esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sparse Filtering  
Sparse filtering is an unsupervised feature learning method that attempts to ensure learning features satisfy three principles: population sparsity, lifetime sparsity, and high dispersal [23]. To realize these properties, sparse filtering trains a weight matrix through the optimization of a cost function.  
As shown in Figure 1, the collected raw vibration signal is used as input data. Firstly, the vibration signal is separated into \(M\) samples to compose the training set \(\left\{\mathbf{x}^{i}\right\}_{i=1}^{M}\), where \(\mathbf{x}^{i}\in\Re^{N\times 1}\) is a training sample which contains \(N\) data points. Then, the training set is used to train the sparse filtering model to obtain a weight matrix \(\mathbf{W}\in\Re^{N\times L}\). Finally, each sample is mapped into a feature vector \(\mathbf{f}\in\Re^{L\times 1}\) by the weight matrix. For sparse filtering, an activation function is needed to calculate the nonlinear features.  
We consider the situation that sparse filtering computes linear features for each sample:  
\[f_{j}^{i}=\mathbf{W}_{j}^{\mathrm{T}}\mathbf{x}^{i} \tag{1}\]  
where \(f_{j}^{i}\) is the \(j\)th feature value.  
The feature matrix comprises the features \(f_{j}^{i}\). Firstly, each row is normalized to be equally activated by its \(\ell 2\)-norm:  
\[\widehat{f}_{j}=\mathbf{f}_{j}/\left\|\mathbf{f}_{j}\right\|_{2} \tag{2}\]  
Then, each column (or each sample) is normalized by its \(\ell 2\)-norm. As a result, each feature is constrained to lie on the unit \(\ell 2\)-ball:  
\[\mathbf{\hat{f}}^{i}=\mathbf{\hat{f}}^{i}/\left\|\mathbf{\hat{f}}^{i}\right\|_{2} \tag{3}\]  
Finally, the normalized features are optimized for sparseness using the \(\ell 1\) penalty. For the training set \(\left\{\mathbf{x}^{i}\right\}_{i=1}^{M}\), the sparse filtering objective is shown as follows:  
\[\mathrm{minimize}\;\mathrm{J}(\mathbf{W})=\sum_{i=1}^{M}\left\|\mathbf{\hat{f}}^{i} \right\|_{1} \tag{4}\]  
Generally, the activation function \(g(\cdot)=\left|\cdot\right|\) is recommended and can be rewritten as the inner product form:  
\[f_{l}^{i}=g(\mathbf{W}_{j}^{\mathrm{T}}\mathbf{x}^{i})=\left|\mathbf{W}_{j}^{\mathrm{T}} \mathbf{x}^{i}\right|=\left|\left\langle\mathbf{W}_{j}^{\mathrm{T}}\mathbf{x}^{i}\right\rangle\right| \tag{5}\]  
This suggests sparse filtering can be interpreted as a measurement of the similarity between the input signals and a series of weight vectors, such as wavelet transform [32].
# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 4 Discussion
### Classification Evaluation  
The final classification accurately mapped mountain pine stands in the complex treeline ecotone. However, few misclassifications were apparent. Our classification underpredicted mountain pines slightly with a higher user's accuracy (UA: 90.28%) than producer's accuracy (PA: 86.1%), which aligns with the results of other studies [8; 10; 23; 26]. Additionally, single conifers within mountain pine stands could not be detected due to the spatial resolution of the satellite imagery (Figure 8c). In some areas, confusion also occurred in areas where the regrowth of mountain pines was visible (Figure 8d). This was probably due to the low textural and spectral contrast between the small regrowing mountain pines and surrounding grasslands. Additionally, omitted mountain pine stands can be found in some edge areas around larger mountain pine patches (Figure 8b,c). One possible explanation could be that, due to the relatively large GLCM\({}_{\text{MAX}}\) window sizes, the textural signal of the smaller and more heterogeneous edge mountain pine stands were obliterated during the GLCM statistic calculation [43; 46]. A smaller GLCM window size could reduce this error to some degree but would introduce misclassifications in other regions of the study area (e.g., at forest boundaries). To account for misclassifications at the boundary areas of mountain pine patches, an object-based approach implementing a region growing algorithm might produce beneficial results [26].  
