esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Cycled Uplink Resource Allocation over NB-IoT
## 2 System Model and Problem Formulation
### Problem Formulation
The target problem has the following constraints:  
_Requirement Constraint:_ Each UE \(d\) should transmit data size \(\psi_{d}\). \[\sum_{p=1}^{p}\sum_{l^{h}\in I_{RU}}\sum_{u=1}^{U}\sum_{m=1}^{M}X_{p,d}^{u,m} \eta(l^{h}_{u},m)\geq\psi_{d},\forall d \tag{2}\]  
_Bandwidth Constraint:_ The subcarriers used by the UEs and NPRACHs cannot surpass the total subcarriers at a subframe. \[\mathcal{S}_{p}^{s}+r_{p}^{s}\leq F,\forall s,p \tag{3}\]  
_Signaling Constraint:_ After UE \(d\) receives a DCI in its NPDCCH subframes, the UE can transmit data using uplink subframes. \[\sum_{u=1}^{U}\sum_{m=1}^{M}X_{p,d}^{u,m} =1,\forall p,d \tag{4}\] \[\sum_{c=1}^{\xi}\mathcal{DCI}_{p,d}^{c} =1,\forall p,d \tag{5}\]  
The objective of this paper is formulated as:  
\[\underset{X_{p,d}^{u,m},\mathcal{DCI}_{p,d}^{c},\mathcal{S}_{p}^{s},l^{h}_{d} }{\text{minimize}}P\times L \tag{6}\]  
with constraints (2)-(5). The notations are listed in Table 1. \begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Depiction** \\ \hline \(D\) & The number of UEs \\ \(p\) & The consumed NPDCCH periods \\ \(F\) & The subcarriers in a subframe \\ \(M\) & The number of modulation and coding indexes \\ \(\psi_{d}\) & UE \(d\)’s data requirement \\ \(N_{d}^{u,m}\) & The data repetition requirement of UE \(d\) as unit type \(u\) and modulation \(m\) is adopted \\ \(N_{Rep}\) & The set of repetition numbers for delivering data \\ \(N_{Rep}^{p}\) & The set of repetition numbers for delivering a DCI \\ \(I_{RU}\) & The set of resource unit numbers \\ \(U\) & The number of different unit types \\ \(\eta(l^{h},m)\) & The TBS as \(l^{h}\) resource units with modulation \(m\) is transmitted. \\ \(f_{u}\) & The subcarriers occupied by unit type \(u\) \\ \(r_{p}^{s}\) & The subcarriers used by NPRACHs in subframe \(s\) of the \(p\)-th NP \\ \(t_{u}\) & The subframes needed by unit type \(u\) \\ \(L\) & The length of an NP \\ \(R_{max}\) & The number of NPDCCH subframes \\ \(G\) & The system parameter for determining the length of NPDSCH in an NPDCCH period \\ \(R\) & The repetitions using for delivering one DCI \\ \(n_{p}^{c}\) & The last subframe in a series of NPDCCH subframes for delivering DCI \(c\) in the \(p\)-th NP \\ \(\mathcal{DCI}_{p,d}^{c}\) & The function records whether DCI \(c\) of the \(p\)-th NP is assigned for UE \(d\) or not \\ \(\hat{D}_{d}\) & The DCI repetition requirement of UE \(d\) \\ \(K\) & The set of scheduling delays \\ \(t_{0}^{c}\) & The NPUSCH start subframe as DCI \(c\) with delay \(k_{0}^{i}\) \\ \(\mathcal{S}_{p}^{s}\) & The subcarriers assigned for UEs in subframe \(s\) of the \(p\)-th NP. \\ \(X_{p,d}^{u,m}\) & The function records whether the cell decides UE \(d\) using unit type \(u\) and modulation \\ \(X_{p,d}^{u,m}\) & \(m\) in the \(p\)-th NP or not \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations.
# Dynamic SDN Controller Load Balancing
## 5 DCC Two Phase Algorithm
### Phase 1: Initial Clustering
#### 5.1.2 Initial Clustering Based on Load Only  
If the overhead generated by additional traffic to distant controllers is not an issue (for example due to broadband link) then we should consider this type of initialization, which put an emphasis on the controllers' load. In this case, we must arrange the controllers into clusters according to their loads. To achieve a well-distributed load for all the clusters we want to reach a \"_min-max_\", i.e., we would like to minimize the load in the most loaded cluster. As mentioned earlier (in Section 4.1) we assume the same number of controllers in each cluster. We enforce this via a constraint on the size of each cluster (see further Heuristic 2).  
