esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Cooperative Low-Power Wide-Area Network for Energy-Efficient B5G Systems
## 3 Error Performance Analysis
### Outage Probability  
From the definition of outage probability, it is straightforward to calculate the outage probability. Generally, an outage occurs when the channel capacity defined \(\log(1+\text{SNR})\) is below the target rate \(R\)[17].  
In non-cooperative LPWAN, the outage probability at the GW \(i\) is given from (2) as follows.  
\[P_{o,i}=\Pr\left\{\log\left(1+Z_{\gamma_{i}}^{K_{i}}\frac{P}{N_{0}}\right)<R \right\}=\Pr\left\{Z_{\gamma_{i}}^{K_{i}}<\frac{2^{R}-1}{\rho}\right\}=\Pr \left\{Z_{\gamma_{i}}^{K_{i}}<R^{\prime}\right\}, \tag{7}\]  
where \(\rho=P/N_{0}\) denotes the SNR and \(R^{\prime}:=(2^{R}-1)/\rho\) is the outage channel threshold related to the target rate. Note that the cumulative distribution function (c.d.f.) of Erlang distribution \(Z_{\gamma_{i}}^{K_{i}}\) for all \(i\) is given as \(F_{Z_{\gamma_{i}}^{K_{i}}}(z)=1-e^{-z\gamma_{i}^{-1}}\sum_{K=0}^{K_{i}-1}\frac {z^{K}\gamma_{i}^{-K}}{K!}\). Therefore, the outage probability \(P_{o}(i,K_{i})\) is derived as  
\[P_{o}(i,K_{i})=\Pr\left\{Z_{\gamma_{i}}^{K_{i}}<R^{\prime}\right\}=F_{Z_{ \gamma_{i}}^{K_{i}}}(R^{\prime})=1-e^{-R^{\prime}\gamma_{i}^{-1}}\sum_{K=0}^{ K_{i}-1}\frac{(R^{\prime})^{K}\gamma_{i}^{-K}}{K!}. \tag{8}\]  
Considering the effective channel gain of the uplink cooperation, the outage probability of that is given as follows  
\[P_{o}=\Pr\left\{Z<R^{\prime}\right\}, \tag{9}\]  
Since the hyper-Erlang distribution is given as the sum of \(N\) polynomials, which are the \(K_{i}\) polynomials of the product of the coefficient \(A_{ij}\) and the p.d.f. of each Erlang distributions as \(j\) is the antenna index, the cumulative distribution function (c.d.f.) of hyper-Erlang distribution is obtained from the summation of \(N\) Erlang distributions c.d.f as follows.  
\[F_{Z}(z)=\int_{0}^{Z}\sum_{i=1}^{N}\sum_{j=1}^{K_{i}}A_{ij}f_{Z_{\gamma_{i}}^{ j}}(y)dy=\sum_{i=1}^{N}\sum_{j=1}^{K_{i}}A_{ij}f_{Z_{\gamma_{i}}^{j}}(y)dy= \sum_{i=1}^{N}\sum_{j=1}^{K_{i}}A_{ij}F_{Z_{\gamma_{i}}^{j}}(z). \tag{10}\]Therefore, the outage probability of the uplink cooperative system in LPWAN is exactly derived as follows:  
\[P_{o}=\Pr\left\{Z<R^{\prime}\right\}=F_{Z}\left(R^{\prime}\right)=\sum_{i=1}^{N} \sum_{j=1}^{K_{i}}A_{ij}F_{Z_{ij}^{i}}\left(R^{\prime}\right)=\sum_{i=1}^{N} \sum_{j=1}^{K_{i}}A_{ij}P_{o}(i,j). \tag{11}\]
## 4 Experiments
### Performance Comparison  
To prove the superiority of the method put forward in this article, DETDNet was compared with some advanced methods in current CD tasks, including FC-EF [7], FC-Siam-conc [7], FC-Siam-diff [7], CDNet [34], STANet [10], BiT [20], SNUNet [14], and ChangeFormer [21]. To be fair, we conducted comparative experiments in the same environment, that is, the same software environment, hardware environment, and dataset processing methods.
