esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain-Based Distributed Patient-Centric Image Management System
## 4 Overview of the PCIM System  
In the proposed PCIM system, medical images are not stored in the blockchain to avoid scaling to the unmanageable size and thereby, a resulting blockchain bloat. Therefore, in the present study, we utilized the Ethereum blockchain for the proposed POC framework to efficiently manage the identity database and access control across participants. This action allows reducing the fees associated with storing images and managing the related database state. The fundamental purpose of this system is to provide distributed immutable on-chain and off-chain storage to facilitate patient-centric management for complex health record data. Figure 1 illustrates the blockchain ledger data structure with a PCIM data field added, as it is designed to store the data that patients want to include in a transaction.  
Figure 1: Blockchain ledger data structure.  
In the proposed scheme, the PCIM data field contents include such information as an image hash value (endpoint of an encrypted medical image), patient addresses, timestamp, encryption public key, image description, and a block hash to form an unchangeable record, as each block is linked with the hash of its previous blocks to connect and verify transactions. Every block is updated in the ledger after transactions are approved and recorded by a patient in the network. A transaction consists of a part corresponding to the ledger content signed and sent by a patient to execute SC by paying ether. Then, transaction validation is performed by the selected and approved consortium within the healthcare ecosystem. As the blockchain is implemented in the healthcare ecosystem, participants seek to achieve decentralizing the process of medical data management, i.e., these blockchain are accessible only to a privileged group based on SC protocol. The trusted participants follow the SC protocols that would limit the image requestor roles to effectively prevent any misbehavior in the blockchain. The trusted participants are, for example, the patient and the radiologist. The overall architecture of the PCIM system framework is illustrated in Figure 2. As it can be seen, it consists of Ethereum and IPFS networks. The Ethereum network is comprised of PCAC-SC and of a blockchain ledger to manage identity and access control within the network. The resulting encrypted medical images are stored in the IPFS network. We discuss the participant interactions with system module in the following subsections (see Figure 3).  
Figure 3: Interaction model of the PCIM system.  
Figure 2: Architecture of patient-centric image management (PCIM) system. The architecture component split into two main decentralized modules: IPFS and Ethereum network.
## 2 Related Work
With recent developments in deep learning algorithms, there has been active research on the application of CNN-based deep learning methods in the process of diagram recognition [36; 37; 38; 39; 40]. In [36] recognition of a simple logic diagram with general application targets was done. In the work in [37], although P&ID was a recognition target, preprocessing of the diagrams was not considered, and the recognition of lines and tables was not investigated. In [38], a method for recognizing symbols, lines, and texts included in P&IDs was proposed. However, for line recognition, the target was limited to continuous lines. In [39], R-CNN was employed to recognize symbols in P&IDs, but there was no discussion on the recognition of texts or lines. In [40], a method for the recognition of various types of symbols and texts was presented for high-density P&IDs. Similar studies related to line recognition in P&ID include [25; 38]. However, in both studies, only continuous lines in vertical and horizontal directions were recognized by performing pixel processing-based line recognition. Accordingly, the diagonal continuous line, and types of lines other than continuous lines could not be recognized. In addition, the flow arrow could not be processed.
# Vibration Fatigue Analysis of a Simply Supported Cracked Beam Subjected to a Typical Load
## 3 Vibration Fatigue Analysis
### Loose Coupling Analysis Method  
In the vibration of the cracked beam, the interaction occurs between the cracked beam vibration and crack's growth. Crack growth will change the modal and dynamic response of the beam, and affect the stress field distribution; variation of the stress field distribution of the cracked beam will also change the crack growth rate. In this paper, a loose coupling analysis method is proposed to conduct the vibration fatigue analysis of the cracked beam. Assume that the crack length is constant in each cycle of vibration, and the crack will grow at the end of each cycle. Crack growth and cracked beam vibration analysis are in segmented data transmission form, which means that data transmission occurs at the end of each vibration cycle. The flow chart of loose coupling analysis method is shown in Figure 3.  
Paris Law [23] can be applied to describe the crack growth rate in this paper:  
\[\frac{\text{d}a}{\text{d}N}=C(\Delta K_{I})^{n} \tag{15}\]  
where \(C\), \(n\) are the constants of beam material; and \(\text{d}a/\text{d}N\) is the crack growth rate.  
Figure 3: Flow chart of loose coupling analysis method.  
