esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Test Stimulus Compression Methods for Ultra-Large-Scale Integrated Circuits
## 2 Overview of Test Stimulus Compression Techniques
### Scan Chain Structure Optimization-Based
#### 2.2.2 Broadcast Scan  
When multiple sub-modules within the CUT have test vectors with interdependencies, test stimulus compression can be achieved through the optimization of a broadcast scan architecture [19]. This method involves broadcasting the same test data to multiple scan chains, effectively using a single test channel to drive multiple scan chains. The broadcast scan architecture includes fan-out circuits, which are typically implemented in hardware using a shared scan input structure [28]. This approach allows the same set of test vectors  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Article** & **Year** & **No. of Ref.** & **Compressed Objects** & **Researches** \\  & & & & **Overview** \\ \hline
[18] & 2006 & 35 &
\begin{tabular}{c} Code-based, Linear-Decompression-based, \\ and Broadcast-Scan-based \\ \end{tabular} & Yes \\ \hline
[19] & 2006 & 49 &
\begin{tabular}{c} Code-based, Linear-Decompression-based, \\ and Broadcast-Scan-based \\ \end{tabular} & Yes \\ \hline
[20] & 2009 & 24 & Code-based & Yes \\ \hline
[21] & 2010 & 6 &
\begin{tabular}{c} Code-based, Linear-Decompression-based, \\ and Broadcast-Scan-based \\ \end{tabular} & No \\ \hline
[10] & 2013 & 38 &
\begin{tabular}{c} LFSR Based, BIST Based, \\ Low Power Scan Based, \\ and Low-Power DFT \\ \end{tabular} & Yes \\ \hline
[22] & 2020 & 29 & \
\begin{tabular}{c} Code-based, Linear-Decompression-based, \\ and Broadcast-Scan-based \\ \end{tabular} & Yes \\ \hline This paper & - & 102 &
\begin{tabular}{c} Code-based, \\ Scan Chain Structure Optimization-Based, \\ and Enhancing Encoding Compression Efficiency \\ \end{tabular} & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of previous reviews and this paper.  
to test different sub-circuits within the CUT, significantly enhancing test efficiency and achieving test vector compression [29]. However, due to the varying fault models of different sub-circuits, the fault coverage of this technique is limited, necessitating the input of additional test vectors in a serial scan mode to ensure comprehensive coverage [30].
## 3 Experimental Design
### Instruments  
To evaluate the achievement of learning outcomes, the gamified tool had an extensive database of isomorphic questions, with around 100 problems for each expected learning outcome. It is important to note that the experimental and control groups responded to the same pre- and post-test questions designed to assess the same learning outcomes. Although specific questions for each test were randomly selected from a larger pool, all questions covered the same learning objectives and had an equivalent difficulty level.  
Figure 1: The process used in the experiment.  
The quiz questions were randomly selected from a pre-developed question bank. This bank contained equivalent questions in terms of content and difficulty, all aligned with the defined learning outcomes. However, within each group (control and experimental), the questions were identical, ensuring that differences in results were not due to variations in the tests.  
Additionally, the problems each student had to solve during the activities were randomly drawn from a predefined set, ensuring that all activities maintained the same consistency regarding challenge and content. This allowed for measuring the intervention's impact without introducing biases about the difficulty or type of problems presented.  
Furthermore, to explain the determinants of users' technological acceptance, five questions based on the Technology Acceptance Model (TAM) [104] were applied to provide a vision of the degree to which students believed that using the learning tool would improve their performance and productivity in addition to determining whether it would require little or no effort. This model has been used and psychometrically validated in existing studies. These factors directly influence the intention of use, which determines the effective use of the technological system. This model has been widely used in information systems and interactive technologies research. For example, in [105], TAM is used to determine the relationship between gamification features and the satisfaction of intrinsic needs, reaching results concerning gamification's ability to satisfy needs of competence, autonomy, and relationship. In the case of [106], researchers used TAM to study how social influence affects the intention to use a gamified exercise application; the results showed that social influence positively affects perceived usefulness and intention of use. The authors of [107] studied how different game design elements (dynamics) impact user engagement in gamified systems; in addition, they used an adaptation of the TAM model, determining as a result that elements such as points, levels, and leaderboards were positively correlated with the predictors and therefore increased the intention of use. The questions associated with TAM that were used in our study had a binary response (yes/no) and were:  
1. Did you like playing a game to practice programming?
2. Did you find the tool useful to learn programming?
3. Could you use the tool correctly?
4. Did you feel satisfied with your results with the tool?
5. Did you feel comfortable with the tool?
## 4 Hardware Description
### Gateway Node Design  
The diagram of the gateway node can be seen in Figure 5.  
