esa-sceva/satcom-chunk-collection huggingface dataset:
# Stratosphere: A Fast Virtual Circuit Switch Layer for Data Center Network
## 3 Prototype Design
### Processing of VCS Modules
#### 3.2.2 Network Congestion Controlling  
The virtual channel of VCS is fixed, and the rate is also determined. The rate of data is specified and controlled to prevent network congestion.  
Rate limit. Cells of packets are transmitted by the source node at the rate assigned in QoS SLA, and the bridge port of source nodes controls the rate based on the token bucket. If the quota is exceeded, cells are cached in port queues of the node. The rate is the bandwidth of a virtual channel because it is transmitted in a virtual link. Packets could be dispatched in multiple virtual links, so the rate is the sum of multiple virtual channels.  
Virtual channel conflicts. The output ports of VCS are synchronized and divided into virtual channels according to time slots. Based on the configuration of the controller, cells received from input ports are forwarded to different virtual channels in different or the same output ports. Due to different transmission delays, packet cells from different input  
Figure 5: The relationship between Cells and Ethernet frame.  
Figure 6: Transmitting of cells.  
ports may cause overlap in the same output port, which creates a conflict between virtual channels. In output ports of VCS modules, cell-level storage is set up to cache one cell from the other virtual channel.  
As illustrated in Figure 7, cell x of channel 1 from input port 1 and cell y of channel 2 from input port 2, which are transmitted to different channels of the same port, are only overlapped. Cell x is transmitted normally, but cell y is cached. Cell x and cell z of channel 1 from input port 4, which are transmitted to the same channel and same port, are competition. Cell z is dropped to normal. With respect to priorities, cell z could be degraded and cached to be transmitted in the next slot, but cell y is dropped. Priorities are combined with time slots of ports, which provide adaptive forwarding to alleviate network congestion.
###### Abstract  
In practice, fresh-cut fruit and fruit salads are currently stored submerged in sugar syrup (approx. 20%) to prevent browning, to slow down physiological processes and to extend shelf life. To minimize browning and microbial spoilage, slices may also be dipped in a citric acid/ascorbic acid solution for 5 min before storage in sugar syrup. To prevent the use of chemicals in organic production, short-term (30 s) hot-water treatment (sHWT) may be an alternative for gentle sanitation. Currently, profound knowledge on the impact of both sugar solution and sHWT on aroma and physiological properties of immersed fresh-cuts is lacking. Aroma is a very important aspect of fruit quality and generated by a great variety of volatile organic compounds (VOCs). Thus, potential interactive effects of sHWT and sugar syrup storage on quality of fresh-cut apple slices were evaluated, focusing on processing-induced changes in VOCs profiles. Intact 'Braeburn' apples were sHW-treated at 55 \({}^{\circ}\)C and 65 \({}^{\circ}\)C for 30 s, sliced, partially treated with a commercial ascorbic/citric acid solution and slices stored in sugar syrup at 4 \({}^{\circ}\)C up to 13 days. Volatile emission, respiration and ethylene release were measured on storage days 5, 10 and 13. The impact of sHWT on VOCs was low while immersion and storage in sugar syrup had a much higher influence on aroma. sHWT did not negatively affect aroma quality of products and may replace acid dipping.  
Keywords:minimal processing: sugar syrup immersion; volatile organic compounds; chemical prevention; ready-to-eat fruit salads +
Footnote †: journal: Foods
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 2 Particularities and Constraints
### Unreliable Communication Channel
#### 2.2.1 Long and Variable Propagation Delay  
The propagation speed of underwater acoustic wave is approximately 1.5 \(\times\) 10\({}^{3}\) m/s, which is five orders of magnitude lower than the radio propagation speed (3 \(\times\) 10\({}^{8}\) m/s) in air. Moreover, the speed is affected by some factors including the temperature, depth, and salinity, which can be calculated by the equations below [9]:  
\[V_{1}=1449.2+4.6T+0.055T^{2}+0.00209T^{3}+(1.34-0.01T)(S-35)+0.06D \tag{1}\]  
\[V_{2}=1449+4.6T+0.055T^{2}+0.003T^{3}+(1.39-0.012T)(S-35)+0.017D \tag{2}\]  
\[V_{3}=1449.2+4.6T-0.055T^{2}+0.00029T^{3}+(1.34-0.01T)(S-35)+0.016D \tag{3}\]  
\[V_{4}=1448.96+4.591T-0.05304T^{2}+0.0002374T^{3}+(1.34-0.0102T)(S-35) \tag{4}\]  
\[+0.0163D+1.675\times 10^{-7}D^{2}-7.139\times 10^{-13}TD^{3}\]  
\[V_{5}=1492.9+3(T-10)4.6T-0.006(T-10^{-2})-0.04(T-18)^{2}\]  
\[+(S-35)(1.39-0.01T)+D/61 \tag{5}\]  
where \(V\) is acoustic speed in m/s, \(T\) is temperature in degrees Celsius, \(S\) is salinity in parts per thousand, and \(D\) is depth in meter. From the above, using different equations will get different acoustic speed. To simplify analysis and calculation, it is often assumed that the salinity and temperature is constant. Unfortunately, in coastal areas or near rivers, these assumptions are generally invalid making make the acoustic propagation speed variable. Moreover, the propagation delay is long and variable, and can be calculated as follows:  
\[t=\frac{d}{v} \tag{6}\]  
where \(t\) is propagation delay in second, \(d\) is distance between sender and receiver in meter, and \(v\) is acoustic speed in m/s. As discussed above, due to a long propagation delay and variable sound speed, the time synchronization and the localization are difficult to achieve in the underwater scenario.
# Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks
## 5 Neural Networks for Synthesis and Optimization of Antenna Arrays
### Benefits of the Taylor-Neural Network Architecture
While the linear assumption facilitates analytical modeling and initial validation, the proposed Taylor-neural network framework is not intrinsically constrained by geometry. Future developments will extend the approach by incorporating geometric position encodings and training on synthetic datasets representing diverse topologies. These adaptations will enable the model to handle non-uniform or platform-integrated arrays with varying curvature and element spacing. We have explicitly outlined this limitation in the revised manuscript and defined it as a key direction for future research.
## Appendix B Model with Non-Gaussian Linear Scalar Filter
### Moment Equations and Stationary Solutions for Non-GLSF Model
Next, equating the derivatives in Equations (A22)-(A30) to zero and taking into account conditions (A31), (A32), we obtain  
\[E[z_{x_{2}}^{2}(l)]=\frac{(\gamma_{x_{1}}^{l})^{2}}{2\beta_{x_{1}}^{l}},\ \ \ E[z_{x_{3}}^{2}(l)]=3\Bigg{(}\frac{(\gamma_{x_{1}}^{l})^{2}}{2\beta_{x_{1}}^{l}} \Bigg{)}^{2},\ \ \ E[z_{x_{2}}z_{x_{3}}(l)]=0\] (A33)  
\[E[z_{x_{2}}(l)z_{x_{4}}(l)]=3\Bigg{(}\frac{(\gamma_{x_{1}}^{l})^{2}}{2\beta_{x_ {1}}^{l}}\Bigg{)}^{2},\ \ \ E[z_{x_{4}}^{2}(l)]=15\Bigg{(}\frac{(\gamma_{x_{1}}^{l})^{2}}{2\beta_{x_{1}}^{l }}\Bigg{)}^{3},\ \ \ E[z_{x_{3}}z_{x_{4}}(l)]=0\] (A34)  
\[E[z_{x_{1}}(l)z_{x_{2}}(l)]=q_{x_{1}}^{l}\Bigg{(}\frac{(\gamma_{x_{1}}^{l})^{ 2}}{2\beta_{x_{1}}^{l}}\Bigg{)}+3q_{x_{3}}^{l}\Bigg{(}\frac{(\gamma_{x_{1}}^{l })^{2}}{2\beta_{x_{1}}^{l}}\Bigg{)}^{2},\] (A35)  
\[E[z_{x_{1}}(l)z_{x_{3}}(l)]=\frac{1}{2\beta_{x_{1}}^{l}}\Bigg{[}(\gamma_{x_{1} }^{l})^{2}E[z_{x_{1}}(l)]+\alpha_{x}^{l}\frac{(\gamma_{x_{1}}^{l})^{2}}{2 \beta_{x_{1}}^{l}}+2q_{x_{2}}^{l}\frac{(\gamma_{x_{1}}^{l})^{4}}{2\beta_{x_{1 }}^{l}}\Bigg{]},\] (A36)  
\[E[z_{x_{1}}(l)z_{x_{4}}(l)]=3q_{x_{1}}^{l}\Bigg{(}\frac{(\gamma_{x_{1}}^{l})^{ 2}}{2\beta_{x_{1}}^{l}}\Bigg{)}^{2}+15q_{x_{3}}^{l}\Bigg{(}\frac{(\gamma_{x_{1 }}^{l})^{2}}{2\beta_{x_{1}}^{l}}\Bigg{)}^{3}.\] (A37)  
Substituting quantities (A31)-(A37) to Equation (A21), we obtain  
\[\frac{dE[z_{x_{1}}^{2}(l,l)]}{dt}=2\alpha_{x}^{l}E[z_{x_{1}}(l,l)]\ -2\alpha_{x}^{l}q_{x_{2}}^{l}\frac{(\gamma_{x_{1}}^{l})^{2}}{2\beta_{x_{1}}^{l}}\] (A38)  
Hence, from Equation (A38) and equality (A32), we find the nonstationary solution for the second moment of the process \(z_{x_{1}}(t,l)\)  
\[E[z_{x_{1}}^{2}(t,l)]=(\alpha_{x}^{l})^{2}t^{2}+2\alpha_{x}^{l}\alpha_{0_{x}} ^{l}t-2\alpha_{x}^{l}q_{x_{2}}^{l}\frac{(\gamma_{x_{1}}^{l})^{2}}{2\beta_{x_{1 }}^{l}}t+c_{0_{x}}^{l}\] (A39)  
where \(c_{0_{x}}^{l}\) is an integration constant.
