esa-sceva/satcom-chunk-collection huggingface dataset:
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 7 Evaluation and Discussion
### Proof of Concept Demonstration of Data Plane Forwarding
#### 7.1.6 The Dynamic Behaviour of VNFs  
The architecture restricts the VNFs from altering the SFC headers in order to let the VNFs themselves decide the next hop in the SFC. In order to enable such functionality together with Encrypted Links, the E-VNFs must be pre-provisioned for every alternative route. Additionally, the VNFs must have access to information about what header attributes are available for modification, such as the next SFC hop. This enforcement of encryption is considered as a security feature since it restricts the VNFs from sending traffic to random VNFs. Corrupt VNFs can possibly both inject malicious traffic into other VNFs or make Denial of Service attacks towards other users. However, when a set of predetermined SFC paths is set, it gives the VNF a choice of multiple next-hops that network administrators and the network controllers have considered as secure paths. This also allows for multi-tenancy VNFs, where a predetermined set of multiple users can share one single VNF. This functionality is not focused on in our work, but the architecture is considered to be easily adaptable for such scenarios.
## 3 Improving on Answering Reachability Queries
We consider \(pa(v)\) and then replace \(L(w)\) in Equation (1) as follows:  
\[L(v)=L(pa(v))\times lpf(\mathrm{q}) \tag{2}\]  
where  
\[L(pa(v))=L(w)\times\mathrm{q}^{\prime} \tag{3}\]  
such that \(\mathrm{q}=\mathrm{q}^{\prime}\times lpf(\mathrm{q})\). If \(\mathrm{q}^{\prime}\) is still a composite number, \(L(w)\) in Equation (3) can further be rewritten as \(L(pa(v))=L(pa(pa(v)))\times lpf(\mathrm{q}^{\prime})\), where \(L(pa(pa(v)))=L(w)\times\mathrm{q}^{\prime\prime}\), such that \(\mathrm{q}^{\prime}=\mathrm{q}^{\prime\prime}\times lpf(\mathrm{q}^{\prime})\). This process can be continued until we have a node label \(L(pa(...pa(v)))=L(w)\times spf(\mathrm{q})\). Now, assume a node \(r=pa(...pa(v))\). Applying the same proof as above for the case where \(\mathrm{q}\) is a prime, we are able to determine the ancestor-descendant relationships between \(r\) and \(w\). If \(r\) is determined to be a descendant of \(w\), \(v\) must be a descendant of \(w\), because \(v\) is a descendant of \(r\). If \(r\) is determined not to be a descendant of \(w\), \(v\) must not be a descendant of \(w\) because every descendant of \(r\) is not a descendant of \(w\). We devised an algorithm based on the theorems to test the reachability of two ordered nodes, which is outlined in Algorithm 2. In implementation level, we used BigInteger class in Java to handle very large integers. An iterative algorithm was implemented to realize line 7-12. The algorithm begins with the first prime factor \(x=2\) of \(Lw\). We check if \(x\) is larger than \(spf(\mathrm{q})\). If so, the algorithm terminates and returns FALSE (line 9). Otherwise, we compute \(Lw^{1}\) by iteratively dividing \(Lw\) with \(x\). In other words, \(Lw^{1}\) is obtained by removing all prime factors \(x\) in \(Lw\) (e.g., if \(Lw=2\times 2\times 3\) and \(x=2\) then \(Lw^{1}=3\)). We move \(x\) to the next prime factor (e.g., 3) and repeat the above process until \(x\) is smaller than \(Lw^{k}\) in \(k\)-th iteration. It returns TRUE (line 11) if the iteration finishes.
