esa-sceva/satcom-chunk-collection huggingface dataset:
# Label-Free Model Evaluation with Out-of-Distribution Detection
## 2 Related Works  
**Model generalization prediction.** The goal of the model generalization prediction task [2; 14; 15; 16] is to predict the difference in performance between a classifier on the training set and the test set. Ref. [17] explored how to estimate the test error of classifiers on datasets with randomly labeled assignments or added Gaussian noise. Ref. [18] proposed a persistent topological metric that can be used to determine the error of DNNs on unlabeled datasets. Ref. [2] used unseen unlabeled data to predict distribution generalization. However, the above methods usually assume that there is no semantic shift between the training set and the test set. In addition, unlike the objective of the above works, we focus on predicting the accuracy of the classifier on a specific dataset.  
**Out-of-distribution detection.** OOD detection methods are generally used in environments that are highly dependent on safety [19; 20; 21], such as autonomous driving. Refs. [19; 22] propose the use of softmax scores to identify OOD data. Ref. [20] used temperature scores to discriminate OOD data and increased the difference between OOD data and normal data by adding perturbations to improve the accuracy of OOD detection. Refs. [12; 13] proposed that energy scores could distinguish OOD data from ID data more accurately and fine-tune the model to improve accuracy. In addition, the distance-based method [23; 24] used the distance between the sample and the class center or sample similarity as the criterion for determining whether the sample is OOD data. Ref. [25] proposed combining feature-based methods with classification-based methods to avoid information loss and to further improve the accuracy of OOD detection. Our main goal using OOD detection is to detect abnormal test samples, i.e., images that do not belong to the categories contained in the training set.  
**Data-centric AutoEval.** The main purpose of the data-centric AutoEval [5; 10; 11; 26; 27] is to predict the accuracy of the model on an unlabeled test set. Ref. [2] presented an unsupervised framework that used only unlabeled data and mild assumptions to estimate the error rates of the classifier. Ref. [6] demonstrated a strong linear relationship between classification task accuracy and rotation prediction task accuracy. Refs. [3; 4] used training sets to generate meta-datasets and to predict model accuracy through dataset representation. Ref. [11] found a relationship between the difference of confidences (DoC) of a classifier's predictions and classifier performance on unlabeled datasets. However, none of these methods consider the effect of OOD data on accuracy prediction. We use the AutoEval method with OOD detection to minimize the impact of OOD data and to improve AutoEval performance.  
In addition, we note that [26; 28; 29] used OOD information in the presence of covariate shift to predict the accuracy of the classifier. In contrast, the aim of our work is to predict the accuracy of the classifier on datasets with OOD data that have semantic shift rather than distributional shift.
# An IoT-Based System for Monitoring the Health of Guyed Towers in Overhead Power Lines
## 3 System Architecture
### Hardware Design of the System
#### 3.5.3 Sensors
This capability allows light measurement over a 23 bit effective dynamic range. Measurements can be either continuous or single-shot. The control and interrupt system features autonomous operation, allowing the processor to sleep while the sensor searches for appropriate wake-up events to report via the interrupt pin. The sensor reports data over an I2C- and SMBus-compatible, two-wire serial interface. The low power consumption and low power-supply voltage capability of the OPT3001 enhance the battery life of battery-powered systems. Figure 9: The tension sensor: (**a**) diagram and (**b**) actual photo. Weather station: Although general weather information is broad, it is primarily local information. For example, patterns and cycles in the atmosphere impact the oceans, the patterns and cycles of the ocean affect the atmosphere, the larger-scale features affect the smaller-scale features, and the smaller-scale features can affect the large-scale features. Then, the local conditions are somehow random and can uniquely affect the tower structure. That is why the logging of local weather is vital for data analysis. According to Figure 10, the weather station consists of three sensing elements: (a) rain gauge, (b) wind speed, and (c) wind direction. Figure 10d shows a photo of the sensors installed in the tower structure. The choice of the sensing elements for this framework is irrelevant since there is a variety of them. However, the designer must observe the most appropriate equipment for the considered environment, such as robustness and long-term lifetime. For example, the most popular anemometers are the cup with a wind vane, the sonic, and the propeller. For a rain gauge, the principle upon which it works is the method of the tipping bucket principle. Since the interface of these devices is simple, it is easy to connect different sensing elements to the on-demand port of the leaf/router nodes.
