esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Method for Evaluating the Bearing Capacity of the Bridge Pile Socketed in the Soft Rock
## 3 Field Test and Numerical Simulation
### Prediction of the Vertical Bearing Capacity  
The pier fundamental frequency 23.665 Hz was used in Equation (23), and the other parameters of the test pier are shown in Table 2 based on the design data. The identified vertical constraint stiffness of the pile foundation \(K_{\upsilon}\) was \(2.715\times 10^{9}N/m\) by Equation (23). The design vertical constraint stiffness \(K_{\upsilon}\) based on the code was calculated as \(2.696\times 10^{9}N/m\) by Equation (25).  
Figure 5: Field photograph of the test pier.  
Figure 6: (**a**) The vertical acceleration time history curve; (**b**) the measured pier vertical fundamental frequency.  
The constraint stiffness identified by the frequency synthesis in this paper is about 0.7% larger than the calculated design value. The reasons are that the contribution of the pile side friction to the vertical stiffness was not considered in the code, and some parameters in the analytical formula were determined by interpolation. However, the difference between them is only about 0.7%, which indicates that it is accurate and feasible to identify the pier foundation vertical constraint stiffness by the frequency synthesis method.  
Based on Equation (31), the bearing capacity coefficient \(\lambda\) of the test pier is calculated as 15.62, and with the vertical constraint stiffness identified in the previous section, the pile's bearing capacity is estimated at 42.41 \(MN\).
## 3 Results and Discussions
### Effects of Phyto-Derivatives Treatments
One interesting aspect is the similar F\({}_{\text{v}}\)/F\({}_{\text{m}}\) values for all treatments; the level indicating the presence of no viable photosynthesis was not determined. However, F\({}_{\text{v}}\)/F\({}_{\text{m}}\) values lower than 0.1 have been proposed for other biofilms to indicate a total loss of photosynthetic activity [47; 48], which is similar to the values found in this study and may explain a great deal of the similarity in the response of the biofilms to the treatments. Concerning the effect of the treatments on the heterotrophic bacteria, in Table 2 are reported the microbial load and the number of bacterial strains found in the biofilms after the first and second application of the different phyto-derivatives in the well plates. Figure 5: Changes in photosynthetic yield before each treatment (Pre) and after 5 days (5d) (n = 12). Control (CTRL), benzalkonium (BENZ, positive control), lavender at 5% (LAV), liquorice at 10% (LIQ) and a mixture of lavender 5% and liquorice 10% (LIQLAV). number of isolated strains was reduced to three, all belonging to Proteobacteria. The treatment with lavender alone, although it did not reduce biodiversity greatly, did induce a species replacement with the appearance of strains belonging to Firmicutes and Actinobacteria.
## 3 Methods  
Six models, including three light use efficiency models (the MOD17 algorithm [44], the two-leaf light use efficiency (TL-LUE) model [45], and the Vegetation Photosynthesis Model (VPM) [46]), two process-based models (the Breathing Earth System Simulator (BESS) model [47] and the Growing Production Day (GPD) model [26]), and the eddy covariance and MODIS data-driven approach (the EC-MOD model [48]) were studied and compared for the biome of deciduous broadleaf forest. The six models tested here are representative as they have been applied to a broad range of studies and some have already been used to produce routine products. Details on the used datasets and the studied models are described as follows.
# Empowering Healthcare: TinyML for Precise Lung Disease Classification
## 3 Materials and Methods
### Dataset  
In this study, we used a publicly available dataset from King Abdullah University Hospital in Jordan [37]. The dataset comprises 310 records from 105 patients with various respiratory conditions, including normal, asthma, pneumonia, heart failure, bronchiectasis, and chronic obstructive pulmonary disease. The duration of each recording ranges from 5 to 30 s, for a total of 88 min of data. The patients' ages range from 12 to 90 years, with a mean \(\pm\) SD age of \(48\pm 18\) years. For each patient, three types of recordings were obtained, each using a different filtering mode--bell mode filtration, diaphragm mode filtration, and extended mode filtration--to minimize interference from heartbeats and external noise.  
The audio recordings were captured by using a single channel at a sampling rate of 4 kHz. The audio files, in WAV format, were captured by using a single-channel stethoscope-based acquisition system (electronic stethoscope 3200; [PERSON]) positioned at various locations on the chest wall. Table 2 presents the quantity of patients in each disease classification along with the associated count of recordings utilized from this dataset. The dataset demonstrates an unequal distribution, containing more data for the normal and asthma categories compared with the others. Given this situation, we decided to distinguish between two distinct kinds of lung sounds: normal and asthma. Before uploading data to  
Figure 1: Workflow of lung sound analysis and model training process.  