# Improved FraSegNet-Based Rock Nodule Identification Method and Application
###### Abstract  
Extracting nodal features is crucial for analyzing rock structure stability and plays a significant role in designing engineering projects. This study presents an enhanced version of the FraSegNet algorithm, focusing on improving its ability to identify nodal features in images. The updated FraSegNet incorporates the ResNet101 backbone and integrates the Squeeze-and-Excitation (SE) attention mechanism, enabling better concentration on key nodal characteristics. The primary improvements are as follows: (1) Multi-scale feature extraction: Leveraging the ResNet101 architecture for the effective extraction of detailed information from nodal images. (2) Better attention mechanisms: The SE module focuses on nodal regions, resulting in clearer and more refined feature representations. (3) Dynamic learning strategies: I incorporation of cosine annealing and warm-up techniques to optimize training efficiency. The algorithm was validated with the Barton-Bandis model and Hoek-Brown criterion. The experimental results demonstrate its superior performance, achieving 97.1% accuracy in nodal feature detection with an average error of only 1.5% compared to the rock mass parameter. This small error proves the model works well. FraSegNet offers accurate segmentation and precise geometric parameter extraction, making it a valuable tool for advancing rock stability analysis and practical applications in rock mechanics.  
deep learning; joint recognition; Barton-Bandis model; Hoek-Brown criterion; rock mass parameter characterization Article
# Evaluation of pH and Optical Properties of Dual Ringe HEDP Irrigating Solution
## 2 Materials and Methods
### Statistical Analysis  
After obtaining data from pH measurements and spectral analysis, statistical analyses were conducted using IBM SPSS Statistics software (v23.0; IBM Corp, Armonk, NY, USA). Descriptive statistics were employed to characterize the pH values, while alpha values, derived from spectral measurements, underwent a comparative analysis between different solutions using the Kruskal-Wallis test. This non-parametric test was chosen due to the nature of the absorbance coefficient data and their non-normal distribution as derived from the Kolmogorov-Smirnov test. All data are presented as means \(\pm\) standard deviation (SD). A \(p\) value less than 0.05 was set to indicate statistical significance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Product** &
\begin{tabular}{c} **Concentration** \\ **\% (w/v)** \\ \end{tabular} & **Brand Name** & **Manufacturer** \\ \hline Distilled water & 100 & N/A & N/A \\ Sodium hypochlorite & 2 & Chloraxid 2\% & Cerkamed, Stalowa Wola, Poland \\ Ethylenediaminetetraacetic Acid & 17 & E.D.T.A. & Imicryl Dental, Konya, Turkey \\
1-hydroxyethylidene & 9 HEDP & Dual Rines HEDP & Medcem, Weinfelden, Switzerland \\
1,1-disphosphonate + sodium hypochlorite & 2 NaOCl & Chloraxid 2\% & Medcem, Weinfelden, Switzerland \\ Citric acid & 20 & Citric Acid 40\% & Cerkamed, Stalowa Wola, Poland \\ Chlorexidine digluconate & 2 & Gluco Chex 2\% & Cerkamed, Stalowa Wola, Poland \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tested irrigating solutions with their corresponding concentrations and sources.
## 4 Results  
The main objective of the evaluation is to assess the practical feasibility of the proposed pipeline and to determine the performance impact of using privacy-preserving analysis of wearable sensor data compared to plaintext analysis.  
All experiments and evaluations are conducted using the Physionet 2017 Challenge dataset. The proposed classifiers are evaluated with 5-fold cross-validation. In each fold, 80% of the recordings are used to train the privacy-preserving deep neural network on the homomorphically encrypted input data, while the remaining 20% are used for inference. The partitioning of the data splitting is done in such a way that the class distribution between the folds is similar.  
The overall performance in terms of accuracy, precision, sensitivity, specificity, and F1-score is computed on the held-out subsets of the data and is listed in Table 1. For a more detailed analysis, the classification performance for each class is shown in Table 2. To tackle the class imbalance problem, we experimented with loss weighting based on class frequency. This strategy is usually used during training to prevent the classifier from being biased towards the most frequent classes. However, the results obtained were comparable to the models trained without this strategy.  
