esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Characterization of Signal Variation by Temperature Variation--Test Model 1  
The system response for temperatures ranging from 40 \({}^{\circ}\)C to 140 \({}^{\circ}\)C is shown in Figure 9.  
For the temperature range between 40 and 100 \({}^{\circ}\)C, S21 increases continuously, from \(-1.4\) to \(-0.65\) dB, with a variation of +0.71 dB, indicating that for this transducer/test specimen set, there is a small improvement in signal transmission up to approximately 100 \({}^{\circ}\)C. Above 100 \({}^{\circ}\)C, the transmission efficiency between the transducers declines rapidly. However, communication is maintained up to the maximum tested temperature of 140 \({}^{\circ}\)C, with a signal decay of approximately \(-1\) dB, as indicated in Figure 9.  
It should be noted that this signal remains robust for the proposed application. It presents values that maximize the S21 signal, ranging from \(-1.6\) dB to almost 0.6 dB, which results in excellent communication between the transducers.  
Figure 8: Schematic of the Test Model 4—the tensile test with temperature variation. **(a)** Photograph of the Instron—model E10000; **(b)** Test Model 2 diagram. (1) The test controller computer; (2) Instron model E10000; and (3) the climatic chamber coupled to the Instron machine; (4) the vector network analyzer which sends and receives signals S21; **(5)** zoom-in on the test specimen inside the thermal chamber coupled to the Instron machine.
## 3 Results and Analysis
### Failure Mode of Jointed Specimens with Various JRCs and Joint Inclination Angles  
The failure mode of jointed specimens was recorded and analyzed after the impact tests. Figure 5 displays failure photographs of jointed specimens with various joint inclinations when JRC is 0, and Figure 6 presents failure photographs of jointed specimens with various JRCs when the joint inclination angle is 45\({}^{\circ}\).  
The final failure of jointed specimens can be attributed to joint surface slip, shear, or tension, and the failure mode varies with JRC and joint inclination angle. The failure mode of the specimen can be mainly classified into three types.  
1. Splitting tensile failure parallel to the joint surface. As depicted in Figure 5a,e and Figure 6e, jointed specimens predominantly exhibit splitting tensile failure parallel to the joint surface, resulting in the upper and lower parts of jointed specimens fracturing into multiple uniform-shaped small pieces, along with a few fragments. Regardless of the JRC, the failure of joint specimens with joint inclination angles of \(0^{\circ}\) or \(90^{\circ}\) is mainly attributed to splitting tensile, accompanied by a notable lateral expansion phenomenon.
2. Shear slip failure along the joint surface. As shown in Figure 5c, the joint surface with JRC of 0 is smooth at this time, hence the shear strength is very low, and the relative dislocation of the joint surface occurs, leading to the shear slip failure. Moreover, the cement mortar on both sides of the slip surface are almost complete, and no penetrating cracks appear.
3. Compound failure of splitting tensile and shear. As depicted in Figure 6b-d, when the joint inclination angle decreases or the JRC increases, the failure mode of jointed specimens transfers from shear slip failure to compound failure of splitting tensile and shear. The failure surface partially extends through the cement mortar and partially through the joint surface. Figure 6c,d illustrate that the failure surface involves both cement mortar and joint surface, resulting from the combined failure of tension failure in the cement mortar and shear failure along the joint surface.
## 4 Experimental Evaluation
### Experimental Setup
#### 4.1.2 Equipment Requirements  
During the training phase, the Adam optimizer is used to train the model. The learning rate is set to 0.003 based on preliminary experiments, where this value provided a good balance between convergence speed and stability. We observed that smaller learning rates led to slower training processes, while larger rates often resulted in unstable training dynamics. The entire network is optimized over 50 epochs, which was determined by monitoring the validation loss; performance improvements plateaued around 50 epochs, indicating that further training would not significantly benefit the model. A batch size of 64 was chosen after evaluating several options. This size allowed us to effectively utilize GPU memory while maintaining a stable gradient estimate, which is crucial for model convergence. We trained with PyTorch 1.9.3 as the software environment, Intel\({}^{\circledr}\) Core\({}^{\circledr}\) 9-1900K @ 3.50 GHz (Intel Corporation, Santa Clara, CA, USA), 64 GB RAM(Kingston Technology Company, Inc., Fountain Valley, CA, USA) and NVIDIA GeForce RTX 3090 (NVIDIA Corporation, Santa Clara, CA, USA) as the hardware environment.
