esa-sceva/satcom-chunk-collection huggingface dataset:
# LW-FIRE: A Lightweight Wildfire Image Classification with a Deep Convolutional Neural Network
## 3 Materials and Methods
### LW-FIRE: A Lightweight CNN for Wildfire Image Classification  
In recent years, deep learning has demonstrated very promising results in a range of different engineering applications. DL-based models have the ability to automatically learn features from various data sources, and it has more robustness than other ML-based methods. Over the years, various DL-based models and applications have been developed [20; 21; 22; 23; 24]. However, one of the downsides of DL methods is an extensive training phase, which usually requires a large training dataset to attain the required functionality. More data always mean more training time and more computational resources--typically high-end GPUs or CPUs. Due to memory constraints, a model with a large number of learnable parameters and arithmetic operations is computationally expensive and can be trained for days to attain reasonable accuracy [25]. In this paper, we propose a new lightweight wildfire CNN for image classification. The CNN model can be trained in a reasonable time and computational resources (CPUs or GPUs, and memory), and is also suitable for real-time fire detection applications. LW-FIRE is an abbreviation for the Lightweight WildFIRE classification model. When a model has the real-time performance we add the \"RT\" prefix to the name of a model. The architecture of the basic Lightweight CNN for wildfire image classification is illustrated in Figure 5. The specification of layers and number of parameters is provided in Table 2. The structure of the CNN model was created with an extensive experimentation process and commonly observed practices from the DL community. From the experiments, we observed some favorable characteristics of the model on the validation dataset. Once the model achieved satisfied accuracy on the validation set, we tested our models on the three different testing sets. The process of evaluation is described in Section 5. Figure 5: Illustrated architecture of LW-FIRE150 CNN model used for wildfire image classification. The input layer uses a set of 3-channel input images. The total number of images depends on the sub-image configuration chosen in the previous stage. We considered two configurations, as shown in Table 1: (i) \(N\) = 12 and (ii) \(N\) = 42, where the number of images \(T_{N}\) = 6000 and \(T_{N}\) = 21,000, respectively. The first convolution layer uses 32 output filters (3 \(\times\) 3) followed by batch normalization and a max-pooling layer with \(2\times 2\) strides. The second convolution layer uses 64 output filters followed by batch normalization and max-pooling layer. Then, an additional convolution layer with 128 output filters is followed by batch normalization and max-pooling layers. Outputs from the last max-pooling layer are flattened and connected with several fully connected (FC or Dense) layers, where ReLU and Sigmoid activation functions are used, respectively. Non-linearity is introduced in all convolution layers with the ReLU activation function. The ReLU activation function
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 4 Key Distribution
### Identity and the Internet of Things  
Consider an example IoT system using SRUP: consisting of an Internet-hosted C2 server and one or more devices under the server's control. In order to perform the initial registration of the device, with the system, there are four pieces of information that need to be exchanged between the server and the device.  
1. The device needs to know the address of the MQTT broker being used to route messages.
2. The device also needs to know the server's public key, in order to validate messages from the server.
3. The server needs to know the device's identity.
4. The server needs to know the public key corresponding to the device's identity.  
This device identity corresponds to a UUID generated using Boost [20]. Boost UUIDs are 128 bit in length, yielding \(2^{128}=340\times 10^{36}\) or more than enough to ever avoid collisions in any conceivable system. Since SRUP uses cryptographic keys to assure the authenticity of messages, we can define having a given identity as meaning that the device in question possesses the private key corresponding to the public key associated with that identity's UUID.  
Depending on the specific implementation to be used, the device identity could be assigned by the server (guaranteeing uniqueness for a given system), or it could be generated by the device itself (although this must be validated by the server in order to ensure uniqueness within the system). A randomly-generated 128-bit UUID is extremely unlikely to ever result in collision (hence the fact that such values are used as an Universally Unique Identifier), but a check is required to prevent a deliberate collision attack against the system.
## 6 Experiment and Result Analysis
### Fault Diagnosis Case Analysis of Different Datasets  
This section will compare the performance of AlexNet using original data, data enhanced by the SMOTE model, the CTGAN model, and the SA-CTGAN model as inputs. The specific scheme is as follows: the original dataset is divided into the training set, verification set, and testing set in a ratio of 7:2:1, which are input into the AlexNet model for training and testing. For the datasets enhanced by the three data-derived models, they are divided into a training set and a validation set according to the ratio of 7:3, while the original data are used as the testing set. The early stopping mechanism is set during model training based on the accuracy of the verification set; if the improvement is less than 0.05% over 20 iterations, the training will be stopped in advance.  
