esa-sceva/satcom-chunk-collection huggingface dataset:
# MT-FBERT: Malicious Traffic Detection Based on Efficient Federated Learning of BERT
## 3 Mt-Fbert  
### Framework  
The proposed MT-FBERT contains two stages: pre-training for learning generic traffic representations with large-scale unlabeled data, and privacy-preserving fine-tuning for adjusting the pre-trained model for malicious traffic detection. The overall framework of MT-FBERT is illustrated in Figure 1.  
In the pre-training stage, the unlabeled traffic flows are transformed into vectors by an encoding layer, which contains token semantic encoding, word position encoding, and word segment encoding. Subsequently, these encoded vectors are then passed through a  
Figure 1: The framework of MT-FBERT.  
representation layer, which consists of a sequence of transformer layers. Each transformer layer (T-Layer) comprises a multi-head attention mechanism and a feedforward neural network. The attention mechanism establishes connections between each token to enable its representation by combining each token with other tokens. Finally, the token vectors are input into the task layer. The tasks in the pre-training stage include Masked Burst Modeling (MBM) and the Next Burst Prediction (NBP) tasks. The model computes gradients to optimize its parameters based on the losses received from these two tasks.  
In the malicious traffic detection stage, the model is initialized with the parameters obtained from the pre-trained stage, and then the federated learning method is adopted to collaboratively train the detection model among multiple organizations on labeled malicious traffic data. In order to save the communication cost of federated learning, there are two key modules: neuron compression and knowledge distillation, which are designed to compress the parameters of the model. The neuron compression performs layer compression in each transformer layer. The knowledge distillation conducts layer-level and neuron-level distillation before and during fine-tuning, respectively.
## 3 Nonlinear FE Analysis
### Validation of FE Model  
In order to verify the developed FE model, the ultimate shear capacity obtained from simulations was compared with the experimental data in Section 2.3. As shown in Figure 8, the numerical and experimental ultimate shear capacity of S-SWJ was 519.3 and 520 kN, respectively, and the difference between them was less than 1%. In addition, a simulation of S-CWJ was also conducted by using the same FE modeling method. The numerical result shows that the ultimate shear capacity of S-CWJ was 270.7 kN, and the corresponding experimental result was 300.2 kN; thus, the difference is 9.8%. It can be seen that the ultimate shear capacity obtained from the simulations has a good agreement with experimental results. Therefore, the present FE modeling method was validated as accurate and effective, and the same modeling method was used in the rest of this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Density \(\rho_{s}\)** & **Young’s Modulus \(E_{s}\)** & **Poisson’s Ratio \(v_{s}\)** & **Yield Strength \(\sigma_{ys}\)** \\ \hline
7900 kg/m\({}^{3}\) & 206 GPa & 0.3 & 400 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 5: Properties of steel bars.
## 4 Future Directions for Technology
### Physical Consistency Modeling and Interpretability Enhancement  
Although Deep Learning has demonstrated excellent fitting and generalization capabilities in reservoir seepage prediction, it is essentially a \"black box model\" with poor interpretability, and it is difficult to validate the results, especially in seepage systems with complex physical mechanisms and high nonlinear coupling, where the reasonableness of the model prediction and the engineering usability of the model are often questioned [34]. In order to enhance the interpretability and credibility of the model, researchers such as [PERSON] have gradually tried to integrate physical knowledge, a priori experience, and neural network structure and to explicitly guide the model learning process through the mechanism of \"physical constraints\", so as to realize the unity of data-driven and physical laws.  
[PERSON] et al. [2] proposed a physics-regularized DNN (PINN), in which the residuals of seepage control equations are embedded as a loss function term in the training process, to achieve physically consistent modeling for pressure field prediction in reservoirs with high WC [2]. [PERSON] et al. [21] further constructed Physics-Informed Neural Networks (PINNs) for multiphase seepage flow. Physics-Informed Neural Networks (PINNs) can reconstruct a high-dimensional continuous saturation field under incomplete boundary conditions and data scarcity conditions [20]. [PERSON] et al. [15], on the other hand, adopted a joint data-physics-driven DNN framework and explicitly introduced coupled control variables into the model structure, which improved the controllability and interpretability of the complex response process in the reservoir [15].  
