esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Validation of the Simplified Design Method
### Parametric Study on Asymmetric Double-T Cross-Section
Figure 10: Comparison of bending resistance for composite behavior of studied configurations—analytical model vs. finite element model (FEM): (**a**) R30; (**b**) R60; (**c**) R90; (**d**) R120. The ratio between the bending resistance computed with the simplified method and the values obtained with SAFIR is analyzed in Figure 11, for composite behavior and in Figure 13, for non-composite behavior, respectively, while Figure 14 shows the results of all the simulations. Figure 12: Comparison of bending resistance for non-composite behavior of studied configurations—analytical model vs. FEM: **(a)** R30; **(b)** R60; **(c)** R90; **(d)** R120. Figure 13: Assessment of the studied configurations with non-composite behavior: (**a**) Gauss curve; (**b**) boxplot assessment. situations. Figure 11a, show the Gaussian curve of the studied data, and Figures 11b, 13b and 14b show a boxplot assessment. It can be noted from Figure 14b that the median value of the data is 0.933, for composite behavior cases and 0.853, for non-composite behavior. The first quartile is 0.86 (25% of the values are below 0.86), for composite behavior and 0.722 for non-composite behavior cases. The third quartile is 0.97 (75% of the values are below 0.97), for composite behavior, and 0.905 for non-composite behavior. For 95% of cases (613 configurations) the simplified method offered a ratio less or equal to 1. For the remaining 5% of cases (35 configurations), the computed bending moment was higher than that obtained with SAFIR, with a maximum ratio of 1.05.
## 1 Introduction  
Oranges contain vitamin C, folic acid, potassium, and pectin, as well as high levels of phytochemicals that facilitate antioxidant activities [1]. Recently, the market for premium-quality orange juice has significantly grown. Such juice is freshly squeezed and minimally processed and is popular among consumers because of its excellent flavor and high nutritional value [2].  
Thermal treatment is used primarily in the food industry to increase shelf life and maintain food safety at low cost [3]. However, the treatment not only inactivates microorganisms and enzymes but also changes the quality and freshness. Therefore, alternative technologies to traditional thermal treatment have been investigated to inactivate microorganisms at temperatures lower than those commonly used in heat treatments [4]. Non-thermal treatments inactivate microorganisms without extensive quality changes of foods. Typical examples of these non-thermal techniques are high hydrostatic pressure, pulsed electric field, intense pulsed light, irradiation, and ultrasound [5, 6]. Among non-thermal techniques, the use of pulsed electric field is one of the most attractive because of its short processing time compared to other technologies and it is more energy efficient [7]. The PEF method is a non-thermal technique involving the discharge of high-voltage electrical short pulses through food items [8]. Many studies have assessed the effects of PEF treatment on microorganisms and fruit juice quality, such as orange juice [9, 10, 11], apple juice [12, 13], and blueberry juice [14]. However, according to our investigation, there were no other studies that found the correlation with volatile compounds data and sensory attribute of pulsed electric filed (PEF) treated orange juice through principal component analyses (PCAs) and partial least-squares (PLS) regression analyses. The objective of this study was to characterize the PEF- and heat-treated orange juice in terms of volatile composition and sensorial properties.
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 3 The Proposed Approach
### The Training Objective  
Our training objective combines supervised bi-directional flow losses with the unsupervised cycle consistency loss to promote accurate, coherent, and invertible transformations. By simultaneously supervising both the forward and backward displacement predictions at multiple resolutions and additionally enforcing consistency between these two directions, the network is guided towards robust and anatomically plausible lung registrations.
## 4 Conclusions  
Petroleum hydrocarbon loss in the operation of industrial facilities results in the contamination of sandy soils, thus adversely impacting their composition and properties. The origin of such changes lies in the physical-mechanical interaction between polar groups of oil hydrocarbons and active centers of sand grains. The result of this interaction consists in the sorption of oil hydrocarbons by sandy soils, in the process of which changes in grain-size distribution and hence physical-mechanical and hydrophysical properties of soils occur. To establish regularities in sand transformation under hydrocarbon contamination, laboratory studies were performed. Summing up the results, it can be concluded that:  
1. Oil hydrocarbon sorption causes the formation of hydrocarbon films on the grain surface of sandy soils and promotes the development of microaggregates, ranging in size from 0.5 to 5.0 mm, which are cemented by detected films.
