esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Energy Consumption Tasks Scheduling Strategy for Multi-Radio WSNs
## 3 Multi-Tasks Schedule Formulation and Energy Consumption Model
### Energy Consumption Model  
For the \(i\)-th multi-radio node \(N_{i}\), the \(h\) packets allocated to the \(j\)-th RF transceiver \(R_{j}\) is denoted as \(w_{i}^{R(j)}=\left\{w_{i,k},w_{i,k+1},\cdots,w_{i,k+h}\right\}\). The required time for transmitting the \(q\)-th packet \(w_{i,q}\) is denoted as \(t_{i,q}\). Thus, the total required time for sending \(w_{i}^{R(j)}=\left\{w_{i,k},w_{i,k+1},\cdots,w_{i,k+h}\right\}\) can be obtained as follows  
\[t_{j}=\sum_{q=k}^{q=k+h}t_{i,q} \tag{1}\]  
where \(t_{j}\) is the total required time of \(R_{j}\) for transmitting the \(h\) packets \(w_{i}^{R(j)}=\left\{w_{i,k},w_{i,k+1},\cdots,w_{i,k+h}\right\}\).  
Therefore, we can obtain the time of each RF transceiver required based on Equation (1) and denote them as \(t_{1},t_{2},\cdots,t_{n}\). The maximum value of \(t_{j}\) is denoted as \(t_{\max}\) and  
\[t_{\max}=\max\left(t_{1},t_{2},\cdots,t_{n}\right) \tag{2}\]  
Energy consumption for transmitting \(w_{i,q}\) is denoted as \(S_{i,q}\), and  
\[S_{i,q}=P_{trans,j}*t_{i,q} \tag{3}\]  
where \(P_{trans,j}\) is the transmission power of \(R_{j}\), \(t_{i,q}\) is the required time for transmitting the packet \(w_{i,q}\).  
The total energy consumption for transmitting \(w_{i}^{R(j)}=\left\{w_{i,k},w_{i,k+1},\cdots,w_{i,k+h}\right\}\) is denoted as \(S_{j}\), and  
\[S_{j} = \sum_{q=k}^{q=k+h}S_{i,q}\] \[= \sum_{q=k}^{q=k+h}P_{trans,j}*t_{i,q}\]  
Figure 1: Task scheduling process.  
The status of \(R_{j}\) is fixed to idle listening after \(w_{i}^{R(j)}=\left\{w_{i,k},w_{i,k+1},\cdots,w_{i,k+h}\right\}\) are transmitted completed until the last packet that belongs to the task \(T_{i}\) and is allocated to the other RF transceivers is transmitted completed. Therefore, the energy consumption in idle listening state is denoted as \(I_{j}\) and  
\[I_{j}=p_{idle,j}*\left(t_{\max}-t_{j}\right) \tag{5}\]  
where \(p_{idle,j}\) is the idle listening power of \(R_{j}\).  
Thus, the total energy consumption of \(R_{j}\) is denoted as \(E_{j}\) and  
\[E_{j} = S_{j}+I_{j}\] \[= \sum_{q=k}^{q=k+h}P_{trans,j}*t_{i,q}+p_{idle,j}*\left(t_{\max}-t _{j}\right)\] \[= \sum_{q=k}^{q=k+h}\left(P_{trans,j}-p_{idle,j}\right)*t_{i,q}+p_{ idle,j}*t_{\max}\]  
Hence, the total energy consumption of \(N_{i}\) is denoted as \(E\) and  
\[E = \sum_{j=1}^{j=n}E_{j}\] \[= \sum_{j=1}^{j=nq=k+h}\left(P_{trans,j}-p_{idle,j}\right)*t_{i,q}+ \sum_{j=1}^{j=n}p_{idle,j}*t_{\max}\]  
Equation (7) reflects that the total energy consumption mainly consists of the transmission energy consumption and the idle listening energy consumption. The first part is related to the transmission power, the idle listening power and the required time. The second part is related to the idle listening power and the maximum transmission time. Suppose the transmission power and the idle listening power are constant, the first part of the total energy consumption is linearly correlated with the transmission time. Similarly, the second part is linearly correlated with the maximum transmission time.
