esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mountain-Based Test Results
### Analysis of GPS and BD Open-Loop Tracking Results
From the curve, we can see that the excess phase changes obtained by GPS and BD are relatively robust, and there is no deterioration in signal capture quality due to drastic changes in the signal. 3. The OL tracking effect of GPS and BD are similar, and both have a greater improvement compared to closed-loop tracking. Figure 6: Excess phases retrieved from observations of B1/BD in OL/closed-loop tracking mode. Red/green lines represent the phases in closed-loop/OL tracking mode, respectively. (**a**) C24/BD occultation; (**b**) C28/BD occultation; and (**c**) C20/BD occultation. Figures 9 and 10 show the refractivity profiles obtained from the observations of L1C/GPS and B1/BD in the OL/closed-loop tracking mode, respectively. In these figures, the refractivity profiles obtained from the observations in the OL/closed-loop mode in the overlap height have the same trend along altitude. The minimum height of refractivity profiles obtained from L1C/GPS signal in OL mode are about 0.4 km, and that of the closed-loop mode are about 1.6 km. The minimum height of refractivity profiles from B1/BD signal in OL mode are about 1.2 km, and that of closed-loop mode are between 1.3-1.5 km. Figure 11 visualizes the difference of refractivity profiles retrieved from observations of B1/BD and L1C/GPS in OL/closed-loop mode, from which it is concluded that 1. the bias of the refractivity retrieved from observations of L1C/GPS and B1/BD in OL/closed-loop mode is within 1N; and 2. The trend of the refractivity profiles retrieved from observations of L1C/GPS in OL/closed-loop mode is slightly better than the result of B1/BD. Figure 8: Difference of excess phase retrieved from observations of B1/BD and L1C/GPS in OL/closed-loop tracking mode. Left panel, B1/BD; Right panel, L1C/GPS. Figure 7: Excess phase retrieved from observations of L1C/GPS in OL/closed-loop tracking mode. Red/green lines represent the phases in closed-loop/OL tracking mode, respectively. (**a**) G19/GPS occultation; (**b**) G24/GPS occultation; and (**c**) G12/GPS occultation. Figure 10: Refractivity profiles retrieved from observations of L1C/GPS in OL/closed-loop tracking mode. Red/green lines represent results in closed-/OL mode tracking, respectively. (**a**) G19/GPS Occultation; (**b**) G24/GPS Occultation; and (**c**) G12/GPS Occultation. Figure 9: Refractivity profiles retrieved from observations of B1/BD in OL/closed-loop tracking mode, respectively. Red/green lines represent results in closed-/OL mode tracking, respectively. (**a**) C24/BD Occultation; (**b**) G28/BD Occultation; and (**c**) C12/BD Occultation.
# Optimization Design of the Spaceborne Connecting Structure for a Lightweight Space Camera
## 3 Optimization Design of the Spaceborne Connecting Structure
### Topology Optimization of Connecting Structures
In addition, the second mirror of the camera in this study is supported by a single pole, and the optomechanical structure is not circularly symmetrical, which results in the connecting structures in three positions not being the same. The results showed that the topology optimization design has carried out a reasonable material distribution to ensure the performance of the camera, rather than blindly and evenly distributing the material to three connecting structures.
###### Abstract  
Structured grid-based sparse matrix-vector multiplication and Gauss-Seidel iterations are very important kernel functions in scientific and engineering computations, both of which are memory intensive and bandwidth-limited. GPDSP is a general purpose digital signal processor, which is a very significant embedded processor that has been introduced into high-performance computing. In this paper, we designed various optimization methods, which included a blocking method to improve data locality and increase memory access efficiency, a multicolor reordering method to develop Gauss-Seidel fine-grained parallelism, a data partitioning method designed for GPDSP memory structures, and a double buffering method to overlap computation and access memory on structured grid-based SpMV and Gauss-Seidel iterations for GPDSP. At last, we combined the above optimization methods to design a multicore vectorization algorithm. We tested the matrices generated with structured grids of different sizes on the GPDSP platform and obtained speedups of up to 41\(\times\) and 47\(\times\) compared to the unoptimized SpMV and Gauss-Seidel iterations, with maximum bandwidth efficiencies of 72% and 81%, respectively. The experiment results show that our algorithms could fully utilize the external memory bandwidth. We also implemented the commonly used mixed precision algorithm on the GPDSP and obtained speedups of 1.60\(\times\) and 1.45\(\times\) for the SpMV and Gauss-Seidel iterations, respectively.  
