esa-sceva/satcom-chunk-collection huggingface dataset:
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 2 Related Work
### Feature Synthesis for Zero-Shot Learning  
Feature synthesis has become a dominant approach for zero-shot learning, with various generative models proposed to create visual features for unseen classes [25, 36, 37]. Early approaches primarily relied on Generative Adversarial Networks (GANs) [38] and Variational Autoencoders (VAEs) [39] for feature generation.  
[PERSON] et al. [36] proposed a multi-modal cycle-consistent GAN for generating visual features from semantic descriptions, while [PERSON] et al. [25] combined VAE and GAN architectures to improve feature generation quality. However, these methods often struggle with mode collapse and lack of diversity in the generated features, limiting their effectiveness for zero-shot detection tasks.  
Recent advances have focused on improving the quality and diversity of synthesized features. Diffusion models [40, 41] have emerged as a promising alternative to GANs and VAEs, offering more stable training and diverse generation capabilities. These models define a forward diffusion process that gradually adds noise to data and a reverse denoising process that learns to recover the original data distribution.  
In the zero-shot detection domain, [PERSON] et al. [12] introduced RFDM, which adapts diffusion models to synthesize region features for object detection. By leveraging knowledge-enhanced conditioning, RFDM can generate diverse and discriminative features for unseen object categories, demonstrating superior performance compared to GAN-based approaches.  
Our work adapts the RFDM framework to the food safety domain, introducing several innovations to address the unique challenges of synthesizing features for food safety hazards. We modify the conditioning mechanism to incorporate domain-specific knowledge from our Food Safety Knowledge Graph, enabling the model to generate features that can capture the subtle visual cues characteristic of different safety hazards. Additionally, we introduce a new training strategy that ensures the synthesized features are well-separated across different hazard categories while maintaining the intra-class diversity necessary for robust detection.
## 3 Background
### The CA Algorithm Used to Handle Dynamic Trustee Profiles
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **FIRE Model** & **CA Model** \\ \hline Initiation of Trust Evaluation & Trustor initiates trust and selects a trustee & Trustee decides whether to engage with a task \\ Sources of Trust Information & Interaction trust (IT), witness reputation (WR), role-based & Single source: trust learned via synaptic-style \\  & trust (RT), certified reputation (CR) & feedback from experience \\ Trust Update Mechanism & Weighted aggregation of direct and indirect trust evidence & Adjusts connection weights dynamically based \\  & Leverages indirect trust (WR, CR) when no prior interaction & Trustee initializes connection weight using \\  & exists (IT is unavailable) & prior experience or a default value \\  & Dependent on network structure. May suffer in large-scale & High: fully decentralized, with no inter-agent \\  & systems due to reliance on witness availability & trust exchange \\ Communication Overhead & Moderate to high: requires trust to query multiple & Very low: no inter-agent trust exchange; all \\  & witnesses for WR or providers for CR & Trust is stored and updated locally \\ Resilience to False Reports & Vulnerable to dishonest witnesses or biased CR & Resistant to recommendation attacks \\ Trust Storage & Distributed. Consumers store IT values locally, and providers & Fully local: trust is stored as neural-like \\  & store CR values locally & weights in each trustee/provider \\ Biological Inspiration & None & Yes—based on synaptic plasticity and neural \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between FIRE and CA trust models. or exceeds a predefined threshold. This promotes cautious behavior by ensuring that the agent only commits to tasks where sufficient trust exists in the cooperation. If task execution is successful, the trustee increases the connection weight; otherwise, it decreases it (lines 22-26). This CA algorithm also accounts for dynamic trustee profiles as follows (lines 27-31). If a connection's weight falls below the threshold, the trustee interprets this as an indication of its incapability to complete the task successfully and stops attempting it to save resources. However, if the trustee performs well on an easier task, it may infer that it has likely learned how to execute more complex tasks within the same category. In such cases, it increases the connection weight of those previously failed tasks to the threshold value, allowing itself an opportunity to attempt them again in the future. To improve the understanding of Algorithm 1, Table 2 summarizes the main symbols and their meanings. ```
1:while True do
# -- broadcast a request message when a new task is perceived --
```
2:when perceived a new task = (c, r)
3:broadcast message m = (request, i, task)
## 2 Experimental Section
### GlobAlbedo-Derived LAI and fAPAR
#### 2.1.5 Time Period and Data Size  
GlobAlbedo-derived fAPAR and LAI products have been generated for the years 2002-2011, globally, at each separate resolution. For 1 tile of either LAI and fAPAR (and associated QA information), for 1 year at 1 km the data are \(\sim\)400+ MB (with size varying substantially between tiles) and \(\sim\)145 GB for 1 year globally; the 5 km and 25 km resolution datasets are \(\sim\)4.3 GB and \(\sim\)200 MB globally per year respectively. Snow-fraction areas are flagged in the processing during generation of the 1 km GlobAlbedo data, and are flagged and processed as such in the 5 km and 25 km aggregated data (_i.e._, the aggregation of snow pixels is considered at those lower resolutions).
