based Tolerance Rough Sets
### Illustrative Example
#### 3.5.1 Training Phase  
During the training phase, only \(\mathbf{Z}_{11}\) needs to be computed by the training patterns. If we use \(\mathbf{x}_{1}\) as the reference pattern, \(\Delta_{\text{max}}\) and \(\Delta_{\text{min}}\) are 3.1 and zero, respectively. For \(Y(\mathbf{x}_{2}\), \(\mathbf{x}_{1})\), \(\xi_{1}(\mathbf{x}_{2}\), \(\mathbf{x}_{1})\), \(\xi_{2}(\mathbf{x}_{2}\), \(\mathbf{x}_{1})\), \(\xi_{3}(\mathbf{x}_{2}\), \(\mathbf{x}_{1})\), and \(\xi_{4}(\mathbf{x}_{2}\), \(\mathbf{x}_{1})\), \(\xi_{1}(\mathbf{x}_{2}\), \(\mathbf{x}_{1})\) can be computed as:  
\[\xi_{1}(\mathbf{x}_{2},\mathbf{x}_{1})=\frac{0+0.5\times 3.1}{1.9+0.5\times 3.1}=0.449 \tag{31}\]  
Moreover, \(\xi_{2}(\mathbf{x}_{2},\mathbf{x}_{1})\), \(\xi_{3}(\mathbf{x}_{2},\mathbf{x}_{1})\), and \(\xi_{4}(\mathbf{x}_{2},\mathbf{x}_{1})\) are 0.463, 0.608, and 1.0, respectively. \(Y(\mathbf{x}_{2},\mathbf{x}_{1})\) can thus be computed as:  
\[Y(\mathbf{x}_{2},\mathbf{x}_{1})=\
icefrac{{1}}{{4}}(0.449+0.463+0.608+1)=0.630 \tag{32}\]  
In a similar way, \(Y(\mathbf{x}_{3},\mathbf{x}_{1})\) can be computed as 0.589. Finally, \(\mathbf{Z}_{11}\) is as follows:  
\[\mathbf{Z}_{11}=\left[\begin{array}{cccc}0&0.712&0.671\\ 0.630&0&0.558\\ 0.589&0.558&0\end{array}\right] \tag{33}\]  
During the training phase, the grey direct influence matrix \(\mathbf{Z}\) is generated as follows:  
\[\mathbf{Z}=\left[\begin{array}{cccccc}0&0.712&0.671&0&0&0\\ 0.630&0&0.558&0&0&0\\ 0.589&0.558&0&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\end{array}\right] \tag{34}\]  
As \(\lambda=1.383\) occurs in the first row of \(\mathbf{Z}\), the normalized matrix \(\mathbf{X}\) derived from \(\mathbf{Z}\) is:  
\[\mathbf{X}=\left[\begin{array}{cccccc}0&0.515&0.486&0&0&0\\ 0.456&0&0.403&0&0&0\\ 0.426&0.403&0&0&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\\ \end{array}\right] \tag{35}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Pattern**} & \multicolumn{4}{c}{**Conditional Attribute**} & \multirow{2}{*}{**Decision Attribute**} \\ \cline{2-4} \cline{6-6}  & **1** & **2** & **3** & **4** \\ \hline \(\mathrm{x}_{1}\) & 51.1 & 35.2 & 14.0 & 2.0 & 1 \\ \(\mathrm{x}_{2}\) & 53.0 & 37.0 & 15.0 & 2.0 & 1 \\ \(\mathrm{x}_{3}\) & 50.0 & 32.1 & 12.0 & 2.0 & 2 \\ \(\mathrm{x}_{4}\) & 52.0 & 27.0 & 39.0 & 14.6 & 1 \\ \(\mathrm{x}_{5}\) & 59.0 & 30.0 & 42.3 & 15.0 & 2 \\ \(\mathrm{x}_{6}\) & 56.7 & 25.4 & 39.0 & 11.0 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: An example decision table.  
The grey total influence matrix \(\mathbf{T}\) can be easily generated by \(\mathbf{X}(\mathbf{I}-\mathbf{X})^{-1}\):  
\[\mathbf{T}=\left[\begin{array}{cccccc}2.833&3.253&3.172&0&0&0\\ 2.872&2.632&2.859&0&0&0\\ 2.791&2.851&2.505&0&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\end{array}\right] \tag{36}\]
### Research Objectives  
This study aims to create a high-quality, comprehensive dataset of Manchu music to advance AI research in the field of ethnic music and cultural heritage preservation. By focusing on this unique musical tradition, the research seeks to address critical gaps in digital archives and datasets, which often overlook ethnic and traditional music forms. The dataset is intended to serve as a foundational resource for both academic research and practical applications in AI-driven music analysis and generation.  
