esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Methodology  
The methodology applied to carry out the research is presented in Figure 2.  
To carry out the research, the data were collected from the available literature and the specifications of individual VTS systems operated in Poland were examined. Three VTS systems, including VTS Zatoka Gdanska, VTS Lavica Slupska, VTS Swinoujscie-Szczecin, were reviewed and compared.  
In order to conduct the research, materials from official websites were used and the internal documents of particular VTS systems were analyzed.  
The criteria for VTS comparative analysis were identified, including:  
1. System location;
2. Organization of the system;
3. Technical infrastructure;
4. Traffic management;
5. Risks related to vessel traffic in the area covered by the system;
6. Traffic management functions in relation to the implementation of Regulation (EU) No 1315/2013 [14];The above mentioned criteria were identified considering the literature review result and the authors' experience in the operation of VTS systems, addressing both technical and organizational aspects. Criteria 6 and 7 refer to the purpose of the undertaken research and the need to assess the adjustment of VTS systems operating in Poland to the two current selected regulations introduced by the IMO [13] and European Parliament [14]. The assessment of these criteria allowed us to show the importance of VTS systems operating in Poland in relation to TEN-T development.  
A comparative analysis of the VTS systems according to identified criteria was performed. An expert method was used to determine the impact of VTS systems operating in Poland on the implementation of individual TEN-T tasks. The authors were the experts and provided the assessments based on their knowledge and practical experience. Experienced VTS managers from each of the analyzed VTS systems shared the data and information (one person per VTS system).  
The International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA) Waterway Risk Assessment Program (IWRAP MK II) was applied to assess the probability of the occurrence of undesirable situations on selected parts of the analyzed fairways. The IWRAP MK II software includes the development of a waterway model and the geometry of the relevant routes and uses data on traffic volume and composition, waterway bathymetry and other factors. Once the model is defined, IWRAP MK II calculates the expected annual average number of collisions and groundings.  
The impact of VTS systems on the development of the trans-European transport network was examined. Moreover, a discussion was carried out, focusing on the level of difficulty to implement the provisions of the regulations introduced by the IMO [13] and European Parliament [14]. Then, recommendations to improve VTS systems were formulated and conclusions were drawn.
## 3 System Model and Proposed Scheme
### The Proposed Interference Mitigation Scheme
#### 3.2.1 Soft Fractional Frequency Reuse  
Interference management is a major challenge with regard to deploying D2D communication into LTE-A cellular networks. In the proposed scheme, soft fractional frequency reuse (FFR) is used as an interference management method between CUEs and DUEs as shown in Table 2. A serving eNB can manage the interference between CUE and DUE pairs using soft FFR. According to Table 2, D2D pairs are allowed to use any partition of frequency (\(F_{c}\), \(F_{1}\), \(F_{2}\), and \(F_{3}\)) except that which is allocated to its CUE in the same area. In this way, D2D pairs can reuse the same frequency with CUEs located in the neighbor sectors and avoid interference from CUEs in the same sector. However, D2D pairs located at the cell edge area can experience serious interference from CUEs of neighbor sectors because those are allowed to use any frequency except the one allocated to CUEs in the same sector. To resolve the inter-sector interference caused by CUEs of neighbor sectors, the proposed scheme exploits the ABS method with soft FFR.
## 3 Results and Discussions
### The Quality of Entrances and Exits  
Figure 13 displays the surface appearance of the exit and entrance of the CFRP normally reamed hole and the ultrasonically reamed hole at a rotational speed of 600 rpm. In the experiment, the ultrasonic amplitude was 6 \(\upmu\)m. The entrance of the ultrasonic reamed hole is relatively smooth, and the surface produces less fiber pull-out. However, the hole of CFRP has more fiber pull-out and more fiber protrusion at the exit of CR. When ultrasound was applied, the exit defects of CFRP were significantly reduced, indicating that UVR is effective in improving the surface quality of CFRP entrance and exit. The reason for this result is that the ultrasonic vibration of the reamer cutting edge helps to remove the carbon fibers by shear fracture mode and the surface quality of CFRP exit and entrance is improved.  
