esa-sceva/satcom-chunk-collection huggingface dataset:
# Reproducibility and Accuracy of Two Facial Scanners: A 3D In Vivo Study
## 3 Results
### Points Measurements  
Table 6 reports the descriptive analysis of the points measurements, showing that all the cephalometric points, except for the subnasal point, have shown a difference between Eiscan and Vectra within the range from 0.5 mm to \(-\)0.5 mm, considered highly reproducible. The ICC values are all above 0.90 and significantly different from 0 (\(p<0.05\)), indicating excellent repeatability (Table 7).  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \begin{tabular}{c} **Minimum** \\ **(mm)** \\ \end{tabular} & **Mean (mm)** & **SD (mm)** & \
\begin{tabular}{c} **Percentage 25** \\ **(mm)** \\ \end{tabular} & **Median (mm)** &
\begin{tabular}{c} **Percentage 75** \\ **(mm)** \\ \end{tabular} & **Maximum** \\ \hline Trichion & \(-\)0.9576 & 0.0691 & 0.4653 & \(-\)0.2040 & 0.0798 & 0.3323 & 1.0571 \\ Nasion & \(-\)1.0919 & \(-\)0.1232 & 0.4209 & \(-\)0.4476 & \(-\)0.1005 & 0.2036 & 0.4930 \\ Pronasion & \(-\)2.0569 & 0.0481 & 0.6468 & \(-\)0.0249 & 0.1048 & 0.3227 & 1.0907 \\ Subnasal & \(-\)1.2152 & 0.7387 & 1.2711 & \(-\)0.1802 & 0.5868 & 1.5739 & 3.5538 \\ Upper lip & \(-\)1.4785 & 0.2705 & 0.9270 & \(-\)0.1695 & 0.1616 & 1.0000 & 1.8152 \\ Lower lip & \(-\)1.6162 & 0.0867 & 1.2139 & \(-\)0.9781 & 0.3960 & 1.0638 & 2.2534 \\ Pogonion & \(-\)1.7705 & \(-\)0.3067 & 0.8115 & \(-\)0.8301 & \(-\)0.1784 & 0.2539 & 1.1284 \\ Left labial & \(-\)1.8904 & \(-\)0.0425 & 1.6089 & \(-\)1.5048 & \(-\)0.2079 & 0.9305 & 3.3753 \\ Commissure & \(-\)2.9855 & 0.2858 & 2.1971 & \(-\)0.9978 & \(-\)0.0780 & 1.3238 & 6.9258 \\ Left alare & \(-\)1.4099 & 0.1304 & 0.8296 & \(-\)0.4880 & 0.3358 & 0.6662 & 1.6933 \\ Right alare & \(-\)1.3692 & 0.0872 & 0.9847 & \(-\)0.6098 & \(-\)0.0315 & 0.5907 & 2.6395 \\ Left zygomatic & \(-\)0.9621 & \(-\)0.1617 & 0.3322 & \(-\)0.3351 & \(-\)0.1484 & 0.0686 & 0.4402 \\ Right zygomatic & \(-\)0.9740 & 0.0570 & 0.7289 & \(-\)0.2995 & 0.1185 & 0.3025 & 2.5619 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Descriptive analysis of points measurements.  
A single-sample Wilcoxon test was conducted in order to verify whether the value is statistically different from zero. It was observed that there were no significant differences, so the median does not deviate from zero (\(p>0.05\)) (Table 8).
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 4 Over-the-Air Demonstrations  
The primary objectives of WISCANet are (a) to accelerate the implementation and validation of novel radio applications and (b) to make over-the-air experimentation accessible to users that are not hardware experts. To demonstrate that WISCANet enables these capabilities, we prepared several over-the-air demonstration applications that showcasethe capabilities and ease-of-use described in the previous section. We executed these applications on the large WISCANet deployment that we maintain in the WISCA laboratory [27, 30, 73], which is pictured in Figure 2 and depicted in Figure 9. As a frame of reference compared to standard solutions (ASICs, FPGAs, RFSoCs, SDRs, etc.), these applications - from conception to OTA validation - were implemented in under 2 weeks each by a single graduate student. Notably, these demonstrations were all performed over-the-air on Ettus USRPs, so real-world channel effects, transmitter noise, receiver noise, RF system impairments, and front-end non-linearities are all present.
