esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Measurement Procedure
### Data Processing Procedure
#### 2.2.2 Multi-Angle Stitching Process
\begin{array}{c}x_{real}\\ y_{real}\\ z_{real}\end{array} \right]=\left[ \begin{array}{cccc}1&0&0\\ 0&\cos\Delta\theta&\sin\Delta\theta\\ 0&-\sin\Delta\theta&\cos\Delta\theta\end{array} \right]\left[ \begin{array}{ ccc}\cos\Delta\beta&0&-\sin\Delta\beta\\ 0&1&0\\ \sin\Delta\beta&0&\cos\Delta\beta\end{array} \right]\left[ \begin{array}{c}x_{ axq}\\ y_{axq}\\ z_{axq}\end{array} \right] \tag{10}\]  
In the above formula, the alignment \(\Delta\theta\), \(\Delta\beta\) can be controlled within 10', so \(\sin\Delta\theta\sin\Delta\beta\approx 0\), \(\sin\Delta\theta\approx\Delta\theta\), \(\sin\Delta\beta\approx\Delta\beta\), \(\cos\Delta\theta\approx 1\) and \(\cos\Delta\beta\approx 1\). After the proper simplification, this equation can be expressed as:  
\[ \begin{array}{l}x_{real}=x_{axq}-\Delta\beta z_{acq}(x_{acq},y_{acq})\\ y_{real}=y_{axq}+\Delta\theta z_{acq}(x_{acq},y_{acq})\\ z_{real}(x_{real},y_{real})=\Delta\beta x_{acq}-\Delta\theta y_{acq}+z_{acq}(x_ {acq},y_{acq})\end{array}  \tag{11}\]  
We can derive another expression of \(z_{real}(x_{real},y_{real})\) according to the first order Taylor formula, namely:  
\[z_{real}(x_{real},y_{real})\approx z_{real}(x_{acq},y_{acq})-\Delta\beta z_{ acq}(x_{acq},y_{acq})\frac{\partial z_{real}(x_{acq},y_{acq})}{\partial x}+ \Delta\theta z_{acq}(x_{acq},y_{acq})\frac{\partial z_{real}(x_{acq},y_{acq})} {\partial y} \tag{12}\]  
Figure 5: Illustration of tilted local ray approximation algorithm (TLRA) algorithm for DHM system in transmission. sub-surface \(W_{j}\). The parameter \(\Delta\theta\), \(\Delta\beta\) of misalignment can be calculated by making the partial derivative of the above formula to 0:  
\[\frac{\partial E}{\partial\Delta\theta_{i}}=0,\frac{\partial E}{\partial\Delta \beta_{i}}=0 \tag{18}\]  
After introducing the parameter \(\Delta\theta\), \(\Delta\beta\) into Equation (10), the real sub-surfaces \(z_{real\_i}\) without the alignment errors is obtained, and the fully surface of sample is recovered accurately by stitching this sub-surfaces with point-to-point.
## 4 Discussion
### Flood Control Effectiveness of IFCS and its Operation
Thus, the operating water level of the gate plays a very important role in floodwater mitigation, which still needs further study of the operating water level for a particular gate to obtain the optimized water level reduction, especially in a complex system that includes several gates to be required in the IFCS. Additionally, some other factors of the gate (e.g., the width, height, and gate opening, etc.) that affect the operation of the gate were not incorporated in the current model, since the current study focuses on applying and estimating the feasibility for flood mitigation of the IFCS in a large lake. Therefore, future study might be necessary to consider the abovementioned factors to obtain the optimized water level reduction.
# Finsformer: A Novel Approach to Detecting Financial Attacks Using Transformer and Cluster-Attention
## 2 Related Work
### Attack Detection Methods Based on Deep Learning Models
#### 2.2.1 Recurrent Neural Networks  
A recurrent neural network (RNN) is another deep learning model, particularly suited for processing sequential data [38; 40]. In financial attack detection, RNNs are capable of handling the temporal dependencies of transaction data, identifying potential anomalous transaction patterns. A characteristic of RNNs is the introduction of loops in the model, allowing the network to retain information from previous moments. However, traditional RNNs are prone to gradient vanishing or exploding problems; thus, in practical applications, variants such as long short-term memory (LSTM) networks [41] or gated recurrent units (GRUs) [42] are commonly used. The basic formula of an RNN is expressed as [41]  
\[h_{t}=\sigma(W_{hx}x_{t}+W_{hh}h_{t-1}+b_{h}) \tag{4}\]  
Here, \(h_{t}\) represents the hidden state at time \(t\), \(x_{t}\) is the input, \(W_{hx}\) and \(W_{hh}\) are weight matrices, \(b_{h}\) is the bias term, and \(\sigma\) is the activation function.
