esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving GNSS Landslide Monitoring with the Use of Low-Cost MEMS Accelerometers
## 6 Integration of Accelerometers into Prototype Monitoring Stations  
The capacitive accelerometer used in this work displayed an accuracy for tilt measurements of 20 mgon (approximately 1.1 arc minutes, 0.3 mrad) (Table 4). It requires a calibration to remove bias and improve the accuracy of raw data by an order of magnitude. This accuracy level is consistent with the value declared by the manufacturer relative to the residual noise. The calibration of eight accelerometers of the same type show very different calibration values among them. Each accelerometer must therefore be individually calibrated.  
In real time tilt monitoring operations, the voltage signal is converted to acceleration and angles. This requires linking the sensor to an interface that sends the data to a microcomputer.  
To limit the system's total cost, a control unit consisting of a NANO SG20-type PC and a wi-fi or GSM transmission unit (Figure 9) was created. These accelerometers are part of a more complex system that also integrates a single-frequency low-cost GNSS receiver, which allows a positioning accuracy of a few centimetres in real time, or better in post-processing, for monitoring slow surface deformations.  
In Figure 7, we observe the design and implementation of a multi-sensor monitoring system located on the \"Madonna del Sasso\" site (north of Piedmont, Italy). The mechanical support allows the GNSS antenna and the accelerometer to be installed with precision. The X axis of the accelerometer was oriented to the north to refer the tilt values measured in the north and east directions, such as the displacements obtained from the GNSS measurements.  
The tilt values computed in real time are sent to a control center together with values of the external temperature, the humidity, and the voltage of the backup battery.  
Tilt measurements can be difficult to perform in different ways, for example the multi-antenna GNSS systems are more complicated and less accurate.  
These devices can be used to monitor the inclination of structures, rock masses, or simply, in our case, the inclination of the pillar on which the GNSS antenna is anchored.  
The next steps of this research are further algorithm enhancements that include acceleration and GNSS measurements to improve system reliability.  
Figure 8: Residual accelerometer tilt with respect to the angular measure of the total station.
## 5 Results and Discussion
### Seasonal Evaluation  
To evaluate the seasonal precipitation rain-gauge observations with their corresponding grid points on satellites and models, all days of the corresponding seasons are compared. Based on seasonal scale, Figure 12 and Table 12 indicate that all three products tend to underestimate in all seasons except spring in the Guilan region. IMERG has a better value of Bias, RMSE, Mbias and Ribias while ERA-Interim represents significant agreement.
## 3 Case-Study School Building  
The analysis of the main features characterizing the aforementioned existing school building stock [6] has allowed us to identify a representative case-study school building, namely the primary school Antognini, located in Ancona (Italy). This school building was built in central Italy around the 1960s, and it is composed of three stories, including an underground level. The floor area is about 690 m\({}^{2}\), and the inter-story height is 3.83 m for the underground level and first story and 3.77 m for the second story. The longest side of the building is 57 m long, and the shortest is 12 m. The plan view and the south elevation of the school building are provided in Figure 2. In situ test reports made available during the survey of the building provided detailed information regarding material properties and reinforcement content for several structural elements; for those elements with no available steel reinforcement information, a reasonable value was estimated through a simulated design approach. Mean compressive strength of the concrete of 14.4, 10.8, and  
Figure 1: Research methodology.  
8.7 MPa at the first (ground), second, and third stories, respectively, was obtained from the in situ tests; furthermore, the yield strength of the reinforcement bars was reported to be 381 MPa. Additional variability in the structural response at both global and local levels, deriving from a non-uniform distribution of the material strength at the member level or plan level [20], was not accounted for in the structural model.  
The floor slab system, which was a hollow-core concrete structure, is the most common in Italy for RC buildings built in the 1960s, and, considering the thickness of the reinforced concrete slab on top of the hollow bricks, it was assumed as fully rigid.
## 4 Discussion
### Clinical Implications
#### 4.6.3 Scaffold-Free Constructs for Immunocompromised Patients  
Scaffold-free tissue engineering represents an alternative approach with specific clinical advantages for immunocompromised patients. By leveraging the self-organizing capabilities of cells, such as PDLCs, scaffold-free constructs eliminate the risks associated with synthetic scaffold implantation, such as foreign body reactions and delayed integration [61]. For patients at higher risk of immune-related complications, this approach aligns closely with the body's natural healing mechanisms and offers a viable pathway for complex tissue regeneration.  
