esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 5 Simulation and Results
### Results and Discussion
#### 5.2.2 Packet Delivery Ratio  
Packet delivery ratio shows a successful packet delivery ratio. It is defined as the total number of packets sent from all source nodes over total number of packets received at all sink nodes. When  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Area & 1000 \(\times\) 1000 m\({}^{2}\) \\ Nodes Data Set & 50, 100, 150,..., 500 \\ Sink Nodes & 5 \\ Data Packet Size & 1000 Bytes \\ Communication Medium & Wireless \\ Wireless Channel & Radio and Acoustic \\ Radio Speed & 3 \(\times\) 10\({}^{8}\) m/s \\ Acoustic Speed & 1500 m/s \\ Transmission Range & 70 m \\ Frequency & 15 KHz \\ Energy & Energy Model \\ Initial Energy & 1000 J \\ Transmission Power & 0.5 W \\ Receiving Power & 0.1 W \\ Idle Power & 0.008 W \\ Sleeping Power & 0.01 \\ Physical Layer & UnderwaterPhy \\ Mac Layer & UnderwaterMac \\ Antenna & OmniAntenna \\ Nodes Mobility & Random \\ Network Topology & 2D \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation Setting.  
Figure 7: Reliable Paths Creation Time in TBRL.  
maximum nodes are present in the network, as shown in Figure 8, delivery ratio of TBRL approximately reaches to 99%, whereas, at small number of nodes, data delivery ratio is not good. Distance plays a major role for these results, fewer nodes means that there is higher distance between them, therefore the probability of packets dropped becomes high.
# The Impact of Prompting Techniques on the Security of the LLMs and the Systems to Which They Belong
## 2 Domain Overview
### PaL: Program-Aided Language Models
#### 2.5.2 Algorithmic Reasoning  
For algorithmic reasoning, the authors test two algorithmic tasks:  
Figure 4: A close-up of a single example from a PaL prompt. Chain-of-thought reasoning is highlighted in blue, and PaL programmatic steps are highlighted in gray and pink [3].  
Figure 5: An example from a PaL paper [3] which shows a prompt used in the colored objects task together with its corresponding question. They omit the code that creates the list objects for space considerations.  
* Object counting entails responding to inquiries regarding the quantity of items that fall under a specific category; see Figure 6.
* Repeat copy necessitates the creation of a series of words in accordance with specified guidelines.  
Q: I have a chair, two potatoes, a cauliflower, a lettuce head, two tables, a cabbage, two onions, and three fridges. How many vegetables do I have?  
The results show PaL dominance over CoT and DIRECT prompting.  
PaL sets a new state-of-the-art top 1 across math reasoning datasets. It even manages to beat \(CoT_{Minerva540B}\), which is an explicitly fine-tuned model for mathematical content. The newly created GSM-HARD dataset hits the performance of DIRECT (19.7% to 5.0%) and CoT (65.6% to 20.1%). To find the real cause of these performance hits, with the lack of arithmetic abilities or blurred reasoning due to the size of the numbers leading to irrational intermediate rationales, the authors use the following technique. They prompt the model using the two versions of the same question (with and without large numbers). In 64% of the cases, CoT generates nearly identical NL \"thoughts\". This shows us the main reason behind the failure of the LLM is the inability to perform accurate arithmetic operations.  
PaL prompting fits very well the symbolic and algorithmic tasks as well. PaL is very close to a perfect score when approaching the object counting (96.7%) and colored object (95.1%). In this case, both CoT and DIRECT prompting are outperformed by a huge margin.  
The authors mention that the main requirement to use PaL is that the LLM must have sufficiently high \"code modeling ability\". If the LLM is not proficient enough in coding, then CoT might be a better choice, and the authors show that in this particular case, CoT performs better.  
This paper takes an important step further, showing the fact that some of the LLMs weaknesses can be fixed by delegating some parts of the task to \"external\" tools like an interpreter/compiler to obtain a consistent performance. Security-wise, things become trickier, because now, our LLM provides code which is executed inside our system. Theoretically, this code should be generated only by the model, but in practice, injections from outside could occur.
## 1 Introduction
Based on this, a mathematical model of the induced electromagnetic field generated by the angular oscillation of magnetic objects at a certain angle has been established, and further theoretical analysis was conducted on the phase characteristics of the three components of the induced electric field generated by the angular oscillation of the magnet. Finally, the correctness of the theoretical model and analysis results was verified through experiments. This manuscript has important research significance for in-depth study of the mechanism and characteristics of ship electric field generation, detection of ship electric field signals, platform adaptation of electric field sensors, and research on electric field fuses for underwater weapons.
