esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 RIM Formulation Summary, Discussion, and Prospects
### Discussion
The RIM method addresses the fundamental issue of optical errors between fluids and solids. Nevertheless, there are discrepancies in the physical properties of the matching fluids and the actual fluids, including hydraulic characteristics such as density, viscosity, surface tension, and so forth. Consequently, researchers must select suitable matching fluids to ensure flow similarity. It is also necessary to consider other physical differences. (iv) Relatively low stability. Some studies have proposed that experiments be conducted in a shorter time frame to prevent changes in the properties of the organic liquid, such as density and viscosity. Additionally, there have been studies conducted on the stability of the matching fluids. (v) Relatively low compatibility with other materials. Organic liquids are easily intermiscible or reactive with several common materials. When selecting a liquid for RIM experiments, it is crucial to consider both the optical matching requirements and the specific experimental conditions. Here are some practical tips and guidelines: (i) Optical matching. The primary goal is to match the refractive index of the liquid to that of the solid material used in the experiment. (ii) Temperature effects. The refractive index of liquids can change with temperature. It is advisable to perform experiments at a controlled temperature or to select liquids with minimal temperature dependent refractive index variation. For example, in the study by [PERSON] and [PERSON] [66], the temperature was kept constant to ensure accurate RI matching. (iii) Pressure sensitivity. High pressure conditions can affect the density and therefore the refractive index of liquids. Select liquids with stable refractive indices under the expected pressure ranges of the experiment. (iv) Duration of the experiment. Some liquids can change their properties over time, affecting the longevity of the RIM match. Choose fluids with proven stability over the duration of the experiment. As noted in [PERSON] et al. [64]'s study, the stability of the RIM fluid was a critical factor in the long-term visualization of multiphase flow. (v) Toxicity and Safety. Consider the toxicity and safety profile of the fluids, especially when working in a laboratory environment. Use appropriate ventilation and personal protective equipment when handling potentially hazardous fluids. By considering these factors, researchers can make informed decisions when selecting fluids for RIM experiments, improving the accuracy and reliability of their studies.
## 2 Related Work
### Sparse Electrode Configurations  
For BCI systems to transition from laboratory environments to practical, everyday use, they must be portable, non-intrusive, and easy to set up. This has driven a significant research effort toward developing systems that function effectively with sparse electrode configurations, i.e., using a minimal number of EEG channels [36; 46]. While traditional, high-density EEG caps provide excellent spatial resolution, their long preparation times, need for conductive gel, and user discomfort make them unsuitable for many real-world applications, particularly for stroke patients or home-use scenarios [47]. However, reducing the number of channels introduces a fundamental trade-off between portability and performance. As noted by [PERSON] et al. [48], using fewer channels can lead to the loss of critical information and an increase in susceptibility to noise and artifacts, often resulting in degraded classification accuracy. A primary challenge, therefore, lies in intelligently selecting the most informative subset of channels. A large body of work has focused on this, using various criteria for channel ranking and selection. For instance, [PERSON] et al. [48] propose a novel entropy-based method to identify channels with the highest information content, demonstrating that a carefully selected subset can outperform a full-channel configuration. Other approaches have utilized correlation-based metrics [49] or advanced algorithms like the binary gravitational search algorithm (BGSA) to optimize channel placement [50]. More recently, research has shifted towards developing wearable, few-channel devices that are inherently designed for practicality. [PERSON] et al. [47] introduced a wearable headband with just four wet electrodes, specifically targeting the motor and occipital cortices. Their work demonstrates that such a sparse system can achieve high online classification accuracy (over 76%), comparable to a mature commercial wired system, while drastically reducing setup time. This highlights the feasibility of creating effective, low-channel count BCIs for rehabilitation. Despite these successes, the challenge of inter-subject variability remains; the optimal sparse configuration for one user may not be ideal for another [51]. Our work directly engages with this problem by using channel dropout not only as a regularization technique but also as a way to simulate the structural variability inherent in sparse-channel BCI systems, thereby building models that are more robust to these practical constraints.