The gateway node is also based on WiPy 3.0. In addition to sensors managing, the gateway node has extended functions such as:  
Figure 4: Sensor node 3D view of the PCB.  
Figure 5: Gateway node architecture diagram.  
* A MicroSD slot card for data storage. Three pins are used for SD managing. DAT0, SCLK and CMD are driven by P8, P4 and P20 using a UART1.
* A GPRS communication module (SIM800, SimComm, Shanghai, China) powered directly from the battery and controlled by AT commands through the UART of the Pycom module. The WiPy module has three UARTs, which can be easily redirected to several pins for a flexible design. The Tx and Rx signals of the SIM800 are connected to P5 and P6 pins and the reset line is fed through P7 of the WiPy. UART1 is dynamically assigned to drive the AT commands because the UART1 is shared with SD storing.
* A CO\({}_{2}\) sensor (COZIR-GC16 CO2 Meter, Ormond Beach, FL, USA) for commodities' breathe monitoring. This sensor has a 0-20000 ppm range with an accuracy of 70 ppm \(\pm\)5%. The sensor is serial controlled through UART2 directed to P20 (Tx) and P19 (Rx) pins of WiPy module. LDR light sensor is connected to P18 pin in the gateway node.
* A DC/DC boost switching regulator (MIC2288, Microchip Technology Inc. Chandler, Arizona, USA) that provides 12 V power supply to additional sensors.
* Two analogic inputs are either voltage (0-3 V) or current (4-20 mA) for additional sensor measurements, P14 and P15.  
The power supply is provided by the WiPy module (3.3 V) to the microSD and the CO\({}_{2}\) sensor. It is switched using a transistor for optimum energy saving as explained in Section 3.  
The size of the gateway node board is 82 \(\times\) 40 mm. The 3D view of the PCB is shown in Figure 6.  
The GPRS SIM800 module is placed on the board using the JP01 and JP02 plugs. A push-button S1 is installed to run the setup sequence.
## 3 Methodology
### Distance Estimation Using Position Information  
A pinhole camera's geometry can be used to calculate object distances because a monocular lacks the ability to detect depth information. A distance estimate is often done in one of two ways: based on positional information or size information. Referring to the findings of the research by [PERSON] et al. [36], if an object is on the same plane as the camera, the position of the object in pixel coordinates can be converted to the real-world coordinates. The distance between the object and the camera can be estimated by a single image from a monocular camera, of which the FOV is known. FOV is the maximum area of a sample that a camera can image. It is related to the focal length of the lens and the  
Figure 1: The overall architecture of NFD. The NFD modeling procedure calibrates the device camera to obtain the ground truth of the object distance and estimate the FOV on the bounding boxes image after YOLOv4 detection.  
sensor size [37]. The implementation of the FOV model can be divided into two scenarios: (1) horizontal camera axis, and (2) arbitrary pitch angle.
## 1 Introduction
### Contributions  
In order to provide efficient services in a resource-limited vehicular network, we employ stochastic geometry to analyze the interference in the platooning of vehicles. We then present a resource allocation optimization method that reduces the re-allocation rate and increases the number of guaranteed services. The key contributions of this study are the following:  
1. The co-layer and cross-layer interferences in the vehicular network are quantitatively analyzed using stochastic geometry.
2. To guarantee the services in the dynamic network, the definition of the guaranteed service is given based on the changes in the data queue.
3. To increase the number of guaranteed services at a low cost in a resource-limited vehicular network, we propose a time dynamic optimization method that constrains the network re-allocation rate.
4. To decrease the computational complexity, we convert the proposed time dynamic optimization problem into a deterministic optimization problem using the Lyapunov optimization theory to determine the set of served services based on the dynamic changes in the network at each slot.
