esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Production Demand Forecast
The calculation results are shown in Table 1, where REQM is the root mean square error, MAE is the mean absolute error, and MAPE is the mean percentage of the absolute error. From Table 2, it is possible to verify that the multiplicative model has the lowest error considering all metrics; that is, its performance is better for forecasting production demand. Therefore, the errors of the multiplicative model were statistically tested in order to identify \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **REQM** & **MAE** & **MAPE** \\ \hline Additive & 18,928.18 & 14,549.97 & 62.30 \\ \hline Multiplicative & 15,047.58 & 13,076.68 & 28.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model performance measures. Figure 8: [PERSON]–Winters additive and multiplicative model. normalities. The Shapiro-Wilk test resulted in W = 0.929 and \(p\)-value = 0.0152, thus rejecting the null hypothesis. This indicates that the multiplicative model is normal. Afterward, the production demand forecast was carried out for the next 12 months, as shown in Table 2.  
It is noted that the production forecast presents a variation between 45,862.00 kg and 97,713.00 kg, representing 46.9%. The demand forecast performed will be used in aggregate production planning. Because to do it, it is necessary to know the demand forecast.
# Application of Deep Learning and Symmetrized Dot Pattern to Detect Surge Arrester Status
## 3 Partial Discharge Signal Acquisition  
For the PD signal acquisition, an autotransformer was used to implement voltage regulation for a high tension molded potential transformer. The high tension side was connected to the power supply terminal of a surge arrester and an HFCT was connected to the ground terminal of a surge arrester, so as to detect the current pulse signal in the ground wire of the surge arrester. A high-speed data acquisition card was used to receive the signal from the HFCT, after which the detection system performed an online analysis.  
The PD of the surge arrester fault model was tested on a testing platform, so as to analyze the differences among the PD phenomena generated by different faults. The testing platform is shown in Figure 5. The bandwidth of the HFCT ranged from 1 MHz to 60 MHz. The sampling rate of the NI PXI-5105 high-speed data capture card was 20 MS/s. The PD signal and data of the surge arrester were uploaded to LabVIEW for analysis and storage.  
The detected PD signals are shown in Figures 6-9. Figure 6 shows the PD signal of a normal surge arrester, Figure 7 shows the PD signal of a surge arrester valve plate after aging, Figure 8 shows the PD signal of a surge arrester with internal dampness, and  
Figure 4: Fault model of salt damage to the ceramic insulator of a surge arrester.  
Figure 5: Surge arrester testing platform.  
Figure 9 shows the PD signal of salt damage to the ceramic insulator of a surge arrester. The surge arrester PD signals were based on three cycles of a 60 Hz power supply with a sampling time of 50 ms and a data length of one million points.  
As Type 1 was the surge arrester in a normal state, its discharge signal was only background noise. The discharge positions of Type 2 were approximately distributed over the positive and negative half-cycles of the power supply. The number of discharges was  
Figure 8: PD signal of a surge arrester with internal dampness (Type 3).  
Figure 6: PD signal of a normal surge arrester (Type 1).  
Figure 7: PD signal of a surge arrester valve plate after aging (Type 2).  
Figure 9: PD signal of salt damage to the ceramic insulator of a surge arrester (Type 4).  
smaller than that of the other faults, but the voltage amplitude was larger. The discharge positions of Type 3 were approximately distributed over the positive and negative half-cycles of the power supply, and the positive half-cycle had a larger number of discharges. The discharge positions of Type 4 were distributed over the positive and negative half-cycles, and the voltage amplitude was lower.
# Deep Class-Guided Hashing for Multi-Label Cross-Modal Retrieval
## 3 Methodology
### Hash Learning
#### 3.3.1 Proxy Loss  
Proxy-based methods can achieve satisfactory performance in single-label cross-modal retrieval, but when considering multi-label cross-modal retrieval, proxy-based methods have been proven to produce poor performance with limited hash bits because they fail to deeply express multi-label correlations and neglect the preservation of inter-class structural relationships [20]. However, since only the relationships between data and proxy points need to be considered, the learned hash codes can maintain intra-class aggregation well. Therefore, we first learn hash codes for intra-class aggregation through proxy loss.  