GPDSP; SpMV; Gauss-Seidel iteration; parallel algorithm +
Footnote †: journal: Applied sciences
## 7 Results and Discussion
### Model Comparison  
The proposed method allows us to flexibly choose an adaptive refresh rate because we need minimal system tweaking and only adjust when the end system gives the location feedback. This should allow the setup to operate in real time and also allow it to operate on par with other systems.  
There are many more comparative advantages of using this system compared to others and, as can be seen from the table below, the results are clear. With a proper cost-benefit analysis, our system surpasses several more systems and can be applied in industrial settings. The only trade-off would be accuracy for range and also its three-dimensional capabilities stripped away in return for maintaining the use of one antenna. However, these methods are not mutually exclusive and hence can be combined to work together--for example, allowing a different system to have multiple phases of operation. An example is the COCKTAIL algorithm. COCKTAIL is a more comprehensive localization algorithm. It also runs in two phases. The first phase is the same as the SA phase of SA-LANDMARC, which aims to determine a subarea where the target object exists [21]. These results can also be logged to allow for deep learning to distinguish and observe any hidden patterns, as when deep CNN is applied to RFID multi-tag localization with the joint fingerprint features of the RSSI and the phase difference of arrival (PDOA). The CNN for RFID localization has great advantages, such as the capability of processing a large amount of data, extracting and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Components Used** & **Refresh Rate** \\ \hline Single core & 3.2 s \\
1 core + 1 cluster & 9.6 s \\
1 core + 2 clusters & 16 s \\
1 core + 3 clusters & 22.4 s \\
1 core + 4 clusters & 28.8 s \\
1 core + 5 clusters & 35.2 s \\
1 core + 6 clusters & 41.6 s \\
1 core + 6 clusters + All sectors & \textgreater{}45 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Refresh rate.  
training fingerprint features, sharing the parameter structure and reducing the complexity of the neural network [22]. The model comparison results are shown in Table 5.
## 3 Methodology
### Shadow Direction Acquisition
#### 3.2.3 Shadow Direction Confirmation  
To confirm shadow direction, we first check the latitude and the collection date of an image to determine the shadow directly. Table 2 shows the general shadow directions at different times and locations.  
In Table 2, shadow direction is roughly south, north or uncertain, using Table 2 and \(\theta\), the shadow direction can be confirmed. If the image belongs to the uncertain cases, we use the gray and topological features to ascertain the direction, as shown in Figure 5.  
In Figure 5, \(\theta\) refers to shadow angle without direction, and areas (\(B1\), \(B2\)) are extracted at the sides of the shadow on the shadow angle of SH\({}_{\rm c}\). A shadow direction candidate \(\alpha_{1}\) and \(\alpha_{2}\) are the two directions of a shadow angle. If the building is on the B1 side, the shadow direction is \(\alpha_{2}\). If the building is on the B1 side, the shadow direction is \(\alpha_{1}\). To ascertain the shadow direction in this case, we make gray value comparison between the \(B1\)s and \(B2\)s:  
\[f(B)=\left\{\begin{array}{ll}1,&G_{B1}>G_{B2}\\ 0,&G_{B1}<G_{B2}\end{array}\right. \tag{3}\]  
\[M=\sum_{i=1}^{N}f(B) \tag{4}\]  
\[\alpha=\left\{\begin{array}{ll}\alpha_{1},&M<N/2\\ \alpha_{2},&M>N/2\end{array}\right. \tag{5}\]  
In Equation (3), \(G_{B1}\) is the average gray value of \(B1\), and \(G_{B2}\) is the average gray value of \(B2\). In Equation (4), shadow direction is \(\alpha\) and \(\alpha\in[-180^{\circ},180^{\circ}]\). In Equation (5), \(N\) represents all the shadows involved in the calculations. In theory, it is unlikely that \(M=N/2\)  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Latitude (\({}^{\circ}\))** & \(-90\)\(-\)\(-\)\(23.5\) & \(-23.5\)\(-\) & \(0\)\(-23.5\) & \(23.5\)\(-\)\(90\) \\ \hline
21 May/22 May–21 June/22 June & south & south & uncertain & north \\
21 June/22 June–22 September/23 September & south & south & uncertain & north \\
22 September/23 September–22 December/23 December–21 May/22 May & south & uncertain & north & north \\ \hline \end{tabular}
\end{table}
Table 2: Shadow direction.  
