esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
## 2 Domain Adaptation
### Unsupervised Domain Adaptation Techniques
#### 2.3.1 Pseudo-Labelling  
The goal of pseudo-labelling (PL) [43] is to generate a set of predicted labels for unlabelled samples with a model trained on labelled data. This idea is an intuitive and straightforward application that can help overcome the challenge of collecting large labelled datasets. Several works in the literature have explored different algorithms for creating pseudo-labels. In [44], pseudo-labels are assigned to unlabelled samples using neighbourhood graphs. The idea of pseudo-labelling is extended in [45] by incorporating confidence scores for unlabelled samples. The authors of [46] present a new optimisation framework to iteratively update the obtained pseudo-labels. Our approach is inspired by the works in [43; 47], where pseudo-labels are generated directly as the predictions of a trained neural network.  
Our solution for pseudo-labelling domain adaptation can be described according to the three following steps:  
1. **Train source model**: first, an initial model is trained in a supervised way on the source domain.
2. **Predict target labels**: the initial model is then used to predict speech presence or absence for the unlabelled target domain.
3. **Adapt using predicted labels**: finally, the initial model is retrained in a supervised way using the pseudo-labels as if they were true labels.  
Furthermore, besides performing a fine tuning of the initial source model to obtain the target model, this solution could also be used to train a target model from scratch using the obtained pseudo-labels or a combination of the source labelled data and the obtained pseudo-labels. Pseudo-labelling techniques have been used in several audio processing applications ranging from acoustic classification problems [48], diarisation [49] or speech recognition [50] with relevant results. In this work, we aim to extend the pseudo-labelling techniques to the SAD task and evaluate its performance in the framework of domain adaptation.
# CRSAtt: By Capturing Relational Span and Using Attention for Relation Classification
###### Abstract
In order to solve the above problems, we start from the perspective of \"How can we accurately focus on the connection between two entities?\". We started to think and study deeply from this perspective. The existing approach is to remove the meaningless edge information and keep the meaningful core information by sentence compression. However, this will increase the computation and make the model become complicated [12]. We find that the description of entity relations in a sentence exists between two entities in most cases. The words outside of this are meaningless information for the relation classification. Therefore, we truncate the sentence span between the two entities and merge the semantics of this span with the semantics of the whole sentence. The feature vector of \"Strengthen Relational Semantics\" is obtained. Using this feature vector, it is possible to greatly improve the model's understanding of what kind of relationship should exist between a pair of entities, thus enabling the model to focus on the semantics of the relationship between entities. To be able to make full use of the semantic features extracted by the model, a completely new prediction structure was designed for relation classification. The various feature vectors extracted by the model are fed into this structure, and the final prediction results are obtained after Multi-Class Attention. In this way, we are able to capture not only the overall semantic information of the whole sentence but also focus on the relational information between entities. Various semantic features are fully utilized to enable the model to better handle the relational classification task. Therefore, to address the problems in existing methods, we propose a method called capturing relational span and using attention for relation classification. The innovations in this paper are as follows:  
1. A feature fusion method called \"SRS (Strengthen Relation Semantics)\" is proposed. We fuse the global information of the whole sentence and the relational information between entities to form the vector \(sr_{feature}\) for strengthening relational semantics, which is used to solve the problem that existing methods cannot effectively focus on the relational semantics between entities. 2. A new attention-based prediction structure is designed. In our known work, we are the first to use full attention instead of fully connected layers to predict the probability distribution of each category for multiple classifications. With this prediction structure, we can make full use of various feature information and reduce the loss of semantic information.