For proxy loss [4], a learnable proxy is first generated for each label category. The hash representation is learned by bringing samples closer to their relevant proxies and pulling away irrelevant data-proxy pairs. For \(\mathbf{P}=[\mathbf{p}_{1},\mathbf{p}_{2},\ldots\mathbf{p}_{C}]\), \(\mathbf{P}\) represents the learnable proxies for each label category, where \(\mathbf{p}_{i}\) is a \(K\)-bits vector. When the samples and the proxy are relevant, we can calculate the cosine distance between the binary-like hash codes and relevant proxies by using \(\cos_{+}(h,p)\), and for proxies that are not related to the samples, the distance between binary-like hash codes and irrelevant proxies is pushed away by using \(\cos_{-}(h,p)\).  
\[\cos_{+}(h,p)=-\frac{|h\cdot p|}{|h|\cdot|p|}\quad,\quad\cos_{-}(h,p)=\max( \frac{|h\cdot p|}{|h|\cdot|p|},0) \tag{1}\]  
Hence, the proxy loss of image \(\mathcal{L}_{\text{proxy}}^{x}\) could be calculated as shown in Equation (2).  
\[\begin{split}\mathcal{L}_{\text{proxy}}^{x}&=\frac{ \sum_{i=1}^{N}\sum_{j=1}^{C}I\big{(}l_{ij}=1\big{)}\cos_{+}\big{(}h_{i}^{x},p_ {j}\big{)}}{\sum_{i=1}^{N}\sum_{j=1}^{C}I\big{(}l_{ij}=1\big{)}}\\ &+\frac{\sum_{i=1}^{N}\sum_{j=1}^{C}I\big{(}l_{ij}=0\big{)}\cos_{ -}\big{(}h_{i}^{x},p_{j}\big{)}}{\sum_{i=1}^{N}\sum_{j=1}^{C}I\big{(}l_{ij}=0 \big{)}}\end{split} \tag{2}\]  
where \(I\) is an indicator function. The denominators represent the number of relevant data-proxy pairs and irrelevant data-proxy pairs, respectively, aiming for normalization [4]. Similarly, the proxy loss of text, \(\mathcal{L}_{\text{proxy}}^{y}\), can be calculated as shown in Equation (3).  
\[\begin{split}\mathcal{L}_{\text{proxy}}^{y}&=\frac{ \sum_{i=1}^{N}\sum_{j=1}^{C}I\big{(}l_{ij}=1\big{)}\cos_{+}\big{(}h_{i}^{y},p_ {j}\big{)}}{\sum_{i=1}^{N}\sum_{j=1}^{C}I\big{(}l_{ij}=1\big{)}}\\ &+\frac{\sum_{i=1}^{N}\sum_{j=1}^{C}I\big{(}l_{ij}=0\big{)}\cos_{ -}\big{(}h_{i}^{y},p_{j}\big{)}}{\sum_{i=1}^{N}\sum_{j=1}^{C}I\big{(}l_{ij}=0 \big{)}}\end{split} \tag{3}\]The total multi-modal proxy loss \(\mathcal{L}_{\text{proxy}}\) is calculated, as shown in Equation (4).  
\[\mathcal{L}_{\text{proxy}}=\mathcal{L}_{\text{proxy}}^{x}+\mathcal{L}_{\text{proxy }}^{y} \tag{4}\]
## 4 Discussion
### The Effect of Cloudiness  
The coefficient \(\gamma\) in Equation (1) is approximately \(2.5\times 10^{-3}\) and \(3.5\times 10^{-3}\)\(\upmu\)m\({}^{-1}\) at 230 and 345 GHz, respectively, with weak temperature dependence [69]. This means that LWP of 100 \(\upmu\)m (which is equivalent to 100 g m\({}^{-2}\)) contributes about 0.25 to the opacity at 230 GHz. The median values of LWP for different cloud classes are from \(\sim\)10 to \(\sim\)40 g m\({}^{-2}\)[70,71]. In [5], the LWP statistics are presented for the existing and candidate EHT sites. The median values are well below 100 \(\upmu\)m for most sites. Therefore, in most cases, the opacity in clouds at 1.3 mm is \(\lesssim\)0.1 and cannot fully prevent radio astronomical observations.  
