esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
### The Proposed Method  
Compared to traditional ICP algorithms and existing hybrid methods, the proposed MKD-ICP algorithm introduces several key innovations. First, by incorporating a multi-level KD-Tree structure, it enhances the efficiency and accuracy of matching complex product models, particularly when dealing with large-scale and high-dimensional datasets. Second, the MKD-ICP algorithm integrates case-based reasoning (CBR) principles to intelligently guide the initial alignment process, thereby improving convergence speed and reducing the risk of falling into local optima. Unlike previous approaches that treat modelmatching and dimension extraction as independent processes, the MKD-ICP method tightly couples them into a unified workflow, which significantly improves the adaptability to models with varying complexity and dimensional requirements. These innovations collectively advance the automation and precision of engineering drawing generation, addressing the key limitations of traditional ICP-based methods.  
To ensure the accuracy of dimensioning and the standardization of completeness checks in engineering drawings, this paper proposes an intelligent dimensioning method based on Case-Based Reasoning (CBR) and the MKD-ICP algorithm. The overall framework of the proposed method is illustrated in Figure 1. By leveraging CBR technology as the core and integrating the MKD-ICP algorithm, the method achieves a precise and efficient dimensioning process.  
Figure 1: The overall framework for automatic dimensioning in engineering drawings.  
First, the CBR technology is utilized to construct a case library containing historical drawings, documenting geometric features and dimensioning information across various engineering drawings. When a new drawing requires dimensioning, the CBR system retrieves similar cases from the library by calculating similarities between drawings based on Euclidean distance and angular similarity. The most similar historical cases are selected, providing not only rich dimensioning references but also initial annotation positions and key feature information for the subsequent MKD-ICP algorithm, significantly improving dimensioning accuracy and efficiency.  
After the CBR system recommends similar cases, the MKD-ICP algorithm leverages this reference information to perform precise dimension mapping and alignment. By using KD-Tree for efficient spatial data queries and integrating the ICP algorithm to extract key component dimensions, the algorithm completes the dimension mapping and alignment process [49; 50]. During this process, the algorithm accurately identifies critical mating dimensions and overall model dimensions, efficiently annotating them in the engineering drawing. Benefiting from the high-quality initial references provided by the CBR system, the MKD-ICP algorithm enables a more efficient and intelligent dimensioning workflow.  
In summary, the proposed method combines CBR and MKD-ICP technologies in a seamless manner, ensuring both efficiency and precision in dimensioning engineering drawings while laying a solid foundation for intelligent dimensioning automation.
## 3 Background Work
### ECDSA Signature Scheme
#### 3.3.1 Key Generation Algorithm  
A point on the EC and an integer are used as the private key \(PrivK\) and public keys-\(PubK\), respectively, in the \"ECDSA\" key-pair: \(PubK=PrivK*G\).  
* \(PrivK\) is a \([0\ldots n-1]\) random integer.
* \(PubK\in EC\) point multiplication used to calculate the EC--\(PubK=PrivK*G\).
## 3 State-of-the-Art in Warning Systems Based on Radio Frequency Technologies
An OpenWrt device captured all WiFi frames from the defined area of interest (i.e., not only probe request frames sent by mobile devices, but all existing frames). To do so, they employed the promiscuous listening mode. Each MAC address was associated with a unique identifier (ID). Other information such as RSS was also stored. An RSS threshold was set to avoid observers that did not participate in the event but were temporarily within the region of interest. The number of people was obtained simply by eliminating those IDs with RSS values that were below the set threshold and by adding the remaining unique IDs. The robustness of this system was assessed in an indoor scenario. The accuracy achieved was 83% compared with visual accuracy. Nevertheless, the proposal presents some limitations. First, privacy concerns arise due to the use and storing of unique information from mobile devices. Second, the crowd estimation is obtained after the end of the event. In other words,it is not useful for real-time estimations. As stated by the authors, further improvement is needed, for instance, it is not clear whether it could be used with a moving crowd since a RSS threshold should be defined in advanced, among other concerns. Following the same approach, a crowd estimation system based on capturing the probe request frames from the users' mobile devices was proposed in [48]. The most remarkable particularity of this proposal is that it was designed and tested on a bus, thus providing a novel scenario with interesting insights for intelligent transportation systems (ITS). The algorithm only used timestamps and RSS measurements for its operation, leaving the use of other available information from the probe request frames (e.g., MAC, SSID, WiFi card, driver, channel frequency, etc.) for future work. Each bus was equipped with a scanner node, the goal of which was to gather the probe request frames. All captured frames were then sent to a central server that processed them. Two intervals were defined, namely, the active interval and present interval. The present interval was the time that elapsed since the first probe request frame from device A was received until device A was considered to be within the bus. Active interval was the maximum allowed time between consecutive probe request frames from the same device. By using these two intervals and a RSS threshold, the system was expected to estimate the number of passengers on the bus. The values for these parameters were heuristically obtained based on two bus trips with only one scanner node and without implementing communications with the central server. Although the proposal was simple, the accuracy was very low, given its very incipient state.
