esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Study Area  
The NEC consists administratively of three provinces in China: Liaoning, Jilin, and Heilongjiang provinces (Figure 1a). It has a total area of 787,300 km\({}^{2}\) and had a population of about 108 million in 2019. The region is separated from Far Eastern Russia to the north primarily by the Amur, Argun, and Ussuri Rivers, from North Korea to the south by the Yalu and Tumen Rivers, and from the Inner Mongolian Autonomous Region to the west by the Greater Khingan Range.  
Across from mid to high latitude (approximately 39-53\({}^{\circ}\) N), the NEC has a large variation in annual average temperature from 10 \({}^{\circ}\)C in south to \(-\)5.5 \({}^{\circ}\)C in north. Moreover, located at the northernmost region of China, the NEC contains the coolest region in China.  
The accumulated temperature greater than 0 \({}^{\circ}\)C is around 2000-4200 \({}^{\circ}\)C, and the average temperature during summer is 20-25 \({}^{\circ}\)C. Influenced by the Eastern Monsoon of China, the NEC has a great variation in annual precipitation, decreasing from wet southeast (900 mm) to semi-arid northwest (400 mm), and a majority of precipitation occurs in summer (July to September).  
There are three mountains: the Greater Khingan Mountain (GKM), the Lesser Khingan Mountain (LKM), and the ChangBai Mountain (CBM) (Figure 1a) that form important natural barriers for the NEC, so that the NEC can be divided into four zones (one plain and three mountains). The heartland of the NEC is the Northeast China Plain (Figure 1a, including Sanjiang Plain, Songnen Plain, and Liaohe Plain), which mainly located at the western NEC and grows a lot of crops (Figure 1b) for its rich, deep, black soil. Owning these plains, the NEC is one of the most important centers of commercial agriculture in China for both food grains (wheat, rice, and maize) and economic crops (soybean and sugar beets). The rest three mountainous zones also play a crucial role in ecosystem function. There are a great amount of forests growing in these three mountains, e.g., the LKM and the CBM are widely covered with broadleaved deciduous forests, and the GKM owns the special bright coniferous forest which is the southern boundary of the Boreal forests of Eurasia.  
Figure 1: (**a**) Location of the study area with elevation from a Digital Elevation Model (DEM) and (**b**) Vegetation types of the Northeast China (NEC). (Vegetation type is provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD12Q1 datasets [38] in 2015).
# Improvement of Speech/Music Classification for 3 GPP EVS Based on LSTM
## 2 Conventional 3 GPP Enhanced Voice Services
### GMM-Based Method  
The GMM has been estimated via the expectation maximization algorithm [15] on a speech/music database, and is a weighted sum of L-component Gaussian densities, given by the following equation:  
\[p(z|\theta)=\sum_{k=1}^{L}\omega_{k}N(z|\mu_{k},\Sigma_{k}) \tag{2}\]  
where \(N(z|\mu_{k},\Sigma_{k})\) are the component Gaussian densities, \(\omega_{k}\) are the component weights, and \(z\) is a normalized N-dimensional feature vector. The GMM generates two probabilities, \(p_{m}\) and \(p_{s}\), for the music probability and the speech probability, respectively.  
By analyzing the values of music and speech probabilities in each frame, a discrimination measure between music and speech can be obtained by subtracting the log-probabilities, as:  
\[y=\log(p_{m})-\log(p_{s}) \tag{3}\]
# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 1 Introduction
Since the cluster-based methods for image retrieval might struggle to trade-off between clustering quality and efficiency for large-scale dataset scenarios, our proposed approach may inspire further research on the field of ISRO to integrate features between high-dimensional data and training using a small batch of data to improve the clustering efficiency. The main contributions of this paper are summarized as follows:  
1. We present a new deep multi-view subspace clustering approach which is beyond the conventional self-expressive model and breaks the limitation of using all data as a batch every time while training. By reconstructing the self-expression coefficients using a small amount of data, our method can scale to arbitrarily large datasets. 2. Given the intrinsic relationship between the samples, our approach uses shared layers and specific layers to integrate local and global representation of each sample, which leverages both common and specific features simultaneously and explicitly and enables fuller use of information between multiple views. 3. We conduct extensive experiments on Fashion-MNIST, COIL-20, COIL-100, and YTF datasets. The results show that our proposed approach has the best performance compared with several multi-view subspace clustering methods. The remaining sections of this paper are structured as follows: Section 2 provides an overview of the related work in the field of subspace clustering. Section 3 introduces our proposed network, loss function and algorithm training procedure. Section 4 describes the experimental datasets, analyses the quantitative results of different methods and visualizes the clustering performance. Section 5 concludes the paper with discussion and summary.
