esa-sceva/satcom-chunk-collection huggingface dataset:
# Impacts of GPS Spoofing on Path Planning of Unmanned Surface Ships
## 3 GPS Attack Designs and Spoofing Analysis
### GPS Attacks  
As we know, the signal structure, spread spectrum code, and modulation method of civilian GPS signals are public. Military GPS signals are encrypted. Therefore, civilian GPS signals are insecure and may be exploited by some attackers. We focus on GPS C/A code in this paper. GPS attacks are classified into jamming attacks and spoofing attacks.  
* A jamming attack happens when a GPS jammer transmits strong jamming signals at the same frequency, which suppress the front-end signal of the GPS receiver so that the GPS receiver loses the ability to work or receive attenuated satellite signals. Jamming attacks have the advantages of simple operation and easy implementation. However, the power required for a jamming signal is large;
* A spoofing attack happens when a GPS spoofer transmits the same or similar signal as a GPS signal to guide the GPS receiver to deviate from the original correct navigation and positioning. Spoofing attacks have the advantages of strong concealment.  
Next, we introduce GPS jamming and GPS spoofing in detail.
# Current Trends on Green Wireless Sensor Networks
## 2 Energy Conservation
### Energy-Efficient Routing
The first data-centric protocol was SPIN (Sensor Protocol for Information via Negotiation) [136]. When a node has new data to share, it advertises this fact by transmitting an ADV message containing metadata (that identifies data). When the neighboring nodes receive this ADV message, if not repeated, they send a REQ message to the source node requesting complete sensor data. After receiving data, the process is repeated in the second node. This protocol is based on negotiation, but data-centric algorithms are mainly query-based, as is the case with Direct Diffusion [137] and Rumor Routing [138]. In location-based routing, all the nodes calculate the distances to the neighboring nodes based on incoming signal strength. Another option is to use a GPS signal, but  
Figure 2: Classification of Energy-Efficient Routing Protocols used in [135]. in this case, nodes should go to sleep mode whenever the communication is not active. Some major routing protocols of this type are Geographic Adaptive Fidelity (GAF) [139] or SPAN [140], that is integrated with IEEE 802.11 to improve transmission latency, and extend the network lifetime. As already explained in Section 2.2, since in most WSN applications data flows from sensor nodes to BS, and data from nearby nodes contains redundancy, clustering favors data reduction by exploiting the spatial correlation. This is the basis of hierarchical routing protocols, where nodes are grouped in clusters, and a node is selected as the CH, that is responsible for collecting data from the sensor nodes inside the cluster and routing it towards the sink, directly (single-hop) or through several CHs (multihop). Hierarchical protocols are the most popular and the preferred option for WSNs and they are clearly the most studied protocols presently. For this reason, we will focus on them.
# The Housing System Contributes to Udder Health and Milk Composition
## 1 Introduction
Numerous enzymes are released in milk when somatic cells are lysed, including lipuges (such as lipoprotein lipase), oxidases (such as catalase and lactoperoxidase), glycosidases (such as lysozyme), and proteases (such as cathepsins, elastase, and collagenase) [17]. Therefore, a high SCC can induce changes in the biochemical properties of milk, including the fatty acid profile, which determines the health benefits of milk and dairy products [18]. Some milk fatty acids, including conjugated linoleic acids (CLAs) and omega-3 polyunsaturated fatty acids (n-3 PUFAs), have anticancer and antidiabetic potential [19]. Diets with a lower n-6/n-3 ratio reduce the risk of many diseases [20]. Previous research has shown that cattle housing systems influence the health status of animals, especially udder health, which affects the quality of produced milk [4; 5; 8]. In most cases, milk from cows with clinical disease symptoms is not fit for consumption or processing due to changes in its composition and the presence of pharmaceuticals [21]. However, milk from cows with subclinical mastitis is widely consumed and processed in the dairy industry. We hypothesize that milk from such cows shows changes in composition and quality, and this is influenced by the housing system. Therefore, the aim of the present study was to determine the effect of the housing system (i.e., maintenance system, manure removal, milking system, feeding method) and increased SCC on milk composition and the fatty acid profile of milk fat, with special emphasis on the contents of nutritionally essential functional fatty acids.