## 2 Materials and Methods
### Sensors Calibration  
To validate the performance of the designed system, a testing clamp that matched the cone that was manufactured. Based on the universal testing machine (Figure 4), the load cell and the film sensor were calibrated through vertical loading force and side loading force, respectively. Eight loading forces (50, 100, 150, 200, 250, 300, 350, and 400 N) were applied in the calibration experiments. The data output from the sensor and the film sensor was stored on the SD card during each gravity loading.  
Figure 4: Sensor calibration test.  
Figure 3: Flexiforce sensing signal processing.  
Figure 5 shows the calibration results of the sensors. The calibration results were fitted by polynomials, which show the linear relationship between each sensor and the loading forces. Note that the fitting coefficient between loading force and the load cell is up to 0.9974 (Figure 5a), and the fitting coefficient between loading force and the output from the film sensor is 0.9867 (Figure 5b). The goodness-of-fit is close to 1 for both of the two sensors, suggesting that our calibrations are reasonable. Hence, the cone tip resistance and the side pressure can be determined as follows:  
\[V_{lc}=0.0014\cdot R+0.0564 \tag{3}\]  
\[V_{fs}=0.0024\cdot P+0.2239 \tag{4}\]  
where \(V_{lc}\) is the output from the load cell; \(R\) is the cone tip resistance; \(V_{fs}\) is the output from the film sensor; \(P\) is the cone side pressure.
## 3 Technology
### Current Research Challenges
#### 3.7.3 Detector Memory Effect  
The possibility to operate a SPAD in TG regime, by extending the dynamic range, highlights a previously unknown source of background noise affecting these devices, which was hidden orders of magnitude below their response peak [111; 112]. This phenomenon was named as \"memory effect\" as it arises in SPADs when exposed to strong illumination before being turned ON, giving a sort of memory of photons impinging on the active area before the gate-ON window [110]. This is different from the standard after-pulse noise, which arises when the detector is illuminated during the ON state and consists in avalanches triggered in the detector due to the late release of a charge carrier trapped in deep trap energy levels during a previous avalanche multiplication. The after-pulsing causes an additional background noise contribution with respect to the primary dark count rate [111]. The contribution of the memory effect to the IRF is similar to an additional diffusion tail. Probably, it is due to chargecarriers generated in the deepest layers of the SPAD, well beyond the neutral region giving the classical response tail described in Section 3.7.2[112]. This hypothesis is in agreement with the amplitude of this decay (attaching different orders of magnitude below the SPAD response peak due to the low probability that such carriers have to succeed in reaching the depleted region to generate an avalanche) and with its time constant (in the range of hundreds of ns due to the long diffusion time needed to reach the depleted region). Similarly to the classical diffusion tail, this phenomenon generates a spread in time of the collected photons, which results in a further contamination of late photons with a residual memory of early ones [110]. Due to the long time constant, which is orders of magnitude longer than the classical diffusion tail, the memory effect is detrimental in TG measurements as it is not possible to increase the delay of analysis along the DTOF to reach times where this contamination is negligible.
# The Emerging Energy Internet: Architecture, Benefits, Challenges, and Future Prospects
## 3 Types of Energy Routers
### Multiport Converter (MPC) and Power Line Communication (PLC)-Based ER Model  
In order to achieve the high degree of reuse and integration for SST-based ER, [PERSON] [52] presented multiport converter- (MPC) and PLC-based ER. The MPC- and PLC-based ER are considered as improved versions of the SST-based ER. In MPC-based ER, all types of distributed energy devices are connected and controlled with bidirectional power electronic converters, as shown in Figure 6. The communication and energy transmission layers run in parallel at each level. At the microgrid  
Figure 4: Energy hub model.  
Figure 5: Organization of an energy router (ER) in a FREEDM structure. DGI—distributed grid intelligence.  
control center, the functions of energy control and information management are performed. All of the DRERs and DESDs, along with the loads, are connected with the local DC bus, which is managed by a bidirectional multiport buck-boost converter, capable of operating in a grid tie or islanded mode [53].  