## 2 Methods
### Practical OTA-Based Gyrator  
Parasitic properties of a real OTA are commonly modeled by shunt resistances and capacitances of the input and output terminals. Input resistance is usually very large, especially in CMOS technology; whereas, input capacitance is typically in the range of tens to the low hundreds of femtofarads when integrated on a substrate of standard CMOS processes. The output resistance of OTA generally reaches several hundreds of kiloohms and output capacitance tens to a few hundreds of femtofarads [24; 25]. For some OTA implementations, the parasitic properties may be dependent on the OTA operating conditions, e.g., the terminal conductance may change depending on the set transconductance, which is also the case of the amplifier used in the computer simulation example in this article.
# Efficient Bloom Filter-Based Routing Protocol for Scalable Mobile Networks +
## 1 Introduction  
Ad hoc mobile networks, also known as mobile ad hoc networks, have become more important due to the widespread use of wireless technology in recent years (MANETs). Because of its unique properties, such as its dependence on a scarce energy supply and/or its susceptibility to rapid topological changes, this class of networks has been the focus of several investigations. Proactive protocols, reactive protocols, and hybrids of these two types are the conventional buckets into which researchers have placed most forwarding protocols suggested for use in MANETs. Maintaining routes to all feasible destinations, regardless of network activity, is a hallmark of proactive routing techniques. In contrast, reactive protocols set up new paths only when needed by a source node. Three examples of proactive protocols are the Destination Sequence Distance Vector (DSDV), the TemporallyOrdered Routing Algorithm (TORA), and the Optimized Link State Routing protocol (OLSR) (TORA). Dynamic source routing (DSR) and Ad hoc On-demand distance vector (AODV) protocols are two examples of reactive routing systems. Hybrid regimens, which combine reactive and proactive features, are another option. The Zone Routing Protocol (ZRP) is one such protocol [1; 2; 3; 4; 5].  
Several studies have been undertaken comparing the different possibilities. After modelling both proactive and reactive protocols in medium-sized networks, the authors of found that the latter produced better outcomes in terms of latency and packet delivery %. These protocols, however, also provide the most unnecessary work for the network to carry out if more routes are requested. This research also confirms that proactive protocols lead to a greater number of lost packets and worse quality of first routes. In contrast, if we look at the total number of control messages, we find that this group of protocols is more effective [6; 7]. In order to effectively store and transmit topological information, HRAN employs Bloom Filters (BFs). Nodes in an HRAN network do not relay information amongst themselves. If nodes receive messages containing topological information, they may conserve bandwidth by combining it with what they already know about the network via a binary OR operation. This operation's output is the only thing sent to the other nodes. The HRAN is able to efficiently search for routes thanks to this function, which involves the dissemination of topological information. When a route has to be fixed or improved in terms of the number of nodes (NoNs), this approach may be utilised to create topologically informed zones around the route [8].  
Currently, there are some solutions already proposing the use of BFs in forwarding protocols for ad hoc networks [9].
## 3 Results  
Our literature search (Search #1, February 2020), secondary search (Search #2, April 2021), and hand search (September 2021) yielded 10,806 studies for review. Once duplicates were removed, 5613 studies were reviewed by title and abstract only. From here, 51 studies were read in full. A total of 14 studies were excluded at this point due to having the wrong measure (\(n=12\)), not enrolling human subjects (\(n=1\)), and being a conference abstract (\(n=1\)). Finally, 37 studies met criteria for inclusion in the scoping review [1, 2, 4, 5, 6, 7, 8, 9, 10, 12, 13, 24, 27, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]. Please see Figure 2 for a flowchart on the review process.