However, the problem is that clouds are usually very inhomogeneous, which leads to spatial and temporal fluctuations of opacity and sky brightness. Their influence can hardly be sufficiently suppressed, even by the usual beam-switching technique. As a result, these fluctuations make observations of weak sources practically impossible, especially in continuum. Spectral line observations are less affected because the fluctuations are synchronous in all channels and can be subtracted at the data reduction. However, a more frequent calibration is needed.
# The Influence of Binaural Room Impulse Responses on Externalization in Virtual Reality Scenarios
## 2 Experimental Setups
### Experiment I: Desktop-Based Experiment  
In Experiment I, subjects listened to the test stimuli presented over a pair of Sennheiser HD800 headphones. The influence of the headphone transfer function (HpTF) to the binaural signals was compensated by calculating its inverse filter. The HpTF was measured on the dummy head with ten repetitions (the headphone was repositioned by each measurement), and the compensation filter was calculated by using the least-squares inversion approach [16]. Subjects were asked to evaluate externalization of the binaurally generated signals and the hidden anchor. They were able to repeat each test stimulus and listened to the stimuli in arbitrary order. The experiment was tested twice, resulting in 16 stimuli (4 stimuli/session \(\times\) 2 directions \(\times\) 2 presentations/subject) to be evaluated. The externalization rating for each subject was taken as the average of these two scores. Subjects were  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Degree** & **Meaning of the Degree** \\ \hline
3 & The sound is well externalized and at the speaker’s position. \\
2 & The sound is externalized but not at the speaker’s position. \\
1 & The sound is not well externalized. It is very close to me. \\
0 & The sound is in my head. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rating scale for perceived externalization.  
Figure 3: GUI of the externalization experiment **(top: desktop-based listening test (Experiment I); bottom: VR-based listening test (Experiment II)).**  
asked to rate externalization of each stimulus and to ignore audible artefacts that do not affect their externalization perception [17]. Before starting the experiment, subjects listened to speech signals reproduced over loudspeakers for about 3 min to become familiar with the acoustic of the listening room. In addition, they could walk around the room. This study focused on externalization of virtual sounds in static situations (no head tracking), i.e., the position of virtual sound sources changed with listeners' head movements. Several studies have reported that externalization of static sound sources can be reduced when listeners rotate their heads without head tracking [18; 19; 20]. Hence, subjects were not allowed to rotate their heads during the listening experiment (monitored by the supervisor through a transparent window). When a large head movement was observed, the listener was asked to stop the experiment immediately and restart it.
# Application of Blockchain Technologies in Verification of Software Metrics
## 1 Introduction  
Software metrics represent a fundamental approach to the evaluation of software quality and complexity. The term \"metrics\" refers to a quantitative measure that provides insight into specific characteristics of a system or process. The development and application of metrics are closely related to the need for a more objective assessment of software in all phases of its lifecycle--from planning and design, through implementation and testing, to maintenance. Reliable metrics should provide insight into software quality. However, contemporary trends in software development have significantly altered the perception and approach to the use of software metrics. Traditional metrics, primarily oriented toward quality assessment within clearly defined development phases, often failed to adequately support emerging, more dynamic software development practices. Consequently, industry practice has begun recognizing the need to redefine existing metrics and introduce new ones better aligned with the requirements of modern methodologies such as Agile and DevOps [1].  
With the emergence of Agile and DevOps practices, crucial changes have occurred in the understanding and application of metrics. The focus shifted toward rapid delivery, continuous improvement, and close team collaboration, leading to a demand for metrics that can be collected and analyzed in real time [2].  
However, although metrics have advanced to more sophisticated forms that offer deeper insights into software quality, they have become susceptible to manipulation [3].  
Team members responsible for software quality assessment may intentionally \"tweak\" metrics to meet predefined goals. Such actions can lead to compromised trust and poor managerial decisions [4].  
Keeping in mind all that was previously mentioned, we came up with the idea to develop a methodology for the calculation of software metrics that will be verified through blockchain smart contracts, ensuring immutability and data integrity. Our approach assesses software developed using scripting languages by applying metrics whose relevance for this type of analysis was evaluated and verified.  
