esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
As discussed above, due to the small number of the sample, the ESs were analyzed to discuss the results obtained from the pre- and post-test on anthropometric, physiological and biomechanical characteristics. This is the first study that analyzes the biomechanical changes in the running segment in elite triathletes during a season. Firstly, concerning the anthropometric characteristics, large ESs and significant differences were found in the decrease of \(\Sigma 6\) skinkolds and fat mass. It should be highlighted that although the athletes came from a period of inactivity (two weeks of full rest and four weeks of adaptation mesocycle), these subjects have several years of experience in triathlon training and competition during previous seasons. This, in addition to the use of the ISAK method for estimating body composition, could explain the low body fat mass and fold sum values obtained. Although there would not be enough data to define the impact of the modifications of the anthropometric characteristics on performance, it could be speculated that there was an improvement in performance as a result of this factor since body fat correlates negatively with running success in triathlon because it is essentially a dead weight that must be carried throughout the event [20]. Secondly, regarding the physiological variables, large ESs were found in all parameters, except heart rate and peak speed. The most important predictor of performance [48], the speed, improved 0.45 km h\({}^{-1}\) in VT1 and 0.97 km h\({}^{-1}\) in VT2, which is the specific physiological intensity of the short-distance triathlon [15], while peak speed did not change. Previous studies [49] partially support the results of the present study, showing an increase in the ventilatory threshold speed but also in peak speed after strength and endurance training in recreational marathon runners. It might be that more concurrent pylemetric training could improve the peak speed, VO2 max and modify the biomechanical parameters, as previous investigations have shown [19]. The improvements at different speeds could be explained by the changes in the percentages of VO2 max at which VT1 and VT2 are
## 3 Results
### Total Nitrogen (TN) and Phosphorous (TP) Removal of the SBR System  
Similar to the COD treatment, the removal efficiency of TN and TP was not high in the first week (22% for N and 28% for P), but after two months of operation, this parameter was gradually increased to around 83% and 75%, respectively. The value of TN loading rate was about five times higher than those of TP, and maintained at this value in the second month. The loading rate value changed correspondingly with N or P removal efficiency and reached the highest amounts of 0.26 kg N/m\({}^{3}\cdot\)day (Figure 2b) and 0.05 kg P/m\({}^{3}\cdot\)day (Figure 2c).
# Cooperative Caching and Resource Allocation in Integrated Satellite-Terrestrial Networks
## 4 Problem Formulation
### Cooperative Caching and Resource Allocation Problem  
The formulated problem can be viewed as two subproblems of storage resource usage and network bandwidth consumption with multiple constraints. To better describe the formulated problem, more variables are provided as follows.  
Firstly, binary decision variable \(x_{u,w}\) indicates whether user \(u\) retrieves content \(w\), and \(x_{u,w}=1\) indicates that user \(u\) retrieves content \(w\), otherwise \(x_{u,w}=0\). Binary decision variable \(y_{w,v}\) indicates whether content \(w\) is deployed on satellite \(v\), and \(y_{w,v}=1\) indicates that content \(w\) is deployed on satellite \(v\), otherwise \(y_{w,v}=0\).  
Considering the different geographical distribution of users and the restriction of access services on each satellite, the same content \(w\) can be deployed on multiple satellites. Therefore, binary decision variable \(z_{u,v}^{w}\) indicates whether user \(u\) can retrieve content \(w\) on satellite \(v\), \(z_{u,v}^{w}=1\) indicates that user \(u\) can retrieve content \(w\) on satellite \(v\), otherwise \(z_{u,v}^{w}=0\).  
Furthermore, when users retrieve contents from the integrated satellite-terrestrial network, a path composed of multiple ISLs will be used to route the data. In that case, binary decision variable \(q_{u,e}^{w}\) indicates whether user \(u\) retrieves content \(w\) by link \(e\), and \(q_{u,e}^{w}=1\) indicates that user \(u\) retrieves content \(w\) by link \(e\), otherwise \(q_{u,e}^{w}=0\).  
Additionally, binary decision variable \(a_{u,dc}\) indicates whether user \(u\) retrieves content \(w\) from cloud center \(dc\), \(a_{u,dc}=1\) indicates that user \(u\) retrieves content \(w\) from cloud center \(dc\), otherwise \(a_{u,dc}=0\).  
The main symbols for the formulated problem are summarized in Table 1.  
