esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results and Analysis
### Analysis of Experimental Results
#### 6.3.6 Comparative Analysis of BDP-LR Model Results with Other Models
The AUC value of the DP-DT model is 0.7535, and the AUC value of the DP-RF model is 0.8684. The higher the prediction accuracy, the closer the AUC value is to 1. Therefore, based on the ROC curves, it can be seen that the BDP-LR model has the highest classification accuracy, followed by the GDP-EBM and DP-NB models. The experimental results show that the four main evaluation indicators of the BDP-LR model are better than those of the other models when the privacy budget is 0.8, and the four main evaluation indicators are 0.9721, 0.9975, 0.9664, and 0.9816, respectively. Therefore, the logistic regression optimization model based on hybrid feature selection and differential privacy proposed in this paper not only provides high privacy to protect the patients' privacy but also provides superior classification results.
## 5 Experimental Data Analysis
### Dial Gauge Data Analysis
#### 5.1.1 Roof Subsidence Analysis  
From Figure 10, it can be observed that during the first 10 loading cycles, the cumulative subsidence of the two dial gauges under the roof was relatively consistent, with both showing minor subsidence. Gauge 2 on the left side recorded a cumulative subsidence of 4.426 mm, while Gauge 3 on the right side measured 4.676 mm. At this stage, the model rock mass was in the initial void compaction phase.  
During the 11 th to 16 th loading cycles, the roof subsidence accelerated significantly, with Gauge 2 recording a cumulative subsidence of 10.695 mm and Gauge 3 measuring 11.610 mm. This increase can be attributed to a larger crack located 33.3 cm from the left side of the roof, which showed signs of potential roof fall. Consequently, the subsidence on the right side of the opening cut was greater than on the left, indicating that the model rock mass was into the elastic deformation phase.  
From the 17 th to the 20 th loading cycles, the roof subsidence increased rapidly. By the end of the experiment, Gauge 2 recorded a cumulative subsidence of 21.882 mm, while Gauge 3 reached 26.260 mm. At this point, numerous cracks appeared in the overburden rock layer within a 4 cm range above the roof, and a fracture occurred in the roof 33.3 cm from the left side, accompanied by rock fall. This observation suggests that the model rock mass had entered the plastic failure phase.  
Figure 10: Dial gauge cumulative displacement variation curves.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **1\#** & **2\#** & **3\#** & **4\#** & **5\#** \\ \hline Initial readings & 2.619 & 3.031 & 1.068 & 0.902 & 5.778 \\ Final cumulative readings & 16.636 & 24.913 & 27.328 & 10.363 & 6.514 \\ Cumulative displacement & 14.017 & 21.882 & 26.260 & 9.461 & 0.736 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental data from dial gauges.
# Classification of OCT Images of the Human Eye Using Mobile Devices
## 4 Experimental Selection of Neural Networks
### Prediction on Mobile Device  
This part of the study examined the performance of the mobile application on three different mobile devices listed in Table 10.  
The aim of the research was to observe the influence of a given mobile device's camera and its image display method during image acquisition on the results and time of image classification. For each device, application tests were performed under the following three conditions:  
* OCT scan selected directly from the mobile device gallery--control test.
* An OCT scan displayed from a projector, followed by image acquisition using the mobile device's rear camera; image editing involved cropping to show only the OCT scan, excluding non-tomogram elements of the photo.
* An OCT scan displayed on a matte monitor, followed by image acquisition using the mobile device's rear camera; image editing consisted of cropping, to show only the OCT scan, excluding any non-tomogram elements of the photo.  
For each class to which a given image can be assigned, one OCT scan was selected, giving five images in total. These scans were used during tests under three different conditions on all mobile devices. The selected images were not used during the training of the neural networks. The obtained results are listed in Table 11.  
The above classification results show that the control sample (i.e., scans selected from the gallery), provided identical results regardless of the device. Only the duration of the classification varied depending on the available computing power. In all situations, the Joint Attention Network + MobileNetV2 network operated fastest, and VGG16 slowest.  