## 3 Results

Table 3: A priori models ranked by Bayesian information criterion (BIC) from acoustic data collected off the Eastern Shore of Virginia, USA, 2012–2019. Displayed are the model names and numbers as referenced in Table 1, approximate degrees of freedom, \(-log\) (Likelihood), BIC, and ABIC. Our final model was highly predictive. It contained a mean MCCV AUC value of 0.852-95% CI (0.828, 0.877). The optimal cutoff for predicting occurrence or non-occurrence was 0.393, which we used as a threshold to predict on withheld data. The model appeared to correctly predict occurrences as indicated by the confusion matrix (Figure 6). Therein, the number of false positives and false negatives were generally low (117 and 65 out of 730 data points). Sensitivity (true positive rate) and specificity (true negative rate) values were 0.826 and 0.671, respectively. Figure 5: Visualizations of all smooth effect components, \(f(x)\), of the final model which was fit using acoustic data off the Eastern Shore of Virginia, USA, 2012–2019. The fits (solid lines) and 95% confidence intervals for the fits (black dotted lines) are displayed. Smooths, \(f(x)\), can be interpreted as the effect of the variable, \(x\). Nightly occurrence probability is positively associated with larger smooth values along the range of variable. These smooth components, \(f(x)\), are on the logit scale, for example, the dotted line at 0 on the logit scale indicates 50% probability of occupancy. The final model used smooth effects of nightly means of temperature (**a**), wind speed (**b**) visibility (**c**), and the ordinal date (**c**). The shape of the smooth effect was separated by roost availability, by limited (**d**), or viable (**e**). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\beta\)**Parameter** & **Estimate** & **Standard Error** & **Z-Score** & \(p\)**-Value** \\ \hline \(\beta_{0}\) (Intercept, Cedar Island) & \(-1.170\) & 0.086 & \(-13.69\) & \(<\)0.05 \\ \(\beta_{1}\) (Assateague Island) & 3.133 & 0.209 & 14.96 & \(<\)0.05 \\ \(\beta_{2}\) (Hog Island) & \(-0.444\) & 0.101 & \(-4.38\) & \(<\)0.05 \\ \(\beta_{3}\) (Silver Beach) & 0.311 & 0.113 & 2.74 & \(<\)0.05 \\ \(\beta_{4}\) (Smith Island) & \(-0.335\) & 0.104 & \(-3.21\) & \(<\)0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Beta parameters names, estimates, standard errors, and \(p\)-values for site-specific intercept modifiers in the final model fit using acoustic data off the Eastern Shore of Virginia, USA, 2012–2019. Estimates of \(\beta_{1}\)–\(\beta_{4}\) should be added to the intercept (\(\beta_{0}\)) to interpret the intercept modifying effect of the specific locality correctly.
# Discrete Environment-Driven GPU-Based Ray Launching: Validation and Applications
## 2 The DED-RL Algorithm
### Environment-Driven RL  
Efficiency has been improved with respect to conventional RL algorithms, by launching rays only toward actual objects (tiles) present in the environment, instead of using a constant angular discretization: this feature is called \"environment driven RL\" and is particularly useful for outdoor application where obstacles are sparse or there are large open sky sectors where launching many rays would be useless or even detrimental to computational efficiency [21].
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
## 4 Results  
In this section, we first compare the proposed method with the methods in the literature to show its benefits. Next, we summarize the principal features of the proposed design. Finally, we present possible practical applications and introduce potential future work.
## 2 Solution Method
### Numerical Method
#### 2.2.2 Boundary Conditions  
Proper boundary conditions play an important role in obtaining good simulation results. Figure 2 shows the domain and boundary condition settings for the simulations. The flow field is divided into a rotation domain and a static domain. The cylinder with a diameter and height of 3 m is used to simulate the MAS, a smaller cylinder that encloses the rotor is used as the rotational domain, and the intersection interface is used at the intersection of the two cylinders. The rotor is placed at the center of the entire computational domain and the bench is set in the static domain. The rotation domain encloses the rotor. The static domain encloses the bench, which is 1 m, 0.3 m, and 0.03 m in length, width, and height, respectively. The rotor has the same dimensions as the experiment, which is 500 mm, 40 mm, and 1 mm in diameter, chord length, and thickness, respectively. The rotor is 0.2 m above the bench.  
To simplify the calculation, the fixture, sleeve, and motor that has little influence on the aerodynamic performance is removed in the model.  