In addition, in order to improve the observability of the internal structure of neural networks, some studies in recent years have introduced techniques such as the attention mechanism, feature attribution methods, and local response visualization to analyze the decision logic of the network and preliminarily reveal the influencing factors and physical motives behind the prediction results [34]. Although the relevant research is still in the exploratory stage, the combination of physical-data fusion modeling and interpretable AI techniques provides an important path to enhancing the engineering credibility and the value of Deep Learning models for generalized applications [62]. It will be necessary to carry out more in-depth research in the form of physical constraints, multivariate constraint synergy mechanism, and visualization and diagnostic tools.
## 3 Description of Research Methods  
### Automatic Modulation Recognition  
Automatic modulation recognition is a method that employs machine learning techniques to classify and identify radio signals. In this task, deep learning can be used to autonomously learn the features of modulation signals and classify them. The advantage of deep learning is its ability to automatically extract features, eliminating the need for manually designed feature extraction algorithms. The input data to the network can be the raw signal data, and the deep neural network, acting as a combination of a feature extractor and classifier, can self-learn patterns in the data. Based on these patterns, it can classify the data, realizing an end-to-end modulation pattern-recognition process.
significant environmental impacts that require systematic evaluation using energy and environmental assessment methods to identify improvement potential.  
However, some characteristics of cooling systems make it difficult to analyze their energy efficiency and environmental impact. Firstly, the uniform quantification of the physical benefits of cooling units across plants is challenging. In life cycle assessment (LCA), the 'quantified performance of a product system for use as a reference unit' is called the functional unit (ISO 14040, [2]). Previous studies defined the functional unit of a cooling tower as '1 kWh of electricity produced by the [upstream] plant [...] referred to [...] 1 year' [3] (p. 1079), the '1 MW heat rejection capacity for a period of 1 year' [4] (p. 50), or the 'cooling of 1 kg water from 35 \({}^{\circ}\)C to 28 \({}^{\circ}\)C in Germany for the overall usage time' [5] (p. 140). These different functional units make comparing the performance of different units nearly impossible.  
Furthermore, cooling tower operation highly depends on ambient temperature and humidity. Dynamic analysis reveals this correlation, unlike a static analysis integrated over time. In addition, cooling systems often consist of several coolers and chillers, the interaction of which differs depending on the ambient conditions. In many cases, free cooling is sufficient at low outside temperatures, and yet, chilling technology must be installed for periods of high ambient temperatures. Therefore, it is difficult to evaluate the efficiency of individual cooling units. The electricity consumption of cooling towers is lower at high temperatures because the refrigeration unit covers the cooling demands and not because of higher efficiency.  
Several methods and indicators exist to evaluate the thermal performance of cooling towers. These indicators assess the evaporated water and heat transfer rate and include the tower characteristic ratio 'KaV/L' [6], the number of transfer units (NTU), effectiveness-NTU [7], and the Merkel number, as per [PERSON] [8], [PERSON] [9], and others. Moreover, the cooling tower effectiveness \(\epsilon\) is the ratio of actual and maximum heat transfer [10]. Hence, these indicators evaluate the extent to which the required temperature (\(\Delta\)t) [11] or the required heat transfer is achieved. However, more heat transfer is useless if the required heat transfer or temperature has already been achieved. Furthermore, electricity consumption is omitted; water consumption is only considered by the amount of evaporated water. These approaches calculate the water outlet temperature to design cooling towers but do not evaluate their energy efficiency and environmental impact.  
The following sections show that these challenges are handled differently across the different energy and environmental assessment methods. Based on these challenges and further aspects, we defined comparison criteria to allow for the selection of the most suitable method in each specific case.
## 4 Experimental Design
### Comparative Experiments on Attentional Mechanisms  
To assess the efficacy of the channel-spatial attention mechanism ECA-CBAM introduced in this research, we incorporated several modules into the YOLOv7 detection algorithm under identical experimental settings to examine the influence of each module on the model's performance. The results are shown in Table 1. The symbol \"\(\uparrow\)\" in the table indicates that the higher the value, the better.  