# Basic Approaches for Reducing Power Consumption in Finite State Machine Circuits--A Review
## 5 Saving Power by State Assignment
In [92], a probabilistic swap search state assignment algorithm is proposed. It is based on (1) assigning probabilities of each pair of code swaps and (2) probabilistically exploring pairwise code swaps. As a result, both area and power consumption are minimized for multi-level FSM circuits. As follows from this short analysis, the outcome of state assignment significantly influences the power characteristics of FSM circuits. In this survey, we mostly analyzed FPGA-based FSMs. As a rule, LUT-based FSM circuits are multi-level. To decrease the static power, it is necessary to diminish the number of literals in SBFs (5)-(6). This allows a reduction in the chip area occupied by an FSM circuit. To reduce the dynamic power consumption, it is necessary to optimize Hamming distances between state codes for pairs of states with a high switching probability. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **One-Hot** & **JEDI** & **NOVA** & **OLT** & **[122]** \\ \hline Power & 8085 & 7754 & 9159 & 7732 & 7135 \\ Area & 6053 & 3280 & 3781 & 4012 & 3598 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of experiments [122].
## 2 Materials and Methods  
### Fisher Kernel Function  
Named after the contribution of [PERSON] [20,21], the Fisher kernel in the field of statistical classification can help measure the similarity between two objects on a statistical model set of a generated model set. It can combine the advantages of generating models with those discriminating classification methods, such as Support Vector Machine. Also, the Fisher kernel model is a dynamically generated probability model. Therefore, serving as a bridge between file generation and the probabilistic model, it can take the entire corpus as background information into account.  
The Fisher kernel is generally used in speech recognition, image classification, etc. [22, 23, 24], which, compared with the traditional kernel function, is featured by its direct relation to the sample. Another feature of the Fisher kernel is that it is suitable for variable-size training and test sample, thus making Fisher kernel a successful application in speech signal recognition. Since the Fisher kernel is a kind of local kernel, whose parameters can be less than that of general hybrid kernel after mixing Fisher kernel with other global kernels, thus capable of effectively reducing the time consumption. Besides, it is expected that such hybrid kernels still have good learning ability and generalization ability.  
The Fisher kernel uses the Fisher score method. The Fisher score is defined as follows:  
\[U_{X}=\
abla_{\theta}\log P(X|\theta) \tag{1}\]  
where \(\theta\) denotes the parameter vector, which can be estimated by the Gaussian mixture model using the Expectation Maximum (EM) algorithm.  
The Fisher kernel is defined as follows:  
\[K(X_{i},X_{j})=U_{X_{i}}^{T}I^{-1}U_{X_{j}} \tag{2}\]  
where \(I\) refer to Fisher kernel information matrix, which is defined as follows:  
\[I=E_{x}[U_{x}U_{x}^{T}] \tag{3}\]  
According to Formula (1)-(3), the Fisher kernel depends entirely on the original training sample information.  
In general, the steps to calculate the Fisher kernel are as follows: First, construct a Gaussian mixture model, then use the EM algorithm to estimate the parameters of the model, and finally derive the Fisher kernel based on the Fisher score.  
The Fisher kernel is correlated with the number of training samples to some degree. The more training samples, the more compact the Fisher kernel value is, thus indicating a better the classification performance.
# Spatial Sound Rendering Using Intensity Impulse Response and Cardioid Masking Function
## 6 Discussion of Results
The demonstrated effectiveness of the proposed technique for 5.1 surround sound allows us to assume that the use of room intensity impulse responses also makes sense for systems with more channels in both 2D (surround sound) and 3D (immersive sound) systems. The data consolidation from the pressure microphone and intensity probe, along with various 2D and/or 3D spatial masks (virtual microphone directivity models) at the post-production stage, makes it possible to generate virtually any set of impulse responses. Consequently, this enables the spatial rendering of virtual sources independently of a reproduction system without database extension.
## 3 Event Cameras
### DVS Main Parameters  
The bandwidth of the DVS pixel is related to the maximum stimulus frequency that the pixel circuit can respond to. The latter is governed by the physical characteristics of the pixel construction, namely the parasite capacitance added by the photodiode, and transistors among other causes. On the other hand, the bandwidth of DVS cameras is defined as the maximum number of events that can be sent through the output bus simultaneously. In optical communication systems, a noisy environment can reduce the link performance  
Figure 2: DVS architecture. This diagram illustrates all steps involved in transforming analog light intensities into events. All the pixels share the same AER arbiter and output bus.  
due to the saturation of the output bus with events that do not carry information (noise). Another cause of bus saturation is the use of multiple blinking transmitters. Therefore, event cameras with large throughput values are more suitable in outdoor environments or when multiple transmitters are used.  
