esa-sceva/satcom-chunk-collection huggingface dataset:
# Fingerprinting Acoustic Localization Indoor Based on Cluster Analysis and Iterative Interpolation
## 4 Experimental Validation
### Analysis of the Two-Level RPs Matching Method  
According to Section 3.3, more subsets in the positioning database partitioned means more online positioning efficient improvement by two-level RPs matching method. However, the same as the coordinate space partition method, when the subsets reaches a certain number, distinguishing between sub-databases partitioned by feature clustering partition method is no longer obvious. Then, the adjacent RPs may be divided into different sub-databases, which will cause RP matching error.  
To investigate the effect of the division number on location accuracy, we explored the localization results with division number from 1 to 6, where 1 means the the matching process without partition, that is, global linear matching localization.  
As shown in Figure 6, when division number increased from 2 to 4, positioning accuracy slightly improved compared with global linear matching. That is mainly because, according to the clustering results, the outlier points with large measurement errors in the sub-database can be eliminated by outlier test method. However, when the number of sub-databases increased to 5, the localization result began to deteriorate significantly. Moreover, when the division number increased to 6, the average error exceeded 0.18 m, while the maximum error reached 0.2780 m and 61.5% of the test point positioning accuracy could not meet the 0.20 m positioning requirements.  
Figure 5: The fingerprint-based acoustic localization system and experiment scene.  
Figure 6: The effect of sub-database number on positioning.  
To compare the positioning effect between the coordinate space partition method and feature clustering partition method, the average matching amount, matching time and the average positioning error were considered when the division number is 4. As shown in Table 1, the average matching amount and average matching time of the two different partition methods in the RPs matching process are basically the same, and their online positioning efficiencies greatly improved compared with the global linear matching method. Among them, the feature clustering partition method can reduce the average matching amount and the average matching time to 30.13% and 29.89% of the global linear matching method, respectively, while the coordinate space partition method was 30.97% and 30.13%. In comparison to the positioning accuracy, the positioning error of 0.0813 m based on feature clustering partition method is significantly superior to the 0.1214 m based on the coordinate partitioning partition method, and the positioning accuracy is improved by 13.97% compared with the traditional linear matching method.
# Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information
When considering multiple weather variables, such as the wind direction _WD_, wind speed _WS_, and temperature \(T\), the data scale increases exponentially. The effective integration of these large-scale datasets into TBO systems presents a major current challenge in the field. Addressing this challenge is crucial for realizing the full potential of weather-integrated TBOs and improving the overall flight efficiency and safety. Some institutions are already providing 4D-trajectory (4 DT) services. For instance, the UK MET Office offers a 4 DT application programming interface (API) service that utilizes 10 km grid global model data to provide high-resolution weather information [23]. The 4 DT API service developed in this study has two significant differences from existing services:  
1. A multi-model-based trajectory service that combines the strengths of various weather models to provide more accurate and reliable weather information;Buffer zone information provision, which offers additional information around the aircraft trajectory, allowing for flexible responses to unexpected situations. In particular, by implementing an on/off function for the buffer zone, we have configured the system so that the buffer zone provides spatial awareness around the aircraft trajectory, allowing for flexible responses to unexpected weather conditions, which can enhance decision making in flight operations. These features enable our 4 DT API service to provide a more comprehensive and flexible approach than existing services. This service rapidly extracts and delivers weather elements from numerical models and provides real-time weather data interpolation tailored to the dynamic requirements of the aircraft trajectory. Such capabilities ensure that ATM systems can access the most relevant and current weather information, crucial for effective decision making and strategic flight planning. Moreover, the reliability and accuracy of the 4 DT API service are rigorously validated through a comparison with real-time observational data from the ADS-B system. This paper discusses the technical implementation of this advanced 4 DT API service, its integration into existing ATM frameworks, and its anticipated impact on air traffic operations. The data sources, study area, and technical framework for the 4 DT-Wx prototype system are presented in Section 2. The performance evaluation of the system, including the accuracy of meteorological data processing and the efficiency of the distributed processing system, is presented in Section 3. The results are interpreted in Section 4, highlighting the system's practical applicability, benefits, and potential areas for further research. Finally, Section 5 summarizes the key findings of the study and their implications for future ATM and TBO implementations.