Figure 5: Diagram of shadow direction confirmation.  
If \(M=N/2\), obtain another area threshold and repeat this step. Lastly, a manual sampling check is applied in the event that an extreme case occurs; for example, if \(\theta\) equals 0.
## 4 Performance Evaluation of Parameter Estimation
### Comparison between Direct-FFT Method and the Proposed Method  
Although the direct-FFT method has a good spectrum resolution, it suffers from significant estimation variance. Besides, as it employs a rectangular time-window to restrict the infinite time signal to a finite time horizon, it faces spectrum leakage which further enhances the amplitude of the useless side peaks of the spectrum. Whereas the proposed technique uses a nonrectangular time window to achieve good property for leakage reduction. The time averaging also minimizes the effect of noise.  
Figures 3 and 4 show the impulse responses of the direct-FFT method and the averaging-FFT method with a window time L of 256 points for C/N\({}_{0}\) of 45 and 38 dB-Hz, respectively. In the simulations, we assume two multipath signals. The first one has the same power as the direct signal with a time delay of a half chip and the second one has attenuation factor \(\alpha\) of 0.5 and time delay \(\tau\) of one chip. Because of the noise, the normalized amplitude of three impulses (one is for LOS and the other two are for multipath signals) after IFFT are random variables fluctuating at the value of 1 and 0.5, instead of constants. When C/N\({}_{0}\) is 45 dB-Hz, the two methods are all able to estimate the amplitude and time delay of both the direct signal and two multipath signals well. But, the noise variance of the direct-FFT method is obviously higher than the aFFT method. Whereas for the case of 38 dB-Hz, the two methods both get worse. The aFFT method is still able to provide the information of multipath parameters accurately. But the direct-FFT method can hardly distinguish the impulses from noise, especially for the second reflected signal with smaller amplitude of 0.5. So for this method, longer data are needed to achieve the performance improvement and minimize the noise. But meanwhile, the required computational load will increase accordingly, and the real-time capability will definitely get worse. Consequently, the implement of direct-FFT method requires relatively good C/N\({}_{0}\) condition, whereas the proposed method can keep acceptable performance in poor noise conditions.  
Figure 3: (**a**) Impulse responses of the averaging-FFT method with C/N\({}_{0}\) of 45 dB-Hz; (**b**) impulse responses of the direct-FFT method with C/N\({}_{0}\) of 45 dB-Hz.
## 3 Proposed Solution
### Proposed Architecture  
The filtering mechanism is based on the application of different filtering modules, where each module examines a number of features of the email header and body and, based on these features, classifies the email as possible spam or ham. The combination of the results of the different modules will allow the email to be forwarded or rejected. For this purpose, a message is considered junk if at least one module classifies it as spam. Our proposal supports the definition of filtering exceptions for messages that (i) have been sent from a specific IP address, (ii) whose source address belongs to certain domains, and/or (iii) whose sender has a specific email address. The modularity of the system gives great flexibility to incorporate new filters and even to disable some filtering modules that are not of interest. In addition, this modular system makes use of concurrency techniques that allow the simultaneous execution of each module in a different thread. Figure 1 shows the architecture of our proposal.  
As shown in Figure 1, LiSB supports the execution of a wide range of filtering modules. Among them, we highlight the following: (i) _FromFilter_ that checks that the _From_ email header and SMTP MAIL FROM command are consistent, (ii) _ToFilter_ that ensures that the _To_ email header and SMTP RCPT TO commands are consistent, (iii) _ReturnPathFilter_ that ensures _ReturnPath_ and _From_ email headers are consistent, (iv) _SPFFilter_ that executes an SPF authentication [18], (v) _XFilter_ that verifies that optional headers (those starting with X) from a particular domain are always the same, (vi) _DKIMFilter_ that ensures that the s and d parameters of emails sent from the same domain always have the same values, (vii) _BlackListFilter_ that implements a dynamic blacklist scheme, and (viii) ML schemes applied on message content.  
