esa-sceva/satcom-chunk-collection huggingface dataset:
This variable (for the sake of accessibility for measurements) is commonly chosen as the outlet temperature of the stack (either the anode or cathode gas outlet), which is then also treated as the primary controlled thermal state variable. Obviously, this value is, on the one hand, corrupted by measurement errors and, on the other hand, cannot fully represent the complete internal state of the stack module. Therefore, each of such offline generated look-up tables (as well as analytic interpolations of these data leading to explicit functional relations) are characterized by unavoidable errors. Especially, they are not capable of capturing the dependency of variable gas inlet manifold temperatures on the steady-state gains between the hydrogen mass flow supplied at the anode and the achievable maximum electric power. However, such kind of dependency was shown experimentally by [PERSON] et al. (2017) to have a severe influence on the accuracy and stability of electric power control strategies that use the hydrogen mass flow as the primary control variable. For this reason, this paper proposes to substitute the offline identification of current-voltage and current-power characteristics, respectively, by a real-time capable Kalman filter-based parameter identification. The respective filter is implemented in such a way that a locally quadratic approximation of the electric power as a function of the electric stack current is estimated online during the fuel cell operation in terms of a mean value with corresponding covariance information. This covariance information helps one to quantify the reliability of the estimate and to judge to which precision a maximum power point tracking procedure can work in practice. Since this Kalman filter solution is based on a locally quadratic approximation, it is only necessary to estimate three parameter values in real time. This reduces memory demands significantly in comparison with multivariate look-up tables and allows for a real-time capable adaptation of the current-power characteristic due to the influence factors described above. This paper is structured as follows. Section 2 summarizes the fundamental current-voltage as well as current-power characteristics of an SOFC stack module on the basis of measured data of a test rig available at the Chair of Mechatronics at the University of Rostock. Based on an offline identified quasi-static mapping, a simplified quadratic representation of the current-power characteristic is proposed which serves as the basis for a real-time parameter identification. The derivation of a real-time capable Kalman filter for the online parameter identification, replacing classical offline computed quasi-static mappings for the current-power characteristic, concludes this section. Then, a strategy towards a robust maximum power point tracking is proposed in Section 3. The algorithmic properties of both the online parameter identification and maximum power point tracking procedures are investigated in detail by means of real measured data as well as with the help of a simulation case study in this section. Conclusions and an outlook on future work are finally given in Section 4.
# A Short Review of Through-Silicon via (TSV) Interconnects: Metrology and Analysis
## 1 Introduction
Other types of defects, such as misalignment, non-uniform deposition, and incomplete filling, can also occur during the fabrication process and cause TSV failure. In addition, there are the following defects: (i) The thermal expansion coefficients of copper, silicon oxide, and silicon filler materials in TSV differ greatly, triggering a thermal mismatch that can lead to plastic deformation of the copper pillar and, thus, generate cracks, damaging the surrounding cover layer and metal connection layer [10; 13; 14]; (ii) along with the shortening of the interconnect line length and the increase in signal transmission rate comes a significant increase in power density, which will lead to a sharp increase in temperature, thus, inducing stress concentration, electromigration, negative bias instability, and the diffusion of copper atoms [15; 16; 17]; (iii) TSV interconnect lines have delay, loss, and crosstalk problems, and electromigration effects tend to trigger Kirkendall holes to form cracks [18; 19]; (iv) since TSVs are affected by a variety of factors under the joint action of thermal-electrical-force fields in service, an analysis method that can be simulated, modeled, and monitored in real time is needed [20; 21]. Due to the significant increase in power density per unit area, the issue of thermal reliability in 3D integrated systems has become more prominent. At the same time, thermal stresses are introduced into 3D integrated systems due to a mismatch in the coefficient of thermal expansion (CTE) between materials during TSV fabrication, which affects the carrier mobility of the electronic device. When voltage is applied to the integrated system, Joule heat is generated. Joule heating increases temperature, which affects electrical parameters and thermal conductivity. The reliability of TSVs is a multi-physical field coupling problem that requires simultaneous consideration of thermal, electrical, and mechanical reliability [9]. To mitigate these defects, various strategies have been proposed, including improving the TSV manufacturing process, such as optimizing the deposition parameters and improving the CMP process. The use of advanced metrology techniques, such as X-ray microtomography and focused ion beam (FIB) milling, has also been proposed to detect and characterize defects in TSVs. In addition, post-fabrication testing and analysis methods, such as electrical and thermal analysis, can be used to identify and evaluate the impact of TSV defects on the performance of the integrated circuit. Overall, as TSV technology continues to miniaturize, the manufacturing process must be optimized to reduce defects and improve the reliability and performance of integrated circuits. The development of effective defect detection and characterization techniques will play a crucial role in achieving this goal [21] (Figure 1). As TSV processes reach today's level of maturity, reliability investigations become critical. This review will summarize the progress made by industry and academia on this issue in recent years.