Figure 12: Comparison of roundness at different levels of tool rotation speed: (**a**) machined CFRP hole; (**b**) machined Ti hole.  
Figure 13: _Cont._  
Figure 14 presents the surface appearance of the entrances and exits of the titanium alloy by CR and UVR. At a rotational speed of 600 rpm, the entrances machined by CR have chipping. In the experiment, the ultrasonic amplitude was 6 \(\upmu\)m. In contrast, the entrances machined by UVR have a complete surface. When the reamer enters the CFRP-titanium alloy interface, the reamer is subjected to force fluctuations that cause damage to the area. The ultrasonic action reduces the cutting temperature, which makes the exit surface of the titanium alloy more complete than that of CR.  
Figure 14: _Cont._  
Figure 13: Surface morphology of CFRP: (**a**) entrances in CR; (**b**) entrances in UVR; (**c**) exit in CR; (**d**) exit in UVR.
## 4 Mobile Localization Algorithms
### Centralized Localization Algorithms  
In this section, five centralized localization schemes are surveyed: 1) Absolute Positioning Scheme (APS), 2) Energy-Efficient Ranging Scheme (EERS), 3) Motion-Aware Self-Localization (MASL) scheme, 4) Collaborative Localization Scheme (CLS) and 5) Three-Dimensional Underwater Target Tracking (3 DUT) Scheme.
## 4 Discussion
### Strengths of the Proposed Architecture  
The case study underscores several key strengths of the architecture:  
* **High Diagnostic and Triage Accuracy.** By combining data-driven models with rule-based safeguards, the system achieved high sensitivity for emergencies and good overall accuracy. It effectively prioritized true urgent cases. Moreover, the inclusion of LLM-derived suggestions provided diagnostic guidance that went beyond a traditional triage algorithm. For instance, in some urgent (Level 2) cases, the system not only flagged urgency but also hinted at a likely diagnosis. This is a value-add for physicians receiving the patient, potentially accelerating the diagnostic process.
* **Privacy-Preserving Learning.** The use of federated learning proved to be practically viable and did not diminish performance, which is a major win for data privacy. Hospitals can collaborate to train better models without exposing patient data to each other. This strength means the system can scale to multi-center deployments. As more hospitals use it, the models can become even more robust by learning from a larger collective dataset.
* **Scalability and Performance.** The architecture of the system handled large data volumes and concurrent user interactions without performance loss. The use of streaming and big data frameworks (Kafka, Spark) ensures that it can scale horizontally--more machines can be added to handle more patients or more data sources.
* **Interoperability and Modularity.** The layered, API-driven design allowed us to plug in upgrades easily. For example, during experiments, we replaced the initial model with the more advanced LLaMA-3-based model without changing the rest of the pipeline--the contract of the Decision Engine with the LLM module remained the same (input: text of note, output: extracted info). This modularity is a strength in fast-moving AI tech: as better models emerge, they can be slotted into the architecture.
* **User-Centric Design.** Features like the UI integration and feedback loop illustrate the user-centric nature of the system. It was built not just to produce predictions, but to fit into how clinicians work. The positive simulated user feedback suggests that it could achieve the elusive goal of being embraced rather than resisted by healthcare providers. In particular, the ability of the UI to capture feedback and corrections fromclinicians and route that back to model improvement is a practical implementation of a learning health system.
## 4 Experimental Results  
In this section, we present the experimental results of the proposed method for protecting real-time mouse data. The dataset used in these experiments is the one constructed in Section 3.2. To evaluate the effectiveness of the proposed approach, we compared it with the traditional random method of generating fake data, using various feature combinations. The results of these comparisons are shown in Figures 4 and 5. Figure 4 compares the classification performance from the attacker's perspective using the feature combination of the Elapsed Time, X Coordinate, and Y Coordinate, while Figure 5 presents the performance comparison results after adding the X Distance and Y Distance features.  