## 3 Analysis of Test Results
### Fractal Characteristics of Crushing Products
Figure 14c shows the mass fractal dimension of coal under the same saturation and velocity (6 m/s) and different temperature conditions; from this, the slopes follow the following trend: \(-20\)\({}^{\circ}\)C \(>0\)\({}^{\circ}\)C \(>10\)\({}^{\circ}\)C \(>-10\)\({}^{\circ}\)C \(>20\)\({}^{\circ}\)C. Comparison with the longitudinal wave speed shows that the magnitude of the wave speed is inversely proportional to the slope. The wave speed reflects the denseness of the material, and the slope is negatively correlated with the mass dimension number; thus, the lower the wave speed, the smaller the slope of the mass dimension regression curve and the larger the mass size number. Figure 14d shows the mass fractal dimension of the rock for the same saturation and velocity (18 m/s) and different temperature conditions. The pattern is similar to that of the raw coal, and both show that the temperature can cause some changes in the samples. The slopes follow this trend: \(0\)\({}^{\circ}\)C \(<10\)\({}^{\circ}\)C \(<20\)\({}^{\circ}\)C \(<-20\)\({}^{\circ}\)C \(<-10\)\({}^{\circ}\)C. The difference is that (1) At the same saturation and temperature, the slope of the maximum velocity of the original rock is higher than the slope of the minimum velocity of the original coal. This result shows that the degree of dynamic compressive resistance of the rock is evidently much higher than that of the raw coal. The energy absorption of the rock is greater than that of raw coal. The rock sample has stronger resistance to deformation. (2) At different temperatures, the change in the original rock is not as sensitive as that of the original coal. The slope change is minimal. This result shows that the rock is less affected by temperature than the original coal. Under the influence of the temperature and velocity, the mass dimension of rock is dominated by the influence of velocity. Figure 14: Statistical graphics of cumulative particle size distribution. (**a**) Different saturation levels and impact speeds for coal samples; (**b**) different saturation levels and impact speeds for rock samples; (**c**) different temperatures for coal samples; and (**d**) different temperatures for rock samples. Overall, the slope of the fitted curves decreases with increasing impact velocity and decreasing saturation. Raw coal and raw rock were also affected to varying degrees at different temperatures. The two inflection points at \(-10\) and \(0\)\({}^{\circ}\)C are the most pronounced. The decrease in the slope of the fit and, correspondingly, the increase in the fractal dimension indicate an increase in the degree of fragmentation of the coal or rock mass and a smaller volume. By observing the collected sample fragmentation and quantifying the fractal dimension D, it can be concluded and mutually corroborated by the waveform plots in Figure 14 that coal bodies are more brittle and rocks are somewhat more plastic.
# Enabling the Orchestration of IoT Slices through Edge and Cloud Microservice Platforms
## 4 Validation of the Solution
### Latency Measurements  
For the latency measurements, the test environment described in Figure 5 is used as well. The time required to process the readings by both the gateway and the server is negligible compared to the latency introduced by the network, and particularly to the one introduced between the home router and the Google Compute Engine. Hence, for latency calculation the time between the reading taken at the IoT gateway and the reading taken in the IoT server will be compared for the same temperature measurement for each slice (i.e., when the IoT gateway is deployed in the Raspberry Pi cluster, slice 1, and when it is installed in the GKE cluster, slice 2). In this case only emulated devices using JMeter load test tool connected to both slices are used. It is manually ensured that the Raspberry Pi cluster uses the same NTP [90] server as GKE cluster and time is forced to be synchronized just before the testing.  
Latency measurements change considerably depending on the impairments of the network at the moment of taking the measure. Figure 8 shows a situation where the network latency measured between the home router and the GKE cluster is around 100 milliseconds.  