# One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation
## 5 Results Analysis
### How Does Segmentation affect Detection?  
The OFA-Net model is evaluated on the detection validation sets and the results are shown in Table 2. An interesting discovery is that when training with the segmentation data, the confidence of the objects with \"enough\" instances [28] (Cars, Persons) becomes higher. This arises from more detailed scene information supplied by the segmentation task. Due to the small amount of validating instances of the cyclist category, it cannot benefit from this. Besides, some cyclist objects were classified into the person category with segmentation data added as the supplementary data source. Therefore, if applying the OFA-Net model and the \"1-N Alternation\" training strategy to the object detection applications, \"enough\" validation instances for object detection are needed. Now, the question is what \"enough\" means. To give an answer to it, the number of instances over the number of images ratio for each category, say \(R_{[category]}\), is calculated as the Formula (4). Notice that \(R_{Car}\) and \(R_{Person}\) are 4.7 and 1.3, respectively, while \(R_{Cylcist}\), \(R_{Van}\), \(R_{Tuck}\), and \(R_{Tram}\) are much smaller than 1, specifically, 0.07, 0.52, 0.17, and 0.03 respectively. What can be concluded from these ratio values is that the \"enough\" instances cannot be less than the number of images, or the \(R_{[category]}\) at least should be greater or equal to 1. Based on this finding, one suggestion is that the amount of validating instances for each category in the detection datasets should not be smaller than the number of images in those datasets.  
\[R_{[category]}=\frac{Num\ of\ Instances\ for\ Category}{Num\ of\ Images} \tag{4}\]
## 2 Materials and Methods
### Explainable Machine Learning for APS Detection
#### 2.3.2 XGBoost Model Training  
Gradient boosting is an ensemble learning method that integrates several weak learners such as decision trees to improve the overall algorithm performance. The trees are constructed consecutively, with each tree aiming to rectify the errors of the preceding tree. The prediction of the constructed model is the sum of the predictions of all individual weak learners weighted by their corresponding importance [28].  
XGBoost is a parallelized and optimized gradient boosting algorithm that accelerates the training process by enabling the utilization of multiple central processing unit cores or graphics processing units [22, 29]. XGBoost trains numerous instances of decision trees on various subsets of the training dataset while employing techniques such as gradient boosting, regularization, and efficient tree construction to improve precision and accuracy while avoiding overfitting. We implemented the XGBoost algorithm, specifically the XGBClassifier from the XGBoost library (version 2.0.3), with the objective set to 'binary' to perform binary classification. The XGBoost Python library is designed to be highly efficient and flexible for training and deploying gradient boosting models [22]. We trained the XGBoost model for APS detection using the physiological signals GSR, HR, ST, and the three-axis ACC [12]. To prevent overfitting, we employed early stopping by halting the training process if the model accuracy on the validation dataset did not improve, setting the early_stopping_rounds parameter to 10.
# MIMO Signal Detection Based on IM-LSTMNet Model
## 3 Experiments and Results of Signal Detection  
### Data Preprocessing and Training of Signal Detection  
Before the formal signal detection, it is necessary to preprocess the signal data and train the model offline. In this section, the preprocessing of signal data and the training of IM-LSTMNet model are mainly introduced.  
Figure 8: Framework diagram for the detection of the MIMO-OFDM signal by a neural network.
