esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results
### Performance Evaluation
#### 6.3.1 Andrew Protocol  
To present the experimental results, we assume the delay values: \(D_{1}=2\), \(D_{2}=3\), \(D_{3}=4\), and \(D_{4}=5\). For the Andrew protocol (Figure 3), we conducted a comprehensive analysis and identified two paths indicative of Man-in-the-Middle behaviour (though not a complete attack). In these paths, locations 74 or 94 were found to be reachable. Additionally, we observed two paths that correspond to honest executions (paths where location 4 or 9 was reachable) and eight paths representing executions involving the Intruder (paths where locations 14, 24, 34, 44, 54, 64, 84, or 104 were reachable). The term _executions with the Intruder_ encompasses two scenarios: firstly, executions in which the Intruder behaves as a legitimate user (paths where one of the locations 14, 54, 64, or 104 is reachable); and secondly, executions in which the Intruder unsuccessfully attempts to impersonate honest users (paths where one of the locations 24, 34, 44, or 84 is reachable).  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Protocol** & **Advantages** & **Disadvantages** \\ \hline NSPKT [46] & - & Foundational in cryptographic communication - Mutual authentication via public-key cryptography & - \\ \hline NSPKT\({}_{L}\)[1] & - & Addresses NSPKT vulnerability Enhanced security with minimal structural change & - & Increased complexity Higher computational requirements \\ \hline DS [54] & - & Reduced reliance on timestamps Key confirmation for increased trust & - & Relies on uncompromised third parties Susceptible to session hijacking \\ \hline WLP [34; 55] & - & Simplified message authentication Mutual authentication without complex cryptographic functions & - & Potential replay attacks Assumes session key confidentiality \\ \hline WLP 1 \& 3 [55] & - & Security tailored for different applications Mitigates replay attack vulnerabilities & - & Increased complexity May require further modifications \\ \hline AP [56] & - & Efficient for lightweight environments Nonce-based verification & - & Replay attacks possible without checks Not robust for high-security scenarios \\ \hline A\({}_{L}\)P [57] & - & Improved security addressing AP vulnerabilities Enhanced replay attack protection & - & Increased computational cost More complex than original AP \\ \hline MIS [58] & - & Designed for mobile information security Scalable and flexible for modern needs & - & High resource consumption Limited compatibility with older devices \\ \hline \hline \end{tabular}
\end{table}
Table 1: Short characteristics of security protocols and their advantages and disadvantages.  
It was found that the minimum lifetime values for the assumed delay values that ensure only honest executions occur (i.e., values that prevent Man-in-the-Middle behaviour) are \(L_{1}=L_{2}=14\). The _minimum values_ that permit Man-in-the-Middle behaviour to manifest at locations 74 and 94 are \(L_{1}=L_{2}=21\).
# Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images
## 4 Experimental Results
### Ablation Studies  
We firstly examine the impact of ABMIL on classification performance. In this experiment, the feature map generated by the feature extractor is reduced to a single feature vector via mean pooling, and all models are initialized with the pre-trained weights. The corresponding confusion matrices are shown in Figure 11. As illustrated in Figure 11, the number of false negatives (missing targets) increases under this setting except for ViT. The results suggest that ABMIL is better suited than the global pooling approach for this classification task. In other words, ABMIL effectively addresses the challenge posed by the small spatial footprint of AP landmines.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **BinaryCNN** & **ResNet** & **ConvNeXt** & **ViT** & **OpenCLIP** & **InfMAE** \\ \hline Number of false positives & 52 & 2 & 0 & 15 & 25 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Prediction results on the synthetic landmine-free images for the models trained with the inpainting-augmented train set.  
