esa-sceva/satcom-chunk-collection huggingface dataset:
# Korean Passori Vocal Note Transcription Using Attention-Based Segmentation and Viterbi Decoding
## 4 Experimental Results
### Frame-Level Transcription
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **VR** & **VFA** & **RPA** & **RCA** & **OA** \\ \hline One-layer SA + F & 68.05 & 10.53 & 59.91 & 61.38 & 78.74 \\ \hline Two-layer SA + F & 64.68 & 7.75 & 57.69 & 58.97 & 79.83 \\ \hline Three-layer SA + F & 59.72 & 8.94 & 52.73 & 54.60 & 76.86 \\ \hline One-layer SA + CE & 60.75 & 7.74 & 54.75 & 55.75 & 78.25 \\ \hline Two-layer SA + CE & 63.68 & **7.55** & 58.30 & 59.12 & 80.25 \\ \hline Three-layer SA + CE & 67.08 & 9.62 & 59.22 & 60.43 & 78.90 \\ \hline Channel attention + F & 65.84 & 9.02 & 60.56 & 61.71 & **80.33** \\ \hline Channel attention + CE & 65.73 & 8.77 & 59.40 & 60.63 & 79.87 \\ \hline DSM [35] & **88.4** & 48.7 & **72.0** & **74.8** & 66.2 \\ \hline Lu and Su’s [31] & 77.9 & 22.4 & 68.3 & 70.0 & 70.0 \\ \hline SegNet [30] & 73.7 & 13.3 & 65.5 & 68.9 & 79.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of various F0 estimation methods on the MedleyDB test data. visualized result from our method shows similar tracking performance in comparison with other methods. To visualize the F0 tracking in the spectrogram, we first extract the F0 values of the audio sample from our method as well as other pre-trained models. The fundamental frequency values extracted from each model are then overlaid into the spectrogram. This comprehensive visualization aids in assessing the accuracy and consistency of our method in tracking F0 variations throughout the audio sample. The cyan lines in all four plots in Figure 3 represent the fundamental frequency values estimated by the model, providing insights into the pitch dynamics of the Pansori performance. This visual analysis is crucial for evaluating the efficacy of our approach in comparison to other state-of-the-art pitch estimation techniques. These visualized results contribute to the evaluation and validation of our approach in the broader context of pitch estimation for traditional Pansori vocal performances. Figure 3: The tracking of F0 in one of the audio samples of Pansori. From top to bottom: pYin, PyWorld, Segnet, and channel attention + F (ours).
Metal oxides NPs are used as water treatment catalysts [27; 28]. In cosmetics, raw materials of solar cells have been reported [29]. Metal oxides nanoparticles, covering zinc oxide, titanium, cerium, and yttrium oxide NPs, have been extensively studied. Each nanoparticle has been shown to have unique features. The current study shows that Y\({}_{2}\)O\({}_{3}\) with rare-earth ions doped can also be used for solid-state lighting purposes [30]. Y\({}_{2}\)O\({}_{3}\) with a maximum of 9.71% rare earth is used in LEDs. The first demonstration of a high-spatial-resolution and multicolor imaging technique for observation of biological cells using cathodoluminescence (CL) from nanophosphors has been observed. CL is emission from materials irradiated by the accelerated electron beam. For CL imaging, Y\({}_{2}\)O\({}_{3}\):Tm, Y\({}_{2}\)O\({}_{3}\):Tb, Y\({}_{2}\)O\({}_{3}\):Eu were used as the phosphors. The spectral bandwidth of the phosphors was narrow enough to distinguish the types of the phosphors. Yttrium oxide doped with europium (Y\({}_{2}\)O\({}_{3}\):Eu) is a luminescent material with red-orange emission, excitation by electrons (cathodoluminescence). Rare-earth doped ceramic materials show efficient fluorescence under near-infrared (NIR) excitation. Current investigation shows that Y\({}_{2}\)O\({}_{3}\):Er particles and the surface modification of these particles with double layers of PAAc (acrylic acid) as an interfacing agent and acetal-PEG-b-PAMA led to successful NIR imaging nanoparticles. Grain size of these particles is found to be 10 nm to 200 nm, which are found to be good for near-infrared excitation. Among the mentioned, yttrium oxide NPs have shown to be the priority of several research efforts, which is attributable to their unmatched number of applications in diverse fields, including biomedicine. However, the available published resources on the Y\({}_{2}\)O\({}_{3}\) synthesis methods and its emerging biomedical applications are very scarce. Hence, the present review article highlights the methods of Y\({}_{2}\)O\({}_{3}\) synthesis and mode of action and discusses their biomedical applications in some detail.
