esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptation of Object Detection Algorithms for Road Indicator Lights in Complex Scenes
## 3 Results
### Datasets and Evaluation Metrics
The size of the images is 1080 \(\times\) 1920 pixels, including four labeling categories of red light (red), green light (green), yellow light (yellow), and off (off), with the numbers 4080, 6175, 375, and 1530, respectively. The numbers of the training set, test set, and validation set are 3967, 1128, and 1000, respectively. The Bosch Small Traffic Lights Dataset is a publicly available dataset released by Bosch Group (Bosch) to promote the further development of traffic light detection technology in the field of autonomous driving. The dataset focuses on the detection of traffic signals in small sizes, which is a common and challenging task in real road scenes. Since traffic signals often occupy only a small pixel area in the long-range field of view of a self-driving  
Figure 4: Sample diagram of headlight dataset in vehicle inspection environment. Figure 3: Headlight dataset visualization. car, they are nevertheless crucial for the correct response of the decision-making system. This dataset is divided into two parts, the training set and the test set, where the images are in 1280 \(\times\) 720 resolution and contain a total of 13,427 images, from which 10,563 are selected for training and learning, 1864 are used as the test set, and 1000 are used to validate the training results. The dataset is labeled with four categories: red, yellow, green, and false. In this paper, mean Average Precision (mAP) including mAP50 and mAP50:95 was used as the accuracy evaluation index of the model. mAP50 and mAP50:95 are the common indices in the area of object detection, which were used in Pascal VOC 2012. Also, those two indices were used by YOLOv8 to evaluate the model performance, and the values of the indices are both positively correlated with the model performance. The calculation is as follows:  
\[P=\frac{TP}{TP+FP} \tag{6}\]  
\[R=\frac{TP}{TP+FN} \tag{7}\]  
\[mAP=\frac{\sum AP}{N(class)} \tag{8}\]  
where TP represents the number of correctly detected objects, FP represents the number of incorrectly detected objects, FN represents the number of missed objects, P represents the accuracy rate, R represents the recall rate, AP value refers to the area under the P and R curves, AP represents the sum of the AP values of all the classes, and N (class) represents the total number of classes. mAP50 denotes the intersection and union ratio (Intersection over Union (IOU) of 0.50 to calculate the mean value of AP for each category. And mAP50:95 refers to the mean AP value when the IOU is from 0.50 to 0.95 with an interval of 0.05. Both P and R were calculated with an IOU threshold of 0.50.
# On the Need for a General REST-Security Framework
## 5 Related Work Analysis
### Oscore  
Object security for CoAP (OSCORE) [59] is a draft standard providing encryption, integrity and replay attack protection for CoAP messages. The CoAP message payload and a set of security-relevant headers are protected by OSCORE. Still, some other security-critical header entries including the token length, message ID, token and max-age option as well as the meta information for the body length are left unprotected. The reason why leaving out the first four meta-data lies in the fact that these entries may be changed by intermediate nodes. However, not considering these meta-data elements opens the door for man-in-the-middle attacks. Possible attack vectors can be spoofing the message ID and token in order to provoke a mismatch between requests and responses. Also, sending the max-age option without any protection is critical, as it has a similar functionality like the cache-control header in HTTP. When it gets tampered, the freshness of the response is corrupted analogously to attack six in Table 1. On the contrary, signing these header entries to avoid the aforementioned attacks prevents middleboxes from changing these elements. Thus, leaving out these header entries from the protection sphere and protecting these meta-data elements induce issues on both sides. To resolve this problem, an enhanced approach has to ensure the integrity of these header entries and it must allow intermediate systems to modify the meta-data elements simultaneously. Moreover, OSCORE does not consider protecting the integrity of the body length meta information. The reason behind this might be that no header for the body length is specified in the CoAP standard, as this information must be extracted from the UDP packet. This omission enables the manipulation of the body as manifested by attack four in Table 1.  
According to the specification, messages protected by OSCORE are not intended for being cached. Each response is strictly bound to its corresponding request. Not supporting the option to cache messages may lead to a low scalability and violates the cacheability principle, which is one of the most vital REST constraints. Also, the OSCORE specification does not provide a protection for acknowledgment and reset messages. As both messages are utilized to confirm or reject a CoAP request or response, they must be secured as well. This prevents attacker from replacing an acknowledgmentmessage by a reset message or vice versa. Another issue of OSCORE is a missing description that lists additional or application-specific header entries to be signed and/or encrypted.