Besides, the further differentiation of subclasses within the \"other land cover\" class might increase the classification accuracy. However, high-quality, expert-based reference data were only available for the mountain pine class, and detailed knowledge about the land cover in the study area was not available. Hence, a binary classification model was chosen, which also reduces the amount of work required to sample reference data.  
Current mapping methods used by local foresters have consisted of intensive field campaigns and time-consuming orthophoto interpretation so far. Therefore, our automated mapping process reduces the required resources for mapping mountain pines tremendously and fosters a continued monitoring of the mountain pine areas. In the future, our approach will be used to map mountain pine stands in the Sarntal Alps over time to support the local forest authorities in assuring their sustainable use. Recent logging activities, for instance, could be detected by comparing maps in time and PlanetScope or Sentinel-2 date back to 2016 and 2015. For the mapping of mountain pine stands of the more distant past, other comparable optical satellite imagery (e.g., RapidEye [28], Landsat 5-8 [6]), or multispectral orthophotos [22; 25] could be used. Additionally, long-term monitoring could also inform of the climate-induced expansion of mountain pines towards the alpine latitudinal zone [7; 12; 15]. Furthermore, this methodology could possibly be transferred to other alpine regions if sufficient reference data are available.
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 3 Ship Ad Hoc Network Construction
### Networking and Network Management  
In this part, we will describe the network establishment process and network management methods. Firstly, a network trigger condition is established by using a model of the ship collision risk index. The ship will have an initialized network mode, and when the node joins, it can communicate. Due to the switching of the scene or the change in the number of ships, we can switch the networking mode. Finally, the node can be disconnected from the network and the ship can destroy the network after completing all interactive tasks. The specific algorithm flow is shown in the following Table 1.
## 2 Transmission Principle of Trochoidal Roller Pinion Rack  
The trochoidal roller pinion rack is a transmission device that can convert linear motion into rotational motion. The structure of the roller pinion rack is shown in Figure 2, and the whole mechanism consists of three parts: 1 rack, 2 roller pinion, 3 roller pin. The tooth profile of the rack is trochoid, and several circular holes are evenly distributed on the circumference, and the roller pins are cylinder installed in the evenly distributed circumferential holes of the roller pinion. The rack has a linear motion state as the driving element, the rack and the roller pins transmit the linear motion to the roller pinion through meshing, and the roller pinion as the output element has a rotational motion around its axis.  
Figure 2b shows a schematic diagram of each parameter in the trochoidal roller pinion rack, the meaning and calculation method of each parameter are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Symbol** & **Unit** & **Geometric Relationship** \\ \hline Number of roller pins & \(z\) & - & - \\ Module & \(m\) & mm & - \\ Reference radius of roller pinion & \(r\) & mm & \(r=mz/2\) \\ Rack tooth profile offset & \(c_{y}\) & - & - \\ coefficient & \(a\) & mm & \(a=mc_{y}\) \\ Rack tooth profile offset distance & \(R\) & mm & \(R=mz/2+mc_{y}\) \\ Pitch radius of roller pinion & \(R\) & mm & \(p=\pi m(1+2c_{y}/z)\) \\ Back pitch & \(p\) & mm & \(p=\pi m(1+2c_{y}/z)\) \\ Diameter coefficient of roller pin & \(c_{f}\) & - & - \\ Diameter of roller pin & \(d_{f}\) & mm & \(d_{f}=mc_{f}\) \\ Addendum coefficient of rack & \(c_{h}\) & - & - \\ Addendum of rack & \(h\) & mm & \(h=mc_{h}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculation method of parameters of trochoidal roller pinion rack.  
Figure 2: Trochoidal roller pinion rack: (**a**) Schematic diagram of the structure; (**b**) Schematic diagram of the parameters.  
