esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Antenna Single Element
### Manufactured Prototype  
The presented antenna was manufactured and measured. A picture of the fabricated prototype can be seen in Figure 3. The antenna construction requires a correct assembly in order to assure the electrical continuity between the ground plane and the walls. This construction model also allows an easy interconnection of several antennas by means of intermediate blocks in order to build the later array.  
The measured S-Parameters are depicted in Figure 4. Port 1 and 2 excite the horizontal and vertical polarization, respectively. The measurement shows an excellent agreement with the simulation. It can be seen that both polarizations achieve a return loss level below -10 dB within the bandwidth of 150 MHz. The center frequency of operation is slightly shifted (around 3%) towards lower frequencies, due to fabrication and construction tolerances. The port isolation \(S_{21}\) between both polarizations is greater than 30 dB at the center frequency of operation.  
\begin{table}
\begin{tabular}{c c c c} \hline Top patch length & 59.6 mm & Bottom patch length & 65.6 mm \\ Top patch width & 58.8 mm & Bottom patch length & 63.8 mm \\ End Line 1 length & 18.5 mm & End Line 2 length & 28.5 mm \\ Line 1 length & 25.95 mm & Line 2 length & 52.3 mm \\ Line 1 width & 1.85 mm & Line 2 width & 4.15 mm \\ Slot 1 length & 40 mm & Slot 2 length & 37 mm \\ Slot 1 width & 2.5 mm & Slot 2 width & 2.5 mm \\ Edge 1 length & 11 mm & Edge 2 length & 11 mm \\ Edge 1 width & 2.5 mm & Edge 2 width & 2.5 mm \\ Slot offset 1 & 17 mm & Slot offset 2 & 10 mm \\ \hline \end{tabular}
\end{table}
Table 2: Antenna geometrical values.  
Figure 4: Simulated and measured S-parameters.  
The radiation pattern of the antenna is measured at the Compact Test Range facilities of the German Aerospace Center [37]. The measured pattern meets the predicted results very well. A comparison between simulation and measurement for the two polarizations is shown in Figure 5. The measured antenna gain, 6.1 dB and 6 dB for the horizontal and vertical polarization, respectively, differs approximately 0.3 dB from the simulation. The simulated efficiency of the antenna is 90%.
## 3 Results and Discussion
### Microbiological Analyses

Table 2: Color differences (\(\Delta\)E) between groups and between storage time. characterized by a strong proteolytic activity, are frequently isolated from aerobically spoiled meat stored at refrigeration temperature [38]. Therefore, from a first analysis of microbial trends recorded in the current work, it is not possible to understand the actual changes in microbial ecology that occurred in the samples tested. Today, despite numerous studies, there is still much uncertainty on the evolution of species and bacterial strains in the presence of specific inhibiting factors used in packaging and, therefore, on the consequent development of altering phenomena [33]. [PERSON] et al. [39] tested the efficacy of an absorbent pad containing pinosylvin inclusion complexes for the control of chicken meat spoilage bacteria, obtaining a reduction in lactic acid bacteria, psychotrophic and total viable counts, but not in pseudomonads levels, after seven days of refrigerated storage. [PERSON] et al. [40] have investigated the effectiveness of the antimicrobial action exerted by of N-halamine, 1-chloro-2,2,5,5-tetramethyl-4-imidazolinone loaded absorbent pads in raw beef. The results showed a reduction in total aerobic bacteria, _Enterobacteriaceae, Pseudomonas_ spp. and lactic acid bacteria. Furthermore, the authors pointed out that to obtain a better result it would be useful to combine the treated pad with vacuum packaging. Similarly, [PERSON] et al. [15] evaluated the antibacterial ability of a silver-loaded absorbent pad (1%) during storage of beef meat in MAP. The pad was effective at lowering microbial contamination of exuded fluids by an average of 1 log CFU/g for total aerobic bacteria, _Pseudomonas_ spp., _Enterobacteriaceae_ and LAB, even though the latter were less sensitive. In general, new packaging technologies are able to: (i) improve food safety and quality, (ii) increase the attractiveness of the packaged product to retailers and consumers [41] and at the same time have the potential to (iii) reduce the environmental impact and food waste. However, only some of these active packaging technologies are applicable for fresh meat and meat products [19; 31; 42; 43; 44; 45]. In particular, the addition of active compounds in the packaging can modify the sensory characteristics of the meat [41] compared to packaging that uses a mechanical action, such as the one tested in this study. On the other hand, the antibacterial effectiveness at different levels of contamination needs to be further investigated and may represent a limit for the practical application of this technology.
