esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Quality Assessment for Gibbs Ringing Reduction
## 5 The Effect of Varying the Threshold, \(\epsilon\), on \(\ell_{e}^{0}\) and Huber Loss
\(\epsilon=0.03\) and \(\epsilon=0.06\) approximately bracket the SSIM curve, meaning that the maximal agreement between \(\ell_{\epsilon}^{0}\) and SSIM is in this region of \(\epsilon\). Larger values of \(\epsilon\) result in an erratic plot, which does not inspire confidence in the recommendation of the 'best' \(\sigma\). Next, we consider Huber loss, in the lower plot of Figure 8. For the \(\epsilon=0\) case, we use the \(\ell^{1}\) norm. We observe that small values of \(\epsilon\) (\(\leq 10^{-2}\)) cause Huber loss to track the SSIM curve rather well. The SSIM curve almost acts as a limiting case of Huber loss: no value of \(\epsilon\) causes Huber loss to recommend significantly lower \(\sigma\) than SSIM. This means  
Figure 8: **(top)** SSIM (thick blue) and \(\ell_{\epsilon}^{0}\) for various values of \(\epsilon\). **(bottom)** SSIM (thick blue) and Huber loss for various values of \(\epsilon\). that Huber loss is biased towards less aggressive filtering, favouring less blur over better ringing suppression. Next, we consider the upper plot in Figure 9. This shows the mean squared error between the curves in Figure 8 and the SSIM curve as a function of \(\epsilon\) (which is log-scaled for clarity because of the distribution of values tested). The blue curve depicts the MSE between the recommendations of \(\ell_{e}^{0}\) and SSIM. It suggests that, when \(\epsilon=0.03\), the MSE between \(\ell_{e}^{0}\) and SSIM is minimized. The red curve shows the MSE between Huber loss and SSIM. It shows that, when \(\epsilon=0\), the MSE between Huber loss and SSIM is minimized. With the caveats noted earlier, values close to 0.03 and 0 are therefore the optimal values of \(\epsilon\) for \(\ell_{e}^{0}\) and Huber loss, respectively, given test images normalized to a peak value of 1. Both metrics correlate well with SSIM for these choices of \(\epsilon\) and therefore presumably with human perception. This is depicted in the lower plot of Figure 9.  
We have shown the significance of \(\epsilon\), the threshold used in \(\ell_{e}^{0}\) and in Huber loss. We have determined optimal values of these thresholds.
## 3 Results  
### Chitosan Amine Groups Are Involved in the Crosslinking Mechanism  
FTIR spectroscopy was used to compare the characteristic chemical groups of chitosan and PVA and confirm the crosslinked network formation with type IV collagen and integrin \(\alpha_{3}\beta_{1}\). Figure 1 shows the FTIR spectra of the CTS, PVA, CTS/PVA, CTS/PVA/type IV collagen and CTS/PVA/type IV collagen/integrin \(\alpha_{3}\beta_{1}\) scaffolds. Figure 1A shows the spectrum of pure chitosan. The band at 3100 cm\({}^{-1}\) corresponds to the N-H groups; two bands at 1640 cm\({}^{-1}\) and 1540 cm\({}^{-1}\) were observed which has been reported as amine I (O=C-NH) and II (NH\({}_{2}\)), respectively. The broad band at 1336 cm\({}^{-1}\) is related to the O=C-N stretching vibration in chitosan. The FTIR spectrum of PVA (Figure 1A) shows the broad band observed at 3280 cm\({}^{-1}\) associated with the hydrogen bonds in the polymeric chains. At 2850 cm\({}^{-1}\), the band refers to the stretching (C-H) from alkyl groups and conformation of C-OH groups between 1232 cm\({}^{-1}\) and 1417 cm\({}^{-1}\). The IR spectra of the CTS/PVA (Figure 1B), CTS/PVA/type IV collagen (Figure 1C) and CTS/PVA/type IV collagen/integrin \(\alpha_{3}\beta_{1}\) (Figure 1D) scaffolds show a reduction of intensities at 1336 cm\({}^{-1}\), corresponding to the O=C-N stretching vibration, this suggest a crosslinking from CTS, PVA, type IV collagen and integrin \(\alpha_{3}\beta_{1}\).  
