esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Hyperspectral Imaging System and Image Acquisition  
Two HSI systems covering two different spectral ranges (VNIR: 380-1030 nm; NIR: 874-1734 nm) were used to acquire hyperspectral images of Longjing tea. Details of the hyperspectral imaging system can be found in the literature [20].  
The tea leaves for image acquisition were placed as in Figure 1. To acquire a non-deformable and clear image, three system parameters, including the distance between the camera lens and the moving plate, the camera's exposure time, and the moving speed of the moving plate, should be determined. For VNIR HSI, these three parameters were determined as 15.6 cm, 0.14 s, and 0.8 mm/s, respectively; for NIR HSI, these three parameters were determined as 12.6 cm, 3000 us, and 11 mm/s.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Geographical Origin**} & \multicolumn{3}{c}{**VNIR**} & \multicolumn{3}{c}{**NIR**} \\ \cline{2-7}  & **Total** & **Calibration Prediction** & **Total** & **Calibration Prediction** \\ \hline Bashan Village & 1074 & 806 & 268 & 1160 & 870 & 290 \\ TongWu Village & 781 & 620 & 161 & 886 & 665 & 221 \\ Meijiawu Village & 845 & 634 & 211 & 785 & 589 & 196 \\ Tiedian Village & 926 & 695 & 231 & 1037 & 778 & 259 \\ Xinxin Village & 1171 & 878 & 293 & 1036 & 777 & 259 \\ Huijialing Village & 1409 & 1057 & 352 & 1489 & 1117 & 372 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of samples in the calibration set and the prediction set of Longjing tea from different geographical origins. VNIR: visible/near-infrared spectroscopy; NIR: near-infrared spectroscopy.  
Figure 1: Red, green, and blue (RGB) images of Longjing tea from different geographical origins for hyperspectral image acquisition: (**a**) Bashan Village; (**b**) TongWu Village; (**c**) Meijiawu Village; (**d**) Tiedian Village; (**e**) Xinxin Village, and (**f**) Huijialing Village.  
After image acquisition, the image correction procedure was conducted to transform the raw hyperspectral images into reflectance hyperspectral images [20]. The image correction is based on Equation (1):  
\[I_{c}=\frac{I_{raw}-I_{dark}}{I_{white}-I_{dark}} \tag{1}\]  
where \(I_{c}\) is the corrected image, \(I_{raw}\) is the raw hyperspectral image, \(I_{white}\) is the white reference image acquired by using a white Teflon tile with nearly 100% reflectance to calibrate the light intensity, \(I_{dark}\) is the dark reference image acquired by covering the camera lens completely with its opaque cap without any light to remove the influence of dark current in the camera.
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 4 Experiment
### Quantitative Comparison with Different Methods  
#### 4.4.1 Compared Methods on LOLv1 and LOLv2  
This study conducts a comprehensive quantitative and qualitative comparison of various low-light image enhancement methods across three datasets: LOLv1, LOLv2-real, and LOLv2-synthetic. Table 2 presents the performance metrics of these methods in terms of PSNR, SSIM, and LPIPS, as well as their model parameter counts (in millions). Traditional methods, such as LIME [10], consistently underperform compared to deep learning approaches. For instance, LIME achieves a PSNR of only 16.760 on the LOLv1 dataset, while our proposed method reaches 27.923. The zero-parameter deep enhancement method, Zero-DCE [50], achieves a PSNR of 14.861 on the LOLv1 dataset, demonstrating a lightweight parameter count of 0.33M but lagging behind most deep learning methods in performance. GAN-based methods, including EnlightenGAN [67] and KinD [68], achieve PSNR values of 17.483 and 20.870, respectively, on the LOLv1 dataset. While these methods excel in certain aspects, they perform less competitively on the LOLv2-real and LOLv2-synthetic datasets. KinD++ [69], an improvement over KinD, attains a PSNR of 21.300 on the LOLv1 dataset but has a relatively high parameter count of 9.63M. Transformer-based methods, such as MIRNet [70] and LLFormer [71], achieve PSNR values of 24.140 and 25.758, respectively, on the LOLv1 dataset, showing strong performance in handling complex scenes but with higher parameter counts. LLFlow [72], a flow-based model, achieves a PSNR of 25.132 on the LOLv1 dataset and excels particularly on the LOLv2-real dataset with a PSNR of 26.200. Diffusion model-based methods, such as Diffusion-LL [25] and GSAD [28], achieve PSNR values of 26.336 and 27.839, respectively, on the LOLv1 dataset, demonstrating robust performance. PyDiff [27], while performing well on the LOLv1 dataset with a PSNR of 27.09, shows slightly weaker performance on the LOLv2-real and LOLv2-synthetic datasets.  