## 3 Results and Discussion
### Vulnerable Areas for Emergency Medical Services (EMS)
#### 3.1.2 Comparison of Speed Limit and Real Speed of Traffic Flow  
We established a road network map that can be used to analyze accessibility in 5-min units by combining a standard node link and traffic communication information. We compared the legal speed limit of each road to the real speed in the established road network.  
Data on the speed range showed a descriptively statistical range within 5-min for 3 months. The speed of vehicles in urban areas is not as easily available as the speed limit, but in rural areas, the speed of vehicles can be sufficiently calculated at a speed corresponding to the speed limit of the road (Figure 3). In the capital city (Seoul), for example, the highest population density, on average, vehicles traveled 20-30 km/h on roads with a 60 km/h speed limit and 40-60 km/h on roads with an 80 km/h speed limit (Figure 2(a)). However, the vehicle speed decreased remarkably around the morning and evening rush hour (8 a.m. and 5 p.m.). By contrast, vehicles in Hongcheon, a typical rural area located in a mountainous region, on average, vehicles can travel at a similar speed to a speed limit of 60 km/h and 60-70 km/h on the road with an 80 km/h speed limit (Figure 2(b)).
# An Accident Model with Considering Physical Processes for Indoor Environment Safety
## 3 Accident Model
### Stamp-Pp
#### 3.2.1 Performers System  
The indoor environment is adjusted by indoor environment adjustment services with respect to various home appliances. To differentiate the home appliances with other indoor items, e.g., router or furniture, we define the concept of Performer.  
**Definition 1** (Performer).: _A performer is a network-enabled home appliance that can adjust the indoor environment independently._  
There are two points to explain this definition. First, a Performer has networking capability so that it can be used by indoor environment adjustment services. Second, it has functions of adjusting the indoor environment, e.g., adjusting indoor temperature, or dehumidification.  
There are two types of Performers based on the way it adjusts the indoor environment. One is direct adjustment, e.g., an air-conditioner which heats or cools indoor air directly; another is indirect adjustment, e.g., an electric window which adjusts, e.g., indoor temperature by introducing an air flow or solar radiation of the outdoor environment. In the latter case, the outdoor climate is passively used to adjust the indoor environment. Then, concept of Performer is used to define the Performers System.  
**Definition 2** (Performers System).: _It is a system of all installed Performers in a house that are connected to the same home network._  
By connecting to the same home network, we can ensure that the Performers System can be used by the same indoor environment adjustment service. The Performers System has a goal prescribed by the indoor environment adjustment service. The goal is achieved by taking advantage of the functions of the Performers of the Performers System. Each Performer is taken as a subsystem of the Performers System. However, there is no need to have all related Performers working at the same time. Figure 2 shows an example of the Performers System. It consists of an air-conditioner, an electric window, and an electric curtain, which are taken as Performers. They connect to the same network and have the ability to adjust the indoor temperature. When adjusting the indoor temperature for thermal comfort (the goal), the indoor temperature adjustment service could use any combination of the Performers, but not necessarily all of them. The indoor temperature adjustment service is executed in the smart home system core. The Performers can of course be operated by occupants who are also the beneficiaries of the adjustment.  
The Performers System can adjust physical processes with respect to various physical properties, e.g., temperature and humidity, by utilizing the functions the Performers provide. These physical processes may have different forms, for example, increasing or decreasing indoor temperature to the prescribed temperature level for thermal comfort.
## 4 The Proposed TOCA-IoT Framework
### Causal Inference Using LiNGAM
This visualization helps in understanding the inferred causal relationships between features and identifying potential root causes of anomalies. LiNGAM is used for causal effect analysis, which measures the strength of the causal relationships and finds the key factors that have a strong influence on the occurrence of anomalies, as shown in Figure 3. LiNGAM attempts to disentangle the effects of confounders and estimate the causal effect of the exposure on the outcome. \"Does the presence of specific network traffic characteristics (exposure) causally increase the likelihood of a Distributed Denial of Service (DDoS) attack (outcome), considering the potential confounding effects of network congestion or other background network activity?\"
# CGT-YOLOv5n: A Precision Model for Detecting Mouse Holes Amid Complex Grassland Terrains
## 3 Experimental Results and Discussion
### Evaluation Criteria  
Four evaluation metrics were employed in this study, namely, the mean average precision (mAP), model size, inference speed latency, and transmission rate in frames per second (FPS), to assess the effectiveness of the original model compared with the improved model for mouse hole detection.  