## 3 Literature Review
### Smart Construction Monitoring Using Drones  
The widespread adoption of digital twin technology in construction project management ensures real-time synchronization between actual project development and virtual plans, through the use of drones, enabling monitoring at appropriate heights to capture clear images for project landmarks [22]. In the context of continuous technological advancement, the construction and building sector witnesses a radical shift in project monitoring methods and plan execution. Although traditional techniques continue to be used in project monitoring and execution, they often remain unable to adapt to the instant changes required at the last moment. In contrast, digital twin technology, relying on smart tools, isconsidered an innovative solution, providing accurate real-time information that reflects construction progress [23]. On the other hand, the smart monitoring system utilizes organized real-time data, collected using advanced tools such as drones and installed sensors like cameras (photo/video) and thermal imaging cameras, and infrared sensors. Using advanced software, this data are analyzed, and better planning and adjustments are made. Important applications of unmanned aerial vehicles (UAVs) in construction monitoring include the following [24]:  
1. Creating three-dimensional maps, which contribute to providing accurate data for creating three-dimensional models of the construction area. These data can be regularly updated and stored online for interactive viewing, making it easier for stakeholders to track progress and provide the latest information.
2. Aerial photography and three-dimensional surveys of projects, which facilitate providing accurate and comprehensive images and videos for clients, enabling them to effectively monitor developments and plan landscapes and interior design.
3. Monitoring progress in the construction process, which allows the determination of flight paths to provide visual and periodic reports to developers and stakeholders, enhancing communication and transparency.
4. Volumetric measurement using precise aerial survey techniques, enabling high-precision measurements with minimal disruption to daily site operations.  
These modern technologies demonstrate the importance of emerging technological means in the field of construction monitoring, contributing to enhancing work efficiency, and transparency, and achieving project goals effectively and sustainably, as shown in Table 1.
# Chromatographic Separation of Rare Earth Elements as MGDA Complexes on Anion Exchange Resins
## 3 Results and Discussion
### Sorption Behavior of REE
#### 3.2.2 Effect of the Acid Concentration on REE Separation  
The effect of HCl concentration on the retention of MGDA-REE complexes is shown in Figure 6. All REEs are eluted faster when the HCl concentration increases from 0.01 M to 0.05 M; additionally, the peaks become sharper. This results in higher peak concentrations for every REE. However, the peaks significantly overlap. Overlapping becomes more severe with the increasing acid concentration. Evidently, 0.05 M HCl is already too acidic to achieve good separation of the REEs. On the other hand, concentrations lower than 0.01 M are not suitable because the MGDA-REE complexes already have a high retention with 0.01 M HCl; it would cause long process cycle times and low productivity. For an isocratic process, the mobile phase of 0.01 M HCl with pH = 1.92 was deemed suitable even though the Eu profile spreads widely. For sharper elution and smaller retention of the MGDA-Eu complex, a pH buffer gradient could be used as the eluent.  
Figure 5: Effect of temperature on the retention of the MGDA–REE complexes shown as \(\Sigma\)[REE] (resin = IRA–410; flowrate = 2.98 BV/h; feed (#2) = 0.05 BV, eluent = 50 mM HCl).  
Figure 6: Effect of HCl concentration in the eluent on the retention behavior of REEs on IRA–410: (**a**) 10 mM HCl; (**b**) 50 mM HCl (flowrate = 2.98 BV/h; feed solution = #2; feed volume = 0.05 BV).
## 6 Implementation and Discussion of Results
### Ablation Study
#### 6.10.1 Effect of Using the VMD on the Price Prediction Phase  
Table 6 emphasizes that utilizing the VMD layer improved performance, which reached 80% improvement in some cases. This improvement is achieved because VMD can remove the noisy data and extract the hidden important signals from the original signal.