In clinical practice, scaffold-free constructs could be particularly beneficial in reconstructive surgeries or periodontal repair, where maintaining native tissue compatibility and minimizing the immune response are critical [62]. These constructs also have the potential to reduce the procedural complexity of scaffold placement, offering streamlined regenerative solutions [47].
# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 2 Materials and Methods
### Process Description  
In our analysis, atmospherically corrected images (level 2A) were converted from reflectivity to reflectance values, utilising the scaling coefficients provided in the product documentation, and clouds and cloud shadows were masked by employing the Quality Mask (QA band).  
Given the 8-day temporal resolution of Landsat missions, we adopted a multi-month approach to ensure comprehensive coverage of our study area while eliminating any potential pixel blurriness and increasing map accuracy, as recommended by various studies [15; 51; 52]. Whenever multiple high-quality images were available for a specific area, we calculated the median of these images. To create a cohesive dataset, we pasted together the selected images and subsequently delimited the study area using the boundaries outlined by the Valencian Cartographic Institute for the year 2022.  
For the training dataset, we manually handpicked specific pixels and assigned them to their respective classes. Our classification system consisted of five distinct categories: Water Bodies, comprising rivers, lakes, and artificial reservoirs; Built-up Areas, encompassing settlements, roads and industrial buildings; Dense Vegetation, including forests and irrigated crops; Sparse Vegetation, covering mowed crops, scrub and bushes, often accompanied by soil components; and Other, representing riverbeds, beaches, and rock mines.  
These classes were selected due to their distinctiveness at a 30 m spatial resolution. The positioning of training points relied on the reference of RGB images constructed from bands 3, 2, and 1 for Landsat 5 and bands 4, 3, and 2 for Landsat 8 and 9. Thirty per cent of these points were reserved for validation purposes. To assess efficiency, the accuracy and kappa values of the algorithms were compared. Despite recent criticisms of the kappa metric for its limitations in map studies [53], we have chosen to use it due to its widespread acceptance and ease of comparison with previous studies. A flowchart illustrating the entire procedure is shown in Figure 2.  
Figure 2: Flowchart of the methodology applied in this work to obtain land cover maps.  
For a comprehensive understanding of urban changes, we conducted a comparative analysis of Built-up Areas class maps from 1985 to 2020. The resulting map was colour-coded to distinguish urban areas across different years, highlighting urban construction expansion. Additionally, linear regression analyses were performed for each class type over the period from 1984 to 2022 to further explore temporal trends. All statistical analyses were conducted using Python, version 3.12.
# Brain-Inspired Data Transmission in Dense Wireless Network
## 3 Ultra-Dense Wireless Networks and Their Biological Counterparts
### Inspirations for Wireless Network  
In our research, we considered the application of the following, bio-inspired functionalities of the dense wireless system:  
* Neurons: They may be treated as the rudimental kind of wireless node, i.e., a simple transmission-reception point in a wireless network.They may possess capabilities to receive (neurotransmitters in a neuron; radio waves reception in a wireless network), process (cell body in a neuron; a processor in transmission point), and transmit (axons and synapses in a neuron; radio waves emission in a wireless network). In HNS, a neuron will be fired (i.e., it will pass on the nervous pulse), if the incoming pulse from the preceding neuron is strong enough. In the context of wireless communication, such a scheme may be mapped to the case where the wireless transceiver relays the message only of the received signal power is above some predefined threshold. For further investigation, in our dense wireless system, such transceivers are marked as neuron-nodes.
* Astrocyte: Astrocyte function can be embodied in wireless communications as the connection point between different kinds of parts of the network, i.e., some parameters of one network can influence the functionality of another network through the astrocytes. A practical example could be the presence in the wireless network of some dedicated sensors deployed for the detection of various emergency situations. Once detected, the network can react by, e.g., redirecting data traffic omitting unstable parts of the network. In other words, this function can be implemented as a short-term scale control plane that can reconfigure some part of the network in sudden situations. For further investigation, in our dense wireless system, such transceivers are marked as astrocyte-nodes.
* Microglia: These glial cells may be seen as nodes providing some fault tolerance to the network. It could react in several ways, for example, it may inform other devices or some network controller about the identified problem. Moreover, it could take over tasks that were done by the currently-not-responding device. Microglia functionality in this case is very similar to astrocyte functionality. However, the microglia inspired device is part of the transmission network, whereas the astrocyte-like device can be treated as an external input to the network from other networks. For further investigation, in our dense wireless system, such transceivers are marked as microglia-nodes.