## 3 Result and Discussion
### Relation between Parameters  
#### 3.5.1 Relation between Porosity and Permeability  
Figure 7 shows the relationship between porosity and permeability of sandstone immersed in acid solution under different F-T cycles. As seen from Figure 7, porosity and permeability are exponentially correlated for sandstone immersed in solution with pH equal to 2.8, 4.2, 5.6 and 7.0. They show a strong relation with correlation coefficients of 0.967, 0.993, 0.930 and 0.965. When the porosity of sandstone is small, pores of different sizes, especially macro pores, are relatively less, and the connectivity of pores is low, while permeability stays at low level. As porosity increases, micro pores nearby combine and expand to meso pores and macro pores. Macro pores are relatively more frequent, the connectivity of pores are high, and permeability increases rapidly. Therefore, permeability increases exponentially with porosity growth.
## 2 Models about Joint with Clearance  
### Model of Kinematic Pairs with Clearance  
In the case of rotating joints with clearance, two kinematic constraints are removed, and two degrees of freedom are introduced. The forces working on the shaft pin and sleeve control the dynamics of joints including contact-impact processes. Consequently, there is a requirement for accurate modelling of the contact-impact process in the joint clearance to combine the clearance model with a kinetic model [31; 32; 33; 34].  
A rotating joint with clearance is shown in Figure 1a and the radial clearance is defined as follows:  
\[c=R_{B}-R_{I} \tag{1}\]  
where \(R_{B}\) and \(R_{J}\) are the radius of the shaft pin and sleeve, respectively.  
Figure 1b, \(O_{i}\) and \(O_{j}\) indicate the centers of the bearing and journal. \(r_{i}^{o}\) and \(r_{j}^{o}\) are the position vectors of the shaft pin and sleeve in the overall inertia coordinates, respectively. Thus, the clearance vector in Figure 1b is represented as follows:  
\[\mathbf{e}_{ij}=\mathbf{r}_{i}^{o}-\mathbf{r}_{i}^{o} \tag{2}\]  
The eccentricity of the clearance vector can be expressed as:  
\[e_{ij}=\sqrt{e_{x}^{2}-e_{y}^{2}} \tag{3}\]  
The unit normal vector where the contact between the shaft pin and sleeve occurs at the clearance is expressed as  
\[n=\frac{e_{ij}}{\sqrt{e_{x}^{2}-e_{y}^{2}}} \tag{4}\]  
\(\delta\) can be used to determine whether the shaft pin and sleeve are in contact, the movement contact condition between the shaft pin and sleeve can be given by Equation (5):  
\[\delta=\left\{\begin{array}{l}e_{ij}-c>0\;(\text{with contact})\\ e_{ij}-c=0\;(\text{without contact})\\ e_{ij}-c<0\;(\text{detachment})\end{array}\right. \tag{5}\]  
Figure 1: The relationship between shaft pin and sleeve: (**a**) Rotating pair with clearance; (**b**) Contact between shaft pin and sleeve.
# A Model-Based Investigation of the Performance Robustness of the Deflector Jet Servo Valve
## 5 Results of Model Validation  
### Description of Internal Flow Distribution  
As noted above, due to the tiny structure of the hydraulic amplifier, compared with experiments, the numerical method is a more effective and realizable method to obtain the internal flow information. Based on the three-dimensional numerical model, the velocity distribution inside the deflector is verified. The velocity distributions in the deflector can be calculated according to Equation (7), with the relevant simulation results displayed in Figure 14.  