# Creating a Remote Choir Performance Recording Based on an Ambisonic Approach
###### Abstract  
The aim of this paper is three-fold. First, the basics of binaural and ambisonic techniques are briefly presented. Then, details related to audio-visual recordings of a remote performance of the Academic Choir of the Gdansk University of Technology are shown. Due to the COVID-19 pandemic, artists had a choice, namely, to stay at home and not perform or stay at home and perform. In fact, staying at home brought in the possibility of creating and developing art at home while working online. During the first months of lock-down, the audience was satisfied with music performances that were fairly far from the typical experience of a real concert hall. Then, more advanced technology was brought to facilitate joint rehearsal and performance of better quality, including multichannel sound and spatialization. At the same time, spatial music productions benefited from the disadvantage of remote rehearsal by creating immersive experiences for the audience based on ambisonic and binaural techniques. Finally, subjective tests were prepared and performed to observe performers' attention behavior divided between the conductor and music notation in the network-like environment. To this end, eye-tracking technology was employed. This aspect is related to the quality of experience (QoE), which in the performance area-and especially in remote mode-is essential.  
ambisonics; virtual concert; remote music performance; eye-tracking; quality of experience; networked music performance +
Footnote †: journal: _Sci._  
or improvisation together in real-time. [PERSON] [4] calls this way of performing music the \"bridge approach\". In such cases, the inevitable delay caused by the physical transit time of network packets is known to affect performance [5; 6; 7; 8; 9; 10]. Therefore, attempts have been made to account for these delays by design or composition [11; 12; 13].  
This paper briefly provides an overview of distance concert performance and the difficulties involved. It also refers to the theoretical basis of ambisonics, which is one of the sound reproduction formats that focuses on listener immersion in the sound scene. The process of making the recording of the Academic Chair of Gdansk University of Technology concert is shown in detail, starting from the preparation stage, through recording the musicians, to postproduction. It includes a description of the sound and video engineer's workshop and discusses problems related to sound synchronization. Finally, the eye-tracker is employed to observe performers' attention behavior divided between the conductor and music notation in the network-like environment. This aspect is related to the quality of experience (QoE), which in the performing domain is essential.  
The summary presents overall conclusions concerning the design of subjective tests to evaluate the realized recordings and further plans related to the Academic Chair of Gdansk University of Technology concerts.
# A Novel Software Architecture Solution with a Focus on Long-Term IoT Device Security Support
## 4 Device Architecture
### Cloud  
Implementing the application and business logic in the cloud enables high availability, scalability and easily supports the increase in the number of supported devices. The cloud application provides endpoints for data collection as well as device management capabilities, such as managing device location, generating usage reports, and providing real-time alerts in case of anomalous behaviour. The application picture is shown in Figure 12.  
