esa-sceva/satcom-chunk-collection huggingface dataset:
# SecureLLM: A Unified Framework for Privacy-Focused Large Language Models
## 5 Potential Applications and Future Work  
### Use Cases and High-Risk Domains  
* **Healthcare:** Patient records can be encrypted locally and integrated into an LLM for medical diagnosis or recommendation systems, reducing the risk of data leakage. Differentially private inference ensures that patients' details are not extractable through repeated queries.
* **Finance:** Banks and financial institutions might leverage SecureLLM to analyze large volumes of transaction data without revealing sensitive customer information. MPC-based approaches enable multiple branches or institutions to collaboratively train on encrypted data.
* **Legal and compliance:** Law firms or government agencies can benefit from large-scale language models while mitigating confidentiality risks. Adaptive scheduling algorithms for decentralized training can efficiently manage the workloads across agencies.  
Table 2 presents potential high-risk use cases and relevant SecureLLM features. Beyond these primary examples, SecureLLM's layered approach can be adapted to diverse industries handling sensitive intellectual property. For instance, biotechnology companies working with genome data can use encryption and differential privacy to protect genetic codes or medical trial results from reverse engineering [56], while defense contractors may deploy decentralized fine-tuning to incorporate external partners without exposing critical documents or sensitive information. SecureLLM can also facilitate flexible business models in marketplace settings, enabling various parties to share proprietary datasets under strong confidentiality controls [57].  
Where the data locality requirements are stringent, incorporating hardware enclaves or secure coprocessors can further mitigate the risks of data leakage. These enclaves may be particularly relevant in regulated environments where local regulations dictate that sensitive data must remain within specific geographical jurisdictions [58].  
Recent studies such as [59; 60] have demonstrated the potential to deploy large language models for non-traditional applications for security-oriented objectives. These papers provide a view into, e.g., how one could apply an LLM to identify financial transactions as forgeries and perform smart contract vulnerability detection. On a related note, as far as including such capabilities inside the SecureLLM system goes--such that data are made confidential via robust cryptographic practices and privacy-preserving techniques--our proposed solution could be extended to securing decentralized finance applications, as well as blockchain networks. This extension would not only broaden the applicability of SecureLLM but also reinforce its potential as a comprehensive security solution in a variety of high-risk domains.
# Robust Low-Sidelobe Transmit Beamforming under Peak-to-Average-Power Ratio Constraint
## 4 Numerical Results and Analyses
### Experiment 1: The Proposed TBF versus Several Typical Beamformers
Considering that the interference region is \([-59.5^{\circ},-20^{\circ}]\), which is far away from the mainlobe, and \([-44.5^{\circ},-5^{\circ}]\), which is close to the mainlobe, the interference interval is \(0.5^{\circ}\), the INRs are set as 40 dB, and other parameters are unchanged. Figure 3 shows the TBF beampatterns formed by all five methods when the interference region is close to the mainlobe. Note that the mainlobes of SMI and WCPO are offset by \(3.1^{\circ}\), and the gains are \(-5.99\) dB. Compared with the beampatterns in Figure 2a,b, SMI and WCPO can still form deep notches against the interferences. However, the sidelobe levels on both sides significantly increase, especially when the snapshot is limited, leading to marked performance degradation. Figure 2: The beampatterns of the proposed TBF method and four typical counterparts with different numbers of snapshots \(k\). (**a**) \(k=64\); (**b**) \(k=500\); (**c**) \(k=32\); (**d**) the proposed beampattern in (**c**). As shown in Table 4, compared to SMI and WCPO, NCCB and static beamformers have a better mainlobe performance, and the sidelobe level facing the air is relatively low; however, NCCB's sidelobe level is obviously much higher than that in Figure 2, and the notches are shallower to interference as well. Compared with the four comparative methods, the proposed method can achieve a smaller PAPR and fully improve the transmission efficiency. It effectively suppresses the sidelobe level on the ground side and reduces the impact of interferences from the ground with high stability due to not being affected by the accuracy of the sampling covariance matrix. As the air area is relatively clean and the electromagnetic interference is relatively low, the requirement for interference suppression in the airspace is low. Even if the sidelobe level in the airspace is quite high, the transceiver performance of the mainlobe and the overall radar performance will not be significantly affected.
