esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobility-Aware Hybrid Flow Rule Cache Scheme in Software-Defined Access Networks
## 4 Proposed Hybrid Flow Rule Cache Scheme
### Heuristic Algorithm  
Since the above ILP problem is NP hard [36], this paper provides a simple heuristic algorithm (Algorithm 1) for the hybrid flow rule cache scheme. It is assumed that the proactive rule caching is determined before the mobile flow physically moves to the target AD and multiple target ADs can be predicted based on the prediction methods. As we explained above, since the reactive flow rules should be installed in an AD as they are from actual moved flows after handover into the area of each AD, the algorithm aims to determine whether the proactive flow rule for mobile flows is needed or not based on the estimated controller's response delay and delay requirement of flows.  
```
0: Set of ADs \(N\), Set of mobile flows \(M_{i}\) from \(i\)th AD, Maximum rule capacity \(R_{i}^{max}\) of \(i\)th AD, Delay constraint of \(i\)th flow \(d_{j}^{th}\).
0: Flow tables including proactive flow rules in ADs
1: for \(i\in N\)do
2: Estimate the controller's response delay \(D_{i}\)
3: for \(j\in M_{i}\)do
4: Select the flow \(j\) which has lowest \(d_{j}^{th}\)
5: if \(D_{i}>d_{j}^{th}\) then
6: if \(R_{i}<R_{i}^{max}\) then
7: Insert flow \(j\) rule in \(i\)th AD
8: \(R_{i}=R_{i}+1\);
```  
**Algorithm 1** Heuristic hybrid flow rule algorithm  
For each AD, the controller first estimates the response delay (i.e., not for each flow), which can reduce the complexity. Then, among the flows in each AD, the controller finds the flow which has the minimum delay constraint, because a low delay requirement means that the flow is delay-sensitive. After the delay constraint of the flow is compared with the estimated controller's response delay, whether proactive rule caching is performed or not is determined. After the decisions of the mobile flows are completed, the static flows are then processed. Although the delay constraint for the static flows can also be considered, the controller handles them without priority as much as possible for simplicity. The time complexity of the proposed heuristic hybrid flow rule algorithm can be expressed as \(O(N\times w\times M\times\text{log}M)\).
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 4 Proposed Sub-Band BadNets Backdoor Attack in Remote Sensing Imagery  
Remote sensing imagery typically has multiple bands in addition to the RGB bands that are commonly found in natural images. Deep learning models often focus on sub-bands differently in classification or regression tasks. We propose planting the backdoor trigger in a subset of these bands to enhance stealthiness and achieve superior performance against traditional defense approaches. Most defense strategies are developed with natural image settings in mind and do not account for the unique aspects of remote sensing modalities. Traditional backdoor defense approaches, such as Neural Cleanse [12] and TABOR [13], assume that attackers plant the backdoor trigger in all bands. Our sub-band-based attack will exploit this assumption, resulting in stronger attacks.  
We utilize an explainable AI-based method, Score-CAM [52], to identify the important and less important bands and then plant the backdoor in all bands, as well as in the important or less important bands for comparison, as shown in Figure 2. The important bands usually contain semantic information for the target object, making it easier for an attacker to hide a backdoor pattern. Conversely, the less important bands have less semantic information, and planting a trigger in these bands should more easily poison the deep model.  
Figure 2: Proposed sub-band BadNets backdoor attack in remote sensing imagery. The EuroSat dataset [51] is used for illustration. (**a**) Clean deep convolutional neural network (DCNN) model training using the clean EuroSat dataset. (**b**) Saliency maps computed by the Score-CAM method [52] for the target class ‘River’. Six bands, including Red, Green, Blue, Red-Edge2, Red-Edge3 and Cirrus, were marked as important for correctly classifying the input image as ‘River’, while other bands were identified as less important. (**c**) Backdoor trigger embedding in the important bands identified by the Score-CAM method. (**d**) Poisoning the DCNN model by fine-tuning with the combination of clean and poisoned dataset.