# FL-MD3 QN-Based IoT Intelligent Access Algorithm for Smart Construction Sites
## 4 Simulation and Analysis  
### Simulation Parameter Settings  
To validate the FL-MD3 QN algorithm in smart construction site scenarios, this section presents experimental results for dynamic spectrum access. Core algorithm parameters include channel count, learning rate, and discount factor. Privacy protection parameters include data encryption rate and data leakage risk threshold to meet security requirements. Experimental environment configurations ensure stable and efficient user access by setting 5G IoT key parameters: interface latency, mobility support, bandwidth, and operating frequency bands. The learning rate is set to 0.95 and the discount factor to 0.96 to accommodate the real-time decision-making requirements of dynamic spectrum access with a high learning rate, while the discount factor balances long-term rewards with the continuous nature of 5G services. The federated learning noise scale is set to 0.01 based on the differential privacy mechanism to balance model accuracy and privacy strength [24]. The data encryption rate is set to 100% and the data leakage risk threshold is set to 0.01%, complying with industrial IoT security standards [27]. The air interface delay is set to 5 ms, the bandwidth to 20 MHz, and the operating frequency band to 2.6 GHz, strictly adhering to the 3 GPP 5G NR standard to accommodate dense device access and high-throughput requirements for video services in construction sites [28]. These parameters comprehensively evaluate the FL-MD3 QN algorithm's optimization capability for multi-modal, multi-user IoT access while ensuring privacy and data security. The proposed model parameter settings are listed in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Setting** \\ \hline Channel Count & 80 \\ Learning Rate & 0.95 \\ Discount Factor & 0.96 \\ Batch Size & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameter settings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**User Type** & **Service Mode** & **Typical Devices** \\ \hline Environmental Monitoring Terminal & 0 & Temperature/Humidity/PM2.5 Sensors \\ Voice Communication Device & 1 & Smart Safety Helmets, Emergency Intercoms \\ Personnel Positioning Tag & 2 & UWB Positioning Modules, RFID Terminals \\ Video Surveillance Device & 3 & 4K HD Cameras, Drone Inspection Systems \\ \hline \hline \end{tabular}
\end{table}
Table 3: Smart construction site configuration.
# Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones
###### Abstract  
The NASA CYGNSS satellite constellation measures ocean surface winds using the existing network of the Global Navigation Satellite System (GNSS) and was designed for measurements in tropical cyclones (TCs). Here, we focus on using a consistent methodology to validate multiple CYGNSS wind data records currently available to the public, some focusing on low to moderate wind speeds, others for high winds, a storm-centric product for TC analyses, and a wind dataset from NOAA that applies a track-wise bias correction. Our goal is to document their differences and provide guidance to users. The assessment of CYGNSS winds (2017-2020) is performed here at global scales and for all wind regimes, with particular focus on TCs, using measurements from radiometers that are specifically developed for high winds: SMAP, WindSat, and AMSR2 TC-winds. The CYGNSS high-wind products display significant biases in TCs and very large uncertainties. Similar biases and large uncertainties were found with the storm-centric wind product. On the other hand, the NOAA winds show promising skill in TCs, approaching a level suitable for tropical meteorology studies. At the global level, the NOAA winds are overall unbiased at wind regimes from 0-30 m/s and were selected for a test assimilation into a global wind analysis, CCMP, also presented here.  