\(mAP\) is a crucial performance metric in target detection. The \(mAP\) (mean average precision) value reflects the detection performance of the model across the entire dataset. A high \(mAP\) value indicates that the model exhibits a good detection performance, while a low \(mAP\) value may suggest issues with the model's detection capabilities. Calculated using Equations (1)-(4), it quantifies the detector's performance by calculating the area under the precision (\(P\))-recall (\(R\)) curve at various thresholds [30]. In Equations (1)-(4), \(P\) represents the precision rate, which denotes the percentage of positive samples (true positive) correctly detected by the model out of all samples. \(R\) represents the recall rate, which refers to the proportion of positive samples (true positive) correctly detected by the model out of all positive samples (true positive + false negative) [31].  
\[P=\frac{TP}{TP+FP}\times 100\% \tag{1}\]  
\[R=\frac{TP}{TP+FN}\times 100\% \tag{2}\]  
\[AP=\int_{0}^{1}P(R)dR \tag{3}\]  
\[mAP=\frac{\sum_{0}^{n}AP}{n} \tag{4}\]  
TP represents the number of samples correctly predicted as positive, \(FP\) represents the number of samples where negative samples are incorrectly predicted as positive, and \(FN\) represents the number of samples where positive samples are incorrectly predicted as negative [32]. Additionally, \(n\) denotes the number of categories, \(AP\) denotes the average precision, and \(mAP\) represents the mean average precision.  
The intersection over union (IoU) is a crucial metric for assessing the localization accuracy of object detection models, and this study utilizes two thresholds, IoU = 0.5 and IoU = 0.5:0.95, to evaluate the model performance. The model's size affects its deployment flexibility and efficiency, with larger models requiring more resources. The inference speed, measured as frames per second (FPS) [33], is an essential indicator of the model's image processing speed and suitability for real-time applications  
These four detection metrics are recognized and widely used measurement standards in object detection, boasting strong generality and comparability.
# Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems
## 2 Method
### Energy Efficiency  
The energy efficiency (EE) of a cellular network (measured in bit/Joule) is categorized as how much energy it takes to transmit a number of bits accurately. As previously mentioned, most sensors and actuators encounter limited energy issues in a UAV-based IoT environment. For this reason, guaranteeing sustainable energy for these devices represents another critical challenge. Drones, in particular, require significant energy to maintain flight and perform various tasks such as remote sensing. This motivated us to analyze the energy efficiency of our proposed irrigation system. Accordingly, we drive the expression of EE as follows [30]:  
\[\mathbf{E}\mathbf{E}=\frac{\mathbf{T}}{\mathbf{PC}}, \tag{5}\]  
where:  
* \(\mathbf{T}\) denotes the throughput of the proposed system and is measured in bit/s/cell. This throughput can be calculated using the downlink (or the uplink) SE expressions given in the previous subsection;
* \(\mathbf{PC}\) denotes the power consumption of the proposed system and is measured in W/cell. This power is equal to the sum of the effective transmit power (\(\mathbf{ETP}\)) and the circuit power (\(\mathbf{CP}\)), i.e., \(\mathbf{PC}=\mathbf{ETP}+\mathbf{CP}\). Note that the circuit power is consumed by the transceiver hardware at the GBS and different IoT devices. In this work, we adopt the circuit power model in [31].