# Shelf-life Assessment on European Cucumber Based on Accelerated Temperature-Humidity Stresses
## 3 Results
### Degradation and Statistical Analysis
It was observed that as weight loss increases, firmness, \"Brix, and pH are significantly altered. Moreover, the scale indicates weight loss values between 1 and 19%. On the basis of the correlation plot and the effect of changes on quality characteristics, the point of failure for cucumbers without a cover was determined to be 5%. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Source** & **Degrees of Freedom** & **Sum Squares** & **Mean Squares** & **F-Value** & _p-Value_ \\ \hline Model & 13 & 601,594,098 & 46,276,469 & 116.28 & 0.000 \\ \hline Linear & 5 & 458,050,430 & 91,610,086 & 230.19 & 0.000 \\ \hline Temperature & 2 & 173,051,457 & 86,525,729 & 217.41 & 0.000 \\ \hline Humidity & 2 & 157,847,555 & 78,923,778 & 198.31 & 0.000 \\ \hline Cover & 1 & 12,204,962 & 12,204,962 & 30.67 & 0.000 \\ \hline
2-Way Interactions & 8 & 134,191,318 & 16,773,915 & 42.15 & 0.000 \\ \hline Temperature*Humidity & 4 & 93,275,372 & 23,318,843 & 58.59 & 0.000 \\ \hline Temperature*cover & 2 & 44,029,652 & 22,014,826 & 55.32 & 0.000 \\ \hline Humidity*cover & 2 & 1,145,869 & 572,935 & 1.44 & 0.243 \\ \hline Error & 88 & 35,022,097 & 397,978 & & \\ \hline Lack-of-Fit & 3 & 19,282,700 & 6,427,567 & 34.71 & 0.000 \\ \hline Pure Error & 85 & 15,739,397 & 185,169 & & \\ \hline Total & 101 & 636,616,195 & & & \\ \hline \end{tabular}
\end{table} 
Table 6: Regression ANOVA table with significance of factors of weight loss. * is indicating the interaction between the factors.
## 3 Results
### Thermal Analysis Results  
The DSC graphic of polyureas A and B is shown in Figure 3. In this graphic, it is possible to observe that polyurea A has two peaks which correspond to two degradation stages. The first peak, around 330 \({}^{\circ}\)C, is attributed to the degradation of the hard segment part of the polyurea due to the relatively low thermal stability of the urea group. Previously, studies using hyphenated techniques have revealed that this initial decomposition step leads to evolution of carbon dioxide primarily. The subsequent mass loss, around 410 \({}^{\circ}\)C, occurs due to the pyrolytic decomposition of the soft segments, polyether linkages in the present case, leading to evolution of hydrocarbons in larger amounts [25]. The mass loss associated with the first and second stages vary with the percentage of hard segment present in the composition. The DSC curve of polyurea B is similar to the DSC curve of polyurea A. However, the first degradation stage starts closer to 300 \({}^{\circ}\)C, while the second degradation stage of this polyurea is not so distinguishable as was in the case of polyurea A.  
Figure 2: Comparison of FTIR spectra of polyureas A and B.  
The TGA analysis performed on polyurea A is shown in Figure 4. In analyzing the TGA graphic presented, it is possible to observe that the DTG curve that corresponds to the derivative loss of mass for polyurea A (blue line) has two peaks, representing the two degradation stages defined in the DSC graphic of Figure 3. These two degradation stages of polyurea A correspond to a total mass loss of 93.1%, represented by the black line.  
The blue line in Figure 5 represents the DTG curve of polyurea B. In this curve the presence of two stages of polyurea degradation is clearer, contrary to what was seen in the DSC curve of this material. The first degradation stage starts at around 300 \({}^{\circ}\)C, while the second one is near 400 \({}^{\circ}\)C. The polyurea B tested sample presented a mass loss of 96.3%,  
Figure 4: TGA analysis of polyurea A.  
Figure 3: DSC plot of polyureas A and B.  
represented in the graphic of the Figure 5 by the black line. From the TG curves, presented in Figures 4 and 5, it is possible to observe that the first step is shorter than the second degradation stage, on the case of polyurea A, meaning that this polyurea has less hard segments in its composition. The reverse happened in the case of polyurea B, that has a longer first step of degradation, thus has more hard segments in its compositions than soft segments. Like it was referred beforehand, the hard segments are responsible for the shock-wave attenuation ability of polyurea.