CAM denotes the channel attention module with a compression rate of 16, and ECA-CAM denotes the lightweight CAM. From Table 1, we can see that the E-CAM reduces the number of parameters by 4.51 \(\times\) 10\({}^{6}\), the computational cost by 7 \(\times\) 10\({}^{9}\) operations, the detection accuracy by 0.6%, and the detection speed by a factor of 14 in comparison to the CAM. The analysis concludes that the feature compression introduced by the CAM in the fully connected layer caused information loss. At the same time, E-CAM accomplishes cross-channel interaction using 1D convolution, which not only does not cause information loss but also reduces the complexity and is more effective. The ECA-CBAM enhances the detection algorithm's accuracy by 2.3% among the added attention modules. At the same time, the ECA-CBAM also ensures the use of several parameters, computation and the real-time performance of the algorithm.  
The precision-recall curves for the YOLOv7 model using the CBAM attention mechanism and the ECA-CBAM attention mechanism are shown in Figure 9. Figure 9a represents the use of CBAM, and Figure 9b represents the use of ECA-CBAM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Params/10\({}^{6}\)** & **FLOPs/10\({}^{9}\)** & **[EMAIL_ADDRESS]/\% \(\uparrow\)** & **FPS \(\uparrow\)** \\ \hline YOLOv7 & 36.54 & 104.6 & 85.1 & 87 \\ YOLOv7 + CAM & 42.61 & 129.4 & 85.7 & 53 \\ YOLOv7 + E-CAM & 38.10 & 122.4 & 86.3 & 67 \\ YOLOv7 + CBAM & 42.62 & 129.6 & 86.8 & 45 \\ YOLOv7 + ECA-CBAM & 38.10 & 122.4 & 87.4 & 67 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of comparative experiments on attentional mechanisms.
# Energy Analysis of Kalina and Kalina Flash Cycles Driven by Renewable Energy
###### Abstract  
The Kalina cycle (KC) has been recognized as one of the most efficient conversion systems of low-grade heat sources. The Kalina flash cycle (KFC) is a recently proposed novel cycle which is equipped with an additional flash process to the KC. In this study, the exergy performance of KC and KFC driven by a low-grade heat source are investigated comparatively. The dependence of the exergy destruction at each component as well as the system's exergy efficiency on ammonia concentration, separator pressure and, additionally, flash pressure for KFC, are systematically investigated. Results showed that KFC can be optimized with respect to flash pressure on the base of exergy efficiency, and the component where largest exergy destruction occurs varies for different separator pressure and ammonia fraction in both systems. It is also shown that the maxima of net power production and exergy efficiency in KFC with optimal flash pressure are superior to those in KC.  
Keywords:efficiency; exergy; exergy destruction; Kalina cycle; Kalina flash cycle; renewable energy; thermodynamics +
Footnote †: journal: Applied Sciences
## 4 Dual-Sharding Mechanism
### XOR Mapping Mechanism  
We propose a shortest suffix sharding mapping mechanism, based on the Kademlia protocol. When a new block is packaged in the blockchain network, it generates a unique hash value, which we select as the unique ID of the new block and represent in the binary tree. The block ID value is subjected to shortest suffix XOR operation with the ID value of the nodes in its assigned cluster, The specific number of bits of the operation is \(\lceil\) log\({}_{2}\)n\(\rceil\), and the \(s\) nodes associated with the operation from smallest to largest are selected as the nodes assigned for storage of this sharding. When some nodes in the network fail, the integrity of the data in the network can be maintained. The robustness of the system is ensured, even with the advantage of reduced redundancy of system storage.  
Because our solution uses a dual-sharding mechanism, it is necessary to determine which cluster a block is assigned to after it has been generated. The scheme performs a specific operation based on block number \(b\) and cluster number \(i\), to determine whether or not the cluster keeps the block, with the following operation rules:  
Define the operation \(\mid\), for any non-zero integers x, y.  
If x%y is not equal to 0, x \(\mid\) y = x%y.  
Otherwise: x \(\mid\) y = y.  