The specific iteration process of the four data-sets is shown in Figure 10.  
From the perspective of the iterative process, due to the small amount of data in the original data without data enhancement, the early stop mechanism was triggered after 121 iterations, the loss value of the verification set was always greater than that of the training set in the whole iteration process, and there was  
Figure 10: The iterative process of different data-sets: (**a**) original data-set; (**b**) data-set enhanced by SMOTE; (**c**) data-set enhanced by CTGAN; (**d**) data-set enhanced by SA-CTGAN.  
phenomenon. The dataset that underwent data enhancement using SMOTE exhibited the highest number of iterations, albeit with evident oscillation throughout the training process. In contrast, the dataset enhanced with the CTGAN model terminated training after 147 iterations, displaying significantly less oscillation compared to the first two datasets. The data enhanced by the SA-CTGAN model established in this paper experienced 182 iterations, and there are obvious oscillations in the first 50 iterations of the model, but the oscillations gradually disappear after 50 iterations. Table 4 shows the accuracy of the four data-sets.  
From the accuracy of the four types of data-sets, it can be seen that the accuracy of the validation set is about 5% lower than that of the training set, and the accuracy of the testing set is about 6.5% lower than that of the training set. This is due to the lack of training data, as the model can only over-extract features unrelated to the direction of diagnosis and only learn the patterns related to the training set data. This pattern is wrong or irrelevant for the new data (validation set and testing set), resulting in the trained model not being universal. The accuracy of other data-augmented datasets is relatively close to that of the training set, validation set, and test set. Among them, the SA-CTGAN model constructed in this paper has the highest data accuracy and the smallest gap between the training set and the test set, which indicates that the model trained with the dataset generated by SA-CTGAN has higher versatility.
## 3 Numerical Simulation of Tunnel Excavation
### Analysis of Calculation Results
#### 3.2.2 Deformation Patterns of the Bifurcated Tunnel Envelope at Different Construction Steps  
From the perspective of stress in the surrounding rock during the construction stage, the RTM has higher requirements for the support structure of the two-lane tunnel and the five-lane section. The key monitoring sections for crown displacement near the bifurcation tunnel junction are shown in Figures 12 and 13. From the figures, it can be observed that the excavation area of the five-lane section increases with the progress of excavation steps, and the differences in construction step sequences between the two methods result in different deformation patterns in the surrounding rock. As shown in Figure 12a,b, when the second-layer excavation of the large section is completed, the crown displacements of the two-lane tunnel and the three-lane tunnel increase rapidly. The growth rate is more pronounced in the RTM, with the maximum displacement at K1+870 in the two-lane tunnel being 1.4 times that of the ACM, and the peak displacement at the monitoring point K1+870 in the three-lane tunnel being 1.2 times that of the ACM. The displacement-time curves of the crown and shoulder of the large section at K1+890 are shown in Figure 13a,b, respectively. When the first-layer excavation of the large section starts, the crown and shoulder displacements increase rapidly. As the excavation steps progress and the displacement growth rate decreases, the displacements gradually reach equilibrium. It can be observed that the initial excavation unloading of the first layer of the large section has a significant impact on the stability of the surrounding rock. After excavation, the crown displacement of the RTM is 1.3 times that of the ACM, while the deformation is smaller in the ACM, indicating its superiority over the RTM.  
Figure 14 presents the contour maps of the distributed rock mass displacement during the key steps of the large section (including the first-layer excavation of the large section, the second-layer excavation, and the stepping excavation of the three-lane tunnel). It visually demonstrates the changes in rock settlement influenced by the aforementioned factors. Compared to the RTM, the settlement of variable-section rock mass in the bifurcation tunnel is smaller in the ACM at each construction step.
### Background  
The piping network of a DH system comprises interconnected pipeline zones, including a transmission pipeline (used when the heat source is distant [6; 7] or multiple districts require heat supply [4]), a distribution network, building connection service pipes, and building indoor piping systems. Each segment consists of two pipes: the supply line, which carries the heated medium from the heat source to end-users, and the return line, which carries the cooled medium back to the heat source. Together, these zones form a closed hydronic loop where various parts, systems, and parameters influence each other. Ensuring the highest overall efficiency of the system requires careful evaluation of the interactive thermal and hydraulic effects of these sub-parts [8; 9; 10; 11; 12; 13; 14].  