Figure 2: Example of cooperative content service for the proposed system model.
# Cybersecurity Challenges in the Maritime Sector
## 4 Cyberattack Cases from the Maritime Transport Sector
The infection came as a macro-enabled Word document attached to an email, and multiple workstations on the administrative networks were affected [33, 58]. In 2021, multiple Greek shipping companies were hit by a ransomware attack that spread through the systems of an IT consulting firm [58]. This incident showed the reality of IT supply chain risk for shipowners, shipmanagers, and the shipping industry, where a large number of them were affected by the hack. A few days later, one vessel was hijacked and up to six others reported the loss of steering control in the Gulf of Oman. These incidents were considered as \"cyberpiracy\" [59]. In another cyber incident, a newly built dry bulk ship was delayed from sailing for several days because its ECDIS was infected by an unknown virus. The source and means of infection, in this case, remain unknown [60]. According to [60], the delay in sailing and costs in repairs totalled hundreds of thousands of dollars (USD). The IT systems of ports have also had a burst of associated cyber incidents that affected the maritime infrastructure. The most frequent types of attacks are phishing, malware, social engineering, brute force, and denial of service. In March 2020, the port of Marseille s was hit with the \"Mespinoza/Pysa\" ransomware. In this incident, the maritime infrastructures were affected by the attack due to their interconnection with information systems in Aix-Marseille-Provence, which was the main target of the attack [61]. In another large-scale incident, the port system of Maersk fell victim to a major cyberattack caused by the \"NotPetya\" malware, which also affected many other shipping companies globally. Maersk's ships are still at sea, and its 76 port terminals around the world have stopped [62]. This incident was followed in 2020 by a serious ransomware attack on the shipping company CMA CGM SA, which impacted some servers on its network and prevented customers from having external access to the company's IT applications and booking systems [63]. This year, the Port of Houston was the target of an attack that involved a password management program that contained a formerly unknown vulnerability. The hackers exploited that to install malicious code that granted access to the networks, which they used to exfiltrate login credentials needed to control network access [64]. Luckily, the hack attempt was successfully defended, and \"no system was impacted\". All these incidents confirm that modern cyberattacks go beyond manipulating navigation or tampering with cargo; they can disrupt local and global supply chains and even put the lives of the crew or passengers on board the ship at risk. Table 3 presents examples of recent cyber incidents in the maritime transport sector.
## 3 BDS-3 Constellation Configuration and ISL Establishment Constraints
### Satellite Visibility Constraints  
The fundamental condition for establishing a link between two satellites is that they are mutually visible in space, meaning there is no Earth obstruction between them. Additionally, to avoid interference and effects from the troposphere and ionosphere on the ISL signals, the visibility constraint can be strengthened by requiring that the inter-satellite communication zone be at least 1000 km above the Earth's surface.  
As shown in Figure 4, \(SAT_{i}\) and \(SAT_{j}\) represent the two satellites establishing the link, \(O\) denotes the center of the Earth, \(R_{e}\) is the Earth's radius, and \(H\) is the height of the ionosphere. The vectors \(\stackrel{{\rightarrow}}{{R_{i}}}\) and \(\stackrel{{\rightarrow}}{{R_{j}}}\) represent the direction vectors from the Earth's center to satellites \(i\) and \(j\), respectively. \(L_{ij}\) is the distance between the two satellites, \(h_{ij}\) is the distance from the Earth's center to the line connecting satellites \(i\) and \(j\), \(\theta_{i}\) is the angle between the line connecting satellite \(i\) to satellite \(j\) and the line connecting satellite \(i\) to the Earth's center, and \(\theta_{j}\) is the angle between the line connecting satellite \(j\) to satellite \(i\) and the line connecting satellite \(j\) to the Earth's center. \(\alpha_{i}\) and \(\alpha_{j}\) are Earth occlusion angles of satellites \(i\) and \(j\).  
To ensure unobstructed inter-satellite signal transmission and guaranteed communication quality, the spatial positional relationship between satellite \(i\) and satellite \(j\) must satisfy the following equation:  
\[h_{ij}\geq R_{e}+H \tag{1}\]  
Figure 4: Schematic diagram of satellite visibility constraints.  