# Changes of Air Pollution between Countries Because of Lockdowns to Face COVID-19 Pandemic
## 3 Results and Discussion
### Europe
During these restrictions, NO\({}_{2}\), SO\({}_{2}\), and PM\({}_{2.5}\) concentrations exhibited reductions compared with average values of the previous period, given as \(-37.9\%\), \(-18.6\%\), and \(-14\%\), respectively. SO\({}_{2}\) exhibited the highest reduction, in January 2021 (\(-169\%\)), while CO concentration did not change (see Figure 4, Croatia). [PERSON] et al. [92] compared concentrations of PM\({}_{1}\) and NO\({}_{2}\) between the lockdown period and the previous year's period and found a remarkable decrease in pollutant concentrations associated with traffic emissions. In Poland, the lockdown policy was imposed on 12 March 2020 [35]. The maximum reduction for CO (\(-41\%\)), NO\({}_{2}\) (\(-16\%\)), PM\({}_{2.5}\) (\(-48\%\)), and PM\({}_{10}\) (\(-63\%\)) was recorded in February 2022, while the highest decrease in SO\({}_{2}\) was observed in January 2022 (Figure 4, Poland). Reductions were also observed in pollutant concentrations (CO, NO\({}_{2}\), SO\({}_{2}\), PM\({}_{10}\)) in March 2020, when containment policies started. [PERSON] et al. [35] measured atmospheric pollutants in Poland during the policies of restrictions and found significant reductions compared with values in 2018 and 2019. Finally, in Turkey, the government announced the first restrictions on March 12, 2020 [93]. In March 2020, variations in the concentrations of all air pollutants were: \(-36\%\) for CO, \(-21\%\) for NO\({}_{2}\), \(-14\%\) for SO\({}_{2}\), \(-20\%\) for O\({}_{3}\), \(-45\%\) for PM\({}_{2.5}\), and \(-15\%\) for PM\({}_{10}\), respectively (compared with the average values of the pre-pandemic period 2015-2018). Among these air pollutants, SO\({}_{2}\), O\({}_{3}\), and PM\({}_{10}\) showed a tendency to decrease more in March 2020, also without lockdown measures. Except for February 2019, maximum reductions were recorded for PM\({}_{10}\) pollutant concentration for all periods investigated (see Figure 4, Turkey). Orak and Ozdemir [93] compared air pollutant concentrations in Turkey during lockdown restrictions with the previous five years' data. Results suggested that PM\({}_{10}\) and SO\({}_{2}\) concentrations are due to transportation emissions based on workplace mobility.
## 3 Experiment and Analysis
### Network Training Results  
The experimental results of network training in this study are shown in Figure 9. From the figure, it can be observed that the improved Deeplabv3+ network exhibits a rapid decrease in loss during the initial stages of training. Following a training duration of 10 epochs, the loss value descended from an initial 1.248 to 0.200. As the number of training iterations increases, the loss gradually stabilizes. In the first 80 epochs of training, the improved model exhibits lower training and validation losses compared to the original model. At the 80 th epoch, the model's loss value diminished to 0.084. Due to the inclusion of Focal Loss and Dice Loss in the proposed method, the overall loss values during training may be larger compared to Deeplabv3+, U-Net, and PSPNet. This phenomenon arises due to the fact that Focal Loss and Dice Loss, when employed to address intricately segmented boundary regions and diminutive targets, steer the model's attention toward these challenging samples, consequently augmenting the magnitude of the loss function. However, they still remain significantly lower than Deeplabv3+. After 100 training epochs, each model tends to stabilize and converge in the later stages of training.
## 5 Conclusions  
The proposed dynamic viewpoint selection method improved the classification of maturity by 6% and 5% compared to a single viewpoint, reaching a classification accuracy of 96% and 84% for red and yellow peppers, respectively, and reducing costs by 52% and 12%. When using all six viewpoints, an accuracy of 96% and 82% could be reached, however, with 69% and 38% higher costs compared to the proposed method. When using a perfect next-best-viewpoint selection method, classification results of 98% and 95% for red and yellow peppers could be reached to distinguish between mature and immature peppers.  