# Spatialization and Analysis of China's GDP Based on NPP/VIIRS Data from 2013 to 2023
## 5 Discussion
### Comparative Analysis Results of GDP
#### 5.1.2 Comparative Analysis of GDP Rankings of Various Provinces  
Figure 6 shows the ranking comparison of the official GDP and GDP data obtained by NTL in different provinces in 2013, 2018, and 2023. This ranking result is consistent with the spatial distribution of the GDP, with a high GDP in the east and a low GDP in the west, and large differences between the east and the west.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **2013** & **2015** & **2018** & **2021** & **2023** \\ \hline Official GDP * & 630,009.3 & 722,767.9 & 914,707.5 & 1,137,743 & 1,250,931 \\ NTL GDP * & 642,302.06 & 723,799.48 & 916,854.55 & 1,142,995.42 & 1,251,395.70 \\ Difference & 12,292.76 & 1031.60 & 2147.08 & 5252.01 & 464.39 \\ RE & 1.95\% & 0.14\% & 0.23\% & 0.46\% & 0.04\% \\ \hline \hline \end{tabular}
*Unit: 100 million yuan.  
\end{table}
Table 5: GDP data obtained by different statistical methods and their errors.  
Figure 6: Changes in provincial rankings in 2013, 2018, and 2023.  
Among the 31 provincial administrative units, the ranking of most provinces has not changed much, and the ranking change is between \(\pm 2\), such as in Shanghai, Jiangsu, Zhejiang, Shandong, Guangdong, Tibet, Gansu, Qinghai, Xining, and other provinces. Among them, the GDPs of Shanghai, Jiangsu, Zhejiang, Shandong, Guangdong, and other provinces are the top 10 province GDPs in China, and the GDP of Tibet, Gansu, Qinghai, and other provinces are the bottom 5 province GDPs in China. The ranking of regions with a better and worse GDP is relatively stable; the ranking of a small number of provinces has changed greatly, such as Hunan, Xinjiang, Hubei, and other places. Among them, due to the development of light industry in Hunan and Hubei provinces, factories do not work outdoors or rarely work outdoors, and the NTL values will be low. In addition, due to the insufficient power generation capacity, there are phenomena such as \"power outages\" during peak power consumption and extreme weather conditions, which will also affect the NTL values. Xinjiang is located in the northwest inland, with rich oil and gas resources and relatively well-developed heavy industry. Mining, transportation, construction, fuel burning, and other phenomena may occur at night, resulting in higher NTL values. In addition, the area is relatively large, and there are many snow-covered mountains, dry riverbeds, or deserts that can reflect strong and weak light, which will also affect the changes in NTL values. In general, the large differences in rankings are caused by factors such as the dominant industry and power generation capacity.
## 4 Conclusions  
The conclusions drawn from this extensive microstructural study of ferritic-martensitic P91 and ferritic, stainless, Laves phase strengthened Crofer(r)22 H steel under thermomechanical fatigue loading are:  
1. The pronounced initial drop in stress response of ferritic-martensitic P91 is associated to recovery of excess dislocations, remaining from martensitic transformation even after tempering.
2. Microstructural instability manifests in transition to the stable stress range phase of the fatigue curve, leads to polygonization of the martensite lath structure and finally results in a refined sub-grain size matching the original martensite lath width after tempering.
3. Polygonization is not associated to stress concentrators like crack tips, crack edges or inclusions, but a result of thermomechanically induced dislocation activity within the entirety of martensite laths.
4. Utilization of the polygonization phenomenon for the assessment of fatigue damage susceptibility and remaining life assessment is plausible, but necessitates further examination on the impact of cycle parameters and the relation of polygonization to macroscopic cracking as well as broadening to other AFM steels.
5. Creep strength of pre-fatigued AFM microstructures shall be revisited.
6. The ferritic, stainless, Laves phase-strengthened Crofer(r)22 H steel exhibits superior microstructural stability and consequently TMF lifetime, based on:  
Figure 10: Crack branching at deformation induced sub-grain boundaries in Crofer®22 H after TMF failure (SEM micrograph).  
Crack obstruction by thermomechanically triggered precipitation of small Laves phase particles, stabilization of the HAGB structure and restriction of polygonization to strongly deformed areas (cracks tips and edges, PFZs at HAGBs) and crack branching at sub-grain boundaries and active crack obstruction by thermomechanically triggered precipitation of Laves phase particles in front of propagating crack tips are suspected other mechanisms, which cause the encountered decrease in stress range degression per fatigue cycle after crack initiation, but further investigation is necessary to substantiate these postulated mechanisms.
