esa-sceva/satcom-chunk-collection huggingface dataset:
# Damping of Oscillations of a Rotary Pendulum System
## 4 Shaper Design  
In most cases, the fixed shaper is designed, based on a linearized version, around the operating point of the nonlinear system, which causes linear behavior (when the payload swing angles are relatively small). By eliminating the corresponding component of the spectrum in the angular acceleration signal, we will simultaneously eliminate this part of the spectrum in the angular velocity signal as well (since the zeros in the velocity spectrum are the same as the zeros in the acceleration spectrum but divided by the velocity itself). Typically, the shaper is designed for shaping the acceleration spectrum. In applying the structure according to Figure 6, when we eliminate the relevant spectrum components in the angular acceleration (velocity) control signal, we can successfully suppress oscillations in the tangential direction [40].  
Figure 5: Block diagram of the carousel model in polar coordinates.  
Figure 6: Structure of linear shaper.  
Based on the relations derived in Section 3, (1)-(3), it is clear that it is also necessary to shape the spectrum of the square of the angular velocity, which is proportional to the centrifugal acceleration. At large angular velocity values, oscillations can also occur in the radial direction due to the action of centrifugal forces, which causes a non-linear effect. As a result, the spectrum of the input signal is distorted, and the problem of shaping is more complex in this case. If we apply the scheme according to Figure 7, it is possible to suppress oscillations in the radial direction and vice versa, so oscillations in the tangential direction will appear [40].  
One of the possible solutions to effectively eliminate oscillations in both axes and remove the second component of the oscillation is to connect a series shaper extended by nonlinearity (Figure 8) [40].  
This combined filter gives the square root in the branch for centrifugal acceleration, which linearizes the given term, but to shape the spectrum correctly, the square root must be given in front of the shaper [40].
## 4 Results
### Development of the Global MODIS Time-Share Two-Stage CI Product
#### 4.2.2 Comparison of Different Developed Algorithms  
Table 4 shows that the percentage of \(\text{CI}_{\text{LOS}}\) < \(\text{CI}_{\text{LFS}}\) in the second algorithm is greater than that in the first algorithm for both the first and second vegetation cycles. Since the leaves exhibit a clumped state during the LOS, \(\text{CI}_{\text{LOS}}\) should be smaller than \(\text{CI}_{\text{LFS}}\). This suggests that the second algorithm generally performs better, capturing the seasonal variations in CI more accurately across the southern and northern hemispheres, as well as the tropics. Therefore, this study selected the second algorithm to generate a global MODIS time-share two-stage CI product. According to the second algorithm, the percentage of \(\text{CI}_{\text{LOS}}\) < \(\text{CI}_{\text{LFS}}\) during the first vegetation cycle shows little variation between the southern and southern hemispheres. The second algorithm is more accurate than the second algorithm, and the second algorithm is more accurate than the second algorithm.  
Figure 5: Accuracy evaluation results of the estimation of typical pixel vegetation phenology parameters from the NDVI time series. (**a**) RMSE of the estimated SOS from the NDVI. (**b**) RMSE of the estimated EOS from the NDVI. (**c**) Bias of the estimated SOS from the NDVI. (**d**) Bias of the estimated EOS from the NDVI.  
and northern hemispheres and the tropics, with all values exceeding 60%. In 2008 and 2018, the global percentages were 68.23% and 61.98%, respectively. During the second vegetation cycle, except for the northern hemisphere in 2018, the percentage of CI\({}_{\text{LOS}}\) < CI\({}_{\text{LFS}}\) exceeded 50%, with global percentages of 56.14% and 53.71% in 2008 and 2018, respectively. This suggests that CI typically displays seasonal variation, but its ability to capture seasonal variation is somewhat reduced during the second vegetation cycle, particularly for biannual vegetation. Additionally, during the first vegetation cycle, the southern hemisphere had the highest percentage of CI\({}_{\text{LOS}}\) < CI\({}_{\text{LFS}}\), with 74.15% in 2008 and 68.66% in 2018, significantly surpassing the percentages in the tropics and the Northern Hemisphere. This suggests that the seasonal variation in CI is most pronounced in the southern hemisphere, likely because of its significantly lower number of vegetation-covered pixels compared to the northern hemisphere and the tropics. The southern hemisphere is less influenced by topographic diversity, human activities, and latitudinal variation. In contrast, needleleaf forests, which exhibit relatively stable temporal trends due to their lower renewal frequency and are predominantly located in the Northern Hemisphere (\(\sim\)96%) [35; 67], have contributed to the reduction in the percentage of CI\({}_{\text{LOS}}\) < CI\({}_{\text{LFS}}\) in the northern hemisphere.