# Sensors for Sustainable Smart Cities: A Review
## 3 Results
### Sensors
#### 3.3.6 Sensors for Energy Efficiency
Neural networks, wireless sensors, algorithms, and statistical methods are used to estimate the energy consumption and the corresponding costs [129; 135; 136]. Environmental factors, pedestrians' flow, weather conditions, and brightness levels influence light intensity [129; 144]. The urban space adopts the most advanced Information and ICTs to support value-added services to manage public affairs, connecting the city and its citizens while respecting their privacy [20; 145]. Most of the time, the monitoring of the variables influencing energy consumption in buildings and public lighting is carried out wirelessly [18; 129]. Various intelligent sensors [135], such as artificial vision, are used to measure temperature, relative humidity [17], electric current, gas flow, air quality [139], lighting, luminosity [129], solar radiation [139], and acoustic emission [146]. Measurement devices include light-dependent resistor (LDR) [139], IR radiation [140; 147], semiconductors, magnets, and optic fiber. Intelligent sensors based on IoT are key to real-time monitoring of the many variables involved in energy management; these sensors can be adapted to microcontrollers and virtual sensors [129; 137]. The main problem with wireless sensors is their battery life; therefore, alternative energy sources (thermal, solar, wind, mechanical, etc.) is vital, although these energies are usually available in minimal quantities [18]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sources of** & **Energy Consumption** & **Variable** & **Sensor** & **Application** \\ \hline Air & & & & \\ Conditioning & Heat/Cooling & Temperature/Humidity & Thermohydrometer & Temperature and relative humidity \\ \hline \multirow{3}{*}{\begin{tabular}{c} Indoor Air \\ Quality \\ \end{tabular} } & & \begin{tabular}{c} CO\({}_{2}\)/CO \\ \end{tabular} & NDIR (Non-Dispersive & \begin{tabular}{c} CO\({}_{2}\)/CO Concentrations \\ Infrared \\ \end{tabular} & \begin{tabular}{c} NO\({}_{2}\)/NO Concentrations \\ HVAC system/equipment, building \\ automation, vents \\ \end{tabular} \\ \cline{3-5}  & & & \begin{tabular}{c} Presence \\ \end{tabular} & Passive Infrared (PIR) & \begin{tabular}{c} Count/Occupation/Movement of \\ people and \\ vehicles/Temperature/Security \\ \end{tabular} \\ \cline{3-5}  & Indoor/Outdoor & Light & Light Depending Resistor (LDR) & \begin{tabular}{c} Color/Resistance/Security alarms, \\ Lighting On/Off \\ \end{tabular} \\ \cline{3-5}  & & \
\begin{tabular}{c} Brightness \\ \end{tabular} & Phototransistor &
\begin{tabular}{c} Home networks, Wi-Fi LED \\ luminaires/Indoor lighting \\ apps/Proximity Light level \\ \end{tabular} \\ \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sources of energy consumption in cities. Figure 9: Physical and non-physical factors in IEQ studies [134].
# Seismological and Engineering Demand Misfits for Evaluating Simulated Ground Motion Records
## 2 Study Area
### Simulated Records of the 2009 L'Aquila (Italy) (Mw = 6.3) Earthquake  
The 2009 L'Aquila (Italy) event with Mw = 6.3 occurred in the Abruzzo region, located in central Italy in a close vicinity of the L'Aquila town. The event had the moderate size of Mw = 6.3 but it caused nearly 300 casualties, 1500 injured people in addition to a total cost of 2-3 billion Euros structural damage [53]. The fault plane in which the 2009 L'Aquila event occurred has a normal mechanism.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Station** & **Code** & **Latitude** & **Longitude** & **Site Class** & **R\({}_{epi}\)** & **PCA-EW** & **PCA-NS** & **PCA-Sim.** & **PCV-EW** & **PCV-NS** & **PCV-Sim.** \\  & C & **C** & **C** & **(NEMFR)** [51] & **(Mw)** & **(cm/s)** & **(cm/s)** & **(cm/s)** & **(cm/s)** & **(cm/s)** & **(cm/s)** \\ \hline Duzce & DZC & 40.8436 & 31.1488 & D & 9.314 & 520.41 & 328.03 & 302.32 & 86.54 & 54.53 & 70.69 \\ Goyank & GYN & 40.396 & 30.7830 & D & 55.163 & 22.17 & 25.79 & 55.50 & 5.84 & 4.49 & 4.53 \\ Iranik & ZEN & 40.4116 & 29.7168 & D & 123.67 & 20.06 & 21.25 & 32.76 & 1.97 & 2.27 & 5.16 \\ Izmit & ZZT & 40.7665 & 29.9172 & C & 100.7 & 16.41 & 18.73 & 28.40 & 2.27 & 1.73 & 5.75 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information on the selected stations corresponding to the 1999 Duzce (Turkey) event (Mw = 7.1).  