Moreover, the refractory period is a parameter that limits the maximum firing rate of individual pixels [22], preventing a small group of pixels from monopolizing the entire bus capacity. After a pixel reports an event to the output bus, a reset signal is generated, setting \(\Delta V\) to a reset state value independent of the input; therefore, no new events can be spiked. \(\Delta V\) is held in reset state for a time known as the \"refractory period\". Consequently, if the light stimulus changes rapidly, a single pixel of the event camera cannot capture high-speed changes, due to the refractory period effect. Therefore, the refractory period limits the maximum speed rate at which the optical communication link can operate. To overcome this limitation, data from multiple pixels could be considered for detecting fast-change incident light sources, as illustrated in Figure 3.  
Finally, event cameras have a programmable bias sensitivity, which adjusts the system's sensitivity, bandwidth, contrast threshold, and refractory period values [25]. Typically, manufacturers provide the option to configure the bias, but the exact reference value to which each corresponds is not given. This lack of information can be a limitation when trying to model a system accurately. In the DVS camera used for the experiments, five possible values of the bias sensitivity existed: the \"VeryLow\", \"Low\", \"Default\", \"High\", and \"VeryHigh\" that could be set in the camera. Across this range of parameter settings, the camera can detect more (\"VeryHigh\" sensitivity) or less (\"VeryLow\" sensitivity) events. Lower bias sensitivity values result in increased noise robustness (defined as non-data-carrying light signals) but require a greater stimulus for the camera to generate events. Conversely, higher values of bias sensitivity enable the camera to detect events with smaller light intensity changes but increase sensitivity to noise. This parameter could be used for adaptive optical communication systems [26], similarly to the automatic gain control loop used in conventional image sensors [22].
## 2 Related Works
### Networks with Obstacle Routing  
Obstacle-avoidance routing algorithms in wireless sensor networks have been widely studied [8]. However, many of these routing algorithms are derived from Internet routing mechanisms and they do not work well in large scale sensor networks. Geography-based routing algorithms [9-11] have been investigated for providing low-complexity routing algorithms that are scalable and stable. In these schemes, packets are routed to the location of the destination node by a greedy algorithm. However, if there are obstacles in such kinds of schemes, greedy or shortest path routing would lead to a \"local minimum\". For such situations, some researchers have introduced techniques such as planarization and face-traversal to forward packets around the obstacles. Meanwhile, the authors of [12] have demonstrated that traditional shortest path algorithms and greedy geographic algorithms can lead to heavy throughput losses, especially at the borders of obstacles. During the routing discovery process, source-destination links set up routes without knowledge of other flows in the sensor networks, so greedy or shortest path routing can lead to spatial congestion. Some strategies to forward around obstacles were introduced in [13]. However, such schemes are likely to fail in networks with typical obstacle configurations, and even when they work, they may provide lower throughput.  
Geometric obstacle avoidance is proposed in [14]. It uses the geometric properties of a node to determine if a message can be stuck at that node. An algorithm is developed to find holes in the network, defined as areas of the network bounded by the stuck nodes. The disadvantage of this technique is the high complexity of hole detection. Additionally, this does not guarantee delivery when the destination is inside the hole.  
The cost based approach [15] consists in assigning a cost to each node, proportional to the distance to the destination. When greedy forwarding fails, a node will forward a packet to a neighbor with a lower cost than itself. Although the complexity and the overhead of the algorithm is average, it does not choose optimal paths. Flooding based techniques [15,16] use broadcasting to forward the message once a packet is stuck. Although their complexity is low, the overhead is high. Although they guarantee delivery, path optimality is not a concern.  
Hybrid techniques use at least two obstacle avoidance techniques. The motivation is the improved efficiency of the path and the guaranteed delivery of the message, and they are used when only one of the two techniques is not enough to achieve these requirements. The disadvantage is the increased overall complexity. In [17], a protocol combining greedy routing and adaptation of the transmission range to bypass obstacles is described. Indeed, the protocol manages to \"jump over\" obstacles, but the routing path created is not optimal and the energy costs can become high, In [18], the authors propose a variation of the right hand rule and manage to bypass even hard obstacles. The paths created are quite efficient, but not optimal.
