esa-sceva/satcom-chunk-collection huggingface dataset:
# Addressing ZSM Security Issues with Blockchain Technology
## 8 Conclusions  
ZSM networks are expected to lead the way toward the development of self-managed and self-optimized networks to form NGNs. The increased automation, in combination with the minimum human intervention, increases the performance of the network and at the same time exposes several security concerns. The current research examined the use of blockchain to tackle the security issues of the ZSM framework. The proposed blockchain architecture was found to address these security issues which further encouraged our interest in selecting the appropriate blockchain/platform on which such a solution would be implemented (to guide prospective implementers). In this course, we defined the selection criteria and examined the representative set of platforms. The conclusion was that for all of them, a modification will be needed as none meeting all the criteria was found. Although blockchain introduces valuable characteristics such as the ability to form a network of trust in a trustless environment without the existence of a trusted third party, the immutability of the data recorded as validated transactions in the ledger and the traceability feature, no technology is weakness-free. There are some drawbacks in this technology, which should be carefully considered during the design of the blockchain-based solution and deserve further research: (a) scalability is one of the main drawbacks of this technology since transactions written in the ledger cannot be deleted afterward; (b) the transaction validation time affects the dynamicity the network can support and mostly depends on the consensus mechanism used in the blockchain software [37; 38]; (c) the support of tokens is also anticipated to add value in this environment for trading the resources. Moreover, the use of cryptocurrency could lead to the development of a modern marketplace where NPs could rent, buy or lease resources using the ZSM framework in a secure manner.  
Conceptualization, M.X. and H.C.L.; methodology, H.C.L.; investigation, M.X.; resources, M.X., D.G.K. and P.A.K.; writing--original draft preparation, M.X.; writing--review and editing, D.G.K., P.A.K. and H.C.L.; visualization, M.X.; supervision, H.C.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data availability Statement: Data are available upon request. Please contact the authors for further information.  
The authors declare no conflict of interest.
###### Abstract  
With the development of wireless sensor network technology, the routing strategy has important significance in the Internet of Things. An efficient routing strategy is one of the fundamental technologies to ensure the correct and fast transmission of wireless sensor networks. In this paper, we study how to combine deep learning technology with routing technology to propose an efficient routing strategy to cope with network topology changes. First, we use the recurrent neural network combined with the deep deterministic policy gradient method to predict the network traffic distribution. Second, the multi-hop node state is considered as the input of a double deep Q network. Therefore, the nodes can make routing decisions according to the current state of the network. Multi-hop state-aware routing strategy based on traffic flow forecasting (MHSA-TFF) is proposed. Simulation results show that the MHSA-TFF can improve transmission delay, average routing length, and energy efficiency.  
multi-hop state-aware; traffic flow prediction; deep reinforcement learning; wireless sensor networks +
Footnote †: journal: Applied Sciences
# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 5 AI-Powered VANET Solutions
### Security  
The security of VANETs is of paramount significance due to their continuous transmission of messages between vehicles and roadside facilities [144; 145]. The content of these messages is sensitive and crucial to ensure the preservation of reliability, genuineness, secrecy, and message privacy. Additionally, factors such as data integrity, trust level, relevance, and real-time safety-related applications are crucial to consider [145]. Dissemination of false or inaccurate information regarding the vehicle's situation, road accidents, and conditions may also provide potential security threats [146]. Key security problems include malicious vehicles, Distributed Denial of Service (DDoS) assaults, authentication vulnerabilities, SQL injections, anti-intrusion measures, and physical attacks. Security challenges in VANET include SQL injections, attack protection, and physical assaults [147]. The k-means [148] approach is especially well suited for identifying harmful nodes since it can easily adjust to the changing structure of VANET. Effective management is necessary for internal attacks, precisely CAN threats. The GRU defines a specific sort of RNN that can be utilized in intrusion detection. However, it is essential to combine it with additional methods