## 3 Results
### Effect of Tile Overlap  
The required number of tiles necessary to fully cover the region varies depending on the location of the target ROI. A case study for an ROI of approximately 51.2 km \(\times\) 51.2 km in Scotland for the week from the 3 May 2021 to the 9 May 2021 is analysed to give an indication of the processing times required by the framework to generate multi-modal ARD as a function of the required number of tiles for full coverage. Sentinel-2 products are available in pre-defined tiles of approximately 100 km \(\times\) 100 km area and 800 MB file size [45], with each tile assigned a unique identifier [64]. In this case study, three ROIs of equal area are defined, A, B and C, as shown in Figure 12, spanning one, two and four tiles, respectively, dependent on their location on the Sentinel-2 tile grid. Each Sentinel-2 product could require multiple Sentinel-1 products to generate paired ARD patches. The framework is executed for scene discovery, download and processing of Sentinel-1 and Sentinel-2 products. Both the VV and VH polarization bands are downloaded and processed for Sentinel-1, while, for Sentinel-2, all spectral bands in Level-2A, along with opaque cloud, cirrus cloud and scene classification masks for cloud shadow and medium- and high-probability thin cirrus, are downloaded and processed. The patch size is set to 256 \(\times\) 256 pixels.  
The framework is executed on a 10-physical-core CPU (Intel i9-10900X) Linux server with 126 GB RAM and 9 TB storage. The time required to produce multi-modal ARD for the three scenarios, i.e., A, B, C, is shown in Table 2. The selection time includes the time for querying products using the Sentinel-API and selecting Sentinel-1 and Sentinel-2 products using the scene discovery algorithm, as discussed in Section 2.2. The processing time includes the processing for Sentinel-1 and Sentinel-2 products, collocation using the SNAP GPT and the creation of patches, as described in Section 2.4. The total time is the aggregate of selection and processing times. The download time is not considered due to the variability of network connections and data provider. As shown in Table 2, the time for processing increases if the ROI falls within the boundaries of the Sentinel-2 tile grid, attributable to the increased number of tile pairs processed. However, the processing time does not scale linearly, due to the varying number of overlapping segments for each collocation.  
Figure 11: Sentinel-1 tile_SIA_IW_GRDH_1 SDV_20191012T063817_20191012T063842_029422_0358 AF_5701 Amplitude VH footprint. The footprint provided by Copernicus is highlighted in red, no-data layer is highlighted in yellow, buffered footprint is highlighted in green.
# DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks
## Appendix A DARAL Messages Type Appendix  
This appendix describes the different control messages present in _DARAL_, which totals 15 different messages, used for connection, deletion and routing tasks. The following is a short description of these messages:  
As it is well known, every layer in a network communication stack adds a control header to the packet. Table 11 shows the _DARAL_ header right after the _IEEE 802.15.4_ MAC header in a network layer packet. It totals 27 octets, distributed as: operation code, which identifies the different control messages; packet length, used to read the payload length; routing type, which indicates the type of routing; a hop limit, not used by the moment; checksum and message id, used to avoid errors and duplication; source and destination virtual network id, used to primarily address the messages; source and destination address used to address the messages in the last step.  
Operation code defines the type of control message used by _DARAL_, which totals 15 different messages, used for connection, deletion and routing tasks. These messages are described below:  
* ASSOCIATION_REQ: Each node sends this message as a broadcast, to indicate that it requests a connection.
* ASSOCIATION_REP: A VC answers the _ASSOCIATION_REQ_ message with this one, indicating that it can accept the node.  
* ASSOCIATION_REP_ACK: If a node configures itself as EN, it sends this message to its VC.
* ASSOCIATION_PAN_ID_REQ: If a node configures itself as VC, it sends this message to its VC (also known as father), requiring a new vID. This message contains the information that identifies the node.
* ASSOCIATION_PAN_ID_REQ_ACK: This message is the answer from the network root to the _ASSOCIATION_PAN_ID_REQ_, and it contains the updated information (the new vID) for the node.
* ASSOCIATION_PAN_ID_ASSIGN: The VC (or father) assigns the new vID to the new VC.
* ASSOCIATION_PAN_ID_ASSIGN_ACK: This message is an answer to the _ASSOCIATION_PAN_ID_ASSIGN_ message, and it confirms that the new sub-network is currently operative.
* ASSOCIATION_INFORM: A certain VC sends this message to the network root when a new node (VC or EN) is connected in its sub-network.
* ASSOCIATION_INFORM_ACK: It is the network root acknowledgement to an _ASSOCIATION_INFORM_message.
* KEEP_ALIVE_REQ: This message is the request of a VC for every node in its sub-network, in order to test the nodes' connectivity
* KEEP_ALIVE_REQ_ACK: A specific answer for a _KEEP_ALIVE_REQ_ message.