* The alloying system offers potential for a further increase in fatigue resistance by enhanced precipitate volume fraction and adjusted precipitation kinetics.  
Conceptualization, B.K.; methodology, B.K.; validation, B.K., J.L.B. and T.F.; formal analysis, B.K., J.L.B. and T.F.; investigation, B.K., J.L.B. and T.F.; resources, B.K.; data curation, B.K., J.L.B. and T.F.; writing--original draft preparation, B.K.; writing--review and editing, B.K. and T.F.; visualization, B.K. and J.L.B.; supervision, B.K.; project administration, B.K.; funding acquisition, B.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to express their gratitude to [PERSON] for mechanical testing, [PERSON] and [PERSON] for metallographic preparation and [PERSON] and [PERSON] for microstructural characterization work.  
The authors declare no conflict of interest.
## 6 Simulation Results
### Computational Overhead
#### 6.1.1 Computational Overhead of Encryption and Decryption  
The characteristics of end-to-end encryption protocols and hop-by-hop encryption protocols indicate that the latter one increase the encryption and decryption operations in aggregators. DAA needs \(T+2\) encryption and decryption processing in the intermediate nodes [9]. A comparison shows that the computational overhead of data confidentiality decreases while protecting data integrity.  
Figure 4: The total number of computations (which includes the computations of encryption, decryption and MACs) _versus_ the number of network monitoring nodes for FESA and DAA.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of nodes & 250 \\ Terrain dimensions & 400 \(\times\) 400 m\({}^{2}\) \\ Transmission range & 50 m \\ Data rate & 0.1 Mbps \\ Propagation pathloss model & Two-ray \\ MAC protocol & IEEE 802.11 \\ Routing protocol & DSDV \\ Simulation time & 200 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 4 The Proposed Scheme
### Determination of the Optimal \(\delta\)  
Next, we will explain how to find out the optimal solution of \(\delta\). The physical resource block (PRB) is the minimum time unit that an LTE device can use. It is 180 kHz wide in frequency and 0.5 ms long (1 slot) in time. The contention-free period, during which LTE users can access the unlicensed band, must be a multiple of \(T_{slot}\), as follows:  
\[(1-\delta)\cdot L=x\cdot T_{slot},\ x\in\mathbb{N} \tag{13}\]  
In (12), we can obtain the upper and lower bounds of \(\delta\) which limits the possible solution of \(\delta\). According to (13), a feasible solution is a set of points within \(\delta\)'s upper and lower bounds. We denoted this set by \(\Delta\). We examine the utility values for all points in \(\Delta\). The optimal \(\delta\) maximizing the utility function can be found by comparing all the calculated values. This is essential for a one-dimensional mixed integer programming problem. The process for finding the optimal \(\delta\) is given in Algorithm 1.  
```
1://List all feasible solutions between the upper and lower limits
2:\(\Delta=\left\{\delta_{x}\in\Delta\ |\textit{lower bound}\ \leq\delta_{x}\leq upper\ \textit{bound},\ x\in\mathbb{N}\right\}\)
3:Initial:\(U=0\)
4:for\(x=1\); \(x\leq\) sizeof(\(\Delta\)); \(x\)++do
5:\(U^{\prime}=U(\delta_{x})\)
6:if\(U^{\prime}>U\)then
7:\(U=U^{\prime}\)
8:\(opt=x\)
9:endif
10:endfor
11:\(\delta_{opt}\) is the optimal solution.
```  
**Algorithm 1** Determination of the Optimal \(\delta\)  
Figure 8: User filtering mechanism flow chart.
# Enhancing Privacy in Large Language Model with Homomorphic Encryption and Sparse Attention
## 3 Materials and Methods
### Proposed Method
#### 3.3.2 Operator Replacement Technique
**Advantages and Applications of the Design:** The design of the above operator replacement techniques enables the transformer model to be trained and inferred without any knowledge of the data content, thereby offering robust privacy protection for users, as shown in Figure 4.  