Compare the velocity distributions of theoretical calculation and the simulation at four locations in the deflector (Figure 15). Overall, the calculation remains consistent with the data extracted from the simulation, as depicted in Figure 16. Yet, the theoretical potential core is wider; this results from Equation (4), the formula of the potential core length, not being absolutely accurate in this situation, since the initial jet velocity is relatively susceptible to the wall friction at such a high flow rate and is not uniform in practice [16].  
Figure 14: Velocity distribution in the mean section.  
Figure 15: Locations for comparisons in the mean section of the deflector.  
Meanwhile, the location of the stagnation line in the simulation is measured in Figure 14. The comparison made in Table 4 shows that the theoretical model can give an accurate location of the stagnation line. It means that the model can properly reflect the flow pattern in the deflector and explain the mechanism of stagnation forming.  
As regards the deflector jet, a comparative study of the impact pressure on the shunt wedge is carried out in this research. As represented in Figure 17, the overall consistency of the calculation and the simulation is available; however, influenced by the receiver opening, the simulation curve is deformed at the edge of the shunt wedge.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Coordinate** & **Theoretical Calculation** & **Simulation** \\ \hline \(x_{r}\) & 0.695 mm & 0.697 mm \\ \(y_{r}\) & 0.114 mm & 0.113 mm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of stagnation line locations.  
Figure 16: Comparisons of velocity distributions in deflector. (**a**) x = 0.147 mm. (**b**) x = 0.245 mm. (c) x = 0.413 mm. (**d**) x = 0.485 mm.  
Figure 17: Comparison of impact pressures on the shunt wedge.
# A Power-Line Communication System Governed by Loop Resonance for Photovoltaic Plant Monitoring
## 2 Materials and Methods
### Loop Resonance  
A PLC system dictated by loop resonance, becomes attributed with the observed tendency of one-wavelength loop resonant antennas, to maintain high levels of voltage/current signal amplitudes along each point of its full length. This allows for consistent carrier power across each point along the loop between the transmitter and receiver. The loop when tuned presents a high Q-factor at resonance which allows itself to act as a narrowband filter to filter out-of-band signal noise around its resonance frequency.  
In order to implement our ring-shaped communications signal path, we propose to install a bypass capacitor across the terminals of the combiner box which closes the series PV string line in a loop for AC signals. Our previous work [16] illustrates the theoretical approach derived for loop resonance communications and presents the implementation of the solution towards the problem of dynamically retuning the loop, when facing inevitable changes that could affect the loop resonance condition.  
In addition, we have designed low-cost communication electronics connected across each solar module leads with a series coil that blocks the AC signal from flowing across the module as shown in Figure 1. This will avoid the possibility of a very low impedance path through the module (depending on its operating point) that would short circuit the communication signal. This low impedance path will occur for a module working in the right section of the I-V curve, where the dynamic impedance is effectively very low. Capacitive coupling is used to inject the communication signal into the loop from the designed transceiver.
## 3 The Clustering Protocol
### AGNES Algorithm  
The AGNES (AGglomerative NESting) [30] algorithm is a hierarchical clustering algorithm. First, several objects are input, each one constitutes an initial cluster by itself. Then the two clusters with the shortest distance are continuously merged into one cluster until the number of clusters obtained reaches the number of clusters \(k\) satisfying the termination condition. Finally, the resulting \(k\) clusters are the target clusters of our algorithm.  
In this algorithm, each cluster equals a sample set, and the merger between clusters equals the merger between sets. The merging standard is the distance between the two clusters, which usually assumes three forms: (1) the longest distance, (2) the shortest distance, and (3) the average distance. For example, given two clusters (\(C_{i}\) and \(C_{j}\)), the distance between the two clusters can be obtained from the following three equations:  
The longest distance:  
\[D_{\rm max}(C_{i},C_{j})=\max_{p\in C_{i}\
eq C_{j}}|p-q| \tag{23}\]  
The shortest distance:  
\[D_{\rm min}(C_{i},C_{j})=\min_{p\in C_{i}\
eq C_{j}}|p-q| \tag{24}\]  
Figure 5: Flowchart of the clustering protocol, including the determination of \(k\), cluster setup, cluster heads (CHs) election, and data transmission.  