Using \(\overset{\rightharpoons}{R_{i}}\) and \(\overset{\rightharpoons}{R_{j}}\) to represent \(h_{ij}\), the distance constraint formula for inter-satellite visibility can be derived as follows:  
\[\frac{\left|\overset{\rightharpoons}{R_{i}}\times\overset{\rightharpoons}{R_{i }}\right|}{L_{ij}}=\frac{\left|\overset{\rightharpoons}{R_{i}}\times\overset{ \rightharpoons}{R_{j}}\right|}{\left|\overset{\rightharpoons}{R_{i}}-\overset{ \rightharpoons}{R_{j}}\right|}\geq R_{e}+H \tag{2}\]  
When the line connecting satellite \(i\) and satellite \(j\) is tangent to the ionosphere boundary, the angle between the satellite connection line and the line from the satellite to the Earth's center can be calculated, resulting in the angle constraint formula for inter-satellite visibility:  
\[\left\{\begin{array}{l}\theta_{i}\geq\alpha_{i}=\arcsin\frac{R_{e}+H}{\left| \overset{\rightharpoons}{R_{i}}\right|}\\ \theta_{j}\geq\alpha_{j}=\arcsin\frac{R_{e}+H}{\left|\overset{\rightharpoons}{R_{j}} \right|}\end{array}\right. \tag{3}\]
## 1 Introduction
### Related Work  
Various examples of medical free-text de-identification exist, which typically reference the HIPAA Safe Harbor Method as a guideline for their approach. Methods for different languages are found in the literature, such as those for English [8; 9], Spanish [10], Dutch [11; 12], Swedish [13], Polish [14], Portuguese [15], Arabic [16], Indian [17], Japanese [18], and Chinese [19] text data. Most solutions published use either rule-based systems or algorithmsbased on machine learning, which have both found success. [PERSON] et al. developed an open-source solution for the English language called Philter [9]. Their solution is based on blacklists and regular expression rules to remove PHI and whitelists to explicitly keep medical information. Later advancements resulted in a type 2 error-free algorithm [20]. For English, examples with machine learning also exist [8; 21]. For the Spanish language, deep learning approaches emerged from the MEDDOCAN community challenge as the best performing [10]. Similarly, an analysis by [PERSON] et al. of Dutch texts showed that approaches with machine learning can beat rule-based systems [12]. On the other hand, [PERSON] et al. found the opposite results in their experiments with Japanese texts, where machine learning methods were outperformed by rule-based systems [18]. [PERSON] et al. also detail the difficulties in de-identification due to the intricacies and ambiguity of the Chinese language [19], which likely requires laborious manual curation. These examples from the literature highlight how differences in languages make specialized tools essential.  
For the German language, [PERSON] et al. investigated different methods of de-identifying texts from the cardiology domain, including a rule-based approach with regular expressions and gazetteers (i.e., geographical dictionaries) [22], which was further improved with deep learning approaches [23]. They used admission letters that followed a basic document structure with designated fields for headers, a salutation, diagnoses, and a summary. [PERSON] et al. also used neural networks to de-identify a manually annotated set of structured or semi-structured discharge summaries and transfer letters [24].  
Both research groups worked with official documents intended for formal correspondence with others (e.g., admission or discharge notes, transfer letters), ensuring a certain level of proof-reading and thus quality of text. Also, these texts are unlikely to include abbreviations (besides those common in the German language), nicknames, or colloquial language. Furthermore, such documents are typically written exclusively by doctors and thus are not subject to text variation due to educational backgrounds.
## 3 Results and Discussion
### Diffusion Analysis of Composite Interface Elements  
To further investigate the distribution pattern of each element on the composite interface, a line sweep of the composite interface area was carried out using an electron probe, where the line-sweep length was 150 \(\upmu\)m. Figure 10 shows the results of the electron probe analysis of the composite interface. It can be seen from the figure that C, Fe, Ni, Cr all diffused obviously at the composite interface, and the diffusion distance of Cr (\(\approx\)35 \(\upmu\)m) is significantly larger than that of Ni (\(\approx\)10 \(\upmu\)m). This phenomenon can be explained by the following two reasons: (1) the difference in the concentration of Cr on both sides of  
Figure 9: Metallographic organisation of the composite interface of the clad rebar: (**a**) carbon steel side; (**b**) stainless steel side.  
the composite interface is greater than that of Ni, so Cr has greater kinetic energy while diffusing, and (2) the diffusion coefficient of C is greater than that of Ni [28].  
