esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of pH and Initial Concentration on Cr(VI) Removal by FeBC  
The Cr(VI) removal efficiency of adsorbents with the initial pH ranging from 2.0 to 10.0 is presented in Figure 6a. The removal efficiency of Cr(VI) remained above 99.0% across the whole pH range from 2 to 5 and then slowly decreased with the pH increasing. The efficiency remained as high as 92.5%, even when the pH reached 10. The pH value affected the charge property of the adsorbent surface and the existing form of chromium in the solution [23]. In the acidic environment, the surface functional groups of adsorbents formed positively charged functional groups (i.e., -OH\({}^{2+}\) and -COOH\({}^{2+}\)) because of hydroxyl protonation, which facilitated the adsorption of \(\mathrm{HC{O_{4}}^{-}}\) and \(\mathrm{Cr_{2}O_{7}^{2-}}\) in the solution by electrostatic attraction [24; 25]. Electrostatic attraction became weaker at higher solution pH due to the reduction of hydroxyl protonation on the adsorbent surface and the competition of OH\({}^{-}\) in the aqueous solution, resulting in the decrease of the Cr(VI) removal efficiency. In this study, the removal efficiency of Cr(VI) remained above 90%, with the solution pH ranging from 6.0 to 10.0, indicating that there existed other effects on the Cr(VI) removal efficiency. The reason may be that the Cr(VI) oxyanions can be reduced and Cr(III) precipitated as \(\mathrm{Fe_{n}Cr_{(1-n)}(OOH)}\) and \(\mathrm{Cr(OH)_{3}}\) due to the oxidation/reduction reaction between Cr(VI) and FeBC [26; 27].  
The effect of initial concentration on the Cr(VI) removal by FeBC is shown in Figure 6b. The removal efficiency was kept at 99.0% with a concentration of Cr(VI) ranging from 2 to 10 mg/L, and decreased with the initial concentration of Cr(VI) increasing from 10 to 50 mg/L. Apparently, an adsorbent with a certain mass had a limited number of active sites and these sites progressively approached saturation as the Cr(VI) concentration increased. Thus, the saturated adsorbent cannot adsorb more Cr(VI) ions, which resulted in a decrease in removal efficiency in high concentration of Cr(VI) [28].  
Figure 5: Effect of (**a**) modifier mass ratio and (**b**) dosage of FeBC on Cr(VI) removal.
# A Novel Method for Ocean Wind Speed Detection Based on Energy Distribution of Beidou Reflections
## 3 Coastal Experiment Description  
### Experimental Setup  
The coastal experiment was conducted to observe typhoon events in the Yangjiang site during the summer of 2013 within the cooperation of the ESA-China in GNSS Reflectometry [25].  
As shown in Figure 3, the antennas were mounted directly on the roof of the building in the mountain (21.56\({}^{\circ}\)N, 111.86\({}^{\circ}\)E), with approximately 120 m altitude above the sea's surface. Both antennas are compatible with frequencies of 1575.42 MHz and 1561.098 MHz. The right-hand circular polarization antenna was used to collect direct signals from GNSS satellites with zenith-looking and the left-hand circular polarization antenna pointing toward the sea surface was used to collect reflected signals, as shown in Figure 3. To collect the weak reflected signal, the left-handed circular one has a high antenna gain of 12 dB and a narrow beam width of 38\({}^{\circ}\). With a detailed parameter shown in Table 2, a two channels GNSS receiver was employed to collect GPS and Beidou signals.  
During the experiment, Beidou signals were collected with a fixed time length of 250 s each time. In this paper, the reflected signals from the Beidou GEO4 satellite (with the elevation of 31\({}^{\circ}\) and azimuth of 108\({}^{\circ}\)) are processed. The in situ wind speed measurements from the Zhapo meteorological station (No. 59674) are used to assess the performances of the results.