###### Abstract  
In response to the need for sustainable soil stabilization alternatives, this study explores the use of waste materials and biopolymers to improve the mechanical behavior of clay from Cartagena, Colombia. Crushed limestone waste (CLW), ground glass powder (GG), recycled gypsum (GY), xanthan gum (XG), and the combination of XG with polypropylene fibers (XG-PPF) were used as stabilizing agents. Samples were compacted at different dry densities and cured for 28 days. Unconfined compressive strength (UCS) and ultrasonic pulse velocity (UPV) tests were conducted to assess the strength and stiffness of the treated mixtures. Results were normalized using the porosity/binder index (\(\eta\)/\(B_{ii}\)), leading to predictive equations with high determination coefficients (\(R^{2}\) = 0.94 for UCS and \(R^{2}\) = 0.96 for stiffness). However, XG-treated mixtures exhibited distinct behavior that prevented their inclusion in a unified predictive model, as the fitted exponent x in the porosity/binder index (\(\eta\)/\(B_{ii}^{x}\)) differed markedly from the others. While an exponent of 0.28 was suitable for blends with mineral binders, the optimal x values for XG and XG-PPF mixtures were significantly lower at 0.02 and 0.03, respectively, reflecting their unique gel-like and fiber-reinforced characteristics. The analysis of variance (ANOVA) identified cement content and compaction density as the most influential factors, while some interactions involving the residues were not statistically significant, despite aligning with experimental trends. The findings support the technical viability of using sustainable additives to enhance soil properties with reduced environmental impact.  
construction waste; xanthan gum; porosity/binder index; strength; stiffness Article
# Split-And-Delay Unit for FEL Interferometry in the XUV Spectral Range
## 2 Split-And-Delay Unit for XUV Interferometry
### Experimental Design of the SDU  
The SDU designed for XUV experiments utilizes two lamellar gratings of different but complementary design. The first grating is a 60 \(\times\) 35 \(\times\) 1 mm\({}^{3}\) Si wafer with the central 10 \(\times\) 20 mm\({}^{2}\) area processed as a slotted grid with a circular diamond saw. The structure of 250 \(\upmu\)m period comprises 150 \(\upmu\)m wide slits separated by 100 \(\upmu\)m wide reflective facets. The second grating comprises 100 \(\upmu\)m wide, rectangular ridges projecting (protruding) from the substrate for 1.25 mm. The ridges are spaced 150 \(\upmu\)m apart and fit into the slits of the first grating. When interleaved, the gratings form an alternating pattern of 100 \(\upmu\)m facets separated by 25 \(\upmu\)m gaps giving a fill factor of 0.8 for the assembled device.  
The interleaved gratings are installed on a specially designed mount equipped with motors necessary for grating alignment and translation as shown in Figure 2. The slotted grating is rigidly fixed to the mount while the ridged grating has three degrees of freedom driven by piezo actuators. One of them is used to translate the grating and thus delay the reflected beam. The two others control the grating rotation in two planes and keep both gratings parallel. The delay piezo stage has a travel range of 250 \(\upmu\)m in the closed loop control mode, which translates in the delay ranging from \(-\)50 to +574 fs at the incidence angle of 22\({}^{\circ}\) with respect to the surface. We note in passing that the resulting peak fluence of unfocused FEL beams on the optics surface in this geometry is well below the melting and ablation thresholds of bulk Si.  
