esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment and Result Analysis
### Determination of Network Structure Parameters  
In light of the dataset's size, the number of convolution kernels in the first layer was set to 8. Simultaneously, experiments were conducted to compare varying numbers of residual attention units in order to select the optimal number. Assuming that the number of convolution kernels of the residual attention unit in the first layer is 16, the number of convolution kernels in the nth layer will be \(16\times 2^{n-1}\). We assess the model's performance with the number of residual attention units ranging from 1 to 6, and the experimental results are displayed in Figure 8.  
In Figure 8, the horizontal axis represents the number of residual attention units, while the vertical axis represents the value of each evaluation index. As can be seen from the figure, when the number of residual attention units is 3 and 5, the three evaluation indicators of MeanRE, MaxRE, and mean RMSE are significantly smaller than other values. Therefore, the optimal number of residual attention units should be either 3 or 5. Upon closer observation, it is apparent that when the number of residual attention units is 3, the three evaluation index values of MeanRE and RMSE are smaller than when the number of residual attention units is 5. Simultaneously, when the performance of the model does not vary significantly, a smaller model is preferable, consider the constraints of model size and physical memory.  
Ultimately, the structure of the MRAB-CNN model is determined as follows. The first layer consists of the mixed features after screening. After convolution and fusion of the two convolution kernels, the feature fusion layer is obtained. The attention mechanism is then added to the feature fusion layer to obtain the fusion attention layer (Attention 1), followed by the connection of three residual attention units, with the number of channels being 16, 32, and 64. Global average pooling is performed on the last layer of the residual  
Figure 8: Residual attention unit comparison diagram.  
attention unit, leading to the fully connected layer. Simultaneously, the fusion attention layer is connected to the fully connected layer to form a large residual structure. Finally, the model prediction is made.
## 6 Conclusions and Future Directions  
The C-IoT shows great potential in supporting pervasive connectivity and providing wide area coverage in the near future. In this paper, we review the C-IoT architecture for MTC and present the challenges when deploying green C-IoT. Based on the unique features and challenges, the energy efficient and energy harvesting schemes are proposed. To validate the effectiveness of the proposed schemes, a practical use case is provided. With the proposed three-tier C-IoT architecture, it is our aim to provide ubiquitous and seamless connectivity to MTC devices, while the network management and control are not taken into account. To address this issue, the SDN can be combined with the C-IoT, where the centralized controller can carry out the functions of flexible and programmable network management and control. This remaining issue will be left for future studies.  
In the future, the research directions for implementing the C-IoT are summarized as follows:  
* The C-IoT is a fresh and emerging network architecture for supporting massive connectivity, whose market model is not mature in practice [48]. Therefore, the service mode, charging policy, and integration with the traditional service need to be carefully considered. Meanwhile, the inter-inhibitive relationships among subscribers, ISPs, and stakeholders ought to be balanced.
* The fog nodes should not only be limited as the communication relay with computing capabilities, but should also be equipped with more functions, such as resource allocation, scheduling optimization, and some self-adaptive predictions. All these functions mentioned above pose great pressure on fog nodes themselves, both in the hardware and software design, which will be left for further research.
* When IoT devices are employed in the cellular network, the compatibility issue is worth considering. Confronted with different spectrum bands and control signaling, some related  
Figure 5: The total power consumption under different scheduling modes.  
changes and updates may have to be carried out. At the same time, the cost of these changes and updates should be controlled at a lower level.
* The realization of software-defined C-IoT is still a challenging issue. According to different IoT applications and various service requirements, the software design seems tough, and the subsequent changes in hardware should also be considered.  