Edge Impulse, each audio file was processed with a 5 th-order Butterworth bandpass filter, with upper and lower cut-off frequencies set to 100-1800 Hz, to reduce external noise [4; 34].  
Upon uploading our dataset, we proceeded to create an ML pipeline called an \"impulse\". This impulse comprises three primary building blocks: the input block, the processing block, and the learning block. The input block identifies the data type employed during the training of the model. It can either be an image or a time series. In our specific case, we utilized time series as the input data. The window size was set to 5000 ms, and the window increase was set to 5000 ms [34]. This duration is sufficient to cover at least one respiratory cycle, given the average resting respiration rates for adults (12-20 breaths per minute) [37].
# Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation
## 3 Test Results
### Forecasts with Temperature Calibration  
The actual temperature data indicate that temperature forecasting biases increase rapidly with large values (around 3 degrees). Figure 5 presents the MAPE time plots of two STLF models using original and adjusted temperature forecasts as inputs. From Figure 5 and Table 3, it can be seen that, after calibrating the forecasted temperature through bias correction based on the previous 28 days' temperature forecasting biases, the forecasting accuracies are significantly improved.
###### Abstract  
In addition to the traditional densely deployed cases, widely distributed wireless sensor networks (WDWSNs) have begun to emerge. In these networks, sensors are far away from each other and have no network connections. In this paper, a special application of data collection for WDWSNs is considered where each sensor (Unmanned Ground Vehicle, UGV) moves in a hazardous and complex terrain with many obstacles. They have their own work cycles and can be accessed only at a few locations. A mobile sink cruises on the ground to collect data gathered from these UGVs. Considerable delay is inevitable if the UGV and the mobile sink miss the meeting window or wait idly at the meeting spot. The unique challenge here is that, for each cycle of an UGV, there is only a limited time window for it to appear in front of the mobile sink. Therefore, we propose scheduling the path of a single mobile sink, targeted at visiting a maximum number of UGVs in a timely manner with the shortest path, according to the timing constraints bound by the cycles of UGVs. We then propose a bipartite matching based algorithm to reduce the number of mobile sinks. Simulation results show that the proposed algorithm can achieve performance close to the theoretical maximum determined by the duty cycle instance.  
data collection; widely distributed sensor networks; Unmanned Ground Vehicle; mobile sink; precedence constraints; trajectory planning +
Footnote †: journal: Sensors  
and
## 3 Chain Communication Model
### Message Structure  
The MHC method forms a message hash chain from unrelated data packets. The receiver can use the node value of the message hash chain to verify the integrity of the current message content and ensure the immutability of the data stream. When the receiver is affirming the packet, the verification succeeds only if the value calculated by using the digest of the previous message and the node value of the message hash chain is equal to the node value of the chain carried in the message. The node value of the chain corresponding to each message in the data flow constitutes a message hash chain, as shown in Figure 2. Through this message structure, the integrity of the message can be verified, but the reliability of the transmission process can be improved.
## 3 Results
### Different Time Scales Assessment (Hourly to Yearly)  
For the different time scales, only the IMERG product was evaluated, using the point-gridded approach, given that this product appeared to perform best at the daily time scale (SS 3.2). Figure 7 depicts the evaluation of IMERG at different time scales (hourly to yearly) against the gauge data over the Ankavia catchment. The coefficient of determination increases with increasing aggregation time scales from hourly to yearly. Specifically, IMERG exhibits good correlation at the yearly time scale (R\({}^{2}\) = 0.97; Figure 7a) and at monthly time scale (R\({}^{2}\) = 0.87; Figure 7b), and reasonable correlation at the daily assessment (R\({}^{2}\) = 0.65; Figure 7c). The correlation is poor at the hourly time scale (R\({}^{2}\) = 0.20; Figure 7d). Especially at the daily time scale, there is a tendency to underestimate the events \(>\) 150 mm (Figure 7c). IMERG also tends to underestimate yearly and, to a lesser extent, monthly rainfall. Additionally, it is apparent that the variability in yearly (Figure 7a) and monthly (Figure 7e) rainfall across the watershed is greater than the variability in IMERG rainfall data, especially for the high-rainfall months.  