## 2 Materials and Methods  
### Feed Wastewater and Membrane  
In this study, synthetic wastewater consisting of sodium acetate anhydrous (C\({}_{2}\)H\({}_{3}\)NaO\({}_{2}\), Duksan Chemicals, Korea) and sodium propionate (C\({}_{3}\)H\({}_{5}\)NaO\({}_{2}\), TCI Chemicals, Korea) yielding chemical oxygen demand (COD\({}_{\text{Cr}}\)) was prepared. Ammonium chloride (NH\({}_{4}\)Cl, Duksan Chemicals, Korea), potassium phosphate monobasic (KH\({}_{2}\)PO\({}_{4}\), Daejung Chemicals, Korea), and sodium bicarbonate (NaHCO\({}_{3}\), Duksan Chemicals, Korea) were prepared and applied as feed wastewater for SDCMD experiments. The synthetic wastewater tested in this study was based upon the low-strength feed wastewater prepared as feed for laboratory-scale anaerobic bioreactors [27; 28]. The characteristics of the synthetic wastewater used in this study are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Chemical Oxygen** & **Ammonia Nitrogen** & **Total Phosphorus** & **Conductivity** & \\
**Demand (mg COD/L)** & **(mg NH\({}_{3}\)-N/L)** & **(mg PO\({}_{4}\)\({}^{3}\)-P/L)** & **(\#5/cm)** & **pH** \\ \hline
282.6 \(\pm\) 5.7 & 32.2 \(\pm\) 1.3 & 5.3 \(\pm\) 0.1 & 971.4 \(\pm\) 4.0 & 7.7 \(\pm\) 0.1 \\ \hline \end{tabular}
\end{table}
Table 1: Composition of synthetic wastewater for the submerged direct contact membrane distillation (SDCMD) process.  
The polyethylene (PE) hollow-fiber membrane (Econity Co. Ltd., Yongin, Korea) was available with high hydrophobicity (contact angle is about 130\({}^{\circ}\)) and used for operating SDCMD system. The mean pore size of the commercially available PE membrane was customized as 0.1 \(\upmu\)m and the porosity of the membrane was 50%. The outer and inner diameter of PE hollow-fiber membrane was 1100 and 750 \(\upmu\)m, respectively. In this study, twenty fibers were assembled into a membrane holder specially designed and sealed by epoxy resin. The effective surface area of the hollow-fiber membrane tested was 84.3 cm\({}^{2}\). The fiber length and distance between each fiber were 9.9 and 1.6 cm, respectively.
# Dynamic Phase Comparison Planar Direction-Finding Algorithm on Satellite Radio Receiver
## 4 Discussion  
The simulation analysis from the previous section indicates that the dynamic phase comparison algorithm, when applied to a high-speed moving satellite platform for the direction finding of radiation sources, shows that different target directions significantly affect the direction-finding accuracy. Moreover, as the amplitude comparison algorithm presents a different error curve, integrating both algorithms has led to a new direction-finding method. Planar direction-finding simulation results demonstrate that this method retains a broader direction-finding range while achieving higher accuracy. Additionally, simulations were conducted to analyze the impact of three important factors in the dual orthogonal dipole antenna direction-finding system, which include the SNR, receiver sampling frequency, and coherent accumulation. Given that the spatial amplitude comparison algorithm is typically used in tri-orthogonal dipole antenna systems for direction find  
Figure 12: Average absolute error of the dynamic fusion algorithm under different (**a**) SNRs, (**b**) receiver sampling rates, (**c**) and the numbers of coherent accumulation.  
ing, further research will continue on the performance of the dynamic phase comparison algorithm in the three-dimensional direction finding system.
## 1 Introduction
Critically, the heterojunction formed between CdS and g-C\({}_{3}\)N\({}_{4}\) facilitates efficient segregation of charges [24], minimizing recombination and significantly improving photocatalytic performance [25]. Relative to other composites utilizing g-C\({}_{3}\)N\({}_{4}\), these attributes collectively ensure that the CdS/porous g-C\({}_{3}\)N\({}_{4}\) system demonstrates higher stability, reusability, and efficiency, positioning it as a strong contender for wastewater treatment applications [26]. This study aims to synthesize CdS/porous g-C\({}_{3}\)N\({}_{4}\) containing different amounts of CdS through an in situ co-precipitation approach. Comprehensive characterization of the materials' compositions and morphologies was performed using techniques such as SEM, XRD, FT-IR, XPS, and BET. Furthermore, photoluminescence (PL) and UV-Vis DRS analyses were conducted to examine charge separation and light absorption properties. This research not only highlights the outstanding performance of the CdS/porous g-C\({}_{3}\)N\({}_{4}\) system in photocatalysis but also provides insights into the underlying mechanisms of charge separation and material stability, offering a promising route for efficient wastewater treatment.