The described SDU is designed for time-resolved ionization experiments of low-density gas targets by XUV photons with detection of ionization products, i.e., electrons and ions. It follows from Equation (1) that diffraction orders from an XUV beam generated by a grating with a 250 \(\upmu\)m period in the focal plane will be separated only by some tens of microns. For example, \(\Delta r=46\)\(\upmu\)m for \(\lambda~{}=~{}38\) nm and \(f=300\) mm [22]. Therefore the detection system must have high spatial resolution in order to distinguish signals from individual orders. The ionization volume can be readily imaged with a resolution of few microns by an elongated velocity map imaging (VMI) spectrometer operated in the spatial imaging mode, which proved to be sufficient for 38 nm wavelength [22]. Higher resolution required for shorter wavelengths can be achieved by extending the focal length of the mirror and using a spectrometer purposely designed for magnified spatial imaging of charged particles, i.e., an ion microscope [36].
## 3 IoT Standard Architectures
### ITU-T Reference Architecture  
International Telecommunication Union--Telecommunication (ITU-T) Reference architecture (as shown in Figure 6) presents a broader representation of all upper layers and capability levels [24]. The abbreviations mentioned in the above figure are elaborated as follows:  
_DC_: Device Capabilities  
_GC_: Gateway Capabilities  
_NC_: Network Capabilities  
_TC_: Transport Capabilities  
_GSC_: Generic Support Capabilities  
_SSC_: Specific Support Capabilities  
_IOTA_: IoT Applications  
_GMC_: Generic Management Capabilities  
_SMC_: Specific Management Capabilities  
_GSEC_: Generic Security Capabilities  
_SSC_: Specific Security Capabilities  
DC deals with sensing and actuating, ad hoc networking, and communication with upper layers. GC deals with multiple interface support, protocol conversion, application logic, and the execution environment. NC deals with connectivity and mobile management and AAA (authentication, authorization, and accounting). TC deals with transport services for data and control information. GSC deals with data processing and storage. SSC deals with support functions of various IoT applications. IOTA deals with applications written  
Figure 5: ESTI Reference Architecture.  
by the developer and executed by the user. GMC deals with device management, such as activation and deactivation, firmware and software upgrades, network topology, and congestion management. SMC deals with application-specific requirements. GSEC deals with authorization, authentication, integrity, data confidentiality, and privacy protection. SSEC deals with application-specific requirements.
## 5 Experiment Results
### Experiment Results
#### 5.3.3 Parameter Experiment  
The experiment results in Table 3 show the F1 score when different dynamic thresholding settings are used. In the experiment, \(\beta_{1}=0.3\) and \(\beta_{2}=0.4\) can achieve the highest F1 score on the actual production dataset.  
Figure 9 shows how \(S_{wp_{p}}\) affects the forecasting result and the error extraction output results. Figure 10 shows the F1 result using different \(S_{wp_{p}}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Forecasting Network** & **Error Extraction Network** & **Dynamic Threshold** & **Precision** & **Recall** & **F1** \\ \hline \(\surd\) & & & 0.5 & 0.85 & 0.615 \\  & \(\surd\) & & 0.111 & 1 & 0.230 \\ \(\surd\) & \(\surd\) & & 0.75 & 1 & 0.857 \\ \(\surd\) & \(\surd\) & \(\surd\) & 0.895 & 0.85 & 0.872 \\ \hline \end{tabular}
\end{table}
Table 2: Ablation experiment result.  
Figure 8: Convergence speed comparison between sliding windows with the mini-batch and single-batch methods.  
Figure 10: F1 results using different \(S_{\mu_{y}}\). \(S_{\mu_{y}}=4\) and 5 achieves the best F1 result.  
The experiment shows that a smaller value of \(S_{wp}\) results in underfitting. Because the smaller \(S_{wp}\) is, less data is needed to reconstruct the normal sequence. But meanwhile, the error extraction network uses the entire \(S_{w}\) data to generate \(S_{w_{j}}\) error sequences. The error extraction network will be trained faster than the forecasting network during training. Therefore, it is more likely to mistake the entire series as an anomaly to reduce the loss in the early stages. By contrast, a larger \(S_{wp}\) value results in error extraction network underfitting. In the experiment, setting \(S_{w_{l}}=4\) and \(5\) achieves the best F1 result on the tested dataset.