## 3 Analysis of Bibliographic Results on Road Network Pricing
### Co-Citation Analysis
#### 3.4.1 Implementation Impacts  
This theme analysed the social, economic, and environmental impacts of road network pricing. The literature points to several road network pricing instruments that are available to regulators. The most common instrument is congestion charging in urban centres [19], which has been implemented in a number of jurisdictions around the world such as Oregon, Vancouver, New York, London, Stockholm, Gothenburg, Milan, and Singapore [17]. The benefits reported are compelling [9]. In the 12 years since the scheme started in London, traffic congestion was reduced by 10 percent, which included a 34 percent drop in private cars entering the city centre, and a 28 percent increase in the number of cyclists accessing the city [54]. The scheme also reduced emissions by 16 percent within the study area, amounting to a reduction of 30,000 tonnes of carbon monoxide annually. A road safety study also identified that it resulted in reducing traffic collisions by 40 percent over a 10-year period between 2000 and 2010 [76]. London's congestion pricing scheme also raised more than USD 300 million per year in revenue, which was invested in public transport and active transport infrastructure.  
Stockholm's congestion pricing scheme reduced peak traffic by 20 percent and increased public transport ridership by 9 percent [77]. In the city's densely populated centre, emissions decreased by 14 percent. The scheme generated an estimated USD 500,000 in daily revenue, which was reinvested in expanding bike lanes and new, dedicated bus lanes. Sweden's Gothenburg scheme was also a success [78] and reduced congestion by 12 percent during peak hours and shifted a large number of private car users to public transport.  
Singapore's congestion pricing scheme was established in 1975 [3]. Initially, it was known as an area license system using a flat rate fee. It cut traffic by 45 percent and vehicle crashes by 25 percent [79]. The system was upgraded to electronic road pricing (ERP) in 1998, resulting in a further 15 percent reduction in traffic congestion [80]. More recently, the ERP was found to have reduced congestion by 45 percent and road crashes by 25 percent, as well as reduced traffic volumes entering the central business district by 15 percent [81]. The literature also points to benefits of road pricing based on distance or travel time [48]. This can also be fixed or variable. In New York, the \"Move NY\" plan proposed to charge taxis based on distance and also time spent within the city [17]. Likewise, in Oregon, a state-wide scheme \"OReGo\" was introduced, where charging was based on distance. Recently, Singapore's ERP 2.0 (from 2020) commenced using satellite communications for distance charging [54]. These success stories provide unique insights into the pathways for implementation [9; 11; 82]. These findings demonstrate that road pricing can be effective in reducing congestion, lowering emissions, improving amenity, and addressing inequity in transport taxation systems.
# A New Hybrid Hierarchical Roadside Unit Deployment Scheme Combined with Parking Cars
## 1 Introduction  
As one of the most promising technologies in intelligent transportation systems [1], vehicular ad hoc networks (VANETs) play a crucial role in driving assistance, traffic accident warnings, traffic management, and internet services. VANETs are a specialized form of mobile ad hoc networks (MANETs) in the transportation field [2]. The basic communication forms of VANETs are mainly vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). V2V communication facilitates the exchange of information such as road congestion and traffic accidents among vehicles. However, the high density of nodes and their high-speed movements can negatively affect the information transmission capabilities of VANETs, resulting in increased packet loss and delays. Roadside units (RSUs) can mitigate these issues; however, the installation and maintenance costs of RSUs are substantial. Therefore, devising an applicable and effective RSU deployment strategy is imperative, particularly for urban areas. To this end, it is essential to introduce the new concept of \"t-RSUs\", which refers to using parked cars as temporary RSUs (t-RSUs), as illustrated in Figure 1.  
Figure 1 depicts a scene where parked cars along the roads form a mesh network automatically, which can be utilized as t-RSUs. The working mechanism of the t-RSU deployment scheme based on V2V and V2I connections in a VANET environment with t-RSUs can certainly solve the dilemma of difficult and complex radio signal attenuation due to buildings and other obstacles by enhancing the signal strength in those areas. In the research scenario, it is assumed that all communication terminals of mixed traffic flow are uniformly connected to the traffic information network. To better achieve the goal of serving users and improving the connectivity of the network, roadside units are used toassist in achieving efficient communication in the VANET. Then, the connectivity problem in the urban traffic information transmission network needs to be solved. To enable each vehicle user to access the traffic information network to the greatest extent, under the existing RSU equipment layout conditions, the connectivity of all vehicles needs to be maximized, and a practical roadside unit deployment scheme is required for roads in urban areas.