The characteristic of the trochoidal roller pinion rack with no backlash in both directions is mainly due to its special meshing state. Figure 3 is schematic diagram of the meshing state. As shown in the Figure 3, the pitch circle of the roller pinion is tangent to the pitch line of the rack during the meshing process. In the static state, the meshing points a, b, and c are all in contact with the rack. When the rack moves to the left, the meshing point is a, b. When the rack moves to the right, the meshing points are b and c. Because of its unique meshing characteristics, it has the characteristics of bidirectional movement without backlash.
# A Comprehensive Review on Network Protocol Design for Autonomic Internet of Things
## 3 Review of Protocol Designs
### RF-MAC  
[PERSON] et al. in [14] proposed the Radio Frequency MAC (RF-MAC) protocol for WSNs that perform energy harvesting from radio waves. The harvesting activity is conducted by placing some Energy Transmitters (ETs), which will do the transfer of energy to the nodes in the network via radio waves. These ETs are divided into two groups with two different frequencies. Through a mathematical model, they have shown that by the two frequencies to transfer energy, the two groups can be active simultaneously for energy charging. RF-MAC provides a method for the determination of the amount of ET, the placement of ET, and the frequency selection in order to provide the optimum energy charging to the nodes in the network. RF-MAC also defines the required communication control packet for this energy harvesting activity.  
The RF-MAC protocol has been validated through simulation. As the result, it showed an improvement compared to the unslotted CSMA, in terms of energy gained and average throughput of the network. The weakness of the RF energy harvesting is the amount of energy gained relatively smaller than other sources, so that this energy harvesting needs to combine with other energy sources in order to meet the energy need for the operation of a node.
## 1 Introduction
A couple of studies have conducted experiments using this free PPG sensor in swimming under different conditions: one has evaluated the agreement between a temple- and an arm-worn sensor with a chest strap in an intermittent and progressive protocol [15]. However, it remains unclear if different intensities would jeopardize the readings or the analyses. The other study reported the validity of the devices using the mean values of minimum HR, maximum HR, and mean HR within different intensity zones [13]. The measure of agreement was presented for the whole test, and the large limits of agreement reported (around 25 and \(-\)27 for the upper and lower limits, respectively) leave it unclear exactly when and why the lack of accuracy occurred. The literature reports mixed findings about exercise intensity and the accuracy of optical sensors in on-land measurements. It has been described that the accuracy rises alongside the exercise intensity [8; 13; 16], probably due to the increased blood flow. However, at rest it tends to be more accurate than at lower intensities [17; 18; 19]. On the other hand, there are reports of high exercise intensity being negatively related to accuracy [8; 17; 20; 21], probably due to increased arm swing. Thus, the aim of the present study was to assess the agreement of two different optical devices for HR monitoring with the chest strap while swimming and to check for bias in different intensities directly from rest to maximum intensity and back to rest again. This analysis will help build the body of knowledge and provide a comprehensive understanding of how to interpret possible biases and, ultimately, help monitor, evaluate, and prescribe exercise intensity with rigor. It was hypothesized that the temple sensor would be an accurate method for HR assessment in swimming and would outperform the wristwatch. By confirming the hypothesis, the present study can also provide the user with guidance on which device to use.
## 4 A Privacy-Aware and Secure Decentralized Framework
### General Framework Overview  
In this section, we discuss the architecture that facilitates the interoperability between aforementioned contributions so that the system can benefit from the desired features of individual modules in an additive way. As shown in Figure 1, the global view of our framework consists of communities of nodes that communicate (1) within a community and (2) between communities, always in a decentralized fashion, which provides supports for multiple levels of interaction.  
We assume different configuration of WSNs depending on requirements ranging from private homes, large residential buildings, and public buildings. There should be at least one sink node per WSN deployment. However, depending on the requirements of availability, redundancy can be added to increase the fault tolerance of sink nodes. Sink nodes expose a RESTful API, either to the external or internal network depending on the security requirements. The API serves as an entry point for querying the system, in our use case, building data mining models. Data are secured using cryptographic methods described in the corresponding section. Nodes provide data access under the conditions enabled in the RBAC component of the node. It is then possible to send queries the framework (in our case, monthly average). A query goes through the nodes and realizes the actions necessary to produce the expected result, while using onion routing technique to protect from external monitoring. Upon receiving a request, the sink node prepares the onion so that the path starts and ends at the sink. Knowing the public key of the actor, who initiated the request via the API, the sink node can encrypt the computation results and interact with the blockchain(RBAC) and the DHT. The query results are then stored on the DHT, while the access control is used to secure the pointer to the file on the blockchain. The RBAC is used to limit access to the exposed API on sink nodes and protect the results of the computation so that only the actor who submitted the query can decrypt the result from the DHT.  