Our proposed method achieves the highest PSNR values of 27.923, 29.818, and 28.741 on the LOLv1, LOLv2-real, and LOLv2-synthetic datasets, respectively. Additionally, it excels in the SSIM and LPIPS metrics, indicating its superior ability to maintain image structure and perceptual quality. Specifically, our method achieves SSIM values of 0.882, 0.889, and 0.942 on the respective datasets, and LPIPS values of 0.105, 0.095, and 0.042, which are among the lowest. In terms of parameter count, our method has a moderate parameter size of 19.86M, striking a balance between performance and efficiency. This suggests that our model is well suited for both high-performance requirements and resource-constrained environments.
## 2 Materials, Methods and Equipment
### Experimental Setup  
The parameters related to roughness are usually reliant on factors such as manufacturing conditions, depth of cut, cutting speed, feed, and machine tool used. For this particular study, the three primary variables chosen were the spindle speed (\(n\)), the feed rate (\(V_{f}\)), and the depth of cut (\(a_{p}\)). Data from 48 experiments conducted on the machining of PLA material were collected and are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test No.** & \(n\), **[rev/min]** & \(V_{f}\), **[mm/min]** & \(a_{p_{f}}\), **[mm]** & \(R_{a}\), **[um]** \\ \hline
1 & 3000 & 400 & 0.3 & 2.058 \\
2 & 3000 & 400 & 0.6 & 2.845 \\
3 & 3000 & 400 & 1 & 2.41 \\
4 & 3000 & 600 & 0.3 & 2.108 \\
5 & 3000 & 600 & 0.6 & 3.18 \\
6 & 3000 & 600 & 1 & 2.601 \\
7 & 3000 & 800 & 0.3 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental data for model construction.  
Figure 5: Spindle cutter.  
Figure 6: Talysurf-6 measuring system.
###### Abstract  
Today's software-defined networking (SDN) applications have many challenges. Its main applications are focused on networks with nodes with high processing capacity. Applying SDN technology in nodes operating on batteries with limited computing capabilities is challenging. In this context, this paper proposes SDN-enabled algorithms for the remote configuration of the default route to be applied in multi-hop wireless sensor networks (WSNs) with tree-type topology using the IEEE 802.15.4 protocol. The routing algorithm to define the default route of each node is executed in an SDN-enabled WSN controller (SDWSN). The SDWSN controller receives information on the state of the network, executes the Djikstra or Kruskal algorithms, and configures the default route of the nodes remotely. The best route selection is based on the battery level of the nodes and the distance between them. The results show that using network protocols to configure the nodes remotely is unnecessary.  
IEEE 802.15.4; wireless sensor networks; SDWSN; Djikstra algorithm; Kruskal algorithm
# Silver Sintering for Silicon Carbide Die Attach: Process Optimization and Structural Modeling
## 2 Materials and Methods  
### Sample Preparation and Reliability Test  
The considered device under test is a molded package with silicon carbide MOSFET power devices attached on an active metal brazed (AMB) substrate by means of a sintering process shown in Figure 1. It was remarked that the molding compound injection and curing occur after sintering and it is not reported in the schematic. As depicted in this figure, paste with silver powder is screen printed on the silver plating in the front side of bare substrate. More in detail, a commercial sintering paste, made by silver powder and an organic vehicle, is used for the process. The typical dimension of silver flakes is in the order of 0.5 \(\upmu\)m. Drying is performed to eliminate the solvent content of the paste. Then, the chip is placed by automatic equipment for pick and place on the dried silver paste. Finally, the chip is pressed by a tool applied on AlSiCu front metallization on the substrate at high temperature and for a certain time. The proposed process optimization relies on this final process step, considering three different pressure levels (\(p_{1}<p_{2}<p_{3}\)), between 10 and 30 MPa and applied for 2 min. Temperature is fixed between 200 and 250 \({}^{\circ}\)C.  