Figure 6: Precipitation detection ability of the six GPPs based on daily rainfall: (**a**) Probability of detection (POD), (**b**) False alarm ratio (FAR), (**c**) Critical success index (CSI). Ground-based rain gauge data from September 2018 to August 2020 are used as reference. Note logarithmic (Log10) scale for X axis.  
Figure 7: Comparison of ground-based precipitation data with IMERG at different time scales: (**a**) yearly, (**b**) monthly, (**c**) daily, (**d**) hourly, (**e**) barplot of monthly rainfall with standard deviation. R\({}^{2}\) value is determined for regression passing through (0,0).
## 1 Introduction
This paper proposes a model and data dual-driven transportation modeling approach based on simulation model development and LBS data-driven analysis. Using the mesoscopic simulation software MATSim (v.13.0) as a case study, the proposed approach is evaluated through the utilization of LBS data provided using AMap ([https://ditu.amap.com/](https://ditu.amap.com/), accessed on 12 October 2022), a leading digital map and navigation service provider in China. First, the original LBS data with partly missing information (trajectories and mode choices in some trips) are used to extract the initial travel demand and link/route traffic information, and the information is utilized to build and calibrate a mesoscopic multi-modal transportation simulation model, which reflects the actual traffic conditions. Second, the calibrated simulation model is adopted to complete the missing information in the original LBS, which achieves simulation-based data completion and the recalibration of the initial multi-modal travel demand. The main contributions of this paper are threefold: (a) Solving the problem of data dependence and data completion for transportation simulation development simultaneously; (b) the dual-driven simulation model (DDSM) provides a new paradigm for the construction of subsequent transportation simulation models and the completion of LBS data; (c) the completion process of DDSM has a higher accuracy for recognizing travel modes, and the completion of missing travel paths is more authentic and reliable than other methods. We tested our proposed DDSM in the urban area of Hangzhou, and the results demonstrated that our algorithm outperforms other completion algorithms in LBS data complement. The remainder of this paper is organized as follows. Section 2 reviews the transportation simulation modeling methods and LBS data completion methods. Section 3 describes the research methodology for traffic simulation modeling and calibration, as well as LBS data completion, based on a dual-drive approach incorporating both data and models. In Section 4, we analyze the results of multi-modal simulation modeling and calibration, as well as simulation-based data completion. We show the model calibration and data completion performance compared to other completion algorithms. Concluding remarks and future work are presented in Section 5.
# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 2 Methodology
### Semantic Segmentation Architecture
#### 2.2.2 SegNet  
SegNet consists of encoder-decoder networks with an additional softmax layer for pixel-wise classification as shown in Figure 3. The encoder is composed of 13 convolutional layers which correspond to the first 13 convolution layers in the VGG-16 network [58] that were initially designed for object classification. Nevertheless, the fully connected layers of VGG-16 were discarded to retain higher resolution feature maps and also reduce the number of parameters [60]. The SegNet architecture consists of an encoder followed by a corresponding decoder network which then feeds into a softmax classification layer. Each encoder was connected with the decoder layer. Hence, there were also 13 convolutional layers in the decoder network. In the encoder network, each encoder performed convolutions, where batch normalized and then rectified-linear non-linearity (ReLU) were applied. Next, a 2 \(\times\) 2 max-pooling layer with stride 2 was performed that produced the output to be sub-sample by a factor of 2. SegNet architecture differs from other architectures in the manner in which the decoder upsamples its lower resolution input feature maps. The decoder network upsampled its input using the memorized max-pooling indices from the corresponding encoder network to perform non-linear upsampling. Then, the resultant maps were convolved with a trainable decoder filter bank to restore the maps to the original resolution. Finally, a softmax classifier was applied in the final decoder output to predict the class for each pixel. Weights from the pre-trained VGG-16 network were used to initialize the training process in this paper.  
Figure 2: An illustration of DeepLabv3+ architecture [61].
# Application of Single-Electron Transistor to Biomolecule and Ion Sensors
## 4 Fabrication and Structure of Biosensors with a Single-Electron Transistor with Multiple Islands  
In the SET biosensors, geometrically defined multiple islands were fabricated using EB lithography. A non-doped channel structure was adopted for pH detection [20], and a highly doped channel structure was adopted to operate in the accumulation mode for the detections of streptavidin and PSA [21; 22]. The final thickness of the channel was less than 20 nm. The final island size and nanowire width were less than 20 nm and 10 nm, respectively. The nanowire region (constrict region in the channel) acts as a tunnel barrier due to the quantum size effect. The channel region, including all barriers and islands in the SET, was inside the solution chamber. The length of channel region was 3.0 \(\upmu\)m. The diameter of the chamber was 0.5 cm. An Au reference electrode was used to control the \(V_{g}\) of the SET through a buffer solution.  