As it can be observed, the latency when deploying both the gateway and the server in GKE is much smaller (around 5 milliseconds in Figure 8b) than when the gateway is  
Figure 8: Latency measured between the IoT gateway and the IoT server when the gateway is executed in the Raspberry Pi Kubernetes cluster (**a**) and when the gateway is executed in the Google Kubernetes Engine cluster (**b**).  
Figure 7: Temperature generated by BME280 and DHT22 sensors together with some emulated background traffic measured at the IoT gateway deployed in Google Kubernetes Engine cluster.  
Pi cluster, that is around the latency introduced by the network connecting the IoT gateway and the IoT server (100 milliseconds in Figure 8a).  
Obviously, the deployment of the IoT gateway at the edge is always a good choice when trying to execute some pre-processing activities in the gateway since it reduces the bandwidth between the gateway and the server. However, with these tests it is also possible to prove that deploying at the edge is better for applications with significant latency requirements. For instance, if a fast reaction on an actuator connected to the device is needed, as a result of a measurement obtained from a nearby sensor (e.g., onboarded at the same device), this reaction is executed faster if this trigger is accomplished in the gateway deployed at the edge (slice 1).
## 2 System Synthesis
### Communication Subsystem  
A **Communication Subsystem** will be needed for the communication of the SailREC System with other interfaces, e.g., a control centre onshore, other SailREC Systems, ships, or vessels in the vicinity. The Communication Subsystem will be used for the transmission of telemetry data, which will include the sensor and condition monitoring data and can also include scientific data. The Communication Subsystem will be required to receive control commands from an external, onshore control centre. There is also the possibility of using the same transponders to act as an alternate network at sea for ship communication.  
The hardware is mainly expected to comprise satellite communication and very high-frequency radio links. A combination of a low-cost and high-capacity, very small aperture service for use in most cases and an alternative service in the L-band can be an option. To ensure that the connection to shore is reliable, two independent communication channels with different systems and frequencies can be used. The end-to-end communication would need to be encrypted so as to protect it from access and threats arising from hostile parties [15; 16].
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 10 Future Research Directions
### Lightweight Cryptographic Solutions
Lightweight and Robust Anomaly Detection/Prevention Systems  
By utilizing machine learning and statistical techniques, lightweight anomaly detection/prevention systems can be implemented in smart grids with greater efficiency. To find unusual activity and unapproved access, these systems monitor device connections and network traffic. They function smoothly in smart grid activities, requiring little computational resources and latency. To remain safe from cyberattacks and operational interruptions, regular upgrades and fine-tuning are essential for keeping up with changing threats.
# Recent Updates on the Synthesis of Bioactive Quinoxaline-Containing Sulfonamides
## 2 Synthesis and Biological Activities
### Bentzothiazole Quinoxaline Sulfonamide Derivatives with Diuretic Activity  
Medications designed to increase the excretion of water and salt in urine are termed as diuretics. Diuretics are used as therapeutics in liver cirrhosis, epilepsy, edema, hypertension, heart failure, hypercalciuria, diabetes insipidus and in some kidney diseases. Their mechanism of action on distinct sites of nephrons involves the production of diuresis and inhibition of sodium ions re-absorption in the renal tubules of the kidney.  
A new multistep synthetic methodology was developed for the synthesis of a novel promising class of substituted quinoxaline sulfonamides as diuretic agents, assessed in vivo by [PERSON] and coworkers [127]. The condensation of \(o\)-phenylene diamine **34** with pyruvate acid **35** in methanol produced 3-methylquinoxaline-2\(H\)-one **36** with an 80% yield that was further reacted with substituted aldehydes **37** in the presence of piperidine to afford the substituted quinoxaline derivatives **38**. The condensation of 2-amino-benzothiazole-6-sulfonamide (**41**) with **38** in ethanol yielded the final quinoxaline sulfonamide derivatives **42** in moderate to good yield (60-82%) (Scheme 1) [127]. The derivatives with electron-donating groups showed the maximum yield, while the electron-withdrawing groups resulted in a minimum yield.  
The Lipschitz method was adopted to determine the diuretic activity of the thiazole moiety-based quinoxaline sulfonamide hybrids. Fifty-four healthy adult Wistar albino rats (180-200 g) were selected and divided into nine groups, and these were acclimized in standard environmental conditions for one week.  