# Architecting an Agent-Based Fault Diagnosis Engine for IEC 61499 Industrial Cyber-Physical Systems
## 4 Evaluating the Architecture of the Engine
### Iso 4.2.5 Reliability  
Reliability is also addressed by running the engine on its own hardware. QAS 4 requires the engine to operate reliably even when the FB application is failing or has failed. The sub-category of Recoverability is addressed by the DP ability to re-connect automatically if they lose their connection to the engine. Data is exchanged asynchronously between the agent and the DPs it is capturing data from or sending test data to. A First-In, First-Out (FIFO) queue structure is used by the engine. A non-blocking socket TCP socket protocol was implemented rather than use UDP. Transmission Control Protocol (TCP) [90] is a connection-oriented protocol that provides guaranteed packet delivery and error correction. In contrast, the User Datagram Protocol (UDP) [91] is a raw, connectionless protocol that provides no error checking or guarantee of delivery. The quality attribute trade-offs of reliability and performance were considered between the extra overhead of TCP versus the simplicity of packet management that guaranteed, reliable delivery provided. TCP does not require additional packet buffering for the FIFO queue manager since only complete packets are received. This also simplified the client C++ code for the DP function blocks. Further scenario testing is recommended with a large number of DPs to evaluate the latency of high-traffic environments.
# Use of Binary Classification in Non-Invasive Load Monitoring
## 2 NILM--Key Elements  
The NILM concept, developed by [PERSON] [1], assumes that the measurement takes place at only one point, as shown in Figure 1a. Thus, the measured signals result from the energy consumption of multiple devices. On the basis of the values obtained in this way, the devices operating in the energy system are identified. From a formal point of view, the task of the NILM system is to identify energy consumers by disaggregating the measured quantities to improve energy efficiency or cost-effectiveness. This is a highly underdetermined task, which means that it cannot be solved by analytical methods [2].
## 1 Introduction
[26] assessed the forecast Vienna Mapping Function 1 for real-time tropospheric delay modeling in GNSS, and concluded that the model performs of the VMF1-FC are better than GPT2 and GPT2w. [PERSON] et al. [27] conducted a comprehensively accuracy analysis on nine ZTD models (UNB3m, EGNOS, Saastamoinen+GPT2/GPT2w/GPT3, Hopfield+GPT2/GPT2w/GPT3, and IGGtropSH models) at 65 Antarctic stations. The results showed that the Saastamoinen+GPT2/GPT2w/GPT3 models are better than the other six models. It is unquestioned that newer, more elegant models generally have higher accuracy than those older and more complicated one. For example, the VMF models, with centimeter discrepancies to IGS ZTD products, have already been recognized as a standard for precise positioning users [26]. Although this kind of grid-wise model can be easily applied at anytime and anywhere via interpolation, requisition for frequent updates still restricts its application for real-time positioning, in which empirical models, such as the Saastamoinen and UNB series models, are still preferred due to higher operational efficiency. In these cases, the uncorrected residual zenith tropospheric delay would still sway the GNSS SPP solution remarkably. However, how much the uncorrected residual tropospheric delay would sway the GNSS positioning solution on a global scale are still unclear. In this paper, with the purpose of evaluating the biases caused by the uncorrected residual tropospheric delay on SPP solutions globally, nine conventionally used ZTD models are analyzed and discussed, including Saastamoinen+norm/GPT/GPT2/GPT2w/GPT3, UNB3, UNB3m, EGNOS, and VMF3 model. Firstly, the residual ZTDs were obtained by comparing the model estimation values with the corresponding IGS ZTD products at 400 IGS monitoring stations, to evaluate the accuracies of the ZTD models. The spatial and temporal variationsof the residual ZTDs by different models are here discussed. Then the biases on SPP solutions introduced by the residual ZTDs were computed, and the temporal and spatial characteristics were analyzed. This paper is organized as follows. The calculation procedure for the influence of residual tropospheric delay on SPP are described in Section 2. Then, the materials, methodology and the data processing strategy used for the assessment are introduced in Section 3. Next, the results of the residual ZTDs obtained from different models and their impacts on SPP solution are presented in Section 4. The results are briefly discussed in Section 5 and the conclusions are summarized in Section 6.
