esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Details of System Performance Metrics  
With each simulated layout proposal, extended system performance metrics can be retrieved, e.g., the time spent performing tasks of an assembly sequence can be classified into value-adding tasks and non-value-adding tasks [30]. This study presents this categorization of task times within the assembly station into three types, Green, Yellow, and Red times:  
* Green time: Represents value-adding activities, such as the actual assembly tasks performed on the battery.
* Yellow time: Encompasses necessary but non-value-adding activities, such as picking parts from racks.
* Red time: Represents non-value-adding activities, such as walking time.  
These classifications enable a nuanced analysis of how different layout configurations impact the efficiency and effectiveness of the production process. They are designed to assist stakeholders in making informed decisions when selecting the most appropriate solution from the Pareto front. By considering these additional factors alongside worker well-being evaluations (e.g., the OWAS Lundqvist Index), the study enhances its analytical depth and contributes to a more comprehensive understanding of operational dynamics in factory layout design.  
The Green, Yellow, and Red times serve as extended system performance metrics that complement the primary objectives of this study: worker well-being (assessed via the OWAS Lundqvist Index), area utilization (as a system performance index measuring the area used by the proposed layout setup), and distance walked (affecting cycle time and efficiency). While these primary metrics provide a direct evaluation of system performance and human-centered factors, the Green, Yellow, and Red times offer additional details by distinguishing between value-adding, necessary non-value-adding, and non-value-adding activities. This breakdown allows for a more nuanced analysis of how different layout configurations impact task efficiency and overall production effectiveness.
# Study the Effect of Operating Temperature on Performance in Dry Scroll Vacuum Pump
## 3 Results and Discussion
### Influence of Operating Temperature on Torque and Power of Dry Scroll Vacuum Pump  
The torque of orbiting scroll is caused by the compression of instantaneous gas, and the evolution of torque with respect to orbiting angle under different operating temperatures is shown in Figure 10. Under different operating temperatures, the torque of orbiting scroll changes with the rotation angle in the same trend. The power can be calculated from the torque and the rotational speed, and the calculation results are listed in Table 7. The power is increased by approximately 4.32% when the operating temperature is from 338.15 K to 298.15 K.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Suction Pressure/kPa** & **Operating Temperature/K** & **Power/W** \\ \hline
1 & 298.15 & 25.1 \\
1 & 318.15 & 24.7 \\
1 & 338.15 & 24.1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Power consumption.  
Figure 9: Temperature distribution when rotation angle is 70\({}^{\circ}\): (**a**) 298.15 K; (**b**) 318.15 K; (**c**) 338.15 K.  
The gas compression process of the scroll pump can be regarded as a polytropic process. According to the thermodynamic principle, the compression power (\(P\)) can be calculated as Equation (14):  
\[P=P_{1}S_{th}\frac{k}{k-1}\Bigg{[}\bigg{(}\frac{P_{2}}{P_{1}}\bigg{)}^{\frac{k-1 }{k}}-1\Bigg{]} \tag{14}\]  
where \(P_{1}\) is pump suction pressure, \(P_{2}\) is pump discharge pressure, \(k\) is the polytropic index, \(k\) = 1.3-1.4, and \(S_{th}\) is the actual pumping speed of the pump.  
In this study, the inlet and outlet pressures of the pump keep constant. It can be seen from the above equation that the compression power of the gas is mainly affected by the actual pumping speed. As shown in Table 7, the greater the heat dissipation capacity of the pump, the lower the operating temperature, the larger the pumping speed, and the larger the gas compression power.
# The Perpetual Vector Mosquito Threat and Its Eco-Friendly Nemeses
## 5 Eco-Friendly _Lysinibacillus sphaericus_ and _Bacillus thuringiensis_ subsp. _israelensis_
### Brief History of Lysinibacillus (Formerly Bacillus) sphaericus (Ls)
#### 5.2.1 Bacillus thuringiensis  
subsp. _israelensis_, Bti  
Despite the trove of Bt isolates cataloged by the mid-1970s, none were active against dipteran larvae. This changed in 1976 with the isolation of Bt (60A) in Israel. The bacterium was isolated from dead _Culex pipiens_ larvae found in a stagnant pond in the north central Negev Desert and it was shown to be lethal to larvae of Nematoceran insects (e.g., mosquitoes, blackflies, and chironomid middges) [248]. The _Bt_ subsp. _israelensis_ (Bti) 60A isolate initially demonstrated rapid toxicity against larvae of five different species assayed, i.e., _Anopheles sergentii_ (Theobald), _Uranoteania unguculata_ [PERSON], _Culex unititatus_ [PERSON], _Aedes aegypti_ and _Culex pipiens_, and the activity was 30-100 times greater than that of _Lysinibacillus_ (_previously Bacillus) _sphaericus_ SSI-1.  