* Oligodendrocyte: Myelination could be understood as a network learning capability. This function can be simply implemented as a long-term scale control plane.
## 4 Results
### Ablation Study  
#### 4.3.1 Evaluate the Performance of SGAN in Spatial Domain  
We analyzed the objective evaluation metrics of reconstructed images to evaluate the contribution of the SGAN reconstructed images based on spatial information. Additionally, to assess the improvement in the network training on the GF_Sen dataset, this paper trained different networks based on the simulated dataset and GF_Sen, and then tested them on the real LR images to obtain different image quality evaluation indicators.  
Table 5 compares the test accuracy of the networks trained based on the two training datasets. It was found that the accuracy of most networks trained with the GF_Sen was improved, as shown in the fact that the PSNR and SSIM values were generally higher than the network accuracy trained on the simulated datasets. The PSNR or SSIM cannot fully represent the reconstruction quality. In this paper, the SD and IE were added based on the statistical characteristics of images, which aim to evaluate the network effect through gray-level distribution and the amount of information the image has, as shown in Table 5.  
Further analysis of the evaluation results showed that SRGAN and ESRGAN were higher than Bicubic, SRCNN, and ESPCN in four indicators, which proved that the GAN can better utilize spatial information than Bicubic and CNN. At the same time, the ESRT also demonstrated advantages over the GAN in some indicators, believing that the combination of a self-attention mechanism and transformer can reconstruct images with more saturated  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Type** & **pair\_A** & **visin\_A** & **pair\_B** & **visin\_S** & **pair\_C** & **visin\_C** & **pair\_D** & **visin\_D** & **pair\_E** & **visin\_F** & **visin\_F** & **visin\_F** & **pair\_C** & **visin\_C** \\ \hline \hline Airport & 20.049 & 0.055 & 37.62 & 0.944 & 37.08 & 0.091 & 25.59 & 0.730 & 30.399 & 0.047 & 29.963 & 0.059 & **0.190** & **0.057** \\ \hline \hline Batch & 36.207 & 37.492 & 39.440 & 0.057 & 24.399 & 36.336 & 0.635 & 37.341 & 0.532 & **0.614** & **0.619** & **0.605** \\ \hline \hline Endup & 34.183 & 0.942 & 34.792 & 0.842 & 34.729 & 0.846 & 35.855 & 34.74 & 0.557 & 34.435 & 0.577 & **34.93** & **0.658** \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**C**} & \multirow{2}{*}{**pair\_C**} & \multirow{2}{*}{**pair\_C**} & \multirow{2}{*}{**pair\_C**} & \multirow{2}{*}{**pair\_D**} & \multirow{2}{*}{**pair\_D**} & \multirow{2}{*}{**pair\_D**} & \multirow{2}{*}{**pair\_E**} & \multirow{2}{*}{**pair\_F**} & \multirow{2}{*}{**pair\_F**} & \multirow{2}{*}{**pair\_F**} & \multirow{2}{*}{**pair\_F**} & \multirow{2}{*}{**pair\_C**} \\ \cline{2-2} \cline{6-12} \clcolors. Finally, the SGAN obtained the highest PSNR and SSIM values on the GF_Sen test set, which were 19.552 and 0.645, respectively, which proved that the combination of the self-attention mechanism and GAN can make the reconstructed images also have a more diverse range of grayscale levels and abundant information in the spatial domain.