## 2 Background and Related Work
### Self-Organization  
A self-organizing system consists of (simple) components that realize the system goals through repeated local interaction. Self-organization [24] often draws inspiration from natural systems and promotes _emergence_[25; 26], i.e., the creation of novel macro-level effects out of decentralized micro-level activity. Swarm intelligence exhibited by ants is probably the best-known example [27]. Such macro-effects are often connected with the ability of the system to be resilient to environmental perturbations (ranging from human in-the-loop effects to faults in physical devices or communications--which are the norm in large-scale distributed systems), or to deal with changing functional/non-functional requirements that have to be satisfied in a wide variety of conditions. Relevant application of self-organization include: study of human social behavior [28], crowd tracking and steering [15], energy demand allocation [29], terrain exploration [30], smart camera coverage [31], task allocation of autonomous vehicles [32], and ICT resources coordination [33].  
Most of these works show a trend in self-organization, shifting from natural inspiration to the identification of \"artificial\" mechanisms for engineering the collective part of adaptive systems,namely to develop so-called _collective adaptive systems_ (CASs). Research on CASs [34] study how a _collective_ (i.e., a dynamic group of \"similar\" autonomous entities--e.g., a swarm of robots) can support macro-level adaptivity and how such insights can be leveraged for engineering large-scale socio-technical systems.  
The approach studied in this paper takes somewhat inspiration from these latter approaches, looking for ways to abstract from the actual deployment of physical systems, hence aiming to apply self-organization techniques in heterogeneous deployment settings such as those relying on multiple layers of cloud/fog/edge computing.
# TSKT-ORAM: A Two-Server \(k\)-ary Tree Oblivious RAM without Homomorphic Encryption +
## 7 Comparisons
### Practical Comparisons
#### 7.2.1 Communication Cost  
CNE-ORAM reports a communication cost of more than \(40\cdot B\) per query. TSKT-ORAM incurs a lower or comparable communication cost with \(k=128\). Specifically, when \(2^{16}\leq N\leq 2^{20}\), the communication cost of TSKT-ORAM is about \(22\cdot B\) per query; when \(2^{21}\leq N\leq 2^{27}\), the cost is less than \(34\cdot B\) per query; when \(2^{28}\leq N\leq 2^{34}\), the cost is less than \(<46\cdot B\) per query.
# Using Multimodal Foundation Models for Detecting Fake Images on the Internet with Explanations
## 2 Materials and Methods
### Prompting and Experimentation  
To meet the respective model nuances, the prompting strategies and responses to the given prompts for each of the models differed slightly. Llava 1.6 was best at interpreting the prompt as a multiple-choice question. An example prompt for the Llava 1.6 model is as follows:  
Is the image real or fake? (A) Real (B) Fake  
The model outputs responses as an answer to the multiple-choice question, either A/B or (A) Real/(B) Fake.  
For OpenAI's CLIP model, since it is a binary classification model, the prompts are passed in as labels. The first label provided is \"You are an image forensics expert. This is a real image with no technical interference\". The second label provided is \"You are an image forensics expert. This is a fake image with technical interference\". From there, the softmax function is used to get the models to output the respective probabilities for each label. The label with the higher probability determines if the model classifies the image as real or fake.  
The Moondream2 model had a slightly modified prompt. It had difficulty answering the prompt as a multiple-choice question. The prompt that worked best with this model is as follows: \"Determine if the image is real or fake and answer clearly\". This prompted the model to respond in a concise statement whether it believed the image provided was real or fake. The response from this model is of the form \"This image is real/fake\", followed by a brief description of the image. The following are examples of real responses that the model output:  
1. The image is real, as it shows a horse standing in a field of tall grass.
2. The image is a fake or computer-generated (CGI) image of a city street with a traffic light.
3. The image is real, as it shows a large ship sailing across the ocean.
4. The image is a fake, as it is a 3D rendering of a cat sitting on a blue background.  
String parsing was then used to determine the prediction the LLM made and classify the prediction as real or fake.  
Google Gemini answers the multiple-choice question similarly to the Llava model. However, at first, its responses were a bit indecisive, and on some occasions, it would not come to a clear decision. Thus, we had to use contextual priming to obtain an updated prompt: \"You are an image forensics expert. Analyze the image and state concisely whether it is (A)real or (B)fake? Choice:(\"  
The following are examples of responses generated:  
1. Choice: \"\"(A)real\"\" This image appears to be a real photograph. There are no obvious signs of manipulation or digital alteration.