## 4 Results and Discussion
### Additional Insights from the Path Model Analysis
#### 4.4.2 Direct and Indirect Effects  
For outdoor physical variables and metabolic CO2 production rate, the presence of mediation effects \(\beta\) implies the distinction between direct, indirect, and total effects on the output model variables \(y_{i}\). The direct effects are those presented in Table 4, while the indirect effects consist of path combinations including at least one feedback loop path \(\beta\), for example, \(\overline{T}_{\text{out}}\rightarrow\overline{T}_{\text{in}}\rightarrow \overline{\text{acc}}^{*}\). Total effects are the sum of direct and indirect effects. For the air change rate, which has discernible indirect effects due to the larger \(\beta_{12}\) value linking indoor air temperature to air change rate, Figure 8 distinguishes standardized direct and indirect effects for the trimmed path model variables \(\overline{T}_{\text{out}}\), \(\sum E_{\text{sol}}\), \(\sum R^{*}\), \(\overline{S}_{c}^{*}\), \(\overline{T}_{\text{in}}\), and \(\overline{\text{acc}}^{*}\).  
Figure 8 emphasizes the importance of the indirect effect of outdoor air temperature on classroom ventilation; at 0.542, it is almost as large as the direct effect of indoor air temperature (0.637; see Table 4). Other relevant indirect effects on ventilation are those associated with air change rate and solar energy (\(-\)0.093 and 0.085, respectively). The indirect effect of the air change rate on itself (by means of the negative feedback loop paths \(\beta\)) tends to reduce the air change rate, having a stabilizing effect that is opposed to disturbances in occupancy and outdoor environmental variables. For solar energy, both the indirect and direct effects contribute to increases in the air change rate, resulting in a total  
effect of 0.238 (=0.153 + 0.085), which is the largest after air temperature. The indirect effects associated with indoor air temperature and precipitation are \(-\)0.059 and 0.019, respectively. The metabolic CO\({}_{2}\) production rate has the lowest standardized indirect effect, at 0.015.  
Comparing the standardized direct effects on air change rate presented in Table 4 with the corresponding total effects derived from Figure 8, the effect of indoor air temperature decreases to (0.637 \(-\) 0.059=) 0.578, the effect of solar energy increases to (0.153 + 0.085=) 0.238, the effect of precipitation decreases to (\(-\)0.204 + 0.019=) \(-\)0.185, and the effect of occupancy increases to (0.154 + 0.015=) 0.169. These changes are small but meaningful, especially the 55% increase in the standardized effect of solar energy on the air change rate.
# Intelligent Environment-Adaptive GNSS/INS Integrated Positioning with Factor Graph Optimization
## 5 Further Analysis and Evaluation
### Comparisons between Receivers and across Integration Methods (KF-Based vs. FGO-Based)
Apart from the discussion on the overall 920 epochs, the evaluation comparing receivers and integration methods is given in Figure 15 only using epochs with available GNSS information. It depicts that all GNSS/INS integration methods still outperform GNSS in terms of the 2D RMSE and STD for Xiaomi. As for U-Blox and NovAtel, integration methods (e.g., KF in U-Blox, and methods except FGO in NovAtel) may perform either less accurately (higher RMSE) or less smoothly (higher STD) than GNSS. Possible explanations are that integration methods applied on the certain receiver still require the GNSS updates for several additional epochs once GNSS becomes available again after outage before coming back to the normal level of positioning errors. Such \"recovery periods\", counted as the GNSS-available epochs for the evaluation in Figure 15, would elevate the overall RMSE and STD. Different from the full evaluation using 920 epochs, the only anomaly in Figure 15 stopping us from concluding that AFGO generates the best solutions is that AFGO has a higher RMSE and STD than FGO for the NovAtel receiver. This is anticipated because of NovAtel's worst GNSS availability while having the most accurate GNSS positionings among the three receivers when free from outage. Balance between these two aspects would demand instant and precise uncertainty adaptations based on CNN predictions. However, such low-tolerance targets would hardly be feasible with our limited-quality CNN model when helping to further improve FGO towards a better performance than the GNSS solutions with low RMSE and STD. Nevertheless, in terms of the overall performance of 920 epochs (in Figure 14), which contains both the GNSS outage and GNSS-available scenarios, AFGO still provides the best performance among integration methods for NovAtel data. Figure 14: Summary of integration methods in terms of the 2D RMSE and STD for the three receivers during all 920 epochs. The data regarding the RMSE and STD come from Tables 5–7.