In addition, Figure 10b clearly reveals that the C in the diffusion process undergoes an \"uphill\" phenomenon. This is because C diffusion to the stainless steel side will react with Cr to form carbon-chromium compounds, which reduce the activity of C and slow the diffusion rate. However, under the action of chemical potential, C originating from the carbon steel side steadily diffuses to the stainless steel side, giving rise to the uphill phenomenon. A similar phenomenon was observed by [PERSON] et al. in their study of elemental diffusion in composite plates [29].
# Thrust Measurements on the High Efficient and Reliable Vacuum Arc Thruster (HERVAT)
## 2 Experimental Setup
### Vacuum Facility  
The vacuum facility is evacuated with a two-staged system consisting of a root pump (Pfeiffer DUO LINE 25) and a turbo-molecular pump (Pfeiffer HiPace 700 TMP). The chamber pressure for the experiments was \(2.5\times 10^{-6}\) mbar. The vacuum facility was preheated to ensure a high quality vacuum level for the experiments. To avoid unintended pollution, a plasma trap device was used to prevent condensation of the emitted plasma and particle stream from the thruster on the thrust balance or the chamber walls as illustrated in Figure 4.
# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 4 Case Study
### PPPM Analysis and the Derivation of Answers to Questions
#### 4.5.4 PPPM Analysis of PRETSA (Q1.2.1)  
In this section, we present the quantitative results of applying the _PRETSA_ PPPM model on our real-life event logs under two different aspects as result and time utility qualities. Here, the parameters \(k\) and \(t\) regulate the risk of attribute disclosure.  
Result Utility Quality  
The Figure 10 heatmap shows the applied _PRETSA_ model for the PM quality metrics with respect to five different \(k\) and \(t\) parameter values behavior on our real-life event logs. For all the results, the baseline metric values without any anonymization were included in Table 3 and employed for the comparative interpretations. Since the \(k\) and \(t\) values are proportional to privacy protection, but the runtime performance is very low (>30 min timeout limit), this model does not apply to the Road Traffic Fines event log for \(k>4\) values.  
All the _PRETSA_ anonymized event logs showed alignment with their baseline fitness and generalization values. However, for all the event logs, there was a decrease in precision for a strong privacy budget (when \(k\) goes high) in comparison with their baselines. Specifically, the precision and generalization applied to the CoSeLog event log exhibited the highest increase in this context. On the other hand, all the metric results showed steady trends for each parameter configuration. The Sepsis event log showed the optimum result quality metrics for this model.  
Time Utility Quality  
Figure 11 includes the runtime (s) performance metric results of the applied _PRETSA_ PPPM model model on our real-life event logs. During the experimentation with the _PRETSA_ algorithm, an inversely proportional decrease in model runtime was observed with increasing privacy levels as a consequence of specific \(k\) and \(t\) parameter modifications, attributable to reduced model complexity besides instance count size. On the BPIC13 and Road Traffic Fines event logs, the runtime for the anonymization values was increased with  
Figure 10: Discovered PRETSA anonymization model quality results.  
the \(t\) parameter value explicitly. However, for the Sepsis event log, the runtime values were very steady for all the configurations byproduct of the anonymization algorithm's behavior. The BPIC13 event log had the highest runtime performance for the lowest \(k\) and the highest \(t\) values.  
Figure 11: Discovered PRETSA anonymization runtime (s) metric results.
## 2 Electrodynamic Scattering: Mie Coefficients
Figure 3: The total scattering efficiency (blue line), in logarithmic scale, as a function of the permittivity (lossless case) for a fixed size parameter \(x=0.75\). Additionally, the lines \(a_{1}\), \(a_{2}\), and \(a_{3}\) (dashed colorful lines) depict the absolute value of the first three electric multipoles, while \(b_{1}\), \(b_{2}\), and \(b_{3}\) (dash-dot colorful lines) illustrate the magnetic dipole, quadrupole, and octupole terms, respectively. The inset figure (left column) depict the corresponding surface charge distributions for the \(a_{1}\), \(a_{2}\), and \(a_{3}\) electric multipoles, while the inset figure (top) the corresponding surface electric current distributions for \(b_{1}\), \(b_{2}\), and \(b_{3}\) magnetic multipoles, respectively. Note that the \(a\) multipoles resonate at both plasmonic (\(\varepsilon<0\)) and dielectric (\(\varepsilon>0\)) regions, exhibiting an identical charge distribution. The amplitude of \(a_{n}\) and \(b_{n}\) coefficients is bounded at the \([0,1]\) interval. Figure 2: The scattering efficiency of a homogeneous sphere (in logarithmic scale), normalized for better visualization of the results, as a function of the permittivity (lossless case) and the size parameter. The yellow regions correspond to radiation enhancement regions. Note two different regions, one for negative (plasmonic region) and for positive (dielectric) permittivity. The inset figure shows an magnification on the plasmonic region, where the first three electric multipole resonances are visible. (Adapted with permission from [63], IEEE, 2017).