# Method for Recognition of Communication Interference Signals under Small-Sample Conditions
## 4 C-ResNet Classification Network  
### Spatial and Channel Attention Mechanism  
In the time-frequency plots of communication jamming signals, a significant portion of the area consists of noise information. To focus the classification network on the key feature areas of the jamming signals, the Convolutional Block Attention Module (CBAM) is introduced [19]. This spatial and channel attention mechanism includes a spatial and channel attention layers, which dynamically adjust the focus on different spatial positions. Lower weight parameters are assigned to the noise areas in the time-frequency plots, while higher weight parameters are allocated to the key feature areas of the signals.  
The spatial attention layer enhances the response to key feature areas in the time-frequency plots by calculating the weights of different pixel positions. The input feature \(\mathbf{F}\) is first processed by two convolutional layers with a kernel size of \(1\times 1\), followed by two layers of activation functions to produce an attention map. Finally, each spatial position of the original input feature map \(\mathbf{F}\) is weighted according to the corresponding attention map. The formula for the spatial attention layer is:  
\[\mathbf{F^{\prime}_{s}}=\mathbf{F}\otimes\gamma(f(\sigma(f(\mathbf{F})))) \tag{11}\]  
Unlike the spatial attention layer, the channel attention layer employs global average pooling to capture the overall features of each channel and assigns weights to each channel based on their importance. The formula for the channel attention layer is:  
\[\mathbf{F^{\prime}_{c}}=\mathbf{F}\otimes\gamma(f(\sigma(f(Avgpool(\mathbf{F}))))) \tag{12}\]  
where \(Avgpool\) represents the global average pooling.
## Preliminaries
### Certificateless Cryptography  
A Certificateless Cryptography (CLC) [31] scheme is specified by seven randomized algorithms.  
1. Setup: This algorithm takes security parameter \(k\) as input and returns the system parameters _params_ and master-key.
2. Partial-Private-Key-Extract: This algorithm takes _params_, master-key, and entity's ID as inputs and returns a partial private key \(\mathrm{sk}_{ID}\) for the entity.  
Set-Secret-Value: This algorithm takes _params_ and entity's ID as inputs and outputs this entity's secret value \(y_{ID}\).
* Set-Private-Key: This algorithm takes _params_, entity's ID, partial private key \(\text{sk}_{ID}\), and secret value \(y_{ID}\) as inputs and outputs private key \(S_{ID}\).
* Set-Public-Key: This algorithm takes _params_ and secret value \(y_{ID}\) as inputs and outputs public key \(Y_{ID}\).
* Encrypt: This algorithm takes _params_, message m, and public key \(Y_{ID}\) as inputs and entity's ID and generates ciphertext \(\sigma\) of the message m if success.
* Decrypt: This algorithm takes _params_, \(\sigma\), and \(S_{ID}\) as inputs and returns message m.  
By making use of certificateless cryptography, we will construct new authenticator of data block.
# Designation of the Quality of EGNOS+SDCM Satellite Positioning in the Approach to Landing Procedure
###### Abstract
The main contribution of the authors of the article consists in:  
* Defining a multi-system Multi-SBAS solution that takes into account individual EGNOS and SDCM solutions to determine the model of the resultant position of an aerial vehicle;
* Employing the Multi-SBAS solution to calculate the quality parameters of the aerial vehicle, i.e., the accuracy, continuity, integrity, and availability parameters;
* Developing a weighting plan to integrate and combine individual SBAS solutions to the Multi-SBAS model;
* Conducting navigation analyses that confirm the correctness of the developed research methodology.
# A Spatio-Temporal Encoding Neural Network for Semantic Segmentation of Satellite Image Time Series
## 5 Discussion
Figure 2 provides a correspondence between the crop type numbers and their specific crop names. drop, particularly a 23% drop in mIoU. Additionally, we trained our proposed spatial encoding module on single temporal images from August and May. We observed a significant decrease in both OA and mIoU, confirming the importance of the annual phenological cycle of crops in agricultural land classification.