CYGNSS; ocean surface winds; tropical cyclones; microwave remote sensing Article Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones  
[PERSON], [PERSON], [PERSON], [PERSON], T. Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones  
17 November 202111 December 20211 December 202116 December 2021  
16 December 2021
## 3 Results and Discussion
### Model Validation Using TCAD Data
Figure 14 illustrates the comparison between the simulation results and the predictions of the compact model for different numbers of fins and fin widths, which are consistent with the degradation trends reported in [12; 16]. The root-mean-square error is kept below 12%. The results show that the effects of these two factors on HCI degradation gradually decrease as the number of fins increases or the fin width increases to a certain extent. The lifetime of devices under HC stress is the time taken for the threshold voltage to degrade by 10%. Under HC stress conditions, the device experiences accelerated degradation. Table 4 presents the stress lifetime obtained from both the simulation and model predictions with substrate bias voltage. As reported in [6], HCI degradation is exacerbated under forward bias, resulting in a shorter lifetime, while reverse bias alleviates degradation to some extent. As shown in Table 4, the improvement in lifetime due to reverse bias is minimal. Furthermore, the device's lifetime under operational voltage was predicted using both the conventional model (self-saturation model) and the compact model for comparison. The results were obtained under 0.74 V DC mode, and the lifetime extracted from [11] was also listed. The results are shown in Table 5. The lifetime prediction from the conventional model is overly optimistic, as it only accounts for interface traps, neglecting the impact of oxide traps, which play a significant role in long-term degradation. Additionally, the conventional model does not incorporate the self-heating effect. In contrast, the model developed in this study predicts a device lifetime of approximately 10 years, which is consistent with the typical operational lifespan of similar devices. Figure 14: Analysis of HCI degradation under different fin numbers and widths. (**a**) \(V_{t}\_shift\)-stress time curves with different fin numbers; (**b**) \(V_{t}\_shift\)-stress time curves with different widths. Figure 13: Analysis of HCI degradation with self-heating and without self-heating. (**a**) \(V_{t}\_shift\) versus stress time; (**b**) mobility versus stress time.
## 2 Materials and Methods
### Implemented Models
#### 2.3.1 Model_0  
As shown in the block diagram shown in Figure 5, upon using Model_0 (the model that the authors published earlier), a labeled dataset was built accordingly from the produced flat files after completing the sequence preprocessing pipeline. The sequence data were dealt with as text data in this stage, and _KnerFIDF_ was then obtained. _KnerFIDF_ is a novel feature extraction method that primarily leverages the _Kner_ counting method that splits (i.e., tokenizes) and counts the terms' frequency sequences with respect to their order in the original sequence. This step produced a kmer counts matrix of each kmer term in the datasets. This matrix was then processed using the _KnerFIDF_ equation given by Equation (1) [11].  
\[KnerFIDF(t,d,D)=KmerCount(t,d)\cdot idf(t,D) \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specification** & **Value** \\ \hline Processor & TPU \\ RAM & 35.35 GB \\ Disk Space & 2 T \\ Data Storage space & Google Drive \\ Development platform & Python 3.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of development environment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Implementation Step** & **Tool/Library Used** \\ \hline Sequence files’ download & pysradb \\ Sequences’ quality control & FastQC \\ Sequences’ trimming & Trimmomatic \\ Fastq files’ conversion and manipulation & Biopython \\ Files’ fragmentation & seqkit \\ Random forest model implementation & Skaern \\ LSTM & Tensorflow \\ \hline \hline \end{tabular}
\end{table}
Table 3: Libraries used in the implementation.  
Afterwards, dimensionality reduction using the LDA algorithm was applied to the obtained _KmerFIDF_ matrix to clean the sparse matrix and to eliminate the kmers with low _KmerFIDF_ vectors. Finally, as per the literature, the traditional predictive method was obtained (random forest (RF)). The experiments used Datasets I, II, and IV since Dataset_III was tested in our previous work [12]. Figure 5 summarizes the processing steps of Model_0.
# Enhancing Security in Smart Robot Digital Twins Through Intrusion Detection Systems
## 5 Experimental Results
### Analysis of the CIC Modbus Dataset  
For this dataset, we followed the previously presented model and split the data into 70% for validation and 30% for testing. The results are illustrated below.  
