esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Dependent Probabilistic Model for Hierarchical Structure in Failure Mode and Effect Analysis
## 3 Hierarchical Time-Dependent FMEA Model
### Evaluation of Losses Due to Failure-Cause (FC)  
As introduced in Section 3.2, the loss function is separated by two functions, \(L_{1}(s,\;t_{0})\) and \(L_{2}(s,\;t_{0})\), so we can also evaluate the expected value of the loss separately. That is,  
\[E[L]=E[L_{1}]+E[L_{2}]\;. \tag{11}\]  
Let \(\overset{\rightarrow}{t}=(t_{0},\;\cdots,\;t_{n-1},\;t_{n})\) be a vector in the set \(\Omega\subset\mathbb{R}^{n+1}\) satisfying \(t_{0}\geq\cdots\geq t_{n-1}\geq t_{n}\geq t_{n+1}=0\). Let \(\overset{\rightarrow}{T}=(T_{0},\;\cdots,\;T_{n-1},\;T_{n})\) be a vector of the random variables of the failure occurrence for each subsystem.  
Then,  
\[\begin{split} E[L_{1}]&=\int_{\Omega}\int_{0}^{ \infty}L_{1}(s,\;t_{0})f_{S_{s},\;\overset{\rightarrow}{T}}(s,\;\overset{ \rightarrow}{t})dsd\overset{\rightarrow}{t}\\ &=\alpha\int_{\Omega}\int_{0}^{\infty}f_{S_{s},\;\overset{ \rightarrow}{T}}\Big{(}s,\;\overset{\rightarrow}{t}\Big{)}dsd\overset{ \rightarrow}{t}\\ &=\alpha\\ &=\alpha\end{split} \tag{12}\]The integration of the probability density function in the whole range is  
\[\int_{\Omega}\int_{0}^{\infty}f_{S,\ T}\bigg{(}s,\ \overset{\rightarrow}{t} \bigg{)}\text{{{\it{dsd}}}}\overset{\rightarrow}{t}=1. \tag{13}\]  
Furthermore,  
\[\begin{split} E[L_{2}]&=\int_{\Omega}\int_{0}^{ \infty}L_{2}(s,\ t_{0})f_{S,\ T}\bigg{(}s,\ \overset{\rightarrow}{t}\bigg{)}\text{{{\it{dsd}}}}\overset{ \rightarrow}{t}\\ &\qquad\qquad\ ;\ L_{2}(s,\ t_{0})=0\text{ for }s<t_{0}\\ &=\int_{\Omega}\int_{0}^{\infty}L_{2}(s,\ t_{0})f_{S,\ T}\bigg{(}s, \ \overset{\rightarrow}{t}\bigg{)}\text{{{\it{dsd}}}}\overset{ \rightarrow}{t}\\ &=\int_{\Omega}\int_{0}^{\infty}L_{2}(s,\ t_{0})\mathcal{S}_{|T_{0} =t_{0}}(s)h_{T_{0}}(t_{0})\text{{{\it{dsd}}}}\overset{\rightarrow}{t}\\ &=\int_{\Omega}\int_{0}^{\infty}L_{2}(s,\ t_{0})\mathcal{S}_{|T_{0} =t_{0}}(s)h_{T_{0}|T_{1}=t_{1}}^{0}(t_{0})\cdot\cdot\cdot\cdot\cdot h_{T_{n-1 }|T_{n-1}=t_{n}}^{n-1}(t_{n-1})\cdot h_{T_{n}}^{n}(t_{n})\text{{{\it{dsd}}}} \overset{\rightarrow}{t}\\ &=\int_{0}^{\infty}\int_{n}^{\infty}\cdots\int_{1}^{\infty}\int_ {0}^{\infty}[\beta+\theta(s-t_{0})]\frac{c_{\mu}}{c+\mu t_{0}}e^{-\mu(s-t_{0}) }\lambda_{0}e^{-\lambda_{0}(t_{0}-t_{1})}\cdot\cdot\\ &\qquad\qquad\ \cdot\lambda_{n-1}e^{-\lambda_{n-1}(t_{n-1}-t_{n}) }\cdot\lambda_{n}e^{-\lambda_{n}t_{n}}\text{{{\it{dsd}}}}t_{0}\cdot\cdot\text{ {{\it{dt}}}}_{n-1}\text{{{\it{dt}}}}_{n}\\ &=\Big{(}\beta+\frac{\beta}{\mu}\Big{)}\frac{c}{\mu}\prod_{i=0}^ {n}\lambda_{i}\cdot\sum_{j=0}^{n}\Bigg{[}\frac{e^{\frac{\lambda_{j}}{\mu}}E_{ \parallel}\left(\frac{c_{\lambda_{j}}}{\mu}\right)}{\prod_{i\
eq j}^{n}(\lambda _{i}-\lambda_{j})}\Bigg{]}\end{split} \tag{14}\]  
(See Appendix A)  
Here, \(E_{1}(x)=\int_{x}^{\infty}\frac{c^{-t}}{t}dt=-\gamma-\ln x-\sum_{k=1}^{\infty }\frac{(-x)^{k}}{k!}\) is a special function named as exponential integral.