Conceptualization, Y.R., X.Z. and G.L.; methodology, Y.R.; investigation, Y.R. and X.Z.; writing, original draft preparation, Y.R.; writing, review and editing, Y.R. and X.Z.; supervision, X.Z. and G.L. All authors read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grants 61801382 and 62071377, in part by the Natural Science Foundation of Shaanxi Province (2020)Q-849), and in part by the Innovation Team Project of Shaanxi Province (2017 KCT-30-02).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Measurement of Intracellular Free Calcium Concentration ([Ca\({}^{2+}\)]\({}_{i}\))  
Spettrofluorimetric measurement was performed according to [PERSON] et al.'s [20] protocol. Briefly, LoVo cells (1 \(\times\) 10\({}^{6}\)/mL) were washed in KRH, dissociated, and loaded for 30 min at 37 \({}^{\circ}\)C, with Ca\({}^{2+}\)-sensitive dye, fura-2 acetoxy-methyl (Calbiochem) ester, added to the final concentration of 2 uM with 0.2% bovine serum albumin. Loaded cells were resuspended in the incubation medium, supplemented with 2.5 \(\times\) 10\({}^{-4}\) M sulfipyrazone to prevent dye leakage, transferred in a quartz thermostated cuvette (37 \({}^{\circ}\)C), and kept under continuous stirring as a homogeneous population. Fluorimetric measurements of [Ca\({}^{2+}\)]\({}_{\mathrm{i}}\) were performed on a homogeneous population of LoVo cells with an LS-50B luminescence spectrometer (Perkin Elmer Life Sciences, Waltham, MA, USA) with a double excitation wavelength (340 and 380 nm) and an emission wavelength (510 nm). Data were collected every 40 ms and calibrated in terms of [Ca\({}^{2+}\)]\({}_{\mathrm{i}}\). The fura-2 fluorescence response was calibrated using calcium standards (Calcium Calibration Buffer Kit, Molecular Probes, Invitrogen, Karlsbad, CA, USA) in the absence of cells, as described in [20; 21; 22]. Baseline [Ca\({}^{2+}\)]\({}_{\mathrm{i}}\) was measured for 100 s without any addition. Then, BDK was quickly added via pump injector at the final concentration of 100 nM. The resultant changes in [Ca\({}^{2+}\)]\({}_{\mathrm{i}}\) were monitored for 8 min. Baseline [Ca\({}^{2+}\)]\({}_{\mathrm{i}}\) levels were 80-100 nM. Cells with baseline \(>\) 100 nM were not considered for the study.
## 3 Experiment Setup for Homodyne Detection
The voltage signal of TeraFET can be defined by the following formula:  
\[V_{\text{det}}(\Delta L)\propto\frac{1}{2\pi/\omega}\int_{0}^{2\pi/\omega}[A_{1 }\sin(kL-\omega t)+A_{2}\sin(k(L+\Delta L)-\omega t)]^{2}dt. \tag{4}\]  
where \(A_{1}\) and \(A_{2}\) are the amplitudes of the first and second beams, respectively, and \(k\) is a wavenumber, which is related to a wavelength \(\lambda\) or a frequency as \(k=2\pi/\lambda=\omega/c\). The equation can be reduced to the following form:  
\[V_{\text{det}}(\Delta L)\propto\frac{A_{1}^{2}+A_{2}^{2}}{2}+A_{1}A_{2}\cos( k\Delta L). \tag{5}\]  
The first term is the average power. By changing frequency, the \(\omega/c\Delta L\) varies from 0 to \(2\pi\), and the envelope of the detector's response dependency on an excitation frequency \(V_{\text{det}}(\omega)\) has a cosine form (so-called fringe spectrum, see the magnification in Figure 3b). We can also change a phase with mechanical XY stages or opaque materials, such as a paper Si attenuator. The detector in the homodyne detection setup exhibits the same noise as for direct detection; however, it provides the difference signal in addition, which is not influenced by the noise of the THz source. The main advantage of homodyne detection is the ability to detect weaker excitation compared to a direct detection scheme.
# Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning
## 4 Discussion  
The purpose of this study is to propose a fault diagnosis method for railway point machines using current signals and machine learning techniques. By making use of the benefits of current-signal measurements, the implementation of sensor monitoring is safer since it can be installed inside the equipment room of the station; therefore, the measurements do not interfere with operations since the current sensor senses the current based on electromagnetic principles. In addition, the installation cost is low because it uses only one sensor. The usage of the current sensor in the suggested technique for monitoring RPM equipment will also contribute to cost efficiency. Time-domain feature extraction based on scalable hypothesis testing aids in the identification of important features in fault diagnosis, avoiding overfitting or underfitting in the machine learning model. The chosen characteristics constitute the dataset for machine learning modeling, and the optimum parameters are obtained and utilized to train the ideal models using grid search 5-fold cross-validation. Two ML algorithms, DT and RF, performed very well in classifying RPM conditions, and the ANN and SVM algorithms also showed good accuracy. However, the other algorithms, LR and KNN, performed poorly, especially in classifying conditions 1 and 2. The primary explanation for this is the similarity of the current-data patterns between conditions 1 and 2. However, the DT and RF algorithms could manage these issues and correctly classify the RPM situations.  
Table 4 compares earlier works on current-signal-based fault diagnosis for railway point machines. The method proposed in this study for classifying RPM equipment conditions using current signals demonstrated excellent accuracy in the application to fault diagnosis with multi-classifications, whereas previous works only investigated binary classifications in the fault detection of railway point machines.