* Systematic Collection and Digitization: This involves gathering a diverse range of Manchu music recordings, including folk songs, ceremonial pieces, and dance music, from historical archives and field recordings. The digitization process ensures high quality digital formats, with preprocessing tasks like noise reduction and the removal of duplicates to maintain dataset integrity.
* Comprehensive Metadata Annotation: Detailed metadata will be annotated for each recording, capturing basic information (e.g., title, performer, date) and complex musical features (e.g., rhythm, tonal structure). Cultural and historical contexts will also be documented to enrich the dataset and support AI analysis and ethnomusicological research.
* Validation Through AI Experiments: The dataset's utility will be tested through AI experiments in music classification, generation, and emotion analysis. These tasks will demonstrate the dataset's versatility and its potential to advance both AI and ethnomusicology research.  
By achieving these objectives, this study not only contributes to the preservation and analysis of Manchu music but also establishes a framework for integrating AI technology into the broader effort of safeguarding intangible cultural heritage. The dataset and associated methodologies will provide a model for future initiatives in documenting and analyzing the musical traditions of other ethnic groups.
# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
## 4 Trends and Prospects of Carbon Emission Research on Biomass Combustion
### Assimilation Technology and Dynamic Monitoring  
(1) Application of data assimilation techniques: By employing atmospheric chemistry transport models (e.g., GEOS-Chem) and data assimilation techniques, researchers can integrate multi-source observational data with model simulations to generate high-precision post-processing emission inventories. This method has the additional benefit of correcting systematic errors in the model and improving the accuracy of carbon emission estimates. For instance, the GEOS-Chem model, when used in conjunction with OCO-2 satellite data, enables the real-time updating of fire emission estimates, thereby markedly enhancing the responsiveness to sudden fire occurrences [3]. (2) Realization of dynamic monitoring: The dynamic monitoring of biomass combustion emissions has become a more feasible undertaking with the advent of near real-time data processing and analysis systems. In recent years, dynamic monitoring systems have been employed to facilitate rapid responses to emergencies such as forest fires and to generate emission estimates within a short period of time. Such systems have been implemented for the monitoring of forest fires in the United States and Australia, thereby markedly enhancing disaster response capabilities [39].
## 5 Simulation Experiments and Result Analysis
### Simulation Experiments on Intelligent Power Distribution and Utilization Dataset (II)  
In this section, the simulation experiment took the medium voltage basic data of 171 towns in power quality on-line monitoring system, from 2015 to 2016, for performance comparison of heterogeneous data integration in the JetBrains PyCharm 2017.2 environment. In this simulation experiment, the incomplete sample proportion in the total samples was set to be 20%, and information loss rates were set to be 5%, 15%, 30%, respectively. Incomplete data sample and missing indexes were randomly selected. According to the incomplete sample proportion, parts of samples were randomly selected from total 342 data samples as the incomplete samples. Then, in these chosen samples, parts of indexes were randomly picked out to delete their corresponding information, and formed a set of data samples to be repaired, according to the information loss rate. Normalized data of typical samples is as shown in Table 7 (original data in Table A2). In order to verify the data integration performance of the proposed GANs-HDI algorithm on IPDU heterogeneous dataset, k-nearest neighbors (k-NN), and error-back propagation (BP) were chosen as control groups with parts of the model parameters selected by experience. The cluster number was equal to the sample class number in k-NN algorithm. The number of hidden neural nodes was set to be 10 in BP algorithm with Sigmodal function as the activation function. In the proposed GANs-HDI algorithm, the threshold of discrimination rate was set to be \(c=0.05\), the reducing pace was set to be \(\alpha=0.0005\), the number of hidden neural nodes was set to be \(L=10\), initialized threshold values as \(R_{1}=0.85\) and \(R_{2}=0.6\), clustering threshold values as \(k=2\), and Sigmodal function was chosen as the activation function. Repair the incomplete data samples with k-NN, BP, and GANs-HDI algorithms, respectively, and calculated the deviation rate with the real values. Repeat 10 trials independently, and calculate the averages and RMSE of the accuracy values of data restoration results, as shown in Table 8.  
