esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting
## 5 Experimental Results and Analysis  
In this section, we compare the performance of three groups models, respectively, for one-step, three-step, five-step, and seven-step electricity load forecasting. Common evaluation metrics used in forecasting include SSE, MSE, RMSE, MAE, MAPE, \(R^{2}\), and so on. In this paper, we employ root mean squared error (RMSE) and mean absolute error (MAE) to evaluate our models, both of which are scale-dependent and widely used in time series prediction. In addition, we use mean absolute percentage error (MAPE) to evaluate the prediction deviation proportion in terms of the ground truth. Compared with other metrics, these three metrics reflect the accuracy of our model more intuitively and accurately. The formulas for these three metrics are Equations (13)-(15):  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(\widehat{y_{i}}-y_{i})^{2}} \tag{13}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|\widehat{y_{i}}-y_{i}| \tag{14}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\frac{|\widehat{y_{i}}-y_{i}|}{y} \tag{15}\]  
where \(\widehat{y_{i}}\) represents the prediction value and \(y_{i}\) represents the actual value of electricity load.  
In the second part of this section, we compare the prediction result and actual data on testing set and observe some local differences between the prediction result and actual data.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Model** & **Parameter** & **Configurations** \\ \hline OCBL & Number of nodes in parameterized FC layer of NODE & [100, 100] \\  & Number of nodes in output FC layer of NODE & [10] \\  & Number of nodes in BiLSTM layer & [50] \\  & Number of nodes in LSTM layer & [30] \\  & Number of nodes in FC layers & [100] \\  & Kernel parameter of Conv1D layer & kernel size = 3, filters number = 64 \\ \hline OBLL & Number of nodes in parameterized FC layer of NODE & [100, 100] \\  & Number of nodes in input FC layer of NODE & [10] \\  & Number of nodes in BiLSTM layer & [50] \\  & Number of nodes in LSTM layers & [30, 10] \\  & Number of nodes in FC layers & [100, 50, 20] \\ \hline OBBLL & Number of nodes in parameterized FC layer of NODE & [100, 100] \\  & Number of nodes in output FC layer of NODE & [10] \\  & Number of nodes in BiLSTM layers & [50, 30] \\  & Number of nodes in LSTM layers & [20, 10] \\  & Number of nodes in FC layers & [100, 20] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter setting of the proposed models.  
In the third part of this section, we discuss the experimental results and explain why NODE can improve the accuracy of the combination of LSTM and BiLSTM.
# Effective PSCCH Searching for 5G-NR V2X Sidelink Communications
###### Abstract  
Cooperative Intelligent Transport Systems (C-ITS) are essential for increasing road safety and to make road transport more efficient, sustainable, and environmentally friendly. The implementation of C-ITS technology is only possible through the connectivity of Vehicle-to-Everything (V2X), which allows the interconnection of vehicles in a network and with road support infrastructure. However, real-time systems require efficient signal processing in order to respond within the necessary time. Some of this processing is related to searching the Physical Sidelink Control Channel (PSCCH), where a blind algorithm is commonly used. However, this algorithm is quite inefficient to searching the PSCCH, since all the processing should be completed several times before successful decoding it. Therefore, the aim of this paper is to design a more efficient algorithm to search/decode the PSCCH. In the proposed algorithm, we firstly compute all the correlations between the received signal and the Demodulation Reference Signal (DMRS), and the remaining conventional processing to decode the PSCCH is only performed over the subchannels with higher correlation, which leads to a strong complexity reduction. The proposed algorithm is evaluated and compared with the conventional blind algorithm. The results have shown a significant performance improvement in terms of runtime.  