Substitute Equation (14) into Equation (15), and the crack growth rate can be obtained:  
\[\frac{\mathrm{d}a}{\mathrm{d}N}=C[Y(r)\sigma(x_{c})\sqrt{\pi a_{0}}]^{n} \tag{16}\]  
After \(\Delta N_{j}\) periodic vibration, the crack growth increment can be calculated:  
\[\Delta a_{j}=\int_{N_{j-1}}^{N_{j}}C[Y(r)\sigma(x_{c})\sqrt{\pi a_{0}}]^{n} \mathrm{d}N \tag{17}\]  
Assume \(\Delta N_{j}=N_{j}-N_{j-1}=1\), and \(\frac{\mathrm{d}a}{\mathrm{d}N}\approx\frac{\Delta a_{j}}{\Delta N_{j}}\). Equation (17) can be transformed as follows:  
\[\Delta a_{j}=C[Y(r)\sigma(x_{c})\sqrt{\pi a_{0}}]^{n}\Delta N_{j} \tag{18}\]  
where \(\Delta a_{j}\) is the crack growth increment after \(j_{th}\) periodic cycle.  
According to the fatigue damage accumulation theory, the depth of the crack can be calculated:  
\[a_{j}=a_{0}+\sum_{j}^{k}\Delta a_{j} \tag{19}\]  
where \(k\) is the number of cycles; and \(a_{k}\) is the depth after \(k\) periodic cycles.
# Theoretical Zero-Thickness Broadband Holograms Based on Acoustic Sieve Metasurfaces
## 3 Results
### Theoretical Zero-Thickness Holograms Based on Acoustic Sieve Metasurfaces
Each blue circle represents an RMSE between \(E_{\text{hole}}(x_{k},0,4\lambda_{0})/\text{MAX}(E_{\text{hole}})\) (color curves in Figure 2b) from a \(d\)-diameter hole and \(E_{\text{PS}}(x_{k},0,4\lambda_{0})/\text{MAX}(E_{\text{PS}})\) (black curve in Figure 2b) from the ideal PS. Each red dot represents the logarithm of the simulated \(E_{\text{hole}}(0,0,4\lambda_{0})/\left.E_{\text{hole}}(0,0,4\lambda_{0}) \right|_{d=\lambda_{0}}\) from a \(d\)-diameter hole. The RMSE decreases as the hole size decreases, as shown in the figure, implying that the smaller the hole size, the better the effect of the hole simulating the PS. Furthermore, the figure shows that as the hole size decreases, the transmitted energy also decreases, and when the hole diameter is less than 0.2\(\lambda_{0}\), the energy decay rate is significantly accelerated. As a result, \(d=0.2\lambda_{0}\) (i.e., 2 mm) is chosen as the diameter value of the ASM holes to balance the effect and efficiency. Figure 3: Mimicking of a PS by a small hole. (**a**) Illustration for a simulation model of PS (or holes of various diameters) radiation. The green part is a rigid circular boundary with a diameter of 30\(\lambda_{0}\); (**b**) Normalized energy profiles \(E_{\text{hole}}(x_{k},0,4\lambda_{0})/E_{\text{hole}}(0,0,4\lambda_{0})\) of holes of various diameters calculated using numerical simulation based on the model of (**a**). The various pseudocolors of the curves represent the various hole diameters \(d\). The black curve is the normalized energy profile \(E_{\text{PS}}(x_{k},0,4\lambda_{0})/\left.E_{\text{PS}}(0,0,4\lambda_{0})\right.\) radiating from an ideal PS; (**c**) A quantitative evaluation of radiations from holes of various diameters. Each blue circle represents an RMSE between \(E_{\text{hole}}(x_{k},0,4\lambda_{0})/\text{MAX}(E_{\text{hole}})\) (color curves in (**b**)) from a \(d\)-diameter hole and \(E_{\text{PS}}(x_{k},0,4\lambda_{0})/\text{MAX}(E_{\text{PS}})\) (black curve in (**b**)) from the ideal PS. Each red dot represents the logarithm of the simulated \(E_{\text{hole}}(0,0,4\lambda_{0})/\left.E_{\text{hole}}(0,0,4\lambda_{0}) \right|_{d=\lambda_{0}}\) from a \(d\)-diameter hole. Then, instead of the PSs in Figure 2b, the holes with a diameter of \(d=0.2\lambda_{0}\) are employed to construct the ASM-based hologram shown in Figure 4a. The light gray area is the zero-thickness rigid screen and the small circles are the holes. Figure 4b shows the image of the ASM-based hologram calculated using numerical simulation based on a combination of the FEM and the BEM in the image plane. In the numerical simulation, all of the acoustic and geometric parameters of the holographic imaging system based on the ASM are the same as those of the holographic imaging system based on the PS array. The RMSE between this holographic image and the desired image shown in Figure 2a is 0.1755. As can be seen, this image closely resembles the desired image.