In PLC-based ER, the power line communication technique is used for information management and power transmission. Because of the employment of the PLC scheme, PLC-based ER is compact in size, and saves additional communication network investment. It works on the wires of the existing widely distributed power network structure of the microgrid. However, the PLC technique has drawbacks, such as a loud noise, signal attenuation, constrained bandwidth, and low transmission rate. Furthermore, the PLC technique has a major drawback of no backups for communication. When the power line is damaged or broken, the PLC communication is lost. In the literature [32], the amplify and forward-based relay-aided (AF-based RA) PLC technique is proposed in order to overcome the technological challenges of a low transmission rate in the distribution feeder and substation feeder. The main idea of the PLC-based ER is to send the power into the form of energy packets, similar to the information packets of Internet technology [54]. All of the energy outputs are configured as power packets quantized with the address information of the source and destination device. These are then multiplexed over a transmission line with a time-division multiplexing (TDM) technique. At the receiving end of the transmission line, the E-router will decouple the energy packets and route them to the load destination. A representation of the PLC-based ER is shown in Figure 7. The DESDs play a major role in PLC-based router design, with the source-load balancing by buffering the intermittent energy packets generated by the intermittent nature of DRERs. The amount of supplied power is regulated by changing the length of the packet or by modulation of the power waveform.  
Figure 6: Multiport converter (MPC)-based E-router model.  
Figure 7: Power line communication (PLC)-based E-router control model.
## 3 Results and Discussion
### Analytical Results
#### 3.1.1 Samples with Horizontal Isotropy Planes  
As a result of the chosen study parameters, with the elastic constants \(E^{\prime}\) and \(\
u^{\prime}\) assumed with fixed values, the resulting radial and vertical strains, \(\epsilon_{r}\) and \(\epsilon_{z}\), for samples with an isotropy plane inclination of \(\beta=0^{\circ}\) are not affected by the varied ratios \(E/E^{\prime}\) and \(\
u/\
u^{\prime}\). Also, they remain unaffected by the shear modulus \(G^{\prime}\). For samples with horizontal isotropy planes, the only elastic parameters contributing to the developing strains \(\epsilon_{r}\) and \(\epsilon_{z}\) at orientation angle \(\theta\) are \(E^{\prime}\) and \(\
u^{\prime}\). Figure 9 presents the results for the strain distributions associated with samples having an isotropy plane inclination of \(\beta=0^{\circ}\).
## 3 Solvent-Based Microextraction Techniques
### Liquid-Liquid Micro Extraction (LPME)  
Considering the principles of Green Analytical Chemistry [88], the development of analytical methods that reduce the amount of toxic solvents, or replace them with non-toxic alternatives without sacrificing the efficacy of the extraction procedure, is a major aim for researchers.  
Liquid phase microextraction methods (LPME), in comparison to solid phase (micro) extraction, have shown important innovations for trace analytes in different matrices. LPME is utilized for organic compounds and inorganic trace elements in several application fields, such as the environmental, biological, and food fields. LPME can be divided into three different procedure modes: headspace LPME (HS-LPME), direct-immersed LPME (DI-LPME), and hollow fiber LPME (HF-LPME). In HS-LPME, a drop of extraction solvent--which can be either an organic solvent or a water solution--is suspended at the tip of a micro-syringe needle and exposed to the headspace of the sample; this is very suitable for analyses of volatile compounds. DI-LPME is very similar, except that the extraction solvent must be immiscible with aqueous solutions, and is directly immersed into a stirred sample solution. HF-LPME uses a hollow fiber in order to stabilize and protect the extraction solvent, while the small fiber pore size avoids the interference of large molecules and particles, which could result in a more extensive clean-up of the sample during the extraction process. [PERSON] and co-workers [89] recently reviewed the principal applications in which these LPME techniques are applied.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 5 Distributed Protocols
### Aloha  
The ALOHA was first introduced in the 1970s [56], and almost every cellular network, including mmWave 5G network technology, uses this protocol [57]. ALOHA is a random access protocol in which various STAs transmit the data simultaneously, ignoring the collision realities. The advanced slotted ALOHA was introduced to address the issues present in the pure ALOHA. In slotted ALOHA, a time frame of transmission is divided into multiple discrete intervals, which are referred to as slots. Transmissions in slotted ALOHA are scheduled to commence at the beginning of each time slot. Since devices are synchronized using base STA synchronization signals, slotted ALOHA is an appropriate model for the worst-case analysis of a device-to-device (D2D) network underpinning a cellular network [58]. Furthermore, slotted ALOHA provides an upper bound for the throughput performance of pure ALOHA, in which transmission begins instantly upon the arrival of a new packet [30].  