At higher pepper prices, the method is more likely to add an additional viewpoint. This leads to an increased classification performance until it reaches a maximum. When increasing the robot costs, the method is less likely to add an additional viewpoint, leading to a decreased classification performance for extreme robot costs. However, for the range of expected robot costs, the classification performance is stable.  
The results indicate the benefit of selecting an additional viewpoint when the system is uncertain about the maturity classification. However, although adding an additional viewpoint improves maturity classification, the current method for selecting next-best viewpoint does not yield the best possible results. Future research will be focused on developing an improved viewpoint selection method to select the viewpoint that optimizes the performance.  
To conclude, we showed that it is possible to improve maturity classification of sweet peppers while reducing the economic costs, by only taking an additional viewpoint when profitable. This can be used in future robotic applications to improve efficiency of a classification task that needs multiple viewpoints.  
Conceptualization, R.v.E., B.H. and Y.E.; methodology, R.v.E., B.H., G.K. and Y.E.; software, R.v.E.; investigation, R.v.E., B.H. and Y.E.; data curation, R.v.E. and B.H.; original draft, R.v.E.; writing--review and editing, R.v.E., B.H., G.K. and Y.E.; supervision, G.K. and Y.E.; project administration, Y.E. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the European Commission (SWEEPER GA no. 644313), and by Ben-Gurion University of the Negev through the Helmsley Charitable Trust, the Agricultural, Biological and Cognitive Robotics Initiative, the Marcus Endowment Fund, and the [PERSON] [PERSON] Chair in Manufacturing Engineering.  
The data presented in this study are available on request from the corresponding author.  
We would like to acknowledge Hahn-Robotics for their donation of the Sawyer robot.  
The authors declare no conflict of interest.
# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 3 RFF Identification Scheme  
In the signal collection experiment conducted using real 5G mobile phones, the inherent randomness of the PRACH preamble sequence used by the mobile phones to access the gNB led to an inability to capture all 64 PRACH preambles for each phone. Consequently, we defined two scenarios based on the extent to which the gNB possessed knowledge of the PRACH preambles:  
* Scenario 1: The gNB possesses complete knowledge of all the PRACH preambles used by the phone. In this scenario, the types of PRACH preamble sequences employed for training are the same as those used for testing during RFF identification.
* Scenario 2: The gNB has only partial knowledge of the PRACH preambles used by the phone. This implies that all the types of PRACH preamble sequences used for training become a subset of the PRACH preamble sequences employed for testing during RFF identification.  
In this section, we first introduce the existing DCTF-based CNN structures (i.e., the single-channel CNN and multi-channel CNN). Then, an index-based DCTF identification scheme is proposed for Scenario 1 using a multi-channel CNN, and a _group_-based DCTF identification scheme is proposed for Scenario 2 using a single-channel CNN.
# A Jamming-Resilient and Scalable Broadcasting Algorithm for Multiple Access Channel Networks
## 4 Proposed Broadcast Algorithm
In contrast, in the KTFW algorithm, the tree comprises nodes that were activated during a segment consisting of \(k\) rounds, in which the maximum number of activated nodes is \(k\). After forming the trees, the nodes are processed to broadcast their packets while attempting to minimize collisions. In the existing tree-based algorithm, the processing of the tree uses a randomized approach where the nodes are subdivided through random coin flips. The KTFW algorithm uses a deterministic approach where nodes are subdivided through predetermined ranges of nodes. Clearly, the KTFW algorithm is inherently different from the existing tree-based broadcast algorithms.
###### Abstract  
The present work is aimed at studying the determination method and implementation process of reasonable completion state for the Hunan Road Bridge, which is currently the widest concrete self-anchored suspension bridge in China. The global finite element model and the cable analytic program BNLAS were integrated. The synthesis algorithm of completion state determination was proposed. The contact relationships between the cable and saddles were captured using the refined FE discretization method. The concrete shrinkage and creep effects during the construction and operation periods were predicted using the CEB-FIP 90 model and the age-adjusted effective modulus method. The cable alignments under the free cable state, system transformation condition, and completion state were obtained. Moreover, the multiple-control method for the whole process of system transformation was proposed. The multiple parameters included the hanger tensioning force, exposed amount of hanger anchor cup, and tag line position. A detailed system transformation procedure was formulated and well preformed in the construction site. In addition, the further optimization analysis of final hanger force was conducted based on the actual completion state. The influence on the stress and geometry evolution of girder brought by the final girder alignment was investigated. The measured results of structural alignment and stress show that the target completion state was well implemented. The accuracy and efficiency of the proposed multiple-control method were verified by checking the tag line position of each step. In addition, the optimized final hanger force and girder lifting amount were obtained, which can provide feedback and reference for the construction control and service safety of the similar concrete self-anchored suspension bridges.  