## 4 Experiments and Results  
The functionality and principle check of the experimental system was carried out through experiments in two stages, part A and part B.  
Part A. The first stage in the test campaign comprised performing the tests without the energy source (radiation) concentrated by the mirror cold testing in which the system was a cold gas propulsion system.  
Part B. The second stage comprised the hot gas testing in which the radiation source which simulates the sun was activated and its energy was concentrated with the mirror.  
Due to the fact that the measurements were done in atmosphere, the maximum real temperature of the service tank which was achieved was 125 \({}^{\circ}\)C, the maximum allowed for the operation of the solenoid valve. For this reason, the real case was only simulated for the temperature of 450 \({}^{\circ}\)C. The temperature of 450 \({}^{\circ}\)C is the maximum temperature that can be obtained using the available surface of the mirror, considering that the sun rays are uniformly concentrated on the actual surface of the buffer tank. According to the mentioned hypothesis, the absence of radiation is added, so that by applying the Stefan-Boltzmann law, the mentioned value is obtained:  
\[\varnothing\ =\ \varepsilon\sigma T^{4}\ =>\ T\ =\ \sqrt{\frac{I\ \ S_{mirror}}{\sigma\ S_{tank}}}\ =\ 469\ ^{\circ}\text{C} \tag{2}\]  
where \(\varnothing\) represents the radiant flux, \(I=1361\) W/m\({}^{2}\)--solar irradiance, Stefan-Boltsmann constant \(\sigma=5.67\times 10^{(-8)}\) W/m\({}^{2}\) K\({}^{(-4)}\), \(\varepsilon\)--emissivity, mirror surface \(S_{mirror}=0.2527\) m\({}^{2}\), buffer tank surface \(S_{tank}=0.1465\) m\({}^{2}\) (corresponding to \(\Phi=80\) mm and a length of 400 mm). Decreasing the tank surface area (to \(\Phi=20\) mm, hence a total surface of 0.0253 m\({}^{2}\)) will lead to an ideal temperature of 696 \({}^{\circ}\)C (without radiation losses). Radiation losses \(q\) result according to the heat radiation law:  
\[q\ =\ \varepsilon\sigma\big{(}T_{h}^{4}-T_{c}^{4}\big{)}S_{tank} \tag{3}\]  
where \(T_{h}\)--hot body absolute temperature, and \(T_{c}\)--cold surroundings absolute temperature.
# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 2 Internet of Vehicles and Collaborative Intelligence
### Internet of Vehicles
Some features of IoV are as follows:  
* **Information gathering:** Data are collected through the use of various sensors (pollution detection sensors, cameras, road sensors, etc.) that provide drivers with enough information to react to environmental changes in an adequate and effective manner. * **High mobility:** Vehicular networks (VNs) have highly mobile nodes, related to the speed of the car, and nodes can be added and disappear in a short period of time, resulting in repeated topology changes. * **Type of information transmitted:** Messages are transmitted according to the level of participation in the event triggered. Messages can be sent from an initiator to a destination (unicast), or from an initiator to a specific cluster using multi-hop communication (multicast). Vehicles can also send messages to all other vehicles via broadcast. * **Processing big data:** A large number of VNs generate large amounts of data, and vehicular networks use cloud computing to process and store big data. * **Internet facilities:** The IoV has the unique feature of accessing the Internet. The connected vehicles can benefit from this huge network.
## 2 Related Work
### False Information Detection Based on Content Features  
Content-based detection methods focus on extracting text features, such as lexical features, text length, and grammatical features, to distinguish between false information and genuine information based on content. Early studies utilized classification algorithms, including Bayes [15], Support Vector Machine (SVM) [16], and random forest [17], to classify false information based on content representation. [PERSON] et al. [18] used an optimized genetic algorithm to solve the problem of fake news detection and compared the performance of several common classifiers, including support vector machine, Naive Bayes, random forest, and logistic regression, to test their effectiveness in identifying fake news in different datasets.  