V2X; sidelink; 3 GPP; 5G-NR; PSCCH; real-time systems Article
# Inertial and Flexible Resistive Sensor Data Fusion for Wearable Breath Recognition
## 6 Identifying the Breath Type
Through the comparison and analysis  
Figure 11: A time signal showing sensor capturing various breathing types in a breathing test including deep, normal, and shallow breathing. Figure 12: STFT of the breathing test including deep, normal, and shallow breathing. presented in Table 1, the sensor's readings are evaluated for consistency and accuracy in capturing respiratory rate. Subjects are provided with a spirometer, where using a mouthpiece, they breathe into the spirometer, which detects the flow of air and calculates the respiration rate. This process typically involves the participants inhaling and exhaling into a mouthpiece connected to the spirometer. Sensors within the spirometer then provide real-time breathing signals which are compared with the wearable sensor data and in Table 1 for validation. The results showed that the developed approach could be used to determine the appropriate number of cycles for each breathing type. It can be seen that 6 s time intervals with a 3 s overlap can cause errors of 3 s in the identification of breath types. Another source of slight mismatches between the calculated results and the actual results arises from the identification of breath types during the transition from one breath type to another. It is essential to note that these thresholds can vary among individuals and may be influenced by body type. This variability highlights the importance of personalizing wearable devices. By instructing individuals to engage in these specific breathing types  
Figure 14: Histogram of the most dominant frequency in each window of time for the breathing test including deep, normal, and shallow breathing. Figure 13: Frequency ranges of different breathing types of six participants (3 males and 3 females). and subsequently calibrating or adjusting the device based on their unique thresholds, it becomes possible to personalize the device to each person's individual characteristics.
## 3 Design of the Enhanced Dynamic Regulation Reserve Controller  
### Enhanced Dynamic Regulation Reserve System Architecture  
The architecture of the enhanced dynamic regulation reserve system mainly consists of energy storage units and a monitoring and dispatch management system. It includes components such as the power grid (Grid), power conditioning system (PCS), battery energy storage system (BESS), energy management system (EMS), and controller. The schematic diagram of the system architecture is illustrated in Figure 5. The power conditioning system and battery energy storage system form a bidirectional power system, while the controller establishes a communication link with the energy management system through Ethernet. The energy management system communicates with Taipower for data exchange, creating a comprehensive enhanced dynamic regulation reserve system.  
This paper adopts Advantech Co., Ltd.'s (Taipei City, Taiwan) customized industrial PC (IPC) as the hardware equipment for developing a dynamic regulation modulation controller. In comparison to a personal computer (PC), industrial computers offer higher reliability, a longer lifespan, superior computing power, and capabilities such as dust resistance, waterproofing, and shock resistance. For the software aspect, the National Instruments (NI) LabVIEW programming language is employed for frequency control. LabVIEW, developed by National Instruments, is primarily used in engineering fields for testing, measurement, control, and automation. It provides an intuitive and user-friendly programming environment, allowing users to write complex programs in a graphical manner.  
According to the full text of the power trading platform management regulations and operating procedures announced by Taipower on the power trading platform [30], qualified traders participating in frequency regulation reserve capacity must independently set up system frequency detection equipment. Regarding the specifications for the frequency detection equipment, the minimum measurement accuracy for identifying changes in system frequency, i.e., the minimum resolution for system frequency, must be \(\leq\)0.01 Hz. Within the system frequency measurement range of 60.50 Hz to 59.50 Hz, the absolute value of the measured system frequency error should not exceed 0.01 Hz, ensuring that the measurement error of the system frequency is within 0.01 Hz. This thesis utilizes power sensing instrumentation developed and produced by ADTEK. To meet Taipower's requirement for a frequency detection equipment reading of 10 frequency values per second, this paper selects the CPM-80 and CPM-72 multifunction power quality analyzers. The CPM-72 is used for reading other relevant power parameter measurement devices, while the CPM-80 serves as the frequency measurement equipment.  
Figure 5: Architecture diagram of enhanced dynamic regulation reserve system.