The average distance:  
\[D_{avg}(C_{i},C_{j})=\frac{1}{|C_{i}||C_{j}|}\sum_{p\in C_{i}}\sum_{q\in C_{j}}|p-q| \tag{25}\]
# Resilient Formation Reconfiguration for Leader-Follower Multi-UAVs
## 4 Construction of the Hierarchical BRB Performance Assessment Model for UAVs
### Basics on BRB  
As mentioned in Section 1, BRB is an optimizable expert system with several belief rules as the knowledge base and the algorithm based on the evidential reasoning (ER) rule as the inference machine. In general, the \(k\)th belief rule, \(k=1,\ldots,L\), is of the following structure:  
\[Rule_{k}:\] \[If\ X_{1}\ is\ A_{k,1}\wedge\cdots\wedge X_{F}\ is\ A_{k,F},\] \[Then\ \{(D_{1},\beta_{k,1}),\ldots,(D_{p},\beta_{k,P})\}, \tag{7}\] \[With\ rule\ weight\ \theta_{k},\ antecedent\ attribute\ weights\ \delta_{1},\ldots,\delta_{F}.\]  
Therein, \(X_{f},f=1,\ldots,F\), is the antecedent attribute. \(A_{k,f}\in A_{f},k=1,\ldots,L\), \(f=1,\ldots,F\), is the referential value of \(X_{f}\) in the \(k\)th rule while \(A_{f},f=1,\ldots,F\), is the referential value set of \(X_{f}\). Note that when \(f,f=1,2\) is determined, \(A_{k,f},k=1,\ldots,L\) can be the same. Denote \(f_{f}\) the size of \(A_{f}\), that is, \(f_{f}=|A_{f}|,f=1,\ldots,F\). The amount of belief rules \(L=\prod\limits_{f=1}^{F}f_{f}\). \(D_{p},p=1,\ldots,P\), is the consequent attribute and \(\beta_{k,p},k=1,\ldots,L\), \(p=1,\ldots,P\), is the belief degree of \(D_{p}\) in the \(k\)th rule. In terms of the \(k\)th belief rule, \(k=1,\ldots,L\), the  
Figure 1: The communication topology of the leader–follower multi-UAVs.  
summation of the belief degree of the consequent attribute satisfy \(\sum\limits_{p=1}^{P}\beta_{k,p}\leq 1\). The \(k\)th belief rule is complete in the sense that  
\[\sum\limits_{p=1}^{P}\beta_{k,p}=1,k=1,\ldots,L, \tag{8}\]  
otherwise the belief rule is incomplete. Note that the completeness of belief rules is not mandatory, depending on the degree of certainty of expert knowledge. For more basics about BRB, refer to [37] and references therein.
Sensors_**2012**, _12_, 297-319; doi:10.3390/s120100297  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**Modulation Techniques for Biomedical Implanted Devices and Their Challenges**  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi Selangor, Malaysia;  
E-Mails: [EMAIL_ADDRESS] (S.M.A.); [EMAIL_ADDRESS] (S.A.S.); [EMAIL_ADDRESS] (A.H.)  
* Authors to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +60-3-8921-7014; Fax: +60-3-8921-6146.  
