esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time
## 4 Implementation
### Data Collection  
We gathered data using crawling techniques from 10 different countries. Over 40,000 images were gathered, but only 3000 images were chosen because some were irrelevant to the study purposes. The countries chosen were Saudi Arabia, Russia, Japan, Germany, Sweden, Norway, Turkey, the United Kingdom, Italy, and Spain. Each country received 300 images of its ambulance vehicles, 200 of which were trained on YOLOv8 and the rest were for validation. In addition, we trained the model with 948 background images, as our data on ambulance vehicles were small (i.e., the model would not be able to identify the ambulance vehicles from the background). Two types of models were implemented: one a pre-trained model and the other a non-pre-trained one. Eventually, we collected the data for each country out of need rather than interest because there is no such thing as a universal ambulance car; each country has its own vehicle with its own shape and color, so we had to construct our own data. Nonetheless, we currently have 22 models. Ten are pre-trained models, and ten are non-pre-trained models; each country has each of them, and 2 are the crucial ones, which we term the universal model because they can work in all of the nations that we have listed; one is pre-trained and the other is non-pre-trained. More details regarding the dataset such as the image size, size range of the ambulance objects (in pixels), possible number of ambulance vehicles in each image, etc., can be found in Table 2.  
Data Labeling  
For the training, 2000 of the obtained images were manually labeled and categorized as ambulance vehicles. A total of 1000 images, however, were utilized for testing. One text file per image is used to store the annotations and labeling.
# Comprehensive Evaluation on an ID-Based Side-Channel Authentication with FPGA-Based AES
## 3 ID-Based Side-Channel Authentication System
### ID-Based Side-Channel Authentication Scheme
#### 3.1.3 Trade-Off for the ID-Based System  
As for the trade-off, in conventional side-channel authentication, the verifier is pre-registered only with the secret keys. In ID-based authentication, the secret key and ID number pairs are pre-shared between the verifier and the prover. In this system, we consider a case where the ID does not contain any secret information related to the secret key. The ID works as a tag to help the verifier quickly identify the claimed secret key of the verifier. A privacy problem could also exist for the ID-based authentication system since the ID information is transmitted in air. It is possible to trace the holder of a device by tracing the ID of the device. A possible mitigation of this problem is to introduce a periodical update of the ID.  
Regarding the secret of the ID and secret key, it is assumed that the registration of the ID/secret key is performed in a secure environment. As for other possible leakages of the ID and secret key, if only the ID is intercepted by a non-legitimate source, the attacker can pretend to be a certain device by using the leaked ID. However, since the secret key is unknown to the attacker, the fake device cannot pass the authentication. In the case that both the ID and secret key are intercepted and used by an attacker, the attackers can pass the authentication without any problem. As long as the verifier realizes this situation, a possible mitigation is to register the legitimate users again with new keys.
## 3 Results  
We validated the retrieval results and the Caltech SIF product at both annual and monthly scales using MODIS VI and GPP products. Considering that validation results at high spatiotemporal resolution can better reflect the differences between the two SIF products, the monthly scale validation was conducted only at a spatial resolution of 0.1\({}^{\circ}\) (similar to the footprint size of the GOSAT satellite). In addition to the 0.1\({}^{\circ}\) spatial resolution, the annual scale validation was also conducted at a spatial resolution of 2\({}^{\circ}\), which is commonly used for validating GOSAT satellite SIF products and for generating intensity spatial distribution maps ([PERSON], 2014; [PERSON] et al., 2015; [PERSON] et al., 2016). Vegetation indices (VI) include the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). EVI reduces canopy background signals and atmospheric influences, enhancing vegetation monitoring capabilities. In this study, we primarily used this index to validate the SIF products.
# Analysis of Turbulence Parameters of Typhoon Morakot along the Southeast Coast of China
## 4 Discussion  
Based on the wind speed data from two observation stations during the landfall of Typhoon Morakot, a detailed analysis of the near-surface turbulence characteristics of the typhoon and its variation with time intervals was carried out. Different wind speeds have a greater impact on the turbulence characteristics of typhoons near the surface layer. For the turbulence intensity, gust factor, and peak factor, the results obtained by applying the short and long time intervals are significantly different. The turbulence intensity, gust factor, and peak factor all decrease with the increasing time interval. Therefore, different calculation intervals have a greater impact on the turbulence characteristics in this area.  