The initial value of block number \(b\) is 1, and it increases sequentially, \(b\) = 1, 2, 3,...  
Figure 2: Cluster division.  
Figure 3 shows the process of sequentially assigning new blocks to the cluster and mapping them to the final node storage, with \(m=4\), \(n=3\), and \(s=2\) as an example.  
1. The first generated block number is 1. The storage cluster location calculation formula yields \(11\,4=1\), determining that the block should be stored in cluster 1. Then, according to the last-bit XOR mapping mechanism, the XOR operation is performed between the ID and the last [log\({}_{2}\)n] bit of the ID of the node in the cluster. The two nodes with the smaller operation values are selected as the storage nodes, and the final nodes stored in this block are determined to be nodes 0000 and 0001;
2. The subsequently generated blocks numbered 2, 3, and 4 are calculated and stored in clusters 2, 3 and 4, respectively. The process of mapping blocks to nodes is the same as in (1).
3. When the block number is 5, the storage location calculation formula yields \(5\,|\,4=1\), determining that the block is stored in cluster 1. According to the last-bit XOR mapping mechanism, the final nodes for the block storage are nodes 0000 and 0010.
4. Continuing the process of (2) above, when the resulting block number is 9, the storage location calculation formula yields \(9\,|\,4=1\), determining that the block is stored in cluster 1. Then, according to the last-bit XOR mapping mechanism, the final nodes of the block storage are nodes 0001 and 0010.
## 3 Results
### Inhibitory Effect on Mature biofilms  
After assessing the effect on preventing biofilm formation, the CHG antib biofilm activity, combined in different formulations, on preformed mature biofilm, was evaluated. In this case, the tested concentrations were higher than those used in MIC and BPC assays (15-480 \(\mu\)g/mL). In fact, established procedures to test the antibofilm activity on mature biofilms highlighted that MIC concentrations used for planktonic cells are often quite ineffective against bacteria embedded in biofilms. This is probably due to the fact that a higher antimicrobial concentration is needed to penetrate the biofilm matrix [32].  
Results shown in Figure 2 highlighted that CHG and formulation 1 mouthwash have a significant effect in destructuring the preformed biofilm, even at low concentrations of active principles (60 \(\mu\)g/mL). However, these preparations failed to fully eliminate the preformed biofilm also when used at higher concentrations, and an average reduction of 50% in biofilm amount was reached. Conversely, no significant differences in the percentage of residual biofilm were observed for the other two mouthwashes, regardless of the concentrations tested. The Student \(t\)-test was carried out on data replicates at each concentration by comparing them with the CHG reference compound. Formulation 1 was the only one exhibiting a result statistically comparable to the CHG control.
## 3 Research Questions and Communication Efficient Methods
### RQ1-What Are Some of the Challenges Presented in FL with Regards to Communication?
They could be participating under unreliable network conditions. Factors such as having a difference between the upload speed and download speed could result in delays, such as model uploads by participants [30] to the central server, which could lead to a potential bottleneck, leading to disrupting the FL environment. * _Limited edge node computation:_ The computation is now dependent on edge devices rather than powerful GPUs and CPUs. The edge devices could have limitations towards computation, power resources, storage, and limited link bandwidth [53]. The authors in [53] compared the training time between a central server and an edge device. They elaborated that an image classification model with over 60 million parameters can be trained in just a few minutes over a GPU, reaching speeds of 56 Gbps. However, even with a powerful smartphone connected over 5G, it could take much longer, reaching an average speed of 50 Mbps. * _Statistical heterogeneity:_ Another possible source for a communication bottleneck or where communication costs can rise could be statistical heterogeneity, where the data are non-independent and identically (non-i.i.d.) distributed [54]. In an FL environment, the data are only locally present on each participating device. They are gathered and collected by the participants on their independent devices based on their usage pattern and local environment. An individual participant's dataset in an FL environment could not be representative of the population distribution of the other participants and their datasets [6]. The size of data gathered and distributed amongst devices can typically vary heavily [55]. Therefore, this type of fluctuation in the size of the dataset could affect communication by causing a delay in model updates and other attributes. A device with a larger dataset could take longer to update, whereas a smaller one could be carried out with updates. However, the global model might not be aggregated until all individual client models are trained and uploaded, causing a bottleneck. The aforementioned constraints listed are just some of the discovered challenges that could be possible sources towards creating a communication bottleneck.