# Transferring Sentiment Cross-Lingually within and across Same-Family Languages
## 8 Results and Discussion
### Language Representations in XLM-RoBERTa  
The training setup consists of three components: the shared encoder, training data, and classifier heads. Using the training data, the classifier heads are trained. The shared model is a black box component of the entire system for representing multiple languages. The XLM-RoBERTa model was trained using 2.5 TB data from 100 languages. The various training dataset sizes for the languages under study are listed in Table 7. The text is divided into tokens using a sentence-piece tokenizer. We conducted a simple study to examine these representations in different languages. We ran each dataset's training set through the XLM-R tokenizer. For the obtained sentence-piece tokens, we calculated the intersection of all possible language combinations. Table 8 indicates the number of common tokens in various languages. We observed that the best-performing language combinations have many shared tokens for a given target language's sentence fragments. In the case of Croatian, it shares 5075 sub-tokens with Czech, allowing it to advance under the joint-training system. We would like to note that the Slovene dataset consists of comments from a news website and is therefore highly informal and noisy. Consequently, we hypothesize that, when combined, it adversely affects the Croatian performance metric. The performance of the Czech language decreases when it is combined with other languages. When English is combined with Czech, we observe a slight improvement over the baseline and other combinations. Russian (Latin), Bulgarian (Latin), and English combinations have higher scores for Polish. In the case of Slovak, training alongside Czech led to results that were comparable to those obtained with Slovak, Slovene, and Croatian combined. Czech and Slovene shared the second-greatest number of tokens. In addition, distant high-resource languages (such as English) do not aid in the improvement of the performance of high-resource languages. Adding English data improves performance in five classes, whereas adding same-family language data improves performance in three classes. Although we observe that Bulgarian shares numerous sub-words with Russian, Czech, and English, the languages with the most shared tokens, the precise classification behavior of tokens requires further investigation.  
\begin{table}
\begin{tabular}{c c c} \hline
**Language** & **Size (GB)** & **Tokens (Million)** \\ \hline Bulgarian & 57.5 & 5487 \\ Croatian & 20.5 & 3297 \\ Czech & 16.3 & 2498 \\ English & 300.8 & 55,608 \\ Polish & 44.6 & 6490 \\ Russian & 278.0 & 23,408 \\ Slovak & 23.2 & 3525 \\ Slovene & 10.3 & 1669 \\ \hline \end{tabular}
\end{table}
Table 7: Data size used for training XLM-RoBERTa.
## 2 Point-to-Point Energy Optimization
The basic are the kinematic and dynamic factors of the manipulator: length and mass of the links, inertia, gear ratio, friction, motor torque, winding resistance, robotic parts, temperature, etc. Subsequently, there are applied forces: gravitational, centrifugal, Coriolis, etc. The analysis of the preciseness of these models can be found in [2]. Another way to express energy efficiency is by simplification of the problem to a single optimization criterion, such as minimum effort, the time integral of squared or absolute values of torque, squared joint accelerations, see [21; 22]. However, for the price of seamless simplification and decreased precision. Using the direct approach, we can measure the energy consumption value right from the real robotic arm. Unlike for the inverse approach, this method depends on a specific robotic arm and its hardware and software to enable such a measurement. There is also the possibility to measure the overall power consumption from a tool that was connected to the manipulator power cables [3]. Another challenge lies in the representation of the movement trajectory with given constraints. Because of the difficulties in Cartesian space representations (singularities, rapid joint movements, etc. ), most of the authors prefer the joint space representation. We can define the trajectory itself by a set of points in the joint space, uniform cubic B-splines [18; 19], Bezier curves [23], etc. The commonly used optimization techniques in the field of robotic are GA, exhaustive search, Differential evolution (DE), Firefly algorithm (FA), Artificial Immune system (AIS), PSO, etc. Complete review of robotic arm optimization algorithms is beyond the scope of this paper. Figure 1 shows the average performance of several algorithms in the task of robotic arm torque optimization. The algorithms were tuned for the task and their performance was evaluated in 20 runs. The best possible value of torque (overall sum of torques of all joints during the whole movement) was found  
Figure 1: Average best fitness for torque optimization task. by DE and PSO. Moreover, the PSO algorithm found the best value in the least number of iterations. Therefore, we will use PSO for the following energy optimization task because of the performance in the torque optimization task.
## Appendix B Derivation of the Complex Marine Area Survey Line Optimization Model
### Derivation of Model 2 in Section 2.2.2  
First, determine the starting position of the survey vessel. According to the analysis of Model 1 in Section 2.2.1, as the water depth increases, the coverage width \(W\) becomes larger, and the overlap rate \(\eta\) also increases. By traversing the four boundary points of the rectangular curved surface area, as shown in Figure 9, the point \(H_{1}(0,y_{0},z_{0})\) is selected as the starting coordinate for the survey vessel.  