2. Choice: \"\"(B)fake\"\" The image appears to be generated using AI. The frog's texture and lighting are too perfect and unrealistic, and the water's reflections are artificial. The overall image lacks the natural imperfections and noise found in real-world photographs.
## 1 Introduction
The deep learning models are capable of capturing higher level feature representation while modeling complex problems. Graphical processing unit (GPU)-based systems enable faster execution of the complex processes related to weather modeling. DL models produce good results with a large volume of training data, and hybrid models still can improve the efficiency and accuracy of the prediction. The consequences of disaster have to be reduced, and it depends on the government, private sectors, communities, and many other social factors to work together [16]. It is also important to identify the disaster-related stressors that are harmful to human beings. There exists a relationship between the characteristics of the COVID-19 pandemic and the post-disaster adversarial health effects due to the impact of weather conditions. Deep learning models help to predict the COVID-19 pandemic by taking into account the dynamic weather variables to reduce the spread of disease and to take necessary preventive measures [17]. Flood security, water sufficiency, ecosystem and environmental stability, human security, and sustainable energy and development are the areas that can be aligned with the impact of severe weather events [18]. Deep belief networks take into consideration of all the weather factors that mainly affect the prediction [19]. The impact of weather prediction plays a major role in wind speed and wind power forecasting. The meteorological attributes and the dynamic weather variables can improve the forecasting accuracy. Wind speed is an important attribute for hurricane prediction, and the deep learning models such as long short term memory (LSTM), its variants, and the hybrid model can predict the wind speed accurately [20]. Considering all these inferences, Section 2 reviews the existing literature and research contributions. Section 3 describes the datasets used for hurricane intensity estimation, damage prediction, and the methodology for the classification of severe weather events are revealed. Section 4 describes the performance evaluation metrics of the prediction models. Section 5 discusses the results of the performed detailed analysis. Finally, the conclusions are drawn, and the future scopes are presented in Section 6.
# Coupled Vibration Analysis of Submerged Floating Tunnel System in Wave and Current
## 2 Modeling of the SFT System  
### Simplified SFT Model  
Figure 1 shows a typical SFT system. A simplified mathematical model is also presented here to highlight the coupled-vibration effect between the SFT tube and the cables in ocean wave and current.  
To simplify the calculation process, the SFT tube and cables are respectively regarded as a simply supported Euler-Bernoulli beam without torsional behaviors and standard tensional strings with constant parameters along their longitudinal directions. Material nonlinearity is not considered.  
As the water wave attenuates rapidly with the depth of the water [5], the impact of waves on the cables can be discounted. Additionally, the vortex shedding frequency in the wake region around the tube is generally not close to the natural frequency of the tunnel. Additionally, because the vortex-induced force on the tunnel is much smaller than the wave loads and the restoring forces from the mooring system, in most cases aiming at the hydrodynamic response of SFT [6; 8; 9; 11], only the wave forces on the tube are considered, without the vortex-induced force. Hence, we separately consider the wave force acting on the tube body, and the effect of water flow on the cables.  
It has been proved that there is a phase difference equal to \(\pi/2\) between the horizontal and the vertical wave loads, but with the same amplitudes [10; 22]. For the tunnel tube, the cables play the role of elastic supports with a certain horizontal and vertical stiffness under the small deformation hypothesis [23; 24]. This means that the vertical and horizontal vibrations of the tube are independent. Thus, the movement of the tube in the vertical direction is taken as an example and calculated in the subsequent sections.  
In addition, much research has shown that the cross-flow (CF) vibration in marine risers is dominated by vortex-induced resonance [25; 26]; hence, the cable in-line (IL) vibration is not taken into account in this study.  