# Dynamic Controllability of Processes without Surprises
## 2 Background and Motivation
### Sudden Termination  
To be self-contained, we briefly revise the small example from [11] to explain the sudden termination problem. Consider the simplified procurement process in Figure 1. The controller may choose the duration of semi-contingent task \(A\) for arranging shipment of goods between 5 and 7 days. \(A\) is bound to a contingent task \(R\) of receiving payment that lasts between 3 and 5 days. A _lower-bound constraint_ (_lbc_) demands that the end of \(A\) is at least 3 days after the end of \(R\) to allow a customer to cancel the order. An _upper-bound constraint_ (_ubc_) states that \(A\) has to finish within 4 days after \(R\) to guarantee timely delivery. Now, let's assume \(R\) should start at time point 10, and hence ends anytime between 13 and 15. Is there a choice for the start time and duration of \(A\) satisfying the constraints? After some trials you see: no! \(A\) might, e.g., start at 12 and end between 17 and 19, but 19 is too late, if \(R\) ends at 13, and 17 is too early, if \(R\) ends at 15. At 12 the end of \(R\) is unknown.  
One can observe that it is impossible to determine a duration for \(A\) such that for all possible durations of \(R\) the constraints are satisfied. The end of the ongoing activity \(A\) can only be scheduled when the end of \(R\) has been observed.  
As shown in [11], treating semi-contingent activities for checking dynamic controllability is not obvious and requires special strategies. Indeed, if they are considered as non-contingent activities, they may be subject to a sudden termination. Instead, if they are treated as contingent activities, we are unnecessarily strict and reject processes that could be scheduled without sudden termination. Therefore, in [11], we introduced the notions of _semi-contingent activities_ and _semi-dynamic controllability_ to adequately deal with such activities and their durations.  
There exist various semi-contingent activities for which the end time is decided when they start, and cannot further be changed. An example is executing a batch computation using a configurable cloud Virtual Machine service. A client can choose the VM configuration, setting the memory and number of CPU cores, before starting the computation. The execution time depends on the VM configuration. Once the computation starts, the client cannot force it to terminate to get the results faster, but needs to wait for completion. Another example is shipping a package with express delivery service or regular service. Once the package is handed in to the courier and shipment starts, the delivery time is set and cannot be changed, e.g., after 1 day for express and 3 days for regular service.
# Rethinking Modbus-UDP for Real-Time IIoT Systems
## 4 Experimental Results
### Modbus-TCP and Modbus-UDP Throughput  
The average Modbus-TCP throughput \(T_{\text{TCP}}\) in b/s is defined as the ratio between the amount of data \(L_{7}\) transferred in one transaction (Table 2), expressed in bits, and the mean round-trip time \(\mu_{\text{TCP}}\) (Table 3). The average Modbus-UDP throughput \(T_{\text{UDP}}\) is defined in the same way:  
\[T_{\text{TCP}}=8L_{7}/\mu_{\text{TCP}},\quad T_{\text{UDP}}=8L_{7}/\mu_{ \text{UDP}}. \tag{5}\]  
The throughput depends on the number of registers exchanged \(N\) and, marginally, on the kind of Modbus transaction (RH or WM). Figure 9 shows the results obtained in the experiments described in Section 4.3. For reference, the figure also shows the baseline TCP and UDP receive-side throughput measured as discussed in Section 4.2. In addition to the significant difference between \(T_{\text{TCP}}\) and \(T_{\text{UDP}}\), which is a direct consequence of the difference between \(\mu_{\text{TCP}}\) and \(\mu_{\text{UDP}}\) through (5), two other facets of these results are worth mentioning:  
1. The Modbus-TCP throughput \(T_{\text{TCP}}\) is significantly lower than \(T_{\text{UDP}}\) even though the baseline TCP throughput is significantly higher than the baseline UDP throughput. This discrepancy can be attributed to the fact that TCP data aggregation mechanisms, which enhanced throughput in the baseline tests, cannot be used when dealing with request-response transactions whose messages are significantly shorter than the maximum TCP segment size.