Shortly after the safety and efficacy of Bti were demonstrated, commercial products were developed for applied use worldwide [249]. At present, at least 26 different formulations (wettable powders and suspensions, granules, and briquettes) based on Bti, or a combination of Bti and Ls, are used in mosquito control programs globally, including, Vectobac, Bactimos, ABG6138G, and Teknar; and VectoMax, Culinexcombo, FourStar, and BTBSWAX, respectively [250; 251].
# A 0.18 um CMOS Millimeter Wave Antenna-on-Chip with Artificial Magnetic Conductor Design
## 2 Materials and Methods  
Figure 1 shows the chip antenna proposed in this paper by using an 0.18 \(\upmu\)m CMOS process design and GSG feed structure. Figure 1a is the top view of the proposed chip antenna. The chip antenna is composed of a patch as the main body, a strip slot and an L-shaped slot are dug on the patch antenna, and an inverted C branch is added to form the antenna body. In addition, the chip stacking structure is shown in Figure 1b. The Metal_6 layer is used as an antenna, and the square patch is introduced in the Metal_1 layer to form an AMC structure with the ground plane, reducing the high dielectric loss caused by the silicon substrate.  
This paper also simulated and analyzed the AMC unit's reflection coefficient and phase. Figure 2a is the structure diagram of the AMC unit model. To get close to the actual situation, the PEC boundary was used in the XZ plane, the PMC boundary was used in the YZ plane, and the wave port was used in the +Z direction of 1/4 wavelength from the AMC. The reflection coefficient and phase simulation results of AMC are shown in Figure 2b. In the 72 GHz operating frequency band, it had phase bandwidth and a high reflection coefficient.  
Figure 1: _Cont._  
Figure 3 shows the proposed chip antenna geometry and the value of dimensions shown in Table 1. The detailed geometric dimensions of the strip slot, L-slot, and inverted C-branch are clearly indicated in the table. The geometric size of the AMC unit is shown in Figure 4 and Table 2. According to the literature [28], the square patch was selected in this paper due to its larger bandwidth and lower loss.  
Figure 1: The proposed antenna-on-chip architecture: (**a**) top view; (**b**) chip stack-up.  
Figure 2: AMC unit cell simulation: (**a**) structure; (**b**) simulated result.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value (\(\mu\)m)** & **Parameter** & **Value(\(\mu\)m)** \\ \hline W & 1200 & L & 1200 \\ W\({}_{1}\) & 537 & L\({}_{1}\) & 30 \\ W\({}_{2}\) & 512 & L\({}_{2}\) & 650 \\ W\({}_{3}\) & 490 & L\({}_{3}\) & 270 \\ W\({}_{4}\) & 50 & L\({}_{4}\) & 50 \\ W\({}_{5}\) & 170 & L\({}_{5}\) & 210 \\ W\({}_{6}\) & 140 & L\({}_{6}\) & 50 \\ W\({}_{7}\) & 208 & L\({}_{7}\) & 790 \\ W\({}_{8}\) & 20 & L\({}_{8}\) & 50 \\ W\({}_{9}\) & 50 & L\({}_{9}\) & 50 \\ W\({}_{10}\) & 50 & L\({}_{10}\) & 150 \\ W\({}_{11}\) & 200 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Antenna-on-chip geometry parameters.  
Figure 3: The proposed antenna-on-chip geometry.
## 2 Materials and Methods
### Micro-Meteorological Data Collection and Analysis  
Thirty-one types of micrometeorological data were collected by the scientific weather station (Figure 1). Net radiation (\(R_{n}\)), downward short-wave radiation (\(R_{sd}\)), downward long-wave radiation (\(R_{ld}\)), upward long-wave radiation (\(R_{lu}\)) and upward short-wave radiation (\(R_{su}\)) was measured by a four component radiometer (CNR4, Kipp and Zonen, Delft, Netherlands) at the height of 2.5 m. Air temperature and humidity at the height of 0.5 m, 1.0 m, 1.5 m, 3.0 m, 4.0 m and 6.0 m were measured with six HC2S3 (Campbell Scientific, Logan, UT, USA), respectively. Soil temperature (\(T_{soil}\)) was measured by Hydra Probe II at the depth of 5.0 cm (Stevens, Portland, OR, USA). Soil heat flux (G) was measured by HFP01 SC (Hukseflux, Delft, Netherlands). Wind speed (\(u\)) was measured by 2-D sonic wind sensor (Wind Sonic C1-L, Campbell Scientific, Logan, UT, USA) at the height of 2.5 m. Soil heat flux plates was placed at the depth of 5 cm. All sensors have been calibrated before carrying  
Figure 1: Study site.  