The thiazole moiety-containing quinoxaline sulfonamide derivative **43** showed a remarkably high diuretic activity and Lipschitz value (1.13 and 1.28 values, respectively) compared to the standard reference drugs, acetazolamide and urea. The derivative **44** exhibited a moderate diuretic activity (value of 0.78) and Lipstchitz value (0.88). The structural motif **45** exhibited the least diuretic action among the three compounds (value of 0.56) and a Lipschitz value of 0.63 (Table 1). These results showed that diuretic activity is directly proportional to the Lipschitz value. The SAR showed that the presence of a strong electron-donating group (EDG), such as methoxy, on the benzene ring of scaffold **43** increases the diuretic activity, while the presence of strong electron-withdrawing group (EWG), such as an -NO\({}_{2}\) group, decreases the diuretic activity of the analog **45**. The presence of an -N(CH\({}_{3}\))\({}_{2}\) moiety on the benzene ring of derivative **44** induced a moderate diuretic effect (Figure 7). The SAR study revealed that the diuretic activity decreased in order of -OCH\({}_{3}\) > -N(CH\({}_{3}\))\({}_{2}\) > -NO\({}_{2}\). This indicated that EDG increases the diuretic activity, while EWG decreases the diuretic activity [127].  
Figure 6: General preparation of quinoxaline sulfonamides.
## 3 Proposed Methodology
### Characterization of the Root Cause of Fault
#### 3.1.3 Time-Frequency Domain Analysis Using Discrete Wavelet Transform (DWT)
The step is iterated until at the agreed level, which are four levels in this case, and this is known as iterated filter bank. The accumulation of detailed information is measured by resolution of the signal, which is altered by filtering operations and the scale is rectified by the down sampling and up sampling operations. The relation between low pass and high pass filters with the mother wavelet or known as scalar function \(\psi(t)\) and the wavelet function \(\phi(t)\) can be defined as follows:  
\[\phi(t)=\sum_{k}g[k]\phi[2t-k] \tag{22}\]  
\[\psi(t)=\sum_{k}h[k]\phi[2t-k] \tag{23}\]  
The relation between the low pass filter and high pass filter is not independent to each other, but instead they are related by:  
\[h[L-1-n]=(-1)^{n}g[n] \tag{24}\]  
where \(g[n]\) is the low pass filter, \(h[n]\) is the high pass filter, \(L\) is the filter length (total number of points). The impulse response \(h[n]\) is involved while dispatching the signal across a half band low pass filter. The mathematical of convolution operation of the signal while filtering processes in discrete time is defined as follows:  
\[A^{0}[n]\times h[n]=\sum_{k=-\infty}^{\infty}A^{0}[n]\cdot h[n-k] \tag{25}\]  
Here \(h[n]\) can be any filter's impulse response. \[y_{n}[k]=\sum_{-\infty}^{\infty}x[n]\cdot h[2n-k] \tag{26}\]  
Based on Equations (27) and (28), the high and low frequency are derived as following definition:  
\[y_{high}[k]=\sum_{n}A^{0}[n]\cdot g[2k-n] \tag{27}\]  
\[\psi\ (t)_{(a,b)}=\frac{1}{\sqrt{a}}\psi\left[\frac{t-b}{a}\right] \tag{19}\]0where \(y_{high}[k]\) and \(y_{low}[k]\) are the yields of the high pass and low pass filters after subsampling by 2. Here \(y_{high}[k]\) is denoted as the detailed component and \(y_{low}[k]\) denoted as the approximate component. \(A^{0}\) denoted as input signal. The algorithm of sub-band filter for high-pass filter and low pass filter of wavelet decomposition in levels of approximated and detailed coefficients were denoted in [32]. After the decomposition process, the wavelet signal will be reconstructed and all reconstructed energy level will be combined to obtain the fix value. At this stage, the higher noises, where the fault signal deviates more from the normal one, will produce more energy. However, an absolute energy value is evaluated above the set up threshold. In this case, the initiated threshold value is 10% of the maximum magnitude. Figure 10 illustrates the reconstructed coefficient energy that is extracted from voltage dip fault signal and used in this study.