The process flow of the SET fabrication is as follows [20; 21; 22]. The thickness of the buried oxide on the B-doped (100) silicon-on-insulator (SOI) wafers used was 400 nm. For the SET with a highly doped channel, the doping (\(n\)-type) of the top silicon layer was carried out by POCI\({}_{3}\) diffusion. The doping level of the channel and source/drain regions was about \(3.5\times 10^{20}\) cm\({}^{-3}\). For the SET with a non-doped channel, no channel doping was performed. The fabrication process of a channel region includes EB lithography and dry etching. Subsequent isotropic wet etching in a solution of NH\({}_{4}\)OH/H\({}_{2}\)O\({}_{2}\)/H\({}_{2}\)O reduced the dimension of the channel and the damage introduced during the dry etching process [45]. The numbers of island were 11. Smaller numbers of island tended to increase the island size due to the proximity effect of the wide source/drain region in the EB lithography. Larger numbers of island, on the other hand, tended to increase the probability of disconnection in the nanowire barrier region. For the SET with a non-doped channel, the source and drain areas were formed by ion implantation of As\({}^{+}\). Then, a Si\({}_{3}\)N\({}_{4}\)/SiO\({}_{2}\) stacked gate insulator was fabricated: A layer of SiO\({}_{2}\), about 10 nm thick, was thermally grown, followed by the deposition of about 90-nm-thick Si\({}_{3}\)N\({}_{4}\) by low-pressure chemical vapor deposition. This Si\({}_{3}\)N\({}_{4}\) layer prevents the invasion of ions such as Na\({}^{+}\) and K\({}^{+}\) from the buffer solution into the inside of gate insulator, and enables reliable SET operation. After contact holes and Al electrodes were fabricated, the samples were annealed at 400 \({}^{\circ}\)C in an H\({}_{2}\) atmosphere. Then, a solution chamber or a fluidic channel was attached to the Si\({}_{3}\)N\({}_{4}\)/SiO\({}_{2}\) gate insulator.  
For the fabricated SET sensor with multiple islands, clear Coulomb diamonds (Figure 7) were observed at room temperature in the contour plot of \(I_{d}\) as functions of \(V_{d}\) and \(V_{b}\) under the absence of buffer solution [20].
# Geolocation, Calibration and Surface Resolution of CYGNSS GNSS-R Land Observations
## 4 Analysis of Coherent Surface Resolution Using Ancillary River Width Data
**(Left)** From 3 July 2018 to 28 February 2019, exclusively comprising 1 Hz data. **(Right)** From 3 July 2019 to 28 February 2020, exclusively comprising 2 Hz data. Green traces indicates Least-Squares estimated transition distance between coherence and non-coherence \(PR\) threshold.
## 5 Analysis of the Scheme
### Analysis of Correctness
(2) the positive integer \(m\) coordinates in \(\hat{\mathbf{e}}_{i}\) are from the uniform distribution \(\mathbb{Z}\cap[-\hat{B},\hat{B}]\), so there is \(\|\hat{\mathbf{e}}_{i}\|\leq\sqrt{m}\hat{B}\). (3) \(m\) coordinates in \(\hat{\mathbf{k}}_{\rho(i)}\) are from the uniform distribution \(\mathbb{Z}\cap[-\hat{B},\hat{B}]\), so there is \(\|\hat{\mathbf{k}}_{\rho(i)}\|\leq\sqrt{m}\hat{B}\). And \(m\) coordinates in \(\widetilde{\mathbf{k}}_{\rho(i)}\) are statistically close to the truncated discrete Gaussians distribution \(\widehat{D}_{\mathbb{Z}^{m},\sigma}\), so there is \(\|\widetilde{\mathbf{k}}_{\rho(i)}\|\leq\mathsf{m}\sigma\). To sum up, for \(\|\mathbf{k}_{\rho(i)}\|\), \(\mathbf{k}_{\rho(i)}=\hat{\mathbf{k}}_{\rho(i)}+\widetilde{\mathbf{k}}_{\rho(i)}\), so the boundary on \(\|\mathbf{k}_{\rho(i)}\|\) is \(\|\mathbf{k}_{\rho(i)}\|\leq\mathsf{m}\sigma+\sqrt{m}\hat{B}\). (4) if \(\mathbf{t}=(1,\mathbf{\hat{t}})\), where \(\mathbf{\hat{t}}\) comes from a truncated discrete Gaussians distribution \(\widehat{D}_{\mathbb{Z}^{m-1},\sigma}\), then it holds \(\|\mathbf{t}\|<m\sigma\). Therefore, given the above conditions, we have that:  