## 3 Results and Discussion
### Cooling Section  
As water vapor drastically reduces the HHV of the final biomethane, it was necessary to eliminate the water fraction contained in biogas exiting the scrubber. For this purpose, a biogas dehumidification system consisting of a heat exchanger, chiller, and droplet separator was used.  
Using a heat exchanger and chiller, the biogas temperature was lowered to 4 \({}^{\circ}\)C. Then, condensate was removed in the droplet separator. After this step, a percentage of water vapor below 0.05%vol was measured. The condensed liquid was composed  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Compound** & \multicolumn{3}{c}{**December-May**} & \multicolumn{3}{c}{**June-November**} & \multicolumn{3}{c}{**Annual**} \\ \cline{2-7}
**(mg/Nm3)** & **Min** & **Max** & **Mean** & **Min** & **Max** & **Mean** & **Mean** \\ \hline Total VOCs & 532 & 1246 & 1022 & 306 & 584 & 422 & 722 \\ limonene & 185 & 648 & 442 & 50 & 96 & 72 & 251 \\ \(\alpha\)-pinene & 101 & 213 & 159 & 17 & 62 & 33 & 96 \\ \(\beta\)-pinene & 117 & 215 & 137 & 34 & 77 & 40 & 89 \\ p-cymene & 84 & 241 & 162 & 14 & 74 & 28 & 95 \\ \(\gamma\)-terpinene & 0 & 1 & 0 & 0 & 6 & 2 & 1 \\ myrcene & 0 & 0 & 0 & 0 & 3 & 1 & \textless{}1 \\ \(\Delta\)-3-care & 0 & 0 & 0 & 0 & 5 & 2 & 1 \\ propane & 0 & 8 & 1 & 2 & 33 & 23 & 12 \\ butane & 0 & 11 & 2 & 1 & 35 & 24 & 12 \\ pentane & 0 & 1 & 0 & 0 & 1 & 0 & \textless{}1 \\ hexane & 0 & 6 & 1 & 6 & 24 & 15 & 8 \\ heptane & 0 & 6 & 2 & 7 & 26 & 20 & 11 \\
2-methyl-pentane & 0 & 0 & 0 & 0 & 2 & 1 & \textless{}1 \\ benzene & 2 & 32 & 21 & 24 & 110 & 38 & 34 \\ toluene & 0 & 21 & 16 & 22 & 31 & 24 & 20 \\ ethyl-benzene & 0 & 23 & 21 & 20 & 34 & 24 & 22 \\ xylene a  & 5 & 37 & 24 & 28 & 69 & 36 & 34 \\ mestiylene & 0 & 3 & 1 & 0 & 0 & 0 & \textless{}1 \\ cumene & 2 & 35 & 26 & 30 & 68 & 33 & 31 \\ acetone & 0 & 1 & 0 & 0 & 4 & 2 & 1 \\ methyl-ethyl-ketone & 0 & 2 & 1 & 0 & 6 & 2 & 2 \\ hexanal & 0 & 2 & 0 & 0 & 0 & 0 & 0 \\ heptanal & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ octamethylcyclotetrasiloxane & 0 & 1 & 0 & 0 & 1 & 0 & \textless{}1 \\ decamethylcyclopentasiloxane & 0 & 1 & 0 & 0 & 2 & 0 & \textless{}1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: VOC speciation (biogas exiting chemical scrubber).  
not only of an aqueous phase but also of an organic layer. In the aqueous fraction, an average concentration of NH\({}_{4}\)+, Cl-, 2-, and VOCs of 1.2 g/L, 660 mg/L, 135 mg/L, and 980 mg/L, respectively, was quantified. The organic layer was composed of terpenes (84%vol), hydrocarbons (12%vol), siloxanes (0.8%vol), water (0.2%vol), and unidentified compounds (3%vol). Consequently, in addition to water condensation, partial abatement of NH\({}_{3}\), HCl, H\({}_{2}\)S, siloxanes, and VOCs occurred at the biogas dehumidification stage.  