In a DH system, thermal and hydraulic interactions can vary widely. The 'Valve Authority' illustrates how components in a hydronic closed loop operate harmoniously, as it measures the ratio of an automatic control valve's hydraulic resistance to the overall resistance in the piping loop [15; 16; 17; 18]. Variables such as differential pressure, hydrostatic pressure, and supply temperature can alter the operational characteristics of thermostatic radiator valves [18; 19; 20]. Differential pressure control valves ensure a constant pressure difference for the secondary loop regardless of the primary side's pressure, thus stabilizing secondary-side equipment operation and maintaining hydraulic balance on the primary side [14; 17; 21; 22; 23].  
In addition to the aforementioned interrelationships, temperature significantly affects the functioning of DH system components. The historical evolution of DH systems reveals a trend of decreasing temperature levels. The phases of this development are: (i) steam in the first generation (1G) systems, (ii) super-heated pressurized water above 100 \({}^{\circ}\)C in the second generation (2G), (iii) pressurized water below 100 \({}^{\circ}\)C in the third generation (3G), (iv) water with supply temperatures as low as 55 \({}^{\circ}\)C and return temperatures around 25 \({}^{\circ}\)C in the fourth generation (4G), and (v) the advanced heating and cooling supply via fifth-generation (5G) technology. More detailed information on these developments can be found in references [24; 25; 26]. This overall reduction in operating temperatures over DH generations has facilitated easier access to various energy sources [27].
## 5 Discussion  
DL presents an opportunity to extend research and the application of the basis for classification of plant diseases using digital images. The rapid and accurate models are required to detect the diseases on time. For this work, the dataset used presents nine classes of tomato diseases and a healthy class, which have a total of 18, 160 images. The dataset was divided into 80-20% (training-testing). Therefore, five-state-of-the-art pre-trained models, namely AlexNet, GoogleNet, Inception V3, ResNet 18, and ResNet 50, were trained by the concept of fine-tuning, which consists of replacing the last three layers, where the final output layer has to be compatible with the number of classes.  
All of these pre-trained models were evaluated by various metrics: accuracy, precision, sensitivity, specificity, F-Score, and AUC, using the same hyper-parameters. Based on Table 6, which shows a general result performance, GoogleNet achieved better results than the other architectures. In addition, Inception V3, which is one of the deepest CNN models, shows low performance in Table 3.  
Firstly, a confusion matrix (Table 7) was achieved, and it shows that two of ten classes produced a 100% rate of correct classifications, representing mosaic virus and the healthy class. Secondly, the performance measures were calculated for each class, in order to know which class obtained lower percentage of the lowest error, based on: sensitivity, specificity, F-Score, and precision, which is because these measurements represent the model performance; it is possible to determine that the classes with the smallest error were: leaf mold, mosaic virus, and the healthy class; the class with greatest error was early blight. All of these were based on the precision metric.  
Figure 5 shows that the performance of the GoogleNet model is excellent by the multiclass ROC curve, and it achieves a statistically significant AUC value of 99.72%, which means that the model presented a sensitivity and specificity rate that is able to classify the data with only 0.28% error.  
On the other hand, one of the limitations of this work was related to the number of images used. It could be interesting to test the model on a set of images taken under controlled conditions or being able to classify images of diseases as it presents itself on the plant.  
In addition, it could be beneficial to create a mobile application that implements the GoogleNet model for an automated diagnosis of tomato plant diseases, so users or farmers with a little or no knowledge can use it to perform their work effectively in the detection of tomato plant diseases.  
Figure 5: Multiclass receiving operating characteristic (ROC) Curves. (**a**) Multiclass ROC Curve. (**b**) ROC Curve for each class.  
It is important to mention that training of the models takes a lot of time (around one or two hours on a high performance CPU computer), while the classification is very fast on a Graphics Processing Unit (GPU).
# A Reconfigurable Formation and Disjoint Hierarchical Routing for Reshapeable Bluetooth Networks
## 3 Mesh-Tree Topology Design
### Scatternet Performance  
In the scatternet performance simulation, it was assumed that Bluetooth nodes were randomly located within a rectangular area of \(40\times 40\) m\({}^{2}\), while a radio transmission range of \(10\) m was assumed. The number of simulated nodes ranged from \(60\) to \(150\). As shown in Figure 2, the \(k=4\) case almost achieves the lowest hop length of a sample topology, while the \(k=5\) case achieves a performance similar to the \(k=4\) case. However, the \(k=6\) case generates a poorer performance than that of the \(k=5\) case, as some tree branches did not satisfy the criterion to execute the return connection mechanism, even with the largest \(k\). In addition, the difference between the lowest hop length and the highest hop length increases as the number of nodes increases. As a result, the lowest average hop length is either the fourth or fifth case for various sizes of network; both cases improved in hop length by approximately \(20\%\) more than the \(k=2\) case through averaging the sample hop length for various numbers of nodes. In addition, the improvement ratio of the hop length increases as the number of nodes increases.  