To compare the performance of the proposed privacy-preserving classifiers with the performance reported in [29], we also analyze the F1 measure. The assessment metric used to rank the models is defined as the mean F1 measure of the classes \"Normal\", \"AF\" and \"Other\". A comparison between our privacy-preserving models and the plaintext  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{5}{c}{**Performance**} \\ \cline{2-6}  & **Accuracy** & **Precision** & **Sensitivity** & **Specificity** & **F1-Score** \\ \hline MLP & 0.83 & 0.77 & 0.75 & 0.92 & 0.76 \\
1D-CNN & 0.79 & 0.72 & 0.68 & 0.90 & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overall performance of the proposed classifiers.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{5}{c}{**Performance**} \\ \cline{2-7}  & **Normal** & **AF** & **Other** & **Noise** \\ \cline{2-7}  & **MLP** & **1D-CNN** & **MLP** & **1D-CNN** & **MLP** & **1D-CNN** & **MLP** & **1D-CNN** \\ \hline Precision & 0.88 & 0.86 & 0.81 & 0.70 & 0.77 & 0.67 & 0.63 & 0.65 \\ Sensitivity & 0.92 & 0.89 & 0.80 & 0.66 & 0.70 & 0.64 &  
approach [29] is summarized in Table 3. For the detection of normal recordings, the average F1 measure is 0.9, for the detection of other rhythms, it is 0.73, while the F1 measure for the classification of atrial fibrillation is close to 0.8. Consequently, the final F1 measure is 0.81.  
Table 4 lists the runtime performance results obtained with the proposed framework when performing inference on encrypted data. It shows the runtime required to complete the privacy-preserving machine learning-based analysis on a server running Intel(R) Core i7-6700K CPU running at 4.0 GHz (Intel Corporation, Santa Clara, CA, US).
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 6 Results and Discussions
### Performance Comparison between Federated Learning and Machine Learning
#### 6.1.1 Volume of Traffic  
We first evaluate and compare the volume of traffic for machine learning, federated learning, and federated learning with PCA.  
* _Machine Learning:_ With ML, all the data points need to be sent to the central server. Each data sample consists of 140 data points (each of 8 bytes) and a label (each of 4 bytes) resulting in a data sample that is \(140\times 8+4=1124\) bytes long. There is a total of 5000 such data samples. Hence, for machine learning, 5000 \(\times\) 1124 = 4,496,000 bytes or 4.5 MB of data needs to be communicated.
* _Federated Learning:_ When using FL, each of five devices will send 9132 \(\times\) 4 = 36,528 bytes in one communication round. This results in 182,640 bytes of data transmitted for five devices in one communication round. Hence, the total data sent by five devices in three communication rounds equals 547,920 bytes. The server will also send the updated global model after performing federated averaging in each communication round. The total data sent by the server to end devices equals 109,584 bytes. Hence, the total volume of data that needs to be communicated for federated learning is 657,504 bytes or 657.5 KB.
* _Federated learning with PCA:_ When PCA is employed with federated learning, each data sample is reduced from 140 data points to 20 data points. Consequently, the number of neurons in the input and output layer also get reduced from 140 to only 20, with 32 neurons in the hidden layer. The resultant model comprises 1332 model parameters. Following the similar calculations performed earlier for federated learning without PCA, the total volume of data traffic communicated among five devices and the server in three communication rounds for federated learning along with PCA equals 95,904 bytes or 95.9 KB.  
Figure 10 depicts the relative communication costs of the three methods for a similar level of model performance, which indicates that the volume of data traffic in federated learning is significantly lower in comparison to the typical machine learning approach. The traffic volume is further reduced when the PCA dimensionality reduction algorithm is used. Specifically, with FL using PCA as specified above, the volume of traffic is reduced by 98% relative to an ML model for the same level of model performance.
## 2 Complex Load and Reliability Modeling
### Reliability Modeling under Complex Load
Substitute the mean \(\mu_{i}\) and variance \(s^{2}_{i}\) of each element and the mean \(\mu_{R}\) and variance \(s^{2}_{R}\) of yield strength into Equation (11) to obtain the reliability index \(\beta_{i}\) of each element in the derrick model under complex load:  
\[Z_{i}=R-S_{i} \tag{10}\]  
Figure 2: Flow chart of reliability analysis model of derrick under complex load. \[\beta_{i}=\frac{\mu_{z_{i}}}{s_{z_{i}}}=\frac{\mu_{R}-\mu_{i}}{\sqrt{s_{R}^{2}+s_{ t}^{2}}} \tag{11}\]  
**Step 7:** Reliability index contour plot by APDL. According to the reliability index of each element, the derrick's reliability index under complex load is represented as a visual contour plot.