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 6 Test Results
### Decompression Performance  
As shown in the previous sections, FAPEC, with its new KMALL pre-processing stage, provides an excellent compression speed, only comparable to _Zstd_. This is where we have allocated more effort, aiming at its applicability to remote sensing devices such as  
Figure 16: Histograms showing the range reduction in water column data from an EM2040.  
Figure 17: Histograms showing the range reduction in water column data from an EM304.  
autonomous vehicles running on batteries, or in general, with limited computing power. However, decompression speed should also be high, allowing to directly process or visualize FAPEC-compressed files on the fly, for example.  
We have used the same testing environment to evaluate the decompression speed of the different data compression solutions considered here. In general, _Zstd_ is clearly the fastest single-thread decompressor, reaching speeds around 500 MB/s on this platform. Both FAPEC and _gzip_ exhibit similar decompression speeds on.kmwcd and combined.kmall files (around 100 MB/s), although the FAPEC decompression speed almost doubles on non-combined.kmall files (that is, without watercolumn datagrams). Finally, _bzip2_ is clearly the slowest option, also in decompression, barely reaching 20 MB/s.
## 3 Performance of IWP with Steel Anchor Box
### Parametric Study
#### 3.2.5 Effect of Shear Connector Arrangement  
Figure 29 shows the effect of connector arrangement on the shear force of the steel-concrete interface connectors. The connector arrangement considered the increase in perforated side plate connectors from four columns to seven columns, and end plate connectors from two columns to five columns. When the perforated side plate connectors are increased from four columns to seven columns, the maximum shear force of the perforated side plate connectors decreases by about 24.5 kN; the maximum shear force of the end plate connectors decreases by about 10 kN. When the end plate connectors are increased from two columns to five columns, the maximum shear force of the perforated side plate connectors decreases by about 28 kN; the maximum shear force of the end plate connectors decreases by about 30 kN.  
Figure 30 shows the effect of connector arrangement on the vertical force distribution of the steel-concrete interface, where the slip stiffness of the perforated side plate or end plate is set as the total vertical slip stiffness of the connectors on it. As shown in the figure, when the slip stiffness ratio of the perforated side plate to end plate reaches 2, the perforated side plate transfers more than 50% of the vertical force, which is 1.4 times that of the end  
Figure 28: Influence of the diameter of perforated hole.  
Figure 29: Influence of shear connector arrangement on shear loads: (**a**) total column number of studs on side plate; (**b**) total column number of studs on end plate.  
plate. When the slip stiffness ratio between the two reaches 6, the vertical force borne by the perforated side plate begins to level off, and further increases in the stiffness ratio cannot effectively enable the perforated side plate to bear more vertical force. The vertical force borne by the surface plate remains basically below 5%. This indicates that changes in the connector arrangement alter the slip stiffness ratio between the perforated side plate and the surface plate, thereby causing a redistribution of the vertical force and simultaneously changing the forces acting on the connectors. In addition, the principal tensile stress on the outer side of the pylon wall and the utilization rate of the steel anchor box are basically unaffected by changes in the connector arrangement. The maximum stress difference does not exceed 0.1 MPa, and the difference in utilization rate does not exceed 0.2%.
# A Joining Procedure and Synchronization for TSCH-RPL Wireless Sensor Networks
## 4 Joining Procedure and Time Synchronization Problems
### TSCH Network: Scan Phase and Enhanced Beacon Broadcasting  
In the first stage, nodes that are already connected to the network send EBs periodically according to the EB Transmission Time (EBITT), while nodes that are waiting to synchronize are listening, sweeping the defined channels, during a scan time called TSCAN. The total number of channels is defined by the Number of Channels (NC); however, it is possible to have a total number of different channels for the scanning phase, which is determined by Number of Scan Channels (NSC).  
Figure 3: The TSCH method’s scan and synchronization phase. EB, enhanced beacon.  
Figure 3 represents the two-node slotframes in this TSCH synchronization phase using four frequencies (f1 to f4). There are circumstances in which a node that is scanning does not locate any signalling message. Depending on the configuration of the parameters EBTT, TSCAN, NC, and NSC, it can be observed that the synchronization process can be easier or harder, since it is influenced by the amount of synchronization messages that are transmitted and by the listening time in each channel.