Our framework incorporates a _time_limit_ parameter that prevents excessively slow email classifications. To this end, when the classification of a specific message takes longer than the time defined in the _time_limit_ parameter, the classification process is interrupted and the message is classified as ham.  
Messages classified as ham by the filtering framework are queued for subsequent forwarding. Different threads work on this queue to forward the verified emails to the destination SMTP server. The framework also includes an administration website (see Figure 2), which is used as the user interface of the system and gives access to all implemented functionalities.  
As shown in Figure 2a, the administration website includes a wide range of administration tasks including monitoring, configuration, and backup. In detail, it allows the administrator to monitor system events in real time, access past events, enable and disable the SMTP proxy server, create and restore local and S3 backups, and edit all system configuration files. Figure 2b shows the server status console accessed through the \"Monitoring/Server status\" menu item.  
Figure 1: This is a figure: LisB architecture.
# Elasto-Plastic Short Exoskeleton to Improve the Dynamic and Seismic Performance of Frame Structures
## 3 Mechanical System
### Elasto-Plastic Description of the Yielding System  
The elasto-plastic constitutive behaviour of the yielding system is described by the Bouc-Wen model [22]. According to this model, the restoring force \(f_{\rm exo}\) can be expressed as  
\[f_{\rm exo}(t)=\psi k_{\rm exo}u_{1}(t)+k_{\rm exo}u_{y}(1-\psi)z_{\rm exo}(t) \tag{2}\]  
where \(\psi\) is the ratio between the post-yielding stiffness \(k_{\rm plast}\) and the pre-yielding stiffness \(k_{\rm exo}\), i.e., \(k_{\rm plast}=\psi k_{\rm exo}\). The case \(\psi=0\) represents the elastic-perfectly plastic constitutive behaviour of the yielding system, whereas the case \(\psi=1\) describes its fully elastic constitutive behaviour. Quantity \(u_{y}\) is the yield displacement, beyond which the yielding system undergoes plastic deformations. It is worth observing that the product \(k_{\rm exo}u_{y}\) represents the yielding force of the structure, \(F_{y}\). Finally, \(z_{\rm exo}\) is an auxiliary variable the describes the post-yielding behaviour and is defined by an ordinary differential equation  
\[\dot{z}_{\rm exo}(t)=\frac{1}{u_{y}}\big{[}A-|z_{\rm exo}(t)|^{n}(\beta_{1}+ \gamma_{1}{\rm sign}(\dot{u}_{1}(t)\cdot z_{\rm exo}(t)))\big{]}\dot{u}_{1}(t) \tag{3}\]  
where parameters \(A\), \(\beta_{1}\), and \(\gamma_{1}\) depend on the shape of the hysteretic cycles.  
Figure 2: Mechanical system: (**a**) geometric and mechanical characteristics of the system; (**b**) deformed configuration and positive directions of the Lagrangian parameters.  
The parameters of the Bouc-Wen model are functionally redundant; there are multiple sets of values of the parameters that produce identical responses for a given excitation. This redundancy can be removed by fixing one of the parameters. A possibility is to fix \(A=1\) so that the physical meaning of the initial stiffness \(k_{\text{exo}}=F_{y}/u_{y}\) is restored [39].  
Parameters \(\beta_{1}\) and \(\gamma_{1}\) control the shape and size of the hysteretic loop, as demonstrated in [22]. However, they lack any physical interpretation. As suggested in [40], it is assumed \(\beta_{1}+\gamma_{1}=1\). The two constraints \(\beta_{1}+\gamma_{1}=1\) and \(A=1\) reduce the Bouc-Wen model to a strain-softening formulation with a defined meaning of the mechanical quantities \(F_{y}\), \(u_{y}\), \(\psi\). Under such constraints, the dimensionless hysteretic parameter \(z_{\text{exo}}\) takes values in the range \([-1,1]\). Finally, the exponential parameter \(n\) governs the abruptness of the transition between the elastic and post-elastic branch of the hysteresis model. For large values of \(n\), the response approaches that of a bilinear model. All simulations presented in this paper are performed considering \(n=2\).  