out the experiment. CR3000 data loggers (Campbell Scientific, Logan, UT, USA) were used to collect the meteorological data and the data sampling frequency of all the sensors is 0.5 Hz. The collection interval of all the sensors was 10 min. Continuous and long-time series meteorological data were collected from 10 November 2016 to 19 July 2018. They were micro-daily maximum/minimum air temperature at five heights (\(T_{mean0.5}\), \(T_{mean1.5}\), \(T_{mean2.0}\), \(T_{mean3.0}\), \(T_{mean4.5}\) and \(T_{mean6.0}\)), daily maximum and minimum temperature at six heights (\(T_{max0.5}\), \(T_{max1.5}\), \(T_{max2.0}\), \(T_{max3.0}\),\(T_{max4.5}\) and \(T_{max6.0}\), and \(T_{min0.5}\), \(T_{min1.5}\), \(T_{min2.0}\), \(T_{min3.0}\), \(T_{min4.5}\) and \(T_{min6.0}\)), daily mean relative humidity at six heights (\(RH_{0.5}\), \(RH_{1.5}\), \(RH_{2.0}\), \(RH_{3.0}\), \(RH_{4.5}\) and \(RH_{6.0}\)) and daily mean \(T_{soil}\), \(G\), \(R_{n}\), \(R_{sd}\)\(R_{ld}\), \(R_{lu}\)\(R_{su}\) and \(u\). Ninety-seven frost events were recorded in the experimental tea farm during the experiment.
## 7 Results
### Training and Testing Data Sets at Different Levels of SNR and Results from the Adaptive Approach
Figure 11: Comparison of the different techniques using the weather radar data set and the adaptive proposed approach in the DIFFSNR method. Figure 12: Evaluation of the adaptive approach: differences between the optimal values obtained with different machine learning algorithms and the ground truth for the application of CWT with CNN for different mother wavelets. The following Table 4 summarizes the numeric results obtained for the accuracy among the different approaches and shows the results of the selection of the ML algorithms among the mother wavelets. In a similar way to what was shown in the previous Section 7.2, we also present the box plot of the accuracy for one time-frequency (CWT-G) with CNN. We choose this mother wavelet because it provides superior performance for DIFFSNR to the other MWs. Figure 13 shows that the variability (i.e., the standard deviation of the distribution of the results) in the DIFFSNR method is slightly larger than in the SAMESNR method. This is also to be expected because the structure of the data can be quite different in the training data set taken at SNR = 30 dB and in the testing data sets at different values of SNR. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**SNR (dB)** & **CWT-S** & **CWT-D** & **CWT-M** & **CWT-G** & **WVD** & **SWVD** & **Spectrogram** & **1D** \\ \hline
**25** & 0.9802 & 0.9762 & 0.9633 & 0.9677 & 0.9722 & **0.9820** & 0.9672 & 0.9572 \\ \hline
**ML** & KNN, DT & & & SVM & &  
To complete the evaluation of the DIFFNSR method, we also show in this subsection similar results to what is shown in the previous Section 7.2. Figure 14a,b show the confusion matrices obtained using the CWT-G at SNR = 0 dB and the 1D or Original time representation. It can be seen that we obtained similar results for the DIFFNSR method as in the SAMESNR method. The TDL-D and TDL-E fading conditions are more difficult to distinguish. At the same SNR, the application of CNN with the original time representation is much worse than the combination of CWT MW = Gaussian and CNN because there is a relevant number of FP and FN for all the fading conditions.
## 6 Experiments Results and Discussion
### RNNs Models Results
#### 6.2.2 BLSTM Model Results  
The error analysis contains the worst speaker recognition rate (most errors compared to others). The average overall results of the five-run experiments of the system are shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Run Number** & **Accuracy** & **Recall** & **Precision** & **F1 Score** \\ \hline
1 & 84.34\% & 85.54\% & 84.34\% & 84.94\% \\
**2** & 85.07\% & 86.14\% & 85.07\% & 85.50\% \\
**3** & **85.22\%** & **86.55\%** & **85.22\%** & **85.88\%** \\
**Average** & 84.88\% & 86.08\% & 84.88\% & 85.69\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The three LSTM experiment runs’ results.  