## 3 In-Silico Simulation
### Reinforcement Learning, T1 DM and the Artificial Pancreas  
Because of the fact that applying reinforcement learning to any problem assumes an underlying Markov decision process, we need to take this into account when designing the state and action spaces for the T1 DM case. There are several factors influencing whether or not we can interpret the glucose insulin dynamics as a Markov decision process, most notably the delayed action caused by the use of subcutaneous insulin infusion. Depending on the type of insulin used, the maximum effect of insulin is delayed and can last up to four hours [64]. On top of this comes the delay between the subcutaneous CGM measurements and the true blood glucose values, which is typically between 5 and 15 min. [65]. One of the fundamental properties of reinforcement learning algorithms, is the fact that they can control systems with delayed reward [66]. This implies that an action in a state can still be considered to be good even if the immediate reward from taking that action is not considered good. Furthermore, we note that, since the insulin infusion is the action taken by the RL agent, the environment will not change its state immediately because of the delayed insulin effect. Therefore, in this work we consider 30 min. time intervals as the time between each updated state from the environment. The insulin basal rate is kept constant during these 30 min. This will allow for the environment enough time to change significantly between each time step.  
The final component involved is the reward function. In this work, we used two different reward functions, a symmetric Gaussian reward function and an asymmetric reward function, previously introduced in [67]. The Gaussian reward is given as:  
\[r(g)=\exp\left\{-\frac{1}{2h^{2}}\left(g-g_{ref}\right)^{2}\right\},\]  
where \(g\) is the current blood glucose value, \(h\) is a smoothing parameter, and \(g_{ref}\) is the reference blood glucose value fixed at 108 mg/dL. The asymmetric reward function was, in [67], designed to reduce hypoglycemia and, at the same time, encouraging time-in-range. It is built as a piecewise smooth function and gives a strong negative reward for severe hypoglycemia, followed by an exponentially decreasing negative reward for hypoglycemic events starting at severe hypoglycemia, and zero reward when hyperglycemia occurs. Positive rewards from a symmetric linear function are given for glucose values in normoglycemic range. Concretely, the function is given as:  
\[r(g)=\left\{\begin{array}{ll}-100&:g<g_{hypo^{-}}\\ \exp(\frac{\log(140.9)}{g_{hypo}}g)-140.9&:g\in[g_{hypo^{-}}g_{hypo}]\\ \frac{1}{36}g-2&:g\in[g_{hypo},g_{ref}]\\ -\frac{1}{72}g+\frac{5}{2}&:g\in[g_{ref},g_{hyper}]\\ 0&:g>g_{hyper},\end{array}\right.\]  
where hyperglycemia is defined as values above \(g_{hyper}=180\) mg/dL, hypoglycemia as values below \(g_{hypo}=72\) mg/dL and severe hypoglycemia as values below \(g_{hypo^{-}}=54\) mg/dL. Thus, the normoglycemic range are values between \([g_{hypo},g_{hyper}]\) mg/dL. The parameters of the reward were found experimentally. Figure 3 shows a graphical representation of the reward function.
# SMS Spam Detection System Based on Deep Learning Architectures for Turkish and English Messages
## 4 Evaluation
### Datasets  
Two datasets were used in order to evaluate our approach. The first dataset is named TurkishSMSCollection [56], consisting of Turkish messages, and the second one is the SMS Spam dataset from the UCI database [26]. The first dataset contains a public set of 4751 Turkish messages, labeled as spam and non-spam. In the second one, there are a total of 5574 messages, specified as spam and non-spam. Table 1 presents the number of messages in the dataset created by combining the two datasets and the ratios allocated for training and testing. Corresponding to 4827 messages, 86.6% of the 5574 messages in the UCI SMS Spam dataset were labeled as non-spam, and 13.4% of these, corresponding to 747 messages, were labeled as spam messages [26]. In the Turkish SMS Spam Collection dataset, on the other hand, 53.3% of 4751 messages, corresponding to 2536 messages, were labeled as spam, and 46.7% of these, corresponding to 2215 messages, were labeled as non-spam messages [56]. In consequence, the numbers and percentages of the spam and non-spam messages in the datasets on which the spam detection was to be performed were considered at an acceptable level. For this reason, the datasets were deemed to be balanced, so the addition of any synthetic data to the datasets through different methods was not considered necessary. Any kind of overfitting problem was not encountered in the datasets during the experimental studies conducted. In the study, data cleaning was performed in the data preprocessing stage due to language rules. Then, Turkish and English words were combined. Examples within both datasets are presented in Tables 2 and 3.  