Figure 1: Spectra of Fourier Transform Infrared Spectroscopy (FTIR): (**A**) Chitosan (CTS) and polyvinyl alcohol (PVA), (**B**) CTS, PVA and CTS/PVA, (**C**) CTS, PVA and CTS/PVA/type IV Collagen and (**D**) CTS, PVA and CTS/PVA/type IV Collagen/integrin \(\alpha_{3}\beta_{1}\).
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 4 Solution Architecture
### Network Security Design
#### 4.3.3 Private Endpoints and Private DNS Zones  
One of the most significant security enhancements in modern Azure architectures is Private Link, which enables private connectivity to Azure PaaS resources. Private Endpoints create a network interface in the customer's VNet, mapped to the service resource, effectively making it accessible via an internal IP address. Eliminate public ingress for all critical resources.  
Private DNS Zones automate name resolution for Private Link connections, removing the need for manual IP management or custom DNS servers. Key benefits include the following:  
- Automatic resolution of service FQDNs to private IPs;  
- Simplified deployment and maintenance;  
- Fully private network communication.  
This combination of subnets, delegated subnets, Private Endpoints, and Private DNS Zones represents an industry-standard approach for securing cloud-native deployments.  
By ensuring no public ingress to critical data stores and minimizing exposure, the design aligns with the zero-trust security model promoted by Microsoft and industry frameworks, such as the NIST framework.
# A Credit-Based Congestion-Aware Incentive Scheme for DTNs
## 6 Conclusions  
In this paper, we have proposed a credit-based congestion-aware incentive scheme, called CBCAIS, to stimulate selfish nodes to cooperatively forward messages in DTNs. In CBCAIS, a delay-based credit reward scheme is proposed to effectively stimulate selfish nodes to forward messages to  
Figure 23: Overhead ratio for varying transmission speeds.  
Figure 24: Energy for varying transmission speeds.  
others according to delivery delay. In addition, a check and punishment mechanism is proposed to prevent forwarding nodes from discarding message deliberately. To further enhance the performance, a message acceptance selection mechanism is proposed to allow nodes to decide whether to accept the messages of other nodes, according to self congestion degree. We have also verified the effectiveness of CBCAIS through extensive simulations. This work focuses on single-copy routing; our future direction is to explore an effective congestion-aware incentive scheme for multi-copy routing and data dissemination in DTNs.  
This work was fully supported by Natural Science Foundation of Heilongjiang Province (e683875-7-11014), the Science and Technology Research Projects of the Education Department of Heilongjiang Province (12543005) and the Natural Science Foundation of Daqing Normal University (14 zr21).  
[PERSON] constructed the model and the algorithms and wrote the manuscript; and [PERSON] designed the experiment. [PERSON] organized and refined the manuscript. All authors have read and approved the final manuscript.  
The authors declare that there is no conflict of interests regarding the publication of this paper.
## 3 Test Results and Discussion
### Barrier Maximum Moment Associated with the Diagonal Tension Crack in the Deck Slab Overhang
The tensile capacity for a concrete section of width b at the diagonal crack is calculated as follows, considering a parabolic distribution of the tensile stress along the diagonal crack length, \(\ell_{\rm dc}\). \[T=\frac{2}{3}f_{r}\cdot b\cdot\ell_{dc} \tag{8}\]  
By substituting equations 6 and 8 into Equation (2), the maximum moment at the centerline of the deck overhang at the inner side of the barrier wall is as follows. \[\rm M_{b}=0.6\ f_{r}\cdot b\cdot d_{d}\cdot\ell_{dc}.\rm sin\alpha \tag{9}\]  
Equation (9) is applied to specimens B-3 and B-4 where the barrier is mounted over a deck overhang. The concrete compressive strengths are taken as the characteristic values in Table 1, with 39.4 MPa and 43.57 MPa for specimens B-3 and B-4, respectively. For a concrete cover to the center of the GFRP bar in the tension side of the deck overhang of 58 mm, the effective depth, \(\rm d_{d}\), is taken as 192 mm. The width of the section is taken as 1000 mm. Per Figure 14d, the diagonal crack angle was calculated as 37.5\({}^{\circ}\). The maximum moments at the deck overhang centerline when the diagonal tension crack occurs are 142 kN.m/m and 150 kN.m/m, for specimens B-3 and B-4, respectively. The corresponding experimental ultimate moments are taken as the maximum experimental transverse load in Table 3 times (\(\rm Ht+0.5 td\)), which is (0.99 + 0.25/2 = 1.115 m). Thus, this leads to corresponding experimental ultimate moments of 161 kN.m/m and 203 kN.m/m for specimens B-3 and B-4, respectively. The ratios between the experimental ultimate moment and the theoretical moment are 1.13 and 1.36 for specimens B-3 and B-4, respectively. When using the average concrete compressive strength rather than the characteristic values in Table 1, the ratios between the experimental ultimate moment and the theoretical moment are 1.04 and 1.24 for specimens B-3 and B-4, respectively. One may observe that Specim B-3 and B-4 are identical except that the vertical GFRP bars at the front of the barrier are doubled in the latter, and the middle vertical bars pass through the diagonal crack at the deck-barrier corner. This may be the reason the experimental-theoretical moment ratio for specimen B-4 is greater than that of B-3. In any case, the moment ratios of being slightly more than 1 confirm the applicability of the developed equation 8 in the design to preclude the failure due to diagonal tension crack at the barrier-deck corner.