In the following, we present a greedy technique to partition the controllers into clusters (Heuristic 2). The basic idea is that in each iteration it fills the less loaded clusters with the most loaded controller.  
In Heuristic 2, Line 1 sorts the controllers by loads. In Line 2-9, each controller, starting with the heaviest one, is matched to the group with the minimum cost function, \(Cost_{g}(C)\), if the group size is less than \(K\), where  
\[Cost_{g}(C)=CurrentClusterSum+C_{load}. \tag{13}\]  
The \"\(CurrentClusterSum\)\" is the sum of the controllers' loads already handled by cluster g, and \(C_{load}\) is the controller's load that will be handled by that cluster. Regarding the time complexity, sorting \(M\) controllers takes \(O(M\cdot log_{2}M)\) time. Adding each controller to the current smallest group takes \(M\cdot K\) operations. Therefore, Heuristic 2 has \(O(max(M\cdot K,M\cdot lgM))\) time complexity.  
```
0:\(C=\{c_{1},c_{2},...,c_{M}\}\) Controller list Masters \(CVL_{i}\)'s (average flow-request number (loads) for each controller) integer \(K\) for number of clusters output:\(P=\{p_{1},p_{2},...,p_{K}\}\) Clusters list, where \(P_{i}=\{c_{i1},c_{i2},...,c_{i(M/K)}\}\)
0:
1:\(SortedListC\leftarrow\) descending order of controllers list according to their loads
2:\(Candidates\gets P\)
3:for all\(c\in SortedListC\)do
4:\(P_{min}\leftarrow\) the cluster with minimal \(Cost_{g}(C)\) from candidates
5:\(P_{min}\gets P_{min}in\)\(cupC\)
6:if\(\mid P_{min}\mid=M/K\)then
7:\(Candidates\gets Candidates-P_{min}\)
8:endif
9:endfor
10:return\(P\)
```  
**Heuristic 2** Load initialization
# Research on Tbps and Kilometer-Range Transmission of Terahertz Signals
## 3 Enabling Technologies for THz Communication Systems  
In this section, to enhance the effective transmission distance of photonic-assisted THz communication systems, we introduce five key enabling technologies, specifically, improving receiver sensitivity; adopting low-order modulation formats characterized by large constellation point spacing and strong noise immunity; leveraging MIMO architectures and MIMO diversity gain for polarization-multiplexed optical THz signals; utilizing high-gain antennas and high-gain lenses; and employing PAs with high gain and high saturated output power coupled with a heterodyne coherent detection configuration at the receiver. Through this optoelectronic co-design optimization strategy, which simultaneously reduces the signal baud rate and the bandwidth requirements of the devices, we provide a feasible path for the long-distance deployment of photonic-assisted THz communications [63].  
Figure 5: Photonic-assisted terahertz 1 Tbps transmission system; LNA: low-noise amplifier; OC: optical coupler; PC: polarization controller [62].
# Second-Order Text Matching Algorithm for Agricultural Text
## 5 Experiments
### Optimal Parameter Determination
#### 5.2.1 BERT Model Determination  
The objective of this experiment is twofold. Firstly, to fine-tune a pre-trained BERT model to enable it to learn more about agricultural crops, leading to higher accuracy in text matching in the agricultural domain. Secondly, to select BERT model with the highest accuracy from different iterations during fine-tuning. This experiment requires the use of the train set and the validation set. BERT model is iteratively trained on the train set. The validation set is used to evaluate the effectiveness of the model. After each training iteration is completed, classification accuracy (defined as the number of correctly predicted samples divided by the total number of samples), is used as the evaluation metric for the validation set, and the model with the highest classification accuracy is chosen.  