There are three cases investigated in this study to verify the method, including an investigation of the influence of the bench and the chamber wall, respectively. Boundary conditions are varied according to the cases as follows.  
Case 1: to verify the method by comparing with experiment results, all settings are made the same as experiment conditions. The chamber wall and the bench are set as wall condition. The interface is set as freestream condition, with flow speed set to 0 Mach and temperature set to 288 k.  
Case 2: to determine the influence of the test bench on aerodynamic performance, the bench is removed from the static domain. All the other settings are the same as case 1.  
Case 3: to determine the influence of the chamber wall and temperature, the chamber wall is set as freestream, whose flow speed is also set to 0 Mach, but the temperature is set to 210 k, which is the same as the average temperature of Martian air.  
It is worth highlighting that the interface and chamber wall in case 3 are set to freestream rather than velocity inlet because this is conducive to the compressible flow simulation, according starccm++ help files.  
Table 1 provides the parameters used in the simulation, since the established method is verified based on MAS data; therefore, the air temperature was set to 288 k, which is close to room temperature on earth.
## 4 Discussion
### Induction of 4 MSOB Accumulation in Young Leaves by Se through the GSLs Transporters  
Within-leaf distribution of defense compounds, such as GSLs and 4 MSOB, is plants' strategy to defend against insect herbivory, whose accumulation in the tissue resulted from in-situ synthesis and long-distance transportation [57]. The study showed that exogenous Se treatment significantly enhances the Met-derived GSLs contents in fresh leaves of broccoli, especially the 4 MSOB concentration, but has no remarkable changes in senescent leaves. The results corresponded to the previously described study, broccoli with Se addition via foliar spraying remarkably enhanced the nitriles and ITCs concentrations in the florets and leaves, but slightly reduced those in broccoli roots [58]. Results revealed that the increase of the 4 MSOB content in young broccoli leaves after Se treatment was largely attributed to the up-regulated expressions of the GSLs biosynthesis genes, such as _BCAT4_, _MAM1_, and _CYP79F1_ genes. However, the _MYB28_ gene expression in fresh leaves was suppressed by Se treatment, probably because the response of _MYB28_ gene was time-dependent and earlier than that of the structural genes [59]. Se-induced upregulation of the structural genes involved in the GSLs biosynthesis could make up for the function [47]. In addition, the effects of exogenous Se treatment on the GSLs and 4 MSOB accumulation could have tissue-specificity, and the 4 MSOB accumulation may be preferentially distributed to fresh leaves of broccoli. Previous studies indicated that the GSLs transporters _NPF2.10_/_GTR1_ and _NPF2.11_/_GTR2_ play an important role in the transport and allocation of GSLs among the different plant tissues [60,61]. In _Arabidopsis thaliana_, _GTR1_ and _GTR2_ genes are responsible for the long-distance movement of GSLs compounds, especially Met-derived GSLs between the rosettes and roots [62,63]. Likewise, the functions of GTR proteins in regulating the GSLs transport and accumulation in different tissues of _Brassica_ crops were also demonstrated [64,65]. However, the regulatory mechanism of Se in the GSLs and 4 MSOB translocation has not been well elucidated. The study showed that Se treatment promotes the transport of Met-derived GSLs from senescent leaves and roots to young broccoli leaves, yet more studies are needed to confirm this hypothesis.
## 4 Simulations
### Experiment 1 - Maximum Wormhole Effect  
In Exp. 1, we verify the avoidance rate of the maximum wormhole effect. That is, the wormhole is placed in the middle of the communication pair and other normal nodes are placed at random. Figure 7(a) shows the avoidance rate with different legal boundaries (\(\alpha\), \(\beta\)).  
We apply three kinds of boundaries in this experiment. With loose boundaries of (0.25, 0.75), only the routes with hop-counts in the middle 1/2 distribution are selected as legal. As a result, the avoidance rate is higher than 98% when RREP\({}_{\mathrm{lim}}\geq\) 4. With stricter boundaries of (0.33, 0.67), the avoidance rate is higher than 99% when RREP\({}_{\mathrm{lim}}\geq\) 3.  