* Advantages: The fundamental benefit of ALOHA's multi-hop context is that it seeks to send a packet as far as feasible in a mobile network. Due to the simplicity of ALOHA, it can be considered one of the most reliable wireless protocols for mobile communication systems.
* Disadvantages: Since the advanced version of aloha introduces a slotting mechanism, based on the scheduling necessities, few slots might stay idle during a transmission. This reduces the throughput of the network, causing the protocol to not perform at its peak occasionally. It also requires queue buffers for packet retransmission, and clock synchronization still remains an issue.  
Figure 8: HetSNet routing operation.
## 5 Conclusions  
This paper presents the construction of a ship knowledge graph using science knowledge graph technology, which allows for extracting ship violation features and identifying specific fake ship license plates through inference and case analysis verification. The results of this research can significantly enhance the decision-making ability of maritime for ship safety supervision. The ship static and dynamic knowledge graph facilitates ship portrait and ship hidden relationship mining and analysis, allowing for the rapid identification of maritime key tracking ships, ships with expired certificates, and the consistency of ship trajectories and reported ports. It also enables the identification of multiple trajectory anomalies of ships and the retrieval of ships that have not reported ports for a long time. Through the analysis of the ship graph, the existence of abnormal ships navigating across inland rivers and seas, as well as the violation of the ship's license plate, can be determined. Furthermore, in conjunction with the identification of ship names under on-site supervision, ship graph comparison analysis can be conducted to enable rapid screening and key monitoring of abnormal risks similar to \"Ning Shuangshun 5568\", thereby enhancing the ability to prevent accidents beforehand. The ship graphical portrait provides full information on the ship belonging to the company, certificate information, the last port from where it departed, the next port where it will arrive, local cargo, and the ship's crew, as well as violation behavior characteristics. This information allows for timely response to ship monitoring and tracking business requirements and enhances after-accident investigation, tracing, and control disposal capability through the use of graph path query and source tracing methods.  
In summary, this research enhances the maritime industry's pre-accident prevention capability and post-accident handling capability through the construction and analysis of a ship knowledge graph, providing valuable insights for ship safety supervision decision-making.  
Conceptualization, validation, writing, original draft, methodology, and formal analysis--H.W., S.F. and M.Z.; writing, review and editing, supervision, funding acquisition--H.W., S.F. and Y.X. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the National Natural Science Foundation of China under grants 52271363 and Shanghai Rising-Star Program 22 QC1400600.  
Not applicable.
## 3 Materials and Methods
### Waveform Prediction
#### 3.5.3 Machine Learning Models  
Different models were trained using either the custom Pearson correlation regression loss layer or the standard mean absolute error loss. The final model will first be discussed, after which a brief description of the other models will be provided.  
The final network is a sequence-to-sequence regression network consisting of the input layer, three hidden layers, and the output layer, which used the custom Pearson correlation loss function. The hidden layers are standard fully connected (fc) layers of 1024 neurons each, followed by batch normalisation layers and rectified linear unit (ReLU) activation layers. It is common to use ReLU activation, since they are computationally inexpensive and prevent vanishing gradients. Activation layers were followed by _dropout_ layers with dropout factors of 0.2. The dropout factors result in a proportion of neurons being ignored, which prevents over fitting [26]. It effectively simulates many more architectures with different numbers of units in the layers. The architecture is illustrated in Figure 3.  
A total of 70% of the observations were used to train the model, with 15% used for validation during training and the remaining 15% for testing. The model was trained using the stochastic gradient descent with momentum (SGDM) optimiser.  
Other architectures were attempted, but only those which achieved over 0.75 correlation (as seen in Section 4) are discussed. Since the objective is to output a time-series waveform, various architectures involving LSTM networks and bidirectional LSTM (biLSTM) networks were modeled. The second approach used standard ANN fully connected layers in different configurations, which only produced correlated results when used with a Pearson correlation regression loss layer. A summary of the different architectures is presented in Table 1.
## 2 Design and Analysis Methods
### _Stator Dimension_  
For the slot shape given in Figure 4, some variables such as the slot opening width \(b_{so}=2\)-\(3\) mm, slot opening height \(h_{so}=0.5\)-\(1\) mm, and wedge height \(h_{s1}=1\)-\(4\) mm are given in [31].  