To determine the optimal \(k\) layer with the lowest hop length for various topologies, it is essential for a root node to collect all scatternet topology information of various \(k\) layers. For the \(100\)-node topology example, a root node forms a mesh-tree scatternet with \(k=2\), \(k=3\), \(k=4\), and up to \(k=5\) cases. The root node then computes the average hop length of these four cases and determines that \(k=4\) is the optimal layer with the shortest hop length. However, the optimal layer \(k\) is an NP (nondeterministic polynomial) complete problem, since the root node can only acquire the formation topology of its own \(k\) layer case instead of all other cases. As a result, the design goal of a reconfigurable mesh-tree algorithm  
Figure 2: Average path length in a scatternet.  
attempts to determine the optimum layer \(k\) in terms of the mesh-subnet size with lower communication and computation overheads. In addition, the reconfigurable mesh-tree can dynamically compute the optimum layer \(k\) when the size of the network changes in the topology maintenance phase.
## 2 Materials and Methods
### Statistical Analysis  
Sequence data were processed using QIIME2 (version 2022.11) with taxonomic classification against the SILVA 132 database for bacteria and the UNITE 8.0 database for fungi. Alpha and beta diversity metrics were calculated and visualized using QIIME2 and R software (version 4.0.2), with differences in microbial communities assessed using PERMANOVA. Metabolomic data were processed with XCMS for peak detection, alignment, and retention time correction; metabolites were identified using public databases, such as HMDB and METLIN. Differential metabolites were analyzed using partial least squares discriminant analysis (PLS-DA) with SIMCA-P 14.0. Volatile compound data were expressed as mean \(\pm\) standard deviation (SD), and statistical analyses were performed using IBM SPSS Statistics 23.0, with significance set at \(p<0.05\). All experiments included at least three biological replicates.
# Stability and Control of Surrounding Rock of a Trapezoidal Roadway Retained with Hard Roof Cutting
## 5 Discussion
### Stress Distribution and Deformation Characteristics  
The trapezoidal top-cutting roadway's unique geometry profoundly affects stress distribution, causing asymmetrical stress, especially at the roof and sides. Post-excavation and mining, stress concentrations lead to high-stress zones and plastic deformation, with vertical stress shifting to compressive on one gob side and tensile on the other, causing significant roof subsidence. However, top-cutting reduces vertical stress on the roof and solid coal, indicating substantial pressure relief and decreased roof subsidence from 66 mm to 32 mm, demonstrating its effectiveness in stress redistribution and deformation control. The gob side shows increased deformation, reflecting the asymmetric nature of rock deformation common in mining. This asymmetry is due to the cantilever beam effect from top-cutting, which allows roof deflection and sliding shear failure along the cut line, crucial for reducing overall deformation and stabilizing the surrounding rock.
## 3 Active Optics Correction of the 4m SiC Primary Mirror  
The main purpose of the active support system is to ensure the surface shape and pose accuracy of the primary mirror, thereby ensuring a good imaging quality of the telescope. Therefore, active optics includes two parts: the primary mirror shape and pose corrections. This section introduces the correction methods and discusses the relevant mathematical derivation. In addition, to ensure the accuracy of the surface shape and pose, a method for compensating for the force on the hardpoints was proposed.
# Mutated Channelrhodopsins with Increased Sodium and Calcium Permeability
## 2 Materials and Methods  
### Plasmids and RNA Generation for Xenopus Laevis Oocyte Expression  
ChR2, XXM, and XXL in the pGEMHE vector were described in previous studies [18; 19; 21]. PsChR (from Platymonas subcordiformis, Accession No.: JX983143) and Chronos (from Stigeoclonium helveticum, Accession No.: KF992040) were synthesized by GeneArt Strings DNA Fragments (Life Technologies, Thermo Fisher Scientific), according to the published amino acid sequences, with the codon usage optimized to Mus musculus. The synthesized DNA segment was inserted into the pGEMHE vector with N-terminal BamHI and C-terminal Xhol restriction sites. Yellow fluorescent protein (YFP), together with a plasma membrane trafficking signal (KSRITSEGEYIPLDQIDINV) [22] beforehand and an ER export signal (FCYENEV) [22] afterward, the YFP was attached to the C-terminalend. Mutations were made by QuikChange Site-Directed Mutagenesis. The sequence was confirmed by DNA sequencing. Plasmids were linearized by Nhel digestion and used for in vitro generation of cRNA with the AmpliCap-MaxT7 High Yield Message Maker Kit (Epicentre Biotechnologies).