# The Law and Mechanism of the Effect of Surface Roughness on Microwave-Assisted Rock Breaking
## 1 Introduction
Obvious differences are observed in the electric field distribution, temperature rise characteristics, crack evolution, and plastic zone distribution between flat surfaces and rough surfaces under microwave irradiation as well as disparities in the effect of irradiation on different roughness degrees. In this study, basalt was taken as the research object, and microwave-irradiation tests of rock samples with different surface roughness values were carried out. The influence of rock surface roughness on the effect of microwave irradiation was analyzed from the aspects of temperature characteristics, crack evolution, and P-wave wave velocity so as to reveal the mechanisms of microwave reflection, diffraction, absorption, and rock failure on the surface of rocks with different roughness values, which is of great significance for the practical application of microwave-assisted rock-breaking technology. Effect of Roughness on the P-Wave Velocity and Crack Propagation of the Specim 2.1. Characterization of Rock Surface Roughness  
It has been found that the surface roughness of rock has a certain influence on its shear strength [24, 25]. Therefore, the research on the characterization method of rock surface roughness has been relatively perfect. Many methods can be used to characterize the surface roughness of rocks, such as the comparison method, straight edge method, and contour rms method. According to the test method and cost considerations, the EL Soudani roughness quantitative characterization method [26] was selected in this study, as shown in Equation (1), i.e., the contour roughness \(R_{p}\) calculation:  
\[R_{p}=\frac{L_{P}}{L} \tag{1}\]where \(R_{p}\) is the roughness of the rock surface contour line, \(L_{P}\) is the actual length of the contour line of the rock surface (m), and \(L\) is the length of the contour line of the rock surface (m).
## 4 Experimental Results
### Model Performance on DFDC and FaceForensics++  
Table 1 summarizes per-frame classification results on DFDC, where XCeption demonstrates the highest accuracy (89.2%) and recall (90.5%). VGG16 performs competitively (85.5%) but with slower inference, while ResNet-50 achieves faster inference but lower accuracy. MobileNet V2, while lightweight, achieves moderate accuracy (80.2%).  
When tested on FaceForensics++ without fine tuning, all models exhibit accuracy drops, as shown in Figure 2. XCeption shows better generalization with accuracy decreasing from 89.2% to 85.7%. Fine tuning on 10% of FaceForensics++ data improves XCeption's accuracy to 88.6%.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **AUC-ROC** & **Inference (ms)** \\ \hline XCeption & 89.2\% & 88.9\% & 90.5\% & 89.7\% & 0.94 & 85 \\ ResNet-50 & 72.8\% & 74.1\% & 76.2\% & 75.1\% & 0.81 & 270 \\ VGG16 & 85.5\% & 86.3\% & 87.8\% & 87.0\% & 0.90 & 1020 \\ MobileNet V2 & 80.2\% & 79.8\% & 78.6\% & 79.2\% & 0.85 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Per-frame classification on DFDC test set.  
Figure 1: Training and validation loss of XCeption, ResNet-50, and VGG16 over 30 epochs on the DFDC dataset.
## 2 A Nonlinear MC Failure Criterion Considering High-Temperature and High-Pressure Conditions
### Parameter Determination Method for the HTP-MC Criterion  
The principle behind the proposed HTP-MC criterion is to add two correction terms to the MC failure criterion, resulting in a total of four parameters: cohesion \(c\), internal friction angle \(\varphi\), and rock property-related parameters (\(n\) and \(B\)).  
Determination of cohesion \(c\) and internal friction angle \(\varphi\): In the HTP-MC criterion, cohesion \(c\) and internal friction angle \(\varphi\) are consistent with the parameters of the MC failure criterion. To determine the cohesion \(c\) and internal friction angle \(\varphi\) without the influence of temperature and confining pressure, at least two triaxial compression tests on rock samples at different low confining pressures under room temperature conditions are required.  
Determination of parameter \(n\): Based on the known cohesion \(c\) and internal friction angle \(\varphi\), at least one triaxial compression test data on rock samples under high confining pressure is required. The value of parameter \(n\) can then be solved using Equations (3) and (5).  
Determination of parameter \(B\): Parameter \(B\) characterizes the reduction in rock strength due to temperature. To avoid the influence of confining pressure, parameter \(B\) can be determined by conducting uniaxial compression tests at different temperatures. At least two rock samples should be tested under uniaxial compression, one at room temperature and the other at high temperature. Based on the data from these two experiments, parameter \(B\) can be solved using Equations (6) and (8).