Figure 11: MLP model results.  
According to the considered public speech dataset, the male speakers are 106, and the female speakers are 32, each with 136 associated audio files, subdividing 20% of the dataset to equal 28 files for each speaker testing the model. Each run's error is counted based on the system execution log file.
# A Review of Medication Adherence Monitoring Technologies +
## 2 Background
### Why Technology-Based Solutions?  
Solutions to non-adherence demand the contribution of multiple factors. Nonetheless, the effectiveness and reliability of the monitoring method is central to achieving large improvements in adherence [9; 45]. Manual approaches require the attention and effort of the patient. Direct biochemical approaches require the patient to report to a clinic for fluid testing. In addition, the interventions associated with biochemical measurements, especially blood sample drawing, are of great burden for patients.  
The development of Cyber-Physical Systems (CPS) for healthcare is advancing rapidly [46]. More recently, such systems included few sensing and monitoring devices associated with mobile devices such as smart pill bottles, smart watches, smart phones, and wearables. The combination of these smart monitoring devices with interventions that remind the patient in case a deviation is detected has proven to improve medication adherence [47; 48]. Compared to manual approaches, electronic-based approaches can reduce the cost and effort from the user's interest. In addition, the accuracy of adherence measure, which is of great importance from the healthcare provider's point of view can be enhanced when using electronic-based systems. Furthermore, as we live in the era of the Internet of Things (IoT) [49], where everything is connected to the Internet, a connected health paradigm is becoming a more dominant field [50]. One expectation of connected health is the automated capability of communicating the collected adherence measurements to the provider, and the feature of issuing reminder and alert messages based on the processed information [45; 51]. Moreover, electronic measurement systems can be portable and thus provide timely and long-term monitoring without restricting the user's mobility. In spite of the fact that electronic-based modalities can outperform traditional ones, the majority of electronic-based approaches come with limitations that act as burdens on the users, as we will see in Section 5. In fact, some of them have not achieved much success due to these burdens [42]. Based on this, we conclude that there is no optimal electronic-based solution for medication adherence evaluation and, for that, much additional efforts will be required to realize accurate, low cost electronic adherence monitoring.
## 2 Materials and Methods
### The Latest MeteoSwiss Dual-Polarization Weather Radar (RADAR) Network Data  
In 2011, MeteoSwiss started the renewal of its weather radar network: it now consists of five identical, dual-polarization, Doppler, C-band (5.5 GHz) radars [3]. Antenna-mounted, fully-digital receivers have been introduced into the design and implementation of the current systems; each system is equipped with two orthogonal receiving channels, which are able to measure vertical and horizontal linear polarizations. Three of these radars are located near the political borders of Switzerland (to control fronts and detect perturbations in the largest possible range) and close to highly populated areas and large airports; these locations are the same as in the previous network. Two additional radars have been installed, by necessity, at high altitudes (3000 m), to improve the coverage in the inner parts of the Alps. The radar locations (latitude, longitude, altitude) are listed in [25] (Table 1, p. 1250) and shown in [25] (Figure 1, p. 1250). During the period covered by this study, the three radars were installed and operational all time long; the high altitude radars in Valais (above Crans-Montana) and above Davos (close to Austria) entered the mosaic in May 2014, and at the end of 2015, respectively.  
Radar hardware monitoring is run continuously: more than 350 parameters are monitored and submitted from each radar site to the central server after completion of every single sweep, that is 20 times in 5 min. The parameters are automatically checked for anomalies and archived for diagnostic analyses. Daily monitoring of the radar receiver and pointing accuracy of the antenna radiation main lobe are also implemented [31] following the Dutch-Finnish [32; 33] operational monitoring approach. A guiding principle for a comprehensive monitoring is to combine as many independent sources of information as possible. These include various types of weather echoes, ground clutter signals, signals from the sun, signals transmitted by the radar and measured by an external receiver, signals received by an external transponder and transmitted back to the radar with a shift in time, intensity, frequency and polarization, signals reflected by nearby towers, and test signals inserted at various locations by laboratory test equipment during preventive maintenance.