Deep learning approaches facilitate the automated learning of high-level data characteristics and enable more accurate identification of patterns and correlations within the data, establishing themselves as the predominant detection methodology. [PERSON] et al. [19] proposed an innovative hybrid deep learning framework that integrates convolutional neural networks (CNNs) with recurrent neural networks (RNNs) for the classification of deceptive information. [PERSON] et al. [20] utilized foundational attributes from multiple categories, including user, content, and lexical features, and developed a hybrid deep learning model that combines Bidirectional LSTM (BiLSTM) with Multilayer Perceptron (MLP) frameworks to improve detection accuracy. [PERSON] et al. [21] employed BERT and CNN to extract textual information from user comments, followed by LSTM to further extract sentiment features, which were then fused with content features to form false information representations. [PERSON] et al. [22] utilized the pre-trained BERT model to transform raw Weibo text into vector representations, which were then fed into a recurrent convolutional neural network for the identification of false information. [PERSON] et al. [23] proposed a detection method based on graph convolutional networks integrated with an attention mechanism, which incorporates the forwarding relationship features and semantic characteristics of texts within comments. [PERSON] et al. [24] pioneered the integration of the attention mechanism with a residual network for rumor detection. First, the residual network, enhanced by the attention mechanism, captured long-range dependencies within the text. Subsequently, CNN was used to select important components and local features, achieving notable results in false information detection and early detection tasks. Furthermore, to improve the effectiveness of disinformation detection, [PERSON] et al. [25] and [PERSON] et al. [26] employed dynamic feature vectors and large language modeling approaches, respectively, to capture semantic features. However, malicious actors may employ deepfake technology to mimic the writing techniques and expression styles of real events, thereby evading detection. This renders methods relying solely on content features ineffective in capturing relevant features for false information recognition [27].
## 1 Introduction
The contribution of the present work is in investigating the performance of the SBFEM approach in capturing the wave dynamic when isoparametric and high-order spectral elements are used in the discretization of the plate cross-sections. The accuracy in the estimation of the dispersion curves and the wave structure of the wave is evaluated by the comparison between the obtained results using the SBFEM approach (isoparametric and spectral) with the analytical solutions of characteristic equations for symmetric and antisymmetric modes [37]. We also compare the computational time with the number of elements, or the order of interpolation for the spectral elements in functions of the thickness. We present numerical results and infer the performance of the SBFEM method when using spectral elements with Gauss-Lobatto-Legendre nodal distribution. Our results suggest that the method could be useful in terms of performance and effectivity when estimating the dispersion curves for more complex applications associated with different materials,  
Figure 1: Schematic representation of the plate and SBFEM model. such as composites and multilayers. Additionally, there is the possibility of considering the effect of viscoelastic properties or embedded materials. However, their performances have not been reported in the literature, and these materials have not been included in the present work.
# Speech Communication System Based on Piezoelectric Electret Mechanical Antenna
## 2 Materials and Methods
### Speech Communication System Design
#### 2.2.3 Software System  
The overarching architecture of the software system is illustrated in Figure 5. The software system, like the hardware system, is divided into transmitter and receiver components. The software system incorporates a suite of several data processing and control functions, including message input and output, encoding and decoding, and controlling the underlying communication devices to complete actual communication work. The system uses a modular structure that allows for changes in each individual module and covers the implementation of underlying communication devices. Hence, function decoupling and strong scalability are well realized. The following section will delve into the system architecture of both the transmitter and receiver.  
The message input module processes the speech recognition function, recognizes the speech content, and converts it into data. The message processing module processes the transmitted messages and generates the transmitting sequence. To extend the message processing function, the data processing function can be extended by inheriting and rewriting the extension points reserved for this part. The transmitter hardware control module controls the AFG to modulate and transmit the signals based on the transmitting parameters and the transmitting sequence. The hardware control of the AFG was achieved by the NI-VISA library, and the software system packaged the control commands well. Hence, the control commands can be easily entered via the Universal Serial Bus (USB).  
In communication channels, the receiver hardware control module implements signal sniffing. Upon detecting a transmission within the channel, this module receives and demodulates the signals and judges the bit value based on the reference voltage. The message inverse processing module then deciphers the received message from its binary representation, employing user-defined message processing functions such as decompression and decryption as necessary. Following that, the message output module implements the received message's output, such as by displaying messages on the screen or broadcasting messages via voice broadcast.
