esa-sceva/satcom-chunk-collection huggingface dataset:
# CPF-UNet: A Dual-Path U-Net Structure for Semantic Segmentation of Panoramic Surround-View Images
## 4 Ablation Experiment and Discussion  
In this paper, we have extended the classic UNet architecture by integrating three innovative components: the Channel Attention Module (CAM), Position Attention Module (PAM), and Feature Fusion Module (FFM). To systematically investigate the performance gains brought by these three modules individually and in combination, we have conducted a series of ablation experiments. Firstly, we removed the FFM module while retaining CAM and PAM to explore the contribution of CAM and PAM in the process of global feature fusion. Secondly, we eliminated the CAM and PAM modules while only retaining FFM, aiming to independently validate the role of the FFM feature fusion mechanism in the overall network performance. The experiments showed that both groups achieved good results, with only a 0.7% compared to the network with all three modules added simultaneously. Since both the first two ablation experiments involved feature fusion operations, we speculated that the connection between the third and the last layers would bring significant benefits. Therefore, we focused on the third ablation experiment, completely discarding the three aforementioned modules and adopting a simplified feature fusion strategy, as follows: we upsampled the last layer features of the original UNet by four times, while compressing the channel dimensions of the lowest-level features through a \(1\times 1\) convolution kernel to match the dimensions of the third-layer features. After concatenation, the fused features were concatenated again with the corresponding third-layer features in the decoder. The experimental results showed that even without the CAM, PAM, and FFM modules, by skillfully combining high-level semantic information with low-level detail information, we could still achieve similar segmentation accuracy as UNet++. This strongly confirmed our hypothesis that the third and the lowest-level features in the decoder play a crucial role in the process of information transmission and fusion. As seen from the results, the individual CAM, PAM, and FFM modules did not bring significant benefits to the network. We believe that without FFM, the attention-weighted features extracted with CAM and PAM may not be effectively fused and utilized, while without CAM and PAM, FFM can only fuse the original features and cannot perform targeted feature selection based on task requirements. Therefore, only when the three are used jointly can a powerful attention mechanism and feature fusion capability be formed, improving the model's performance and surpassing the accuracy of UNet++. The specific parameters are shown in Tables 4 and 5.  
Among them, UNet-f represents adding only the FFM module, UNet-cp represents adding the CAM and PAM modules, and UNet-cat represents connecting only the third layer and the last layer.
## 4 Proposed LSTM Based Optimization of the CMM \(\mathbf{\Phi}\)
### Implementation Details of the Deep Learning Framework  
The proposed deep learning framework comprises a series of LSTM units and FCDNNs. An LSTM unit summarizes the historical observations into a fixed-dimensional cell state vector \(\mathbf{c}(t-1)\), which serves as a sufficient statistic for optimizing the CMM in the subsequent time instance \(t\). For a particular time snapshot \(t\), the input tensor \(\hat{\mathbf{Y}}(t)\), along with the cell and hidden state tensors \(\mathbf{C}(t-1)\) and \(\mathbf{H}(t-1)\), serves as input to the LSTM unit. The tensors \(\mathbf{C}(t-1)\) and \(\mathbf{H}(t-1)\) are formed by concatenating the vectors \(\mathbf{c}(t-1)\) and \(\mathbf{h}(t-1)\) for all \(B\) scenarios and all layers of the LSTM network. Based on the gate mechanism described in Equations (10)-(15), the cell and hidden states are updated adaptively. We then employ an \(L\)-layer FCDNN to map the cell state information \(\mathbf{C}(t)\) to design the CMM \(\mathbf{\Phi}(t)\) at time instant \(t\). The DNN output at time \(t\) is expressed as  
\[\mathbf{\tilde{\Phi}}(t)=\mathcal{A}_{L}(\mathbf{W}_{L}\mathcal{A}_{L-1}(\cdots\mathcal{ A}_{1}(\mathbf{W}_{1}\mathbf{G}(t-1)+\mathbf{b}_{1})\cdots)+\mathbf{b}_{L}), \tag{16}\]  
where \(\mathbf{W}_{l},\mathbf{b}_{l},\mathcal{A}_{l}\) are the weight, bias, and nonlinear activation function corresponding to the \(l\)th layer of the DNN, respectively. \(\mathbf{\Phi}(t)\) is the real valued representation of the complex valued CMM matrix at time \(t\), i.e., \(\mathbf{\tilde{\Phi}}(t)=[\mathcal{R}(\mathbf{\Phi}(t)\ \mathcal{I}(\mathbf{\Phi}(t)]\).