Figure 10: Examples of heatmaps generated by Grad-CAM on test images for the models trained with the inpainting-augmented train set. Red color represents higher values, while blue color represents lower values.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **BinaryCNN** & **ResNet** & **ConvNeXt** & **ViT** & **OpenCLIP** & **InfMAE** \\ \hline Precision & 0.973 \(\pm\) 0.009 & 0.933 \(\pm\) 0.009 & 0.996 \(\pm\) 0.003 & **1.000 \(\pm\) 0.000** & 0.994 \(\pm\) 0.004 & 0.990 \(\pm\) 0.005 \\ \hline Recall & 0.757 \(\pm\) 0.008 & 0.999 \(\pm\) 0.001 & **1.000 \(\pm\) 0.000** & 0.928 \(\pm\) 0.013 & 0.894 \(\pm\) 0.013 & **1.000 \(\pm\) 0.000** \\ \hline F1-score & 0.851 \(\pm\) 0.007 & 0.965 \(\pm\) 0.004 & **0.998 \(\pm\) 0.001** & 0.963 \(\pm\) 0.007 & 0.941 \(\pm\) 0.006 & 0.995 \(\pm\) 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Precision, recall, and F1-score of positive class (images with AP landmine) when models are trained with the inpainting-augmented train set. The best values are highlighted in bold.  
Finally, to evaluate the effects of transfer learning, we also conducted experiments by training all models from scratch. The resulting confusion matrices are presented in Figure 12. By comparing Figures 9 and 12, we can observe that both the number of false negatives and false positives increase across all models when trained from scratch. The increase in false negatives is particularly pronounced, leading to significant declines in recall. The results indicate that transfer learning is an essential element to improve the performance of models trained from the limited number of training samples.
## 3 Modelling of the Bus Transportation Network
### Bus Consumption Modelling
#### 3.2.1 Consumption Model  
Data-driven models require gathering a high amount of data, which can be collected through sensors. High-resolution data can be used [37; 38], but they are often hard to collect and replicate for other locations. Low-resolution data [39] can also be used; however, they decrease the accuracy of the consumption model. Comparatively, physical models apply [PERSON]'s second law of motion in order to model the electricity consumption of EB. In this category, longitudinal dynamic models consider only the traction of the bus and require driving profiles of EBs as input (i.e., a time series of velocity and elevation) [40]. [PERSON] and [PERSON] [41] have indicated that driving profiles may come from real-world operation [14] or standard dynamometer driving cycles [42; 43]. Moreover, HVAC and other auxiliaries' consumption (lighting and other support systems) can be also considered for more detailed modelling of an electricity consumption profile [44; 45; 46; 47].  
This paper implements an existing physical model [35]. The model needs to be fast enough to compute, to require accessible data, and to take into account both the traction and the auxiliaries' consumption, considering the influence of the latter on the bus' consumption. It should be noted that the presented method does not consider the regenerative braking, the curves of the road, the number of crossroads, the meteorological conditions, or the driver's behaviour. The total consumed power by the bus \(P_{tot}(t)\) is defined by Equation (1) as the sum of the power needed for vehicle motion \(P_{ldm}(t)\) and for auxiliary systems \(P_{aux}(t)\) at each time step \(t\).  
\[P_{tot}(t)=P_{ldm}(t)+P_{aux}(t) \tag{1}\]  
\[P_{ldm}(t)=v(t)\cdot\big{(}m(t)\cdot g\cdot\sin(\alpha)+m(t)\cdot g \cdot C_{r}\cdot\cos(\alpha)\] \[\qquad+\frac{1}{2}\cdot\rho\cdot v(t)^{2}\cdot A_{front}\cdot C_{ d}+m(t)\cdot a(t)\big{)} \tag{2}\]  
\[P_{aux}(t)=P_{HVAC}(t)+P_{other}(t)=P_{HC}(t)+P_{ ventilation}(t)+P_{other}(t) \tag{3}\]  
where \(v(t)\) is the bus speed, \(m(t)\) is the mass of the bus, \(g=9.81\,\,\mathrm{m/s^{2}}\) is the standard acceleration of gravity, \(\alpha\) is the slope, \(C_{r}\) is the coefficient of rolling resistance, \(\rho=1.2\,\,\mathrm{kg/m^{3}}\) is the air density, \(A_{front}\) is the frontal area of the bus, and \(C_{d}\) is the drag coefficient. The auxiliary consumption is modelled as the sum of the consumption of HVAC (the sum of \(P_{ventilation}\) and \(P_{HC}\)[35]) and the other auxiliaries \(P_{other}\). It should be noted that the total bus weight \(m(t)\) consists of the bus curb (\(m_{bus}\)), battery pack (\(m_{battery}\)), and the total number of passengers (\(m_{passengers}\)) inside the bus \(m=m_{bus}+m_{passengers}+m_{battery}\) at time \(t\).