# Extended Reality Application Framework for a Digital-Twin-Based Smart Crane
## 1 Introduction  
The breakthrough in transformative information technologies has meant that the world stands on the threshold of the fourth industrial revolution, or Industry 4.0 [1]. Industry 4.0 is driven by digital technological advances, which have led to fundamental and sustainable digital upgrades in the manufacturing field [2]. Industry 5.0, meanwhile, is regarded as the next industrial revolution, and it will emerge when intelligent devices, intelligence systems, and intelligent automation are fully integrated with the physical world [3, 4]. The aim of Industry 5.0 is to leverage the creativity of human experts in cooperation with smart machines, in which digital twin (DT) and extended reality (XR) could empower humans with access to critical insight as well as control over diverse machines, systems, and processes [4].  
On the one hand, DT realizes real-time and continuous synchronization of data transmission between real products or systems and virtual mathematical models, which represent the state of the real twin and provide data analytics, simulation, and optimization. It can offer significant value for the improvement of the user experience. On the other hand, XR, as an umbrella term for virtual reality (VR), augmented reality (AR), and mixed reality (MR), can improve the human-machine interaction by combining the virtual and physical worlds.  
However, there is a lack of an established technical guideline to support such integration of XR services into DT systems in the manufacturing field. In order to facilitate such integration, this research aims at creating a systematic XR application development framework for the DT-based machine to provide highly supportive HMIs. The framework was derived based on a DT-based overhead crane and validated through several use cases.
# A Survey of Machine Learning Methods for Time Series Prediction
## 5 Experimental Results and Discussion
### Results
#### 5.3.9 Comparative Analysis of Hybrid Models  
Across the surveyed literature, hybrid models often outperform individual stand-alone models, although there are notable exceptions. This section examines these exceptions as well as provides insights into the relative performance of different hybrid configurations.  
Performance of Hybrid Models vs. Individual Models  
While hybrid models generally exhibit a superior performance, a few studies highlight cases where individual models outperform hybrids:  
1. Study [44]: a 2D CNN, 3D CNN, and XGBoost model each outperformed a hybrid RNN-CNN model;
2. Study [80]: RF and XGBoost models surpassed multiple hybrid models, including CNN-LSTM, CNN-GRU, RNN-GRU, and RNN-LSTM configurations;
3. Study [81]: a CatBoost model outperformed a spatio-temporal attention-based CNN and Bi-LSTM hybrid model.  
Hybrid Models Compared to Other Hybrids  
Several papers explored the relative performance of different hybrid configurations, providing valuable insights into the design considerations:  
1. Study [45]: a hybrid CNN-LSTM-Attention model outperformed a CNN-LSTM model, which in turn outperformed an LSTM-Attention model;
2. Study [48]: CEEMDAN decomposition was applied to both an XGBoost and DL model. The hybrid XGBoost-CEEMDAN model performed better than its DL-based counterpart;
3. Study [72]: a Bi-LSTM-LightGBM hybrid outperformed a Bi-LSTM-FFNN hybrid;
4. Study [73]: LSTM models with decomposition techniques, Variational Mode Decomposition (VMD) and Empirical Mode Decomposition (EMD), were compared. The LSTM-VMD hybrid outperformed the LSTM-EMD hybrid;
5. Study [74]: an Attention-based Bi-LSTM hybrid model performed better than an Attention-based Bi-GRU hybrid model;
6. Study [80]: among four hybrid DL models, CNN-LSTM demonstrated the best performance, followed by CNN-GRU, RNN-GRU, and RNN-LSTM;
7. Study [65]: four hybrid models were compared, with relative performances as follows: LSTM-XGBoost > FFNN-XGBoost > LSTM-MLR > FFNN-MLR;
# Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?