# Intelligent Air Cutting Monitoring System for Milling Process
## 3 Air Cutting Monitoring System  
The Human Machine Interface (HMI) is designed and becomes the air cutting monitoring system. The system can detect real-time machining status (cutting/no-cutting) and calculate effective cutting time, air cutting time, and total machining time, as shown in Figure 3. The user needs to input spindle load, vibration threshold, and import NC machining program, then the system will capture the real-time spindle load, vibration signals, and NC machining. Subsequently, the signal and data are analyzed, then combined with the algorithms to perform cutting/no-cutting judgment. When the spindle load current value and vibration signal value are greater than the threshold value, and the current executing NC block is G01/G02/G03, then the system will judge as a cutting state. Meanwhile, the system will record the current time and the \"Cutting\" indicator in the HMI will change to a green color that indicates the machining status. On the other hand, when the spindle load current value and vibration signal value decrease from the cutting load value to the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Target Identified NC Code** & **Regular Expression Characteristic** \\ \hline G00, G01, G02, G03, G04 & /G00.[1,] / /G01.[1,] / /G02.[1,] / /G03.[1,] / /G04.[1,] / \\ Spindle speed S & /S\(\backslash\)d[1,] / \\ Feed rate F & /F\(\backslash\)d[1,] / \\ x-axis X & /\(\times\)d[1,] / \\ y-axis Y & /Y\(\backslash\)d[1,] / \\ z-axis Z & /Z\(\backslash\)d[1,] / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Regular Expression characteristics.  
Figure 2: Cutting/no-cutting vibration signal.  
threshold value, where the vibration signal value is equal to the vibration threshold value, and the current executing NC block is G01/G02/G03, then the system will judge as a no-cutting state. Meanwhile, the system will record the current time and the \"No Cutting\" indicator in the HMI will change to green color. When the executing NC block is M30, which means that the machining is completed, the system will calculate all the recorded cutting time, air cutting time, and machining time, as shown in Figure 3. At the same time, the system exports the detailed information in txt file format, as shown in Figure 4.  
Figure 4: Output txt file of the air cutting monitoring system.  
Figure 3: Air cutting monitoring human machine interface (HMI).  
The quality of measurement is important for the precision machining. However, there may be uncertainty in the measurement. The main uncertainty contributors are machine repeatability, accelerometer sensor repeatability, sampling strategy, DAQ, fitting and evaluation algorithm. The CNC vertical machining center TMV-720A with machine repeatability of \(\pm\)7 um was used in this study. The accelerometer sensor 353B15-PCB that was used in the experiment has non-linearity \(\leq\)1%. Besides, the NI DAQ USB-4431 internal frequency timebase accuracy is \(\pm\)100 ppm.
# Analysis of Key Storage Mechanism of Asymmetric Key-Related Functions in CNG Crypto Library
## 2 Background/Related Works  
### CNG Structure  
To compensate for the disadvantages of capital, which has limitations in coping with rapidly changing environments, the proposed CNG replaces all existing cryptographic primitives. Moreover, it both supports all the cryptographic algorithms from capital and provides new algorithms. Hence, the developer can more actively intervene in how to perform various functions and how to provide various functions by using CNG in a complex manner. Thus, a more flexible design structure is supported. Figure 1 shows the overall structure of CNG [7; 9].  
CNG provides cryptographic primitives related to random number generators, hash functions, signatures, and keys, and the core of its structure is BCrypt. NCrypt provides key storage to support hardware, such as asymmetric keys and smart cards; the B in BCrypt stands for base, while the N in NCrypt stands for new. These names come from the header or DLL filename in the development environment provided by CNG. In addition, CNG provides a framework to support user and kernel modes, unlike existing cryptographic libraries (BCrypt) or where the key storage feature is only supported in user mode (NCrypt).  
There are two main perspectives on the cryptographic primitive provided by CNG. The first perspective considers CNG as a collection of local objects that provide queryable and changeable properties along with methods. These objects include algorithm providers, hash functions, keys, and a secret agreement. Additionally, random number generation is processed internally by the algorithm provider, while all others are provided by the key.  
Figure 1: The overall structure of CNG.  
Here, the key can be expressed in a symmetric or asymmetric form and is used to perform or verify a hash signature. The hash and key objects are obtained from the algorithm provider, and the secret agreement is obtained from the key object pairs. The second perspective is to regard CNG as a software router or intermediary for cryptographic operations. For example, when implementing a particular hash algorithm, CNG can be implemented using a hash interface without excluding hard coding because the API of CNG is built on a set of logical cryptographic interfaces. Most cryptographic APIs use an algorithm-centric approach. However, in the case of CNG, because it uses an interface-oriented approach, it is possible to quickly cope with the problem by providing better agility to the developer and to the application manager, who needs to replace the algorithm that is found to be defective in the application.