# No Free Lunch--Characterizing the Performance of 6 TiSCH When Using Different Physical Layers
## 2 Related Work
### Hybrid Radio Utilization  
[PERSON] et al. [19] integrate multiple PHY layers within the 6 TiSCH stack. The authors perform range-testing measurements on multiple modulations: 2-GFSK (at 1.2, 8, 50, and 250 kbps), 4-GFSK (at 1000 kbps), O-QPSK 2.4 GHz (at 250 kbps). They report the link quality in terms of number of motesreached by each note in the network, and link symmetry. The authors then choose two modulations in the sub-GHz band for integration in the 6 TiSCH stack: 2-GFSK at 1.2 kbps for transmitting Enhanced Beacons (EB), 4-GFSK at 1000 kbps for data traffic. This allows for EB sent by the gateway to reach more nodes, resulting in faster and more accurate network-wide synchronization compared to multi-hop transmissions. The slot templates for the PHY layers are designed in accordance with the IEEE802.15.4 standard, and ported on Contiki-NG implementation of 6 TiSCH. The authors report on the performance of the multi-PHY network in terms of channel utilization and the distribution of synchronization accuracy in the network, in comparison with the distribution of hop-count. They demonstrate that a faster bit-rate of 4-GFSK at 1000 kbps for data packets diminishes the probability for collision and leads to less than 0.1% channel utilization despite the increased re-transmission rate. At the same time, the slower bit-rate of 2-GFSK at 1.2 kbps for EB packets leads to higher channel occupancy, at 2% duty cycle (leading to violation of the license-free band regulation in many regions).  
This research paves the road for a thorough investigation of multi-PHY integration in the 6 TiSCH stack. However, it explicitly assumes an inverse correlation between bit-rate and range. This assumption is challenged by the research results in another practical setting in References [1] and [3], which show that a high bit-rate modulation such as OFDM 1 MCS 3 at 800 kbps can offer competitive performance compared to 2-FSK at 50 kbps in terms of range, reliability, and duty cycle (as explained in Section 2.2). This is a significant observation because if long-range radio is needed for EB synchronization as Reference [19] suggests, higher bit-rates are appreciated for EBs in order to minimize energy consumption and also collision probability since EBs are transmitted on shared cells. Furthermore, it still remains unclear whether there are any side-effects to the choice of one radio or another on the end-to-end performance as a whole so that the costs and benefits of each PHY layer choice can be fully assessed.
### Lorenz Waveforms for Ranging Sensors  
Chaotic Lorenz waveforms have been used in ranging sensors [5]. Some detection, ranging and doppler applications require signals that are difficult to detect or intercept. The chaotic nature of the waveform can make it difficult to distinguish from noise, which in turn can enhance the security and reliability of a given sensor. Other sensor applications benefit in terms of range measurements, velocity measurements and ambiguity properties. Traditional sensor techniques tend to utilize repeating waveforms that lack entropy (non-entropic). The non-repeating and spread spectrum characteristics of chaotic waveforms provide entropy to these sensor schemes. As a result, they benefit ranging and detection schemes by creating near-thumbtack ambiguity functions [5, 10, 11]. The resulting waveforms can give low correlation sidelobe levels, flat power spectral density and reduced ambiguity in range and Doppler measurement.  
Chaotic radar and sonar schemes also support applications that require tracking moving targets, detecting the presence of stationary objects and imaging applications. These techniques have historically constituted chaotic imaging which lead to new opportunities in areas such as remote sensing [10], medical imaging [33], and non-destructive testing [34]. The benefits related to these sensor advances parallel those found in chaotic communication systems including enhanced security, improved range and velocity measurements, and the ability to perform high resolution imaging. Similarly, drawbacks to these techniques also parallel chaotic communication systems as these schemes require sophisticated signal processing techniques and can be computationally intensive.