In particular, the sanitation expectancy period is calculated and enforced through alerting system. Therefore, it consists of the user interface part for management and the REST API segment for data ingestion and service mobile application authentication operation. The user interface operations are authorised by role-based security and the permissions are granulated based on the organisation structure. Multiple devices can be assigned to a single organisation, and each organisation can have multiple administrators. User and device data are stored in the relational database due to referential integrity requirements. The service mobile application intended for service operations uses the protected endpoint and requires standard JWT token authentication. The token is obtained on a separate endpoint using the assigned credentials for the waiting user via a common username and password authentication scheme. Based on the permissions and organisational affiliation, each service user is separately authenticated for each device using cloud authentication and device-based authentication. Data entry does not pose a high security risk, so no dedicatedauthentication is performed other than signing the payload data with the device's private key and verifying it in the cloud with the device's specified public key. Due to the large amount of ingested data, a NoSQL database is used for the efficient storage of sensor measurement data. The data aggregation component processes the data in near real time (NRT) to provide an efficient reporting and alerting system.  
The cloud system allows three levels of device monitoring:  
* device user (restaurant, hotel, private person);
* device owner (brewery, juice factory, hotel chain);
* device manufacturer Oprema d.d.  
Each level has its own monitoring needs and accordingly monitors the device from its own point of view. The equipment user is interested in the energy balance and the equipment flows. The sanitation and fault alerts determine the maintenance schedule. The equipment owner is interested in the equipment flow because most equipment is rented to introduce new beverages on tap in restaurants and stores. Finally, the device manufacturer has information about the reliability of the equipment, which allows it to introduce new hardware and software innovations in equipment models or to create an equipment upgrade specification in the service cycle. This is one of the primary goals of Industry 4.0.
# Dynamic Weight and Mapping Mutation Operation-Based Salp Swarm Algorithm for Global Optimization
## 2 Salp Swarm Algorithm
### The Position Update of Followers  
In the process of moving and hunting, followers move sequentially in a chain without random movement. On the basis of [PERSON]'s law, therefore, the equation for updating the location is utilized to indicate the movement distance of the follower:  
\[S=\frac{1}{2}at^{2}+v_{0}t \tag{5}\]  
where \(S\) denotes the movement distance, \(t\) is time, \(v_{0}\) is the original velocity, and \(a\) represents the acceleration of movement. For an optimization problem, \(t=1\), \(v_{0}=0\) and \(a=\frac{v-v_{0}}{l}\) in each iteration. In addition, because the follower moves closely following the previous salp, the speed \(v=\frac{X_{i-1,j}^{l}-X_{i,j}^{l}}{l}\). Then, Equation (5) can be expressed as:  
\[S=\frac{1}{2}(X_{i-1,j}^{l}-X_{i,j}^{l}) \tag{6}\]  
According to the above explanation, the followers' location update formula is as follows:  
\[X_{i,j}^{l+1}=X_{i,j}^{l}+S=\frac{1}{2}(X_{i,j}^{l}+X_{i-1,j}^{l}) \tag{7}\]  
where \(i>N/2\), \(X_{i,j}^{l+1}\) and \(X_{i,j}^{l}\) are the positions of the \(i\)-th salp of followers in the \(j\)-th space at iteration \((l+1)\) and \(l\), respectively. The flow diagram of the original SSA is presented in Figure 1.  
As shown in Figure 1, the classic SSA can be improved in the following aspects, such as the population initialization stage, the control parameter \(c_{1}\), the position update formula of leaders, the position update formula of followers, and the last stage of the population. In order to ease to fall into local optimum and low convergence accuracy, [PERSON] et al. [23] introduced the adaptive control parameter and the elite gray wolf domination strategy into the position update stage of followers.