Therefore, the nodes are responsible for preserving privacy and network anonymity between the actors interacting with the API, the underlying WSN, and the computation by onion routing the request as described in Section 4.3. In addition, each node contributes to the decentralized data mining by locally processing the data it collects (for example, precalculating local averages). Each node also participates to optimizing the overall communication by contributing to the network through calculating the best routes as well as the roles of each node.
## 5 Field Testing
### Integrated Path Tracking Controller Testing
#### 5.2.1 Pre-Experiment  
With narrow roads and complex road conditions in the underground tunnels, surface experiments are conducted at the vehicle's manufacturing factory in order to ensure safety. The experimental site is shown in Figure 16, and the maximum available road width is 10 m. The width is similar to the roadway path of the underground metal mine.  
In this width condition, vehicle meeting and obstacle avoidance happen frequently. Therefore, a single shift line is selected as the reference path. Road cones are placed as a reference, as shown in Figure 17. The path is divided into three longitudinal sections of 10 m each, with the first 10 m being the acceleration section, the middle 10 m being the shift line section, and the subsequent section to ensure the vehicle is driven straight. The lateral spacing of the shift line section is set to 4.8 m.  
Before path tracking, a speed test with artificial driving is performed. It is designed to identify the maximum speed that can be driven under this path so that the path tracking experiment can avoid dangerous conditions such as a rollover.  
As shown in Figure 18, three drivers participated in the experiment, and each driver drives ten times at the highest speed according to the reference path based on their driving ability. The highest speed is taken as the result.  
Figure 16: Path tracking experimental site: (**a**) top view of the site; (**b**) road surface of the site.  
Figure 17: Diagram of artificial reference cones’ placement.
# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 2 Background and Related Work
Most were human-crafted indices extracted from Landsat-8 and MODIS satellite imagery, but their input also included topology-related parameters, such as curvature, slope, etc. Using a dataset of 1879 composite soil samples and relying on 10-fold cross-validation, they compared the performance of several traditional ML algorithms (support vector machines, multi-layer-perceptron, regression decision trees, random forests, and extreme gradient boosting) with a DL model when predicting SOC. The DL model that showed the best results in the study was a fairly simple fully connected neural net, with seven hidden layers and 50 neurons in each of them, but it still outperformed the other methods tested. The authors reported a comparatively large R\({}_{2}\) value of 0.65, with an RMSE of 0.75% SOC. In a recent study, [PERSON] et al. [42] evaluated the capability of Sentinel-2 time series to estimate soil organic carbon and clay content at local scale in croplands. The pipeline they proposed relies heavily on human engineering, both in terms of the features they derived from Sentinel-2 imagery (NDVI, NB R\({}_{2}\), BSI, S2 WI), as well as in terms of how they were used to create the input to their machine learning models. In terms of modeling, they did not opt for deep neural networks, but the Quantile Regression Forest (QRF) algorithm, QRF with added longitude and latitude as covariates, and a hybrid approach, the Linear Mixed-Effect Model (LMEM), which included the spatial autocorrelation of the soil properties. While the latter takes spatial information into account up to a point, their approach is essentially pixel based, which differs from the one proposed here. In addition, the authors of the study aimed to assess the capability of their approach in a very limited scenario, by creating and evaluating models for each of their test sites separately. No attempt was made to create a single model that could be applied globally, or at least for a large part of the Earth's surface. Thus, the results they achieved could be viewed as a sort of \"blue-sky-performance\", which could be reached by a global model using Sentinel-2 images as the input. The R\({}_{2}\) of the best of [PERSON] et al. 's models ranged from 0.26 to an impressive 0.96 for different locations, with an average R\({}_{2}\) of 0.67. The RMSE (in % SOC) ranged from 0.09 to 0.22 and was 0.152 on average.