## 3 System Model
Therefore, at each round \(t\), node energy consumption is calculated as the maximum power required to sense its own packet or summation of all relayed packets at each round, which can be expressed as  
\[E_{c,i}(t)=\text{max}\bigg{\{}E_{t_{i}}(t),\sum_{k\in N_{ngh,i}}P_{f,k}(t) \bigg{\}}, \tag{4}\]where \(E_{t_{i}}\) is the energy node \(i\) consumes for transmitting its sensing packet and \(P_{f,k}\) is the of number of forwarded packets from node \(i\) neighbors. Each node can communicate with the neighboring nodes in its coverage region. A circular region centered on a node is referred to as coverage region of a node, where the radius of the circle can vary, and it is determined by each node transmission power and pathloss model. There are several pathloss models that are used in the literature. In this paper we model the pathloss using log-distance model [44] as follows  
\[\overline{P_{R_{dB}}(d)}=P_{T_{dB}}-10n\log_{10}(d). \tag{5}\]  
where \(\overline{P_{R_{dB}}(d)}\) is the average received power in dB at separation distance \(d\), \(P_{T_{dB}}\) is the transmission power in dB, and \(n\) is the path loss exponent. Considering the normal distribution, Equation (5) can be written as  
\[P_{R_{dB}}(d)=P_{T_{dB}}-10n\log_{10}(d)+X_{\sigma}, \tag{6}\]  
where \(X_{\sigma}\sim\mathcal{N}(0,\sigma)\) is a Gaussian random variable with zero mean and standard deviation \(\sigma\) in dB. Thus, \(P_{R_{dB}}(d)\) is also Gaussian distributed, and can be formulated as  
\[P_{R_{dB}}(d)\sim\mathcal{N}\Big{(}P_{T_{dB}}-10n\log_{10}(d),\sigma\Big{)}. \tag{7}\]  
When the receiver sensitivity is lower than the received power, the received signal can be readable at the receiver. Therefore, as \(P_{R_{dB}}(d)\) is Gaussian distributed, we use the \(Q\)-function to calculate the probability of \(P\Big{(}P_{R_{dB}}(d)>\gamma_{dB}\Big{)}\) (successful received signal) by calculating the tail probability of the standard normal distribution [44]. Thus, we have  
\[P\Big{(}P_{R_{dB}}(d)>\gamma_{dB}\Big{)}=Q\Bigg{(}\frac{\gamma_{dB}-\Big{(}P_{ T_{dB}}-10n\log_{10}(d)\Big{)}}{\sigma}\Bigg{)}. \tag{8}\]  
Thus, each packet will be successfully delivered to the next hop with a probability of \(P\Big{(}P_{R_{dB}}(d)>\gamma_{dB}\Big{)}\) at each round. Packets might not reach the destination due to disconnected links, shortage of remaining energy in nodes, or channel conditions. In those cases, the packet will be dropped and measurement outage occurs.
## 3 System Validation Methods
### Audio Fingerprint Matching  
An audio fingerprint is a compact content-based signature that summarizes an audio recording. This technology has attracted attention since they allow the identification of audio independently of its format and without the need of meta-data or watermark embedding [30]. The main objective of an audio fingerprint mechanism is to efficiently compare the equality (or not) of two audio files, not by comparing the files themselves, but by comparing substantially smaller sets of information, referred to as audio fingerprints. Furthermore, audio fingerprint length is a lot less than the raw audio data. In order to validate the proposed wireless acoustic sensor, an open source application, termed Chromaprint [31], is used to generate the fingerprints of original and recorded audios. Then, to find an audio matching between original and recorded audios, the Hamming distance is evaluated using both fingerprints.
## 3 CFD Setup
### Mesh  
Two computational domains have been considered taking into account the whole turbine from the spiral case to the standard draft tube (see Figure 2 on the left) or an artificially-extended draft tube (see Figure 2 on the right). Two types of mesh have been considered: two structured hexahedral meshes (see Figure 3 at the top) and one unstructured mesh (see Figure 3 at the bottom) with tetrahedral and prism elements. All the meshes have been built with the ICEM CFD v17.2 software [53]. Considering the hexahedral meshes, five sub-domains are meshed separately using a blocking method. These five sub-domains are the spiral case, the stay vanes, the guide vanes, the runner, and the draft tube. The two structured meshes differ only by the height of the first cell layer at the walls in the guide vanes, runner, and draft tube domains. On the contrary, for the unstructured mesh, the whole turbine is meshed including only three fluid domains and two internal interfaces: one between the guide vanes and the runner and another between the runner and the draft tube, in order to delimit the fixed and the rotating part of the computational domain. Along the solid walls, five prism layers are generated to better resolve the boundary layer. The number of nodes and elements for each mesh are shown in Table 1. The minimum angle and the maximum aspect ratio for each mesh and each sub-domain are provided in Table 2. For the structured meshes, the minimum angle in the runner domain is low compared to the recommended value of 20 degrees; however, the percentage of the mesh with a poor minimum angle represents less than 1% of the runner mesh. The average \(y^{+}\) on several solid walls for the BEP is given in Table 3. It is obvious that the \(y^{+}\) in the runner for the structured mesh is high compared to some standard requirements. This point will be further discussed in the Results Section.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Sub-Domain**} & \multicolumn{2}{c}{**Structured Mesh**} & \multicolumn{2}{c}{**Unstructured Mesh**} \\ \cline{2-5}  & **Nodes (10\({}^{6}\))** & **Elements (10\({}^{6}\))** & **Nodes (10\({}^{6}\))** & **Elements (10\({}^{6}\))** \\ \hline Spiral Case & 3.7 & 3.6 & & \\ Stay Vanes & 2.9 & 2.8 & 2.8 & 8.9 \\ Guide Vanes & 3.7 & 3.5 & & \\ \hline Runner & 2.8 & 2.6 & 2.2 & 6.6 \\ \hline Draft Tube & 1.6 & 1.5 & 0.8 & 2.7 \\ \hline Total & 14.7 & 14.0 & 5.8 & 18.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of nodes and elements for each mesh.  