_Applied_  
_sciences_  
_Article_  
**Sewage Sludge Hydrochar: An Option for Removal of Methylene Blue from Wastewater**  
**[PERSON] 1\({}^{\copyright}\), [PERSON] 2\({}^{\copyright}\), [PERSON] 1, [PERSON] 1 and [PERSON] 1,*\({}^{\copyright}\)**  
Received: 20 April 2020; Accepted: 12 May 2020; Published: 16 May 2020  
**Featured Application: Producing a material--a product--from sewage sludge--a waste--resulting from wastewater treatment plants (WWTPs) is a possible sustainable solution to reduce the amount of sewage sludge to be disposed of. Thus, sewage sludge hydrochar (simply produced as detailed in the paper) having good adsorption capabilities could be used, for instance, in municipal and industrial WWTPs for water remediation.**  
**Abstract:** Municipal sewage sludge was subjected to a hydrothermal carbonization (HTC) process for developing a hydrochar with high adsorption capacity for water remediation in terms of dye removal. Three hydrochars were produced from municipal sewage sludge by performing HTC at 190, 220 and 250 \({}^{\circ}\)C, with a 3 h reaction time. Moreover, a portion of each hydrochar was subjected to a post-treatment with KOH in order to increase the adsorption capacity. Physicochemical properties of sludge samples, raw hydrochars and KOH-modified hydrocarbons were measured and batch adsorption studies were performed using methylene blue (MB) as a reference dye. Data revealed that both raw and modified hydrochars reached good MB removal efficiency for solutions with low MB concentrations; on the contrary, MB in high concentration solutions was efficiently removed only by modified hydrochars. Interestingly, the KOH treatment greatly improved the MB adsorption rate; the modified hydrochars were capable of capturing above 95% of the initial MB amount in less than 15 min. The physicochemical characterization indicates that alkali modification caused a change in the hydrochar surface making it more chemically homogeneous, which is particularly evident for the 250 \({}^{\circ}\)C hydrochar. Thus, the adsorption process can be regarded as a complex result of various phenomena, including physi- and chemi-sorption, acid-base and redox equilibria.  
**Keywords:** hydrothermal carbonization; HTC; sewage sludge; hydrochar; methylene blue; adsorption; water remediation; value-added product; waste-to-products
# Performance Analysis of Dual-Hop AF Relaying with Non-Linear/Linear Energy Harvesting
## 5 Throughput Analysis
### Outage Probability for the Linear EH Model  
The outage probability of the linear EH model is calculated as  
\[P_{out}^{L}=\int_{0}^{\infty}F_{\gamma}(\gamma|W)f_{W}(w)\mathrm{d}w, \tag{47}\]  
where  
\[F_{\gamma}(\gamma|W)=\int_{0}^{\infty}F_{\gamma}(\gamma|Z,W)f_{Z}(z)\mathrm{d}z \tag{48}\]  
and \(F_{\gamma}(\gamma|Z,W)\) is given in (10). Substituting (21) and (10) and after simplification, (48) is obtained as  
\[F_{\gamma}(\gamma|W)=S_{11}-S_{12}, \tag{49}\]  
where  
\[S_{11}=\int_{0}^{\infty}f_{Z}(z)\mathrm{d}z=1 \tag{50}\]  
and  
\[S_{12}= D\exp(-\frac{\gamma}{w\overline{y}^{\prime}})\int_{0}^{\infty}z^{ m_{z}-1}\exp(-z\phi_{2}-\frac{\gamma}{z\overline{x}^{\prime}})\mathrm{d}z\] \[= 2D\exp(-\frac{\gamma}{w\overline{y}^{\prime}})(\frac{\gamma}{x^{ \prime}\phi_{2}})^{0.5m_{z}}\mathrm{K}_{m_{z}}(2\sqrt{\frac{\gamma\phi_{2}}{ \overline{x}^{\prime}}}). \tag{51}\]  
Here, (51) is obtained by using ([50], eq. 3.471-9). Substituting (49) in (47) and simplifying, we have  
\[P_{out}^{L}=S_{21}-S_{22}, \tag{52}\]  
where  
\[S_{21}=S_{11}\int_{0}^{\infty}f_{W}(w)\mathrm{d}w=S_{11}=1 \tag{53}\]  
and  
\[S_{22}=2 DI(\frac{\gamma}{\overline{x}^{\prime}\phi_{2}})^{0.5m_{z}}\mathrm{K} _{m_{z}}(2\sqrt{\frac{\gamma\phi_{2}}{\overline{x}^{\prime}}})\int_{0}^{ \infty}w^{m_{w}-1}\exp(-w\phi_{w}-\frac{\gamma}{w\overline{y}^{\prime}}) \mathrm{d}w. \tag{54}\]Using ([50], eq. 3.471-9), after simplification, the CDF of the SNR at node D, namely, the outage probability is calculated as  
\[P_{out}^{L} =1-4 DI(\frac{1}{\bar{x}^{\prime}\phi_{z}})^{0.5m_{z}}(\frac{1}{\bar{ y}^{\prime}\phi_{w}})^{0.5m_{w}}\gamma^{0.5(m_{z}+m_{w})}\] \[\times\mathrm{K}_{m_{z}}(2\sqrt{\frac{\gamma\phi_{z}}{\bar{x}^{ \prime}}})\mathrm{K}_{m_{w}}(2\sqrt{\frac{\gamma\phi_{w}}{\bar{y}^{\prime}}}). \tag{55}\]  
Finally, the throughput of the linear EH model is obtained by substituting (55) in (34).