# High-Precision Pseudo-Noise Ranging Based on BOC Signal: Zero-Bias Mitigation Methods
###### Abstract  
In high precision applications based on binary subcarrier offset (BOC) signals, zero-bias of the digital discriminator is an error of importance. Unlike the thermal noise error, zero-bias is a fixed deviation that is challenging to eliminate by filtering in the time domain. In this paper, a statistical error analysis model for the zero-bias of BOC signal's digital phase discriminator is established. The evaluation of the zero-bias is inseparable from the spreading code sequence and the initial phase of the signal through defining the concept of statistics maximum and statistics standard deviation. Based on the zero-bias statistical error analysis model, two receiver parameter design methods, namely, the baseband signal sampling frequency and the early-late correlation interval, are proposed. The performance of the algorithm is simulated on account of the limited bandwidth, Doppler frequency offset and thermal noise. The simulation results prove that the proposed algorithm can suppress the standard deviation of zero-bias within one phase resolution, which contributes substantially to the improvement of the measurement accuracy of pseudo-noise ranging.  
GNSS; BOC; noncommensurate sampling; zero-bias Article
# Machine Learning-Based Classification of Mushrooms Using a Smartphone Application
###### Abstract  
Worldwide, a large number of cases of harmful mushroom exposure and consumption result in hallucinations, sickness, and death. One contributing factor is that certain poisonous mushrooms closely resemble their edible counterparts, making it difficult for general public collectors to distinguish one from the other. We propose a method to classify mushroom types from field-collection images using a smartphone application based on a convolutional neural network. The application helps people without proper mycology background or training to distinguish poisonous mushrooms from edible ones with which they may be confused. We showed three case studies to classify two-, three-, and five-class models by optimizing their training steps by cross-validation. An android app was developed by transferring the server-based trained model and allowing users to obtain probability scores for the correct genus classification. Our experiments showed that this method could provide sensitivity and specificity of two-, three-, and five-class mushroom models ranging from 89% to 100% using an image from the field with diverse backgrounds and objects.  
smartphone; mushroom; image classification; machine learning +
Footnote †: journal: Medical Engineering, Purdue University, West Lafayette, IN 47907, USA
machine learning algorithms to assist decision-making when visual observations cannot be used for accurate discrimination of the samples [16; 17; 18; 19].  
Here, we propose utilizing a conventional smartphone as a mushroom-image classifier that can provide reliable categorization of mushroom images and provide necessary safety information for both public and professional mushroom pickers. Multiple approaches to analyzing mushrooms using machine learning employing support vector machines (SVMs) [20; 21], decision trees, naive Bayes, and ensemble algorithms based on mushroom data from the UCI Machine Learning Repository [22], which contains 22 morphological features, have been described. However, the result generated from this mushroom database assumed that morphological characteristics such as gill shape and cap shape had already been extracted objectively and that all the data were already in numerical formats as opposed to the raw images. If an application were developed utilizing the methods, users would still be required to manually extract the mushroom's morphological attributes, which may be based on a subjective interpretation of the morphological traits. In addition, the ecosystem in which particular mushrooms are found is another critical feature mycologists utilize for identification. Regrettably, the morphological feature-based classifications might not incorporate this information into the classifiers. This paper proposes a method to classify mushrooms belonging to several easily confused genera. In order to achieve this task, we trained and tuned deep convolutional neural network models [23]. Subsequently, the models were integrated into an Android mobile application so that anyone could determine the edibility of mushrooms by photographing them in situ.
## 5 Analysis of Factors Affecting Tunnel Deformation
### Sensitivity Analysis of Enclosure Parameters  
Previous research has demonstrated that the depth of the diaphragm wall's embedding has little impact on tunnel deformation [29]. We adjusted the thickness of the diaphragm wall and the stiffness of the support structures to examine how changes in these retaining structure parameters affect the maximum horizontal displacement of the tunnel.  
Figure 14a shows the effect of varying the diaphragm wall's thickness on tunnel displacement. The data reveal that, although increasing the wall's thickness can somewhat mitigate tunnel displacement, the degree of control is limited. For instance, a diaphragm wall 1.4 m thick reduces tunnel displacement by only an additional 1.71 mm compared to a 0.6 m wall, which is insufficient to meet deformation control standards. Additionally,  
Figure 13: Tunnel deformation (\(\times\)200).  
the benefit of increasing wall thickness diminishes progressively; it does not enhance deformation control linearly, as it tapers off instead.  