2. The Modbus-UDP throughput \(T_{\text{UDP}}\) is still much lower than the baseline UDP throughput and never went beyond about 16% of it. The implications of this result are twofold. Firstly, it highlights the ineffectiveness of UDP datagram queuing and pipelining in request-response transactions. In fact, in this kind of transaction, any queues or pipelines that the protocol stack may put into effect are, by definition, completely drained at the end of each transaction. Secondly, it shows that Modbus protocol stack overheads above L4, as well as application-level message processing on both the master and slave side, are significant. In turn, this suggests that optimizing the protocol stack at this level may be the subject of future work.  
Figure 9: Modbus-TCP and Modbus-UDP throughput, \(n=10\),\(000\) samples.
programs should be individualized with respect to playing positions and related to specific competition on-court demands [6].  
This necessity to understand handball's physical characteristics has raised great interest among researchers who have studied these demands using different methodologies [5]. The most widely used method has been time-motion analysis, based on observing players in the competition followed by an analysis of a video, taken with one camera [7; 8] or two cameras [9]. The video-recorded matches are analyzed and the actions encoded. However, this method is time-consuming and depends on a subjective analysis of the observer, thus not being an objective or precise method when determining the different locomotion speeds.  
In order to overcome this inconvenience, the European Handball Federation (EHF), Select(r) and Kinexon(r) jointly developed the Kinexon(r) tracking system for handball players (Kinexon: Munchen, Germany; Select Sport. Glostrup, Denmark) in addition to a monitored ball, the iball, which has been recently validated [10; 11; 12] and used in studies on handball [11; 13] and other team sports [14]. This technology provides us with values regarding movements, accelerations, changes of direction, jumps, as well as data on the speed at which the ball is transferred (game speed) and the speed and position of the throws in real time, opening up new possibilities in the study of handball competition requirements [11]. With this fully automatic tracking system, the inconveniences mentioned for the conventional time-motion analysis are solved.  
To date, there are no studies conducted with Local Positioning System (LPS) technology during a full championship of the highest level. There are also no studies at this level comparing the work done in both phases of the game, offense and defense. Therefore, new information must be provided through the use of this technology in high competition.  
Previous time-motion studies carried out in different kinds of competitions, found differences between playing positions regarding the total distance covered and the distance covered at different locomotion categories [7; 8; 13; 15]. Nevertheless, no studies so far have investigated in detail the activity profile of elite male handball throughout all matches of an European Championship for different playing positions. Therefore, the aim of the present study was to analyze on-court demands of handball players during the EHF European Championship 2020 to define time motion characteristics (time played, distance covered) both in offense and defense and according to their specific playing position, winners and losers, or between top ranked teams and lower ranked teams.
## 3 Signal Quality Index Algorithm
### Pre-Process  
The PPG pulses were segmented from the smoothed PPG signal according to the previous mentioned procedure. However, the Gaussian filter cannot be affected by the wandering noise. In Figure 4, the PPG signal (solid line) is corrupted by the wandering noise, but its smoothed signal (dashed line) is same as the smoothed PPG signal without the wandering noise. Therefore, in the pre-process, the segmented pulses must be verified regarding whether their baselines were corrupted  
Figure 3: Smoothed PPG signal (dash line) is obtained by filtering the PPG signal (solid line) with a Gaussian filter.  
Figure 2: Flowchart of signal quality indexes (SQI) algorithm for each PPG pulse.  
by the wandering noise or not. If the PPG pulse is corrupted by the wandering noise, it will be deleted. As shown in Figure 4, when the peak and two side valleys of a PPG pulse have the same signs, or the two side valleys have different signs, this PPG pulse is considered to be owning severe wandering noise, and it is deleted.
###### Abstract  
Olive anthracose, mainly caused by _Colletotrichum acutatum_, is considered a key biotic constraint of the olive crop worldwide. This work aimed to evaluate the ability of the endophytes _Aureobasidium pullulans_ and _Sarocladium summerbelli_ isolated from olive trees to reduce _C. acutatum_ growth and anthracose symptoms, and to assess _A. pullulans_-mediated changes in olive fruit volatile organic compounds (VOCs) and their consequences on anthracose development. Among the endophytes tested, only _A. pullulans_ significantly reduced the incidence (up to 10-fold) and severity (up to 35-fold) of anthracose in detached fruits, as well as the growth (up to 1.3-fold), sporulation (up to 5.9-fold) and germination (up to 3.5-fold) of _C. acutatum_ in dual culture assays. Gas chromatography-mass spectrometry analysis of olives inoculated with _A. pullulans_ + _C. acutatum_ and controls (olives inoculated with _C. acutatum_, _A. pullulans_ or Tween) led to the identification of 37 VOCs, with alcohols being the most diversified and abundant class. The volatile profile of _A. pullulans_ + _C. acutatum_ revealed qualitative and quantitative differences from the controls and varied over the time course of microbial interactions. The most significant differences among treatments were observed at a maximal reduction in anthracose development. At this stage, a set of VOCs, particularly Z-3-hexen-1-ol, benzyl alcohol and nonanal, were highly positively correlated with the _A. pullulans_ + _C. acutatum_ treatment, suggesting they play a critical role in anthracose reduction. 6-Methyl-5-hepten-2-one and 2-nonanone were positively associated with the _C. acutatum_ treatment and thus likely have a role in pathogen infection.  