Leaving the droplet separator, biogas contained on average 2-4 mg/Nm\({}^{3}\) of NH\({}_{3}\), 0.0-0.1 mg/Nm\({}^{3}\) of HCl, 5-30 mg/Nm\({}^{3}\) of H\({}_{2}\)S, 0.0-0.1 mg/Nm\({}^{3}\) of siloxanes, and 100-300 mg/Nm\({}^{3}\) of VOCs.
# Low-Cost Sensor Based on SDR Platforms for TETRA Signals Monitoring
## 1 Introduction
_Real-time processing capabilities and instantaneous acquisition frequency bandwidth have been improved_ An extension has been added through which the sensor can be used either for the Uplink frequency range or for the Downlink frequency range. In this way, it is possible to monitor the emissions of both the terminal and the base stations. Several SDR platforms on the market have been tested. A platform has been chosen and integrated into the monitoring sensor that allows real-time acquisition of the full range of TETRA signals. 3. _COTS sensor and a small form-factor have been achieved_ In order to improve the portability of the sensor its size and weight have been reduced substantially. The design and implementation of the sensor took into account the choice of COTS hardware components. The SDR platform was controlled with a Raspberry Pi device. This led to the low price of the sensor. The main benefit of our sensor is that the data are collected simultaneously, in real time, for all TETRA channels, and the information, after processing, can be obtained in detail for each TETRA channel separately. As a general goal, we aimed to achieve a sensor capable of providing a complete real-time characterization of TETRA signals in all four dimensions: amplitude, time, frequency, space (location). Next, the work is organized as follows: in Section 2, the measurement sensor based on the SDR platform is presented in detail and the method of monitoring the electromagnetic spectrum specific to the TETRA standard is explained. Section 3 presents the results following the calibration of the system in laboratory conditions and testing in real environmental conditions. Section 4 formulates the general conclusions of the work.
# Residual Stress Relaxation of DRWDs in OSDs under Constant/Variable Amplitude Cyclic Loading
## 1 Introduction
Unfortunately, all of the existing literature investigates the uniaxial residual stress relaxation of simple welded joints under constant amplitude cyclic loading, but seems to lack information on the release behavior of multi-axial residual stress under variable cyclic loading at the heat-affected zone of complex DRWDs. In this paper, a FE analysis method is proposed to calculate the relaxation effect of the welding residual stress under the external cyclic loading of DRWDs in OSDs. First, a refined residual stress analysis model of DRWDs is established based on the element birth and death technology in the FE software ANSYS and the thermal-structure sequence coupling analysis method, and the initial distribution of transverse/longitudinal welding residual stresses are obtained. Then, residual stress tests of the full-size OSD specimen are carried out by using the hole drilling strain-gauge method, and the accuracy of the FE model is verified by the test results. Second, a stress relaxation analysis model, which considers the coupling effect of the welding residual stress and external loading stress, is established on the basis of the cyclic plastic constitutive model. The influence of the number of cycles, stress amplitude, and stress ratio on the multi-axial residual stress relaxation effect under constant/variable amplitude cyclic loading is analyzed. Last but not least, a release formula of welding residual stress relaxation coefficient is proposed, which takes into account the factors of the external loading stress amplitude, stress ratio, and material yield stress. The research results may provide a technical reference for the structural design and fatigue assessment of this type of OSD.
## 2 Research of SLAM Algorithm
### Submap  
The usual scan-to-scan laser-matching accumulates errors. The Cartographer algorithm introduces the concept of submap, which turns the original scan-to-scan matching into scan-to-map matching, i.e., the laser matches with the submap first.  
Several consecutive scans create a submap. When a submap is made, the raster probability is less than \(P_{\text{min}}\) for no obstacle at that point, between \(P_{\text{min}}\) and \(P_{\text{max}}\) for unknown, and greater than \(P_{\text{max}}\) for an obstacle at that point. Each frame of scans will generate a set of raster points called hits and a set of raster points called misses. Its submapis a raster map, and the submap can be considered a two-dimensional array whose value values under each index indicate the probability. To improve the performance and speed of operations, the Cartographer optimizes the probability representation.  