## 4 Animals as Mobile Biological Sensors for Harsh Terrains  
Communications technologies enable rapid and accurate identification, location tracking, and condition monitoring of high-value assets. The applications of mobile biological sensors include identification and monitoring of humans, pets, fish, poultry and other livestock through patented implantable microchips, the location tracking and message monitoring of vehicles and aircraft in remote locations through systems with GPS and/or geosynchronous satellite communications; and monitoring of asset conditions such as temperature and movement, through advanced miniature sensors. The miniaturization of electronic components and sensors (MEMs usage and technology) combined with advances in satellite technology have led to dramatic improvements in the tags, great increasing the ability to pinpoint an animal's geographic location, and researchers are continuing to add new capabilities.
# Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments
## 2 Background and Related Work
### Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Dataset** & **Augmentation** & **Number of Classes** & **Model** \\ \hline
[27] & DAWN & Yes & 1 & YOLOv4 \\ \hline
[28] & BDD-IW & Yes & 1 & YOLOv4 \\ \hline
[29] & MWVD & No & 1 & YOLOv5 \\ \hline
[30] & VOC & & & \\ RTTS & Yes & 5 & YOLOv3 \\ \hline
[31] & Foggy & No & 1 & DSNet \\ \hline
[33] & Collected & & & \\ dataset & No & 11 & YOLOv5 \\ \hline
[35] & Open Image & No & 4 & YOLOv4 \\ \hline
[36] & COCO BDD100K & No & All COCO & YOLOv4 DSSD \\ \hline
[37] & KITTI & No & 3 & Tiny YOLO Complex YOLO \\ \hline \multirow{2}{*}{Ours} & DAWN & No & 6 & YOLO5 \\  & Aug. DAWN & Yes & 3 & YOLOv7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of recent object detection publications in various weather conditions. conditions such as sandy conditions. This omission has caused a bias in object detection model evaluations and results since the results are always impacted by the training dataset. We can summarize the reasons for excluding sandy environments from object detection publications in two points:  
* Limited resources: there is an absence of reliable datasets accurately depicting sandy conditions. * Regional and weather bias: the regions where researchers and institutions are located, as well as the weather conditions in those regions, can inadvertently influence the scope of object detection studies. Our research aimed to fill the gap by shedding light on the performance of one-stage models (specifically, YOLO versions) in sandy weather environments.
# Towards Incidence Management in 5G Based on Situal Awareness
## 2 Background
### Situual Awareness and Information Security  
As defined by [PERSON], the term Situual Awareness (SA) refers to \"the perception of the elements in the environment within a volume of time and space, comprehension of their meaning and the projection of their status in the near future\" [31]. Usually, this definition is simplified as \"knowing what's going on so you can figure what to do\" [32]. Thus, it is clear that its aim is to facilitate decision-making based on what is happening and its projection [13]. In order to acquire Situual Awareness, [PERSON] proposes three stages of information processing: perception, comprehension and projection; the first conducts the tasks of monitoring and identification of incidences, the second their analysis and association, and the last predicts the evolution of the state of the system. As shown in Figure 1, once relevant situations are detected, the countermeasures to be applied are decided and executed. It is important to highlight that there is feedback between action/decision levels and the Situual Awareness; in this way, the countermeasures and their impact on the system are tracked. The observed results have implications on future decisions and facilitate the use of advanced diagnostic methods [14].  
Incident management based on Situual Awareness has been implemented in very different areas, among them smart grids [33], power generation [34] or vehicular collision avoidance systems [35]. In [36], a method for defining the critical information and the relevant information quality elements that are required to build the Shared Situual Awareness (SSA) in disaster response is suggested. The adaptation of the Endsley has proved particularly effective in complexand dynamic environments [37], where the diagnosis is highly dependent on the context in which incidents are reported, reaching to play an essential role in the fight against cybercrime. Many of these contributions are collected in [38], where the predominance of issues related to risk management in emergency situations, industrial systems and networks is observed. As discussed in [12], they improve the three most repeated deficiencies of the Information Security Risk Management: (1) Information security risk identification is commonly perfunctory; (2) Information security risks are commonly estimated with little reference to the current situation; and (3) Information security risk assessment is commonly performed on an intermittent, non-historical basis (a conventional security risk assessment scheme can only give a \"snapshot\" of the risks of the information systems at a particular time [39]). In order to bring solutions to these problems, but without losing focus on the ISRM/ISRA basis, several publications approach the combination of both paradigms. This is the case of [12; 40], where the Situational Awareness is acquired, taking into account the definition of risks, assets and their impact posed by the different standards and platforms for ISRA implementation [7; 8; 9; 10; 11].