## 3 Results
### Impacts of Assimilation on Land Surface Variables Prediction
#### 3.1.1 Leaf Area Index  
Figure 2 shows the prediction of LAI in MLDAS and OL at the Mead site from 2003 to 2005. Since LAI observations are limited, the ground-measured LAI was used as the references in this section, and \(NIC_{RMSD}\) of LAI was not calculated herein. As shown, MLDAS performs better than OL in most scenarios compared to ground-measured LAI. In  
Figure 1: Diagram for four assimilation scenarios. The green arrow represents benchmarks with data assimilation; the blue arrow represents Noah-MP Open-Loop run (without data assimilation); the red arrows represent the prediction of LAI, SM, H, or LE; and the dashed lines in red arrows are the first-month prediction used for statistical analysis.  
general, LAI is tended to be overestimated, especially during DOY 180-220 in OL. MLDAS corrects the overestimation and produces predictions that can better fit LAI observations, suggesting that assimilation can bring better prediction skills to MLDAS. However, as Figure 2 shows, the prediction skill of MLDAS is worsened in scenario I assimilating for 15 days in 2004 and 2005. This is because only one set of LAI observations with relatively low values was assimilated into the Noah-MP model, bringing uncertainties to future prediction. In contrast, longer assimilation then prediction generally leads to better predictions of LAI peaks, which are especially better captured in scenario III from 2003 to 2005. It is also notable that the correction in MLDAS is most obvious in midsummer, when LAI is at peak. Correction brought by MLDAS is no longer evident compared with OL after midsummer (scenario IV).
## 3 Experimental Procedure  
The investigation of the azimuth-based SAST and sensor-based SAST was carried out simultaneously on a sunny clear sky day at IAU, in Dammam city (26\({}^{\circ}\)26\({}^{\prime}\)03\({}^{\prime\prime}\) N, 50\({}^{\circ}\)06\({}^{\prime}\)11 E). The experiment was performed on 10 February 2022, from 9 a.m. until 4:30 p.m. All the systems were facing the south, since KSA is in the Northern Hemisphere with 23 degrees of yearly tit angle, and the IAU latitude is between 25\({}^{\circ}\) and 50\({}^{\circ}\) and based on Equation (8). The ambient temperature was around 22\({}^{\circ}\) and the relative humidity of around 42%. Figure 8 demonstrates an actual representation of the azimuth-based and sensor-based SASTs.  
\[\beta_{Yearly}=(\phi*0.76)+3.1 \tag{9}\]  
where \(\beta_{Yearly}\) is the optimal yearly tilt angle, and \(\phi\) is the latitude.  
Test protocols developed from the International Electrotechnical Commission (IEC) 62,817 standard were used to evaluate solar tracking accuracy in an experimental setting. The overall mean instantaneous solar tracking error was about 0.4\({}^{\circ}\) with a calibrated digital solar sensor. On the other hand, the installation cost of the system with trackers for solar production is given in Table 2. The daily average for the sun tracking system PV panel was  
Figure 7: Flow chart algorithm operation for sensor-based SAST.  
about 9 MJ, and using the cost research analysis conducted with actual prices of 2021, the cost of the sun tracking system was estimated to be USD 3500.