In Figure 1, \(D\) is the tube diameter; \(L\) is the tunnel length; \(x=x_{li}\) is the location of the \(i^{\#}\) cable (\(i=1\), \(2\), \(\ldots\), \(n\)) along the \(X\) direction; \(l_{i}\) is the cable length; \(\alpha_{i}\) is the inclined angle of the cable; \(l_{li}\) is the cable spacing; \(X_{i}\), \(Y_{i}\) are the local axes to describe the motion of cables; \(v_{i}(x_{i}\), \(t)\) is the displacement of the \(i^{\#}\) cable in cross-flow direction; \(z(x,\,t)\) is the vertical displacement of the tube; \(w_{y}\), \(w_{z}\) are the horizontal and vertical velocities of the wave particles at the center of the tube, respectively.
# Internet of Drones: Routing Algorithms, Techniques and Challenges
## 6 Conclusions  
To conclude, it is necessary that, while in the process of devising a network route, the protocol architecture needs to be understood first. Next, it should be considered that all possible security breaches are considered. A strongly connected network needs no bit errors, therefore proper equipment testing and verification should be done before purchasing the equipment. Similar concerns are of timing, throughput, and delay. The battery life can be increased by techniques like green solutions. The greater the mobility of a UAV, the more data it can store, process, and transfer. The processing task is mostly left to the base station, and the UAV acts as the aggregate data transfer machine. The major concerns of UAWSN are lossless data delivery and seamless mobility. Therefore, these become the main concerns in the formation of UAV-based wireless sensor networks.  
In this paper we have discussed the various routing protocols for WSN, the methods and and techniques for efficient routing, routing protocols, and the challenges faced by the UAWSN industry. Some of the main challenges related to WSNs are propagation delay,packet delivery loss, timing and jitter, and security breaches. All of these problems have been addressed in a heuristic approach. The routing algorithms also need to be defined for a WSN, some of which were explained in this survey. It has been seen that the routing protocols for mobile UAV-WSNs should be made robust and able to work in 3D scenarios. An analysis of various routing challenges and techniques was also given in this paper.  
Challenges are faced by every mechanism in wireless data transmission, and UAWSNs are not entirely safe. Techniques and methods have been developed as a result of detailed surveys, because of which UAWSNs have a promising future in academia as well as industry. It has been observed that the UAWSN networks can conform to routing protocols as they perform the different tasks as required by the applications. However, WSNs are also prone to security attacks like any other system, and proper authentication must be used in them.  
Conceptualization, S.K.H. and A.N.; methodology, S.K.H. and A.N.; formal analysis, M.A.J. and A.N.; writing--original draft preparation, A.N., S.K.H. and M.A.J.; writing--review and editing, A.N., A.J. and S.B.; supervision, A.N., M.A.J., A.J. and S.W.K.; project administration, A.N., A.J., S.W.K. and M.A.J.; funding acquisition, S.W.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1A6A1A03039493) and in part by the NRF grant funded by the Korea government (MSIT) (NRF-2022R1A2C1004401).  
Not Applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 5 Simulation Results
Moreover, in order to more clearly demonstrate the superiority of the algorithm proposed in this paper compared with other algorithms, the energy efficiency values of different algorithms under different number of PDs are shown in Table 4. When the number of devices is 50, the total energy efficiency of the algorithm proposed in this paper is about 1, 5 and 8 times higher than that of the equal power allocation algorithm, random power allocation algorithm and random device association algorithm, respectively. Figure 8 shows the relationship between total energy efficiency and the maximum power \(P_{\text{max}}\). It can be seen that for small \(P_{\text{max}}\), the total energy efficiency of the JDAPCOO algorithm proposed in this paper increases with the increase of \(P_{\text{max}}\), indicating that a higher transmission power threshold is needed to achieve the overall energy efficiency optimization. However, when \(P_{\text{max}}\) reaches a certain value, the total energy efficiency increases slowly and gradually converges, and the total energy efficiency of the other two algorithms is always lower than that of the proposed algorithm. Therefore, by comparing the results of the three algorithms, it can be concluded that the JDAPCOO algorithm proposed in this paper has better performance. Figure 7: The relationship between the total system energy efficiency and the number of PDs.