###### Abstract  
The increasing complexity of communication systems, following the advent of heterogeneous technologies, services and use cases with diverse technical requirements, provide a strong case for the use of artificial intelligence (AI) and data-driven machine learning (ML) techniques in studying, designing and operating emerging communication networks. At the same time, the access and ability to process large volumes of network data can unleash the full potential of a network orchestrated by AI/ML to optimise the usage of available resources while keeping both CapEx and OpEx low. Driven by these new opportunities, the ongoing standardisation activities indicate strong interest to reap the benefits of incorporating AI and ML techniques in communication networks. For instance, 3 GPP has introduced the network data analytics function (NWDAF) at the 5G core network for the control and management of network slices, and for providing predictive analytics, or statistics, about past events to other network functions, leveraging AI/ML and big data analytics. Likewise, at the radio access network (RAN), the O-RAN Alliance has already defined an architecture to infuse intelligence into the RAN, where closed-loop control models are classified based on their operational timescale, i.e., real-time, near real-time, and non-real-time RAN intelligent control (RIC). Different from the existing related surveys, in this review article, we group the major research studies in the design of model-aided ML-based transceivers following the breakdown suggested by the O-RAN Alliance. At the core and the edge networks, we review the ongoing standardisation activities in intelligent networking and the existing works cognisant of the architecture recommended by 3 GPP and ETSI. We also review the existing trends in ML algorithms running on low-power micro-controller units, known as TinyML. We conclude with a summary of recent and currently funded projects on intelligent communications and networking. This review reveals that the telecommunication industry and standardisation bodies have been mostly focused on non-real-time RIC, data analytics at the core and the edge, AI-based network slicing, and vendor inter-operability issues, whereas most recent academic research has focused on real-time RIC. In addition, intelligent radio resource management and aspects of intelligent control of the propagation channel using reflecting intelligent surfaces have captured the attention of ongoing research projects.  
intelligent networking; network slicing; network data analytics function (NWDAF); radio access network intelligent control (RIC) +
Footnote †: journal: Sensor and Actuator Networks
## 3 Void Avoidance Algorithm (VAA)
### Algorithm Description  
Let F(n) be the set of neighbors of node n. When node n is initialized, it exchanges its location information with its neighbors F(n) using neighbor discovery protocol. It also receives the hello message from the BS and calculates the ED. After receiving the information of neighbors, node n compares DC among neighbors F(n) and then sets logical directional links to the next hop node that is closer to the BS than itself.  
As shown in Figure 7, node n has an incoming link from node n\({}_{1}\) and an outgoing link to node n\({}_{2}\) according to the DC. If DC(n\({}_{i}\)) \(<\) DC(n), it is said to be an outgoing link, otherwise it is an incoming link. A logical link (n\({}_{i}\), n) means n\({}_{i}\)\(\in\) F(n). The constructed routing graph is acyclic, because all routing graphs made by the logical links are acyclic [20].  
Figure 7: Logical directions. Figure 8: Concave node and dead end node.  