Furthermore, owing to the turbulence characteristics being influenced by elements such as measured height, surrounding landforms, and wind field types, the measured results of the typhoon's near-surface wind characteristics vary by area. Therefore, numerous typhoon data along the southeast coast of China should be studied, common norms during multiple typhoons should be explored, and wind characteristics in various places should be enriched.
# Deep Learning for Sarcasm Identification in News Headlines
## 4 Model Selection
The GlobalMaxPool1D layer captures the most important feature from the temporal sequences, and the dense layers capture higher-level patterns in the data. To further improve the model, we used GlobalAveragePool and pruning techniques for better results. We trained the proposed model for 100 epochs consisting of 10 layers. The hyperparameters used included a vocab size of 1000, an embedding dimension of 32, a maximum length of 50, a learning rate of 0.001, a batch size of 32, and a dropout rate of 0.5. To avoid overfitting the model, we used EarlyStopping with a patience of 10. EarlyStopping is a technique used in deep learning to prevent the overfitting of a model during training. Overfitting occurs when a model becomes too complex and begins to fit the training data too closely, leading to poor performance on new, unseen data. The number of epochs the model is allowed to continue training without improvement on the validation set is known as patience. The value of patience is a hyperparameter that needs to be set by the user. After training, we saved the best model. The loss function used during training was binary cross-entropy, and we employed the Adam optimizer. The loss function was evaluated on both training and validation datasets to assess the model's performance, achieving accuracy scores of 0.999 and 0.925, respectively. Figure 6 represents the overall structure of the proposed GMP-LSTM model. Figure 7 describes the detailed layered architecture of the GMP-LSTM model, in which the model is divided in three sections: embedding, GlobalMaxPooling and dense layers. The embedding layer is the first layer in the model and is responsible for mapping the input text data to a continuous vector space. This is done by assigning a unique vector representation to each word in the input text, such that words that are semantically similar have similar vectors. The embedding layer helps the model to understand the underlying meaning of the text and is often pre-trained using large text corpora. The global max pooling layer is used to reduce the dimensionality of the output of the LSTM layer. It works  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifiers** & **Precision** & **Recall** & **F1** & **Accuracy** \\ \hline Decision tree & 71.2407\% & 70.4845\% & 70.8606\% & 74.6462\% \\ Random forest & 87.8693\% & 83.2642\% & 85.5048\% & 80.8162\% \\ Multinomial & 88.8000\% & 73.4013\% & 80.3697\% & 83.5177\% \\ Bernoulli & 84.7538\% & 80.1688\% & 82.3  
by taking the maximum value across all the output values of each feature map, resulting in a single value for each feature. This reduces the output dimension of the LSTM layer and captures the most important features of the input sequence. The dense layers are fully connected layers that perform nonlinear transformations on the output of the global max pooling layer. These layers help to further abstract the features learned by the model and are often used for classification tasks. The model has five dense layers, which may be used to capture increasingly complex features of the input sequence.
# BMP-2 Delivery through Liposomes in Bone Regeneration
## 2 Components of Bone Regeneration
### Growth Factors  
The most important class of biomolecules in bone regeneration are the bone morphogenetic proteins ( BMPs), members of the TGF\(\beta\) superfamily [26; 52]. They play a fundamental part in differentiation, embryonic development [53], and even tumorigenesis and cancer progression [54; 55]. BMPs were first identified by [PERSON], in 1965 [56; 57], and purified by [PERSON] et al. two decades later [58]. After the identification and cloning of the BMP genes the manufacturing of recombinant human BMPs followed [59]. There are 20 different types of BMPs described in the literature as markers of the osteogenic cell differentiation process. BMP-2 and BMP-7 are currently approved by the FDA for human use [52; 60]. BMP-2 is the most potent molecule in osteoinduction, essential for new bone formation. During fracture healing, the molecule is released during the degradation of the bone by osteoclasts and acts like a beacon for MSCs to find the lesion sites. During in vitro experiments, it is added to the culture media in the process of differentiation of stem cells into osteoblastic lineages for a better and faster osteogenic transformation. Attempts to replicate this in vivo frequently fail, because growth factors have a short half-life, and they are rapidly removed from the lesion site by the circulatory system. Human recombinant BMP-2 (rhBMP-2) is expensive, and its stability and biological activity in vivo is limited [61]. Off label use results, sometimes, in significant complications such as dysphagia [62], airway swelling [63; 64], ectopic and heterotopic bone formation [65; 66], immune response, or tumorigenesis [67]. For this reason, we have to optimize its intake by the cells by adding them to slow-release delivery systems. Pre-differentiation of MSCs, produces BMP-2 and attracts the organisms own stem cells to the injury site [68; 69]. There are no standards of dosage for in vivo animal studies, the use of BMP-2 for cartilage and bone regeneration ranges between 0.015 and 150 \(\upmu\)g/implant and even the FDA-controlled formulations are used in supra-physiological doses [70].