## 3 Approach
### Selection, Application, and Adaptation of AI Methods for Position and Species Determination
#### 3.2.1 Proposed Model Based on [PERSON] et al. [3] Applied to the Aerial Imagery of Kaisersalautern  
[PERSON] et al. trained a convolutional neural network (CNN) using aerial imagery from California, providing a confidence map from which the tree location can be obtained [3]. Based on this architecture (see Figure 4), we conducted experiments with different datasets and backbones, as depicted in Table 1. We observed the best results while using a combination of the Urban Tree Detection Dataset (UTD) from [PERSON] et al. [3] and our dataset as described in Section 3.2.2 together with a ResNet50 backbone. We chose to stick to the basic U-Net [18] architecture with two heads from [PERSON] et al. [3] as they conducted extensive experiments and found this set-up to perform best. As the VGG16 backbone is based on an old architecture, we chose to replace it with a ResNet50. With this model, we obtained a maximum accuracy of 0.778 and a corresponding recall of 0.783 (see Table 1 and Figure 5).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Backbone** & **Precision** & **Recall** \\ \hline UTD & VGG16 & 0.563 & 0.566 \\ \hline KL 40 cm and UTD & VGG16 & 0.763 & 0.794 \\ \hline KL 40 cm and UTD & ResNet50 & 0.778 & 0.783 \\ \hline KL 40 cm and UTD & ResNet34 & 0.661 & 0.883 \\ \hline KL 20 cm and UTD & ResNet50 & 0.68 & 0.788 \\ \hline KL 20 cm and UTD & ResNet50 (\(\sigma\) = 4.5) & 0.683 & 0.818 \\ \hline \end{tabular}
\end{table}
Table 1: Results of the model from [PERSON] et al. [3] trained on a combination of the original dataset (UTD) proposed by [PERSON] et al. and the data obtained as discussed in Section 3.2.2.  
Figure 3: Screenshots from Cyclomedia (**a**) and OpenStreetMap (**b**) depicting the same corner of Kaisersalautern’s City Park.  
Figure 4: The architecture used to obtain the results shown in Table 1. We omitted the detailed view on each layer of the network and the U-Net [19] typical connections from each backbone layer to each head layer for simplicity. For a more in-depth view, we refer the reader to [PERSON] et al. [3].  
Figure 5: Results of the model are exemplified in a map excerpt from Kaiserslautern. It is also challenging for the human eye to discern the exact location of a tree.  
That means that less than 80% of the trees are detected. In addition, about 21% of the detected trees are not trees but something else. Using imagery with higher resolution did not improve the results. However, there are several possible explanations for this outcome and thus promising approaches to improving it. In the data preprocessing, some forest-like areas of Kaiserslautern had to be masked as it was impossible to label trees there (see Figure 6). The handling of those masked areas will be discussed in Section 3.2.2. In addition, the model had four channels (RGBN), whereas the aerial imagery in Kaiserslautern had only three (RGB). Another promising model was trained on trees outside cities and then fine-tuned on urban trees in Munich [4].
## 1 Introduction
### Fixation Unit Anchor System  
#### 1.2.1 Types of Post-Installed Anchor Bolts  
Anchor bolts are installed in concrete by drilling holes using hammer or core drills after the concrete has been poured and hardened. They can be divided into mechanical post-installed anchor bolts, which produce the main bearing capacity by frictional force and mechanical locking devices, and adhesive post-installed anchor bolts, which generate the main bearing capacity by the adhesive force of the attached compounds [5]. Figure 1 shows post-installation anchors commonly used for field installation in Korea, and, in this study, set anchors were targeted.
## 8 Justification for Using a Supervised Approach for Anomaly Detection  
The decision to use a supervised learning approach for anomaly detection in this study is based on the availability of labeled data from historical attack datasets, such as NSL-KDD and CICIDS 2017. These datasets provide detailed information about known attack vectors and normal traffic patterns, allowing the MLP to learn distinct features associated with both normal and anomalous traffic. Although unsupervised methods are often favored when labeled data are scarce, supervised learning offers several advantages in this context.  