If a node does not have an outgoing link, that node is a stuck node. As shown in Figure 8, node C and D are stuck nodes. Let L(n) be neighbor nodes of node n, closer to the BS than node n. Node n is a stuck node if L(n) = \(\emptyset\). L(n) can be defined as:  
\[\text{L(n)}=\{\text{n}_{i}\mid\text{DC (n}_{i})<\text{DC (n), for all n}_{i}\in\text{F(n)}\}. \tag{1}\]  
A node is a non-stuck node if L(n) \(\
eq\emptyset\). Each stuck node falls under one of the following categories:
## 2 Train Dynamical Model and Problem Statement
### Description of Train Dynamical Model with Actuator Faults and Actuator Saturation
#### 2.2.3 Description of Train Dynamical Model with Actuator Faults and Actuator Saturation  
Considering loss of actuator effectiveness faults and actuator saturation simultaneously, the actual actuator output can be described as Figure 2.  
As can be seen from Figure 2, the actual actuator output, i.e., the control force of HSTs, can be written as  
\[F^{\prime}=k_{c}sat(F). \tag{7}\]  
For convenience of analysis, we define \(k_{c}=1-\tau_{c}\). Therefore Equation (7) can be rewritten as  
\[F^{\prime}=(1-\tau_{c})sat(F)=(1-\tau_{c})(F+\sigma(t)). \tag{8}\]  
**Assumption 2**.: _In this paper, it is assumed that \(0\leq\tau_{c}<\delta<1\) is satisfied, where \(\delta\) is unknown._  
**Remark 1**.: \(0\leq\tau_{c}<\delta<1\) _in Assumption 2 indicates that even if the actuator fails, it still can provide necessary traction force or braking force to drive the train tracking the desired trajectories. If the actuator fails seriously and the traction force or braking force produced is not enough to achieve the train tracking task, other measures must be taken. Thus, Assumption 2 is reasonable._  
Considering loss of actuator effectiveness faults and actuator saturation, and combining Equation (8), Equation (2) can be transformed into  
\[m\dot{v}=(1-\tau_{c})(F+\sigma(t))-a_{0}-a_{1}v-a_{2}v^{2}-d. \tag{9}\]  
Note that the parameters \(m\), \(a_{0}\), \(a_{1}\), \(a_{2}\) in Equation (9) are unknown and vary in a certain range due to many factors such as changes of passengers and loads, line conditions, weather, train speed, etc. Thus, it is necessary to add parametric uncertainties to train model dynamics. Taking parametric uncertainties into consideration, Equation (9) can be modified as:  
\[(m+\Delta m)\dot{v}=(1-\tau_{c})(F+\sigma(t))-(a_{0}+\Delta a_{0})-(a_{1}+ \Delta a_{1})v-(a_{2}+\Delta a_{2})v^{2}-d, \tag{10}\]  
where \(m\), \(a_{0}\), \(a_{1}\), \(a_{2}\) represent the nominal values and \(\Delta m\), \(\Delta a_{0}\), \(\Delta a_{1}\), \(\Delta a_{2}\) are the modeling uncertainties of \(m\), \(a_{0}\), \(a_{1}\), \(a_{2}\).  
According to Equation (10), the following expression can be obtained:  
\[m\dot{v}=(1-\tau_{c})F+\sigma(t)-\theta\zeta^{T}(v)+P, \tag{11}\]  
where \(\theta=[a_{0},a_{1},a_{2}]\), \(\zeta(v)=[1,v,v^{2}]\), \(P\) represents the lumped uncertainty which has the following expression:  
\[P=-\tau_{c}\sigma(t)-\Delta a_{0}-\Delta a_{1}v-\Delta a_{2}v^{2}-\Delta m \dot{v}-d. \tag{12}\]
# Real-Time Volumetric Ultrasound Research Platform with 1024 Parallel Transmit and Receive Channels
## 2 Materials and Methods
### System Integration  
One goal in the development was to integrate the 1024-channel system into a standard 19-inch rack case (Figure 8) that can be used as a single ultrasound system even for clinical studies after normative testing regarding electrical safety and electromagnetic compatibility. The mobile rack system on wheels is equipped with telescopic levels in a way that the individual sub-systems and the PC can be easily accessed and maintained. Due to the movable concept, the system can be transported very comfortably from one room to another. In total, the cart has a height of 800 mm.  