## 4 Layout and Post-Layout Experimental Results
### Post-layout Experimental Results of Proposed Delay Element  
To obtain the VTC of the proposed low voltage delay element, a DC sweep of the input voltage was conducted in Cadence and the output voltage was observed. Meanwhile, the DC gain was obtained from the VTC. As shown in Figure 6a,b, the VTC and DC gain of the proposed delay element was presented as under VDD = 24 mV. Figure 6c,d indicate the VTC and DC gain as under VDD = 30 mV. It is obvious to observe that the DC gain becomes larger with the increase of VDD. As indicated in Figure 6e,f, the VTC and DC gain comparison under VDD = 40 mV between the proposed delay element and the stacked inverter previously reported [8] is shown. It features a wider output voltage swing. A 191.7% improvement in DC gain was achieved compared with the stacked inverter.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Transistor**} & **Width** & **Length** & \multirow{2}{*}{**Transistor**} & **Width** & **Length** \\  & **(\(\upmu\)m)** & **(\(\upmu\)m)** & & **(\(\upmu\)m)** & **(\(\upmu\)m)** \\ \hline M1 & 2.28 & 0.25 & M8 & 0.22 & 0.18 \\ M2 & 1.80 & 0.60 & M9 & 0.22 & 0.18 \\ M3 & 3.80 & 0.25 & M10 & 0.22 & 0.18 \\ M4 & 1.80 & 0.60 & M11 & 0.22 & 0.18 \\ M5 & 0.76 & 0.25 & M12 & 0.22 & 0.18 \\ M6 & 5.58 & 0.60 & M13 & 0.22 & 0.18 \\ M7 & 0.22 & 0.18 & M14 & 0.22 & 0.18 \\ \hline \end{tabular}
\end{table}
Table 2: Transistor dimensions of the proposed delay element.  
Figure 5: Physical layout of the cold start-up ring oscillator with 21 stages based on the proposed delay element.
# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 6 Experimental Results
### System Behavior Under Network Constraints (Robustness)  
To test robustness, we simulated a network outage where the edge could not reach the cloud for an extended period (2 h in one test). In that period, our edge agent continued to monitor and even recorded two anomalies (one tachycardia event, one minor fall). It handled them locally (alerting the user directly via phone and logging them). Once connectivity was restored, the edge agent pushed the log of those events to the cloud. The system was designed to queue important messages and deliver later if needed. Thus, no data or event was lost due to disconnection. The only impact was that the cloud could not assist or take over tasks during that time, but since edge is autonomous, it was fine. In contrast, the baseline cloud system would have been essentially blind and frozen during a cloud disconnect--likely missing events entirely or unable to function.  
We also tested with 50% packet loss artificially introduced. MQTT with quality of service was configured to handle retries, so eventually messages got through, albeit delayed. The edge agent also had a safety: if an alert fails to receive acknowledgment, it will retry via alternate path (e.g., send SMS or some fallback if truly cloud is unreachable, but in our test environment that fallback was simulated by just printing a warning). The takeaway: the distributed edge-cloud nature and local processing make the system robust to network issues, an important feature for healthcare where connectivity cannot be guaranteed (e.g., ambulance driving through a tunnel should not stop monitoring).