## 2 Model of the Railway Vehicle
### The 'Flexible Carbody' Model  
In the case of the 'flexible carbody' model, the vehicle carbody is represented by a free-free equivalent beam, with a constant section and uniformly distributed mass, Euler-Bernoulli type. This model allows for the consideration of the vertical bending modes of the carbody. The beam parameters are: \(\rho_{c}=m_{c}/L\)--beam mass per length unit; \(\mu\)--coefficient of structural damping; and \(EI\)--bending modulus, where \(E\) is longitudinal elastic modulus and \(I\) is the moment of inertia in the beam cross-section. The carbody vibration modes are bounce \(z_{c}\), pitch \(\theta_{c}\) and the first vertical bending mode. The carbody displacement \(w_{c}(x,t)\) comes from the overlap of these three vibration modes of the carbody,  
\[w_{c}(x,t)\ =\ z_{c}(t)+\bigg{(}x-\frac{L_{c}}{2}\bigg{)}\theta_{c}(t)+X_{c}(x)T _{c}(t), \tag{7}\]  
where \(T_{c}(t)\) is the coordinate of the carbody vertical bending and \(X_{c}(x)\) its eigenfunction  
\[X_{c}(x)\ =\ \sin\beta x+\sinh\beta x-\frac{\sin\beta L-\sinh\beta L}{\cos \beta L-\cosh\beta L}(\cos\beta x+\cosh\beta x), \tag{8}\]  
with  
\[\beta\ =\sqrt[4]{\omega_{c}^{2}\rho_{c}/(EI)}\ \text{and}\ \cos\beta L_{c}\cosh\beta L_{c}-1\ =\ 0, \tag{9}\]  
where \(\omega_{c}\) is the natural angular frequency of the carbody vertical bending. The vertical motions of the vehicle are described by the equations of the rigid vibration modes of the carbody and bogies--bounce and pitch--as well as by the equation of the carbody vertical bending. The carbody motion equation has the general form  
\[EI\frac{\partial^{4}w_{c}(x,t)}{\partial x^{4}}+\mu I\frac{\partial^{5}w_{c}(x, t)}{\partial x^{4}\partial t}+\rho_{c}\frac{\partial^{2}w_{c}(x,t)}{\partial t^{2}}\ =\ \sum_{i=1}^{2}F_{zci}\delta(x-l_{i})+\sum_{i=1}^{2} \left(M_{ci}-h_{c}F_{xci}\right)\frac{\mathrm{d}\delta(x-l_{i})}{\mathrm{d}x}\, \tag{10}\]where \(\delta(. )\) is Dirac's delta function, distances \(l_{i}\) define the position of the carbody support points on the secondary suspension and \(F_{zci}\), \(F_{xci}\) and \(M_{ci}\) are the forces and the moments, respectively, due to the secondary suspension of the bogie \(i\)  
\[F_{zci}\ =\ -2c_{zc}\bigg{(}\frac{\partial w_{c}(l_{i},t)}{\partial t}-\dot{z}_{ bi}\bigg{)}-2k_{zc}[w_{c}(l_{i},t)-z_{bi}] \tag{11}\]  
\[F_{xci}\ =\ 2c_{xc}\bigg{(}h_{c}\frac{\partial^{2}w_{c}(l_{i},t)}{\partial x \partial t}+h_{b}\dot{\phi}_{bi}\bigg{)}+2k_{xc}\bigg{(}h_{c}\frac{\partial w_{ c}(l_{i},t)}{\partial x}+h_{b}\phi_{bi}\bigg{)} \tag{12}\]  
\[M_{ci}\ =\ -2c_{\theta c}\bigg{(}\frac{\partial^{2}w_{c}(l_{i},t)}{\partial x \partial t}-\dot{\phi}_{bi}\bigg{)}-2k_{\theta c}\bigg{[}\frac{\partial w_{c}( l_{i},t)}{\partial x}-\theta_{bi}\bigg{]} \tag{13}\]  
With the help of the modal analysis method and considering the orthogonality property of the eigenfunction of the carbody vertical bending, the motion Equation (8) is transformed into three second order differential equations with ordinary derivatives, describing the three vibration modes of the carbody--bounce, pitch and the vertical bending.