Mathematically, the homomorphic encryption algorithm ensures that the computations on encrypted data are logically equivalent to those performed in plaintext, meaning that the model's output will not be biased due to encryption. This operator replacement technique has the following advantages:  
1. Privacy Protection: Since all computations are conducted in the encrypted domain, user data are never exposed to the model provider in any form, ensuring the privacy of user data. 2. Security: The security of the homomorphic encryption algorithm is based on mathematically hard problems, such as factoring large numbers or learning with errors, making it difficult for attackers to decrypt the data with current computing capabilities. 3. Model Performance: Through carefully designed operator replacement and approximation techniques, privacy is ensured while minimizing the impact on model performance. Figure 4: Advantages of the FHE-based encryption framework. 4. Compatibility: The operator replacement technique is compatible with existing homomorphic encryption algorithms and can be integrated with the latest FHE optimization technologies to further enhance computational efficiency. 5. Scalability: This method is not only applicable to dialogue systems but can also be extended to other natural language processing tasks that require privacy protection. With rigorous mathematical design and proof, the feasibility of performing complex natural language processing tasks on encrypted data are demonstrated, providing robust technical support for protecting user privacy.
## 3 Results
### Model Performance
Overall, for the RF, BRT, and SVM methods, Model C (combination of backscatter coefficients from multi-temporal SAR data) had a higher prediction accuracy than Model B (single-day Landsat-8-derived variables). This result indicates that SAR data not only provides important information for STN mapping but can also be as effective as optical images. Moreover, the predictive power of multi-temporal Sentinel-1A data was not inferior to the single-day Landsat-8 OLI image. Therefore, SAR data has broad application prospects in digital soil mapping, especially in those areas that are susceptible to cloud. For the three C models, the RF technique obtained the highest R\({}^{2}\) (0.44) and the lowest RMSE (0.27) and MAE (0.22). The R\({}^{2}\) value indicated that Model C, which was constructed using multi-temporal Sentinel-1A images, explained approximately 44% of the STN variation. Using three different machine learning techniques, STN prediction accuracy was improved when multi-temporal Sentinel-1A images were combined with Landsat-8 images. The addition of SAR data using the BRT method resulted in 48.6%, 17.2%, and 17.4% improvements for R\({}^{2}\), RMSE, and MAE, respectively. Improvements in the prediction accuracy of the STN were also observed when multi-temporal SAR data were added to Model A. For example, the addition of multi-temporal Sentinel-1A images using the BRT and RF models increased R\({}^{2}\) from 0.52 to 0.57 and 0.52 to 0.55, respectively. The combination of all environmental variables (Model E) obtained the highest prediction accuracy compared to the other four models (Models A, B, C, and D), with R\({}^{2}\) values of 0.57 and 0.55 for the BRT and RF modeling techniques, respectively. The R\({}^{2}\) values indicated that the RF and BRT modeling techniques can explain 55% and 57% of the STN variation, respectively.
# Tunnel-Free Distributed Mobility Management (DMM) Support Protocol for Future Mobile Networks
## 3 Analytical Modeling and Performance Evaluation
### Modeling the Handover Latency
#### 3.2.5 Handover Latency of Proposed Approach
Hence, it can be given as:  
\[d_{REG}=d_{R}+d_{N}+d_{s}. \tag{14}\]  
Meanwhile, the combined latencies of link layer handover and authentication can be given as:  
\[t_{RS}=\left(\frac{m_{BU}}{b_{WD}}+t_{WD}\right)+\left(\frac{m_{BU}}{b_{WL}}+t _{WL}\right)\times\frac{pf}{1-pf} \tag{7}\]0  
Let \(T_{HO}\) denote the handover latency combining all the factors mentioned above. We can now calculate the handover latency for the proposed approach by summing up the involved timing factors as follows:  
\[t_{RS}=\left(\frac{m_{BU}}{b_{WD}}+t_{WD}\right)+\left(\frac{m_{BU}}{b_{WL}}+t _{WL}\right)\times\frac{pf}{1-pf} \tag{7}\]1  
where the default values used for \(d_{L2}\), \(d_{A}\), \(d_{WRS}\), and \(d_{DAD}\) are the same as in References [8; 30].