## 7 Simulation Results
### Reactive Algorithm  
The reactive algorithm computes the optimal transmission power levels for each posture and each subject using the previously collected experimental dataset. We will call this algorithm \"optimal reactive\". On the other hand, we are also interested in testing how the optimal transmission power levels chosen for the human Subject A work when applied to another human subject, B. Therefore, we are interested in analyzing the generalization capabilities of the policy; we will call this algorithm \"not optimal reactive\".  
The evaluation of both algorithms is done selecting two cases from all of the posture sequences simulated: the best case and a complex case of study that was previously presented in [7]. The best case is referred to as the sequence that showed the greatest energy savings; typically, this sequence includes postures for which Link 2 has a direct line of sight (L2P1, L2P2), and therefore, it allows one to play with a wide range of power values. The case study is the combination of the following postures: L1/P1 + L2/P4, L1/P2 + L2/P3, L1/P3 + L2/P1, L1/P4 + L2/P2 and L1/P5 + L2/P1 (see the description of each one in Section 3).  
Table 5 shows the results for the best case under the \"optimal\" and \"not optimal\" reactive policy. For the algorithm \"not optimal\", data of an extra human subject relative to the five test subjects were chosen randomly. All of the values presented in the table are average values of five subjects during a full sequence of five postures. As can be seen, the optimal algorithm allows one to obtain an energy savings of 29% with an average PER of 5.6% and 1.4% for the sensor nodes of Link 1 and Link 2, respectively. For the \"not optimal\" case, the average PER is similar to the previous case, 5.5% and 1.4%, respectively, but with a reduction in energy savings of almost 6% with respect to the \"optimal\" case. These results show the good energy savings achieved by the reactive policy only for previously characterized subjects; therefore, the reactive policy lacks generalization capabilities and requires a characterization and tuning phase per user.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Reactive** & **TPLn1** & **TPL n2** & **RSSI n1** & **RSSI n2** & **Energy Saving** & **PER n1** & **PER n2** \\ \hline Optimal & \(-5\) dBm & \(-15\) dBm & \(-78\) dBm & \(-70\) dBm & \(29\)\% & \(5.6\)\% & \(1.4\)\% \\ Not Optimal & \(-2\) dBm & \(-15\) dBm & \(-74\) dBm & \(-70\) dBm & \(23\)\% & \(5.5\)\% & \(1.4\)\% \\ \hline \end{tabular}
\end{table}
Table 5: Simulation results for the reactive algorithm in the best case. PER, packet error rate. Transmission Power Levels (TPL), node 1 (n1) and node 2 (n2).
## 3 Cooperative Communication Algorithms
### Model Validation
#### 3.3.1 The Effect of Hops and Number of Cooperative Nodes on End-to-End Reliability  
\(K\) is set from 1 to 10, the number of hops varies from 4 to 16 and the step size is 4. The result is shown in Figure 2. The number of cooperative nodes leads to the increase of reliability, and the increase of hops leads to the decrease of reliability.  
Figure 2: The effect of hops and number of cooperative nodes on end-to-end reliability.