# Analysis of Cultivated Land Productivity in Southern China: Stability and Drivers
## 2 Materials and Methods  
### Study Area  
The Poyang Lake region, located in northern Jiangxi Province and on the southern bank of the junction of the middle and lower reaches of the Yangtze River, covers Pengze, Hukou, Duchang, Chaisang, Lushan, Ruichang, De'an, Yongxiu, Poyang, Leping, Wannian, Yugan, Yujiang, Linchuan, Dongxiang, Fengcheng, Zhangshu, Gao'an, Anyi, and Jinxian counties and the urban areas of Nanchang, Jiujiang, and Fuzhou (Figure 1). The geographical coordinates are from 115\({}^{\circ}\)01\({}^{\prime}\) E to 117\({}^{\circ}\)34\({}^{\prime}\) E and 27\({}^{\circ}\)32\({}^{\prime}\) N to 30\({}^{\circ}\)06\({}^{\prime}\) N. It is characterized by a subtropical humid monsoon climate with superior hydrothermal conditions.  
Figure 1: Location map of the study area. ((**a**) is a schematic diagram showing the location of the Poyang Lake region in China and within Jiangxi Province. (**b**) is a remote sensing image of the Poyang Lake region. (**c**) illustrates the distribution of farmland in the Poyang Lake region in 2022, as well as the spatial distribution of lakes and rivers.)  
The terrain of the Poyang Lake region presents a \"semi-encircled\" pattern of high in the northwest and low in the southeast. Mountains, low hills, and plains are intertwined and dominate the landforms, and the overall terrain is relatively low and flat. The west and north of the region are surrounded by the Lushan and Meiling Mountains, among others. The Lushan Mountains are the highest point in the region at 1474 m above sea level. The central lakeside zone is flat, with the Poyang Lake water system as the core. The surface cover mainly includes paddy soil, red soil, and river alluvium. The vegetation is dominated by subtropical evergreen broad-leaved forests, which are mainly distributed in the upper reaches of the river, accounting for 24% of the total area. The Ganjiang, Fuhe, Xiuhe, Xinjiang, and Raohe Rivers flow into Poyang Lake, making it the largest freshwater lake in China and forming a water network zone with numerous lakes and traversed rivers.  
The Poyang Lake region is a commodity grain production base in China. It is the largest agricultural production base in Jiangxi Province, particularly for grain, cotton, oil, and pigs, making it one of the most economically developed regions in the province. Rice dominates, accounting for >50% of the total cultivated area of crops in Jiangxi Province. However, although the agricultural production conditions in this region are superior, investment in agricultural production is insufficient, resulting in low grain productivity.
# Robust DOA Estimation Using Multi-Scale Fusion Network with Attention Mask
## 5 Experiments and Discussion
### Experiments
#### 5.4.4 The Robustness of Methods to Different Noise Types  
In addition to white noise, experiments on signals in diffuse noise [59] and pink noise [60] were added. The diffuse noise model takes the indoor reverberation into account. For rooms with longer \(RT_{60}\), the diffuse noise field is a more accurate model. Pink noise is one of the most common noises in nature. The energy of pink noise decays from low frequency to high frequency, and traffic sound is a typical pink noise. Therefore, both of them have good practical significance.  
Experiments on the robustness of methods were conducted under different types of noise. At the beginning, the models trained on the white noise signal were directly used to test the two newly added noise signals, and the results were not very satisfactory. It may be that the model has learned some inherent patterns of white noise signal features on the previous dataset. Therefore, a mixed noise dataset was prepared. The new dataset contains the same number of noise samples based on different types, and other settings such as SNR and \(RT_{60}\) are consistent with the previous dataset. The test performance of the new model trained using this new dataset is shown in Table 5.  
For diffuse and pink noise, the performance of all three methods was degraded to varying degrees. Due to the irregular variation of diffuse noise, all methods were relatively poor for diffuse noise. The Acc5\({}^{\circ}\) of CV-CNN and CVREAN decreased to about 80% in diffuse noise, while it maintained 88% in MF-AMnet. In pink noise, the Acc5\({}^{\circ}\) of all methods appeared to decrease within 5%. In pink noise, the Acc5\({}^{\circ}\) of all methods appeared to decrease within 5%. In addition, the performance of MF-AMnet under all three noises in MAE was maintained within 3\({}^{\circ}\), reflecting good stability. The overall performance of the model is relatively stable, indicating that MF-AMnet has a certain ability to deal with different types of noise. White noise was used as the noise setting in the subsequent experiments.