For photos taken with a phone camera, a decrease in classification effectiveness was observed in most cases compared to the control group. Significant differences sometimes occurred between images displayed on the projector and the monitor; however, no common dependencies could be found. A potential reason for the better classification results of OCT scans displayed on the monitor compared to OCT scans displayed on the projector is the better contrast of retina layers.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Phone Model** & **OS Version** & **Rear Camera** & **RAM** \\ \hline Motorola Moto G7 Play & Android 10 & 13 Mpx & 2 GB \\ Samsung Galaxy S8 & Android 9 & 12 Mpx & 4 GB \\ Oneplus 8 Pro & Android 13 & 12 Mpx & 12 GB \\ \hline \hline \end{tabular}
\end{table}
Table 10: Prediction times and accuracy for Motorola Moto G7 Play.  
Although the test sample was small, it can be assumed that the type and resolution of the phone camera should not have a significant impact on the results, since all images are resized to dimensions of \(224\times 224\) pixels. Disturbances occurring in images, resulting from reflections or shadows created during the photographing of the image, seem to be of greater importance.
# Satellite Network Task Deployment Method Based on SDN and ICN
## 3 System Model and Problem Formulation
### Communication Model  
To balance the communication load caused by onboard computing, it is necessary to model the network traffic. It is assumed that all network devices are connected to the task initiator through the satellite-ground link. In the satellite IoT scenario, a large number of monitoring data cannot be stored in one node, yet this can be stored in multiple nodes in the network. Considering that the satellite network provides computing services for the terminal, the computing resources need to be obtained from the terminal and the ground cloud computing center. Therefore, the ground cloud computing center is treated as a node equivalent to a satellite.  
The data required by subtask \(task_{i}(C_{i},R_{i},G)\) need to be obtained from satellite nodes \(j\). The amount of resources that a satellite can provide is \(cache_{1}\), \(cache_{2}\ldots cache_{j}\). The node providing resources to the deployment point of the task needs to go through \(k_{1}\), \(k_{2}\cdots k_{j}\) times. Therefore, the communication volume \(F_{i}\) for a single subtask transmission is:  
\[F_{i}=\sum_{w=1}^{j}k_{w}cache_{w}=k_{1}cache_{1}+k_{2}cache_{2}+\ldots+k_{ j}cache_{j} \tag{1}\]  
The amount of communication caused by completing the task resource transmission composed of \(n\) subtasks is as follows:  
\[F_{n}=\sum_{i=1}^{n}F_{i} \tag{2}\]  
In the communication mode based on IP addresses, the network relies on the address to transmit messages. Network nodes cannot perceive content. This makes it infeasible for the satellite nodes to acquire the data content required by the mission. The content name-based transmission mode of the ICN architecture enables nodes to have the ability to sense the local cache content. The cache content is also placed in the satellite network to reduce the time required to obtain the computing data required by the task and the task processing delay.  
In the SDN-based satellite network architecture, the SDN controllers are deployed on the GEO satellites. The LEO satellite node can sense the local cache content and upload the cache update to the SDN node in time. The data needed by subtasks are searched globally through the SDN. The node sends the find message to the GEO satellite node to find the required data. The SDN controller obtains the dynamic link between LEO satellites in real-time and returns the data to the node requesting the data.