Firstly, supervised learning methods achieve high accuracy in detecting known types of attacks, as the MLP is trained on labeled instances of both normal and malicious traffic. The ability to recognize specific patterns and relationships within the data leads to more precise classifications when the system is deployed in real-world environments. Secondly, the CYO enhances the training process, making supervised learning more practical and efficient for real-time applications. The optimizer accelerates the training process, enabling the MLP to converge more quickly on an optimal solution, which is essential in scenarios where high accuracy and low latency are critical.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **0 dB** & **5 dB** & **10 dB** & **15 dB** & **20 dB** & **25 dB** & **30 dB** & **35 dB** & **40 dB** \\ \cline{2-10}  & **Mean (\%)** & **Mean (\%)** & **Mean (\%)** & **Mean (\%)** & **Mean (\%)** & **Mean (\%)** & **Mean (\%)** & **Mean (\%)** & **Mean (\%)** \\  & **(SD)** & **(SD)** & **(SD)** & **(SD)** & **(SD)** & **(SD)** & **(SD)** & **(SD)** & **(SD)** \\ \hline List 1 & 0 (0) & 0.3 (0.7) & 6.8 (1.47) & 28.5 (7.31) & 66.4 (3.56) & 90.7 (3.35) & 95.3 (2.58) & 98.4 (1.88) & 100 (0) \\ List 2 & 0 (0) & 0.3 (0.7) & 6.9 (1.49) & 28.3 (8.81) & 66.1 (3.89) & 90.8 (5.01) & 95.7 (2.6) & 98.8 (1.26) & 99.7 (0.7) \\ List 3 & 0 (0) & 0.3 (0.7) & 7.1 (1.49) & 28.3 (7.48) & 66.8 (3.19) & 90.4 (3.48) & 95.1 (2.37) & 98.4 (2.03) & 99.9 (0.52) \\ List 4 & 0 (0) & 0.1 (0.52) & 6.7 (1.63) & 27.5 (6.65) & 66.7 (2.99) & 90.9 (3.92) & 95.5 (2.97) & 98.0 (1.85) & 99.9 (0.52) \\ General & 0 (0) & 0.2 (0.65) & 6.9 (1.49) & 28.1 (7.42) & 66.5 (3.35) & 90.7 (3.89) & 95.4 (2.59) & 98.4 (1.76) & 99.9 (0.5) \\ \hline \end{tabular}
\end{table} 
Table 3: Mean percent of the monaural correct scores and standard deviations (SD) for the four 50-item lists (\(p\) > 0.05). the 20 percent to 80 percent correct points. The best-fit, third-degree polynomials for the four word lists are as follows: List 1, y = \(-\)0.972390572 + (-1.785525092)x + 0.373383357 \(\times\) 2 + (-0.006714254) \(\times\) 3; List 2, y = \(-\)0.835016835 + (-1.862542889)x + 0.378820587 \(\times\) 2 + (-0.006805836) \(\times\) 3; List 3, y = \(-\)0.993939394 + (-1.755363155) + 0.371220779 \(\times\) 2 + (-0.006680135) \(\times\) 3; and List 4, y = \(-\)0.914478114 + (-1.879390733)x + 0.379667148 \(\times\) 2 + (-0.006818406) \(\times\) 3. The instantaneous slopes of the List 1 function range from 4.29 percent/dB at 20 percent correct to 4.49 percent/dB at 80 percent correct. The 5.15 percent/dB instantaneous slope at 50 percent is steeper than the 4.9 percent/dB linear slope, which is quite close to an average slope. The instantaneous slopes of List 2, List 3 and List 4 are similar to the slopes of List 1. The instantaneous slopes at 50 percent are steeper than the corresponding linear slopes for all the lists, while a similar steepness of the linear and average slopes is observed (Table 4).
## 2 Modeling and Computational Details  
The exact Boltzmann equation requires great computational effort that is not justified in practical and engineering calculations. The CFD approach was utilized to model a steady-state, laminar, and forced convection flow inside a circular pipe under a slip flow regime and constant wall heat flux conditions, as shown by Figure 1. The adopted CFD approach was based on the usage of the Ansys Fluent 2018 software package. The semi-implicit technique for pressure-linked equations (SIMPLE) algorithm was used to link the continuity equation with the pressure field in the CFD domain. Moreover, the PRESTO algorithm was used to calculate the pressure field. A hybrid second-order accuracy scheme of central and upwind difference was used to differentiate the convective terms. The maximum of the normalized absolute residual across all nodes was taken to be less than \(10^{-6}\) and was considered as a condition for convergence criteria.