# Cloning and Functional Identification of _Sipg49_ in _Solanum Lyopersicum_
## 2 Materials and Methods
### Prokaryotic Expression and Purification of SIPG49  
_SIPG49_ CDS (without the sequence encoding a signal peptide) was cloned into _pMAL-c2x_ to construct the _pMAL-MBP-SIPG49_ bacterial expression vector. The generated _pMAL-MBP-SIPG49_ vector and _pMAL-MBP_ empty vector were transformed into BL21-DE3. The extracted plasmid was verified by sequencing and transferred into _E. coli_ Rosetta strain. Expression of pMAL-MBP-SIPG49 fusion protein and pMAL-MBP protein were induced by 0.05 mM isopropyl b-D-thiogalactopyranoside (IPTG, Sangon Biotech, Shanghai, China) at 37 \({}^{\circ}\)C for 2 h. Cells were harvested by centrifugation (8000\(\times\) g, 8 min, 4 \({}^{\circ}\)C), then resuspended in protein extraction buffer (10 mmol\(\cdot\)L\({}^{-1}\) disodium phosphate, 1.75 mmol\(\cdot\)L\({}^{-1}\) potassium dihydrogen phosphate, 137 mmol\(\cdot\)L\({}^{-1}\) sodium chloride, and 2.65 mmol\(\cdot\)L\({}^{-1}\) potassium chloride, pH 7.4; Sangon Biotech, Shanghai, China). The resuspended cells were sonicated for 30 min (10 s/10 s) and then centrifuged (8000\(\times\) g, 10 min, 4 \({}^{\circ}\)C). For protein purification, supernatants were incubated with amylose resin and then loaded into a purification column (Sangon Biotech, Shanghai, China). The purification column was washed with extraction and washing buffer (1 mmol\(\cdot\)L\({}^{-1}\) ethylene diamine tetraacetic acid, 200 mmol\(\cdot\)L\({}^{-1}\) sodium chloride, and 20 mmol\(\cdot\)L\({}^{-1}\) tris-hydrochloride, pH 8.0). The bound proteins were eluted with elution buffer (10 mmol\(\cdot\)L\({}^{-1}\) maltose) and stored at 4 \({}^{\circ}\)C for later use.
# Federated Learning for Cybersecurity: A Privacy-Preserving Approach
## 3 Proposed Methodology
### Security and Privacy Analysis  
The proposed framework integrates several security and privacy-preserving techniques designed to defend against common threats in FL, including eavesdropping, model inversion, poisoning, and malicious aggregation.  
First, the use of Diffie-Hellman key exchange ensures that communication between clients and the server is protected against passive attackers who could intercept updates. Second, model update encryption combined with Fisher-based pruning and gradient clipping minimizes both the amount and sensitivity of shared information, thereby reducing vulnerability to reconstruction and gradient leakage attacks.  
Differential privacy mechanisms applied during global model aggregation provide formal guarantees that individual client contributions remain statistically indistinguishable. In addition, blockchain-based update logging ensures immutability and auditability, preventing tampering and replay attacks. The integration of post-quantum encryption (Dilithium) further strengthens the system against future cryptographic threats. By integrating these measures, the system delivers a resilient, privacy-focused learning framework specifically tailored for adversarial, heterogeneous IoT environments.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 6 Simulation--Basic Topologies
### Influence of Position of the Branch (Tap) on Throughput  
To see the impact of the position of the branch (tap) on the throughput, the following scenario was considered: constant length between transmitter and receiver of 1200 m, constant branch length of 20 m, and the position of branch varied at 200, 400, 800, and 1000 m from the transmitter.  
CTF is slightly rippled with small notches, therefore the impact of the position of the branch (tap) on the throughput is not significant.
## 3 Case Study: Analysis of Urban Pipeline Sensor Placement Optimization
### System Clustering for Risk Level Classification
Through this analysis, resources can be adjusted and optimized effectively, ensuring the safe operation of the gas pipeline network and comprehensive monitoring. With this approach, gas management departments can more efficiently deploy sensors, ensuring dense monitoring of high-risk areas while applying basic monitoring measures to low-risk areas, thereby maximizing resource utilization efficiency and system safety.