###### Abstract  
Climate change is a global problem facing mankind, and achieving peak CO\({}_{2}\) emissions and carbon neutrality is an important task for China to respond to global climate change. The quantitative evaluation of the trends of urban energy consumption and carbon emissions is a premise for achieving this goal. Therefore, from the perspective of urban expansion, this paper analyzes the complex relationship between the mutual interactions and feedback between urban population, land expansion, economic growth, energy structure and carbon emissions. STELIA simulation software is used to establish a system dynamics model of urban-level carbon emissions effects, and Changsha city is used for the case study. The simulated outputs of energy consumption and carbon emissions cover the period from 1949 to 2016. From 1949 to 2016, Changsha's total energy consumption and carbon emissions per capita have continuously grown. The total carbon emissions increased from 0.66 Mt-CO\({}_{2}\) to 60.95 Mt-CO\({}_{2}\), while the per capita carbon emissions increased from 1.73 t-CO\({}_{2}\)/10,000 people to 18.3 Mt-CO\({}_{2}\)/10,000 people. The analysis of the structure of carbon emissions shows that the industrial sector accounted for the largest proportion of emissions, but it had gradually dropped from between 60% and 70% to about 40%. The carbon emissions of residential and commercial services accounted for less than 25%, and the proportion of transportation carbon emissions fluctuated greatly in 2013 and 2016. From the perspective of carbon emissions effects, carbon emissions per unit of GDP had a clear downward trend, from 186.11 t-CO\({}_{2}\)/CNY10\({}^{4}\) to 1.33 t-CO\({}_{2}\)/CNY10\({}^{4}\), and carbon emissions per unit of land showed two inflection points: one in 1961 and the other in 1996. The general trend showed an increase first, followed by a decrease, then a stabilization. There is a certain linear correlation between the compactness of urban shape and the overall trend of carbon emissions intensity, while the urban shape index has no linear correlation with the growth rate of carbon emissions. The carbon emissions assessment model constructed in this paper can be used by other municipalities, and the assessment results can provide guidance for future energy planning and decision making.  
urban sprawl; carbon emission; system dynamics model; STELIA
###### Abstract  
The dominant low-light object detectors face the following _spectral trilemma_: (1) the loss of high-frequency structural details during denoising, (2) the amplification of low-frequency illumination distortion, and (3) cross-band interference in multi-scale features. To resolve these intertwined challenges, we present UMFNet--a frequency-guided detection framework that unifies _adaptive frequency distillation_ with _inter-band attention coordination_. Our technical breakthroughs manifest through three key innovations: (1) a frequency-adaptive fusion (FAF) module employing learnable wavelet kernels (16-64 decomposition basis) with dynamic SNR-gated thresholding, achieving an 89.7% photon utilization rate in \(\leq\)1 lux conditions--2.4\(\times\) higher than fixed-basis approaches; (2) a spatial-channel coordinated attention (SCCA) mechanism with dual-domain nonlinear gating that reduces high-frequency hallucination by 37% through parametric phase alignment (verified via gradient direction alignment coefficient \(\rho_{G}=0.93\)); (3) a spectral perception loss combining the frequency-weighted structural similarity index measure (SSIM) with gradient-aware focal modulation, enforcing physics-constrained feature recovery. Extensive validation demonstrates UMFNet's leadership: 73.1% mAP@50 on EXDAR (+6.4% over YOLOv8 baseline), 58.7% on DarkFace (+3.1% over GLARE), and 40.2% on thermal FLIR ADAS (+9.7% improvement). This work pioneers a new paradigm for precision-critical vision systems in photon-starved environments.  
Keywords:low-light object detection; adaptive wavelet decomposition; dual-domain attention gating; spectral distortion suppression; edge-aware denoising _Article_
## 3 Results and Discussions
### Effect of Laser Fluence and Pulse Overlap on Microchannel Geometry
Increase in fluence and pulse overlap increases the energy density and laser penetration into the material, which results in a higher depth of material being removed for each laser scan, thereby producing  
Figure 5: Effect of peak fluence and pulse overlap on TWE. Figure 6: Effect of peak fluence and pulse overlap on BWE. a channel with higher depth. Figure 7 shows the effect of peak fluence and pulse overlap on the depth per scan. It can be seen that at lower fluence, the difference in depth is minimum for different pulse overlaps. However, this difference increases significantly as the fluence increases, because at higher fluence energy the density is higher which in conjunction with the overlap increases the depth of material removed. Figure 8 shows the variation of MRR with peak fluence and pulse overlap. In general, it was found that in the selected pulse overlap range a lower pulse overlap of 58.33% resulted in a higher MRR because of the reduced machining time. High pulse overlaps require the laser to move with less speed as compared to the lower overlaps resulting in increased machining time and hence reduced MRR.