using DL [149]. A GRU system establishes a decentralized collaborative framework that assesses network conditions and promptly initiates appropriate measures to defend against jamming attacks [150]. An ANN effectively detects misbehavior [151], whereas a CNN retrieves spatio-temporal features of vehicles [152] from a two-dimensional dataset. LSTM can enhance intrusion detection by autonomously identifying malicious network traffic on the OBU using time-dependent traffic flow data [153]. Trust management systems in VANETs guarantee the reliability and credibility of both information and data nodes. Evaluating the reliability of crucial communications, such as urgent warnings, is intricate and demanding. SVM and Reinforcement Learning might be employed in VANETs to develop trust strategies for the actions of vehicle nodes or third parties. SVMs are reliable ML techniques commonly used in nonlinear classification scenarios [154]. In contrast, the concept of Reinforcement Learning can effectively handle the vast volume of data generated by automobiles [155]. Particle Swarm Optimization can reduce Denial of Service (DoS) assaults by analyzing the collective behavior of vehicles [156]. PSO is a computational algorithm that simulates the dynamics of particles inside a search space. Itadjusts the particles depending on their historical behaviors, using a heuristic technique that simulates the flight of bird flocks.
# Seismic Analysis of Steel Solid Web Girder-RC Tubular Column Hybrid Structure
## 4 Numerical Results and Discussion  
### Dynamic Characteristics  
Table 2 provides the numerical results of natural periods, mode shapes, and participating mass coefficients for the first three vibration modes. As shown in Figure 7a, the first vibration mode of the steel solid web girder-RC tubular column hybrid structure was torsion due to its small torsional stiffness. This is caused by the uniform distribution of stiffness and mass which mainly distribute on the superstructure. Both second and third vibration modes were translations, as shown in Figure 7b,c. Since the torsion is averse to the seismic resistance, the China seismic design code recommended that the first torsion to first translation period ratio should not be greater than 0.85 [17]. For the steel solid web girder-RC tubular column hybrid structure, the calculated period ratio was about 1.03 greater than 0.85, indicating that the torsional effect of this hybrid structure should be considered in the structural design.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multirow{2}{*}{**Natural Period (s)**} & \multirow{2}{*}{**Mode Shapes**} & \multicolumn{4}{c}{**Participating Mass Coefficient**} \\ \cline{5-6}  & & & **X Direction** & **Y Direction** & **RZ Direction** \\ \hline
1 & 1.291 & Torsion & 0.001 & 0.000 & 0.976 \\
2 & 1.251 & Y-direction & & 0.987 & 0.000 & 0.001 \\  & & translation & & & \\
3 & 1.239 & X-direction & & & \\  & & translation & & & \\ \hline \hline \end{tabular} Note: The value in the bracket means the accumulated participating mass coefficient for the first three modes.  
\end{table}
Table 2: Numerical results of dynamic characteristic parameters.  
Figure 6: FE (Finite element) model of the steel solid web girder-RC tubular column hybrid structure: (**a**) Whole model; (**b**) part of the model.
# Automatic Correction of Indonesian Grammatical Errors Based on Transformer
## 6 Result and Discussion
### Case Study  
In this subsection, we utilized a case study to examine the model performance. This subsection introduces a case study to examine the model performance with a real-world example taken from the CC100 Indonesian dataset. Table 5 shows the outputs of different versions of the Indonesian GEC model, including the baseline SAN-GEC (Transformer), SAN-GEC with a copy mechanism and SAN-GEC with a copy mechanism and BPEChar, and also we provided the sources, targets and English translations. The presented example has 21 errors subdivided into 6 categories as follows: the syntactic or grammatical errors are in numbers 1, 8, 11 and 13, while the wrong word selection errors are in numbers 2, 5, 9, 17, 18 and 19. Furthermore, errors 6 and 14 are conjunction usage errors, and the numbers 4 and 20 are incorrect preposition usage errors. Finally, affix usage errors and word order errors are in numbers 3, 7, 12 and 21. The red color refers to all errors in the sentences. In the beginning, the baseline model (Transformer) corrected the syntactic or grammatical, word selection and conjunction usage errors for numbers 1, 2, 4, 5, 6, 8, 9, 11, 13, 14, 17, 18, 19 and 20 and failed to correct the errors for numbers 3, 4, 7, 10, 12, 15, 16, 20 and 21. Moreover, the next model (Transformer with a copy mechanism) corrected all mistakes except the affix usage and word order errors in numbers 3, 7, 12 and 21. The last model, Transformer with a copying mechanism and BPEChar, corrected all mistakes but failed to correct the punctuation errors in numbers 10, 15 and 16, as it left only a comma in the sentences.  