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 1 Introduction
### Land Use Mapping  
The Queensland Government, currently through the Department of Environment and Science, has mapped land use and land use change throughout Queensland since 1999. The Queensland Land Use Mapping Program (QLUMP) maps land use in accordance with the Australian Land Use and Management (ALUM) classification [13]. In 2015, when Panama TR4 was first detected in the Wet Tropics, banana plantations had not been specifically classified, as they did not explicitly appear in the ALUM classification. For DAF and the banana industry to manage the Panama TR4 infestation, Systeme Pour l'Observation de la Terre (SPOT) 6 imagery was acquired over the Wet Tropics, for QLUMP to manually digitize the extent of banana plantations.  
Timely land use mapping is fundamental for responding to biosecurity incidents, and for other applications such as natural disaster response, natural resource management and environmental monitoring [14]. Advances in big data and imagery availability have created an opportunity to develop methods to automatically and efficiently classify land use features over large geographical areas to allow for higher spatial and temporal resolutions and a more detailed classification for commodity level observations.  
Using high-resolution imagery, many different land uses can be identified with human vision, including banana plantations. This is a result of human operators combining a number of image properties including colors, textures, pixel proximity, geometric attributes, and contextual information such as related built infrastructure [15]. Spectral information alone cannot successfully distinguish land use features as some land uses can appear spectrally similar [16] and are usually restricted to a single sensor without cross calibration. Using ancillary datasets and decision trees to derive land use is not always an accurate representation of what is on the ground [14].  
The greater availability of high-resolution imagery introduces more complexity into the data, requiring more computing power to process the imagery and more detailed classifications. The integration of textural properties through object-based segmentation techniques have significantly improved the classification results for remote sensing applications [15]. For land use mapping, it has been found that using spatial as well as spectral information outperforms pixel-based classifications [17]. However, object-based image analysis approaches still require human input [16; 18] and these complex workflows tend to be just as time- and resource-intensive as entirely drawing the land use features manually [19; 20; 21].
## 8 Performance Analysis  
In this section, we introduce the performance evaluations of dynamic path optimization with charging selection based on real-time transportation network information. To verify the effectiveness of our proposed method, numerical experiments are conducted to compare it with relevant and representative methods [28; 29], as these studies consider most of the relevant characteristics in path-planning and charging problems. The characteristics of their studies are complementary and representative of the research in this field, as shown in Table 1.
## 2 Framework of RUL Prediction Based on TCNMABG
### Multi-Domain Mixed Feature Extraction and Selection
#### 2.3.2 ISOMAP Feature Dimension Reduction  
The extracted multi-domain mixed features represent the degraded features of the equipment operating state from multiple dimensions, which makes the fault feature set with high-dimensional, non-linear, redundant, and other characteristics, and even leads to the occurrence of \"dimensional disaster\", which affects the results of the prediction model. Manifold learning [34] This kind of nonlinear dimensionality reduction method [35] can effectively discover low-dimensional manifold components embedded in high-dimensional space, complete dimensionality reduction or data visualization, and is suitable for the dimensionality reduction processing of high-dimensional fault features of rolling bearing equipment.  
The Isometric Feature Mapping Algorithm (ISOMAP) utilizes the geodesic distance matrix between the sample data points instead of the Euclidean distance matrix in the multidimensional scale analysis (MDS) algorithm to obtain the low-dimensional manifold components that keep the geodesic distances between the samples unchanged. Compared with the classical linear dimension reduction methods, the ISOMAP can better mine the nonlinear manifold components hidden in the high-dimensional data, which can deal with the data with complex structures efficiently. Therefore, this paper uses the ISOMAP algorithm to compress the multi-domain mixed features for determining the sensitive features in the degradation process. The implementation process of the ISOMAP algorithm is summarized as follows.  
(1) Construct a neighbor graph by the k nearest neighbors based on the distance or similarity.  
(2) Calculate the geodesic distance between nodes based on the shortest path algorithm. The distance takes into account not only the distance of direct neighbors but also the distance through other node paths.  
(3) Map the high-dimensional data to a low-dimensional space to keep the geodetic distance of the original data as similar as possible.