# IPN HandS: Efficient Annotation Tool and Dataset for Skeleton-Based Hand Gesture Recognition
## 7 Conclusions  
In this work, we introduced IPN HandS, a refined hand gesture dataset that provides frame-level hand skeleton annotations for continuous video sequences, supported by a dedicated annotation tool. To evaluate the impact of annotation quality, we trained a simple LSTM-based recognition model on different annotation sources and showed that models trained on IPN HandS significantly outperformed those trained on MediaPipe-based annotations. Our refined annotations significantly improved overall accuracy and recall, especially for gesture classes with subtle hand pose variations. These findings emphasize the importance of spatially and temporally consistent annotations in enhancing skeleton-based hand gesture recognition. On the other hand, the IPN HandS annotation tool enhances the annotation process by incorporating automatic initialization, manual refinement, and interpolation. This advancement enables the large-scale labeling of the entire dataset at a significantly faster rate compared to traditional interfaces.  
Together, the dataset and tool offer practical and scalable resources for advancing research in hand gesture recognition and related domains. We will publicly release both the IPN HandS dataset and the annotation tool to support reproducible research and enable broader adoption in the community. In future work, we aim to explore more advanced recognition architectures that fully utilize the structure of IPN HandS annotations. Additionally, we plan to expand the annotation tool to support more complex tasks, such as sign language recognition.  
Conceptualization, G.B.-G. and H.T.; methodology, G.B.-G.; software, G.B.-G.; validation, J.O.-M. and G.S.-P.; formal analysis, G.S.-P.; investigation, G.B.-G.; data curation, J.O.-M.; writing--original draft preparation, G.B.-G.; writing--review and editing, G.B.-G. and J.O.-M.; visualization, G.B.-G.; supervision, G.S.-P. and H.T.; project administration, H.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a Research Grant (S) from the Tateisi Science and Technology Foundation.  
Not applicable.  
The IPN HandS annotations and annotation tool introduced in this study will be made publicly available at [The IPN HandS Dataset] [14] under a Creative Commons Attribution 4.0 License. The original IPN Hand dataset used in this work, including the video recordings, is already publicly available and documented in [5]. All annotations provided in this work are derived from that dataset.  
The authors declare no conflicts of interest.
# A Novel Low-Complexity and Parallel Algorithm for DCT IV Transform and Its GPU Implementation
## 4 Implementation
### Overview of CUDA Used Architectures
If the first two matrices have elements based on FP16 representation, for the Volta architecture (Table 3), the last matrix can have elements based on FP16 or FP32 representation. The subsequent architecture supports a better precision representation and brings novel approaches to improve computational performances. The Ampere architecture, based on the third generation of the Tensor Core, adds computation capabilities of BFloat16, TF32, and FP64 representation. In the Ampere architecture, other improvements exist, like the ability to manage sparse matrix mathematics more efficiently. So, CUDA units are designed to perform simple mathematical calculations efficiently, while Tensor units perform complex matrix calculations in the most efficient way possible.