The baseline SAN-GEC with the copy mechanism and BPEChar model proved its ability to detect and correct Indonesian text errors except for some punctuation errors. Basically, the punctuation errors in the given example are not a big issue in Indonesian grammar because they are still acceptable in Indonesian writing. Thus, our model was relatively effective compared with the baseline model (Transformer) because it revealed enhanced improvement over the previous performance.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Type** & \multicolumn{1}{c}{**Example**} \\ \hline \multirow{7}{*}{Source} & Namun demikian1  & saat ini pemerintah Jawa Barat memiliki \\  & uang2  & didik3  & seatu  & guru dan6  & mereka dapat kerja7  & baik-baik8  & Suatu9  & guru juga,10  & disekolah11  & dapat jjar12  \\  & macam-macam13  & yang14  & pelajaran serumpu,15  & misalnya \\  & matematika, fisika, kimia dan biologi,16  & selain itt mereka \\  & dapat17  & tang tamehaha18  & tan tungung19  & keluarga \\  & dari pemerintah Indonesia aatu pemerintah pusat \\  & berbulan-bulan21  & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of different versions of our model, where the incorrect words have been numbered and colored in red.
much faster than humans. So image processing tools plays a major in applications pertaining to remote sensing. Remote sensing methods are considered as a complementary tool in various applications due to the wide range of area the images can cover. The usefulness of satellite images to remote sensing applications depends on the accuracy of the techniques. A good understanding of how well the image processing techniques perform will help in deciding the best choice of methods for various applications. With the enhancement of technology, demand for near-real-time monitoring and visual images for use in emergency services and the public in the case of a natural disaster is increasing. Recent advancements in earth monitoring satellites are making way towards use in such applications. Methods are thus being developed to utilize the available data effectively to make sure that the best possible intelligence is available to emergency services and decision makers on time.  
While a vast amount of digital satellite and aerial imagery is available, the real challenge is in the analysis of raw image, extraction of useful information with high accuracy and applying it to real world decision making and applications. Even though there are various image processing techniques available, conventional methods need to be critically reviewed and image understanding with respect to features of interest is important. With the advances in artificial intelligence and machine learning, there is an improvement in extraction of detailed features from high resolution aerial imagery. The fin different layers are utilized for developing various image processing techniques.  
Several methods have been already explored to process the satellite images efficiently. Still, there are plenty of scopes for the performance improvement of the conventional system. A detailed understanding of the conventional methods is extremely necessary to develop new automated systems for satellite image processing. In this work, an exhaustive survey is presented on the various aspects/applications of satellite image processing. Pros and cons of the methods are also presented which will aid in developing novel solutions for the current problems in the area of satellite image processing.  
The reminder of the paper is organized as follows--Section 2 describes the various image processing techniques in remote sensing, Section 3 explains different performance evaluation metrics in image processing, Section 4 gives a brief discussion on the study with the conclusion given in Section 5.