## Materials and Methods
### Experimental Set-Up and Instrumentation  
As illustrated by Figure 2, the IRT experiments were performed with the interior side of the window facing the camera. Heating was done either in transmission or reflection mode by moving the light source while the rest of the setup stayed in place. In transmission mode, the exterior side of the window is illuminated, while the interior side is illuminated in reflection mode. In particular, the sample was heated by two 500 W halogen lamps, positioned at equal distances and under the same angle with respect to the camera-object axis. The lamps were placed at a distance of 0.80 m, measured from the sides of the window frame, and the camera was positioned at 0.80 m from the center of the window, as shown in Figure 2. Typical emission from a halogen light source ranges from 0.4 um to 3.5 um with the peak wavelength around 1 um.  
Figure 1: Visual light images of the interior side of the inspected window in reflected (**left**) and transmitted light (**right**).  
These measurements were performed in laboratory conditions using a FLIR X6540 sc actively cooled, thermal camera, which has a thermal sensitivity of <25 mK (typically 18 mK), a resolution of 640 \(\times\) 512 pixels and spectral sensitivity in the 1.5-5.5 \(\upmu\)m range. Measurements were carried out without filter and using an L1009 25 mm f = 2 lens, which has a spectral range of 2.5-5 \(\upmu\)m and an Angular Field of View (AFOV) of 22\({}^{\circ}\)\(\times\) 17\({}^{\circ}\). The window was heated for 2 min, while the cooling-down process was recorded for 3 min at a frame rate of 10 Hz. Increasing the heating time or a longer measuring time during cooling did not result in a significant improvement of the results. During the optimization process of the parameters, we strive for minimizing the temperature variation of the glass. Because the coefficient of expansion of glass is different from the other window materials, varying the temperature can potentially generate stress in the window pane. Generally, glass can endure heating up quite well, but cooling down too fast could lead to thermal fractures. The epoxy resin employed for mending broken glass is particularly vulnerable to this. Ideally, leaded-glass windows are kept at 18-20 \({}^{\circ}\)C with a maximum temperature variation of 3 \({}^{\circ}\)C in 24 h [30]. During our measurements the apparent surface temperature of the glass varied by 4 \({}^{\circ}\)C on average. The lead cames appear to cause a temperature gradient in the glass panes, which introduces mechanical stress and bending.  
In order to obtain images with a higher spatial resolution, the upper and lower part of the window panel were imaged separately. The resulting images have one row of glass panes in common by way of overlap. The upper and lower image sets were processed separately. An area of roughly 0.5 mm \(\times\) 0.5 mm is covered by each pixel.
# Efficient Layered Parallel Architecture and Application for Large Matrix LDPC Decoder
## 3 Design and Implementation
### Back-Up Memory  
In the previous section, this paper discussed the error caused by the quantized overflow processing of VN information in the decoder. After tracing the error bits in several decoding processes, a rapidly increasing number of error bits was observed in the late decoding period. If the hard decision is output in this case, it will output a large number of error bits, extremely hindering the performance of the decoder.  
To this end, this paper adds an additional hard-decision memory--back-up memory (BM). Its mechanism is shown in Figure 5. In the early stage of decoding, both BM and HM are updated, and the content stored in BM is the copy of HM. In the later stage of decoding, when the decoder is detected to near convergence, it waits for a period of time. If the decoder does not finish decoding within this period, it is considered that a large number of overflow errors will occur in the decoder. The update of BM is disabled and only HM is updated. At the end of the decoding, output from HM is selected if the decoder successfully interprets, output from BM is selected if the decoding fails and an operation to disable BM is triggered, and output from HM is selected if the decoding fails and no operation to disable BM is triggered.