The rest of the paper is organized as follows. Section 2 discusses related work regarding the verification of software metrics and their usage in the evaluation of software. In Section 3, we present the methodology, which includes blockchain technology and the metrics to be verified, smart contracts, data collection, and system architecture. To test the developed methodology in a real-world case scenario, Section 4 presents a use case study. The paper concludes with Section 5, which provides a summary and outlines future research directions in this area.
## 4 System Design
### Information Storage, Decision Support System and Application Layer  
Information is presented to the client in our proposed system in the form of an online web page with two modes of triggered events and periodic inspection. Algorithms in the BS of the system  
Figure 4: (**a**) Arduino MEGA 2560; (**b**) BLE shield for Arduino; (**c**) photos of sensors; (**d**) circuit diagram of sensors attachment to Arduino MEGA 2560; (**e**) mobile node carried by the miners.  
continuously look for global events. In our system of real-time information sharing, communication between applications, components, standards, and interfaces was created using the Representational State Transfer (REST) application programming interface. A massive amount of data from SNs is efficiently managed by the real-time operational database (RODB). These databases work in combination with extract-transform-load (ETL) to extract and transform user-acquired information. First, the extract function reads data from various sources and extracts a desired subset of data. Next, the transform function works with the collected data and converts it to the desired state. Finally, the load function is used to write the resulting data to a target database. A naming, addressing, and profile server (NAPS) was deployed for assigning an internet protocol (IP) address to all sensor nodes using IPv6 Low-Power Wireless Personal Area Networks (6 LoWPAN) as a link layer protocol. A constrained application protocol (CoAP) was used for application gateways (AGs) and application software for different platforms and tasks. Our system follows a Service-oriented Architecture (SOA) that simplifies complex systems into simple, well-oriented applications and components through common interfaces and standard protocols. Moreover, cloud services are linked to external data storage such as amazon S3, amazon EBS, Google's Cloud Storage, etc. These services can support load, update, and mix operation systems, and can provide analysis languages such as SQL, Hadoop, and MapReduce. They can also provide big data analytics frameworks like Spark, Storm, Flink, and Google's dataflow. The present study also provides an e-science platform for cloud computing, data analysis and real-time information analysis. The e-Science platform serves roles like IaaS (Infrastructure as a Service), SaaS (Software as a Service), PaaS (Platform as a Service) and DaaS (Data as a Service).
# A Combined Dynamic-Kinematic Extended Kalman Filter for Estimating Vehicle Sideslip Angle
## 2 Combined Extended Kalman Filter
### Dynamic Model
The considered measures are the yaw rate and the lateral acceleration. Consequently, the measurement equations may be written as follows:  
\[\mathbf{y}_{k}^{dyn}= \begin{bmatrix}r_{meas}\\ a_{y,meas}\end{bmatrix} = \begin{bmatrix}r_{k}\\ \frac{r_{y}r\cos(\xi_{k})+F_{pp}}{m}\end{bmatrix} =h_{k}(\mathbf{x}_{k}^{dyn},\mathbf{u}_{k}^{dyn}) \tag{9}\]  
System and measurement equations and the respective Jacobian matrices are then used to compute the a priori and a posteriori estimates of the EKF.
# Combined Navigation and Tracking with Applications to Low Earth Orbit Satellites +
## 1 Introduction
Section 4 contains the simulation results with the two-fold objective of validating the deep aiding concept and presenting the positioning performance of the TeseoV Xona receiver. The simulations are all open sky; however, the focus is kept on difficult land scenarios for automotive applications, where the proposed architecture is expected to provide most of the benefits in terms of increased availability and robustness. Finally, Section 5 concludes the paper. Figure 1: Advantages of the Xona’s PULSAR(tm) LEO Constellation.
# Analyzing Impact and Systemwide Effects of the SlowROS Attack in an Industrial Automation Scenario
## 4 Attack Context and Description  
### Underlying Protocol Mechanisms  
In the TCP protocol, a three-way handshake procedure determines how connections between the client and the server are established [49]. Although the way network packets are exchanged during such a procedure may be familiar, the local activities executed on the hosts may need further discussion.  