As we all know now, the size of the dataset is the most important factor for learning in deep learning applications. The bigger the size of the dataset, the better the performance of learning. Moreover, the size of the dataset also increases the time spent on learning and the size of the model obtained at the end of the learning at the same rate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Number of Messages** & **Train** & **Test** \\ \hline Spam & 3321 & 80\% & 20\% \\ Non-Spam & 7004 & 80\% & 20\% \\ Total & 10,325 & 80\% & 20\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about the datasets used.
## 1 Introduction
By optimizing these factors, companies can reduce cycle times, increase production throughput and improve efficiency. 4. Reduced Downtime: By integrating Digital Shadow technology, companies can monitor production processes in real-time and promptly identify potential issues that may cause downtime. By using DOE techniques, companies can identify critical factors affecting machine reliability, such as machine speed or maintenance schedules. As a next step, by optimizing these factors, companies can reduce machine downtime, leading to increased productivity and decreased maintenance costs. 5. Improved Cost Management: Digital Shadow technology can provide companies with real-time data on production costs, such as energy usage, raw material costs and labor costs. As a result, by using DOE techniques, companies can identify the most significant factors that impact production costs and make adjustments to lower costs. Finally, by optimizing production processes and reducing waste, companies can save money on production costs, leading to improved profitability. The contribution of this paper can be summarized as follows. The main advantage of utilizing Digital Shadows technology and robust engineering techniques is the improved decision-making support in PI. The production line is digitized, and the digitalization services in combination with Industry 4.0 technologies, such as Internet of Things, Simulation and Cloud Computing, etc., are utilized. The Digital Shadow of a production line is developed to collect, analyze, and identify potential issues, with decisions made based on the integration of historical data and current dynamic events. The suggested actions can be adapted, taking into consideration the current production situation. The technology can also be used to digitize the knowledge of experienced operators, which is crucial in the food industry, where product quality is sensitive to delicate manipulations. The use of Digital Shadow makes the transition to Digital Twins technology easier. Robust engineering/statistical DOE techniques are used to validate the digital model through a specific number of experiments. ANOVA is employed to identify control factors affecting performance and to propose alternative solutions. The research topic to be addressed in the following paragraphs is \"the definition, design, and development of a framework for the utilization of DOE in combination with Digital Shadow, in order to further improve and automate decision-making in a food industry\". The rest of the manuscript is structured as follows. In Section 2, the state of the art in Digital Shadows, Edge Computing and DOE is investigated. In Section 3 the proposed framework of Digital Shadow is presented and discussed. In Section 4, the problem formulation is described. In Section 5 the system implementation is presented. In Section 6 the results are analyzed. Finally, in Section 7, the manuscript is concluded and future research directions are discussed.
## 3 Adaptive Thresholds for Robust Fault Detection
### Overall Architecture of Fault Detection  
We consider a scenario of ACC with both a constant reference speed and a controlled speed which could often change depending on the safety distance behind a preceding vehicle. For ACC controlling the velocity of a vehicle, a single Kalman filter is built, tuned and then is used to perform vehicle's longitudinal control safely. The overall architecture of the proposed fault detection using adaptive thresholds is presented in Figure 2 and consists of residual generation, residual evaluation, the Kalman filter, and an adaptive  
In practice, the residual generation by comparing the estimated data of the Kalman filter with actual measurements and a proper threshold is chosen in the adaptive threshold pool in real time whenever the velocity of a vehicle is changed to maintain the safety distance. The adaptive threshold pool has many pairs of predefined-adaptive thresholds suitable for individual velocities at which the Jackal UGV can be driven. Each pair of adaptive thresholds consists of a reference point (\(\rho\)) and an upper boundary (\(\sigma\)) as shown in Figure 2 and is determined by our method presented in Section 3.3.2.  
The used Jackal UGV has many sensors including two encoders for the right and left wheels, IMU (inertial measurement unit), and GPS, and the maximum velocity is limited to 2 m/s. In our work, only two encoder sensors are used to measure the velocity as real measurements and are exploited to detect transient faults. The two wheels for each side are connected to each other using the chain. Both encoder sensors provide measurement at 20 Hz. Sensor data are gathered from Jackal by normal driving on straight lines at a constant speed. The velocities of encoders are measured in each operation distinguished by velocities.