Table 4 represents the average values of the performance metrics for all machine learning classification algorithms shown in Figures 4 and 5. As shown in Table 4, for all datasets, the classification performance using various feature combinations significantly decreased when applying the proposed data protection method compared to the method that uses randomly generated fake data. Depending on the performance metrics, a reduction of approximately 30% to 50% was observed. Even in terms of absolute accuracy, the performance was found to be 61.9%, which is only slightly higher than the baseline accuracy of 50% for binary classification, where random selection is possible even without any meaningful classification. This result demonstrates that the proposed data protection method is highly effective.  
Additionally, to verify whether the classification accuracy of fake data generated using GANs is statistically significantly lower than the classification accuracy of randomly generated fake data, a one-tailed paired \(t\)-test was performed based on the accuracies of all machine learning methods presented in Table 4. The confidence intervals and \(p\)-values for each feature set were summarized in Table 5. The results of the paired \(t\)-test show that for all feature sets, there was a decrease ranging from a minimum of 33.7% to a maximum of 42% within the 95% confidence interval, with \(p\)-values that are much smaller than 0.01. Therefore, it can be concluded that, even at the 99% confidence level, the decrease in classification accuracy is statistically significant.  
Figure 4: Comparison of classification performance based on elapsed time, X-coordinate, and Y-coordinate features.  
Figure 5: Comparison of classification performance based on elapsed time, X-coordinate, Y-coordinate, X-distance, and Y-distance features.
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 4 Review of Edge Computing in Power Systems  
Power systems are large-scale and comprehensive electrical networks that support critical electricity services [77]. In recent years, intelligent monitoring, control, and computational equipment have emerged, transforming traditional power systems characterized by unidirectional communication and centralized energy production into smart grids. These smart grids incorporate bidirectional communication and decentralized energy sources, including renewable energy, and utilize intelligent monitoring and control technologies to enhance grid efficiency, reliability, and sustainability. This technological shift is pivotal in addressing the growing energy demands and decarbonization challenges. This section reviews the applications of edge computing within smart grids to illustrate how it offers new opportunities to solve the issues brought by these modern systems.
# Nanomaterials for Healthcare Biosensing Applications
## 4 Organic Nanomaterial Applications for Healthcare Biosensing
### Nanostructured Hydrogels
The main reason for these thermo-responsive volumetric changes can be attributed to a perturbation in the intermolecular hydrogen bonds between pNIPAm and water in the aqueous solution that takes place due to phase transition above a lower critical solution temperature. The sensitivity, accuracy, and durability of biosensors are greatly affected by its membrane and pose a significant challenge. Sun et al. reported a chitosan-pNIPAm nanogel membrane for sensing temperature and ethanol on microchips [248]. The membrane enabled volume shifts and provided a platform of nanovalves in the channels of the chip. This strategy holds promise for in situ smart nanomembrane fabrication for fabricating lab-on-a-chip type sensors, detectors, and controlled release systems. Besides their function as membranes, NHgs may function as other elements in a biosensing system. An interesting application of such a system is the glucose sensitive artificial muscle developed by Lee and co-workers [249]. They harnessed the glucose sensitive nature of boronic acid (BoA) byconjugating it with a nanogel of HyA and cholesterol. This nanocomposite was subsequently used for MWCNT yarn actuation due to their reversible swelling/shrinking. Zhao et al. exploited the negative charge and poor conduction of poly(acrylic acid) (pAAc) to impede electron transfer and repel the [Fe(CN)\({}_{6}\)]\({}^{3-/4-}\) mediator [250]. They used the nanogel as an amplification agent to develop an ultrasensitive impedimetric aptasensor for CEA. The sensor provided a linear impedimetric response in serum from 10.0 fg mL\({}^{-1}\) to 10.0 ng mL\({}^{-1}\) and the LOD was 1.4 fg mL\({}^{-1}\).