The equations of motions, Equations (1)-(3), are solved numerically, using the Runge-Kutta method, embedded in the command NDSolve inside the Wolfram Mathematica(r) environment. The used integration time-step is 0.001 s.
###### Abstract  
The present study investigated the effect of aeration mode on microbial structure and efficiency of treatment of wastewater with a high concentration of suspended solids (TSS) from meat processing in sequencing batch reactors (R). R1 was constantly aerated, while in R2 intermittent aeration was applied. DNA was isolated from biomass and analyzed using next-generation sequencing (NGS) and real-time PCR. As a result, in R1 aerobic granular sludge was cultivated (SVI30 = 44 mL g\({}^{-1}\) MLSS), while in R2 a very well-settling mixture of aerobic granules and activated sludge was obtained (SVI30 = 65 mL g\({}^{-1}\) MLSS). Intermittent aeration significantly increased denitrification and phosphorus removal efficiencies (68% vs. 43%, 73% vs. 65%, respectively) but resulted in decomposition of extracellular polymeric substances and worse-settling properties of biomass. In both reactors, microbial structure significantly changed in time; an increase in relative abundances of _Arenimonas_ sp., _Rlodobacterae_, _Thauera_ sp., and _Dokdonella_ sp. characterized the biomass of stable treatment of meat-processing wastewater. Constant aeration in R1 cycle favored growth of glycogen-accumulating _Amaricoccus tamworthensis_ (10.9%) and resulted in 2.4 times and 1.4 times greater number of ammonia-oxidizing bacteria and full-denitrifiers genes in biomass, respectively, compared to the R2.  
Keywords: intermittent aeration; TSS-rich meat-processing wastewater; extracellular polymeric substances; complete denitrification; microbial structure +
Footnote †: journal: Applied Sciences
## Materials and Methods
### TerraSAR-X Data Base  
SAR data were acquired by the high-frequency (9.6 GHz) X-band sensor of TerraSAR-X with a wavelength of 3.1 cm, operating at 514 km altitude. The data were collected in Spotlight (SL) and High Resolution Spotlight (HRS) mode, which provide ground range resolutions of 1.5-3.5 m [(27)], few images were taken in stripmap mode with a resolution of 3 m. Since the SAR data should be combined with extensive in situ data and to perform spatio-temporal analyses, Geocoded Ellipsoid Corrected images (GEC) were chosen which can be easily imported into geographic information systems (GIS). To allow acquisition times close to the time of low tide on the one hand, and to obtain a sufficient amount of data on the other hand, SAR data had to be collected at varying orbits and incidence angles. This enabled us to acquire extensive and detailed time series, as well as recordings before and after events such as storm and storm tide or ice drift. From the resulting set of more than 100 TerraSAR-X images available for the years 2009 to 2016, the SAR data documented in this study are listed in Table 1. These images were acquired within the time period 1.5 h before and after low tide and apart from the stripmap image recorded in HH-polarization, the data were taken vertically co-polarized (VV).
## 3 5G HetNet Services, Architecture, and Related Technologies
### 5G Technologies
#### 3.3.6 Device-to-Device Communication in 5G HetNet  
Device-to-Device (D2D) communication in 5G HetNet refers to the direct communication between two devices without the involvement of a centralized network infrastructure (i.e., base station). D2D communication in 5G HetNet offers several benefits, making it an attractive technology for various applications. Firstly, D2D communication reduces the burden on the cellular network, leading to improved network capacity and reduced network congestion [72]. This means that more devices can be served simultaneously, leading to higher network utilization and a better user experience. Secondly, D2D communication enables direct communication between devices, leading to reduced latency and increased communication speed. This makes D2D ideal for real-time applications such as gaming, video streaming, and augmented reality. Thirdly, D2D communication provides improved coverage and connectivity in areas with weak or no cellular signals. This is particularly beneficial in remote areas where cellular coverage is limited. Fourthly, D2D communication is energy efficient, making it ideal for IoT devices that have limited power resources. Finally, D2D communication enables personalized and customized communication services, such as social networking and multimedia sharing, which enhance the overall user experience [73; 74; 75].