* PURGE_REQ: A VC sends this message to the network root when detects that a node in its sub-network is down.
* PURGE_REQ_ACK: This message is generated by the network root as response for a _PURGE_REQ_ and it informs each node, until the destination, that a certain node is not available anymore.
* DATA: Sends a data frame, it can contain other protocol frames (e.g., 6 LoWPAN-IPv6).
* DATA_ACK: It confirms the reception of a _DATA_ frame.  
All of these messages are used by the protocol to carry out different actions.
# A Multi-Scale Graph Attention-Based Transformer for Occluded Person Re-Identification
## 4 Experiments and Results
### Implementation  
All experiments were conducted on four NVIDIA V100 GPUs. The fundamental architecture of our approach was based on VIT-B/16, comprising 12 transformer layers. The model's initial weights were pre-trained on ImageNet. In our experiments, we also incorporated methodologies from TransReID, employing both overlapping patch embedding and Scale-Invariant Embedding (SIE). When constructing the adjacency matrix, the threshold \(m\) was set to the mean value of the similarity matrix. The batch size was configured to 128, with each containing four images per identity. The initial learning rate for the experiments was set at 0.00196, and the learning rate was decayed following a cosine annealing schedule.  
We used the Cumulative Matching Curve and Mean Average Precision (mAP) to evaluate different ReID methods.  
The Rank-1 accuracy of CMC is a metric that evaluates whether the correct match can be found at the top of the retrieval list. Specifically, if the image ranked first in the retrieval results corresponding to a query image indeed belongs to the same individual, it is counted as a successful match. The Rank-1 accuracy is, thus, the ratio of successful matches to the total number of queries.  
The Mean Average Precision (mAP) is a more comprehensive evaluation metric that takes into account all correct match positions in the retrieval results. mAP is the mean of the average precision, where precision is defined as the ratio of the number of correct matches to the total number of retrieved items up to a certain position in the retrieval list; recall, in contrast, is the ratio of the number of correct matches to the actual total number of matches. This metric provides a balanced view between precision and recall, offering a more holistic assessment of retrieval performance.
## 3 Design of the Control Scheme
### Controller Parameter Optimization  
This part is used to verify the feasibility of IPSO to optimize and adjust the parameters of LADRC in HSTs. IPSO needs to optimize the parameters \(k_{p}\), \(k_{d}\), \(w\), and \(b\) of LADRC for the first and fourth carriages according to the objective function of (7) to simplify the parameter setting process. The parameters of each carriage are shown in Table 2. IPSO parameters are set as follows: \(c_{1}=c_{2}=1\), \(N=150\), \(M=50\), \(D=8\), \(w_{\max}=1.2\), and \(w_{\min}=0.6\).  
The change curve of LADRC parameters optimized by IPSO is shown in Figure 5, and the change curve of the IPSO objective function value is shown in Figure 6. From  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Road Condition** & **Parameter Setting** & **Starting (\(m\))** & **Ending (\(m\))** \\ \hline \multirow{2}{*}{Ramp} & \(\theta_{1}=10\frac{0}{00}\) & 3000 & 6000 \\  & \(\theta_{2}=-8\frac{1}{00}\) & 14,000 & 15,300 \\ \hline \multirow{2}{*}{Curve} & \(R_{1}=10,000\ m\) & 34,000 & 35,500 \\  & \(R_{2}=12,000\ m\) & 55,000 & 56,000 \\ \hline \multirow{2}{*}{Tunnel} & \(L_{1}=2000\) m & 64,000 & 66,000 \\  & \(L_{2}=1000\) m & 71,000 & 72,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ramp, curve, and tunnel road parameters.  
Figure 4: Ideal speed and displacement curve.  
Figure 5, it can be seen that the parameter \(w\) is the fastest to find the optimal solution in the IPSO search process, and the parameter \(w\) for carriage 1 and carriage 4 are \(w_{1}=200\) and \(w_{4}=200\), respectively. However, the parameters \(k_{p}\), \(k_{d}\), and \(b\) change many times, and finally the optimal solution was found in the 27 th iteration. Their values are \(k_{p1}=4969\), \(k_{p4}=4991\), \(k_{d1}=268\), \(k_{d4}=200\), \(b_{1}=0.111\), and \(h_{4}=0.113\). As can be seen from Figure 6, the objective function value keeps shrinking in the IPSO optimization process, finally reaches the minimum value in the 27 th iteration, and tends to be stable. It shows that the parameters of LADRC obtained at this time are optimal.