## 3 Experiment and Parameter Setup
### Model Parameter  
Statistical methods were used to determine the unknown parameters of the membership functions. For one stage, 900 paralleling \(x\) values were obtained from the corresponding paralleling sludge samples in every September from 2014 to 2018. All 2700 \(x\) values from the three stages were made statistical analysis. Values of \(x_{\text{layer}}\), \(x_{\text{Isd}}\), \(x_{\text{Imin}}\), \(x_{\text{Imax}}\), \(x_{\text{IImin}}\), \(x_{\text{IIaver}}\), and \(x_{\text{IIImax}}\) are 4.8, 2.3, 0.0, 2.0, 15.8, 17.2 and 20.0, respectively. The value of \(x\) is speculated to range from 0.0 to 20.0 based on the minimum and maximum. According to condition (3)., parameters \(u_{1}\) and \(\sigma_{1}\) are expected to be 4.8 and 2.3. Parameters \(u_{2}\) and \(\sigma_{2}\) are required to satisfy the conditions of \(u_{2}\)-2\(\sigma_{2}\) and \(u_{2}\)+2\(\sigma_{2}\) equaling 4.8 and 17.2, respectively, thus, \(u_{2}\) and \(\sigma_{2}\) were calculated to be 11.0 and 3.1. Parameters \(c_{1}\) and \(c_{2}\) are 2.0 and 15.8, and \(\tau_{1}\) and \(\tau_{2}\) should be above 3.45 and 1.64 based on condition (3). To further analysis the effect of \(\tau_{1}\) and \(\tau_{2}\) on the gradients of basic membership functions \(A(S_{\text{II}})\) and \(A(S_{\text{III}})\), \(\tau_{1}\) was set to be 2.5, 3.5, 5.0, 10.0 and 18.0, and \(\tau_{2}\) was set to be 1.0, 1.7, 3.5, 6.0 and 10.0, respectively. Curves of the four basic membership functions in Equation (2) are drawn in Figure 4.  
As is shown in Figure 4, the relatively low \(x\) produces the higher membership of \(S_{\text{II}}\) compared with memberships of \(S_{\text{I}}\), \(S_{\text{III}}\), and \(S_{\text{un}}\), while the relatively large \(x\) generates the bigger \(A(S_{\text{III}})\) compared with other three memberships. With the increase of \(x\), \(A(S_{\text{II}})\) falls rapidly to 0, while \(A(S_{\text{III}})\) climbs lately to 1. As \(x\) increases, \(A(S_{\text{I}})\) goes up to 1 fast and then goes down, while \(A(S_{\text{un}})\) rises slowly to 1 and then descends. \(A(S_{\text{II}})\), or \(A(S_{\text{II}})\), or \(A(S_{\text{un}})\), or \(A(S_{\text{III}})\) predominates within a certain range of \(x\). Furthermore, the larger the values of \(\tau_{1}\) and \(\tau_{2}\), the bigger the gradients of \(A(S_{\text{II}})\) and \(A(S_{\text{III}})\), and the smaller the uncertainties of fuzzy sets \(S_{\text{II}}\) and \(S_{\text{III}}\). When \(\tau_{1}\) and \(\tau_{2}\) exceed 10.0 and 6.0, respectively, the two fuzzy sets almost convert to the non-fuzzy sets with the threshold of \(x\) values being 2.0 and 15.8. In this model, 3.5 and 1.7 are set to be the values of \(\tau_{1}\) and \(\tau_{2}\). Figure 5 describes the trends of the normalized membership functions in Equation (4) with the parameters determined above. As can be seen, \(S_{\text{II}}\) is predominant when \(x\) is below 2.01, \(S_{\text{I}}\) and \(S_{\text{un}}\) play decisive role when \(2.01<x<7.45\) and \(7.45<x<15.45\), respectively, and it is most probable of \(x\) values belonging to \(S_{\text{III}}\) when \(x\) is above 15.45.
# Photonic Crystal Cavity with a Thin Low-Index Layer for Silicon-Compatible Nanolight Source
###### Abstract  
The development of an efficient silicon-based nanolight source is an important step for silicon-based photonic integrated circuits. We propose a high quality factor photonic crystal nanocavity consisting of silicon and silica, which can be used as a silicon-compatible nanolight source. We show that this cavity can effectively confine lights in a low-index silica layer with a high confinement factor of 0.25, in which rare-earth dopants can be embedded as gain materials. The cavity is optimized to have a high quality factor of 15,000 and a mode volume of 0.01 \(\upmu\)m\({}^{3}\), while the resonance has a wavelength of 1537 nm. We expect that the high confinement factor in the thin silica layer and the high quality factor of the proposed cavity enable the cavity to be a good candidate for silicon-compatible nanolight sources for use in nanolasers or light-emitting diodes in the telecommunication wavelength region.  