# A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O\({}^{2}\) Facilities
## 3 Methodology
### Proposed Framework
#### 3.1.3 Anomaly Classification  
Anomaly classification is a crucial component of our proposed framework. Since the topic model operates in an unsupervised manner, it generates a topic distribution that represents the probability of a given log message belonging to all possible topics. Based on this distribution, the framework assigns the log message to the topic with the highest probability. In each topic, n-grams of words are extracted from log messages related to that topic, facilitating downstream applications such as text analysis and the study of log message behavior. However, when applied to anomaly classification, this approach does not inherently produce a binary decision indicating whether a log message is anomalous. To address this, we integrate topic representations with rule-based criteria for anomaly assessment. Specifically, log severity levels (e.g., INFO, WARNING, ERROR, and CRITICAL), which are already included within system-generated log messages, are leveraged to distinguish errors and prioritize critical issues.  
The classification process produces the topic prediction. Log messages that do not align with the identified normal topics and are assigned as a high-severity level (e.g., ERROR and CRITICAL) are flagged as anomalies. This approach ensures that anomalies are not only identified based on their deviation from normal patterns but are also filtered by their potential impact or urgency, as indicated by their severity. Each flagged anomaly is accompanied by its corresponding log message content, providing context for further analysis and rapid response. This combination of topic modeling and log message severity enhances the reliability and interpretability of the anomaly classification.
## 2 Background and Motivation
### Optane Architecture and Instructions Support  
To guarantee data persistence, the integrated memory controller (iMC) operates within the asynchronous DRAM refresh (ADR) domain. Intel's ADR feature ensures that CPUstores reaching the ADR domain will remain intact even in the event of a power failure [20]. In this system, the iMC maintains separate read and write pending queues (RPQs and WPQs) for each Optane DIMM, as depicted in Figure 1a. Once data reach the WPQs, the ADR mechanism ensures their survival during power loss. The actual access to the storage media takes place after address translation. Notably, the Optane controller translates smaller requests into 256-byte accesses of the Optane block, leading to write amplification. As the NUMA configuration is used in this work, address space interleaving (Figure 1b), the effect of the iMC contention will be reduced when the size of the random access is the same or close to the address space interleaving size of 4 KB where the stride size becomes 24 KB for six PM inserted into all DIMMs. Applications and file systems modify the contents of Optane DIMMs by utilizing store instructions, thereby ensuring data persistence. Intel's instruction set architecture (ISA) offers specific instructions for flushing cache lines back to memory, such as _clflush_ and _clflushbort_. The _clwb_ instruction is utilized to write back the cache lines. Additionally, applications employ the _ntstore_ instruction to write directly to memory without involving the cache. To ensure that the data is persistently flushed, applications need to issue the _sfence_ instruction, which guarantees the flushing of data to persistent memory.  
Developing significantly faster data structures for persistent memory necessitates programming changes that can introduce complexity and increase the likelihood of errors [21]. Nevertheless, it remains crucial to consistently store data in the order of the store operation. Standard approaches are typically employed to ensure this consistent storage of data. Consequently, the design of a write-optimized and scalable hash table specifically tailored for persistent memory incorporates these features to achieve the desired performance improvements.
## 5 Discussion
This is mainly due to the fact that landslides with large morphology variables also have more drastic phase changes in the interferograms, so the phase gradients are relatively large. This is why the A7/D7 Xieliupo landslides have the highest values of stacked phase gradients. This method quickly extracts the landslides from the differential interferograms, providing a reference for quickly locating the landslide with deformation and improving the efficiency of large-scale landslide surveys. Since the method is based on wrapped differential interferograms, phase unwrapping is not required during data processing. The elapsed time of the multi-direction phase gradient stacking method and the SBAS method are listed in Table 4. There are a total of 109 interferograms in the ascending data and 107 interferograms in the descending data. The size of the study area is 1507 \(\times\) 1045 pixels, about 927.19 km\({}^{2}\), and the geometric distortion area is removed. The effective gradient points of the ascending and descending tracks are about 1,325,600 and 1,388,900, respectively. Approximately 297,400 effective deformation points were calculated for the ascending track and 429,500 effective deformation points for the descending track. As shown in Table 4, the time applied by the multi-direction phase gradient stacking method in the two data sets is much lower than the time of the SBAS, which is consistent with our expectations and can save a lot of time on the basis of obtaining effective landslide results, thereby improving landslide identification efficiency. Figure 8: Nanyu and Xieliupo landslides (region 2 in Figures 5 and 6). (**a**) Multi-direction phase gradient stacking results of the ascending track; (**b**) Multi-direction phase gradient stacking results of the descending track; (**c**) SBAS deformation rate of the ascending track; (**d**) SBAS deformation rate of the descending track.