## 4 Results
### Analysis of MSC Proliferation  
After cultivation of hMSCs in plasma, each day, the cells were counted. The experiment ended at day 3. In plasma, hMSCs demonstrated a significant increase in proliferation compared to cultivation in DMEM-EM. Figure 2 shows that hMSCs proliferated almost 2.3-fold faster in plasma compared to DMEM-EM.  
Figure 1: Proof of multipotency. Human mesenchymal stem cells’ (hMSCs) differentiation towards osteogenic and adipogenic lineages was compared with those cultivated in DMEM-EM (**A**,**C**) and plasma (**B**,**D**). The adipogenic differentiation was confirmed with the Oil Red O staining (**A**,**B**), showing lipid droplets as red spots. The osteogenic differentiation was confirmed with the Van Kossa staining (**C**,**D**), showing calcium deposits as dark dots.  
Figure 2: Cell proliferation analysis. hMSC proliferation was evaluated. The significance analysis was assessed as described above. Each day the cells were counted. hMSCs in plasma showed a significant enhancement of proliferation compared to cultivation in DMEM-EM at day 2 and 3. * indicates the statically significant changes.
## 2 Results and Discussion
### Evaluation of the Effect of Heating/Brewing Conditions on Atropine and Scopolamine  
#### 2.3.1 In Standard Solutions  
To evaluate how heating conditions affect the stability of atropine and scopolamine, standard solutions were prepared at two different concentrations, 0.2 and 4 ng/mL. The standards were boiled for 5 and 10 min, simulating the more drastic brewing technique by decoction (see Section 4.3.1). The peak areas obtained after HPLC-MS/MS analysis were interpolated into solvent calibration curves. The results obtained are shown in Figure 3. As can be seen, no degradation was observed in standard solutions at any of the concentrations or times studied. These results demonstrate that atropine and scopolamine are quite heat-stable under the studied conditions and that the brewing techniques usually used to prepare infusions (97 \({}^{\circ}\)C and allowed to cool for 5 min) or decoctions (boiling for 5 and 10 min) do not affect the presence of these toxins in the brew. This is an important point because under real tea-making conditions, the TAs found in the weed impurities of tea samples would be less exposed to heat in the plant matrix than when they are added as standard solutions, so they are more likely heat-stable.  
A previous study by [PERSON] et al. [32] that simulated tea-making conditions in vials spiked with standard solutions (100 \({}^{\circ}\)C, left to cool for 5 min) showed degradation of atropine and scopolamine by up to 32%. However, this study was carried out at a high concentration, specifically 5000 ng/mL, which is 4 orders of magnitude higher than the maximum amount allowed for liquid herbal infusions (0.2 ng/mL).
## 4 Demand Side Management System under Evaluation  
For field measurements, three different sites are selected in Lahore, Pakistan, for comprehensive investigation of the NB-PLC-based DSM control technique. The feasibility analysis of the NB-PLC control signal for DSM services is performed on all the sites while considering all possible types of outdoor power line channels. An important aspect of these field measurements is to include the NB-PLC characteristics during the signal transmission through the LV/MV channel and transformer bridge. Figure 1 shows the measurement setup including the NB-PLC system architecture for the DSM control. The sites under evaluation include various types of electricity consumers, which provides the opportunity to access the type of power line channels, from a simple to a hostile environment for NB-PLC channels including industrial, commercial and industrial loads.  
In the following subsection, the key properties for the chosen sites for LV/MV NB-PLC channels are analyzed in detail. Every LV site has an energy meter installed, so that the primary control room receives data from LV sites and stores it. This data is also sent to secondary station MCR, which is present at the primary substation. To make this process efficient, MV couplers enabled with a voltage detection system (VDS) are used. The main reason for using VDS for this purpose is because it decreases voltage fluctuation by employing two series capacitance. Voltage detection system is a very popular method for finding the main voltage by considering the IEC 61234-5 standard [32; 33].