Porosity is a physical parameter strictly linked to the applied pressure during sintering. Porosity tends to decrease applying higher pressure [16] and impacts on the Ag sintering structural behavior in terms of elastic modulus, plasticity and ultimate tensile strength [18]. The pressure modifications affect the silver layer mechanical characteristics. Higher pressure increases the ultimate tensile strength. In contrast, the increase in sinter layer elastic modulus amplifies the thermal stress due to the coefficient of thermal expansion mismatch among chip, silver sintering joint and substrate [19]. Liquid-to-liquid thermal shock (TS) test is indeed employed to compare the reliability performances of the three different pressure values.  
Such a test consists of transferring the device under test between two baths of perfluoropolyether fluid at 150 \({}^{\circ}\)C and \(-65\) \({}^{\circ}\)C. Samples dwell three minutes at each temperature, with transfer time smaller than 10 s. The immersion in a liquid environment allows to reach a higher stress and strain rate than thermal cycles performed under air-to-air conditions. Temperature rate reached in thermal shock is comparable with the aggressive rate enhanced by application, nevertheless, such a test can represent an over-stressed condition considering that the temperature rate involves all the package and not only a limited part (i.e., the front metal e-wire source connection), such as in an application. On the other side, the thermal shock is a practical method to benchmark the performance of sintering interconnections: other methods, as the thermal cycle, are not able to stress enough of a sintering layer up to crack generation in a time-frame compatible with package development schedule.
## 4 Classification of PPDA Protocols
### Heterogeneous protocols
#### 4.3.1 Perturbation  
The perturbation technique has the same meaning as what we defined in the homogeneous protocols. The work proposed by [PERSON] _et al._[36] is only the existing perturbation-based protocol.  
**[PERSON] and [PERSON]'s scheme:** [PERSON] _et al._[36] proposed a privacy-preserving storage scheme which associates a tag with each encrypted data in order to process a range query, by adopting the concept of bucketing [37] and indexing [38]. Their two-tiered network model consists of a powerful sink, regular sensor nodes, and storage nodes equipped with a large storage capacity, as shown in Figure 9.  
In this scheme, every sensor generates environmental data values at a fixed rate and periodically submits the collected data in the form of coarse information to the closest storage node. The storage nodes first bucketize the mixture of original data into a few bins before storing them. Both the sensor node and the sink node have agreed on the same bucket partition. Before transmitting the data to the storage nodes, the data source first encrypts the data by using the key shared with the sink and then attaches a tag to the encrypted data indicating the bucket into which the data falls. When the sink node needs to process a range query over the data stored in the storage nodes, it obtains an approximate result based on the tags corresponding to the query range, instead of having storage nodes decrypting every data point and returning them. Then, through decryption at the base station, the real data set can be derived. However, the main problem of this scheme is the transmission delay, _i.e._, sending data from sensors to the sink, which is critical for many tracking and event detection applications. In addition, this scheme does not consider the scenario of malicious modifications of sensor data.  
Figure 9: Two-tiered network model in [PERSON] and [PERSON]’s scheme.