During training, this paper uses the AdamW optimizer to optimize the loss values, warm-up strategy to adjust the learning rate and gradient cropping to  
It can be found that the choice of the value of \(s\) affects the retrieval accuracy and retrieval efficiency of the algorithm. Overall, as the recall number \(s\) increases, the P@1 value of SOTM algorithm shows a trend of first rising and then falling. When \(s\) equals 10, the P@1 value of the algorithm reaches its maximum. On the other hand, the average time of SOTM algorithm rises monotonically with recall number \(s\). Since SOTM algorithm has high requirements for accurate answers, this paper prioritizes algorithms with high retrieval accuracy rates. In addition, algorithms with faster retrieval speeds are chosen whenever possible, while ensuring that their retrieval accuracy rates are maintained. Considering both retrieval accuracy and speed, this paper finally takes \(s\) equal to 10. The algorithm achieves the highest retrieval accuracy of 88.34% and the average retrieval time for one query is only 2.02 s. The algorithm has high retrieval accuracy and excellent efficiency.
## 3 Results
### Classification Performance  
Using the extracted frequency-domain features, six classification algorithms--Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost, LightGBM and Multi-Layer Perceptron (MLP)--were trained and evaluated to assess their effectiveness in identifying structural changes in the robot.  
Hyperparameter tuning was conducted on the training dataset using 5-fold cross-validation. For SVM, a linear kernel was selected with moderate regularization. KNN yielded optimal performance with 4 neighbors and a distance-based weighting scheme. The best RF configuration employed 142 trees with a maximum depth of 10. XGBoost and LightGBM both performed best with 100 estimators, a learning rate of 0.1, and a maximum depth of 3, using 80% feature and sample subsampling. For MLP, the best results were obtained with one hidden layer of 100 units, ReLU activation, and an initial learning rate of 0.001. These optimal configurations for each model are summarized in Table 1.  
The classification performance of each tuned model was first evaluated on the training set using 5-fold cross-validation. All models achieved perfect performance, suggesting that the acoustic features captured sufficient information for structural classification under controlled conditions.  
The same models were then tested on an independent test dataset to assess their generalization performance. The test results are summarized in Table 2. MLP achieved the highest accuracy (71.4%), followed by SVM (65.7%), LightGBM (62.9%), KNN (60%), XGBoost (57.1%), and RF (51.4%).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Hyper Parameters** \\ \hline SVM & c = 0.3, kernel = linear \\ Random Forest & n estimators = 142, max depth = 10 \\ KNN & n neighbors = 4, weights = distance \\ XGBoost & boosting rounds = 100, learning rate = 0.1 \\ LightGBM & boosting rounds = 100, learning rate = 0.1 \\ MLP & single hidden layer, Relu activation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Best parameters for SVM, RF, KNN, XGBoost, LightGBM and MLP.  
These findings indicate that while all models performed exceptionally well during training, their performance on unseen data varied. Neural network models such as MLP appear to offer better generalization by capturing complex patterns in the frequency-domain features. In contrast, simpler tree-based and distance-based models were more sensitive to subtle acoustic overlaps or variability across conditions. These results highlight the importance of careful model selection and validation in developing robust models.
# Robust Image Watermarking in Spatial Domain Utilizing Features Equivalent to SVD Transform
## 4 Experimental Results Discussion
### Imperceptibility Analysis  
Imperceptibility in invisible watermarking is associated with the human visual system. If both the host and watermarked images are identical, it is argued that the watermarking scheme is imperceptible. To investigate the invisibility of the inserted watermark, the structural similarity measure (_SSIM_) and peak signal-to-noise ratio (_PSNR_) explained above are widely applied in evaluation metrics. This study also used these metrics to investigate the watermarked image's imperceptibility. The _PSNR_ and _SSIM_ values obtained by the proposed watermarking scheme are listed in Table 2, corresponding to each watermark inserted in the given host images. As can be observed in Table 2, the _PSNR_ and _SSIM_ values for each of the images are close to 49 dB and 1, respectively, for each watermark insertion. This demonstrates that the proposed watermarking scheme has a high level of imperceptibility.