The housing is divided into 3 levels. The PC with a height of 4U is integrated in the lower third. The upper two levels contain the ultrasound electronics with the master and the three slave sub-systems. All necessary connections between the individual sub-systems, such as the PCIe interfaces and the synchronization circuitry, are routed in the case and do not have to be made externally. Only the interfaces relevant for the user are located in the front area of the system. These include the four ITT Cannon DLM6-360 ports for the transducer, two USB ports for connecting external peripherals to the PC, such as external data storages, and one trigger input and output each. The power button is also located on the front of the system.  
The PC installed in the system contains a total of 10 PCIe interfaces (Supermicro X10 DRG-O+CPU, X9 DRG-O-PCIE) and integrates a dual Intel Xeon processor (2x E5-2697 AV4, 32 cores). In addition to the four PCIe interfaces of the 256-channel ultrasound sub-systems, further PCIe interfaces are used to connect four additional Nvidia graphics cards, which are intended for further signal processing on the GPUs [22]. In total, the system uses 128 Gbytes of DDR4 memory, which is used for the DMA transfer of the  
Figure 8: Fully integrated 1024 CH system.  
raw ultrasound data, allowing the user to access the single-element channel data of all 1024 channels in parallel.  
The required total power of the four sub-systems as well as the integrated PC is ensured by two AC/DC power supplies. On the one hand, a 1000 W power supply with an output voltage of +12 V (DC) is used to supply the ultrasound systems, on the other hand, a 2000 W power supply provides the necessary power for the PC.
## 3 Proposed Novel Matrix Profile for Similarity Search
### Inputting the Time Series (Its)
#### 3.1.1 Data Preprocessing  
Issues of noise filtering, outlier handling, scale concerns, normalization, and resampling of time series usually arise in time series in realistic situations. All of these problems are usually handled by preprocessing the data. [PERSON] [26] proposed a fast algorithm for filtering noise for time series prediction using Recurrent Neural Networks (RNNs). [PERSON] et al. [27] exploited the Sliding Window Prediction (SWP) model to perform the correction of outliers.  
Figure 2: Proposed model for time series data set supported with NMP algorithm.  
Additionally, they used standard temperature time series data gathered from the Nanjing University of Information Science and Technology (NUIST) weather stations.  
The next concern relates to different variations in the scale of time series. As proposed by [PERSON] et al. (2018), a linear transformation of the amplitudes can handle this problem. [PERSON] (2018) proposed a dynamic computational graph neural network (DCGNN) system, which received an award in the M4 competition. For the preprocessing, the time series data were normalized and divided into different ranges to use as input for obtaining the desired output.  
Because data normalization is a fundamental preprocessing step, we will find an appropriate method to address time series normalization tasks. Here is one of the traditional data normalization methods:  
**Definition 1**.: _A time series \(T\) (please refer to Appendix A for more information) is called Z-normalized when its mean \(\mu\) is \(0\) and its standard deviation \(\sigma\) is \(1\). The normalized version of \(T=t_{1},...,t_{|T|}\) is computed as follows:_  
\[c^{n}=\left\{\frac{t_{1}-\mu}{\sigma},...,\frac{t(Tl)-\mu}{\sigma}\right\} \tag{1}\]  
_where \(T^{n}\) is the \(z\)-based normalized time series of \(T\); \(t_{1},\cdots,t_{|T|}\) is a sequence of numbers \(t_{i}\in\mathbb{R}\), where \(i\in N\) represents the position in \(T\); \(Tl\) is the length or size of time series \(T\); and \(\sigma\) is the standard deviation of time series \(T\)._  
This approach works well in stationary settings where the exact minimum and maximum values of the time series are unknown, but it struggles with nonstationary time series where the mean and standard deviation change over time.  
On the other hand, this method is useful when the minimum and maximum values of an attribute are undefined and can be extended to stationary time series, in other words, time series in which statistical properties such as the mean, variance, and autocorrelation remain constant over time.  
Moreover, z-normalization is an essential operation in several applications because it allows similarity search irrespective of shifting and scaling.