According to the data shown in Table 6, information loss rate and deviation rate shown a significant proportional relationship. Since there is no strong causal link between the indexes in IPDU heterogeneous datasets, the performance of traditional BP algorithm was not satisfactory in the experiments. On the other side, the performance of GANs-HDI algorithm was much better than k-NN and BP on deviation rate with 15 percentage points ahead. Moreover, when the information loss rate took 30%, the deviation rate of k-NN algorithm zoomed up, and the integration results were far away
## 2 Foundations on RISs
### RIS-Assisted Wireless Communication Systems  
In a realistic scenario of wireless communications, the electromagnetic (EM) wave suffers from many effects. As the waves travel through the radio channel, they interact with different objects in the propagation environment, resulting in multipath propagation and power attenuation. In this scenario, the RIS can control both transmitter and receiver channel links [9; 35]. RIS surfaces enhance communication between the BS and the destination by reflecting waves, where the surface elements work together, redirecting the signals in a specific direction [36]. Hence, the received signal strength can be enhanced at the final user device. As a result, the probabilistic wireless channel turns into a deterministic model controlled by software [37]. Nonetheless, RIS devices are effective solutions providing alternative paths between the transmitter and receivers when direct communications are impossible [38].  
Figure 2 shows the RIS-assisted communications system model. The BS is equipped with \(N_{t}\) transmit antennas (Tx), while each \(K\) destination user device has \(N_{r}\) antennas. The system uses \(N\) RISs with \(N_{s}\) reflective elements. The direct link between the BS and the \(k\)th user is a matrix defined as \(\mathbf{H}_{k}^{DP}\in\mathbb{C}^{N_{r}\times N_{t}},k=\{1,2,\cdots K\}\). The link from the \(n\)th RIS to the \(k\)th user is a matrix defined as \(\mathbf{H}_{n,k}\in\mathbb{C}^{N_{r}\times N_{s}}\), and the link from the BS to the \(n\)th RIS is defined as \(\mathbf{H}_{n}^{SR}\in\mathbb{C}^{N_{r}\times N_{t}},n=\{1,2,\cdots N\}\). In this example, each one of the links defined in the scheme is assumed to be quasi-static with a Rayleigh fading distribution, whose elements are complex Gaussian random variables with zero mean and unit standard deviation \(\mathcal{CN}(0,1)\). However, more realistic channels can be considered for better system performance prediction.
## 6 Discussion and Conclusions
### Future Work  
Several research directions emerge from our findings. First, extending our methodology to other Berber languages (Tamazight, Tashelhiyt, Kabyle) would validate the generalizability of our ICL optimization strategies and linguistic proximity effects. Second, investigating bidirectional translation capabilities, particularly Arabic\(\rightarrow\)Tarifit, could provide insights into the asymmetries of cross-lingual transfer. Third, developing multilingual ICL approaches that simultaneously leverage multiple target languages could improve overall translation quality through cross-lingual knowledge sharing.  
Technical extensions should explore the integration of morphological analyzers and cultural knowledge bases into ICL prompts, potentially addressing the systematic errors identified in our analysis. Additionally, investigating prompt engineering techniques specifically designed for morphologically rich languages could further improve performance. Finally, comprehensive comparison with fine-tuning approaches, when sufficient computational resources permit, would establish the relative merits of ICL versus traditional neural machine translation methods for low-resource languages.  
Practical applications should focus on developing user-friendly interfaces that appropriately communicate translation confidence levels and limitations to end users. Given the performance constraints identified, hybrid approaches combining ICL with human post-editing or community-driven correction mechanisms may prove most effective for real-world deployment.  
Our work contributes to the broader goal of linguistic equity in artificial intelligence systems, demonstrating that state-of-the-art language models can be effectively adapted for underrepresented languages through carefully designed in-context learning approaches, albeit with realistic performance expectations. As the field moves toward more inclusive NLP technologies, our methodology provides a template for developing translation capabilities for the thousands of low-resource languages that lack sufficient data for traditional machine translation approaches.  
Conceptualization, O.A. and K.F.; methodology, O.A.; investigation, O.A.; resources, O.A.; data curation, O.A.; writing--original draft preparation, O.A.; writing--review and editing, K.F.; visualization, O.A.; supervision, K.F.; project administration, O.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.
## 3 Network Background
### Sixth-Generation Cellular Networks  
In 6G communication, emerging technologies such as terahertz communication, visible-light communication, and advanced access-backhaul integration are introduced to achieve ultra-high peak rates, massive connectivity, and exceptional reliability in communication networks [7], as illustrated in Figure 2. As shown in Table 2, 6G is expected to significantly outperform 5G in various aspects. However, merely introducing new technologies is insufficient, as the existing architecture cannot fully address these challenges. Therefore, the core network architecture must be restructured to create a more powerful, flexible, and intelligent system. The following section presents a brief vision of a 6G cellular network [1].
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 4 Our Proposal
### Arbitration  
When users and the SP have disputes over electricity bills, AC is responsible for arbitrating the disputes, which consists of the following five steps:  
1. Upon receiving the arbitration request, for the disputed electricity billing ciphertext, AC can pre-decrypt it with the private key using \(B^{\prime}_{ij}=\frac{V_{ij}}{(U_{ij})^{PAC}}\).