\[ \begin{array}{l}\|\sum_{i\in I}\omega_{i}\mathbf{e}_{i}\mathbf{k}_{\rho(i)}^{\top}+ \sum_{i\in I}\omega_{i}\hat{\mathbf{e}}_{i}\mathbf{t}^{\top}\|\\ <|\mathbb{U}|\Big{(}m^{3/2}\sigma^{2}+m\sigma\hat{B}+m^{3/2}\sigma\hat{B}\Big{)} \\ <|\mathbb{U}|\cdot 3m^{3/2}\sigma\hat{B}\\ <q/4\end{array}  \tag{23}\]  
Therefore, with almost negligible probability in \(\lambda\), the MSB of \(sy^{\mathsf{T}}\) is not affected by the noise mentioned above, which is bounded by \(q/4\), and it does not affect the MSB. That is \(\text{MSB}(K)=\text{MSB}(\mathbf{sy}^{\top})\) is the proof of correctness.
## 3 Results and Discussion
### HPLC-PDA Method Validation
#### 3.2.3 Intra-and Inter-Day Precisions  
For intra-day measurements of L7 GC at 10.00, 100.00, and 250.00 ug/mL; C7 dGlu at 5.00, 50.00, and 125.00 ug/mL; and CDR and DSM at 12.50, 125.00, and 312.50 ug/mL produced relative standard deviations (%RSD) of 0.11-1.93%. Similarly, inter-day measurements of L7 GC at 10.00, 100.00, and 250.00 ug/mL; C7 dGlu at 5.00, 50.00, and 125.00 ug/mL; and CDR and DSM at 12.50, 125.00, and 312.50 ug/mL produced relative standard deviations (%RSD) of 0.63-1.95%. The %RSD values for the intra- and inter-day precisions were all <2%, indicating a good precision and accuracy (Table 2).
## 3 Design and Analysis of 7-QAM Constellation
### Analysis of Constellation Distortion  
If the modulated signals are sensitive to the non-linear characteristic of the HPA, the resulting non-linear distortion would reduce the overall performance gain expected with the application of higher modulation orders. In order to analyze the sensitivity of 7-QAM signals to the non-linear effects, the distorted constellation diagrams of different modulation methods over the non-linear satellite channel model are presented in Figure 5. In addition, the characteristic parameters in the Saleh model, presented in Equations (3) and (4), for the non-linear conversion module are set as \(\alpha_{\text{a}}=1.9638\), \(\beta_{\text{a}}=0.9945\), \(\alpha_{\text{p}}=2.5293\) and \(\beta_{\text{p}}=2.8168\) in these simulations [32].  
Figure 5: Original and distorted constellation diagrams. (**a**) 7-QAM; (**b**) 8-PSK; (**c**) 8-QAM.  
In Figure 5, the arrow points from the original constellation point to its corresponding distorted signal point through the non-linear channel model. Over the non-linear satellite channel model, the 8-QAM signals shown in Figure 5c have serious non-linear distortion in both amplitude and phase. The 8-PSK modulated signals shown in Figure 5b are changed a bit in amplitude but are shifted much in phase. The signals mapped to the signal point on the origin of 7-QAM constellation remain constant in both phase and amplitude, and the other 7-QAM signals are compressed a little more than the 8-PSK signals and also have much distortion in phase. Because of the larger \(\theta_{\rm min}\), the 7-QAM signals are less sensitive to the AM/PM distortion than both the 8-PSK signals and the 8-QAM signals.  
In summary, although the 8-PSK signals are robust to the non-linear AM/AM conversion, they are poor at the anti-noise performance and the tolerance of the AM/PM distortion. The 7-QAM signals have much better anti-noise performance and are mildly influenced by the AM/AM conversion and the AM/PM conversion, so that the 7-QAM signals may have better error performance over the non-linear channel model than the 8-PSK signals and 8-QAM signals.