2. The content of microaggregates in contaminated sands increases their coarseness by over 51% up to coarse-grained and very coarse-grained types compared to conditionally pure sands, which are classified as medium-grained.
3. Aggregated state of mineral grains in contaminated sands affects their density. In loose and dense structures, it becomes lower by 0.10 and 0.21 g/cm\({}^{3}\), respectively, in contrast to conditionally pure sands, which have densities of 1.42 g/cm\({}^{3}\) and 1.72 g/cm\({}^{3}\), respectively.
4. Changes in the density of contaminated sands have an impact on their permeability. In loose sands, the filtration coefficient is similar to that of conditionally pure samples, varying within the range of 18.7-20.0 m/day. The filtration coefficient of dense sands decreases to 5.1-6.5 m/day.
5. Studying strength parameters of contaminated sands using the consolidated drain method of the direct shear test shows that, in comparison with conditionally pure sands, contaminated samples have higher internal friction angle \(\phi\) (up to 39\({}^{\circ}\)) and display greater cohesion c (up to 5 kPa). Particular attention should be drawn to the duration of the contaminated sands' shear deformations--depending on the applied normal stress and sample density, the experiment can last up to 10 h.  
Conceptualization, I.L.; methodology, Y.L.; validation, P.K.; formal analysis, I.L.; investigation, I.L.; resources, Y.L.; writing--original draft preparation, I.L.; visualization, P.K. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Distribution curve of acid-base centers on the surface of natural (**a**) and enriched (**b**) quartz sand [13].  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data will be provided when the reader asked for it.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Development of Parameters towards Voice Bifurcations
## 4 Discussion  
### Overall Impression of HDF and BI  
The proposed HDF with the experiment configuration in Table 1 has demonstrated excellent ability to distinguish Type-I segments from others, as shown in Figure 2, corresponding well to visual cues that are present in the spectrograms. This includes both the normal voice in Case 1 as well as during the Type-I segments in the pathological cases. Visual observation of Figure 2 is confirmed by the HDF histogram in Figure 3, which illustrates a wide gap between Type-1 HDF values and those of other types. Further analysis of the histogram predicts the 99.9% detection rate of non-Type-I voice signal while fixing the false alarm rate at 0.01%. While this prediction is made with limited data, it still is a strong indication of the HDF's ability to detect non-Type-I signals reliably.  
The BI (and to lesser extent HDF) are expected to maintain a fixed level for each Type-II voicing mode. This is apparent in the three pathological cases, but most clearly in Case 2 (Figure 2b). After the bifurcation at \(t=0.1\), the BI stays around 0.76 (which corresponds to the \"7:8\" attractor notation of [PERSON] and Herzel [13]) until the next bifurcation at \(t=0.37\). Towards the end of this period, the HDF jumps between 0.875 and 1.143, which are reciprocal of each other, due to near-equal spectral peaks, causing \(f_{0}\) and \(f_{1}\) to flipflop. This behavior is the reason to introduce the BI.  
Case 3, whose acoustic signal is shown in Figure 1, exhibits numerous bifurcations. Its spectrogram in Figure 2c indicates the voice mostly switches between Type I and biphonic Type II. The HDF distinguishes Type-I and Type-II segments, including the momentary returns to the Type-I signal amid biphonic Type-II phonation in \(t\in(0.6,\ 0.9)\) correctly. At the beginning and end, period-2 subharmonic voicing is present, and the insensitivity of the BI to the period-2 subharmonic is prevalent (as expected per Table 2).  
Type-III voice is defined as random or chaotic, and Case 4 (Figure 2d) appears to have such behavior between \(t=0.1\) and 0.4, as per visual inspection of the spectrogram, although the speaking fundamental frequency \(F_{0}\) is observable throughout. As expected from the Type-III signal, the spectrum over this Type-III segment consists of many peaks, which gives an impression of randomness. Most notably, the number of candidates in \(\mathcal{F}_{1}\) is substantially higher than the other cases, which is an indication of the consistent presence of noise-like disturbance. However, a closer inspection of the BI and the frequencies of the peaks suggests that there is a consistent undertone of 7:9 mode (BI = 0.56) and thus has a strong Type-II quality to it. Nonetheless, the expected higher variation of HDF and BI for the Type-III signal is apparent from the Case 4 results.