## 2 Results
### RNA-seq, Assembly and DEGs Functional Annotation  
To explore the molecular mechanisms underlying differences in aroma volatile compounds between Gramny Smith and Jonagold apples, six cDNA libraries (three biological replicates) were constructed from these two apple varieties and sequenced utilizing the Illumina sequencing platform. Approximately 6.15-6.94 G of clean bases were produced from Gramny Smith and Jonagold apples (Table S3). After data filtering and quality trimming, 41.01-46.29 million clean reads were obtained through sequencing of the cDNA libraries prepared from these two apple varieties. The Q30 percentage was over 93.01% for each sample and the average GC content was 47.03% for all six libraries (Figure S1; Table S3). Furthermore, average error rate of the sequenced bases was less than 0.03% (Figure S2). A total of 33.21-40.12 million reads were mapped to the apple reference genome, with match ratios in the range of 78.29-92.30% (Figure S3; Table S4).  
FPKM methods were used to analyze gene expression patterns in Gramny Smith and Jonagold apple libraries (Figure S4). Comparing these two types of libraries with respect to the FPKM calculation, 20,481 and 20,794 genes were identified in the cDNA libraries, while 1954 and 2267 genes were expressed specifically in Gramny Smith and Jonagold apples, respectively (Figure S5). A total of 8117 DEGs were detected, including 3682 upregulated genes and 4435 downregulated genes (Figure 3a). To investigate the differences in gene expression patterns between Gramny Smith and Jonagold, DEGs were subjected to hierarchical clustering analysis of transcript abundances using the FPKM values, and these DEGs had different transcriptome profiles in the two apple varieties (Figure 3b).  
In order to know more about the biological function of the DEGs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. According to the GO enrichment analysis, these DEGs were classified into three categories including biological process, cellular component and molecular function. The most abundant terms of each category were ADP binding, single-organism carbohydrate metabolic and cell wall process (Figure 3c). Based on the KEGG enrichment analysis, DEGs were matched and assigned to 122 pathways. The most enriched pathways were carbon metabolism, biosynthesis of amino acids, plant hormone signal transduction and fatty acid metabolism. Moreover, the pathways of phenylalanine metabolism and linoleic acid metabolism were also enriched (Figure 3d).  
Figure 2: Percentage (%) of each type of volatile compounds in Gramny Smith and Jonagold apples. (**a**) Volatile compounds in Gramny Smith. (**b**) Volatile compounds in Jonagold.
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 3 Background  
### LoRa and LoRaWAN Protocol  
LoRa is an ultra-long-distance wireless transmission technology based on spread spectrum technology [22; 23]. Long Range Wide Area Network (LoRaWAN) is a set of communication protocol and system architecture designed for long-distance communication network [23; 24]. Lora has a great advantage in handling co-channel interference. It solves the problem of not being able to take into account long distance, anti-interference, and low power consumption at the same time. Compared with other communication technologies, LoRa's ultra-low cost, high sensitivity, ultra-low power consumption, strong anti-interference ability, low bandwidth consumption, and long transmission distance make it ideal for this project.
# Symmetric Keys for Lightweight Encryption Algorithms Using a Pre-Trained VGG16 Model
## 2 Theoretical Background
### Symmetric Key
#### 2.1.3 Blowfish  
Blowfish is a symmetric cipher with a variable key length based on a Feistel structure. It features a 64-bit block size and supports key lengths ranging from 32 to 448 bits. The algorithm employs 16 rounds and utilizes a wide S-box that depends on the key. Blowfish uses four S-boxes, and the same algorithm is used for decryption in reverse [7, 12], as illustrated in Figure 4.
## 4 Discussion
### Evaluation of Multi-Angle Observed PRI
In contrast, it has been found that the relationship between SIF and GPP appears to be linear under stress conditions precisely due to the reduced SIF values around midday [45]. At the seasonal scale, considerable evidence based on retrievals from satellite [32; 33; 37; 41; 42] and ground-based measurements [43; 44; 61; 62] has been showing strong empirical linear SIF-GPP relationships. In contrast, the studies conducted at short time scales (e.g., sub-daily) found insignificant relationship (R\({}^{2}\leq 0.3\)) than that in seasonal scale studies [63], which may be nonlinear [64]. [PERSON] et al. [46] demonstrated that SIF primarily responded to changes in APAR rather than GPP and emphasized the needs of LUE information when estimating GPP at a short-term scale.