Figure 2: Selected stations along with the epicenter of the 1999 Duzce event where the rectangular box shows the fault projection.  
Majority of the near-fault strong ground motion stations that recorded the 2009 L'Aquila earthquake are located on stiff soils or rock sites. Yet, local site effects are observed in time histories of some stations which are located on softer soils. In order to investigate the efficiency of ground motion simulation methodology in predicting the real records of this event, a total of four stations having epicentral distances less than 50 km are selected. Figure 3 shows the location of the selected four stations along with the fault plane. Table 3 presents the selected four stations and their information.  
The real records at the selected four stations are taken from the website corresponding to the strong ground motion database of Italy [55]. For ground motion simulations at the selected stations the validated source, path and site parameters proposed by [PERSON] et al. [45] are used. In that study, the simulation parameters of the 2009 L'Aquila event have been calibrated with respect to the observed records at fourteen stations. Table 3 shows the simulated PGA and PGV levels at the selected stations.
# Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost
## 4 Conclusions  
In this study, based on Pearson's correlation coefficient and heat maps, the correlation between total organic carbon content and logging data in the study area was quantitatively analyzed. GR, AC, DEN, CNL, LLS, and LLD were selected as sensitive logging responses. Principal component analysis was used to reduce collinearity between logging data, and four principal components from PC-1 to -4 were determined as input features of the model.  
The prediction models of total organic carbon content based on the BP neural network, GBDT, and XGBoost algorithms were established. Optimal parameter combinations of different models were selected using the particle swarm optimization algorithm. Through comparison and analysis with conventional prediction methods, the XGBoost model was selected as the optimal prediction model for its superior performance on different datasets. Its evaluation metrics also outperformed GBDT, BP neural network, and the \(\Delta\)logR improved method. It is suitable for predicting total organic carbon content in shale reservoirs in the study area.  
The TOC content prediction model established using multiple methods has broad application prospects, is low cost, has strong operability, and has a high reference value for guiding significance for the prediction of TOC content in shale reservoirs in the study area.  
Figure 9: Application results of different models in X2 well.  
**Author Contributions:** Conceptualization, Y.M.; methodology, Y.M.; resources, J.Z.; data curation, Y.M.; writing--original draft preparation, Y.M.; writing--review and editing, C.X.; supervision, T.L. ([PERSON]), L.T. and T.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (No. 42172163).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Exploration and Development Research Institute of Daqing Oilfield Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
## 2 Materials and Methods
### Infrastructure and Functional Components
#### 2.3.1 Trustability and Traceability  
The information flowing through the platform must be encrypted and validated on each transaction between the peers because the information is supposed to flow in every direction to be accessible for all authorized companies and between all of the software tools (that require it). For instance, one order can be sent directly from the client to the manufacturing facility, passing through several actors, and each of them must accept and validate the order. All of these paths must be validated, registered, and auditable by external tools to guarantee that the procedures performed during the manufacturing of the product or sub-product are compliant with all the restrictions that were agreed upon, for instance, on the use of recycled materials or water and energy consumption.
# Image-Based Tactile Deformation Simulation and Pose Estimation for Robot Skill Learning
## 4 Discussion
### Real-World Applications  
This algorithm can also be applied to the real-world tasks described in [8], specifically inserting a pipette with a known pose into a test tube with an unknown pose held by a parallel gripper for liquid aspiration. The grasp of the pipette can be planned using image-based grasp detection methods [25; 26]. Subsequently, with TacTip installed on both fingers, the pose of the test tube can be estimated by matching the real-world TacTip pin displacement with the predicted pin displacement from the proposed algorithm, using contact conditions of the test tube in different orientations as input. The problem with implementing this algorithm for estimating the pose of the test tube lies in the symmetrical features of the test tube. For the same orientation, the contact conditions remain identical if the test tube is flipped upside down, making it impossible to rely solely on the tactile feedback from TacTip for pose estimation. This issue can be resolved by using visual feedback to determine the upright orientation of the test tube in-hand.  