# Polar Region Integrated Navigation Method Based on Covariance Transformation
## 1 Introduction
Papers by [17; 18] both proposed indirect polar navigation methods, using a combination of the wander frame and G-frame or the t-frame to achieve smooth switching of navigation frames. However, indirect polar navigation methods did not fundamentally solve the filter consistency problem during the coordinate frames switching. In order to solve the problem of filter discontinuity caused by the change of navigation frame, this paper proposes a polar-region airborne INS/GNSS integrated navigation method, based on covariance transformation. The transformation relationship between the system error state in the local horizontal geographic frame and that in the grid frame is deduced. Flight experiments at mid-latitudes initially proved the effectiveness of the covariance transformation method. It is difficult to conduct experiments in the polar region. A purely mathematical simulation cannot accurately reflect real aircraft situations [19]. To solve this problem, the authors of [19; 20] proposed a virtual polar-region method based on the t-frame or the G-frame. In this way, the experimental data from middle and low latitude regions can be converted to the polar region. Verification by semi-physical simulations, based on the proposed method by [20], is also conducted and gives more convincing results. This paper is organized as follows. Section 2 describes the grid-based strap-down inertial navigation system (SINS), including the mechanization and dynamic model of the grid SINS. In Section 3, the covariance transformation method is presented. In addition, Section 3 also offers a navigation frame-switching method based on the INS/GNSS integrated navigation method. Section 4 verifies the effectiveness of the proposed method through experimentation and semi-physical simulation. Finally, general conclusions are discussed in Section 5.
## 3 Results and Discussion
### Respiration Rate  
The respiration rate values of the fruit that were exposed to the magnetic field and the fruit in the control group are shown in Figure 12. The respiration rate was expressed as the production of CO\({}_{2}\) in the fruit. The production of CO\({}_{2}\) was lower in the MF-treated fruit compared to the control apples. The magnetic field that affected the fruit for eight hours a day reduced the production of CO\({}_{2}\) compared to the apples that were kept in room conditions. The magnetic and electric fields are elements of plant physiology [51]. The internal electric fields of plants are dependent on those coming from outside impulses, which may cause changes in fruit metabolism that are related to fruit respiration. Many studies [10; 21; 52] have reported that magnetic and electric fields reduce the respiration rate of various products. Respiration plays an important role in adapting plant metabolism to changing conditions [53; 54; 55; 30; 56].  
Generally, the higher the yield of carbon dioxide, the shorter the shelf life of the product [57; 58; 59]. The level of carbon dioxide in the apples that were exposed to MF treatment was determined using samples that were stored for 14 days at 22 \({}^{\circ}\)C and 50% relative humidity. A study by [PERSON] et al. [46] showed that electric fields reduce the activity of enzymes, which is the same feature that inhibits the increase in carbon dioxide yield [46].  
Figure 11: The dry matter versus TSS/TA ratio of Idared apples.  
The carbon dioxide yields were 7.6 and 8.1 mg of CO\({}_{2}\) per gram of fruit and increased to 11.7 and 14.5 mg of CO\({}_{2}\) per gram of fruit during storage for treated and untreated samples, respectively. In fact, the MF treatment of fruit reduced the rate of carbon dioxide yield from 6.2% to 23%, depending on the time after storage. This same treatment may have the ability to inhibit metabolism. Generally, the application of MF treatment to apples had an impact on their respiration rate, which led to a decrease in carbon dioxide production. Thus, it contributed to the extension of the shelf life of the apples.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 3 Methodology
### Evaluation Criteria
#### 3.3.2 FEM Model Validation  
To ensure the accuracy of the finite element method (FEM) simulations, a validation exercise was performed by comparing the simulated ground resistance values with those obtained from field measurements. The comparison was conducted across multiple grounding configurations using identical soil resistivity and geometric parameters. The results indicate a strong correlation between the simulated and measured values. The average absolute error observed was \(\pm\)0.35 \(\Omega\), while the relative error remained below 7% for most cases. These deviations are considered acceptable within the typical range of variability expected in soil resistivity and measurement tolerances. The low error margins confirm that the FEM model accurately reflects the physical behavior of the grounding system and validates the appropriateness of the soil modeling and boundary conditions used in the simulations. This provides confidence in the use of the FEM results for optimizing design parameters and evaluating performance under various grounding scenarios.