# A Reversible Data-Hiding Method with Prediction-Error Expansion in Compressible Encrypted Images
## 5 Conclusions  
We proposed a new algorithm for reversible data hiding in encrypted images that simultaneously accomplishes both high hiding capacity and high compression performance. Our method has three main advantages. First, a compressible encryption method WAS used to attain high compression performance using international standards JPEG-LS and JPEG 2000. Second, our method achieveD high hiding capacity of around 1 bpp. For this purpose, we introduced an MED predictor for pixel prediction and a PEE-HS method for data hiding. Lastly, we could decrypt marked encrypted images without data extraction; marked images containing a payload could accordingly be obtained.  
The proposed method was evaluated in terms of data-hiding capacity, marked-image quality, and lossless compression performance with JPEG-LS and JPEG 2000. We discussed the experimental results with \(L=10\) using the Kodak database, and we confirmed a similar trend in the other databases. The hiding capacity of our method was 0.82 bpp on average. This capacity was approximately 20 times higher than that of a conventional compressible RDH-EI method. In terms of marked-image quality, our method achieved a PSNR of 27.54 dB and SSIM of 0.8960 under hiding capacity of 0.82 bpp. Lastly, we proved that the marked encrypted images could be effectively compressed by using JPEG-LS and JPEG 2000. Regarding the influence of image size, we confirmed that our method further enhanced performance with a larger image. Through our experiments, it was clear that our method was the only technique that possessed all three advantages.  
The method still has a constraint on flexibility. When we first decrypted a marked encrypted image before data extraction, a marked image was derived. However, it is difficult to retrieve the original image and extract a payload from the marked image. To alleviate the constraint, we will properly define the embedding order within and among blocks in the data-hiding algorithm. This would further expand the range of applications.  
Conceptualization, R.M., S.I. and H.K.; methodology, R.M. and S.I.; validation, R.M. and S.I.; investigation, R.M.; writing--original draft preparation, R.M.; writing--review and editing, S.I. and H.K.; supervision, S.I. and H.K.; project administration, S.I. and H.K. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by JSPS KAKENHI, grant number JP21H01327, and the Yutaka Kojima Research Fund of Society of Photography and Imaging of Japan.  
Not applicable.  
Figure 18: JPEG 2000 compression performance of proposed, RDH-EtC [30] and RDH-MSB [29] methods. (**a**) Kodim4; (**b**) pepppar; (**c**) hhc5 (1/16); (**d**) hhc5 (original image).  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.
# Jamming Detection and Suppression Technique for OTFS Systems in an AWGN Channel
###### Abstract  
Orthogonal time frequency space (OTFS) modulation is a promising waveform for future wireless communication, offering robustness against time-varying channels and Doppler effects in high-mobility scenarios. However, the anti-jamming capabilities of OTFS systems remain underexplored, leaving gaps in understanding their potential in jamming-prone environments. In this work, we mainly focus on the jamming detection and suppression methods for OTFS systems in additive an white Gaussian noise (AWGN) channel. A frequency-domain anti-jamming technique is presented, employing a time-domain overlapping window approach to reduce jamming sidelobes while mitigating the signal-to-noise ratio (SNR) degradation caused by the window function. Additionally, a smoothing window method is introduced to provide stable samples for jamming detection, effectively reducing the false alarm rate. Furthermore, a low-complexity improved forward consecutive mean excision (FCME) algorithm is proposed, enhancing its practicality and making it more suitable for real-world applications. Finally, the effectiveness of the proposed anti-jamming methods is validated through simulation analyses under single-tone and partial-band jamming scenarios.  
Keywords:orthogonal time frequency space; jamming detection and suppression; improved forward consecutive mean excision +
Footnote †: journal: Electronics
# A Framework for Constructing a Secure Domain of Sensor Nodes
## 5 Results and Discussion
### Security Evaluation
#### 5.3.2 Sensor Impersonation  
Sensor impersonation is not possible. Each sensor node has a fixed identifier (N_ID), an NSK key, and a special tag (NTAG). All of these parameters are determined during the pre-preparation procedure of the node and used during the registration procedure in the domain. The NTAG tag is important. Determining the value of this tag requires knowledge of the domain name, the node identifier, and other fields that are known only to node B and node M.
## 4 Experimental Results
### Testing the Performance of the WCDPSO-KELM Model  
In this section, the performance of the proposed method is tested by performance degradation indexes from different feature spaces in different working conditions. Experimental data are selected in different working conditions, and the specific information of sample datasets is listed in Table 3.  
The sample data numbered bearing1_5 is processed according to Figure 3 to obtain performance degradation indexes of time-frequency domain, wavelet packet space and EEMD space. From a total of 2442 datasets, the first 2000 datasets are taken as training datasets, and the remaining 442 as the testing dataset. A different performance degradation index is input to the WCDPSO-KELM model. The outputs of the WCDPSO-KELM model and actual outputs are shown in Figure 7.  