# Target-Oriented Multi-Agent Coordination with Hierarchical Reinforcement Learning
## 3 Preliminaries
### Multi-Agent Deep Deterministic Policy Gradient  
Multi-agent deep deterministic policy gradient (MADDFG) is an adaptation of the deep deterministic policy gradient algorithm to multi-agent settings. It is an actor-critic, off-policy method that uses the paradigm of CTDE to learn deterministic policies. In MADDPG, a separate actor and critic is learned for each agent, such that arbitrary reward functions can be learned. Each agent, \(i\), has a deterministic policy, \(\pi_{i}(a_{i}||o_{i})\), parameterized by \(\theta_{i}\) (abbreviated as \(\pi_{i}\)), and let \(\pi=\left\{\pi_{i}(a_{i}||o_{i})\right\}_{i=1}^{n}\) be the set of all agent policies. A centralized and monolithic critic that estimates the joint action-value function \(Q_{i}^{\pi}(s,a_{1}\ldots,a_{n})\) is learned for each agent separately. The critic is said to be centralized as it utilizes information only available to it during the centralized training phase, the global state and the actions of all agents, \(a_{1}\),..., \(a_{n}\), to estimate the joint action-value function \(Q_{i}^{\mu}\), which is parameterized by \(\phi_{i}\). This joint action-value function is trained by minimizing the following loss:  
\[L(\phi_{i})=\mathbb{E}\Big{[}\big{(}y_{i}-Q_{i}^{\pi}(s,a_{1},\ldots,a_{n}) \big{)}^{2}\Big{]} \tag{2}\]  
where,  
\[y_{i}=r_{i}+\gamma Q_{i}^{\pi}\big{(}s^{\prime},a_{1}^{\prime},\ldots,a_{n}^{ \prime}\big{)}\big{|}\big{|}_{a_{i}=\pi_{i}(a_{i})} \tag{3}\]  
Here \(r_{i}\) is the reward received by each agent \(i\), \(a_{1}\),..., \(a_{n}\) is the set of target policies with delayed parameters \(\theta_{i}\), and \(\phi_{i}\) are the parameters of the target critic. The replay buffer \(\mathcal{D}\) contains the transition tuples \((s,s,a_{1}\ldots,a_{n},r_{1}\ldots,r_{n})\).  
The following policy gradient can be calculated individually to update the policy of each agent, \(i\):  
\[\
abla_{\theta_{i}}J(\pi_{i})=\mathbb{E}_{\mathcal{D}}\Big{[}\
abla_{\theta_ {i}}\pi_{i}(o_{i})\
abla_{a_{i}}Q_{i}^{\pi}(s,a_{1},\ldots,a_{n})\parallel_{a _{i}=\pi_{i}(a_{i})}\Big{]} \tag{4}\]  
where the current agent \(i\)'s action \(a_{i}\) is sampled from its current policy \(\pi_{i}\) when evaluating the joint action-value function \(Q_{i}^{\pi}\), while all other agents' actions are sampled from the replay buffer \(\mathcal{D}\).  
Updating the target network parameters of each agent \(i\) is shown in the following formula:  
\[\theta_{i}^{i}\leftarrow\tau\theta_{i}^{i}+(1-\tau)\theta_{i}^{i} \tag{5}\]
# Improving the Safety and Security of Software Systems by Mediating SAP Verification
## 2 Background
### Software Security Verification  
Software security has always been an afterthought during the testing phase of development. In contrast, modern approaches, such as agile, include continuous testing throughout all stages of the SDLC. Every day, hackers and cybercriminals are coming up with new methods to take advantage of software flaws. Security should be a priority throughout the SDLC, allowing developers and stakeholders to identify and resolve possible security concerns early on in the process [11; 12]. Therefore, it is necessary to adopt the concept of SSDLC, as shown in Figure 1.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Abbreviations** & **Full Form** \\ \hline MS-SDL & Microsoft security development lifecycle \\ \hline NFR & Non-functional requirements \\ \hline QA & Quality assurance \\ \hline RE & Requirement engineering \\ \hline ROI & Return on investment \\ \hline SAMM & Software assurance maturity model \\ \hline SCA & Static code analysis \\ \hline SDLC & Software development lifecycle \\ \hline SSD & Secure software development \\ \hline SSDLC & Secure software development lifecycle \\ \hline SSE-CMM & System security engineering capability maturity model \\ \hline SAP & Security and performance \\ \hline SVTs & Security verification techniques \\ \hline ZAP & Zed attack proxy \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: An insight into security verification.  