## 4 Indoor Dynamic Beam Deflection Measurement Experiment and Validation
### Structural Full-Field Dynamic Displacement Field Measurement  
#### 4.2.1 Dense Feature Point Extraction, Matching, and Dual-Step Mismatch Removal  
Using SIFT feature extraction and FLANN matching to measure the structural displacement between two images, a large number of feature matching pairs can be obtained. Taking the 1 st and 20 th frames as examples, the feature matching results between the two frames are shown in Figure 8, with a total of 6838 pairs of matched features.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Equipment** & **Manufacturer/Model** & **Main Parameters** & **Main Usage** \\ \hline Industrial & \multirow{2}{*}{Basler/a2A5320-7 gmBAS} & Resolution 5320 \(\times\) 3032 px; & \multirow{2}{*}{Data acquisition} \\ camera & & & Frame rate 50 fps \\ \multirow{2}{*}{Lens} & \multirow{2}{*}{C11-1220-12M} & Lens focal length 12.0 mm; & Auxiliary \\  & & & Aperture F2.0-F16.0 & \\ \multirow{2}{*}{Computer} & \multirow{2}{*}{MSI/GL62M} & Intel(R) Core (TM) i5; & \\  & & & 7300 HQ CPU @ 2.50 GHz & Data storage \\ \hline \hline \end{tabular}
\end{table}
Table 1: Equipment types and main parameters.  
Figure 8: Dense feature matching pairs between the 1 st and 20 th frames.  
Figure 7: 20 th frame in the image sequence. (**a**) The 20 th frame image. (**b**) The image without background.  
The RANSAC algorithm was applied to coarsely remove the mismatches from all 6838 pairs, and a total of 2513 mismatched pairs were removed, as shown in Figure 9a. After the coarse mismatch removal, 4325 remaining matching pairs were left as shown in Figure 9b.  
A secondary mismatch removal was further performed with the SDCC method, with a total of 1272 mismatch pairs being removed as shown in Figure 10a and 3053 pairs with correctly matched feature points reserved as shown in Figure 10b.  
Results show that the DFEM-DSMR method significantly improves the matching accuracy, and there are no obvious mismatches remaining after DSMR mismatch removal. In outlier removal using DSMR, the number of mismatches removed by the RANSAC and the SDCC accounts for 36.75% and 18.60% of the total number of dense feature pairs (6838), respectively. To further illustrate the applicability and effectiveness of the DFEM-DSMR structural displacement measurement method, the 10 th, 20 th, 40 th, 60 th, 80 th, and 100 th frame image, corresponding to six different times of 0.2, 0.4, 0.8, 1.2, 1.6, and 2 s were analyzed and calculated, and the number of mismatches removed by the RANSAC and SDCC is shown in Table 2.  
Figure 10: Secondary SDCC mismatch removal and correct matching pairs. (**a**) Removed features by SDCC mismatch removal. (**b**) Feature matches after SDCC mismatch removal.  
Figure 9: Coarse removal of mismatches by RANSAC method and remaining matching pairs. (**a**) Removed features by RANSAC mismatch removal. (**b**) Feature matches after RANSAC mismatch removal.
## 4 Performance Evaluation
### Simulation Parameters  
A GEO multibeam satellite with flexibility in frequency, bandwidth and beamwidth and with a four-color frequency plan scheme to avoid co-channel interference is assumed for the analysis. We evaluate the performance of different ML models by numerical simulations.  
In that sense, the Table 3 shows the main features of different ML models that have been evaluated for RRM on GEO multibeam satellite. It is noted that the models based on supervised learning are assumed to be trained offline; therefore, the AI Chipset could be onboard the satellite operating only as an intelligent switch that adapts the resources according to the traffic demand. On the other hand, for the reinforcement learning based models it is assumed that the training will be online, i.e., the model will be trained every time the traffic requirements change drastically so the AI Chipset will be on ground and, by using TT&C, it will send the information to the satellite to update the resources (see Section 3.1). The architecture used for each ML model can be found in the Appendix A.  
In addition, Table 4 provides a summary of the main simulation parameters. The traffic model used is based on the model presented in [34], which represents a service area similar to that of the KaSat satellite [46] with 82 beams. The flexible parameters per beam are power with 8 to 17 dBW with 0.1 dB steps, bandwidth with 100, 150, 200, 250, 250, 300, 350, 400, 450 or 500 MHz and beamwidth with 0.55, 0.60 or 0.65 degrees.