## 2 Materials and Methods
### Data Collection
#### 2.1.4 ERA-Interim Data  
ERA-Interim is a global land surface reanalysis dataset covering the period since 1979 and continuing in real time. This product can be downloaded from the European Centre for Medium Range Weather Forecasts (ECMWF) Data Server at [[http://data.ecmwf.int/data](http://data.ecmwf.int/data)]([http://data.ecmwf.int/data](http://data.ecmwf.int/data)). The time steps are sub-daily, daily and monthly, and a spatial resolution of 0.75\({}^{\circ}\). ERA-Interim is the result of the simulation with the latest ECMWF land surface model driven by meteorological forcing from the ERA-Interim atmospheric reanalysis and precipitation adjustments based on the monthly Global Precipitation Climatology Project (GPCP) [64]. In this study, we downloaded half-daily ERA average air temperature (AAT), 2-m dewpoint temperature (D2M), surface air pressure (P), downward shortwave radiation (SWdw), net longwave radiation (LWnet) and net shortwave radiation (SWnet). Then, we averaged every 16 values to obtain 8-day ERA data to prepare for the global LUE and GPP estimation.
## 8 Multi-Objective Optimization Integrated with Neural Networks ANN
### Dynamic Decision-Making for Process Optimization  
One of the standout features of MOO-ANN is its ability to enable dynamic decision-making in real time. By continuously learning from process data, ANN models integrated with MOO frameworks can adapt to changing conditions, such as variations in raw material quality or shifts in consumer demand.  
For instance, an application in yogurt production utilized MOO-ANN to adjust fermentation times and temperatures dynamically, resulting in a 10% increase in production yield and a 5% reduction in production costs [5]. Such advancements not only enhance operational efficiency but also improve economic viability, supporting SDG 8: Decent Work and Economic Growth.
# Delay Bounded Multi-Source Multicast in Software-Defined Networking
## 2 Related Work
For each multicast stream, the MMForest algorithm can establish a multicast forest to span all destinations and sources under the constraint of achieving the maximal residual bandwidth. The MMForest is designed based on the existing Widest-Path Forest algorithm, which prefers to select those links with higher residual capacity. This simply means that different multicast streams have to design their MMForests cooperatively. However, most multicast streams are usually independent. The MMForest method focuses on the design constraint of optimizing the residual bandwidth under a set of multicast streams. The MMForest \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Name** & **TE** & **Delay** & **Multi-Source** & **Focus** \\ \hline DBMC [19] & ✓ & ✓ & \(\times\) & Delay-bound single source multicast \\ RAERA [24] & ✓ & ✓ & \(\times\) & Recover-aware Steiner Tree \\ MUcPF [3] & ✓ & \(\times\) & \(\times\) & Partially deployed SDNs \\ P-MCF [25] & ✓ & \(\times\) & ✓ & Multicast with uncertain sources \\ This paper & ✓ & ✓ & ✓ & Delay-bound multi-source multicast \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some Related Work on Multicast SDN. basic idea does not deviate much from the most straightforward multicast trees. The destination selects one path with the largest residual bandwidth towards all sources, and then merges those selected paths from all destinations. This method is highly probable to loose non-trivial opportunities to maximize the number of shared links among the individual paths.
# Virtual Reality or Videoconferencing for Online Learning? Evidence from Comparative Meta-Analyses
## 2 Literature Review
### Possible Moderator Variables
#### 2.3.2 VR Technological Features  
Equipment.VR equipment may affect students' immersion experience and learning outcomes. Devices such as HMDs, desktop systems, and VR glasses all offer unique features. HMDs create a highly immersive experience by encompassing the user's field of view [10; 58], which offers an engaging and realistic virtual environment that can facilitate learning and improve knowledge retention by providing a feeling of presence and a high degree of engagement [59; 60]. Brands like HTC Vive (HTC Corporation, Taoyuan City, Taiwan) and Meta Quest (Meta Platforms, Inc., Menlo Park, CA, USA) are globally recognized for their advanced HMD technology. Desktops, meanwhile, enable users to experience virtual environments through interactive devices such as screens, keyboards, and mice. Although they may not offer the same level of immersion as HMDs, they provide a more user-friendly and cost-effective way to access virtual environments [61]. Because desktop VR does not fully isolate the user from the real world, this allows for a more gradual transition between real and virtual experiences. VR glasses, such as Google Cardboard (Google, Mountain View, CA, USA) and Samsung Gear VR (Samsung Electronics Co., Ltd., Suwon, South Korea), are mobile-based VR equipment that creates a virtual environment in front of the user's eyes. These glasses offer users a lighter and more convenient option than HMDs [62; 63].