The security verification of software, also known as security assurance, spins around the following main techniques, as shown in Figure 1:  
* Log inspection;
* Static code analysis;
* Profound penetration testing before release;
* Interactive application security testing (IAST).  
The complete concepts of security adoption and verification are better elaborated in Figure 1. According to Figure 1, organizations need to adopt SSDLC practices during development, which include security requirement analysis, security design, security implementation, security testing, and operation and maintenance. Once software development is complete, it needs security verification before release; this includes applying essential security verification techniques, as mentioned above. Below, we describe the concept of SSDLC and key SVTs.
# Failure Analysis of Gantry Crane Slewing Bearing Based on Gear Position Accuracy Error
## 2 Slewing Bearing Mechanical Analysis and Test
### Gear Meshing Characteristics after Assembly  
As the actual force situation of the gantry crane is more complicated, the slewing bearing will produce certain displacement in operation, which may have an impact on the parallelism of the gear axis, so the gear meshing relationship will be different from the ideal situation. Therefore, it is necessary to analyze the meshing relationship between the gear teeth and the nature of the force on the gear teeth after the assembly of the slewing bearing model with the turntable and cylinder structure and to compare the analysis with the ideal meshing situation.
## 3 Results  
To validate the effectiveness and evaluate the performance of our proposed method, an observation period, covering one week from day of the year (DOY) 031 to 037 in 2021, is selected for the experiments. Figure 1 shows the distribution of stations and none of the stations for PPP experiment are used for deriving the satellite clocks. Thereafter, analysis of the residuals of network processing and satellite and receiver code biases are conducted. Finally, the estimated satellite clocks are compared with the IGS final products and applied in a pseudo-kinematic PPP procedure to evaluate their performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Strategies** \\ \hline Observations & GPS code and phase \\ \hline Priori noises & 0.3 and 0.003 m for raw code and phase observations in clock estimation, respectively; in PPP, 0.003 m for phase and priori noises for code discussed in Section 3 \\ \hline Cut-off elevation & 10\({}^{\circ}\) \\ \hline Differential code bias & CODE P1-C1 products \\ \hline Relativistic effect & Corrected \\ \hline Phase wind-up & Corrected \\ \hline PCOs/PCVs & igs14.atx \\ \hline Tidal displacements & Solid earth tide, ocean tide loading and pole tide \\ \hline Station coordinates & Fixed to IGS weekly solutions in clock estimation, and estimated as white noises in PPP \\ \hline Satellite orbits & Fixed to IGS final products \\ \hline Earth rotation effects & Fixed to IGS final products \\ \hline Receiver clocks & Estimated as white noises \\ \hline Satellite clocks & Estimated as white noises in clock estimation, and fixed to estimated clocks or IGS final products in PPP \\ \hline Zenith troposphere delays & Estimated as random-walk noises (10\({}^{-7}\) m\({}^{2}\)/s) with respect to Saastamoinen model \({}^{1}\), and NMF \({}^{2}\) is used \\ \hline Ambiguities & Estimated as constants over each continuous session \\ \hline Code biases & Estimated as white noises \\ \hline Estimator & Least square filter \\ \hline \hline \end{tabular} \({}^{1}\) Saastamoinen model [30], \({}^{2}\) Niell Mapping Function [31].  
\end{table}
Table 1: Detailed strategies for satellite clock estimation and PPP.
## 4 Evaluation Scope
### Target Domain
#### 4.1.9 Domain-Agnostic XAI Research  
The survey by [PERSON] et al. [84] shows that many instances of XAI research are domain-agnostic, meaning they are not specifically designed and evaluated for a particular real-world application. This also holds true for the evaluation studies in our sample. For this reason, domain-agnostic studies constitute a distinct category where the authors either do not specify a target domain or focus on domain-independent features [34; 51; 61; 103]. Most domain-agnostic studies are either concept-driven or methodology-driven. For instance, [PERSON] et al. [103] did not focus on any specific domain but reflected on evaluation methodologies in general. Various studies focus on universal evaluation concepts. For instance, [PERSON] [34] evaluated consequence-oriented, contrastive explanations. [PERSON] et al. [61] investigated visual explanations for interpreting charts. [PERSON] et al. [51] deliberately used alien scenarios in their evaluation study to abstract from a concrete domain.  