# Internal Force Analysis of Buried-boring Piles in the Yuanzishan Landslide
## 2 Methodology and Validation
### Simplified Calculations of Buried-Boring Piles during a Landslide
#### 2.1.4 Internal Force of the Bored Pile below the Slip Surface
In order to determine the distribution coefficient of the driving force after a landslide has occurred, the internal force of the bored pile can be calculated using the method defined in this paper. The driving force of the landslide, \(R_{t}\), used in the calculation was 100 kN/m; the modulus of subgrade reaction of the soil, \(k_{1}\), was 30,000 kPa/m; the diameter of the pile, \(D\), was 0.4 m; the total length of the pile, \(L\), was 10 m; the length of the pile above the slip surface, \(h\), was 5 m; and the elastic modulus of the pile, \(E\), was \(3.0\times 10^{7}\) kPa. For distribution coefficients of the driving force of 0, 0.4, and 1, according to Equations (12) and (13) and Equations (16)\(and(17)\), the internal force of the bored pile was calculated and compared with [PERSON]'s results (Figure 2). The bending moment and shear force below the slip surface increased as the distribution coefficient of thrust increased (Figure 2). The shear force and bending moment of the bored pile calculated by our method exhibited a similar trend to the monitoring results, but the position of the maximum bending moment and the shear force of the pile below the slip surface were slightly higher. This is potentially due to a difference in the soil resistance in front of the pile, which may have affected internal forces. According to the calculation results of shear force and bending moment, when the distribution coefficient of the driving force was 0.4, our results closely matched the monitoring results of Lirer. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Geotechnical Classification** & **Moisture Content (\%)** & **Density \(\rho\) (g/cm\({}^{3}\))** & **Undrained Strength of Soil S\({}_{u}\) (kPa)** & **Internal Friction Angle \(\varphi\) (\({}^{\circ}\))** & **OCR** \\ \hline Soil & 19 & 2.05 & 50 & 27.23 & 2 \\ Varicolored clay & 15 & 2.22 & 100 & - & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Calculation parameters of geotechnical materials for the Masseria Marino landslide.
## 1 Introduction
### Afterimage and Visual Angle
#### 1.2.2 Visual Angle  
In the visual angle, an object is visible when observed. It is expressed in degrees of arc [36]. Visual angle is formed by the rays reflected from the eye above and below an object. It is used to indicate the size of the retinal image of an object. The larger the angle of view is, the larger the retinal image size will be. Figure 3 shows how the visual angle is formed.  
Figure 1: Positive afterimage instance: (**a**) original image, (**b**) reflection image.  
Figure 2: Negative afterimage instance, (**a**) original image, (**b**) reflection image.  
The angle of view is calculated by two values: the size of the object and the distance of the object from the eye. Equation (1) gives the mathematical expression of the visual angle [36].  
\[Visual\ Angle\ (V)=2\cdot\arctan(\frac{object\ size}{2*\ object\ distance}) \tag{1}\]  
Object size (\(S\)) is the distance between points A and B. It is the real size of the object. Object distance (\(D\)) is the distance of the object from the eye. It is the distance between O and B.  
The perceptual size of the observed object (\(S\)) is expressed by Equation (2) [36].  
\[S=V\cdot D \tag{2}\]  
As can be seen, after finding the \(V\) visual angle, the perceptual size of the observed object can be found.
# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 5 Discussion
### Impact of LoRa Research Proposals in the Smart City Initiative
#### 5.7.2 Data Integration Hypothesis  
A relevant characteristic of previous works based on LoRa is data generation. It is covered by LoRa proposals in the four layers of the Y.2060 recommendation described in the \"Materials and Methods\" section of this study; however, the data are not used significantly in decision-making processes, as mentioned in the \"Research Impact Contribution\" section. Solutions seeking to complement their contribution with the transformation of data into intelligence for decision-making are shown in Figure 22.