The algorithm can also be applied to manipulation tasks introduced in [4], including edge following and surface following. In [4], the approximated TacTip deformation is matched with the real-world pin distribution using a model trained with the pix2 pix architecture. Since the proposed algorithm offers a more detailed shape representation and comprehensively studies and models the relationship between contact conditions and pin distribution using a calculation-based method, utilizing the proposed algorithm may result in better sim-to-real alignment and improved accuracy in executing manipulation tasks.
## 3 Results and Discussion
### Moisture Content Distribution  
Figure 5 shows the 2D and 3D distributions of MC after 1 h SuCO\({}_{2}\) dewatering. The left and right part in Figure 5 indicates the status of MC after dewatering at 10 and 30 MPa, respectively. The mean final MCs and standard deviations of the 25 wood blocks in each dewatering test are shown in Figure 6. The standard deviations indicated as vertical bars present the MC gradients, which decline with decreasing of mean final MCs, showing that the larger MC gradients are present in the stage of higher mean final MC (10 MPa, 55 \({}^{\circ}\)C in  
Figure 4: Effects of pressure and temperature on dewatering rate.  
Figure 6: The mean MCs and standard deviations of six conditions after 1 h SuCO\({}_{2}\) dewatering.  
The color gradients in Figure 5 show MC gradients of wood after dewatering. The color bands are the same or similar in radial direction compared with that in tangential direction, especially for the left part of 10 MPa pressure. This means that MC gradients along radial direction are much smaller than those in tangential direction. Moreover, MCs were higher in the central parts and lower in the surface parts of wood. MC gradients along tangential direction were greater, indicating water in wood was dewatered mainly in this direction. This result coincides with previous studies [32; 36; 41]. These phenomena could be explained by the bordered pits that control the water migration between connected cells. There are more bordered pits in radial cell walls of wood, resulting in further possible paths for water migration along tangential direction. During SuCO\({}_{2}\) dewatering, the dissolved CO\({}_{2}\) expands to bubbles of gas when releasing the pressure; free water is mainly expelled along tangential direction by bubbles of gas through the bordered pits connecting cell lumens towards the surfaces of wood.
## 2 Food Fingerprinting and Vibrational Spectroscopy Techniques
Chief among these are the substantial financial investments required for certain high-performance systems, the relatively small number of commercial manufacturers offering robust solutions, and the considerable computational resources and time demanded for image acquisition, data processing, and model development, particularly for tasks such as classification and predictive analysis [41; 42]. However, advancements in electronic components, improvements in camera technology, and more efficient algorithms are expected to enhance the speed of image acquisition and processing, facilitating the implementation of hyperspectral imaging systems in food authenticity applications. The terahertz (THz) frequency range occupies the space between the end of the IR spectrum and the start of the microwave region. Although the use of THz technology in food analysis is still in its infancy, it holds significant potential. One of the key benefits of THz spectroscopy is its ability to probe far-IR vibrational modes in food, particularly for measuring water content. The resonance of THz waves with hydrogen bond vibrations makes this technique highly sensitive to water, which is an advantage when assessing moisture levels in food [43]. However, this heightened sensitivity to water also presents a challenge, as it can complicate the detection of other compounds in food samples that have high moisture content. Beyond traditional spectroscopic approaches and hyperspectral imaging, integrating vibrational spectroscopic techniques, such as near-infrared, mid-infrared, and Raman spectroscopy, with advanced optical microscopy tools, including confocal and conventional light microscopes, has proven highly effective for visualizing and characterizing a broad spectrum of food substrates, ranging from individual grains to complex cereal structures [32]. This integration enables the analysis of the chemical composition of food samples without requiring staining or complex sample preparation, making it a more efficient and non-invasive method.
## 3 Results and Discussion
### Rapid Chloride Migration
#### 3.2.2 Influence of the Air Content of Concrete  
Figure 14 shows a slight increase in the chloride migration coefficient for Mix 5 (with higher air content), when compared to Mix 3, both at 28 days and at 56 days of age.  