## 3 System Flexibility Digital Representation
### Data Modelling and Data Analysis
#### 3.3.2 Dynamic Prediction Model (DPM)  
Dynamic prediction model (DPM) is used to predict processing capacity of the production system (PS) in the face of device failure, and the model structure is shown in Figure 5. Each device in the PS has the possibility of failure during production. For processing devices, possible faults include tool wear, tool damage, machining accuracy deviation, etc., while for the material transfer devices, the possible failures include material loss, material pick up and put wrong, etc. Therefore, the Digital Twin Dynamic Prediction Model (DTDPM) in Figure 5 takes the random device failure rate as the input parameter of the model, and takes the system dynamic performance description model as the main body, considers the throughput of each schemes under the present device failure rate as the output, which is described by Formula (22).  
\[\text{DTDPM}=\{\text{Designed malfunction, System dynamic property, Predictive performance}\} \tag{22}\]  
System dynamic property \(=\{\text{Equipment layout, Processing technic, Processed products, Time sequence}\}\) (23)System dynamic property is a description model of the system dynamic performance, which is a data set collected from process, product and machine dimensionality cover the information of equipment layout, processing technology, processing product category, processing time sequence and other information reflected in the design scheme of the production system as in Formula (23). In the production system structure description metamodel, the basic structure of this information is described in detail. In the face of a specific production system design scheme, this information is all the instance information in the description metamodel.
## 2 Dynamic Modeling and Linearization
### Derivation of the Inertia Matrix
To construct the regression model, \(M(q)\) and \(\tilde{q}\) are reformulated as the product of the state regression matrix \(Y_{M}\) and the minimal parameter vector \(P_{M}\):  
\[\tau_{\text{inertia}}\ =Y_{M}(q,\tilde{q})\cdot P_{M} \tag{18}\]  
The main characteristic terms include polynomials such as \(Y_{M}\), \(cos(q_{i})\tilde{q}_{i}\), and \(cos(q_{i}+q_{j})\tilde{q}_{k}\), while parameter \(P_{M}\) contains variables related to mass inertia and inertial coupling, as shown in Equation (19),  
\[P_{M}=[m_{1},I_{1 xx},m_{1}x_{c1},\dots,I_{6 zz},m_{6}x_{c6}]^{T} \tag{19}\]  
In identification experiments, the observability of 1 can be improved by designing specific trajectory excitation parameters, thereby ensuring that the regression matrix is of full column rank [20]. The inertia matrix is the dominant component in the excitation-response process, especially during rapid acceleration phases, where parameter distortion can directly cause abnormal outputs in the control system and trigger structural oscillations. Therefore, accurate reconstruction of \(M(q)\) becomes a critical foundation for feedforward compensation and vibration source inference. This structure forms the core regression component of the linear identification process, serving as the basis for inertial parameter fitting. Moreover, its residuals are used as the input residuals for subsequent BLS training, providing the input source for friction disturbance modeling.
## 4 Experiment Setup  
The experiment aims to assess the effectiveness and universality of the proposed FSNT algorithm for feature selection using a public dataset. The benchmark algorithms employed include the XGBFS algorithm with embedded feature selection, the CSFFS feature selection algorithm based on filtering, and the RFIFFS feature selection algorithm based on hybrid methods. Additionally, six real datasets are utilized, employing various mainstream classification and regression algorithms, to perform comparative experiments, and evaluate the advantages and disadvantages of using the FSNT algorithm. Figure 1 presents the overall flowchart of the experiment.