# In-Situ Seawater Gamma Spectrometry with LaBr\({}_{3}\) Detector at a Nuclear Power Plant Outlet
## 3 Results
### Calibration
#### 3.1.2 Efficiency Calibration  
Spiked Experiment  
If the activity concentration of a radionuclide in seawater of volume \(V\) (in L) is \(A\) (in Bq L\({}^{-1}\)), the probability of emission of a particular \(\gamma\)-ray from that radionuclide is \(I_{E}\), and the detector's efficiency in detecting the source peak of that \(\gamma\)-ray is \(\varepsilon_{sp}\), then the detector's full-energy peak count rate \(n\) (in cps, counts per second) for that \(\gamma\)-ray should theoretically be \(n=A~{}V~{}I_{E}~{}\varepsilon_{sp}\). Specific count rate \(y=V~{}I_{E}~{}\varepsilon_{sp}\) (in cps L Bq\({}^{-1}\)) represents the full energy peak count rate of a particular \(\gamma\)-ray emitted by the radionuclide of unit activity concentration in seawater, which is the detector's detection efficiency for that \(\gamma\)-ray.  
The efficiency calibration experiment was performed in two water tanks with a diameter of 2.0 m and a height of 2.3 m to simulate an underwater environment. One of the tanks was spiked with a standard solution of \({}^{137}\)Cs to make the activity concentration of \({}^{137}\)Cs in the tank to be 0.45 Bq/L, and the other tank was spiked with U3O8, making the activity concentration of \({}^{238}\)U in the water tank to be 5.86 Bq/L. Since the half-life of \({}^{238}\)U (4.47 \(\times\) 10\({}^{9}\) a) is much longer than the half-lives of its decay daughters \({}^{234}\)Th (T\({}_{1/2}\) = 24.1 d) and \({}^{234}\)Pa (T\({}_{1/2}\) = 1.17m), this decay system established long-term equilibrium within a few months, and the \({}^{234}\)Th activity and \({}^{238}\)U activity were almost identical so that the activity of \({}^{238}\)U can be reflected by the \(\gamma\)-rays emitted by \({}^{234}\)Th at 92.38 keV (2.18%) and 92.80 keV (2.15%).  
In the experiment, the measurement times for \({}^{137}\)Cs and \({}^{234}\)Th were 118.8 h and 41.1 h, respectively, and the results are shown in Table 1.
# State-of-the-Art Internet of Things in Protected Agriculture
## 5 Disscussion
### Future Prospects  
IoT technology is being applied to many fields of society, economy and life at an unprecedented speed, making human society step into a new era of high intelligence. IoT technology is evolving and mature, and many novel deployments and applications are constantly being built in protected agriculture. Based on the above analysis and discussion of the key technologies and applications of IoT in the protection of agriculture, we will present its future development prospects and key research and development directions in the following aspects. In the perception layer, the development of sensors should focus on new sensitive materials, mechanisms, processes and methodologies as well as low power consumption and low cost. Besides, we must accelerate the development of sensors and rapid detection devices soil, animal and plant life. At the network layer, we should focus on high-capacity data real-time broadband communication standards and technologies for complex agricultural application environments, high-reliability, adaptive, low-power network deployment and management strategies and algorithms. With wide coverage, high capacity, low power consumption and low cost, LPWAN is ideal for some agricultural scenarios where text-based, video and voice service support is not required and coverage, power consumption and cost are critical. Future research and deployment should focus on IoT solutions based on LPWAN. In the application layer, governments and organizations should develop unified standards for the sensors and identification interface devices, data transmission communication protocols, multi-source data fusion analysis processing and application services in protected agriculture through international cooperation or negotiation. Software developers should develop large open source databases and signal processing algorithm libraries for different areas of facility agriculture. For universities and research institutes, cloud-based agricultural intelligent decision-making models, multi-source data-based information fusion algorithms, agricultural Big Data mining technologies, distributed intelligent processing systems and lightweight IoT authentication, encryption and authorization mechanisms will be the key research directions for the future. With an increased presence of IoT technology in protected agriculture, its potential for the refined management of crop, livestock and aquatic animals will be recognized. Besides, as a technical means to monitor the production, processing, circulation and consumption of agricultural products, IoTwill play an increasingly important role in food safety.