2. The SP uses its private key to compute \(W=(U_{ij})^{x_{SP}}\) and generate the signature \(\sigma_{SP}=H(ID_{SP}||W||Bill||T_{SP})^{x_{SP}}\), respectively.
3. The SP sends \(\mathcal{T}_{SP}=(ID_{SP},W,Bill,T_{SP},\sigma_{SP})\) to AC.
4. Upon receiving \(\mathcal{T}_{SP}\) from the SP, AC verifies \(\mathcal{T}_{SP}\) with the following equation: \[e(\sigma_{SP},g)=e(H(ID_{SP}||W||Bill||T_{SP}),pk_{SP}).\] (8)
5. If Equation (8) is satisfied, AC computes \[g_{n=1}^{\sum\limits_{i=1}^{t}r_{ij,n}\cdot p_{m}}=\frac{B^{\prime}_{ij}}{W}.\] (9)After obtaining \(g^{j=1}\sum\limits_{i=1}^{t}r_{ij,m}\cdot p_{m}\), \(\sum\limits_{m=1}^{t}r_{ij,m}\cdot p_{m}\) is obtained by using the Pollard lambda method [35]. Then, AC can arbitrate effectively through comparing with \(Bill\) sent by the user and the SP.  
Furthermore, if users want to query the electricity billing for a certain period of time (for example, from 10 June to 20 June), they can initiate query requests to the SP. The SP can obtain the aggregated ciphertexts for the time period through the FN, then decrypt them with the help of AC and finally send bills to users. Note that this can only be initiated by the user, and the SP cannot query a user's electricity bill in a particular period.
# Prediction of Arrival Time of Vessels Considering Future Weather Conditions
###### Abstract  
International logistics is becoming increasingly active. Marine transportation, in particular, accounts for approximately 90% of the total volume managed in international logistics and plays a vital role in the supply chains of many companies. However, en route factors, such as weather conditions, often delay scheduled arrivals at destination ports, and an accurate prediction of the arrival time is required for supply chain efficiency. The arrival time has been predicted in previous studies by calculating the route to the destination port and the en route voyage speed without considering the influence of future weather conditions. Hence, the prediction accuracy may decrease when weather conditions change. In this study, we propose a prediction method that identifies the route from the voyage results of vessels whose weather condition is similar to the future one and uses Bayesian learning to calculate the voyage speed in consideration of future weather conditions. Consequently, future changes in weather conditions are reflected in the prediction results. The prediction accuracy of the proposed method is projected to be 28% higher than that from previous studies based on historical operational data of vessels carrying home appliance and automobile industry cargoes.  
estimated time of arrival; weather forecast; path finding; Bayesian learning
# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
## 5 Experimental Results  
This paper verifies the proposed method based on the taxi trajectory data from Chengdu. The experiments were conducted in a PC environment (Intel(R) Core(TM) i7-7500U, CPU @ 2.70 GHz, 8 GB RAM, 500 GB hard drive). First, the DBSCAN algorithm optimized by grid search was used to cluster the pick-up and drop-off points to determine the starting and ending areas for passenger searching. Then, the number of passenger pick-up points in each road segment between the start and end points was counted, and their passenger probabilities were calculated. The _BiLSTM_ algorithm was used to predict the passenger probabilities on the roads. Finally, a passenger search path optimization model based on road passenger probabilities was established and solved, resulting in a passenger search path with the maximum passenger probability and the shortest possible travel distance.
## 2 Signal Model and Problem Formulation  
In this section, we introduce the ISAC signal model with the OFDM waveform in a 5G communication system, and we detail the waveform design motivation and constraints.
## 3 Data and Data Processing
### InSAR Data  
Karapinar and its surroundings were investigated using two Synthetic Aperture Radar (SAR) datasets. The first dataset was acquired by the COSMO-SkyMed satellites in StripMap HIMAGE mode and the second dataset by the Sentinel-1 satellites in interferometric Wide (IW) swath mode. Our first dataset consists of 25 COSMO-SkyMed images, which were acquired between 16 February 2016 and 29 November 2017 with 3 m pixel resolution. This X-band (wavelength 3.1 cm) data were acquired in descending mode with VV polarization. The second SAR dataset consists of 87 Sentinel-1A images, which were acquired between 5 October 2014 and 6 March 2018 with 5 m \(\times\) 20 m pixel resolution. This C-band (wavelength 5.6 cm) data were also acquired in descending mode with VV polarization. The footprints of the COSMO-SkyMed and Sentinel-1 satellites cover well the study area, as shown by the blue and white frames, respectively, in Figure 1.