## 3 Results and Discussion
### Characterization of Cockleshell Powder
#### 3.1.3 FTIR Analysis  
According to the existing literature, carbonate groups play a greater role in determining the bonds between the surface of the CS adsorbent and Direct Red 81 dye (DR-81). There are four vibration modes assigned: asymmetric stretching mode (V1), out-of-plane bending (V2), doubly degenerate planar symmetry (V3), and doubly degenerate planar bending mode (V4) [33].  
Figure 4 shows the appearance of the micrometric bands of raw cockleshell powder, with peaks at 711.6, 855.27, 1080.91, 1449.24, and 1786.72 cm\({}^{-1}\). After the adsorption of the DR-81 dye, a slight transition in the position of certain peaks was observed, but no change in structure was observed due to the appearance or disappearance of other peaks. This may be attributed to the conservation of the physicochemical properties [34] of the CS adsorbent after the adsorption of DR-81.  
The presence of the 711.6 cm\({}^{-1}\) band indicates that there is a structural change in the calcium ions with respect to the symmetry of the calcite phase (V4), which also represents the band of the aragonite phase [33]. The appearance of the peaks at 855.27 and 1080.91 cm\({}^{-1}\) represents the inactive carbonate ions (CO\({}_{3}\)\({}^{2-}\)) of the aragonite phase [35] assigned by the vibration modes V2 and V1, respectively. The appearance of a large peak at 1449.24 cm\({}^{-1}\) can also be explained by the structural change in the symmetry of the aragonite phase assigned to vibration mode V3, or by the presence of alkyl groups in the polymorphic aragonite phase [31].  
Finally, the peak at 1786.72 cm\({}^{-1}\) is clear evidence of the presence of a single crystalline domain of the aragonite phase. Additionally, it confirms the fundamental changes in the vibrational mode positions of the sample resulting from the modification of the electrostatic valence of the CaO bond due to changes in the oxygen atoms in the environment [31].  
Figure 4: FTIR spectrum of cockleshell (CS) powder (a) before and (b) after DR-81 adsorption.
# A Time Bank System Design on the Basis of Hyperledger Fabric Blockchain
## 3 System Overview
### Preliminary Assumptions about our Time Bank System  
In the real world, a time bank will encounter lots of challenges and constraints. Therefore, some assumptions and/or conditions must be specified before elaborating on each part of our prototype system. Basic assumptions about the design of the proposed time bank system include the following:  
(1) The time credit, which is the token for quantifying the value of a service. Each unit of time credit (i.e., the time spent of service) is traded equally regardless of the substantiality of the services rendered. This assumption is to make the contributions of different services be valued equally so that there is no discrimination in the service offered.  
(2) The inclusion of rewarding function makes the total time credit amount of our system increase slowly. The possible inflation of time credit is disregarded in this study.  
(3) In a real-world time bank, it is necessary to ensure the safety of all members. Although the time bank committee validates the members' backgrounds in the enrollment stage, no one can guarantee that the enrolled members would always be law-abiding. However, for simplifying the design, we assume that every system participant would always be law-abiding and would not commit a crime.  
(4) In our service-matching stage, the impact of geographical locations on the selection of service proposals is not considered. This assumption ignores the possible time spent on traveling in service offerings, i.e., we assume that all system participants live not too far away. If the location of the service is considered, the matching algorithm needs to calculate the distance and estimate the traveling time between the matched SP and SR; clearly, this will complicate the situation a little bit!
## 3 Results  
Data from 13 participants were included in the results--except for HRV data, where data of one participant were excluded from the research due to data quality issues detected by Kubios HRV Premium. The mean age of the 13 participants was 41.0 (\(\pm\)12.0 SD) and the gender division was seven females and six males. The mean PSS-10 result was 11.9(\(\pm\)6.2 SD), which indicates low stress levels and that the sample population was somewhat less stressed than the general population [25; 26]. Six participants had some prior experience regarding VR head-mounted devices, while seven participants had not experienced any VR application. Only one participant expressed having extensive VR experience. Seven participants had experience with meditation or yoga, while six did not. No participant reported issues regarding technical problems or nausea.