Secondly, the coordinates of each key point in the model are determined based on geometric relationships. The coordinates of \(Z_{1}\) are \((0,y^{1},0)\). Given that the multibeam angle is \(\theta\), we know that:  
\[\frac{y_{0}-y^{1}}{z_{0}}=\tan(90^{\circ}-\frac{\theta}{2})\]  
Since \(H_{2}Z_{1}\) and \(Z_{1}H_{1}\) are symmetric about the angle bisector of the multibeam survey line, the equation of the line \(H_{2}Z_{1}\) is:  
\[z=\tan(90^{\circ}-\frac{\theta}{2})\cdot(y-y^{1})\]  
Since the line \(H_{2}Z_{1}\) must intersect with the intersection line of the slope surface and the multibeam plane, denote this intersection point as \(H_{2}\). By substituting all the data points from Table 1 into the line equation of \(H_{2}Z_{1}\), if a data point satisfies the equation, it is \(H_{2}\). If no data points in Table 1 satisfy the line equation, choose the point on the intersection line of the multibeam plane that is closest to the line \(H_{2}Z_{1}\) as \(H_{2}\), denoted as \(H_{2}(0,y_{1},z_{1})\).  
Using the concept of approximating curves with straight lines, the length of \(W_{1}\) is the Euclidean distance between points \(H_{1}\) and \(H_{2}\):  
\[W_{1}=\sqrt{(y_{1}-y_{0})^{2}+(z_{1}-z_{0})^{2}}\]  
It is easy to see that the coordinates of \(Z_{2}\) are \((0,y^{2},0)\), which can be expressed as \((0,y^{1}+d_{1},0)\).  
Finally, we need to satisfy the coverage width constraint. As shown in the optimization objective (4), the sum of \(W_{1}\) and \(d_{i}^{\prime}\) should be as large as possible to ensure the measurement model covers the entire seabed area, minimizing the uncovered area, and the overlap rate should be less than or equal to 20%. Here, \(d_{i}^{\prime}\) is the difference between the coverage width \(W_{i+1}\) and the overlap portion between \(W_{i+1}\) and \(W_{i}\), i.e., \(d_{i}^{\prime}=W_{i+1}-W_{i}\eta_{i}\). The specific coordinates of \(H_{n}\) can be obtained from Table 1.
# Querying on Federated Sensor Networks
## 3 L-FSN Protocol
### Routing Tables on Gateways
#### 3.1.1 Initialization of Routing Tables  
EGs exchange routing tables at the beginning of network construction and whenever a change occurs on an EG's routing table. Exchanging of routing tables continues until no more information is exchanged between EGs.  
At first, EGs send routing tables to their neighbor EGs by single broadcast in order to detect their neighbors. Receiver IGs on the broadcast path continue routing packets to the neighbor island's EGs. A receiver \(EG\)\(z\) replies back to the sender \(EG\)\(x\) with its routing table. After receiving a routing table from \(z\), \(x\) declares \(z\) as one of its neighbors. Next time, when \(EG\)\(x\) propagates a routing table to its neighbor \(EG\)\(z\), it sends these routing tables directly to the geographical coordinates associated with its neighbor \(EG\)\(z\) instead of broadcasting.  
During routing table exchanges, EGs update routing tables according to the path selection policies. For example, for the shortest path selection policy, EGs exchange and update routing tables with the distance-vector algorithm as explained in Appendix A. With the distance-vector algorithm, every EG  
Figure 1: A layered federated sensor network topology.  
maintains the least-cost path information. Let D\({}_{x}\)(x,y) be the distance from EG \(x\) to EG \(y\) on the routing table of \(x\). For each neighbor \(z\), EG \(x\) updates its routing table for all EG \(y\) in FSN using the Equation (1):  
\[D_{x}(x,y)=min\{D_{x}(x,y),c(x,z)+D_{z}(z,y)\}, \tag{1}\]  
where \(c\)(x,\(z\)) is the cost from \(x\) to \(z\). If \(z\) is \(x\)'s direct neighbor, \(x\) assigns \(c\)(x,\(z\)) to be 1. If two EGs cannot directly communicate, \(c\)(x,\(z\)) is assigned to be the number of gateway hops between \(x\) and \(z\).  