self-anchored suspension bridge; concrete girder; reasonable completion state; concrete shrinkage and creep; system transformation; multiple control
# Scottish Passive House: Insights into Environmental Conditions in Monitored Passive Houses
## 3 Methodology  
The data used in this paper represents one calendar year, 2013. Solar powered Wireless Sensor Technology (WIST) sensors with battery back-up were used to monitor internal environmental conditions, temperature (\({}^{\circ}\)C), RH and CO\({}_{2}\) concentration in the living rooms, kitchens and bedrooms in each dwelling. This equipment was used in conjunction with solar powered door/window contact sensors to monitor window opening patterns (WO) these were fitted to the principal windows in each monitored room. Occupant interviews were also conducted during the monitoring period. In addition to the data collected, other tests which inform this study were: Airtightness testing, a thermographic survey and air flow rate measurements at the supply and extract terminals forming part of the MVHR system.  
This data was transferred to a wireless data capture system, with the capability to transmit data over General Packet Radio Service (GPRS) networks to a central off site server. This enabled greater flexibility of monitoring in a domestic context decreasing the need for an adjacent electrical socket outlet at each sensor, which reduced the risk of equipment being turned off. However, the remote nature of Site A resulted in some data loss due to transmittance problems.  
Qualitative data from occupants includes: semi-structured interviews, survey and occupant diaries, and informs the occupancy behaviour profiling.  
Due to the limited number of completed and occupied Passive Houses constructed in Scotland at the time of this study (16 homes [17]), there is insufficient statistical evidence for clear recommendations to be made. However, of the five houses studied, four of these were at Site A, with another four unmonitored houses of the same house types which experienced similar problems. Based on this data, issues have been identified which warrant further investigation. These issues are not limited to Passive House, and other investigations by this team into low energy buildings has demonstrated similar issues.
# Ultimate Compressive Strength of H-Section Stub Columns Subject to Random Fitting Corrosion Damage
## 3 Results and Analyses
### Equivalent Thickness Method
#### 3.3.1 Equivalent Thickness Based on Minimum Cross-Sectional Area  
Figure 13 displays the ultimate strength of corroded H-section columns, derived from the average thickness and the minimum cross-sectional thickness. The impact of thickness change in the column flange is illustrated in Figure 13a, while Figure 13b demonstrates the influence of thickness change on the column web. Note that in the figure, the blue dashed line corresponding to 1.0 represents the results of columns with random pitting corrosion.  
Clear differences emerge between the predicted ultimate strength based on the average thickness and minimum cross-sectional thickness of randomly pitted columns. This distinction arose from the fact that the failure of columns was primarily influenced by the minimum cross-section in comparison to the average cross-section. However, variation was observed in the predicted results due to the effect of the random nature of pitting corrosion. To assess the precision of different equivalent thicknesses, the mean relative errors were calculated for five levels of pitting intensities. For columns with various flange thicknesses of 7.5, 9.5 and 11.5 mm, depicted in Figure 13a, the average thickness resulted in mean errors of 11.5%, 13.2% and 11.3%, respectively. In contrast, the minimum cross-sectional  
Figure 13: Ultimate strength of H-section columns with equivalent thickness: (**a**) different flange thickness; (**b**) different web thickness.  
thickness yielded significantly smaller mean errors of \(-4.1\%\), \(-2.3\%\) and \(1.0\%\), respectively. Similarly, for columns with different web thicknesses of 7.5, 8 and 9.5 mm, shown in Figure 13b, the average thickness generated mean errors of 11.3%, 10.2% and 8.9%, while the minimum cross-sectional thickness resulted in smaller mean errors of 1.0%, \(-0.7\%\) and \(-1.9\%\), respectively.  