## 4 Performance Evaluation
### Results
#### 4.3.2 Energy Consumption  
Figure 8 presents the energy consumption of different nodes in class 5 and class 3 with BER = \(10^{-5}\). As discussed above, high priority classes achieve higher throughput, which eventually results in higher energy consumption as shown in the figure. The proposed scheme consumes 0.04 joules energy for five nodes in class 5, and this consumption is increased to 0.11 joules for 40 nodes in the same class. This is due to collisions caused by the increasing number of nodes required to send RTS packets. The nodes in class 3 consume less energy as they stay in the backoff stage in the presence of high priority nodes. Figure 9 compares the energy consumption of the proposed scheme with the IEEE 802.15.6 CSMA/CA for different nodes in class 5 with BER = \(10^{-5}\). As the RTS/CTS packets are exchanged in addition to data packets, they consume extra energy due to control packet overhead, especially when the size of packets are small. However, as reporting medical data or emergency events are extremely important in WBANs, it is of relevance that the RTS/CTS scheme is able to achieve maximum throughput in such situations, and therefore, the additional energy consumption is tolerable for life-critical applications. For example, the IEEE 802.15.6 CSMA/CA consumes less energy, but it is unable to report emergency events in the presence of hidden nodes or when the emergency events are generated at such nodes. The proposed scheme achieves better performance and is able to report emergency events generated at hidden nodes successfully.  
Figure 8: Energy consumption of the RTS/CTS scheme for different classes.
# Spectrum Based Power Management for Congested IoT Networks
## 6 Result Comparison  
The same four major communication states (inactive time, Tx, SYNC, and ReTransmission) analyzed in our result portion are used to compare the power consumption of our method with the base standard. Minimizing the power consumption for these four states of communication affects the overall power consumption significantly.  
As shown in Figure 12, the power consumption in standard (Traditional) mechanism is four times higher for retransmission, three times higher for synchronization processes and 30% more consumption for packet transmission compared to our proposed solution. Additionally, our mechanism spend >10% power in the idle/sleep state compared to the standard communication for the same test scenario. Which is a good improvement of our solution on the achieved network performance, since our mechanism executed the same processes in less time and remains in a sleep state for longer time. As a result it saves up to half of the total power consumed in the same network environment and  
Figure 11: Re-transmission power consumption in conventional congested IoT environment.  
increases the lifetime of IoT device by almost 2\(\times\) compared to the same IoT device in a standard network.  
Furthermore, it is noted that the power consumption for our method and the standard network increases linearly over the interval of connected time which also increases. However, our method has a much lower consumption than the standard network. It is observed that our method performs better than the standard communication in the congested IoT network. It is important to clarify that when we say the standard/traditional in this document we referring to any IoT application which uses IEEE802.15.4 standard as a base for its communication.  
We also compared power consumption of successfully received packets without retransmission of our work with the results collected in conventional congested IoT and it is clear that our method is more power saving and has a high network performance.
# A Blockchain-Based Cooperative Authentication Mechanism for Smart Grid
## 6 Implementation and Evaluation
### Authentication Efficiency  
Authentication efficiency is an important metric to evaluate our proposed authentication mechanism. The information related to certificates in our proposed mechanism is stored in the blockchain, so frequent on-chain operations are required, so we mainly record the latency of running smart contracts, which mainly includes write latency and query latency. The write latency is the time required between the invocation of the write operation and the data being written to the blockchain, and the query latency is the time required between the invocation of the read operation to read data from the blockchain and the return of the data to the result. For a legitimate transaction, the average time taken for a single write is 126 ms and the average time taken for a single query is 51 ms. As shown in Figure 5, as the number of transactions increases, the read and write latencies are also increasing in an almost linear trend. The write latency increases faster due to the fact that the call to the chain code looks up the replica pool first, which saves time.
# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 3 Evolution of pavement Image Acquisition Systems  
High-quality image acquisition forms the foundation for accurate distress identification and is crucial for automated detection systems. Since road inspection vehicles were first introduced in the 1970s, imaging technology has evolved significantly worldwide. This evolution includes shifts from analog to digital systems, low-speed to high-speed operation, and single to multi-modal sensing capabilities.