## 5 Biomarkers of Response and/or Resistance to Immune Checkpoint Inhibitors
### Markers of Response and/or Resistance in Tumor Genome
#### 5.1.3 Gene Expression Profiling  
Beyond mutational analysis, gene expression profiling is another potentially useful predictor of the response to ICIs. According to two retrospective studies, the activation of interferon signaling pathways and genes related to inflammation predict a favorable response to ICIs in patients with HCC [61; 84]. The analysis of fresh and archival tumor samples from the Checkmate 040 trial allowed the identification of an inflammatory 4-genes signature able to predict an improved objective response rate (\(p=0.05\)) and OS (\(p=0.01\)) [61]. Very recently, a study evaluating the tumor samples from patients with advanced HCC showed that, among 28 patients treated with anti-PD-1 in the frontline, responders had upregulated IFN\(\gamma\) signaling and MHC II-related antigen presentation [84]. In addition, the authors demonstrated that a signature comprising 11 genes, including among others IFN\(\gamma\) signaling (STAT1, GBP1), antigen presentation (B2M, HLA-DRB5, HLA-DRA), and chemotaxis (CXCL9), was able to predict the prognosis. This signature was shown to predict response and survival also in a separate cohort of advanced HCC and in more than 240 patients with other solid cancers [84]. However, these very interesting findings require prospective confirmation in a larger cohort of patients with HCC.
## 6 Open Challenges, Future Prospects and Research Directions
### FSO Scalability and Reliability for 5G and Beyond Networks
#### 6.1.4 System Integration  
Integrating FSO systems with existing network infrastructure and emerging technologies is vital for scalability. Key integration consideration include compatibility with existing networks. So, ensuring interoperability between FSO systems and existing network equipment, such as routers and switches, can facilitate seamless integration and operation. Also, adopting standardized protocols and interfaces can simplify the integration process and enhance compatibility across different vendors and technologies.  
In addition, for effective integration with 5G and beyond technologies, FSO systems must be designed to support the requirements of 5G and future networks, including high data rates, ultra-low latency, and massive connectivity. Also, integrating FSO with IoT infrastructure and smart city applications can provide high-capacity, low-latency links for different use cases, such as smart lighting, traffic management, and environmental monitoring. Implementing centralized network management systems can provide real-time monitoring and control of FSO links, facilitating proactive maintenance and optimization. Also, leveraging AI and ML techniques can enhance network management by predicting and mitigating issues related to atmospheric conditions, traffic patterns, and potential obstructions.  
In general, the potential of FSO communication systems to scale with the increasing demands of 5G and beyond is promising, provided that key challenges related to network architecture, deployment strategies, resource management, and system integration are addressed. By leveraging hybrid network architectures, adopting efficient deployment strategies, implementing dynamic resource management techniques, and ensuring seamless integration with existing and emerging technologies, FSO can be crucial in the next generation of high-capacity, reliable communication networks.
## 2 Methods  
### Data Sources  
GPP and ET data from 2000 to 2014 were both retrieved from MODIS, which were produced by the Numerical Terra dynamic Simulation Group ([[http://www.ntsg.umt.edu](http://www.ntsg.umt.edu)]([http://www.ntsg.umt.edu](http://www.ntsg.umt.edu))). The MODIS GPP product (MOD17A3) was developed based on a light-use efficiency model [41]. The accuracy and reliability of this model have been validated in many regions [25; 42; 43]. MOD17A3 has been widely used in studies on regional or global ecosystem carbon cycles [44]. The global MODIS ET product (MOD16A3) was estimated based on the Penman-Monteith model, which uses meteorological reanalysis data and vegetation property dynamics (e.g., land cover, leaf area index, and albedo) retrieved from MODIS as input variables [45; 46]. Validations of this product using flux tower data from stations in the USA [47], Asia [48], and other regions have indicated reasonable accuracy [14]. We used the land cover type CMG product (MCD12C1) to analyze the observed changes in the selected datasets. The Central Asian study region is primarily represented by the following five land cover classes: grasslands (26.91%), barren land (24.61%), croplands (9.34%), forest (13.21%), and shrublands (11%). We did not analyze the barren pixels because there were no ET data in barren pixels.