photonic crystals; cavity; Purcell factor; slot mode +
Footnote †: journal: Applied Sciences
# The Impact of Cloud Versus Local Infrastructure on Automatic IoT-Driven Hydroponic Systems
###### Abstract  
Technological advancements in the cloud field are becoming widely used on a large scale in increasing activity sectors. Agriculture is an important domain in everyday life, central to human existence. This research comparatively analyzes two proposed types of infrastructures that optimize the growth flow of plants in a hydroponic system for continuous monitoring, one full-cloud and one full-local. The study's main objective is to determine which of the two infrastructures is more suitable for the hydroponic scenario by conducting seven types of tests. This research aims to fill a gap in the specialized literature through a detailed analysis of the configuration, implementation methods, and all implications of the two approaches from the perspective of the seven indicators. The seven indicators are response time, operational reliability, implementation costs, operational costs, configuration scalability, data accessibility, and data security. The cloud infrastructure uses Microsoft Azure technologies, while the local variant uses custom-made scripts and locally installed services. For both software infrastructures, the hardware components are identical, including an MSStack module with sensors for monitoring temperature, humidity, electrical conductivity, and liquid level in the hydroponic container. The test results highlight that the local infrastructure offers a shorter response time (200 ms compared to 300 ms for the cloud infrastructure). The results also showed lower operational costs for the local infrastructure, making it more suitable for autonomous hydroponic systems. On the other hand, the results showed that cloud infrastructure has greater data accessibility than local infrastructure, and the security measures are advanced. These advantages of cloud infrastructure involve recurring costs of USD 82.57/month. The limitations of this research are associated with the exclusion of human errors and cybernetics simulations from the analysis. Another limitation concerns the real analysis of short-term costs. Future research will explore the real fluctuations of long-term costs. Additionally, infrastructure studies on different plant species and hydroponic farms will also be considered.  
hydroponic system performance; infrastructure comparison; operational costs analysis; data security in IoT; machine learning in agriculture
# Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network
## 3 Thermal Bridge Detection Method Using Neural Network
### Thermal Anomaly Area Clustering  
#### 3.1.1 Temperature Clustering  
Thermal anomalies in building envelopes, including thermal bridges, have a clear temperature difference from normal areas. These characteristics are used to detect the thermal anomalies of buildings through thermal images. Previous studies have confirmed that the temperature distribution of normal areas and abnormal areas with thermal anomalies follows a multi-modal Gaussian distribution [8; 12]. The Gaussian Mixture Model (GMM) is a clustering method that configures multiple Gaussian components, to perform clustering [21]. Due to the nature of clustering based on Gaussian components, this study applied GMM for clustering between similar temperature distributions.  
The temperature clustering based on GMM, proposed in this study, is performed as follows. First, clustering is performed by applying GMM with the temperature of each pixel constituting the thermal image as input. As shown in Figure 1(a), the results produce mixture components with K Gaussian distributions. Figure 1(a) shows the temperature distribution histogram for four mixture components. Figure 1(b) shows the location of each distribution in the thermal image, and the colors between Figure 1(a),b correspond to each other. As shown in the figure, temperatures belonging to the same mixture component are regarded as clusters with the same temperature characteristics. The Bayesian Information Criterion (BIC) is applied to determine K, which represents the number of mixture components. BIC is the most widely used method to determine the optimized K of GMM [22]. Since the K-value to minimize BIC is the optimized K-value in GMM fitting, this study set the range for finding the optimal K from 2 to 8 and selected the K-value with the lowest BIC value.
## 2 Materials and Methods
### Statistical Analysis
#### 2.3.3 Data Insertion Performance of the IoT Platform  
The IoTP was responsible for maintaining communication with multiple central controllers as well as ensuring data security. In the performance test of the IoTP, throughput rate (TR) and response time (IoTP_RT) were important indicators. TR is calculated as shown in Equation (5) and refers to the number of requests processed per second. A larger TR indicates that the IoTP can connect with a greater number of central controllers.  
\[\mathrm{TR}=\frac{\mathrm{NoR}}{\mathrm{T_{c}}} \tag{5}\]  
where NoR is the number of requests processed by the IoTP and T\({}_{c}\) is the total time taken to process all requests in ms.  