# A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques
## 1 Introduction
### Contributions  
In the current work an unconventional data pre-processing method is suggested, firstly to find the important features required, and then the feature drop-out technique is suggested to get rid of low-priority features. This article presents a set of classifiers for anomaly detection in IoT traffic network data using machine learning models, along with comparing the performance ratings of each classifier. The identification of DDoS attack patterns and the proliferation of malware with DDoS capabilities remain the main objectives of the research. This article focuses on the detection of DDoS attack labels and malware propagation in IoT network data traffic using the suggested feature selection and pre-processing methods, which are detailed below in Section 3 of the manuscript.  
This research has used BOT-IOT and TON-IOT datasets to show how effective the proposed model is with respect to its robustness and generalizability. Diverse datasets capture various network traffic patterns, attack types, and network conditions to develop models that effectively detect attacks in different scenarios. Additionally, this helps researchers gain insights into the challenges of detecting attacks in diverse IoT deployments, as different datasets reflect real-world scenarios. By leveraging two datasets, bias can be avoided, more comprehensive models can be developed, and different attack patterns and network conditions can be effectively handled. The proposed model is proven to be lightweight and much more effective than the previous state-of-the-art models, as feature selection methods like ETC are used and feature dimensionality is narrowed down by using only 15 features. The SMOTE technique is used to resample and balance the datasets to avoid overfitting. K-fold cross-validation is used to enhance the accuracy of multiple-class classification and time factor is implemented to depict the training and prediction time to detect the DDoS attacks. Labels on the dataset were used to group DDoS attack tests with other testing. The following are the research's contributions:  
* A modern method of ensemble feature selection for data pre-processing for network IoT datasets.
* Withdrawal of DDoS-attack-related traffic patterns from the TON-IOT and BOT-IOT datasets.  
Figure 1: DDoS attack model in lightweight IoT networks.  
* Comparing and analyzing the performance of machine learning algorithms like LR, NB, RF, ANN, and KNN with binary and multiple-class classification.
# Indoor Localization Using Semi-Supervised Manifold Alignment with Dimension Expansion
## 3 Algorithm Description  
### Location Calibration  
In the target environment, we denote the sets of \(l\) calibrated locations, as well as corresponding RSS measurements as \(\mathbf{H}^{\prime}=\{(\mathbf{c}_{11},\mathbf{c}_{12})\), \(\cdots\),\((\mathbf{c}_{l1},\mathbf{c}_{l2})\}\) and \(\mathbf{G}=\{(\text{rss}_{11},\,\cdots,\text{rss}_{1m})\), \(\cdots\),\((\text{rss}_{1l},\,\cdots,\)\(\text{rss}_{lm})\}\), where \((\mathbf{c}_{i1},\mathbf{c}_{i2})(1\leq i\leq l)\) is the two-dimensional physical coordinate of the \(i\)-th calibrated location. \((\text{rss}_{i1},\,\cdots,\text{rss}_{im})\) is the RSS measurement collected at \((\mathbf{c}_{i1},\mathbf{c}_{i2})\), where \(\text{rss}_{ik}(1\leq k\leq m)\) is the RSS value from the \(k\)-th AP at the \(i\)-th calibrated location and \(m\) is the number of Access Points (AP). To guarantee that the physical coordinates and RSS measurements have the same dimension, we simply replicate the physical coordinate of each calibrated location \(m/2\) times (when \(m\) is odd, the replication will do \((m-1)/2\) times, which is ended by adding the former element of the two-dimensional physical coordinate, such that \(\mathbf{H}=\left\{\left(\underbrace{c_{11},c_{12},...,c_{11},c_{12},c_{11}}_{ m\text{ in total}},\,\cdots,\underbrace{c_{\ell 1},c_{\ell 2},...,c_{\ell 1},c_{\ell 2},c_{\ell 1}}_{ m\text{ in total}}\right)\right\}\)) to obtain the set of \(l\) augmented physical coordinates \(\mathbf{H}=\left\{\left(\underbrace{c_{11},c_{12},...,c_{11},c_{12}}_{ m\text{ in total}},\,\cdots,\underbrace{c_{\ell 1},c_{\ell 2},...,c_{\ell 1},c_{\ell 2}}_{ m\text{ in total}}\right)\right\}\). To be clearer, the result of location calibration is shown in Figure 1.