As shown in Figure 2, the grid points in each hit are given the initial value \(P_{\text{hit}}\), and the grid points in each miss are called \(P_{\text{miss}}\). If the grid point already has a \(p\)-value, the value of the grid point is updated with the following.  
The probability of a lattice probability \(p\) is defined as \(odds(p)\).  
\[odds(p)=\frac{p}{1-p} \tag{1}\]  
\[M_{\text{new}}\left(x\right)=\text{clamp}\Big{(}\text{odds}\ ^{-1}(\text{odds}(M_{ \text{old}}\left(x\right))\cdot\text{odds}(P_{\text{hit}}))\Big{)} \tag{2}\]  
Figure 1: Cartographer algorithm architecture.  
Figure 2: Grid map.  
where clamp is the interval-limited function, \(M(x)\) is the a priori lattice probability of the \(x\) under consideration. Each time, the latest scan obtained needs to be inserted into the optimal position in the submap so that when the bit pose of the point bundle in the scan is transformed and falls into the submap, each point has the highest confidence sum.
# Phase-Type Distributions of Animal Trajectories with Random Walks
## 5 Results
### Validation Results with RMSE  
From Section 5, it can be observed that the virtual traces generated by our simulation approximately match the real animal paths when they are inside or outside the sensor coverage. To quantify the accuracy of the virtual traces, the root mean square deviation (RMSD) method is used to calculate the error between the real and virtual trajectories. Specifically, the calculated \(P_{in}\) values for each animal are used, considering the real trajectory  
Figure 14: \(P_{in}\) and \(P_{out}\) of the real and simulated traces. (a) \(P_{in}\) and \(P_{out}\) for the Mangabey monkey, (b) \(P_{in}\) and \(P_{out}\) for the coelot, (c) \(P_{in}\) and \(P_{out}\) for the bat, (d) \(P_{in}\) and \(P_{out}\) for the duck.  
ries as the true probability values and the virtual trajectories as the predicted probability values obtained from our simulation.  
The RMSD values for the mangabey monkey, coelot, bat, and long-tailed duck were 0.2131, 0.3298, 0.2494, and 0.1613, respectively. These values represent the portion of the total time that each species spends inside the proposed imaginary circular region. The precision of the proposed model can be calculated as in Appendix B, which results in 78.69%, 67.02%, 75.06%, and 83.87% for each animal, respectively. These precision values indicate the accuracy of the model in approximating the movement behavior of the animals.  
In the study conducted by [PERSON] et al. [24], the validation of their movement model for four sheep was carried out using a unique approach. Their validation methodology involved utilizing the behavioral change point analysis (BCPA) segmentation technique and the Kolmogorov-Smirnov test. By analyzing different segments of the overall distribution, specifically, the persistence velocity distribution derived from random walk parameters, they assessed the precision of their predictions.  
To quantify the precision, [PERSON] et al. employed a comparison between observed and predicted movements. This involved calculating the percentage of intersecting minutes in various activities (forage, rest, walk) for both real and virtual movements. The resulting precision values were 87.6% for forage, 68.7% for rest, and 70% for walk. These values provide insights into the accuracy of their model predictions within specific behavioral contexts.  
Comparing these precision results to our proposed model, a notable observation emerges. The average precision for the overall movement of sheep in their study was 75%. This figure aligns well with the precision values achieved in our investigation across four distinct species. This alignment underscores the effectiveness of our proposed model in capturing movement patterns.  
It is noteworthy that achieving higher precision accuracy in the proposed model could be attainable through several strategies. Increasing the number of samples, enhancing GPS accuracy, maintaining controlled environmental conditions, and ensuring uniform step times between samples could collectively contribute to the refinement of precision measurements.