Figure 3: (**a**) Structured hexahedral mesh, meridional plane view. (**b**) Structured hexahedral mesh, zero machine plane view. (**c**) Unstructured mesh, meridional plane view. (**d**) Unstructured mesh, zero machine plane view.  
Figure 2: (**a**) View of the CFD domain with the standard draft tube. (**b**) View of the CFD domain with the extended draft tube.
## 4 Discussion
### Development of Disintegration Model
As indicated by Equation (11), when \(k\) is held constant, incremental variations in \(d\) scarcely impact the shape of the disintegration curve, but they lead to a gradual reduction in the intercept of the curve. Figure 16: Comparison between test results and model for stabilized soil with lime: (**a**) 90% compactness; (**b**) 100% compactness. Figure 17: Comparison between test results and model for stabilized soil with lime and cement: (**a**) 90% compactness; (**b**) 100% compactness. Conversely, with \(d\) held constant, higher values of \(k\) result in varying gradients within the major segments of the disintegration curve and can even induce alterations in its overall shape. Changes in the model parameters of \(d\) and \(k\) correspond to modifications in the disintegration curve, characterizing the disintegration properties of the stabilized silty clay. Notably, the parameter \(d\) signifies the intercept of the disintegration curve, while parameter \(k\) governs the slope of the curve. As a result, using the Weibull model to analyze the progression of disintegration breakage is considered appropriate due to the remarkable agreement observed between the test results, which encompass the \(P_{i}-t_{i}\) correlation and the Weibull model. Furthermore, the parameters \(d\) and \(k\) possess precise and well-defined mathematical interpretations.
## 3 Measurement Weighting and Exclusion (WE)  
This section is aimed at explaining the proposed WE function used to reduce the effects of multipath. The development of the WE function is based on a theoretical and experimental assessment of the relation between the signal propagation conditions and the received correlation recordings at both polarizations. For instance, due to signal propagation, if the LHCP component is stronger than the RHCP one, it means that the signal has been received under NLOS conditions [11]. On the other hand, a positive RHCP to LHCP ratio in dBs but with a high signal strength in the LHCP component, would mean that the signal has been received under multipath conditions. In both cases, the user range errors of the corresponding signals would be increased with respect to the nominal errors. In order to mitigate these effects, in the former case it would be interesting to exclude the signal from the PVT computation, whereas in the second case, it would be interesting to weight it down. The key question is how to derive a proper threshold to exclude measurements and a proper function that provides an appropriate value for the weight. In the following, we will provide answers to these two questions. In particular, the rest of the section is organized as follows: Section 3.1 illustrates the design of the exclusion function, whereas Section 3.2 introduces a novel weighting design based on the 2 pol antenna.