## 6 Conclusions  
In this research, a novel design of optical homodyne and optical heterodyne detection is explained. The optical homodyne technique consists of a simple setup, but the optical heterodyne technique makes use of complex optical components, such as AOM. The optical homodyne detection makes use of the direct modulation of the fundamental optical signal, while the optical heterodyne detection technique makes use of the modulation of the higher order mode of the optical signal. The path length fluctuations are the main problem in optical homodyne detection. The phase fluctuations related to the path length difference can have very little variation in a homodyne setup. This problem can be solved with optical heterodyne detection, as the frequency of the local oscillator is not equal to the  
Figure 38: (a) Deviations of \(HR\) measured with heterodyne system with respect to ECG (chest wall measurement, subject 7); (b) deviations of \(HR\) measured with homodyne system with respect to ECG (chest wall measurement, subject 7).  
signal frequency in optical heterodyne detection (\(f_{s}\
eq f_{LO}\)). In case of measurement of high values of \(HR\) (more than 70 bpm), the optical heterodyne detection has a higher accuracy of results compared to optical homodyne detection. This is due to the fact that variation in the path length difference is very limited in the homodyne setup (\(\Delta L\leq\frac{3}{4}\)). However, results obtained from the homodyne method show much less deviation from results of the heterodyne setup and ECG setup. The deviation of results is less than 3 ms for measurement of the heart period and less than 0.5 bpm for measurement of the \(HR\). Both homodyne and heterodyne detection techniques can be used to accurately measure \(HR\) and \(HRV\). The experimental results of homodyne and heterodyne detection are comparable with contact type measurement methods, such as an ECG. The results are found to be within a less than 1% deviation value from the ECG measurement.  
[PERSON] performed the conceptualization, methodology, software, validation, formal analysis, and investigation. [PERSON] performed the supervision and project administration. The writing, review, editing, and visualization are carried out by [PERSON] All authors have read and agreed to the published version of the manuscript. This research received no external funding. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy issues. We would like to acknowledge [PERSON], Visiting Fellow from Tata Institute of Fundamental Research, Mumbai, India for providing optical components used for the experiment. The authors declare no conflict of interest.
## 5 Proof of Concept
### Testbed Setup  
The experimental network built from Clemson's cluster on the CloudLab testbed is depicted in Figure 3. The topology consists of four back servers, one Open vSwitch (OVS), a client (emulating the aggregated traffic flow of multiple users), and the learning agent-based SDN controller as a VNF. The client is connected to the back server via the OVS. The learning agent decides the forwarding of HTTP requests from the clients to the servers based on their observed performance as measured by the response times in ICMP echo/reply packet exchange and the packet-loss ratios on the links connecting OVS to the back servers.  
The back servers \(s_{1}\), \(s_{2}\), \(s_{3}\), and \(s_{4}\) run on Xen Virtual Machines (VM), each configured with two CPU cores--Intel Xeon 2.0 GHz. Apache-2.4.18 was installed on the back servers to provide the simulated network service that users need to access to either retrieve or send information. Open vSwitch runs from the machine _switch_ and forwards the HTTP requests and responses between the client and back servers. The machine has 56 processors, each having 14 cores, and its model is Intel Xeon 2.40 GHz. The SDN controller is running from the Xen VM _controller_, which has four CPU cores. Artificial delays and packet losses were introduced to the links connecting back servers to the switch using Linux's NetEm tool (Traffic Control) to emulate propagation latencies and the impact of atmospheric channel impairments. A Poisson traffic generator was developed with various configurable sending rates (\(\lambda\)) to emulate aggregated traffic from clients. The client in the Figure 3 emulates the users in Figure 1, the switch represents a router on the satellite, and the back servers represents the links connecting to the ground stations.