biocontrol; _Aureobasidium pullulans_; _Sarocladium summerbelli_; anthracose; volatile compounds
# An Introduction to Nonlinear Integrated Photonics: Structures and Devices
## 4 Nonlinear Photonics Devices
### All-Optical Processing
#### 4.2.3 All-Optical Signal Regeneration
The Si nano-rib waveguides had width, height, and slab height of 500 nm, 210 nm, and 50 nm, respectively; doped regions were created at a distance of 350 nm from the waveguide through implantation of boron and arsenic with concentrations of  
Figure 14: Sketch of the experimental setup for characterization of a SOA-based polarization-independent all-optical regenerator for DPSK data. The principles of operations are schematized within the two dashed boxes. (TL: tunable lasers; MZM: Mach–Zehnder modulator; BPG: bit pattern generator; EDFA: Erbium-doped fiber amplifier; PC: polarization controller; VOA: variable optical attenuator; OF: optical filter; DI: delay line.) Reproduced from [181] under Creative Commons license. \(10^{18}\) cm\({}^{-3}\) for the p- and n- regions, respectively. Keeping in mind development of 5G and 6G networks, data transmission rates as high as 100 Gb/s at the user must be considered; correspondingly, intensity modulation with the NRZ-OOK format will likely be a good solution for optical access networks. [PERSON] et al. [180] have, therefore, investigated the feasibility of all-optical regeneration of these signals using the FWM process in a silicon waveguide with reverse-biased p-i-n junction. The Si rib waveguide is 4 cm long, 450 nm wide, and 70 nm thick, respectively. The p- and n- regions of the junction were 3.6 \(\upmu\)m wide and separated by 1.35 \(\upmu\)m; they had been obtained by implantation of boron and phosphorus ions, respectively. The achieved conversion efficiency was as high as \(-12\) dB, and all-optical regeneration of NRZ-OOK signals with rates of 50 Gb/s and 100 Gb/s were experimentally demonstrated [188]. For completeness, it has also to be mentioned that signal regeneration was obtained by exploiting the nonlinear properties of lithium niobate, e.g., using nonlinear wave mixing in cascaded periodically poled lithium niobate (PPLN) waveguides [189; 190; 191] and in lithium niobate on insulator (LNOI) structures [35]. Before concluding our discussion on all-optical signal regeneration, we summarize some challenges for achieving efficient all-optical regenerators: (1) as optical networks are adopting flexible and modulation format variable transceivers, any dependence on modulation format will reduce the efficiency and upgradability of the regenerators; therefore, optical regeneration should be modulation format and baud-rate-transparent. (2) Optical regeneration should improve the optical SNR by amplifying only the data signal and reducing the signal's amplified spontaneous emission (ASE) noise arising from erbium-doped-fiber-amplifiers (EDFA). (3) The optical regenerator should mitigate any crosstalk and inter-symbol interference that arises in the different domains of the optical wave, such as wavelength, polarization, and spatial modes [50; 51].
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 7 Wireless Technologies in IoV
### Vehicular Communications
#### 7.1.2 IEEE 802.11p (WAVE--Wireless Access in Vehicular Environments)  
IEEE 802.11p, an IEEE 802.11 substandard focused on vehicular communications, provides special attention to the communications needs within vehicular environments [96]. This wireless technology operates in the dedicated 5.9 GHz frequency band designed for the challenges of real-time communication between vehicles and infrastructure. The primary purpose of IEEE 802.11p is to facilitate V2X communications between vehicles and infrastructure, sharing critical information [97]. The technology has been found to be  
Figure 7: Wireless technologies in IoV.  
valuable in applications such as collision avoidance, where immediate sharing of position, speed, and other crucial data elements improves road safety. Furthermore, 802.11p enables V2X communication for traffic information and the development of ITS in optimizing traffic flow.