# Short Landing for Flying-Wing Unmanned Aircraft with Thrust Vector
## 4 Flight Control System Design
### Angle of Attack Control  
For flying wing aircraft, changes in the angle of attack significantly affect longitudinal static stability. Poor longitudinal and lateral static stability can pose a serious threat to flight safety. Therefore, the angle of attack, denoted by \(\alpha\), is chosen as the control object for the outer loop, which generates a pitch angular rate command for the inner control loop. Neglecting the lateral and directional coupling dynamics, the linearized model for the angle of attack can be expressed as follows [17]:  
\[\dot{\alpha}=-Z_{\alpha}\alpha+q+Z_{\delta_{\ell}}\delta_{\ell} \tag{36}\]  
where we have the following:  
\[Z_{\alpha}=\frac{1/2\rho V^{2}SC_{L\alpha}}{mV} \tag{37}\] \[Z_{\delta_{\ell}}=\frac{1/2\rho V^{2}SC_{L\delta_{\ell}}}{mV}\]  
where \(C_{L\alpha}\) is the lift curve slope, and \(C_{L\delta_{\ell}}\) is the derivative of the lift coefficient to the elevator. The value of \(C_{L\delta_{\ell}}\) is insignificant when compared to \(C_{L\alpha}\), resulting in the simplification of the transfer function for the pitch angular rate to angle of attack:  
\[\frac{\alpha(s)}{q(s)}=\frac{1}{s+Z_{\alpha}} \tag{38}\]  
Consider the closed-loop dynamics of the pitch angular rate in the system. If it is assumed to be a first-order low-pass filter, then the transfer function for the angle of attack command to the measured output can be simplified as a second-order nominal model with the angle of attack controller. This can be expressed as follows:  
\[\frac{\alpha(s)}{\alpha_{c}(s)}=\frac{k_{a}k_{q}}{s^{2}+k_{q}s+k_{a}k_{q}} \tag{39}\]  
The control diagram of the angle of attack is shown in Figure 16.  
The controller gain, \(k_{a}\), can be calculated using the desired bandwidth and damping ratio of the closed-loop system. In this case, the damping ratio is selected to be 0.8, and then \(k_{a}\) can be calculated using the following formula:  
\[k_{a}=k_{q}/1.6^{2} \tag{40}\]
# Efficient Distributed Method for NLOS Cooperative Localization in WSNs
## 4 Solution to the Subproblem
### Lipschitz Constant  
The proximal algorithm needs the guarantee of Lipschitz continuity. First, we prove the premise in this subsection. To simplify notations, we defined the functions:  
\[\begin{split} h(\mathbf{z}_{i})&=\frac{1}{2}\text{dist}^{ 2}(\mathbf{H}_{i}\mathbf{z}_{i},\text{C}_{i})=\sum_{j=2}^{\mathcal{N}_{i}+1}\frac{1}{ 2}\text{dist}^{2}((\mathbf{z}_{i})_{1}-(\mathbf{z}_{i})_{j},\text{C}_{i,l})\\ g(\mathbf{z}_{i})&=\frac{1}{2}c\|\mathbf{z}_{i}-\mathbf{A}_{i} \mathbf{x}^{t}+\tilde{\mathbf{\lambda}}_{i}^{t}\|^{2}\end{split} \tag{27}\]  
where \(\text{C}_{i}\) is Cartesian product of balls \(\text{C}_{i,l},\ l\in\mathcal{N}_{i}\), and \(\mathbf{H}_{i}\) has the form of  
\[\left[\mathbf{1}_{\mathcal{N}_{i}},-\mathbf{1}_{\mathcal{N}_{i}}\right]\otimes\mathbf{I}_ {d} \tag{28}\]  
Function \(g(\mathbf{z}_{i})\) in Functions (27) is convex and differentiable, and its gradient is  
\[\
abla g(\mathbf{z}_{i})=c\left[\mathbf{z}_{i}-(\mathbf{A}_{i}\mathbf{x}^{t}-\tilde{\mathbf{ \lambda}}_{i}^{t})\right] \tag{29}\]  
Obviously, this gradient is Lipschitz-continuous, and we could obtain its Lipschitz constant as follows  
\[\begin{split}\|\
abla g(\mathbf{x})-\
abla g(\mathbf{y})\|& =\|\mathbf{cx}-\mathbf{cy}\|\\ &\leq k_{g}|c|\|\mathbf{x}-\mathbf{y}\|\\ \Rightarrow\frac{\|\
abla g(\mathbf{x})-\
abla g(\mathbf{y})\|}{\|\mathbf{x}- \mathbf{y}\|}\leq k_{g}|c|=L_{g},\forall\mathbf{x},\mathbf{y}\in\mathbb{R}^{d(\mathcal{N} _{i}+1)}\end{split} \tag{30}\]  
where \(k_{g}\geq 1\) is a constant.
# Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine
## 3 State of Health Estimation Model  
In this paper, the SVM algorithm is adopted for the SOH estimation based on the partial voltage variation, which can solve nonlinear regression and irregularity problems. First, the SVM algorithm is introduced along with its regression characteristics. Then, the operation steps of the SOH estimation are presented based on the calculation process.
# Path Loss Measurement of Outdoor Wireless Channel in D-band
## 4 Results
The greater the distance, the more reflection paths are generated, and the greater the fluctuation of the received signal.