# A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps
## 3 Methodology
### Model Preselection Algorithm  
Plenty of intelligent models exist, so we should choose the most suitable one for application through the model preselection algorithm from at least two typical models. The validation accuracy of the model is considered in the model preselection algorithm. The model selection process is mathematically defined as follows:  
\[\begin{split}&\text{$\mathit{Sel}\left(M_{1},M_{2},\dots,M_{ \text{p}}\right)$}=M_{i}\\ &\text{$\mathit{s.t.\text{max}}$}\Big{(}M_{1-valid-acc},M_{2-valid-acc}, \dots,M_{\text{p}-valid-acc}\Big{)}=M_{i-valid-acc}\end{split} \tag{13}\]  
where \(M_{i}\) represents the \(i-\)th model of the provided p models. \(M_{i-valid-acc}\) represents the validation accuracy of \(M_{i}\). If the multi-fold cross-validation is applied, \(M_{i-valid-acc}\) represents the mean of multi-fold cross-validation accuracy. The accuracy can be calculated by  
\[\begin{split} Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\end{split} \tag{14}\]  
Figure 3: Fusing all channels’ samples with mean fusion.  
Figure 2: Putting samples of all channels together.  
TP_ means the number of samples correctly detected or diagnosed as positive, _FN_ means the number of samples incorrectly detected or diagnosed as negative, _TN_ means the number of samples correctly identified or diagnosed as negative, and _FP_ means the number of samples incorrectly detected or diagnosed as positive.  
The model with the highest validation accuracy is selected, and the accuracy should be greater than 0.8 according to the standard GB/T 43555-2023 [36] to ensure the selected UAD and FD models are valid. The process can be described in Figure 4. Model \(M_{i}\) is the most suitable model for the same dataset and performs best among the provided \(p\) models.
# Towards the Development of Agenda 2063 Geo-Portal to Support Sustainable Development in Africa
## 3 Geoportal Implementation Framework
### Architectural Design  
The architectural design of the Agenda 2063 Geoportal is based on three application layers as illustrated in Figure 1. PostgreSQL[a] client/server, conceptual model (refer to the PostgreSQL 11.3 Documentation: The PostgreSQL Global Development Group, 2019). An overview of the architectural design of the Agenda 2063 Geoportal is shown in Figure 1 below.  
The first application layer is the database layer that stores and serves all Sustainable Development Goals (SDGs) dataset sourced from different countries. PostgreSQL database with PostGIS extension was selected in the implementation of the geoportal due to its ability to deal with both spatial and non-spatial data types. This then ensured that any data types from 54 African countries can be stored.  
Figure 1: Application layers for the Agenda 2063 Geoportal. Three application layers were implemented for the geoportal.  
PostgreSQL has a proven capacity to deal with a range of large geographical datasets that include vector, raster, and numerical data. PostgreSQL is an open-source object-relational database management system (ORDBMS) that uses a client/server model. In addition to being open-source, some of the attractive features offered by PostgreSQL include its ability to handle complex queries, multi-version concurrency control, triggers, update views, transactional integrity, security enforcement, and its capability to handle foreign key. The backend server-side processes are responsible for managing the database files by accepting connections from client applications and executing the requested database actions using a dataset server program known as PostgreSQL. Some of the basic functions for the server-side include data storage and transformation.  
The second layer consists of a data services layer that allows for access, processing, and sharing of data stored in the database. This layer allows for the publishing of interactive maps and spatial data to the web for consumption by various web mapping applications with map visualization capabilities. MapServer, an open-source platform, was used to publish this data is an application-independent manner by using OGC standards thus achieving data interoperability that will allow the sharing of this data with other organisations.  
The third layer is the visualisation layer which serves two main purposes namely, the display of data served from the database for the visualisation and the capture of data queries from the users and pass it on the database via the services layer. The results of these queries are also viewed from the visualisation layer. Visualisation is achieved via a web user interface that provides user administration functionalities.