However, it is noteworthy that the minimum cross-sectional thickness may occasionally lead to undue underestimation of the ultimate strength of randomly pitted columns, particularly in cases where the flange thickness was thinner. For instance, for a flange thickness of 7.5 mm (H7.5 \(\times\) 7.5\(-\)DF + W), the error amounted to \(-10.9\%\) under the \(DOV\) of 13.8%, while it increased to \(-11.9\%\) under the \(DOV\) of 6.8%, for the flange thickness of 9.5 mm (H9.5 \(\times\) 7.5\(-\)DF + W).
# Dual Partial Reversible Data Hiding Using Enhanced Hamming Code
## 2 Preliminaries  
### Hamming Code  
Error detection and correction are fundamental techniques for ensuring the reliability of data transmission. These processes are based on the principle of redundancy, wherein additional bits are appended to a message \(y\) according to predefined mathematical rules to enable error detection and correction at the receiver. Among various error control coding schemes, Hamming Code (HC) [29] has been widely adopted due to its simplicity and effectiveness in correcting single-bit errors. As a single-error-correcting linear block code with a minimum Hamming distance of three, HC remains a prevalent choice for many practical applications in digital communication.  
In the notation \(HC(n,k)\), \(n\) denotes the total length of a codeword, \(k\) represents the number of information bits, and \((n-k)\) corresponds to the number of parity bits. Given a \(k\)-bit information vector \(x\), the corresponding \(n\)-bit codeword \(y\) is generated through the relation \(y=xG\), where \(G\) is the \(k\times n\) generator matrix. Errors may occur during transmission, resulting in a received vector \(\tilde{y}\). The error vector is defined as \(e=y-\tilde{y}\). If \(e=0\), this implies that no error has occurred and that \(\tilde{y}=y\).  
To detect and correct errors, a parity-check matrix \(H\) of size \((n-k)\times n\) is employed, satisfying the relation \(GH=0\). Applying the parity check matrix to the received word yields the syndrome \(S=\tilde{y}H\). The syndrome \(S\) corresponds to a specific row of \(H\), with its index indicating the position of the erroneous bit.  
This relationship can be further elaborated as follows:  
\[\tilde{y}\cdot H^{T}=(e\oplus y)\cdot H^{T}=e\cdot H^{T}+y\cdot H^{T}=e\cdot H ^{T} \tag{1}\]  
where \(y\cdot H^{T}=0\) by construction; consequently, the syndrome directly reflects the error pattern, enabling accurate and efficient error correction. Consider the example of HC(7,4) with the following parity check matrix \(\mathbf{H}\) (Equation (2)):  
\[H=\begin{bmatrix}1&0&1&1&1&0&0\\ 1&1&1&0&0&1&0\\ 0&1&1&1&0&0&1\end{bmatrix}. \tag{2}\]  
**Example**: Suppose that the received string is \(\tilde{y}=(1110111)\). The syndrome is computed as \(\tilde{y}H=[101]\). This syndrome corresponds to the 4 th row of \(\mathbf{H}\), indicating an error in the 4 th bit position. Therefore, the corrected string is \((1111111)\). If \(\tilde{y}H=(000)\), this implies that no errors occurred.
# Time Series Irradiance Synthesizer for Optical GEO Satellite Downlinks in 5G Networks
## 4 Validation and Numerical Results
### Validation with Experimental Data
It was observed that the long-term irradiance statistics are re-produced by the proposed synthesizer and fully represent the experimental ones. Concerning the first order statistics results, the scintillation indexes computed from the time-series generated are very similar to the measured ones as well as the corresponding normalized PDFs and CDFs which almost coincide. It is clear that the various wind speeds on the vertical (to the slant path) path cause different turbulence conditions and the larger the wind speed, the greater the SI value. Regarding the second order statistics results, the methodology-obtained PSDs show good agreement with the ones computed from the optical experimental data. Moreover, it can be seen that the scintillation effect is dominant in the frequency range under 10 Hz with a 3 dB bandwidth between 6 Hz and 8 Hz. From 10 Hz and after the power spectrum decreases steadily until the 100 Hz where the noise power spectra becomes significant.