## 4 Results
### Attenuation with Distance from Fault Rupture  
Measuring distances from the epicenter or hypocenter, as conducted for \"Aim 2\" of this work, can be an oversimplification in the case of strong earthquakes, when the point-source approximation is no longer valid. Figure 7 shows two examples of earthquakes included in our dataset, namely the 1894 Atalanti, Greece and 1912 Acambay, Mexico events. The surface rupture length along the main fault is 36 km for Acambay [50; 51] and 32 km for Atalanti [52]. IDPs are not distributed uniformly in space; in fact, especially for Atalanti, most of them are located near the fault. Therefore, using the epicentral distance might generate an error in the order of even tens of kilometers, while using a different distance metric, such as from the fault, will reduce this error. Many studies use the distance from the seismogenic plane at depth, or the Joyner-Boore distance; numerical equations to relate the different distance measures have been proposed as well [33]. Due to the paucity of such information for the earthquakes in our dataset, we rely on the horizontal distance from the rupture (D\({}_{\rm RupX}\), see Figure 2b), which we consider better constrained in studies dealing with the ESI-07 scale.  
Figure 5: Distribution of the intensity data points (IDPs) in terms of (**a**) intensity (ESI-07 scale) and (**b**) epicentral distance (km). The red line represents the limit of the data decimation (65 km).  
Figure 6: (**a**) Representation of IDP median values obtained from intensity binning, and the proposed ESI-07 IPE (with confidence bounds of 95%); (**b**) expected ESI-07 values considering various \(\mathrm{I}_{0}\), where the solid lines refer to the equation derived in this study and dashed lines are after [PERSON] et al. (2020) [10]; (**c**) difference between IPEs ([PERSON] et al., 2020) minus the curve derived in this work).  
After the data decimation process, our analysis focused on 726 IDPs. Each earthquake's rupture was delineated end-to-end through the reference literature [46; 50; 52; 53; 54; 55; 56; 57; 58; 59]. The new IPE was computed by employing an approach consistent with that used in Aim 2 (i.e., IPE relative to epicentral distance). This process ensures the comparability of our findings.  
Figure 7: Examples of IDP maps referring to (**a**) 1912 Acambay earthquake, Mexico (IDPs from [PERSON] et al., 2024 [45]; surface rupture from [PERSON] et al., 2000 [50]); and (**b**) 1894 Atalanti earthquake, Greece (IDPs from [PERSON] and [PERSON], 2017 [43]; surface rupture from [PERSON] et al., 2001 [52]).  
Figure 8 presents the new IPE based on the horizontal distance from the rupture (D\({}_{\text{RupX}}\)); numerical coefficients are provided in Table 3. The median value of \(\Delta\)I = 0 is located within 1 km of the fault, while considering a \(\Delta\)I between 1 and 4, a marked attenuation trend can be observed in the interval between about 10 and 15 km. The trend then smooths out, similar to what is observed for the epicentral distance. In fact, the median value of \(\Delta\)I = 5 is located at almost 43 km.
# Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center
## 7 Experiment Result and Analysis
### Experiment Result  
For the experiment, the VMs were randomly allocated to the four compute nodes (naive scheme), the VMs wee allocated to the compute nodes according to the GP, and lastly the VMs were allocated to the compute nodes based on SP. Latencies between VMs in a same dependency group were measured in each placement case, while the VMs communicate to each other through the data network. Figure 13 shows a result of the experiment. As depicted on the figure, the average latency between the VMs in a same dependency group is considerably low in SP comparison to the other schemes. GP reduces the number of required compute nodes by placing the VMs on three compute nodes. However, SP also places the VMs on three compute nodes, and it improves the network performance of the data center by placing highly dependent VMs on a same compute node.
# A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
###### Abstract  
For networks that process 3D data, estimating the orientation and position of 3D objects is a challenging task. This is because the traditional networks are not robust to the rotation of the data, and their internal workings are largely opaque and uninterpretable. To solve this problem, a novel equivariant self-supervised vector network for point clouds is proposed. The network can learn the rotation direction information of the 3D target and estimate the rotational pose change of the target, and the interpretability of the equivariant network is studied using information theory. The utilization of vector neurons within the network lifts the scalar data to vector representations, enabling the network to learn the pose information inherent in the 3D target. The network can perform complex rotation-equivariant tasks after pre-training, and it shows impressive performance in complex tasks like category-level pose change estimation and rotation-equivariant reconstruction. We demonstrate through experiments that our network can accurately detect the orientation and pose change of point clouds and visualize the latent features. Moreover, it performs well in invariant tasks such as classification and category-level segmentation.  
Keywords:equivariant; self-supervised; point clouds; information theory +
Footnote †: journal: _Algorithms_