# Comparing GRACE-FO KBR and LRI Ranging Data with Focus on Carrier Frequency Variations
## 2 Phase Observable
The phase \(\Phi\) is invariant under Lorentz transformations [44], which means that it does not change its value when transformed from the local system of the satellite to the geocentric celestial reference system (GCRS), e.g.,  
\[\Phi(\tau_{\mathrm{A}},\vec{r}_{\mathrm{A}})=\Phi(t_{\mathrm{A}},\vec{r}_{ \mathrm{A}}^{\prime}), \tag{5}\]  
where \((\tau_{\mathrm{A}},\vec{r}_{\mathrm{A}})\) is the four-valued event in the local satellite system, while \((t_{\mathrm{A}},\vec{r}_{\mathrm{A}}^{\prime})\) denotes the same event in the GCRS. The frequency is not relativistically invariant, since it appears in the GCRS as  
\[\
u(t):=\frac{\mathrm{d}\Phi}{\mathrm{d}\tau}(\tau(t))\cdot\frac{\mathrm{d} \tau}{\mathrm{d}t}(t)=v_{0}\dot{\tau}(t)\cdot\left(1+\frac{\mathrm{d}\tau_{ \mathrm{FV}}}{\mathrm{d}\tau}(\tau(t))\right), \tag{6}\]  
where the short form of the derivative \(\dot{\tau}=\mathrm{d}\tau/\mathrm{d}t\) was used. We note that even if a perfect laser system would produce a constant frequency in the satellite frame, the frequency is varying in other systems such as the geocentric one used for gravity field determination. The optical phase is the time-integral of the optical frequency, i.e.,  
\[\Phi=\int_{\tau_{0}}^{\tau}\
u(\tau^{\prime})\;\mathrm{d}\tau^{\prime}=\int_{ t(\tau_{0})}^{t(\tau)}\
u(t^{\prime})\;\mathrm{d}t^{\prime}. \tag{7}\]
## 3 Experimental Results  
An experiment to test a building fire detection warning system based on the multi-sensor and information fusion technology was carried out. The experiment was designed to test the real effect of information fusion technology in the building fire warning system.
## 2 Text Similarity Detection  
### Proposed Approach to Evaluate Text Similarity  
As was mentioned earlier, there are various methods to find similarity between texts but mostly in all of them similarity is evaluated by numerical measures and is based on usage of bag of words. Instead of this, we propose an n-grams based approach and result estimating in two ways: visual and numerical. The scheme of the proposed approach is presented in Figure 1. The approach consists of three main parts: text preprocessing, visualization and clustering, and numerical estimation. The main aim of text preprocessing is to find the numerical expression of texts (to find the frequency matrix), which will be used for visualization, clustering, and numerical estimation. The detailed description of text preprocessing is presented in Section 2.2. After the frequency matrix is created, the matrix is given to the SOM, where the dataset is clustered and visualized in the SOM. It allows detecting texts similarity in a visual form. In parallel, the four similarity measures are calculated (numerical estimation). The combination of these two separate techniques allows performing deeper texts similarity analysis. The SOM helps to see the whole text dataset similarity in one map and the numerical estimation justifies and specifies the results quantitatively.
## 3 Materials and Methods
### Experimental Setup
#### 3.4.2 Hyperparameters and Training Configuration  
To ensure efficient convergence and stability during training, critical hyperparameters were carefully tuned. The Adam optimizer was employed, with an initial learning rate set to \(\alpha=0.001\). A learning rate decay strategy was implemented, reducing the learning rate by a factor of 0.1 every 10 epochs to adapt to the convergence requirements of the model. The batch size was set to 64, balancing training efficiency and model performance. Additionally, a dropout mechanism was introduced to prevent overfitting, with a dropout rate of 0.5 applied to randomly deactivate a portion of neurons.  
In the experimental design, the dataset was partitioned to ensure the scientific rigor and robustness of model training and evaluation. The dataset was split into training, validation, and test sets in proportions of 70%, 15%, and 15%, respectively, to provide sufficient training data while reserving adequate validation and test data for evaluating model generalization. To further enhance the reliability of experimental results, five-fold cross-validation was employed. The training set was subdivided into five mutually exclusive subsets, with one subset used as the validation set and the remaining four as the training set in each iteration. The performance metrics from all iterations were aggregatedto provide an overall evaluation of the model. Cross-validation effectively mitigates biases caused by data distribution differences, enhancing the stability of experimental outcomes.