IoTP_RT is calculated as shown in Equation (6) and refers to the time taken to process a request. The smaller the response time, the faster the IoTP processes the request.  
\[\mathrm{IoT\_RT}=\mathrm{T_{f}-T_{s}} \tag{6}\]  
where T\({}_{f}\) is the time in ms at which IoTP finishes processing the request and T\({}_{s}\) is the time in ms at which the IoTP starts processing the request.
# Pointer Meter Recognition Method Based on Yolov7 and Hough Transform
## 3 Pointer Meter Recognition Method
### PGNet  
Optical Character Recognition (OCR) is the process of converting scanned or digital images of text into editable and searchable text data. The technology behind OCR enables computers to recognize and process written or printed text, making it possible to extract, store, and manipulate textual information from a variety of sources.  
In recent years, end-to-end OCR algorithms have made good progress, including the MaskTextSpotter series, TextSnake, TextDragon, PGNet series, etc. Among these algorithms, PGNet has features that other algorithms do not have: PGNet loss is designed to guide training: no character-level annotation is required, NMS and ROI-related operations are avoided, and the prediction speed is improved. A module for predicting the reading order within a text line Is proposed. A graph-based rectification module (GRM) is introduced to further improve the recognition performance. It makes the recognition accuracy higher and  
Figure 6: The flow chart of Hough transform.  
the prediction speed faster [30]. The schematic diagram of the PGNet algorithm is shown in Figure 7.  
The input image will enter four different branches through feature extraction: the TBO (Text Edge Offset Prediction) module, the TCL (Text Centerline Prediction) module, the TDO (Text Orientation Offset Prediction) module, and the TCC (Text Character Classification) graph prediction) module. The output of TBO and TCL can get the text detection result after subsequent processing, while the TCL, TDO, and TCC are responsible for recognizing the text.
# Analysis of the Effect of Temperature on the Sound Transmission Loss of a Curved Plate
## 3 Model Validation
### Effect of Pitch and Azimuth Angles  
Random incidence is a desirable condition, and realistic scenarios often occur where sound waves are excited at a fixed angle. At \(T_{2}=0\)\({}^{\circ}\)C and \(T_{1}=-50\)\({}^{\circ}\)C, the variation in STL with frequency is shown in Figure 9, where the azimuth angle is kept at \(0\)\({}^{\circ}\), and the pitch angle is set at \(0\)\({}^{\circ}\), \(30\)\({}^{\circ}\), \(60\)\({}^{\circ}\), and \(90\)\({}^{\circ}\). Figure 10 also shows that different angles of incidence of the sound waves have different effects on the STL. When the sound waves are incident at a pitch angle of \(0\)\({}^{\circ}\), the STL is the greatest in the low-frequency range, and as the angle increases, the STL in the low-frequency range decreases. The position of the trough of the STL also changes with a change in the angle of incidence. The reason is that the smaller the pitch angle, the greater the air-solid coupling mode density of the curved-plate structure. Thus, the STL deteriorates when the pitch angle increases.  
Figure 9: Variation in STL at different temperatures.  
Figure 11 shows the variation in STL with frequency for four cases, where the pitch angle is kept at 0\({}^{\circ}\) and the azimuth angle is 0\({}^{\circ}\), 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\). The figure shows that the STL is not affected by a change in the azimuth angle.
# A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-Rays
## 3 Materials and Methods  
### Dataset  
In this paper, we use the publicly available DNS dataset, which is accessible upon request to the authors of [5]. The dataset comprises 543 panoramic X-ray images, split into five folds with a resolution of 1991 \(\times\) 1127 pixels. The test fold has 111 images, while the other four have 108 each. Three folds are for training, and one is for validation. To fit in the GPU, we divide each image into patches of size 512 \(\times\) 512 pixels with an overlap of 10 pixels along both height and width. This corresponds to a stride of 502 pixels, keeping redundancy low while enabling smooth transitions across patch boundaries. For border patches, the final starting index is adjusted by subtracting the patch size from the image dimension, ensuring that the patch fits entirely within the image boundaries. We choose the patch size 512 \(\times\) 512 to balance contextual richness and computational efficiency. Larger patches preserve more semantic and structural information, which is beneficial for tasks such as teeth segmentation. All images are normalized between 0 and 1. In addition, for training the deep-learning models, a unique segmentation label is assigned to each tooth, resulting in a total of 32 segmentation labels. Table 1 summarizes the key properties of the DNS dataset and the preprocessing steps adopted for model training. These include image resolution, fold distribution, patching strategy, and the use of PCA-based OBB generation. The distribution of segmentation labels is depicted in Figure 2. Furthermore, we create oriented bounding box coordinates for each tooth using a PCA-based approach.