# SQRT: A Secure Querying Scheme of Routing Table Based on Oblivious Transfer
## 5 Experimental Results
Figure 6 shows the performance test results of the SQRT scheme in terms of communication traffic when \(N_{k}\) and \(\Delta\) take different values. Similar to Figure 5, as the values of \(N_{k}\) and \(\Delta\) increase, the communication traffic of the SQRT scheme increases accordingly. By comparison, the change of communication traffic value is more affected by the change of \(N_{k}\) value than \(\Delta\) value. When \(N_{k}\) is 1, the traffic is generally less than 100 KB. When \(N_{k}\) is 10, the traffic is generally less than 400 KB. The experimental results show that the secure querying scheme of the routing table based on oblivious transfer proposed in the paper has less computational and communication overhead and is suitable for applications requiring high querying efficiency and security. Therefore, the SQRT scheme has good security and availability. Figure 5: The running time of \(N_{k}\)-out-of- \(N_{k}\) + \(\Delta\) oblivious transfer scheme. Figure 6: The communication traffic of \(N_{k}\)-out-of- \(N_{k}\) + \(\Delta\) oblivious transfer scheme. The authors give a comparison of the analysis results of the degree of anonymity between the SQRT scheme and the existing schemes, as shown in Figure 7. The degree of anonymity is a measure of the degree to which the identity or address information of the routing nodes in the anonymous networks is not recognized by the attacker. In [29], the degree of anonymity of anonymous networks is defined as \(D=\log_{2}h/\log_{2}n\), where \(h\) is the number of routing nodes of which the private information is sniffed by the attacker, and \(n\) is the total number of routing nodes in anonymous networks. Therefore, the smaller the anonymity \(D\), the better the privacy and security protection effect of the secure querying scheme of the routing table. When the number of routing nodes in the anonymous networks continues to increase, the degree of anonymity of various schemes declines, and the decline becomes slower. The degree of anonymity of the SQRT scheme is much lower than other existing schemes. That is because the SQRT scheme can effectively ensure that both user equipment and the directory server faithfully follow the routing querying protocol and protect the privacy information of both parties to the greatest extent. The experimental results better illustrate the security performance advantages of the SQRT scheme compared with the existing schemes.
# SMS Spam Detection System Based on Deep Learning Architectures for Turkish and English Messages
## 6 Conclusions  
In this article, a CNN-and-GRU-based hybrid model was proposed to classify SMS messages as unsolicited and solicited. The disadvantages of the CNN are long periods of training and a high memory capacity. The disadvantage of the GRU model, on the other hand, is high memory consumption. Evaluating the experimental results, it was observed that our CNN + GRU hybrid model achieved a better performance in classifying spam messages compared to the other methods. The results of the study revealed that the CNN + GRU hybrid approach achieved 99.07% accuracy, 99.37% recall, 99.22% F1-score, and 99.06% precision. This model is promising in terms of improving mobile security by preventing unsolicited text messages as well as SMS phishing attacks.  
In the future, we aim to generate a deep learning framework that is able to filter SMS spam messages with better precision. It is our objective to add more functional features such as the investigation of telephone numbers, files, and URLs included in messages by analyzing these in order to improve our model.
## 2 Observing-System and Power-System Considerations  
### Deployment of Observing System in the Arctic Ocean  
As shown in Figure 1a, an observing system was deployed on Arctic sea ice on 9 August 2016 during the 2016 Chinese National Arctic Research Expedition. This observing system was designed by the Polar Research Institute of China (PRIC) and Taiyuan University of Technology (TYUT), which included an acoustic sounder (SR50A, Campbell, Camden, NJ, USA) and an underwater sonar (PSA-916, Teledyne Benthos, North Falmouth, MA, USA) to measure snow depth and ice thickness, a 4.5 m long thermistor string, a temperature sensor (HMP155A, Vaisala, Vantaa, Finland), a battery unit, a data logger developed by TYUT, and an Iridium system. The power of this system was only dependent on the battery unit (13.5 V, 75 Ah). Blizzard weather occurred three days after the observation system was deployed (Figure 1b); these storms occur frequently in the Arctic Ocean. In Figure 1b, we can see that the situation in which the observing system was deployed had dramatically changed, increasing the possibility of equipment damage.  