Figure 14: Rapid chloride migration coefficient, DRCM, for mixes with 50% GGBS, k = 0.6 and different amount of air, cured at 20 \({}^{\circ}\)C.  
The chloride ingress in concrete depends mainly on the diffusivity of concrete, which is directly related to its pore structure. It is therefore expected that an increase in the air content will lead to a certain increase in the diffusivity of concrete, due to an increased number of air voids throughout the paste, which may be partially filled with the pore solution. Furthermore, an increase in the number of air pores in concrete due to the use of air entrainment admixtures often results in a reduced spacing factor, which shortens the continuous paths between the air voids in the paste, therefore facilitating the transport of chlorides. On the other hand, the diffusivity of concrete depends not only on the volume and distribution of voids, but also on the size and continuity of the air pores. Given that it is the capillary porosity which controls the diffusivity of concrete (which increases with an increase in the amount and size of capillary pores), and since the air pores entrained using AEA are usually not fully filled with water in the test, the effect of an increase of the pores in the paste on the diffusivity is not as markedly as one would expect. For this reason, only a slight increase in the chloride migration coefficient is obtained for concrete with increased air content (Figure 14).  
These results also show that, even though an increase in the air content affects the overall resistance against chloride ingress, it does not influence the development of chloride resistance with ageing of concrete. This fact was expected since, as concluded in the tests of the compressive strength resistance, the air content in concrete affects only its air pore structure, and not the chemical reactions and the rate of hydration of concrete.
## 4 Experimental Results
### Ablation Experiment  
In order to explore the impact of each module on the model, based on the YOLOv8s model, the experiment used the same training data and parameter settings, and conducted ablation experiments for each improvement item. The effects are shown in Table 3.  
As can be seen from Table 3, the replaced modules in the network all contribute to the improvement in model performance, with the SPD-Conv module exhibiting the most significant enhancement in mean average precision (mAP), achieving a 3.4% increase. The SPD-Conv module preserves the feature information lost due to previous downsampling, making it easier for the model to extract feature information for small targets, thereby enhancing the performance of the model.  
Figure 11: FSNB_YOLOv8 Defect Detection Result Example. The Chinese characters on the surface of the medicine bag indicate the name of the medicine bag particles and the specific packaging requirements. From the above picture, we can see that small defects can be identified by the model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **FasterNet (F)** & **SPD (S)** & **BiFPN (B)** & **NWD (N)** & **mAP (\%)** & **Precision (\%)** & **Recall (\%)** & **FPS** \\ \hline YOLOv8 & & & & 92.6 & 96.5 & 91.7 & 65 \\ YOLOv8+F & \(\surd\) & & & 92.8 & 96.8 & 92.1 & 76 \\ YOLOv8+FS & \(\surd\) & \(\surd\) & & 96.2 & 98.5 & 94.4 & 73 \\ YOLOv8+FSB & \(\surd\) & \(\surd\) & \(\surd\) & 97.3 & 98.8 & 95.2 & 71 \\ YOLOv8+FSBN & \(\surd\) & \(\surd\) & \(\surd\) & 98.8 & 99.2 & 97.6 & 74 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Impact of different improvement methods on the performance of the YOLOv8 algorithm.  
Figure 12: FSNB_YOLOv8 Model Training Result Example, the x-axis represents the number of training iterations.  
ing the accuracy of detecting small defect targets. By replacing the FasterNet module, the FPS of the model increases by 11 FPS compared to the original model, effectively improving the inference speed of the model. Furthermore, the introduction of the NWD loss function also leads to a notable improvement in mAP, with a 1.5% enhancement. The NWD module significantly reduces the sensitivity to the positional deviation of small defect targets, enabling more accurate identification of minute defects. By integrating the FasterNet, SPD-Conv, BiFPN, and NWD modules, the final model achieves an mAP of 98.8%, with accuracy and recall rates reaching 99.2% and 97.6%, respectively, and a model inference speed of 74 FPS.  
To visually demonstrate the detection performance of the improved algorithm, this study selected a few representative images from the test dataset, as shown in Figures 13 and 14. Figure 13 displays the detection results using the YOLOv8s model, and Figure 14 presents the detection outcomes using the F