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 2 Background
### Scenarios of 6G Use
The ultra-fast and highly reliable communication capabilities associated with 6G will increase data transmission speed and bandwidth, enhancing real-time operational efficiency for smart agricultural applications, including continuous monitoring of crops, soils, and environmental variables through intelligent sensors, autonomous systems, and unmanned aerial vehicles (drones) [18; 25]. These innovations will enable the timely diagnosis of plant diseases and nutritional deficiencies, as well as the execution of distributed agricultural operations in a more robust and reliable manner. AI will play a central role in the 6G ecosystem, acting as the operational brain of intelligent networks [26]. Unlike previous generations, 6G will natively integrate AI to manage dynamic resource allocation, predict network failures, and optimize agricultural processes. In smart agriculture, AI combined with 6G will enable the efficient management of the large volumes of data generated by IoT sensors and connected devices in real time, facilitating advanced analytics and autonomous decision-making in the field. Applications will include diagnosing plant diseases and nutritional deficiencies, analyzing soil moisture, detecting pest outbreaks, monitoring crop growth patterns, and assessing meteorological  
Figure 2: The usage scenarios of 6G in IMT-2030 [22]. cal conditions in real time, offering either automatic recommendations or autonomous actions [25]. Moreover, AI will support predictive agriculture by anticipating phenomena such as droughs or pest infestations based on historical and real-time data patterns [26]. This integration will not only enhance the optimization of agricultural resources but also contribute to the optimization of wireless resource allocation, improving energy efficiency and network coverage--factors that are critical for agricultural sustainability and productivity. Additionally, the integration of decentralized technologies such as blockchain will ensure the traceability and security of agricultural data, promoting more transparent, reliable, and sustainable business models across the sector. The future of 6G in sustainable agriculture is envisioned as a technological revolution, promoting a deeper integration between the physical, biological, and digital worlds. Through its advanced connectivity, low latency, and high-speed capabilities, 6G will enable the mass deployment of IoT sensors, AI-driven systems, and autonomous technologies, optimizing resource use, reducing waste, improving yields, and contributing to food security and environmental conservation in a sustainable manner [27].
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 3 Out-of-Equilibrium Dynamics
### Non-Negativity and Constraints of the Extended Entropy \(\overline{S}\)  
To ensure that the extended entropy \(\overline{S}\) in Equation (8) retains the non-negativity required for a valid thermodynamic entropy, we analyze its structure through variational principles and physical constraints. We demonstrate \(\overline{S}\geq 0\) as follows:
## 3 Materials and Methods
### Feature Reduction
#### 3.1.5. Autoencoders  
Autoencoders (AEs) [24] are deep feed-forward neural networks that include two sections of the encoder and decoder. They compress the input, i.e., reduce the number of features, into a latent-space representation known as a bottleneck with the help of multiple hidden layers. Then, the decoder section reverses the process to reconstruct the output from this representation with the same number of features as the output. The output of an autoencoder is its prediction for the input.  
When given an input \(x\in\mathbb{R}^{n}\) into an encoder section (the transformation defined by \(W_{h}\)), the result is an intermediate hidden layer \(h=f(W_{h}x+b_{h})\), where \(h\in\mathbb{R}^{d}\) and \(W_{h}\in\mathbb{R}^{d\times n}\). This is injected into the decoder section (the transformation defined by \(W_{x}\)). This produces the output \(\widehat{x}=g(W_{x}h+b_{x})\), which is our model's prediction/reconstruction of the input, where \(\widehat{x}\in\mathbb{R}^{n}\) and \(W_{x}\in\mathbb{R}^{n\times d}\). Autoencoders can reduce dimensionality with their encoder section. Hence, we do not inject the result into the decoder section.
# Variable Rate Point Cloud Attribute Compression with Non-Local Attention Optimization
## 4 Experimental Results
### Ablation Studies
#### 4.4.1 Influence of NLAM  
To further make clear the contribution of the introduced NLAM, we set different configurations of network in terms of NLAM. The internal mask branch of NLAM pairs is gradually removed and the network is retrained for performance evaluation. Other experimental conditions remain unchanged and the result is shown in Figure 9a.  
Removing the mask branches of the NLAM pair in the shallow layer in the main encoder-decoders (referred to as \"remove_first\") yields a PSNR drop of about 0.3 dB compared to the orginal setting at the same bit rate. The drop is further enlarged when all NLAM pairs' mask branches are removed (a.k.a., \"remove_all\"), resulting in a network without any non-local characteristics explorations.
## 3 Results and Discussion
### Correction Factors Used for CO\({}_{2}\) Uptake
#### 4.1.1 Strength Classes Involved  
Considering the concrete strength class or category depends on its porosity, four classes of strength have been proposed as follows: concrete structures of low strength, less than 15 MPa; concrete structures of strengths between 15 and 20 MPa, including old houses; concrete structures such as most new homes with strengths of 25-35 MPa; and concrete structures used for infrastructure works such as bridges with strength over 35 MPa. Each of these classes was assigned a factor for calculating the CO\({}_{2}\) uptake.