Fields of a routing table are shown in Table 1. EGs exchange destination gateways' _id_, _island id_ and _geographical coordinates_ along with the _area boundary information_ with northeast (NE) and southwest (SW) coordinates. EGs build routing tables according to the path selection policies; which are defined by the _metric_ field of routing tables. According to the path selection policy, EGs provide the _next neighbor_ and the _weight_ value. Status can be either public or private. An EG does not propagate a private EG's routing information into FSN in order to prevent the network flow through this private island.  
Instead of managing a large island under a single EG, islands can be managed under multiple EGs. Dividing an island with multiple EGs increases the querying efficiency within an island. In this way, each IG forwards queries to the closest EG within the island. This decreases the query resolving time compared to querying with a single EG. In addition, dividing of islands into regions improves the scalability of the inter-island querying protocol.
## 4 Discussion
### Perspectives and Limitation  
This study illustrated the spatial distribution pattern of cropland SOC in the Northeast China Plain using the DSM method and optimal environmental covariates. There are several limitations, as follows.  
Firstly, more advanced feature selection methods should be considered to improve model performance. Though Boruta could greatly decrease the number of environment covariates, there was less improvement in accuracy and uncertainty. Numerous DSM studies performed feature selection (e.g., Forward Recursive Feature Selection, Recursive Feature Elimination) before fitting the models, whereas few studies are focusing on providing the validity domain on the applicability of different feature selection algorithms in different situations [17; 70; 71; 72; 73]. Further studies should solve the knowledge gap and find an algorithm that can select the most important covariates directly related to the target model and improve model performance.  
Moreover, it is essential to map SOC for the areas with less differences and with more sampling points. We found irregular SOC changes in some areas at the vertical scale (e.g., the middle of Songnen Plain). Further studies should focus on fitting models with more sampling points and try to fit the model with train, validation, and test datasets, which could generate more accurate SOC maps and help illustrate the impacts of long-term mechanized operation.  
Finally, more environmental covariates with higher spatio-temporal resolutions need to be proposed and used to model. Although climate has a lagged effect on the spatial distribution of SOC and WorldClim2 from 1970 to 2000 at 1 km spatial resolution has been widely used in DSM, there is a lack of WorldClim2 products for the last 20 years. Feature studies should apply spatio-temporal downscaling methods to produce new climate products to minimize the error and uncertainty in predictions due to covariates.
## 4 Results and Discussion
### Comparison with Other Algorithms  
Most of the watermarking algorithms based on neural networks described in the literature deal with the problem of embedding a static image in another static image or in a video signal frame, which makes it impossible to compare the effectiveness of the algorithm at a given binary capacity. Below is a comparison of the method described in this article with the RivaGAN algorithm [34], which also distinguishes various variants of the embedded binary sequence (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **RivaGAN** & **Our Model** & **RivaGAN** & **Our Model** & **Our Model** \\  & **32 Bits** & **32 Bits** & **64 Bits** & **64 Bits** & **512 Bits** \\ \hline SSIM & 0.960 & 0.933 & 0.950 & 0.947 & 0.949 \\ accuracy & 0.992 & 0.998 & 0.983 & 0.998 & 1.000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the described method with another algorithm.  
Figure 19: Comparison of BER metric values.  
Figure 18: Comparison of PSNR metric values.  
The accuracy of the model was determined as the inverse of BER, i.e., the ratio of the number of bits decoded correctly to all embedded bits. For both variants studied by the authors, i.e., 32 bits and 64 bits, our algorithm is characterized by worse transparency but higher accuracy. In the case of 64 bits, the SSIM metric values are similar (0.950 for RivaGAN and 0.947 for our algorithm). The table also shows a variant for which transparency was achieved at a level almost equal to the RivaGAN algorithm (SSIM = 0.949) with a precision of 1.0 and a much higher watermark capacity--512 bits. Our main goal was to find a balance between the transparency and resistance of the character with the largest capacity of the embedded binary sequence.