###### Abstract  
Spatial Pattern (SP) Description, Identification, and Application Methodology (SPDIAM) was developed for describing and detecting spatial economic, social, and environmental phenomena and providing basic information technology (IT) artefacts that can be used for the spatial analysis development using GIS technologies. SPDIAM allows urban planning and design practitioners to describe SP in a computerized manner, identify SP automatically, and apply them in the spatial planning and design domain. In this article, we explain the general SP and spatial meta-pattern model, used in SPDIAM, that is based on the theory of Complex Spatial System (CSS), spatial configuration, and spatial capital concepts and is presented using UML diagrams as standard used for visualization of project models from structure and behavior points of views. The practical experiment of describing and identifying 6 basic spatial meta-pattern values is conducted using the new algorithm that combines Space Syntax method, Visibility Graph Analysis (VGA), and VGA measures to create a computer model of space and to quantify its configuration, which can then be used to handle geographic and geometric data associated with attribute information, to perform spatial, mathematical, and statistical calculations and to visualize SP. The results of the experiment show that the model and the algorithm are appropriate for spatial meta-patterns identification, and the best results can be achieved using VGA measure Isovisit Compactness. In the future, general SP and the spatial meta-pattern model can be used to describe and identify complex SP and to solve problems in CSS with the help of the spatial meta-pattern values described in this article.  
pattern analysis; geographic information system; space syntax; network theory; graph
# A Road Defect Detection System Using Smartphones
###### Abstract  
We propose a novel approach to detecting road defects by leveraging smartphones. This approach presents an automatic data collection mechanism and a deep learning model for road defect detection on smartphones. The automatic data collection mechanism provides a practical and reliable way to collect and label data for road defect detection research, significantly facilitating the execution of investigations in this research field. By leveraging the automatically collected data, we designed a CNN-based model to classify speed bumps, manholes, and potholes, which outperforms conventional models in both accuracy and processing speed. The proposed system represents a highly practical and scalable technology that can be implemented using commercial smartphones, thereby presenting substantial promise for real-world applications.  
automatic data collection; CNN; road defect detection; smartphone +
Footnote †: journal: Sensors
# Deep Learning-Based Optical Camera Communication with a 2D MIMO-OOK Scheme for IoT Networks
## 4 Implementation Results  
### SNR Measurement  
The SNR parameters were calculated and measured to analyze the relationship among range, power of transmitter, and exposure time. Through adjusting exposure time, we can improve the distance and SNR of a signal. In the experimental setup, multiple measurements were conducted at varying communication distances, ranging from 5 to 20 m. As shown in Figure 2, the transmitter side contained an 8 \(\times\) 8 LED matrix, while the receiver side included a rolling shutter camera (Point Grey camera). Moreover, during the measurements, the camera's exposure time was varied between 150 \(\upmu\)s and 300 \(\upmu\)s to evaluate its impact on communication performance at different distances (5, 10, 15, and 20 m).  
SNR measurements were conducted at distances of 20, 15, 10, and 5 m as shown in Figure 3. The SNR was considered based on the two operating states of the LED. When the LED is ON, the SNR reflects the signal power emitted by the LED. Equally, when the LED is OFF, the SNR represents the background noise level. The SNR (dB) can be determined as in Equation (1):  
\[SNR_{dB}=20\log\left(\frac{\sqrt{\frac{1}{n}\sum\limits_{i=0}^{n-1}{|A_{i}|}^{ 2}}}{\sqrt{\frac{1}{n}\sum\limits_{i=0}^{n-1}{|B_{i}|}^{2}}}\right) \tag{1}\]  
From that, \(A\) denotes the signal power from the LED, corresponding to the pixel amplitudes collected when the LED is \"on\". \(B\) denotes the background noise, measured as the pixel amplitude when the LED is \"off\". The variable \(n\) indicates the number of samples collected. At short distances, the pixel amplitudes are higher due to stronger signal strength, whereas at greater distances, the amplitudes decrease as the signal weakens.  
Figure 2: Devices for SNR measurement.