## 3 Results
### Precipitation Frequency Trends  
Precipitation frequency was shown to increase at high latitudes and decrease at middle latitudes (Figure 3). Significant increases in this parameter were detected in more areas of the Eastern Hemisphere, especially in spring and summer, while the Western Hemisphere has more areas with significant increases in winter. In terms of precipitation categories, the greater the amount, the lower the frequency (Figure S2). Trace amounts accounted for a large proportion of precipitation events (circa 46-86%), but the trend was not significant, while the frequency of small precipitation significantly decreased. In addition, in cropland and forests, the proportion of very heavy precipitation was larger than that of heavy precipitation.  
Figure 3: M-K trends of total precipitation frequency for each 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) grid across the four seasons from 1982 to 2015. The “+” symbols in each sub-figure indicate the statistically significant grids at the 0.05 level.  
Figure 2: Decade trend of GPP values during each season and the annual mean from 1982 to 2015 in every vegetation type. The error bars (mean \(\pm\) 1.96 \(\times\) SE) represent 95% confidence intervals.  
The frequency of seasonal precipitation varied in different land cover areas (Figure 4). In cropland, grassland, and barren land, this parameter showed a decrease in all precipitation intensity categories, with the largest decline detected in winter trace precipitation in shrubland (\(-2.3\%\)/decade). In contrast, the frequency of trace precipitation in forests in summer and in shrubland in spring and summer increased at rates of \(0.5\%\)/decade, \(0.8\%\)/decade and \(1.7\%\)/decade, respectively. The frequency of trace precipitation in barren land declined at rates of \(-0.4\%\)/decade, \(-1.1\%\)/decade, \(-1\%\)/decade, and \(-1.6\%\)/decade in spring, summer, autumn, and winter, respectively.  
It is interesting that the frequency of trace precipitation in forest and shrubland increased while other land cover types decreased. In grassland, the frequency of all five precipitation categories decreased. Inundra areas, precipitation frequency varied slightly for the trace category at an average rate of \(-0.5\%\)/decade. The rates of variation increased in summer and decreased in other seasons, with no obvious trend larger than \(10\) mm/day precipitation, which corresponds to the precipitation frequency data recorded for barren land.
# Underlying Security Transmission Design for Orthogonal Time Frequency Space (OTFS) Modulation
## 5 Security Performance Analysis
### Chosen-Plaintext Attacks  
Assuming that the attacker, [PERSON], has access to the encryption machine, it can construct a ciphertext corresponding to any plaintext. Because the wireless channel has the characteristics of instantaneity and uniqueness, the encryption sequence (\(D_{x}\), \(D_{y}\), \(D_{z}\), \(D_{w}\)), generated according to the wireless channel, is completely dynamic. That is, the IFFT matrix and the FFT matrix controlled by \(D_{x}\), \(D_{y}\), \(D_{z}\), \(D_{w}\) are also completely dynamic. The input OTFS symbols are encrypted into dynamic ciphertext through the IFFT matrix and the FFT matrix. The eavesdropper, [PERSON], must have real-time channel state information between the legitimate users, [PERSON] and [PERSON], in order to process the fully dynamic ciphertext. However, [PERSON] cannot know the channel state information between [PERSON] and [PERSON], so the chosen-plaintext attack is invalid. In other words, the transmission mechanism we propose is sufficient to resist selective plaintext attacks.
## 3 Overview of OMF Architecture
### OMF Experiment Workflow
From the initial specification in the EDL file to the final data collection and processing, each stage of the workflow is designed to facilitate a seamless experiment experience. This robust and modular approach enhances the OMF architecture's applicability to a wide range of research objectives, supporting not only network measurement studies but also more complex, large-scale experimental designs. While the OMF framework provides a robust foundation for managing and automating network experiments, it is primarily a software tool that lacks concrete physical implementation and pre-configured templates tailored to specific research needs, requiring researchers to manually integrate hardware, tools, and configurations. The proposed OMF-enabled test environment addresses these limitations by integrating OMF with physical nodes, third-party measurement tools, and predefined templates, offering a fully implemented, reconfigurable testbed. This setup simplifies the deployment of complex experiments, enables dynamic customization, and bridges the gap between theoretical frameworks and practical applications, providing a scalable and efficient platform for realistic and reproducible network measurements.