Nevertheless, we design a dynamic boundary. We apply different boundaries (\(\alpha\), \(\beta\)) according to the received RREP numbers, e.g., we choose (0.5, 1) for received RREP number = 2, and (0.35, 1) for received RREP number = 3-4, and (0.25, 0.75) for received RREP number = 5-6, and so on. The dynamic boundary gives a better percentage in avoiding wormhole attacks and also enhances the performance by decreasing transmission distance in hops. Refer to Figure 5(a), as long as RREP\({}_{\mathrm{lim}}\geq\) 2, the avoidance rate can reach 100%.  
However, the wormhole attack is unavoidable when RREP\({}_{\mathrm{lim}}\) = 1 for any boundaries, because the only established route goes through the wormhole. As the RREP\({}_{\mathrm{lim}}\) increases, the avoidance rates of all boundaries raise. The effect on a strict boundary is more obvious than it is on a loose boundary.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes & 300 \\ Field dimensions & 2,000 m x 2,000 m \\ Radio range & 250m \\ Node delay & Random \text@{ }0.05–0.075 ms \\ Trial & 50 times \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for experiments.
## 1 Introduction
being one of the main microalgae species investigated in literature [12,13,21]. The cultivation of microalgae using Anaerobic Liquid Digestate (ALD) is of particular interest because it provides a production scheme that falls inside the context of a circular bioconomy, by recycling valuable nutrients and the simultaneously producing of valuable biomass that can be further utilized as a feedstock for a biorefinery platform [17]. However, microalgae cultivated in liquid digestate or wastewaters, which typically contain various hazardous contaminants, and could potentially contaminate the produced biomass, raise various concerns about the safety of their consumption restricting their use for human applications [17]. For this purpose, microalgae used asslow-released bio-fertilizers or bio-stimulants have been recently investigated obtaining promising results and can be considered as an eco-friendly solution avoiding chemical pollution especially by leaching or evaporation of nutrients as it happens when synthetic fertilizers or liquid digestate are directly applied in land [22; 23]. Besides crops growth efficiency due to the utilization of nutrients, microalgae have also been found to have a biostimuling effect and improving crop yields [22; 23]. Microalgae can be applied directly in soil [24; 25; 26; 27] or through foliar application [23; 25; 28]. For instance, [PERSON] et al. (2018) produced _Chlorella vulgaris_ and _Spirulina platensis_ to employ them as fertilizer in rice growth experiments [29]. As a result, the application with microalgae improved the plant growth rate (in dry weight) by 69% and 71% compared to the control (soil alone), respectively for _Chlorella vulgaris and Spirulina platensis_. Similarly, [PERSON] and [PERSON] (2015) cultivated tomatoes using the microalgal _Acuodesmus dimorphus_ as fertilizer. The application of microalgae as fertilizers enhanced the plant growth rate (shoots + roots) by 50-325% and by 25-53% the number of branches in comparison with the associated control [25]. Nonetheless, the application of microalgae as fertilizers for crop production is not yet well studied and more research is needed. Moreover, the use of microalgae cultivated in liquid digestate for slow-released fertilizer is still in its infancy. Thus, in this study, the feasibility of _Monoraphidium_ sp. cultivation in liquid digestate for nutrients remediation coupled with the production of algae-based bio-fertilizers was investigated by means of (1) a preliminary microalgae indoor cultivations tests, where several liquid digestate dilution ratio (1:20; 1:30; 1:50) where investigated; (2) a pilot-scale outdoor cultivation in column photobioreactors of 110 dm\({}^{3}\) at the optimal dilution rate; and (3) performing agronomic trials with nutrient leaching columns tests and plant growth pot tests on tomatoes in comparison with synthetic fertilizers and liquid digestate applied at 170 kg N/ha.
# A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O\({}^{2}\) Facilities
## 3 Methodology
### Datasets
#### 3.2.1 Infologger  
InfoLogger serves as a critical infrastructure component for the collection, storage, and visualization of log messages generated via diverse processes operating across multiple machines in the ALICE detector. This system acts as a core broker, facilitating the aggregation of log data from a wide array of applications and services distributed throughout machines. As the ALICE detector evolves, newly introduced components are seamlessly integrated into this logging framework, ensuring comprehensive coverage of system activities. During active physics operations, the system experiences a high volume of log generation, which can be up to one million in each run due to the concurrent operation of multiple detectors and associated applications. The scale and complexity of the InfoLogger system present significant challenges for effective monitoring and anomaly detection.