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 4 Hardware Implementation
### On-Chip Data Exchange Engine Architecture
#### 4.1.1 DDR3_TOP Module  
Dual-channel read and write access control of two off-chip DDR3A and DDR3B is achieved by the DDR3 TOP module. All the data that interact with DDR3 must go through this module. The sub-modules are: DDR3 Control Unit Register (DCU Reg) is used to control all operation process interacting with DDR3 and monitor the feedback status of the read and write process. MIG is the official memory interface control IP core of Xilinx, which is used to directly read and write data with off-chip DDR3. The primary modules for mapping address generation, read and write access control, and input and output data buffer management are DDR3A CTRL and DDR3B CTRL.
# Study About the Performance of Ascon in Arduino Devices
## 2 The Ascon Cipher Suite
### State of Ascon  
As is well known, a system is said to be stateful if it is designed to remember previous information, which we will call state. Thus, the set of all possible states of a system is called state space. In a discrete system (defined by a finite number of degrees of freedom), the state space is usually finite. The interaction between system states and their environment consist of actions that occur separately, such as the acceptance of an input or the production of an output, which may cause the system to modify or update the state.  
In the case of Ascon, all components of the encryption algorithm operate on a finite state of 320 bits, which is modified through a series of permutations (explained in the next section). The 320-bit state is separated into an external part of \(r\) bits, denoted as \(S_{r}\)  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Name** & **Algorithms** & \multicolumn{4}{c}{**Bit Size**} & \multicolumn{4}{c}{**Number of Rounds**} \\ \cline{3-8}  & & \(K\) & \(N\) & \(T\) & \(r\) & \(a\) & \(b\) \\ \hline Ascon-128 & \(\mathcal{E},\mathcal{D}\) & 128 & 128 & 128 & 64 & 12 & 6 \\ Ascon-128a & \(\mathcal{E},\mathcal{D}\) & 128 & 128 & 128 & 128 & 12 & 8 \\ Ascon-80 pq & \(\mathcal{E},\mathcal{D}\) & 160 & 128 & 128 & 64 & 12 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for the three algorithms of the Ascon suite.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Name** & **Algorithm** & \multicolumn{2}{c}{**Bit Size**} & \multicolumn{4}{c}{**Number of Rounds \(p\)**} \\ \cline{3-8}  & & \(H\) & \(r\) & \(i\) & \(a\) & \(c\) & \(b\) \\ \hline Ascon-HASH & \(\mathcal{X}\) & 256 & 64 & 12 & 12 & 12 & 12 \\ Ascon-HASHa & \(\mathcal{X}\) & 256 & 64 & 12 & 8 & 12 & 8 \\ Ascon-XOF & \(\mathcal{X}\) & \(l\) & 64 & 12 & 12 & 12 & 12 \\ Ascon-XOFa & \(\mathcal{X}\) & \(l\) & 64 & 12 & 8 & 12 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for the hash functions defined by Ascon.  
and an internal part of \(c\) bits, denoted as \(S_{c}\), where \(r\) is the rate and \(c\) the capacity such that \(c=320-r\) (these parameters depend on the version of Ascon). This state structuring is common in sponge constructions, and in this particular case, the MonkeyDuplex construction [13], where the state is split in the mentioned way.  
For a correct application of the round transformations or permutations, the 320-bit state \(S\) is divided into five 64-bit register words, \(x_{i}\), \(0\leq i\leq 4\), such that:  
\[S=S_{r}\|S_{c}=x_{0}\|x_{1}\|x_{2}\|x_{3}\|x_{4} \tag{4}\]  
State \(S\) must always be interpreted as a byte-array for the sponge interface, which starts with the most significant bit (MSB) of \(x_{0}\), which is the byte 0, and ends with the last significant bit (LSB) of \(x_{4}\), which is the byte 39.