## 7 Conclusions  
Maximizing the utilization of the limited spectrum resource is the most critical task in a wireless communication system, as it leads to enhanced capacity, coverage, and QoS provisioning. A cognitive radio network (CRN) attempts to exploit the unused licensed spectrum of an existing network dynamically (primary network) to provision a secondary network that can optimize the spectrum utilization, as well as afford additional communication services. The integration of buffer-aided relaying in wireless networks helps to improve spectral efficiency and coverage, albeit that this could result in increased delays. In this work, the focus is on improving the performance of a CRN that includes buffer-aided (BA) relays. The proposed buffer-aided multi-hop relay selection (BAMR) scheme improves the delay performance through virtual duplexing using a cluster of relays. BAMR takes into account both the inter-relay interference and the transmission power constraint imposed by the primary network. The performance evaluation of BAMR has been carried out using Markov modeling and Monte Carlo simulations. The results, collected in terms of throughput, outage, and average end-to-end delays, have clearly demonstrated the substantial performance improvement achieved with BAMR in comparison to other contemporary schemes. BAMR takes into account the delays both at the source and the relay cluster, while ensuring better spectral efficiency and reduced outage. BAMR performs well in both DTN and non-DTN environments. It not only reduces the queue lengths at the relay buffers through prioritized link selection ability, but also keeps queues at the source to minimum lengths through virtual duplexing. The adaptive data rate feature of this scheme enables it to transmit more than one packet in a single time slot whenever a favorable condition, in terms of SINR, allows for it.  
Conceptualization, S.A.A.; methodology, S.A.A.; software, S.A.A.; validation, S.A.A., R.H., and S.A.M.; formal analysis, S.A.A. and R.H.; investigation, S.A.A.; resources, S.A.A., R.H., and S.A.M.; data curation, S.A.A. and R.H.; writing, original draft preparation, S.A.A.; writing, review and editing, S.A.A. and R.H.; visualization, S.A.A.; supervision, R.H., S.A.M.; project administration; Q.U.H. This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CRN & cognitive radio network \\ BA & buffer aided \\ BAFD & buffer-aided full-duplex \\ DTN & delay-tolerant network \\ IoT & Internet of Things \\ IID & independent and identically distributed \\ LI & loop interference \\ CSI & channel state information \\ MC & Markov chain \\ \end{tabular}
## 3 Underwater Channel Characterization
### EM Propagation in Seawater
#### 3.4.2 Absorption Loss for EM in Seawater  
The absorptive loss for EM waves in seawater is expressed in Equation (8) [7]:  
\[\alpha\approx\sqrt{f\mu\sigma\pi} \tag{8}\]  
Equations (7) and (8) clearly show that the velocity of propagation decreases while the absorption losses increase as EM waves propagate in seawater. Thus, aforementioned characteristics influence the EM wave propagation in underwater.
## 3 Theory
### Breathing Signal Detection and Spectrum Estimation
The channel with the highest signal to clutter noise ratio is then chosen. The breathing rate is estimated for each channel as the frequency \(f_{max}\) of the peak in the periodogram \(P_{x}\)(\(f\)). The signal to noise clutter ratio (SCR) is estimated from the periodogram as the ratio between the power around the maximum of the periodogram and the power outside:  
\[SCR=\frac{\int\limits_{f_{\max}-B/2}^{f_{\max}+B/2}P_{x}(f)df}{\int\limits_{0} ^{\infty}P_{x}(f)df-\int\limits_{f_{\max}-B/2}^{f_{\max}+B/2}P_{x}(f)df} \tag{19}\]  
where \(B=0.05\) Hz is the resolution in the estimation of the periodogram.
# Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
## 4 Applied OMA Methods
### Covariance-Driven Stochastic Subspace Identification (Cov-SSI)  
Cov-SSI is one of the most effective and widely used methods in civil engineering. It is a parametric method in the time domain. It approaches the problem of stochastic realization, identifying a stochastic model from output-only data [1; 7; 11; 19]. A complete and exhaustive mathematical treatment of the method can be found in [1].  