# Business Impact Analysis of AMM Data: A Case Study
## 7 Conclusions
* Integrity assurance: digital signature DSA 3072, EC-DSA-25, RSA 3072
* Hashing: SHA2-256, SHA3-256
* Integrity protection mode: HMAC, CMAC
* Key Management: DH-3072, ECDH-256
* Random bit generator: for SHA2 and SHA3: HMAC DRBG, Hash DRBG. Therefore, the resulting technical requirements must:  
* Ensure safe recovery from error, fault, or failure. * Include a reliable time synchronization. * Include instructions for safe installation. * Supply initialization and operation with the equipment. * Ensure data validation before use--input protection. * Ensure flood (DoS) protection through traffic filtering or network segmentation,
* resource management. * Minimize the interface--disabling all unnecessary services, protocols, and physical interfaces. * Log and report security events. Furthermore, each device must be uniquely identifiable. Data in messages must be encrypted and messages must contain integrity protection. Systems should be implemented to confirm the execution of a command. Access to elements handling sensitive data should require the penetration of a sealed security perimeter. Cryptographic credentials should be unique to the meter and securely stored. If stolen, this should not reduce the security of another meter. In terms of the architecture of the meter itself, the measurement and communication functions should be separated, with the remote updating of security functions and cryptographic primitives, and the remote updating of cryptographic credentials. If these minimum cryptographic and technical requirements are met, the implemented BIA can be expected, with a high degree of probability, to ensure adequate quality and availability in AMM data, and should be usable for secure and stable smart grid operation. Future research on the safety of AMM system implementation can be divided into technical and organizational measures. The focus of this research project was data modeling using BPMN_SC security distributions, analyzing the impacts of not guaranteeing the quality and security of AMM system data on other ICT and ICS systems involved in the Smart City concept. This is a dynamically evolving area, where the quality, security, and availability of data from the energetic networks have a profound impact on the downstream cooperating areas and systems that comprise the Smart City concept. This work and paper were supported by the project \"Application of Artificial Intelligence for Ensuring Cyber Security in Smart City\", n. VJ02010016, provided by the Ministry of the Interior of the Czech Republic. Data Availability Statement: Data sharing is not applicable. I would like to thank my colleague [PERSON] et al., from CEZ Distribuce, a. s., for his cooperation in the analysis of data flows in the real AMM system environment. The author declares no conflict of interest.
## 1 Introduction
#### State of the Art  
It is undeniable that increasingly more applications are being used to consult all kinds of information. The healthcare world is no exception, and is evolving towards a more technological approach, where there are more and more applications that support clinicians in diagnosing pathologies, making decisions and even prescribing medical treatment to ensure that the medication and its dosage are correctly indicated.  
Currently, there is the Clinical Guide to Probiotic Products application, which is a Canadian guide to consult the scientific evidence available for probiotic products. It was created using published data from clinical studies for various probiotic strains [17]. Only commercial products available and approved in Canada were used in this application. There is also an American version created by the same authors, in which the probiotics that appear are those approved in the United States [18].  
Although there are applications that allow consultation of the clinical evidence of probiotics, they only include those approved in Canada and the United States. The information they provide is very useful, but in countries such as Spain, the commercial products are not the same, and it would not be appropriate when recommending a probiotic to a patient for a given pathology.
###### Abstract  
Using chaos for communication can provide more robust channel security, covert transmission, and inherent support for spread-spectrum modulation. Although numerous studies have explored this technology, its practical deployment remains limited due to substantial hardware demands, complex signal processing, and a lack of efficient modulation methods for chaotic signals. In this study, a novel chaotic digital communication system is proposed and studied. A prototype of a frequency-modulated antipodal chaos shift keying (FM-ACSK) system is implemented on an Intel Cyclone V field-programmable gate array (FPGA) along with a complete mathematical model using Matlab R2022a Simulink software. Using FPGAs to implement chaotic oscillators avoids analog system problems such as component drift and high thermal instability while providing determined system parameters, rapid prototyping, and high throughput. The employment of FM over a chaotic modulation layer provides a passband operation (currently at an intermediate frequency of 10.7 MHz) while adding the benefits of carrier frequency offset robustness and constant signal envelope. Within this study, the robustness of FM-ACSK to white noise in the channel was evaluated using bit error rate, which was tested through hardware experiments and simulations. The results show the feasibility and potential performance limitations of this approach to chaotic communication system design.  
chaos; chaotic communications; secure communication system; chaotic systems; signal processing; chaos synchronization; FPGA; chaos shift keying Article