## 4 Simulation Scenario with Ultra-Dense Urban Environment
Otherwise, if it is time to establish a data session, the UE searches for the best nearby cell (i.e., with the highest RSRP value) and sends an attach request to establish a data session. Rejected attach requests for any reason (e.g., all resources are reserved) lead to updating the attach failure counter of the cell that rejects the request, and attempts are made with other suitable cells. UE with an established data session is visualized by circles colored according to the serving cell color. Each cell updates its data rate, resource utilization array, and load status. For users with established data sessions,  
Figure 5: Layout of HetNet simulator environments. several factors are considered to estimate signal strength such as line-of-sight (LoS), non-line-of-sight (NLoS), and log-normal shadowing. These factors influence the calculation of the RSRP and SINR values for each carrier signal. Moreover, the configured MM events are checked as follows: if a UE meets A1/or A2 conditions, it sends an A1/or A2 MR to the serving cell. Similarly, if the A3 event is configured, the UE measures and stores RSRP values for neighboring cells within a specific time frame (TTT). Upon reaching TTT, the average RSRP from stored RSRP values of each neighboring cell is checked with A3 conditions. If any cell meets the condition, it is included in the A3 MR with its average RSRP value. Upon reception of A1 or A2 MRs, the serving cell evaluates the handover necessity. Where A1 MR indicates that the UE has a good signal stat (\(RSRP_{s}+Hys>event_{Thr}\)), the cell proactively responds with a request to remove the A3 event, if present, to avoid unnecessary measurements. Conversely, for an A2 MR that indicates that the UE has a poor signal stat (\(RSRP_{s}+Hys<event_{Thr}\)), the A3 event configuration is triggered as planned to identify a better target cell during the handover evaluation. Upon reception of A3 MRs, a cell selects the best target cell (excluding blacklisted ones) and initiates the handover process by sending an HO request. An HO command is sent to the UE if the request is acknowledged. Rejected requests lead to updating the HO-rejected counter and blacklisting the cell after exceeding a retry limit. The target cell keeps track of HO-rejected requests per UE. Once the UE receives the HO command, the UE disconnects from the serving cell and establishes the connection to the target cell. The UE monitors the new connection in case any RLF or HOPP occurs. If RLF occurs directly after HO for any reason (e.g., sudden signal degradation), the target cell  
Figure 6: Network deployment scenario with cells and connected users. updates the RLF counter and the UE reestablishes to the old cell. Otherwise, the cell updates the RLF counter and the UE attempts to re-attach to the same cell. If re-establishment is impossible, then the attach failure counter of that cell is updated, and UE tries to attach to the second nearby cell with a good RSRP value.
# Programming Real-Time Sound in Python
## 1 Introduction
### On Real-Time Processing  
By definition, real-time processes produce an output within a given time. Concerning sound processing, this time is nominally inversely proportional to the audio sampling rate. Our test environment is an Intel-i5 laptop computer running Windows 10, connected to a RME Babyface Pro external USB audio interface. Contrarily to hardware/software systems specifically oriented to real-time audio [24], in such a standard architecture a sound process can be stopped by the operating system (OS) scheduler for too long or too many times within an allowed time window, hence becoming ultimately unable to regularly refill the audio output buffer at sample rate, with consequent sound glitches and distortions in the output. A common workaround to this problem, which is known as buffer underflow, consists of increasing the size of the audio buffer. Because a sound process normally produces samples much faster than the audio sample rate, this workaround decreases the probability for the audio interface to find the buffer empty.  
However, longer buffer size comes along with a proportionally higher latency of the output. Latency can be a negligible issue in feed-forward sound interactions; conversely, it can annihilate a closed-loop musical perception-and-action, where typically no more than 10 milliseconds are allowed for a computer music setup to respond to musicians. Thus, a buffer size must compromise between probability of audio artifacts and tolerable latency, and only the aforementioned systems can reliably fulfill the low-latency needs of live electronic music interactions.  
Irrespective of the buffer size, higher latency is generally beneficial for sound quality, since the OS scheduler in that case can stop the sound process occasionally for a longer while, e.g., to handle an external interrupt. However, the developer should always avoid to include unbounded time operations in a sound processing thread. Rather, input/output (I/O) instructions, graphic functions, and, in general, all procedures in charge of the interaction with the system, should be implemented by parallel threads sharing lock-free data structures with the sound processing thread. In this regard, practical general suggestions on real-time programming can be found in thematic discussions on the Internet [25]. At any rate, Python is not designed to support the servicing of audio threads within deterministic temporal constraints. For this reason, Python applications should not be programmed with the purpose to guarantee real-time sounds at low latency, regardless of the performances reported for our test environment in the following sections.