The system under evaluation should be observable and controllable [20]; for a system of order N, its observability and controllability matrices must have rank N. In practice, the order of the system is unknown, and its accurate determination is very complex due to the uncertainty and the noise that accompanies the sampling. For this reason, a conservative approach will overestimate the system order. This will cause the appearance of non-physical modes that must be separated from the physical modes by specific methods represented by the stabilization diagrams [1; 7].  
Only the poles that exhibit stability between the different orders of the model represent physical modes. The most used stability criteria, as reported in [1], are generally the following:  
\[\frac{\lvert f(n)-f(n+1)\rvert}{f(n)}<0.01 \tag{3}\]\[\frac{|\zeta(n)-\zeta(n+1)|}{\zeta(n)}<0.05 \tag{4}\]  
where \(f(n)\) is the natural frequency of the order \(n\) model; \(\zeta(n)\) is the damping of the order \(n\) model; \(f(n+1)\) is the natural frequency of the order \(n+1\) model; and \(\zeta(n+1)\) is the damping of the order \(n+1\) model.
# On the Use of Transformer-Based Models for Intent Detection Using Clustering Algorithms
## 4 Results and Discussion  
This section presents the results of this work, which were divided into seven experiments. The first one evaluated the performance according to the pooling methods used. At the same time, the second examined whether the concatenation of two or more layers from the transformer-based models for creating the embeddings could improve the results. The third experiment evaluated the need for using dimension reduction on the embeddings, while the fourth studied the clustering algorithms. These first four examinations allowed the definition of the best pipeline, which was then used in the following two experiments. Specifically, in the sixth evaluation, the Siamese transformers were studied to define a baseline performance that was then used to compare the effect of retraining these models on domain data. The final experiment used the best-performing models identified earlier and combined them using ensemble-based approaches.  
The followed-up sequence of queries and the conclusions reached after each investigation are shown in Figure 2. The clustering labels generated by the models represent their outputs, which were then evaluated by comparing them with the ground truth labels using the clustering evaluation metrics described in Section 3.8.  
Except for the experiments carried out with HDBSCAN and agglomerative in Section 4.4, all other experiments used the embeddings from either the baseline models or from specific layers (indicated in the experiment) of the transformer-based models to feed the K-Means for clustering the data into 34 clusters (the number of intents in the dataset). Afterward, the evaluation metrics were used to assess the quality of the found clusters according to the database intents. The analysis primarily focused on finding the maximum V-measure since this is the most relevant metric.
# Evaluation of Multi-Stream Fusion for Multi-View Image Set Comparison
## 2 Problem and Related Work  
### Assumptions  
Let us make some assumptions to constrain the general problem of image set comparison. First, we constrain the kind of 3D objects we would like to identify and compare. Here we shall distinguish the \"image type\" from \"object type or category\" and from the \"context\". The question, \"are the images of the same type?\", asks whether the object views are the same, i.e., a \"front view\" and a \"side view\" are examples of image types. The question, \"are the visual objects of the same type?\", is related to single foreground objects detected in the image and classified according to our \"context\" (application domain). Object \"categories\" are generalizations of some types having the same basic properties.  
Examples of object types in the category of \"cars\" can be: \"vans\", \"sedans\", \"trucks\", and \"lorries\". Examples of an application domain (context) are: cars for fraud detection for insurance companies or buildings and other urban infrastructure for visual navigation. The context can refer to single object categories (e.g., cars) or many object categories that can appear in the same image (e.g., \"a car next to the tree\" or \"a car on the bridge\"). In this work, we focus on the context of single cars present in an image.
# High-Speed Visible Light Communications: Enabling Technologies and State of the Art
## 2 Potentials of VLC System
### Security/privacy and interference
(Reprinted from Energy Build, 149, [PERSON], Smart lighting: The way forward? Reviewing the past to shape the future. 180–191, Copyright (2017), with permission from Elsevier.) VLC system called AquaOptical [14], which can obtain a data rate of 1.2 Mb/s over 30 m in clear water. While in water with visibility estimated at 3 m, the data rates and distances dropped to 0.6 Mb/s and 9 m.  