## 7 3D Model Analysis for Structural Monitoring of the Aqueduct
### Analytical Vertical Deformation of the Composite Tied-Arch  
The vertical deformation of the composite tied-arch (\(\Delta V\)) in the 3D FE model is computed using the vertical displacements at nodes 7, 8, and 9 specified in Figure 24, of which the positions are in line with those of the static level gauges on the aqueduct as shown in Figure 2. A schematic diagram of the composite tied-arch's deformation induced by water inside the aqueduct trough is shown in Figure 26.  
The model analysis shows that 1 m deep water in the trough will induce a downward \(\Delta V\) of 2.17 mm, while, by contrast, a \(\pm 1\)\({}^{\circ}\)C uniform temperature change in all structural members can only cause a minor \(\Delta V\) of \(\pm 0.022\) mm (+ stands for upward), indicating an insignificant effect of the temperature change on \(\Delta V\), which is in conformance with the non-definitive correlation between \(\Delta V_{dx}\) and \(\Delta T_{dx}\) during the non-operation period as shown in Figure 10. Therefore, the monitored \(\Delta V_{dx}\) is assumed to be primarily dependent on water load by neglecting the temperature influence during operation and plotted with the analytical \(\Delta V\) obtained from the model under the same water level \(W_{dx}\), which is shown in Figure 27. The correlation has an R\({}^{2}\) coefficient of 0.7052 and an RMSE of 0.35 mm. Although  
Figure 25: Analytical \(\Delta L\) versus monitored \(\Delta L\) of the tied-arch during operation.  
the correlation may not seem high, the 3D model can still give reasonable estimations of monitored \(\Delta V\) due to the relatively low RMSE.
## 2 Materials and Methods
### Structural and Optical Characterizations  
TEM and HR-TEM images were recorded from a JEOL-3010 electron microscope (JEOL, Tokyo, Japan). Zeta potential and size distribution of our freshly prepared QDs in water solution measured by Nano ZS (Malvern, Worcestershire, UK). QDs powder (Christ Alpha, Shanghai, China) was used for XRD samples. UV-vis absorption spectra, PL spectra and Lifetime measurements were recorded using a Lambda 950 UV-vis spectrophotometer (Perkin Elmer, Wokingham, UK), F-4500 fluorescence spectrofluorimeter (Hitachi, Tokyo, Japan) and FL920-fluorescence lifetime spectrometer (Edinburgh Instruments, Edinburgh, UK), respectively.  
For determination the fluorescence stability of dendrimer modified QDs, 1.0 mL dendrimer modified CdTe/CdS QDs solution and different amount of test solutions were sequentially added into test tubes, then diluted to the mark with water and mixed thoroughly. The resulting solutions were used to monitor the change of emission fluorescence intensity by means of spectrofluorimeter.
# Development and Field Validation of Wireless Sensors for Railway Bridge Modal Identification
## 2 Description of the Wireless Monitoring System
### Gateway  
The gateway is essentially a cabinet hosting a DC-DC converter, a Raspberry Pi4, a master communication board, and a 4G modem. The acquisition script runs on the Pi4, which is connected through serial communication to the master board. The master board is endowed with a BLE transceiver and an omni-directional external antenna to communicate with sensor nodes. Acceleration data are sent by the sensor nodes and received through the master board, which is read by the Pi4 to gather all the data and store them locally. Collected data are automatically uploaded to the cloud so that can be accessed remotely to be analyzed. Moreover, the BLE communication also allows sending of messages from the master board to the sensors. This functionality is used for changing some acquisition parameters of the sensor nodes. In fact, the gateway can wake up sensor nodes on request and send them a specific message, which can modify some configuration parameters.  
The gateway is fed with a 24 VDC power supply. Due to the low power consumption of the different components, it can also be fed by a battery recharged through a photovoltaic panel suitably positioned on the side of the deck to avoid the presence of any power supply cable.