Figure 7: Actual outputs and prediction outputs in working condition 1. (**a**) phase space; (**b**) wavelet packet space; (**c**) EEMD space.  
The sample data numbered bearing2_5 is processed as described above. From a total of 2304 datasets, the first 1900 datasets are taken as training datasets, and the remaining 404 as testing datasets. The outputs of the WCDPSO-KELM model and actual outputs are shown in Figure 8.  
Figure 8: \(Cont.\)  
The sample data numbered bearing3_2 is processed as described above. From a total of 1625 datasets, the first 1200 datasets are taken as training datasets, and the remaining 425 as testing datasets. The outputs of the WCDPSO-KELM model and actual outputs are shown in Figure 9.  
Figure 8: Actual outputs and prediction outputs in working condition 2. (**a**) Phase space; (**b**) wavelet packet space; (**c**) EEMD space.  
Figure 9: \(Cont.\)
## 4 Discussion of Far-Field Patterns with Varying Dimensions  
The near-field calculation of a very large metasurface, such as a 60 \(\times\) 60 array, necessitates an expanded radiation box as depicted in Figures 8 and 10. However, when the metasurface size is increased to 60 \(\times\) 60, the computational workload grows exponentially, surpassing the capabilities of a single workstation for completing the task. At 12 GHz, Figure 13 illustrates the far-field diagram for metasurfaces of varying sizes. It can be observed that the far field exhibits a hollow circular beam characteristic of vortex waves. In this context, circularly polarized waves are employed to excite metasurfaces measuring 20 \(\times\) 20 and 40 \(\times\) 40 to obtain the far-field patterns.
# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
## 3 Experimental Results and Analysis
### Benchmark Evaluation  
We conducted tests on our IOF-Tracker algorithm using the MOT17 and MOT20 test sets, comparing the performance of our feature fusion combined tracker in Tables 4 and 5. Our tests all used publicly available detectors, and all results were directly obtained from the official MOTChallenge evaluation server. Since the speed of each method depends on the device they are implemented on, and tracking based on detection usually does not calculate the time spent on detection, we do not compare the FPS performance here.  
Through the benchmark scores in Table 4, on MOT17, our feature fusion model combined tracker performs excellently in the main evaluation metrics, namely MOTA, IDF1, IDs, and HOTA, outperforming all other advanced trackers. This indicates that the multi-object tracker with our feature fusion module can exhibit a certain degree of stability in tracking some difficulties. In Table 5, on the MOT20 dataset, the only metrics that reached the optimal performance were MOTA and HOTA. We analyzed the reasons for this: firstly, the population density in the MOT20 dataset is much larger than that in MOT17, resulting in more frequent crowd interactions and a greater impact on the optical flow field, causing frequent interactions between pedestrians and leading to chaos in the optical flow field.  
Figure 6: Qualitative analysis of multi-object tracking performance (Different colored target boxes represent different ID information).  
Secondly, some scenes in MOT20 are in dimly lit night conditions with large variations in lighting intensity. Changes in lighting intensity (such as shadows, reflections, etc.) can cause variations in surface brightness, which poses a challenge for optical flow calculation. Therefore, using our module for multi-object tracking in extremely crowded scenarios like MOT20 presents certain challenges.
# Distributed Load-Balancing Method for CCA Parallel Component Applications
###### Abstract  
Numerous universities and national laboratories in the United States have collaboratively established a Common Component Architecture (CCA) forum to conduct research on parallel component technology. Given the overhead associated with component connection and management, performance optimization is of utmost importance. Current research often employs static load-balancing strategies or centralized dynamic approaches for load-balancing in parallel component applications. By analyzing the operational mechanism of CCA parallel components, this paper introduces a dynamic and distributed load-balancing method for such applications. We have developed a class library of computing nodes utilizing an object-oriented approach. The resource-management node deploys component applications onto sub-clusters generated by an aggregation algorithm. Dependency among different component calls is determined through data flow analysis. We maintain the load information of computing nodes within the sub-cluster using a distributed table update algorithm. By capturing the dynamic load information of computing nodes at runtime, we implement a load-balancing strategy in a distributed manner. Our dynamic and distributed load-balancing algorithm is capable of balancing component instance tasks across different nodes in a heterogeneous cluster platform, thereby enhancing resource utilization efficiency. Compared to existing static or centralized load-balancing methods, the proposed method demonstrates superior performance and scalability.  
parallel component; performance optimization; load-balancing; parallel computing; resource management Article