# Anchor Design of a Ring Joint Based on Reliability in a Precast Shear Wall Structure
###### Abstract  
Precast buildings have been widely used owing to its outstanding features. As these buildings consist of a combination of various components, the mechanical properties of the connections play a critical role in the overall performance of the structure, particularly the vertical connections in shear wall members. A new connection called ring joint is proposed for precast buildings, and test pieces were designed to analyze the anchor performance. The damage phenomena and test data were observed and recorded. The displacement, force, and strain were analyzed, and the results indicated that the anchor length was a key factor and the connection was safe and reliable under reasonable design. Numerical analysis was conducted to explore the mechanical mechanism, and a bearing capacity model was proposed combining the bond and dowel effects simultaneously. Anchor length was determined based on reliability analysis under various conditions, and other suggestions were proposed. All these could guarantee the mechanical properties of the connection, as well as safety and reliability, and promote the popularization and application.  
Keywords:precast structure; ring joint; numerical analysis; bearing capacity model; reliability analysis +
Footnote †: journal: Applied Sciences
## 4 Results and Discussions  
### Analysis of Load Bearing Characteristics of Adjacent Pile Bases without Immersion  
#### 4.1.1 Load-Settlement Curve  
The base was finally loaded to 5 kN, and the load-settlement (Q-S) curve was drawn according to the collected data, as shown in Figure 6. The load-settlement curve had an obvious inflection point. And it can be divided into three stages: initial gradual stage (0-4 kN), rapid drop stage (4-4.5 kN), and later gradual stage (>4.5 kN) ([PERSON] et al., 2023). When the vertical load was 0-4 kN, the settlement value of the pile top increased linearly with the increase in vertical load. When the vertical load was 4-5 kN, the settlement value of the pile top increased rapidly, and the soil at the pile end became destroyed. When the vertical load was 4.5 kN, the settlement was 5.07 mm. When the vertical load was 5 kN, the settlement was 15.44 mm. The settlement of the pile top at the load of 5 kN was more than twice that when the vertical load was 4.5 kN. According to the Technical Code for Testing of Building Foundation Piles [51] for the Q-S curve of steep descent type, the load value corresponding to the point before the turning point of the apparent steep drop was taken as the ultimate bearing capacity of pile bases. So, the ultimate bearing capacity of pile bases was 4.5 kN. Considering the safety factor K = 2, the characteristic value of the vertical bearing capacity of the pile bases is 2.25 kN.
## 3 Results and Discussion
### Results of the Analysis of the Classification Model with GRU-Based Training
For male voices, the optimal choice was the use of the rectangular window with a window size of 20 ms with EZDDMFCC feature parameters and the CNN classifier. For female voices, the optimal choice was the use of the Hanning window with a window size of 35 ms with EZDDMFCC feature parameters and the CNN classifier. We saw better performances for the male class with respect to female. We do not think that the difference in the performances was caused by the imbalance of the two classes. It may be attributed to the difference in the speaking style of female speakers. As a result, it was observed that as the number of novel features increased, the success rate increased for female and male speakers. The windowing time and windowing techniques had an insignificant and similar contribution. The results demonstrate that CNN deep learning classifiers achieved higher classification performance compared to MLP and standard classifiers.
# Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration
## 3 PQ Mitigation Techniques
### Traditional Methods
#### 3.1.5 Static Synchronous Compensator  
The use of PQ correctors such as STATCOM is one of the solutions to recover the deteriorated PQ issues. It aims to help the WPP in situations of voltage dips, voltage regulation, and power factor control. Additionally, STATCOM can improve many PQ issues at the PCC such as improving transient stability and stabilizing power flow [33; 34]. Moreover, the STATCOM stimulates voltage stability by reactive power regulation. It injects reactive power compensation to or from the grid for voltage variations at the PCC. In the case of low voltage, the STATCOM injects reactive power to the grid to increase the voltage. When voltages levels are high, the STATCOM can absorb reactive power to decrease the system voltage. An example is presented in [33], explaining the effect of STATCOM in improving the voltage level in the system containing DFIG WTG. A 60 MVAR reactive load in the system causes a sudden voltage drop. Therefore, the STATCOM reactive power helps to increase the voltage level of the system. Additionally, the provision of reactive power at the WPPs connection point reduces the deviation of node voltage level from 0.946 p.u. to 0.979 p.u.
## 3 High-Performance Modulation Devices Based on Graphene  
In optical telecommunications scenarios, optical modulators are used to convert the encoding of electronic data to the optical domain. Any combination of photonic degrees of freedom can be utilized, for example intensity, phase, and/or frequency [28; 29; 30]. Afterwards low-loss optical fibers are used to achieve long distance communication links. Integrated silicon optical modulators allow an orders-of-magnitude decrease in size, weight and power in modulation systems, and thus have attracted great attention since their first demonstrations [30].  
Among these modulation mechanisms, intensity modulation is most widely applied due to its simplicity. There are two typical methods to realize intensity modulation in integrated silicon chips. One method is to directly alter the absorption of the active medium, and the other is to convert phase modulation to intensity modulation with interference structures, for example using a Mach-Zehnder interferometer (MZI) or a micro-ring resonator (MRR). Both methods rely on the efficient modulation of either the refractive index of the waveguide or the absorption of the active medium. For both types of phase shifter, graphene's impressive optical and electrical properties offer a significant enhancement in key performance metrics, such as bandwidth and power consumption.
## 5 Implementation of the Adaptive SMC and Detailed Circuital Simulations
### Comparison with a Classical Controller
The control structure of Figure 11 was implemented in PSIM, and the performances of both the classical solution (cascade PI structure) and proposed SMC were contrasted. Figure 12 shows the circuital simulation of both solutions, where the proposed SMC (right side) imposes the desired low-frequency behavior \(i_{LP}=i_{o}-i_{R}\) on the battery current; instead, the cascade PI structure (left side) introduces an undesired high-frequency transient  
Figure 11: Cascade PI structure. to the battery current. This undesired behavior is caused by the tracking error of \(G_{PI,c}\) observed in \(i_{L2}\) for the cascade PI structure, which is the result of the bandwidth limitation introduced in the linearization process; moreover, \(i_{L2}\) also exhibits an overshoot caused by the change on the operation point. Instead, the proposed SMC forces \(i_{L2}\) to track \(i_{R}\) with null error, thus ensuring the desired low-frequency behavior of the battery current. Figure 12 also presents, in the bottom waveforms, the behavior of \(v_{C2}\) for both controllers. The \(v_{C2}\) waveform imposed by the proposed SMC has the desired \(MO_{C2}\) and \(t_{s,C2}\) without any overshoot, thus requesting a low-frequency current in the battery in order to restore the energy in \(C_{2}\). Instead, the \(G_{PI,c}\) controller from the cascade PI structure, despite being designed with a unitary damping ratio, cannot avoid the voltage overshoot. In addition, \(G_{PI,c}\) imposes a higher derivative, thus requiring faster changes to the battery current, which is an undesired battery condition. The analog implementation of the cascade PI structure requires two adders, two PI controllers formed by two integrators, two gains and two adders, and a PWM circuit formed by a comparator, a triangular waveform generator and an adder. The analog implementation of the proposed SMC, depicted in Figure 4, requires four adders, two dividers, a multiplier, and a gain; the SMC control circuit is formed by two comparators and an S-R flip-flop. Therefore, the proposed SMC requires integrated circuits for multiplication and division operations, while the cascade PI structure requires analog integrators. Thus, the analog implementation complexity of both controllers is similar. The digital implementation of the cascade PI structure also requires two threads, one for the inner current loop and another for the voltage loop. In addition, a complementary PWM peripheral must be used to produce both control signals. Hence, it has a complexity similar to the digital implementation of the proposed SMC reported in Figure 8.  