Referring to the cross-sectional dimensions in Table 3, we explored the impact of support stiffness on tunnel displacement by modifying the support's cross-sectional area. As shown in Figure 13(b), enhancing the stiffness of the support more effectively controls lateral tunnel displacement than merely increasing the wall's thickness. A support with 1.4 times the stiffness decreases the displacement by an extra 2.82 mm compared to a support with 0.6 times the stiffness, although the restraining effect remains modest. Furthermore, the effectiveness of controlling deformation reduces as support stiffness increases. It is crucial to recognize that choosing supports with lower stiffness could worsen the tunnel's horizontal displacement, making it impractical to reduce support stiffness in engineering applications.
# End-to-End Delay Model for Train Messaging over Public Land Mobile Networks
## 6 Results
### Statistics of the Random Variables \(X_{0}\), \(X_{1}\) and \(X_{h}\)
Due to the reduced number of collected E2E data for \(X_{H}\) statistics, we observe instabilities in the corresponding distributions especially at received power levels below \(-95\) dBm. In Figure 10, we report the PDFs of the random variable \(X_{1}\) obtained from experimental data ranked by technology and by the received power interval. From Figure 10, the experienced E2E delays are about 200 ms, showing that, most of the time, the behavior of the channel is almost regular in the Q state. Furthermore, the left-tails show that when the link bit rate of the radio channel is larger than the minimum required to transmit one message before another is generated by the application, more than one enqueued message can be transmitted at the same time, thus causing rapid emptying of the queue. From a practical point of view, it means that the received time associated to enqueued messages can be lower than 200 ms. The \(X_{1}\) can also assume values even greater than 10 s with non-zero probability as shown in the log-log scale graphs in Figure 10.  
Statistics of \(X_{0}\), \(X_{1}\) and \(X_{H}\) have been obtained from about 3/4 of the E2E delay samples measured with days. The remaining E2E experimental samples have been used for validation (see the next section). Figure 10: Probability Density Functions of the random variable \(X_{1}\) obtained from experimental data ranked by technology and the received power interval: WCDMA 2100 (**a**), WCDMA 900 (**b**), GSM 900 (**c**). Before concluding this section, we would like to point out some aspects concerning the extension of the proposed E2E model to other railway scenarios. For the application of the model, it is important to have characterized the railway line in terms of the handover (roaming) points, the received power profile and the serving radio technologies over the line. These data can be measured and/or can be determined using typical radio planning tools. In this section, we have made evident that the variability of the statistics of \(X_{0}\), \(X_{1}\) and \(X_{H}\) can be (mainly) related to the previously considered rail line parameters. Since networks of different MNOs use similar technologies/apparatus in the radio access and the core, it is expected that they could offer similar performance. In particular, we could safely assume that every MNO should provide a message transport service with E2E delays similar to that measured during our campaign. Thus, it is reasonable to assume that the statistics for \(X_{0}\), \(X_{1}\) and \(X_{H}\) should be similar to that presented in this paper even when considering different MNOs.
## 2 Key Theories and Technical Points of Ground Source Heat Pump  
### Heat Resources  
According to the viewpoints of civil engineers, artificial heating methods are expensive for application in most civil engineering projects. In other words, heating technologies have advanced significantly. The required heat can be supplied from different sources such as electrical heaters, boilers, cogeneration plants, heat pumps, solar thermal collectors, and thermal storage equipment [16].  
Heat boilers use fuels and rapidly release a large amount of heat energy. However, their smooth operation requires large amounts of heat and expensive heat exchangers and radiators. Consequently, boilers have high capital and operating expenses. Electrical heaters run on commercially purchased electricity; hence, they increase the demand for electricity. The construction and operation of boilers and electric heaters is costly during embankment heating, thereby placing undue pressure on environmental conservation and sustainable development. Hence, using renewable energy for embankment heating is the most effective approach.  
Renewable thermal energy includes solar and geothermal energy. Solar energy is inexhaustible and free; however, its intensity and temperature are low in winter. The outputs of solar thermal collectors are affected by numerous factors, including the intensity of the solar irradiation and the flow rate of hot water. Conversely, the ground temperature is primarily influenced by the ambient temperature up to a few meters below the surface. Typically, the temperature of the ground rises with depth at a rate of 0.03 \({}^{\circ}\)C /m. However, the ground temperature may rise to 90 \({}^{\circ}\)C, even at a depth of 100 [17]. High underground heat levels may result from the absorption of solar radiation on the surface. However, during winter, the temperature decreases while the thermal inertia increases with depth. Hence, compared to the almost constant low underground temperature, the rapid changes in the Earth's surface temperature are reduced.  