This overview demonstrates that explanations are used across a wide range of domains, each with very different levels of severity: the potential harm in the domain of product recommender systems is relatively minimal while using AI for legal decision-making can significantly affect individual liberty. Additionally, the requirements are quite different: Explanations for movie recommendations, for instance, can be reviewed at one's leisure, whereas in the transport sector, explanations need to be understood in real time. The target audience can vary greatly not only between domains but also within the same domain. For instance, in the medical field, the level of domain knowledge significantly affects whether the explanations are intended for doctors or patients. The last issue underscores the importance of explicitly defining the target audience in evaluation studies.
## 1 Introduction
[11] proved that the critical properties of nanoscale perpendicular magnetic anisotropy in STT-MTJs, such as magnetoresistance, magnetic field switching, and current-induced switching, are robust against ionizing radiation. The triple modular redundancy (TMR) method is commonly used for better reliability in digital integrated circuits, especially large-scale memory and programmable logic arrays. However, the TMR module itself suffers the potential danger of SEE, which also decreases circuit reliability. Methods of making TMR modules extremely radiation-hardened have rarely been discussed in the literature before; therefore, it is worth considering whether TMR can be strengthened by utilizing the inherent anti-radiation characteristics of MTJ devices. In this paper, a novel triple modular redundancy (TMR) anti-radiation reinforcement design based on STT-MTJ devices is proposed. With STT-MTJ devices contributing to the majority voter circuits, the TMR module itself can be considered truly radiation-hardened. Furthermore, the proposed design was numerically analyzed to verify its functional behavior through simulation. The simulation results demonstrate improvements to the digital system's robustness against ionizing radiation. This paper is organized as follows: in Section 2, the method of using STT-MTJ devices to reduce radiation damage is described with an emphasis on and sufficient details regarding STT-MTJ mechanisms. Then, the radiation-hardening solution of introducing the STT-MTJ devices into the optimized TMR circuits is discussed in Section 3. Afterwards, its enhanced robustness is demonstrated with simulation results in Section 4. Finally, conclusions are drawn in Section 5.
## 2 Related Works
### Multi-Object Tracking
#### 2.2.2 Algorithms  
**Tracking-by-detection**[26; 27; 28] used to be the most popular framework, which treats object detection, re-ID features extraction and data-association as three separate tasks. It uses the object detectors [29; 30; 31] to localize all objects of interest. Then, cropped images are cut out according to bounding-boxes and fed into an identity embedding network to extract re-ID features which are used to link the boxes over time. Finally, standard algorithms like the Kalman Filter [32] or Hungarian algorithm [33] are used to accomplish the linking task.  
A critical dilemma of this framework is that when these three tasks are performed separately, they cannot benefit each other, leading to low quality and efficiency. With the quick maturity of multi-tasks learning [34; 35] in deep learning, a modified framework that simultaneously accomplishes object detection and identity embedding in a single network was proposed. For example, Track-RCNN [36] adds a re-ID head on top of the Mask R-CNN [37] and regresses a bounding box and a re-ID feature for each proposal. Similarly, JDE [38] and FairMOT [39] learn the object detection task and appearance embedding task based on the anchor-free detector, and achieves near video rate inference. **Joint-detection-and-tracking** is a recently proposed framework, which shares knowledge between detection and object association. Integrated detection [40] boosts the detection performance by combining the bounding boxes in the current frame and tracks in previous frames. Tracktor++ [41] directly uses the previous tracking boxes as region proposals and then applies the bounding box regression to provide tracking boxes on the current step, thus eliminating the box association procedure. CenterTrack [42] localizes objects by tracking-conditioned detection and predicts their offsets to the previous frame.  
After the success of transformers in detection, several works directly apply transformers to MOT and have been proven powerful and inspiring. Trackformer [43] builds directly from DETR [44] and is trained to propagate the queries through time. Transtrack [45] extends DETR to MOT by adding a decoder that processes the features at t-1 to refine previous detection positions. And TransCenter [46] proposed the use of dense pixel-level multi-scale queries in a transformer dual-decoder network to detecting and tracking simultaneously.  
In this work, we adapted four state-of-the-art methods (details shown in Section 4.1) to evaluate on our NPSPT datasets.