In conclusion, the proposed SMC performs better than a classical solution based on cascade PI controllers. This performance is observed in the tracking efficiency that ensures \(i_{L2}=i_{R}\), which imposes the desired low-frequency performance in the battery. Finally, the implementation complexity of both solutions is similar since both control solutions can be implemented using a few integrated circuits.
# Repairing Damage Caused by Burrowing Animals in Embarnkments: A Sustainable Proposal
## 1 Introduction
It is well known (e.g., [10; 12; 19]) that the addition of lime can provide the reduction of the maximum dry density attainable from a treated fill, and the optimum moisture content typically shifts towards the wet side. Line treatment tends to produce a relatively flat curve when results of dry density are  
Figure 1: (**a**) Hole on the surface of a Po River embankment; (**b**) levee failure that occurred along the Secchia River in Italy [1]. plotted against moisture content, so that higher void ratios can result together with similar mechanical behaviour, with attention to permeability matters [20,21]. In the present research, the possibility of a sustainable compromise between the two most common interventions was investigated and the injection of lime treated soil, from the Po River embankment soils with their features, was considered as a suitable solution. The aim is to preserve continuity in the levee body by injecting a mix of soil and lime as filling material, without excavation. The continuity between the two materials--compacted in situ soil and injected treated soil--is reached paying attention that the lime treated soil, even if not compacted, might show similar characteristics (e.g., [12,15]) of the more compacted (i.e., higher energy of compaction) soil in situ forming the pre-existing earthen structure (see Figure 2). The sustainability of the proposed intervention, the prevention of interface problems, piping phenomena and, at the same time, the aim of avoiding the weakening of the embankments the uncertainty of the complete filling are all objectives of the present proposal. Hence, the purpose of this work is to investigate preliminarily, by comparison, the behaviour of raw samples (at different initial compaction) and lower compacted treated specimens created from slurries by means of oedometer tests, assessing the effectiveness of lime treatment and the possibility of a new sustainable solution of intervention to repair damages caused by animal burrowing on the peculiar soils coming from the Po River geological environment. Three particle size distributions were tested in order to investigate the effect of clay fractions on the effectiveness of treatments. The specimens, made of soil coming from three real dens of the Po River net embankments, were reconstituted at different void ratios, achieved with different sample preparation, for investigating the effect of initial void ratios. Different curing times were taken selected in order to investigate the effectiveness of the curing period on the behaviour of samples.
# Detecting Deepfake Voice Using Explainable Deep Learning Techniques
## 3 Preliminaries and Dataset
### Speech Analysis  
Speech can be categorized into three types: voiced, unvoiced, and silence. Voiced speech has constrained energy with a periodic impulse sequence, while unvoiced speech is a relatively random non-periodic noise-like sequence. Silence refers to a section with no meaningful signals.  
For categorized speeches, several linguistic features, including formant, are used for analysis. Formant is the frequency with constrained energy, showing a local maximum on the spectrum. For general human speech, three to five formants can be found, and named in ascending order from low-frequency formant to high. In voiced and unvoiced speech, the first three formants form the mainframe of the speech, considered as one of the representative features.  
In voiced speech, pitch and amplitude are also considered major features. These two voiced speech features represent expressions of stress and the accent of the speech. In unvoiced speech, the local center frequency and its amplitude at high frequency are considered key features.  
Based on such acoustic information, speech recognition or speaker recognition can be performed through human or algorithm analysis. In this study, for a higher level of interpretation, such traditional features are used to interpret the attribution scores acquired from the XAI methods applied to the detection models.