# Brief Review of Recent Developments in Fiber Lasers
## 4 Raman Fiber Lasers
### Heat Mitigation in Raman Fiber Lasers
The DW introduces optical loss that partially offsets the growth of the anti-Stokes signal in the RW and hence suppresses the reversed CARS cycles that would otherwise result in heat generation in the RW. It is shown that the frequency-selective dissipative coupling provided by the DW can reduce the heat in active Raman media by a factor of up to five when the CARS phase mismatch is compensated for by the optimum level of coupling between the RW and the DW. The alternative approach to mitigating heat dissipation in Raman lasers is based on an ultralow quantum defect. In the majority of cases, high-power Raman fiber lasers are made of pure silica fibers or germanium-doped fibers. The lowest quantum defect for such a Raman laser pumped at 1064 nm and emitted at 1075 nm is 1.02% [80]. The pump power of the laser is 6.5 W and the output power is 1.1 W. It is difficult to achieve a high-power Raman output with such a low quantum defect, because the Raman gain in the pure silica fiber or germanium-doped fiber is relatively low at such a small frequency shift. In 2020, the phosphosilicate-doped passive fiber was proposed for low quantum defect applications [81]. This fiber has a strong boson peak and a Raman gain peak at a frequency shift of \(\sim\)40 THz in its Raman gain spectrum. The Raman gain peak is related to the asymmetric stretching vibrations supported by phosphorus-oxygen double bonds. This peak has been used for high power generation in Raman fiber lasers [82]. The boson peak in the phosphosilicate fiber is ascribed to an excess density of vibrational states [83]. In ref. [81], an ultralow quantum defect Raman laser based on the boson peak in phosphosilicate fibers has been reported for the first time. In this laser, the amplification process is based on a strong boson peak located at a frequency shift of 3.65 THz in its Raman gain spectrum. At a pump wavelength of 1066 nm and a pump power of 18.6 W, the Stokes output at the wavelength 1080 nm has reached 12.5 W. The quantum defect is 1.3%. For the increased pump wavelength, 1072 nm, the output power at 1080 nm is 10.7 W. In this case, the quantum defect is 0.74%. This is the lowest quantum defect ever reported for Raman fiber lasers.
## 4 Methodology
### Data Acquisition
#### 4.2.2 Hydrological and Topographical Parameters  
The hydrological and topographical parameters were acquired from the different sources that are mentioned in Table 2. The slope for the study area was then acquired from the DEM using ArcGIS tools. The slope attribute extracted for the observed study area is classified into six classes; (i) flat (0-3%), (ii) gentle sloping (3-8%), (iii) sloping (8-15%), (iv) moderately steep (15-30%), (v) steep (30-50%), and (vi) very steep (\(>\)50%) as seen in Figure 4. The Rawal watershed mostly lies in the moderately steep class. The steepness of the slopes determines the momentum of the runoff. Faster run off can cause soil erosion that naturally ends up in the waterways, causing the water to pollute. Thus, the moderately steep slope with a partial runoff is considered to have a good water quality. Similarly, the aspect parameter was derived from the DEM and distributed in 10 classes namely; (i) flat (\(-\)1), (ii) north (0-22.5), (iii) northeast (22.5-67.5), (iv) east (67.5-112.5), (v) southeast (112.5-157.5), (vi) south (157.5-202.5), (vii) southwest (202.5-247.5), (viii) west (247.5-292.5), (ix) northwest (292.5-337.5), and (x) north (337.5-360) as seen in Figure 4. The impact of sun is determined by the aspect parameter which gives an understanding of the plants that colonize the slope and eventually determines the animals that may be seeking food. The Rawal watershed has a south-facing slope which is warmer, and the soil tends to dry out faster in such slopes. The soil type parameter is also an important attribute that plays a part in assessing the quality of the water. Soils with higher infiltration capacity can decrease the runoff to a great degree. The soil types for the Rawal watershed are classified as (i) Be--eutric cambisois and (ii) Rc--calcaric regosois, with a 99:1% ratio. The eutric cambisois class lies in the hydro group B category which means that such soil types have a moderate infiltration rate.  
Moreover, the topographical parameters, i.e., the geological formations, of the study area are classified as Cenozoic and Upper Paleozoic (Dev, Car, Per) with a 44:56% ratio. Lithology for the Rawal watershed has siliciclastic sedimentary consolidated (Ss) and mixed sedimentary consolidated (Sm) rocks with a 44:56% ratio. Such rocks have a high resistance to erosion and poor solubility rate. Additionally, the type of land use is an important factor in determining the behaviour of the watershed as they affect the water infiltration rate. The land use/land cover parameter for the watershed is classified as (i) trees, (ii) shrubland, (iii) grassland, (iv) cropland, (v) built-up, (vi) barren/sparse vegetation, (vii) open water, and (viii) herbaceous wetland.