## 3 Results and Discussion
### Alcohols
& & & & \\ \hline \multirow{8}{*}{Alkanes} & undecane & \(5.11\) & \(25.88\pm 3.79\) & \(17.07\pm 5.76\) & \(16.98\pm 7.10\) & \(12.39\pm 5.02\) \\  & dodecane & \(6.11\) & \(1.63\pm 0.12\) & n.d. & \(3.66\pm 3.65\) & n.d. \\  & pentadecane & \(9.99\) & \(8.47\pm 0.46\) & n.d. & \(5.74\pm 4.90\) & n.d. \\  & hexadecane & \(11.72\) & \(8.03\pm 1.06\) & n.d. & \(4.72\pm 3.14\) & n.d. \\  & heptadecane & \(13.60\) & \(6.26\pm 0.78\) & n.d. & \(3.83\pm 2.37\) & n.d. \\  & octadecane & \(15.53\) & \(5.61\pm 0.27\) & n.d. & \(3.15\pm 1.82\) & n.d. \\  & nonadecane & \(17.49\) & \(5.42\pm 0.94\) & n.d. & \(1.92\pm 0.95\) & n.d. \\  & eicosane & \(19.41\) & \(4.27\pm 0.39\) & n.d. & \(1.28\pm 0.36\) & n.d. \\ \hline \multirow{8}{*}{Alcohols} & 1-hexanol & \(7.89\) & \(2.37\pm 0.24\) & n.d. & \(6.09\pm 5.51\) & n.d. \\  & 3-octanol & \(8.42\) & \(9.39\pm 7.27\) & \(6.32\pm 3.04\) & \(330.6\pm 75.9\) & \(65.42\pm 26.73\) \\  & 1-octen-3-ol & \(9.27\) & \(491.6\pm 208.5\) & \(178.3\pm 29.1\)* & \(341.4\pm 131.5\) & \(90.19\pm 16.59\) \\  & 2-ethyl-1- & \multirow{8}{*}{\(9.89\)} & \multirow{8}{*}{\(1.68\pm 0.34\)} & \multirow{8}{*}{\(0.99\pm 0.44\)} & \multirow{8}{*}{\(3.06\pm 1.44\)} & \multirow{8}{*}{\(0.94\)} \\  & hexanol & \(11.06\) & \(23.26\pm 14.56\) & \(2.98\pm 1.22\) & \(9.47\pm 6.45\) & \(0.59\pm 0.03\) \\  & phenylethyl & \multirow{8}{*}{\(17.79\)} & \multirow{8}{*}{\(1.83\pm 0.38\)} & \multirow{8}{*}{n.d.} & \multirow{8}{*}{n.d.} & \multirow{8}{*}{n.d.} \\  & alcohol & & & & \\ \hline \multirow{8}{*}{Aldehydes} & 2- & \multirow{8}{*}{\(3.17\)} & n.d. & \(7.70\pm 9.16\) & n.d. & n.d. \\  & methylbutanal & \multirow{8}{*}{\(3.29\)} & n.d. & \(14.94\pm 2.35\) & n.d. & \(7.10\pm 1.55\) \\  & pentanal & \(3.89\) & n.d. & n.d. & n.d. & \(15.73\pm 6.19\) \\  & hexanal & \(4.98\) & \(78.91\pm 25.55\) & \(42.08\pm 27.17\) & \(17.26\pm 7.81\) & \(5.50\pm 2.03\) \\  & trans-2-hexneal & \(6.37\) & \(1.59\pm 0.39\) & n.d. & \(0.39\pm 0.19\) & n.d. \\  & octanal & \(7.14\) & \(8.04\pm 2.42\) & n.d. & \(4.89\pm 2.53\) & \(1.68\pm 1.10\) \\  & 2-heptenal & \(7.60\) & \(28.93\pm 15.73\) & n.d. & \(10.32\pm 1.61\) & n.d. \\  & 2-octenal & \(9.04\) & \(42.12\pm 16.00\) & \(15.47\pm 17.66\) & \(14.89\pm 4.34\) & \(1.17\pm 0.14\) \\  & benzaldehyde & \(10.56\) & \(15.40\pm 6.16\) & \(8.71\pm 4.57\) & \(3.60\pm 3.21\) & \(4.21\pm 1.62\) \\  & phenylacetaldehyde & \(12.59\) & n.d. & \(5.61\pm 2.88\) & n.d. & \(12.91\pm 6.05\) \\  & 2,4-nonadien & \(13.74\) & \(11.13\pm 7.91\) & n.d. & \(1.53\pm 0.68\) & n.d. \\  & 2,4-decdenal & \multirow{2}{*}{\(15.81\)} & \multirow{2}{*}{\(2.92\pm 1.43\)} & \multirow{2}{*}{n.d.} & \multirow{2}{*}{n.d.} & \multirow{2}{*}{\(1.73\pm 0.26\)} & \multirow{2}{*}{n.d.} \\  & (\(E\),\(E\)) & & & & \\ \cline{1-1}  & 2-phenyl-2- & \multirow{8}{*}{\(17.92\)} & \multirow{8}{*}{n.d.} & \multirow{8}{*}{n.d.} & \multirow{8}{*}{n.d.} & \multirow{8}{*}{n.d.} & \multirow{8}{*}{\(1.87\pm 1.15\)} \\  & butenal & \multirow{8}{*}{\(20.64\)} & \multirow{8}{*}{n.d.} & \multirow{8}{*}{n.d.} & \multirow{8}{*
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 5 Byproducts and Challenges
### Challenge: Sidelobes
research effort. As a result, the use of ML methods for the structure of the predistortion function as well as the coefficients may be particularly useful in the very-low-frequency, constantly-on environment of the distribution grid. In addition to valuable use in predistortion and other communication issues, ML methods may be particularly useful in the disambiguation of feeder and phase as well as localization of disturbances in geospatial and schematic contexts. ML techniques may result in the ability to rapidly decompose these disturbances or anomalies into a \"feature set\" which could easily be transferred upstream, retaining sufficient fidelity for locating the anomalies among the several feeders and phases that converge at a substation. By locating and categorizing these anomalies, and by assigning a probability of