_Received: 22 November 2011; in revised form: 15 December 2011 / Accepted: 15 December 2011 / Published: 28 December 2011_  
**Abstract:** Implanted medical devices are very important electronic devices because of their usefulness in monitoring and diagnosis, safety and comfort for patients. Since 1950s, remarkable efforts have been undertaken for the development of bio-medical implanted and wireless telemetry bio-devices. Issues such as design of suitable modulation methods, use of power and monitoring devices, transfer energy from external to internal parts with high efficiency and high data rates and low power consumption all play an important role in the development of implantable devices. This paper provides a comprehensive survey on various modulation and demodulation techniques such as amplitude shift keying (ASK), frequency shift keying (FSK) and phase shift keying (PSK) of the existing wireless implanted devices. The details of specifications, including carrier frequency, CMOS size, data rate, power consumption and supply, chip area and application of the various modulation schemes of the implanted devices are investigated and summarized in the tables along with the corresponding key references. Current challenges and problems of the typical modulation applications of these technologies are illustrated with a brief suggestions and discussion for the progress of implanted device research in the future. It is observed that the prime requisites for the good quality of the implanted devices and their reliability are the energy transformation, data rate, CMOS size, power consumption and operation frequency. This review will hopefully lead to increasing efforts towards the development of low powered, high efficient, high data rate and reliable implanted devices.  
**Keywords:** modulation techniques; bio-medical/wireless implanted devices; power amplifiers; inductive coupling link
# Causal Inference for Modality Debiasing in Multimodal Emotion Recognition
## 5 Experiments
### Datasets and Evaluation Metrics  
We conducted experiments on two widely used datasets: IEMOCAP [31] and CMUMOSEI [32]. Refer to Table 1 for a detailed summary of the statistics and evaluation metrics for each dataset.  
* **IEMOCAP**: The Interactive Emotional Dyadic Motion Capture (IEMOCAP [31]) dataset contains recordings of 10 actors engaged in conversations, capturing text, audio, and video modalities. It consists of 4453 video clip samples, with pre-split data including 2717 training samples, 798 validation samples, and 938 testing samples, as outlined in previous works [20; 23]. The dataset comprises nine emotions (anger, happiness, sadness, neutrality, excitement, frustration, fear, surprise, and others). Following previous works [20; 23; 33], four emotions (happiness, sadness, anger, and neutrality) were chosen for evaluation. Moreover, unlike the CMU-MOSEI [32] dataset, the IEMOCAP [31] dataset adapts a multi-label configuration, allowing video clips to simultaneously exhibit multiple emotions, such as both anger and sadness. As in previous works [20; 23], we evaluate the predicted values for each emotional label using binary classification accuracy and F1 score.
* **CMU-MOSEI**: The CMU-MOSEI [32] dataset consists of video clips of movie reviews sourced from YouTube, providing text, audio, and video modality data for each instance. It contains 22,777 video clips, with pre-split data comprising 16,265 training samples, 1869 validation samples, and 938 testing samples, as outlined in previous work [20]. The dataset is assigned sentiment scores ranging from \(-3\) (strongly negative) to \(+3\) (strongly positive). Previous works [20; 23; 24] have used 7-class accuracy (Acc7) to evaluate model performance.
## 5 Discussion  
Our results show that the effect of adding GAN-generated synthetic images to training data on accuracy strongly depends on both the number of original images and the number of synthetic images to be added. For instance, adding 250-750 GAN-generated images to the training data of 1000 images significantly increased the classification accuracy of the modified U-Net (\(p\)-values \(\leq\) 0.0025). Still, using GAN-generated images did not often produce any statistical significance, and adding 10 GAN-generated images to 500 original images significantly decreased the classification accuracy (\(p\)-value: 0.03). While this is partially caused by the multiple comparisons problem, as repeating a statistical test several times increases the likelihood of incorrectly rejecting a true null hypothesis, our results still suggest that the number of GAN-generated images and, in particular, their ratio with the original images should be considered carefully.  
We expected the 1000 GAN-generated images to give a high mean accuracy and be statistically significantly different from the original images. Still, they could have performed better compared to 250, 500, and 750 GAN-generated images. The maximum mean accuracy of 88% was achieved with 500 GAN-generated images. However, the model performed well when the dataset contained less than 500 GAN images against a certain number of original images.  
This model is adaptive to publicly available data and tested on random MRI images. The model has yet to be tested on specifications like T1, T2, Flair, Contrast, and functional MRI or scans with different magnetic field strengths. Likewise, the model has yet to be tested on different image dimensions (i.e., 512 \(\times\) 512, 1024 \(\times\) 1024 pixel image). However, the framework can be adjusted according to the dataset's specifications to improve the model's performance and accuracy. This model generates images with the real-world benefit of using them as oversampling for research purposes.