Slot pitch \(\tau_{s}\), stator tooth width \(b_{ts}\), stator slot height \(h_{s2}\), stator yoke height \(h_{sy}\), the upper \(b_{s1}\) and lower \(b_{s2}\) parts of the slot width, slot area \(A_{u0}\), the active slot area \(A_{u}\), stator inner diameter \(D_{si,}\) and rotor outer diameter \(D_{ro}\) can be expressed, respectively, as follows [31; 32]:  
\[\tau_{s}=\frac{\pi D_{g}}{Q_{s}},\qquad b_{ts}=\frac{\tau_{s}}{k_{fill}}\frac{ B_{mg}}{B_{sat}} \tag{21}\]\[h_{s2}=\frac{6N_{s}I}{\frac{B_{mg}}{B_{sat}}j_{con}k_{fill}}\frac{1}{\pi D_{g}}= \frac{A_{m}}{\frac{B_{mg}}{B_{sat}}j_{con}k_{fill}} \tag{22}\]  
\[h_{sy}=\frac{B_{mg}}{B_{sy}}\cdot\frac{\tau_{p}}{\pi}=\frac{B_{mg}}{B_{sy}k_{sf} }\cdot\frac{D_{g}}{2p} \tag{23}\]  
\[b_{s1}=\pi\frac{(D_{g}-2\cdot h_{s0}-2\cdot h_{s1})}{Q_{s}}-b_{ts} \tag{24}\]  
\[b_{s2}=\pi\frac{(D_{g}-2\cdot h_{s0}-2\cdot h_{s1}-2\cdot h_{s2})}{Q_{s}}-b_{ts} \tag{25}\]  
\[A_{u0}=\frac{2N_{s}t_{ph}}{k_{fill}\cdot j_{con}} \tag{26}\]  
\[A_{u}=\frac{(b_{s1}+b_{s2})}{2}\cdot(h_{s2}+h_{s1}+h_{s0})\]  
\[D_{si}=D_{g}-2(h_{s0}+h_{s1}+h_{s2}+h_{sy}) \tag{27}\]  
where saturation flux density \(B_{sat}\) = 1.8 T, yoke flux density \(B_{sy}\) = 1.4...1.7 T, airgap flux density \(B_{mg}\) = 0.85 T, and slot fill factor \(k_{fill}\) = 0.5 [23; 30; 31; 32]. For efficiency, the diameter ratio \(D_{si}/D_{so}\) should be close to 0.5 [31] or 0.6 [29].
# Synthetic Approaches to 1,3,4-Oxadiazole-Containing Boronic Derivatives
## 3 Synthesis of Boronic Derivatives from Reagents Containing 1,3,4-Oxadiazole Moiety
### Indirect Connection of Boron with 1,3,4-Oxadiazole Ring via Phenotype Linker
#### 3.2.2 Derivatives of Meta-Substituted (1,3,4-Oxadiazol-2-yl)phenylboronic Acid
The reaction can be carried out in dichloromethane at room temperature to yield the 3-[5-(phenylamino)-1,3,4-oxadiazol-2-yl]phenylboronic acid pinacol ester in 77% yield [74], or in acetonitrile at 40 \({}^{\circ}\)C to afford the product in 62% yield (Scheme 13) [67]. The third approach to synthesizing _meta_-substituted phenylboronic acid derivatives of 1,3,4-oxadiazol is the metal-halogen exchange reaction (method A). Commonly, 2-(3-bromophenyl)-1,3,4-oxadiazol derivatives are used as starting materials and reacted with \(n\)-BuLi as a strong base and triisopropyl borate (B(O-\(i\)-Pr)\({}_{3}\)) as the boron source at \(-\)78 \({}^{\circ}\)C in dry THF. Subsequent acidic hydrolysis of the resulting boronic esters yields the desired 3-(1,3,4-oxadiazol-2-yl)phenylboronic acids in 60-80% yield [69; 75]. A similar method can be applied for bis-_meta_-bromo substitution using trimethyl borate (B(OMe)\({}_{3}\)) (Scheme 14) [76]. Alternatively, _meta_-iodophenyl derivatives such as 2-(3-iodophenyl)-1,3,4-oxadiazole can be employed to form Grignard reagents (Scheme 14). Isopropylmagnesium chloride (_i_-PrMgCl) can be used for metal-halogen exchange, followed by substitution with triisopropyl borate. Subsequent acidic hydrolysis leads to _meta_-substituted (1,3,4-oxadiazol-2-yl)phenylboronic acid pinacol ester derivatives in 37-66% yield. The reaction is typically carried out in THF under milder conditions (\(-10\)\({}^{\circ}\)C) compared to the lithium-based approach [77; 78; 79].