# Application of Polymers as a Tool in Crystallization--A Review
## 2 Basic Research
### Discussion and Conclusions
#### 3.2.5 Other Drugs  
[PERSON] et al. reported studies on biomimetic polymer-directed crystallization techniques applied to the crystallization of atorvastatinatin (a popular drug belonging to statins) [82]. In the presence of poly(ethylene glycol) (PEG), polyethylene imine (PEI), hydroxypropyl cellulose (HPC), polyethylene oxide-polpropylene oxide triblock copolymer (PEO-_b_-PPO-_b_-PEO) and PAA, nucleation and growth of atorvastatinatin at improved conditions led to obtaining composite crystals with significant polymer contents and unusual characteristics, such as a decreased melting point, improved stability, and sustained-release patterns.  
[PERSON] et al. presented a natural polyphenol-based supramolecular metallogel system for active pharmaceutical ingredients crystallization such as caffeine, carbamazepine and piroxicam (an anti-inflammatory drug) [83]. Advantages of this system include ease of preparation, use of inexpensive components and the ability to incorporate diverse additives. [PERSON] et al. reported the use of polymer microgels, formed by cross-linking polyethylene glycol diacrylate of varying PEG subchain molecular weights, for achieving selective crystallization of carbamazepine polymorphs [84]. They showed that polymer microgels with tunable microstructure are promising materials for controlling crystal polymorphism.  
[PERSON] et al. presented a novel method for continuous polymer coating of grisgefulvin crystals based on solid hollow fiber cooling crystallization [85]. The Eudragit RL100 polymer precipitated from the solution and coated the suspended crystals due to rapid temperature reduction and heterogeneous nucleation.  
[PERSON] et al. studied in detail the effect of ethyl cellulose on the recrystallization of agomelatine (a drug used in patients suffering from a depressive disorder associated with insomnia) from a molten state [86].  
Finally, it is worth mentioning several review papers which, in depth, tackle the subject of novel solutions regarding crystallization of drugs in the presence of polymers. For example, [PERSON] et al. wrote an overview on the use of template-induced crystal nucleation for formulating drug substances and enabling nucleation and polymorphic control during continuous manufacturing of active pharmaceutical ingredients [87]. [PERSON] et al. in their review paper presented recent findings in the primary heterogeneous crystallization with specific emphasis on its pharmaceutical applications [88]. [PERSON] et al. discussed how excipients or formulation variables such as type of solvent, presence of hydrophilic adjuvants, surfactants, dyes and colorants improve stability, modify release and improve physicochemical properties of dosage forms [89].
# Design and Diagnostics of High-Precision Accelerator Neutrino Beams
## 8 Decay Section
### Flux Monitoring at the Neutrino Factories  
Non-conventional beams like the above-mentioned Neutrino Factories offer interesting opportunities to perform precise flux measurements. Since in a Neutrino Factory the neutrinos are created by muons stored in a ring, most of the diagnostics of conventional accelerators can be embedded in such a ring. The nuSTORM Collaboration has envisaged this possibility in Ref. [98]. The decay ring is a set of FODO cells (focusing and defocusing quadrupole lattice) and steering magnets that confine the muons (\(E_{\mu}\simeq 5\) GeV) in a circular ring with two long straight sessions (Figure 11). The circulating muon intensities is measured by a toroid-based Fast Beam Current Transformer (FBCT). One of these devices has been developed at CERN [142] and is shown in Figure 12. A one-turn calibration winding and a 20-turn secondary winding picks up the induced current by the charged particles revolving inside the ring. The windings are wound on a magnetic core and housed in a 4-layer shielding box. This system achieves a precision of 10 uA in a 10 MHz bandwidth. It is therefore useful for fast extractions where the muons are bunched in tens of us up to ms. This class of devices can be used also in other monitored neutrino beams except when the extraction is very long and the induced current goes below the sensitivity of the instrument. In [98], the expected precision on the muon flux is 1%, which propagate to a precision in \(\phi(v_{\mu})\) and \(\phi(v_{e})\) of similar size since the muon-to-neutrino flux is driven only by the three-body kinematics of the muon decay and the betatron oscillations along the ring.  
Figure 11: Schematics of the nuSTORM facility. **(top)** the pions focused upstream by a horn (not shown) are transported and injected into the storage ring. **(bottom)** the storage ring made of a large acceptance lattice with quadrupoles and bending dipoles. The muons from pion decays produce neutrinos of both electron and muon flavor mostly in the straight session. The Beam Current Transformer is located inside the beampipe of the storage ring.