A heat pump can be used to extract heat from the relatively warm ground and subsequently transfer it. [PERSON] [18] conducted a survey on the feasibility of a geothermal heat pump for residential areas in China. According to the findings illustrated in Figure 1, the utilization of geothermal energy for heating purposes in cold regions is an appropriate and cost-effective approach. Recently, GSHP technology has been extensively investigated as a highly effective HVAC system, exhibiting superior characteristics compared to other technologies.  
The heat capacities, efficiencies, and reliabilities of the four techniques (boilers, electric heaters, solar thermal collectors, and heat pumps) were compared. Furthermore, the drawbacks and benefits of these techniques were outlined for a clear comparison. Subsequently, a GSHP was the most beneficial for embankment heating, since it uses freely available energy, which is abundant even in winter and is inexhaustible.
## 3 Numerical Example
### Training Data Selection from Generated Earthquake Samples  
Figure 12 reveals the distribution of the intensities of the training set using the violin plot format. To compare the statistics of each set, all intensities are scaled between zero and one by min-max normalization based on the entire set. In Figure 12a, the violin plots of the full set are shown. Comparing this set with a randomly chosen sample set, shown in Figure 12b, the issue becomes clearer. Observing the randomly chosen training distribution, one immediately realizes that the most severe earthquakes are not covered. However, using our new selection strategy enables covering a broad range of data that include a large share of high- and low-intensity values, as shown in Figure 12c. Therefore, these events are covered more likely in the training set. The presented distributions in Figure 12c are based on the selection using two intensities, and Figure 12d is based on the selection using three intensities. The strategy clearly shows the anticipated effect on the training set. The distributions based on two or three dimensions do, however, not change significantly.  
Increasing the number of intensities that contribute to the selection process, the number of bins needs to be decreased. Otherwise, the number of training samples increases and the computational benefit of the whole strategy vanishes. Using six intensities for the selection process, the probability density function of each intensity shows an uneven distribution, as shown in Figure 13. The peaks of these distributions correlate with the number of the selected bins, and the share of the higher selected Intensities decreases except for the peak ground acceleration. Therefore, we propose to select training data from a relatively small number of intensities, i.e., two or three different features.  
Figure 11: Numerical example to demonstrate the strategy: (**A**) Two-bay-tree-story frame structure; (**B**) three-bay-two-story frame structure; (**C**) two-bay-five-story frame structure.  
Figure 12: Violin plots [52; 53] show the distribution of the intensities, which are used as inputs for the neural network; **(a)** full set, 10,000 samples; **(b)** 500 randomly chosen samples from the full set; **(c)** 438 samples are chosen as training data based on the proposed strategy using two intensities (velocity spectrum intensity \(\int_{0.1}^{2.5}S_{\varphi}\) and the spectral displacement \(S_{d}(T_{1})\)) with 34 bins each; **(d)** 565 samples are chosen as training data based on the proposed strategy using three intensities (velocity spectrum intensity \(\int_{0.1}^{2.5}S_{\varphi}\), the spectral displacement \(S_{d}(T_{1})\) and the spectral acceleration \(S_{a}(T_{1})\)) with 19 bins each.  
Figure 13: Violin plots [52; 53] show the distribution of the intensities, which are used as inputs for the neural network; 291 samples are chosen as training data based on the proposed strategy using the shown six intensities with 5 bins each.
###### Abstract  
Distribution system state estimation (DSSE) is a valuable step for DSOs toward tackling the challenges of transitioning to a more sustainable energy system and the evolution and proliferation of electric cars and power electronic devices. However, on the LV level, implementation has only taken place in a few pilot projects. In this paper, an LV DSSE method is presented and implemented in four real Hungarian LV supply areas, according to well-defined scenarios. Pseudo-measurement datasets are generated from AACs and SLPs, which have been used in different combinations on networks built with different accuracies in terms of load placement. The paper focuses on the critical aspects of finding accurate and coherent information on network topology with automated management of information systems, real LV network implementation for power flow calculation and managing portions of the network characterized by uncertain or inconsistent line lengths. A refining algorithm is implemented for the integrated network information system (INIS) models. The published method estimates node voltages with a relative error of less than 1% when using AACs, and a meter-placement method to reduce the maximum value of relative errors in future scenarios is also presented. It is shown that the observation of node voltages can be improved with the usage of AACs and SLPs, and with optimal meter placement.  
low-voltage distribution grid state estimation; power system state estimation; pseudo-measurement data generation; averaged annual consumptions; standard load profiles; weighted least square; optimal meter placement; observability; voltage monitoring; real-time energy management +
Footnote †: journal:  
Article