The RF model achieved remarkable performance in classifying industrial network traffic, demonstrating an accuracy of 95% on the validation set and 93% on the test set. The precision and recall of 95% for both classes during validation indicate a perfect balance between correctly classifying benign traffic and attacks, with a minimal error rate, as shown in Figure 22. On the test set, performance remains stable, with a precision and recall of 93%, suggesting good generalization capability. The AUC-ROC of 0.95 on validation and 0.93 on testing, as presented in Figure 23, confirms the model's robustness, indicating excellent separability between normal and malicious traffic. The confusion matrices, as shown in Tables 14 and 15, display low false positive alert values, making this model a strong security solution.  
Similar to the previously analyzed model, SVM did not perform very well here either, unlike RF, with an accuracy of 88% and an AUC-ROC of 0.883, as shown in Figures 24 and 25, which suggests difficulties in correctly separating the classes. Although the recall for attacks is high (88%), indicating a good ability to detect malicious events, the precision for attacks is only 65%, meaning that the model generates a significantly higher number of false alarms. This aspect is also highlighted by the confusion matrices, presented in Tables 16 and 17, which show a tendency for the model to erroneously classify benign traffic as malicious. The F1-score of 0.75 confirms this imbalance between precision and recall, indicating a weaker performance compared to RF. These aspects make the SVM model not such a good choice.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Confusion Matrix—RF Validation** & **0** & **1** \\ \hline
0 & 832,972 & 43,588 \\
1 & 44,936 & 857,248 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Confusion matrix—RF for the test set from CIC Modbus 2023.  
Figure 23: Classification report—RF for the test and validation sets.
Based on the analyzed data, we can draw the following conclusions, as presented in Table 18. RF achieved superior results in all categories, demonstrating high accuracy and a balanced classification of benign traffic and attacks. SVM, although providing decent performance, has a weaker separation capability and produces more misclassifications.
## 2 Materials and Methods  
### Experiments  
The conductance measurements were carried out with a mechanically and electronically isolated \"PicoSPM II\" microscope head controlled by Picoscan-2500 electronics (Agilent) using a homemade PTFE STM cell. Current signals from the STM were captured using a NI-DAQmx/BNC-2110 (National Instruments analogic-digital converter PC-interface acquisition system), analysed with LabVIEW software and plotted employing Python through Matplotlib [33]. All glassware and homemade PTFE cells were cleaned with freshly prepared piranha solution (volume ratio of 3:1 H\({}_{2}\)SO4:H\({}_{2}\)O\({}_{2}\)) before the experiments and subsequently rinsed with 18 M\(\Omega\) cm\({}^{-1}\) Milli-Q water (Millipore(r), Burlingtone, MA, USA). A Au(111) single crystal (10 mm \(\times\) 1 mm, MaTech) of 5N purity and an orientation accuracy of \(<\)0.1\({}^{\circ}\) was employed as a substrate. Before each experiment, the Au(111) crystal was electro-polished to eliminate possible residual contamination, rinsed with Milli-Q water, annealed in a H\({}_{2}\) flame for 10 minutes and then cooled down in Ar atmosphere. The crystal was then placed in the STM cell that was filled with 80 uL of pure mesitylene (purity 99 %, ACROS Organic, Thermo Fisher Scientific). One drop of a 0.5 uM mesitylene solution containing the target carotenoid molecule was added to the STM cell before starting the single-molecule conductance experiments. The employed molecules in this work, _trans_-TMC and _cis_-TMC-syn, were synthetized according to a procedure described by [PERSON] et al. [22] (for synthetic details see Appendix A.4).
# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
###### Abstract  
This article examines the incorporation of the Shopping Assistance Automatic Suggestion (SAAS) model into Virtual Reality (VR) environments in order to improve the online shopping experience. The SAAS model employs sophisticated deep learning methods to offer customized product recommendations, which are conveyed by non-player characters (NPCs) via voice-based interactions. Our goal is to develop an interactive shopping experience that replicates real-life interactions by integrating AI-powered recommendations with immersive VR technology. We gather and standardize data from several open commerce databases, such as Amazon Product and Customer Reviews. The SAAS model, in conjunction with GPT-3, BERT, and T5, undergoes training and testing to evaluate its effectiveness across multiple criteria. The results demonstrate that the SAAS model surpasses other models in delivering contextually aware and pertinent recommendations. The integration process outlines the specific steps involved in capturing, processing, and transforming user interactions in virtual reality (VR) into vocal suggestions provided by non-player characters (NPCs). This strategy improves customization and utilizes the immersive features of virtual reality to effectively engage people. The results of our research establish a higher standard for e-commerce, with the goal of enhancing the user experience of online purchasing by making it more instinctive, engaging, and pleasurable.  
virtual reality; digital twins; data mining; deep learning; e-commerce