## 8 Results and Discussion
### Scenario 2 (20% DR)
For example, at 1:00 a.m., the batteries discharge 280.47 W, increasing to 527.70 W by 4:00 a.m. In the afternoon peak, battery discharge reaches a maximum of 903.24 W at 8:00 p.m. FCs contribute intermittently throughout the day, providing significant support during peak periods. For instance, FCs provide 74.95 W at 1:00 a.m., increase to 237.58 W at 3:00 a.m., and reach up to 200.81 W at 5:00 a.m. During the evening peak, FCs provide 228.66 W at 10:00 p.m. EVs also aid in energy supply, though their impact is less pronounced compared with batteries and FCs. EV contributions include both charging and discharging cycles, with notable discharges of 302.74 W at 1:00 a.m., and \(-\)130 W (charging) at various times like 2:00 a.m., 3:00 a.m., and 4:00 a.m. By the end of the study period, the total cumulative operating expense reaches $753.56. Throughout the day, the LPSP remains well within the accepted range of 0 to 0.05, with the highest value being 0.00081 at 6:00 p.m. as shown in Figure 13.  
In the 20% DR scenario, the residential home adjusts its energy usage patterns to reduce peak load demand, leading to significant energy cost savings and optimized use of backup systems. The implementation of the DR program results in a reduction in peak load demand by 20%, allowing for more efficient use of PV and wind generation, as well as backup systems such as batteries and FCs. The PV and wind generation provide substantial contributions during daylight hours, while the fluctuations in load demand and generation highlights the importance of having a robust energy management system in place. The cumulative operating cost for the day is $753.56, which is lower than in the \"No DR\" scenario, demonstrating the cost-saving potential of DR programs. Figure 13: The LPSP values in Scenario 2.
## 3 Results and Discussions
### Displacement of Underground Granary
In conclusion, because the soils around the granary are dense soils with high strength, its ability to resist unstable failure is strong, which is beneficial to the stability of the granary. Figure 6: The displacement–time curves of target points: (**a**) horizontal direction for loose sands; (**b**) vertical direction for loose sands; (**c**) horizontal direction for medium dense sands; (**d**) vertical direction for medium dense sands; (**e**) horizontal direction for dense sands; (**f**) vertical direction for dense sands. In summary, in the stability stage, the larger the degree of compaction of the soil around the granary is, the longer the time for the granary and the soil to coordinate and interact with each other to resist water buoyancy is. Water buoyancy at the bottom of the granary is less than or equal to the sum of the weight of the silo and the frictional resistance of the surrounding soil to the granary wall. In the floating stage, the floating water levels are 32.5 cm, 34 cm, and 36 cm for the loose sands, medium dense sands, and dense sands around the granary, respectively, which is increased by 10.77%. The reason is that with the increase of the degree of compaction, the mutual friction between soil particles and the friction resistance on the wall of the granary increase, the anti-floating ability of the granary increases, and the vertical displacement of the granary decreases under water buoyancy. In the inclined stage, with the increase of relative compactness of soil around the granary, the horizontal displacement of the granary decreases. The reason is that the embedding between soil particles is more compact, and the self-stability of the soil is improved. The  
Figure 7: Floating water level for different working conditions: (**a**) in loose sands; (**b**) in medium dense sands; (**c**) in dense sands. soil particles per unit volume can resist larger horizontal displacement of the granary. In the unstable failure stage, with the increase of soil compactness around the granary, the time course of the granary from floating to critical failure increases. When the soils around the silo are medium dense sands, compared with loose sands, the vertical displacement of the granary at the critical point of unstable failure decreases by 17%. When the soils around the granary are dense sands, the granary does not experience instability failure. This is because the strength of the dense soil is high and its ability to resist instability failure is improved, which is conducive to the stability of the granary.