## 1 Introduction  
A sensor node is a small, lightweight sensing device. It is composed of a constrained processing unit and small amount of memory for its small operating system. Additionally, a sensor node includes a limited-range transceiver and a battery unit [1]; a mobile node also includes a mobility subsystem. WSNs often manage thousands of sensor nodes. In fact, these sensor nodes communicate with a huge number of small nodes via radio links. WSNs applications distinguish between applications based on the type of data that must be collected in the network [2]. Sensor nodes in a network gather data that are necessary to include in a smart network environment. These environments include homes, transportation systems, military installations, healthcare systems, and buildings. WSNs make it technologically possible to reorganize information and communication technology. The study of WSNs is a significant topic in computer science and engineering. It has an economic impact and affects industry.  
In WSNs, sensor nodes transfer packets from the source to the base station. Because a sensor node is a limited-transmission device, it uses a multi-hop method to send packets to the base station [3]. While the communication between sensor nodes in WSNs is accomplished wirelessly by radio, adversaries can use many types of attacks. Eavesdropping, compromising nodes, interrupting or modifying packets, and injecting malicious packets compromise privacy, and denial of service attacks are threats to the security of WSNs [4]. Attackers compromise the internal sensor nodes from which they launch attacks, which are difficult to detect. A selective forwarding attack is the one of these attacks. It is an attack where a node sends some of messages to other nodes or base stations whilst dropping the sensitive information [5].  
In a selective forwarding attack, malicious nodes attempt to stop the packets in a network by rejecting message forwarding. It is not easy to detect this type of attack due to the unreliability of communications. According to [PERSON] and [PERSON] [6], selective forwarding attacks can impact some routing protocols, such as TinyOS beaconing, DSR, and PSFQ. During the launch of a selective forwarding attack, a compromised node has notable consequences. A compromised node selectively drops packets. Malicious nodes work in the same manner as other nodes in the network field, but these malicious nodes try to find the important messages and drop them before sending the whole packets to the next nodes. The attackers make the sensor network rely on the redundancy forwarding by using broadcast for data to spread in the network. Based on researchers' results, limited power and low memory are obstacles that make conventional security measures inappropriate for WSNs [7]. One of the obstacles in WSNs is also energy consumption, so data transmission between sensor nodes is the major source of energy consumption and it is a serious challenge to design an energy efficient routing scheme for extending a network's lifetime [8].
# How Useful Are Strain Rates for Estimating the Long-Term Spatial Distribution of Earthquakes?
## 3 Results
### Smoothed Scatter Plots  
To give a better insight into changes over time in the correlation of covariates with earthquake locations, we plotted the covariate values for the cells corresponding to target earthquakes versus time over the period 1951-2020. We then fitted a trend curve using locally weighted scatter plot smoothing (lowess) based on [PERSON]'s method in [24]. We observed clear trends in the strain rate covariate values corresponding to target earthquake locations over the period 1951-2020, as shown by Figure 10.  
Figure 8: Spatial distribution of GSS and target earthquakes during 1951–1962.  
Figure 10: Smoothed scatter plots of (**a**) GSS, (**b**) BSS, and (**c**) HWS strain rate covariates for targeted earthquakes against time from 1951 to 2020.  
Figure 9: Area Skill Score (ASS) of Molchan error diagram for covariates over different times.  
The trend is stronger for BSS and HWS than for GSS. However, for all three covariates, the smoothed trend line starts from its lowest level in 1951, rises gradually to a peak in 1999, and then tails off somewhat through to 2020. However, the HWS curve also tails off less than the others. These results confirm and elaborate on the trends over time that was observed in Figures 8 and 10. The HWS model was extracted from the longest period of recordings (1995-2013) and a larger number of GNSS stations compared with the GSS and BSS models. In addition, unlike BSS and GSS that were both based on an older method of Haines and Holt [25; 26; 27], the HWS model was derived using the novel VDOHS technique [14]. Such differences could explain the differences observed in the smoothed scatter plots versus time.  
We observed that FLT was the only covariate to show a clear trend with the magnitude of target earthquakes. Figure 11 plots the FLT values versus the magnitudes of the target earthquakes from 1951 to 2020. The locally weighted smoothed curve, shown by the solid black line, reveals an increasing trend with magnitude. This may not necessarily imply that future large earthquakes are more likely to occur on known faults than smaller earthquakes. It may instead reflect the fact that large earthquakes cause surface ruptures more frequently than small ones; consequently, new ruptures over the past 70 years have revealed the associated faults and allowed them to be mapped.