###### Abstract  
To achieve launch-on-demand for Operationally Responsive Space (ORS) missions, in this article, an intra-satellite wireless network (ISWN) is presented. It provides a wireless and modularized scheme for intra-spacecraft sensing and data buses. By removing the wired data bus, the commercial off-the-shelf (COTS) based wireless modular architecture will reduce both the volume and weight of the satellite platform, thus achieving rapid design and cost savings in development and launching. Based on the on-orbit data demand analysis, a hybrid time division multiple access/carrier sense multiple access (TDMA/CSMA) protocol is proposed. It includes an improved clear channel assessment (CCA) mechanism and a traffic adaptive slot allocation method. To analyze the access process, a Markov model is constructed. Then a detailed calculation is given in which the unsaturated cases are considered. Through simulations, the proposed protocol is proved to commendably satisfy the demands and performs better than existing schemes. It helps to build a full-wireless satellite instead of the current wired ones, and will contribute to provide dynamic space capabilities for ORS missions.  
O 181537 1, 13390/s18051537 1 1 20181537 1
# Transcriptome-Wide Analysis of Stationary Phase Small ncRNAs in _E. coli_
## 2 Results
### STARPA Workflow  
We developed a novel and universal pipeline, which we denoted STARPA, for the analysis of the sequencing data. STARPA fulfills requirements absent from existing tools, such as the capability to handle multiple cDNA libraries in a comparative way and the  
Figure 1: Sequencing library preparation and analysis workflow. (**a**) Schematic representation of sequencing library preparation: _E. coli_ were cultured in two different media, lysogeny broth (LB) and morpholinepropanesulfonic acid (MOPS), and in two biological replicates. Samples were taken at the indicated time points (OD600 = optical density measured at 600 nm, h = hour). From each growth condition, samples were used for total RNA extraction and crude ribosome extraction, followed by RNA extraction. RNA was size extracted from polyacrylamide gels with 20–300 nt cutoffs (nt = nucleotides). Size selected RNA was Tobacco Acid Pyrophosphatase (TAP) treated then reverse-transcribed with 5\({}^{\prime}\)- and 3\({}^{\prime}\)- adapters to make cDNA libraries. cDNA libraries were sequenced. (**b**) Workflow of Stable RNA Processing Product Analyzer (STARPA) analysis: Reads were trimmed from adapter sequences then aligned to the _E. coli_ reference genome. Unaligned reads were discarded in a sorting step. Reads with paired-ends were transformed into pseudo-single-end (SE) reads. The remaining reads were identified, clustered by sequence or by overlap, and then quantified (RPM = reads per million).  
ability to identify potential RNA processing products that are supported by full length reads. STARPA was designed in a modular setup, allowing the application for various types of RNA sequencing data. The pipeline consists of seven steps (Figure 1b). Briefly, raw demultiplexed reads were first trimmed to remove adapters and low-quality nucleotides. Subsequently, trimmed reads were aligned to the _E. coli_ reference genome. After alignment, reads were filtered to remove unaligned, mismatched, and nonspecific reads. Next, PE (paired-end) reads were converted to SE (single-end) reads to allow subsequent analysis by [PERSON] [38], which identifies processing products. To reduce redundancy, the identified RNA processing products were clustered by overlap and by genomic context. Following clustering, quantification was carried out and reported in a normalized manner as read per million of mapped reads (RPM) for the identified RNA processing products (Supplementary Tables S1 and S2). Aside from libraries of ribosome-associated RNA under MOPS conditions, all biological replicates displayed positive correlations (Supplementary Figure S1a,b).
## 3 Results
### Imaging Performance with Limited Data  
This subsection focuses on reconstructions of more challenging imaging scenarios. These include imaging with limited prior information, as well as detecting a target positioned in-between the antenna rings of a 3-D array. We compare 2-D and 3-D reconstructions for these cases to examine possible benefits of using the 3-D algorithm. Similar to the previous results, we have performed twenty DBIM iterations in all our reconstructions below.
## 4 Performance Comparison
### Signaling Cost  
The signaling cost is the total traffic demand encountered while transmitting signaling messages during content transmission. According to [65], the handover rate is defined as the likelihood that the user crosses over to the next cell within one movement period. For this purpose, we focused on several messages that can be sent through the network during handover transmission. A low signaling cost corresponds to better performance of the proposed methodology.