## 4 Results and Analysis
### Thematic Analysis: Findings
#### 4.1.2 Situational Awareness: Findings  
Participants at Smiths Falls discussed that some pilots lack a clear mental model during approach, implying that many are unaware of how to recognise a bad \"picture\" on approach. One pilot commented,  
\"Pilots don't have a clear picture of aim points and speed on approach.\"--SF1  
This statement aligns with the idea that instructors often intervene early, limiting student exposure to decision points during a poorly executed approach, especially when factors like time pressure and financial constraints are significant considerations. Likewise, one instructor at Smiths Falls commented,  
\"[This] sets a weak message to students.\"--SF2  
Training often lacks exposure to adverse weather and standardised go-around criteria, so after completing their training, pilots may lack experience handling unfavourable conditions should they encounter them. Both discussions mentioned training surrounding judging distances and the lack of standardisation when approaching go-around points.  
Figure 4: Themes identified through the thematic analysis.  
This results in delays when they do need to go around, as pilots are suddenly faced with a highly evolving environment.  
Furthermore, both discussions cemented the importance of knowing the traffic conditions in the circuit when flying the approach. Observing circuit traffic improves situational awareness and anticipatory decision-making, consequently improving circuit conditions.  
However, external factors impacting situational awareness were also discussed, such as the impact of night-time VFR approaches, where one pilot at Rockcliffe commented,  
\"Visual cues at night are less.\"--RC1  
Nevertheless, this can be exacerbated by airport design. Sun glare was noted by pilots at Rockcliffe airport to impair depth perception and runway judgement.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Operational Considerations
#### 5.2.3 Air Band Radio  
In manned aviation, air band radio is considered as the second most important aid to situational awareness, sitting only behind visual scanning [81]. There are two considerations for using air band radio with a BVLOS operation. The first is listening to broadcasts on the local frequency. This is a must for a BVLOS operation that is being conducted in non-segregated airspace. This is because listening to position reports will allow the pilot to form a mental model of how traffic is acting in the airspace surrounding them, thus providing useful information to help detect and avoid other aircraft [82]. A recent study showed that just over a quarter of unmanned aircraft users listen in to air band radio when conducting operations, primarily when operating above 400 ft AGL, within 4 km of an aerodrome, or within controlled airspace [21]. These correspond with the types of airspace where this is most useful because there is the greatest potential for manned aircraft to also be operating within these areas [20]. To be able to make sense of radio calls, there will need to be some training, as well as an awareness of where the operation is taking place so that the pilot will be aware of which locations' position reports will be made in relation to them (i.e., 5 nautical miles southwest of \"location\"). For BVLOS operations, it is also advisable to make position reports for the unmanned aircraft to aid another pilot's situational awareness. However, to be able to make radio calls requires a Flight Radio Telephony Operator's (FRTO) rating, which is typically held alongside a pilot's license [20]. Some countries, such as Australia, have made this easier by providing a means for unmanned aircraft pilots to have an FRTO rating independently of a pilot's license [83]. If the operation is taking place in a country where it is not possible for unmanned aircraft pilots to make radio calls, and the pilot in command for the operation does not have a pilot's license, it may be necessary to have someone with a pilot's license in the support crew. This relates back to meeting the requirements of the airspace that is being flown within, which, outside of segregated or controlled airspace, means making position reports so that other pilots can maintain situational awareness of traffic in the area.