Figure 1: **(a)** Observing system deployed in the Arctic Ocean during the 2016 Chinese National Arctic Research Expedition. **(b)** Scene after three days of observing-system deployment.  
The observing system observed the entire ice season for approximately 11 months. As shown in Figure 2, air temperature and power voltage from October 2016 to July 2017 are presented, which covered the period of the polar night. The sampling interval of air temperature and voltage was one hour.  
The air temperature monitored by the observing system in the entire ice season included one peak (Figure 2). Beginning with the deployment of the observing system, air temperature continued to decrease until 18 February 2017. Subsequently, air temperature continued to increase until 5 July 2017. Power voltage was greatly affected by air temperature, and daily changes were relatively severe and showed an oscillating downward trend. Low temperature was the main influencing factor of the power supply of the observing system. Although power voltage stayed above 13 V, which can ensure the normal operation of the observation system, the continuous decline of power and the violent fluctuation of voltage could bring great potential hazards to the normal operation of the equipment. Thus, designing and developing energy-harvesting systems at low temperatures is vital for long-term observations of the polar regions.
## 1 Introduction  
Temperature assessment currently relies largely on remote sensing tools, especially for the acquisition of Land Surface Temperature (LST) data. Many studies use MODIS LST products due to their high spatial resolution, making them significant for climate assessments.  
Analyzing the differences between LST data and air temperature obtained from in-situ measurements can help determine whether LST products can serve as a reliable substitute for in situ measurements, given the strong correlation between the two [1].  
In the case study analyzed in this article, in situ measures, provided by ARPA's monitoring stations (Agenzia Regionale per la Protezione Ambientale), offer high temporal granularity (hourly) but lack spatial detail. Conversely, satellite data provide high spatial granularity but with lower temporal resolution.  
To address the gaps in data from ARPA [2] monitoring stations, this study aims to estimate the correlation between air temperature (Tair) and Land Surface Temperature (LST) to achieve finer spatial granularity in temperature data [3].
## 3 Results and Discussion
### \(\gamma\)-ray Pulses Generation
Despite the intensity of the laser we use being a little higher, our scheme requires only a single laser beam instead of the two lasers in ref. [29], which potentially has practical advantages due to the absence of the lasers' spatial collimation and delay setting. Furthermore, we use a different mechanism comparable to the one in ref. [30] that is based on the wakefield acceleration, but we obtain comparable \(\gamma\)-ray quantities, which indicates that our scheme is indeed an efficient alternative method for generating a high-quality \(\gamma\)-ray. Figure 5: (Color online). (**a**) The electron trajectories after leaving the target surface. (**b**) The variation in electron momentum \(p_{x}\) over time. (**c**) The energy spectrum of photons; the cut-off energy is less than 1 MeV. (**a–c**) are all in the planar target case. The values of \(t_{1}\), \(t_{2}\), \(t_{3}\), \(t_{4}\) and \(t_{5}\) are the same as those in the cone target case. Figure 6: (Color online). Cut-off energy of photons and laser-to-photon energy conversion efficiency for five different open angles (\(30^{\circ}\), \(60^{\circ}\), \(90^{\circ}\), \(120^{\circ}\), \(150^{\circ}\)).
## 5 Experimental Verification
### Experimental Results
#### 5.3.4 Robustness Testing  
In practical applications, the FTS system is greatly affected by the load during the processing process, and changes in the load often cause changes in the tracking error of the system. Therefore, in order to simulate the impact of load during the machining process, we applied a constant force of 1N on the tool end of the FTS system to simulate the impact of the load during the cutting process on the stability of the proposed control structure.  
The experimental results are shown in Figure 9 and elaborated upon in Table 2, the introduction of load demonstrates a discernible augmentation in the iterative tracking performance of ILC-ASMC in contrast to the ASMC. The tracking accuracy is improved by 73.46% and 64.35%, respectively. Therefore, the proposed control structure has better robustness.