# Internet of Drones: Routing Algorithms, Techniques and Challenges
## 4 Routing Algorithms
### IoD Data Security
#### 4.5.2 Deterministic Mobility Data Collection  
For a deterministic mobility of UAV, the system allows the static and mobile nodes to have a specific time for initiation of data transfer. Because of a strict schedule, the nodes know where mobile nodes will be in a given amount of time. For example, in [60], the mobile nodes are studied as part of transportation shuttles visiting sensor notes as per schedule. Sensor nodes calculate the \"wake up\" time by this method and start communicating to transfer information. The authors in [61] have evolved a scheme called multiple enhanced specified-deployed sub-sinks (MESS) for WSNs. This method consists of multiple sub-sinks for data collection. The sub-sinks are created in the coverage area, which provides data collection in a more effective way as compared to a single sink. Wireless high speed routing (Whisper) [62] was also proposed for forwarding of data to a high speed sink. The scheme is based on the assumption that all node locations of sinks and sensor nodes are known by each other along with the displacement from the sink. In addition to this, the nodes have their own, their neighbours', and the sink's location information. If the sensor nodes cannot directly send a message to the sink, they transfer it to a \"meeting point\", which is calculated based on experienced delays in transmission of the message along with the locations of node, its neighbours, and the estimate of sink's location [56].
## 3 Results  
### Temporal Backscattering Thresholds of Permanent Water  
Among the 402 scenes used in this study, only 28,748 scenes had coverage by water bodies. Therefore, the maximum and minimum values and the average of the permanent water pixels in all images were statistically obtained (Figure 6). The distribution trends of the maximum and minimum of VH and VV polarization were similar to a parallel distribution (Figure 6a). In general, the minimum and maximum values of the VV polarization were greater than the VH polarization. Figure 6b shows the average values of the maximum and minimum of VH and VV polarization. It can be seen that the average maximum and minimum of VH were both less than the polarization of VV. The difference between the maximum and minimum of VH was 33.68 dB, which was greater than the 30.85 dB of VV polarization, while the first standard deviation of the maximum and minimum of VH (2.54 dB and 4.02 dB) were both smaller than the VV polarization (3.59 dB and 7.48 dB). The \(\sigma^{0}_{max\_VV}\), and \(\sigma^{0}_{min\_VV}\) values were finally calculated with \(-\)15.05 dB and \(-\)55.29 dB, respectively, while \(\sigma^{0}_{max\_VH}\) and \(\sigma^{0}_{min\_VH}\) were obtained with \(-\)7.69 dB and \(-\)49.60 dB. These results indicate that the range of the final maximum and minimum of VV were larger than that of VH. At the same time, VV polarization also had a larger standard deviation and volatility, and was sensitive to changes in water during the whole year, which is beneficial to the extraction of water information. Therefore, \(\sigma^{0}_{max\_VV}\) and \(\sigma^{0}_{min\_VV}\) were selected as the threshold range for the subsequent extraction of potential rice in this study.  
Figure 6: \(Cont.\)
# Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing
## 3 Results and Analysis
### Analysis of the Characteristics of Urban Heat Island Changes
#### 3.2.3 Annual Timescale  
The annual average intensity of SUHI and CLHI during the four transit times from 2003 to 2020 is shown in Figure 7. The results show that (1) the overall intensity of SUHI and CLHI shows a slight downward trend over time, indicating that the urban heat island phenomenon is slowly diminishing each year. (2) Unlike SUHI, CLHI significantly differs between day and night. The average annual average heat island intensity is close to 2 \({}^{\circ}\)C at night and about 1 \({}^{\circ}\)C during the day. (3) From 2003 to 2020, the annual average centers of SUHI and CLHI moved eastward over time, and the north-south changes were relatively insignificant.  
Figure 5: Distribution map of monthly average intensity of (**a**) SUHI and (**b**) CLHI at the times of four border crossings from 2003 to 2020.  
Figure 6: Distribution map of monthly average area of (**a**) SUHI and (**b**) CLHI at the times of four border crossings from 2003 to 2020.  
The annual average footprint ellipse area and direction of the SUHI and CLHI from 2003 to 2020 are shown in Figure 8. The results show that the annual average area of the SUHI and CLHI has a trend of steadily increasing over time. On an annual time scale, the direction values of the SUHI and CLHI show an increasing trend over time; that is, the heat island direction slowly deflects to the northeast-southwest direction each year.