In particular, referring to Figure 2, before sending the first SYN packet to the server [49], the client allocates a local socket for the connection. When the server receives the SYN packet, it does not immediately allocate a dedicated socket yet. At this point, it merely allocates an in-kernel state vector to keep track of the connection state and puts it in the so-called TCP SYN queue while the handshake proceeds towards full connection establishment.  
The socket is in fact allocated on the server only at the end of the three-way handshake, when the ACK packet has been received and the server has eventually been informed about the new connection by the successful completion of an accept() system call. After accepting the new connection, the server also usually allocates an application slot for it, to store all the information the server needs for its management.
# Automated Generation of Hybrid Metaheuristics Using Learning-to-Rank
## 4 Methodology  
### Method Overview  
The core concept of the LTR-MHA method is to leverage the LTR framework to extract knowledge from historical execution data of multiple metaheuristic algorithms. The trained prediction model dynamically predicts the scores of candidate update behaviors during the search process, thereby intelligently selecting the update behavior most likely to improve the current solution. The overall methodological flow is illustrated in Figure 1.  
First, information is extracted from the update behaviors during the solving process of metaheuristic algorithms to construct a feature set and the corresponding label set. Next, a prediction model is trained to learn the relationship between different update behaviors and solution quality, which is used to predict the effectiveness of each candidate update behavior. Once the prediction model is trained, the relevant feature vectors of all candidate behaviors for the search agent to be updated are input into the model. By evaluating the feature vector of the current search agent, the model predicts a corresponding score, which reflects the potential solution quality after applying the given update behavior. Finally, the behavior with the highest predicted score is chosen to update agents.  
In this way, the search process of algorithm can be guided more intelligently, avoiding blind exploration and thereby improving convergence speed and solution quality. The innovation of the LTR-MHA lies in applying LTR to learn the ranking of metaheuristic algorithm behaviors, enabling the automated generation of new algorithms. This approach breaks through the fixed search framework of traditional metaheuristic algorithms and offers a novel perspective for tackling optimization tasks. Notably, the primary objective of this study is not to identify the optimal algorithm combination but to verify the feasibility of multi-algorithm integration strategies in boosting the performance of individual algorithms. Based on this objective, three representative classical metaheuristic algorithms (GA, WOA, and HHO) are selected as the subjects of investigation in this work.
## 4 Discussion
### Limitations  
Up to the present, data on skin health in dancers was completely lacking. Therefore, a study design was chosen that generates a lot of data through a cross-sectional and longitudinal survey. With the choice of the two dance styles, a relevant and large spectrum of dance was covered. Although a total of \(n=114\) test persons is not representative for all dance styles, it is a sufficient number to suggest a certain transferability to other dance styles. Thus, the results can serve as a first rough orientation regarding skin problems. The examinations took place parallel to the ongoing training. In principle, this was a good method to examine as many dancers as possible in a short period of time. At the same time, more dancers could be motivated to participate in the study. Simultaneously, the varyingload intensity during the training made the dermatological examination slightly more difficult (e.g., to assess seborrhoea and erythema caused by redness and sweat production). Furthermore, the results could be biased by the timing of the study during the year, as certain skin conditions are more prevalent during certain times of the year because of the weather. Furthermore, there has been no second control examination to confirm the findings, as this is also not usual in physician consultations. Theoretically, this includes the possibility of misjudgements.  
Inflammatory skin diseases such as acne vulgaris were counted as folliculitides in this study. This served to provide a better overview, but should also be critically questioned, as numerous different clinical pictures are now subsumed into one entity under the term folliculitis.  
In order to be able to assess the extent of skin changes, further questions on quality of life and skin care behaviour could be a valuable addition to the present questionnaire. For this purpose, there are standardised questionnaires developed especially for dermatology, for example the DLQI (Dermatology Quality of Life Index) frequently used in studies [61]. A detailed analysis of the cosmetic products used and their ingredients would also be helpful. Despite the aforementioned limitations, the present study offers new, already extensively discussed findings in a relevant but hitherto little-considered field of research.