Intelligent transportation  
Congestion has become an increasingly significant issue worldwide, especially in the developing countries, and has leading to a series of problems, such as air pollution, fuel consumption and difficulties in implementing transportation plans. Furthermore, accident risks increase with the rapid growth of the number of vehicles. [PERSON] et al. [15] indicated that almost three fourths of all traffic accidents can be attributed to human error. Thus, one may predict that if there is an early warning system in place, the probability of accidents will be greatly reduced. The above problem can be alleviated by implementing the intelligent transportation. As shown in Figure 2, VLC could be used to send messages with vehicle lights, traffic lights, street lights and the other elements of the lighting infrastructure. According to the Vehicle Safety Communications Project from the US Department of Transportation, the applications including emergency electronic brake lights, cooperative forward collision warning, pre-crash sensing, lane change warning, curve speed warning, stop sign movement assistant, left turn assistant, traffic signal violation warning, and so on [16].
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 2 Background
### Raspberry Pi3 B+
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Node Type** & **Process** & **Description** \\ \hline \multirow{6}{*}{Source node} & \multirow{2}{*}{UDP source} & RP3 unit with an Internet protocol (IP) address 192.168.10.2 and a port number \\  & & 1234 generates data stream with a predefined payload size of 1472 bytes in the \\  & & form of frames encapsulated in user datagram protocol (UDP). \\ \cline{2-3}  & Packet encoder & Converts frames into packets with two samples per symbol (or one bit per symbol). \\ \cline{2-3}  & Gaussian minimum shift keying & \\  & (GMSK) modulator & Converts packets, or non-zero byte stream, into modulated signals at baseband. \\ \cline{2-3}  & &
\begin{tabular}{c} Sets the signal parameters, including the center frequency 850 MHz, channel \\ gain 1 dB, sample rate 1.1 MB, and the intermediate node address, and then \\ broadcasts the signals. \\ \end{tabular} \\ \hline \multirow{6}{*}{Intermediate node} & USRP source & Receives signals from a source node or an upstream intermediate node. \\ \cline{2-3}  & GMSK demodulator & Converts complex modulated signals at baseband into packets with a rate of 2 \\  & samples per symbol. \\ \cline{2-3}  & Packet decoder & Converts packets into frames in bytes. \\ \hline Destination node & USRP sink & RP3 unit receives data stream and run it on application, such as a media player. \\ \hline \hline \end{tabular}  
* Please note that the packet encoder, GMSK modulator, and USRP sink of the source and intermediate nodes are similar. Also, the USRP source, GMSK demodulator, and packet decoder of the intermediate and destination nodes are similar.  
\end{table} 
Table 1: Blocks used in the flow graphs of the GNU radio of the source, intermediate, and destination nodes. more feasible platform compared to a traditional testbed comprised of a PC and a switch.
## 4 Results and Discussion
### Phase 1: Analysis of the current situation
The estimated economic savings at the terminals studied, which exceed EUR 500 per hour on average, reflect the financial viability of adopting technologies such as IoV and blockchain. Automation not only allows for a reduction in operating costs, but also increases processing capacity without the need to physically expand the facility. This is particularly relevant in a competitive environment where efficiency and sustainability are key factors in attracting logistics operators and increasing the competitiveness of Spanish ports in the global market. The results of this study demonstrate that the implementation of IoV and blockchain in Spanish Ro-Ro terminals can generate significant improvements in operational efficiency, sustainability, and cost reduction. When comparing these findings with container terminals in advanced European ports, such as Rotterdam or Hamburg, it is observed that automated systems and dynamic vehicular traffic management have a similar impact on reducing waiting times and optimizing vehicular flow. However, Ro-Ro terminals face specific challenges due to the heterogeneous nature of Ro-Ro cargo, requiring customized solutions that are not always necessary in container terminals. This reinforces the need for additional studies that address the contextual differences and barriers specific to Ro-Ro terminals. The methodology applied in this study is designed to be replicable for future research in Ro-Ro terminals and similar port environments. By detailing the sources of our data and the validation process used, we provide a transparent framework that can guide other researchers interested in automation and digitalization in port operations. The inclusion of direct terminal data, combined with secondary sources and industry benchmarks, ensures that the findings presented in this study are grounded in real operational conditions. Future research could expand upon this by conducting longitudinal studies with real-time data from IoV and blockchain-enabled terminals. This would allow for more precise validation of the efficiency gains and economic benefits predicted in this research.