accuracy, the accuracy estimate would improve over time as more anomalies with better characteristics occurred on each phase/feeder. The ability to map this in real time over a large area, leveraging geospatial data from smart meters as well as electrical information from the matching anomalies, could be quite valuable for distribution operators. As previously described, in most cases, geographic information systems (GIS) are unable to reconcile infrastructure requirements to schematic realities, particularly after a modification event (e.g., storm, phase balancing, etc.). As a result, the architecture proposed here is a form of large-scale \"learning\" that can be bootstrapped by several approaches, including a correlation receiver [41] or equivalent at the upstream location as well as a recognition or decomposition system at the downstream location(s). Subsequent sections of this paper describe conventional ML techniques and proposes the use of these techniques in the context of voltage optimization (\(\overline{\text{DVO}}\)) as well as disambiguation of distribution infrastructure via current disturbances.
# MT-FBERT: Malicious Traffic Detection Based on Efficient Federated Learning of BERT
## 3 Mt-Fbert
### The Pre-Training Stage  
#### 3.2.1 Traffic Data Pre-Processing  
Network traffic differs greatly from natural language and images in that it does not contain human-understandable content and explicit semantic units. In order to effectively leverage the pretraining technique for traffic representation, traffic is transformed into a pattern-preserved token unit for pre-training. Figure 2 illustrates an example of the traffic data pre-processing.  
The traffic data is initially segmented into multiple flows, with each flow subsequently divided into multiple bursts. A flow is defined by a 5-tuple, comprising the source and destination IP addresses, source and destination ports, and the protocol. A burst is defined as a sequence of consecutive packets originating from either the request or the response within a single flow. Each packet is represented as a string of hexadecimal numbers. These hexadecimal strings are encoded in bi-grams, where each unit consists of two adjacent bytes, and each byte is represented as a 4-digit hexadecimal string. The Byte Pair Encoding (BPE) algorithm is subsequently applied for token representation, where each token unit is a 4-digit hexadecimal string with a value ranging from 0 to 65,535. The vocabulary size is specified as a maximum of 65,536. In addition, special tokens such as [CLS], [SEP], [PAD], and [MASK] are added for training tasks. [CLS] is added to the beginning of every sequence as the first token. [SEP] is used to separate sequences. [PAD] pads sequences to the maximum length to satisfy the requirement. [MASK] is used for masked language modeling tasks during pre-training to learn the context of the traffic.  
Figure 2: An example of the traffic data pre-processing.  
Each token is represented by three types of embeddings: token embedding, position embedding, and segment embedding. Token embedding refers to the representation of tokens learned from a sequence of 4-digit hexadecimal string token units, as illustrated in Figure 2. The final embedding dimension of the token is set to 768. Position embedding enables the model to learn temporal relationships between tokens via relative positioning. We assign a 768-dimensional vector to each token to encode its sequential position information. As shown in Figure 2, a burst is equally divided into two segments, which are distinguished by the [SEP] token. The segment embedding is to indicate whether a token belongs to the first segment or the second segment. And the segment embedding dimension is set to 768. The final token representation is constructed by summing these three embeddings as shown in Figure 3. Initially, the embeddings are randomly initialized, where the embedding dimension is 768, and subsequently fine-tuned through multiple iterations of transformer encoding.