esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Security Analysis
### Informal Security Analysis
GCS then verifies \(RP_{ID_{O}}\) from X against its stored \(RPMD_{ID_{O}}\). This confirms OD's knowledge of the correct \(RP_{ID_{O}}\) and \(P_{ID_{O}}\), and its freshness. * 4. OD receives \(M2=\{PIN^{\prime}\}_{KS^{\prime}}\). OD attempts to decrypt using its locally derived KS. By the Message-Meaning Rule, \(OD\mid\equiv GCS\mid\sim PIN^{\prime}\). * 5. Since OD believes \(\#(PIN)\) (as it generated PIN freshly) and \(PIN^{\prime}\) matches its original PIN, by the Nonce Verification Rule, \(OD\mid\equiv GCS\mid\equiv PIN\). This confirms that the GCS successfully decrypted the 'OTAC' using the shared key KS and thus possesses the correct PIN, thereby authenticating the GCS to the OD. * 6. Both OD and GCS can now believe KS is a good shared key, achieving mutual authentication: \(OD\mid\equiv OD\stackrel{{ KS}}{{\leftrightarrow}} GCS\) and \(GCS\mid\equiv GCS\stackrel{{ KS}}{{\leftrightarrow}} OD\). The BAN logic analysis suggests successful mutual authentication and shared key belief establishment under its assumptions.
# Physical Layer Security for Military IoT Links Using MIMO-Beamforming at 60 GHz
## 3 Results and Discussions
Similarly, in this paper, MIMO-beamforming designs that seek to maximize PHY security and privacy are explored for the secure 60 GHz mmWave band. The performance metrics explored for measuring communication secrecy in this paper is the TX-RX range for which eavesdropping is no longer possible. Next, we examine the range of secure communications between TX and RX that is achievable when using the beamforming arrays shown in Figures 5-7. We assume typical 5G user equipment (UE) with a transmitted power of 23 dBm (or 200 mW) [17], a base station (BS) with a transmitted power of 43 dBm (or 20 W) [18], and a receiver sensitivity \(R_{\times}\) of \(-100\) dBm (or 0.1 pW), considered the same for both the BS and UE, for simplicity. Thus \(0.001R_{\times}=0.001\) pW = \(-120\) dBm. The Stanford University interim (SUI) model was assumed for the path loss model (terrain type A or hilly/dense vegetation) [18], and water vapor absorption of 14.6 dB/km for the 60 GHz band shown in Figure 1 was added to the terrain-specific path loss. Figure 8 displays the received power as a function of the TX-RX separation distance, when a UE transmits 23 dBm (200 mW) power at 60 GHz using the antenna arrays in Figures 5-7 corresponding to antenna gains of 18.3, 22.3, and 26.2, respectively. It can be observed from this figure that the useful transmission range for \(R_{\times}\) of \(-100\) dBm is roughly 1.5 km, while the maximum intercept range for \(0.01R_{\times}\) of \(-120\) dBm is roughly 2.5 km. Figure 9 displays the received power as a function of the TX-RX separation distance, when a BS transmits 43 dBm (20 W) power at 60 GHz using the antenna arrays in Figures 5-7 corresponding to antenna gains of 18.3, 22.3, and 26.2, respectively. It can be observed from this figure that the useful transmission range for \(R_{\times}\) of \(-100\) dBm is roughly 2.5 km, while the maximum intercept range for \(0.01R_{\times}\) of \(-120\) dBm is roughly 4 km. These results are important technical contributions. They provide useful guides on the range of secure transmissions that are possible when deploying IoT devices employing beamforming arrays at 60 GHz, assuming that one is operating within contemporary TX and RX parameters stipulated in 5G cellular networks. Figure 7: Directional beams produced by uniform linear arrays (ULA) operating at 60 GHz; 400 array elements with \(\lambda/2\)-spacing; 400 \(\times\) 1 steering vector \(A\) = [1 1 1... 1].
## 2 Data Acquisition and System Design  
### Collection and Transmission of Citrus Fruit Data Set  
The scene of UAV shooting citrus fruit image is shown in Figure 1. To obtain citrus images in a mountain orchard environment, on the one hand, we need some necessary hardware equipment, such as a camera, power supply, visual display screen, and edge computing equipment, to recognize citrus images. On the other hand, while obtaining the above information, the rational use of UAV technology can help fruit farmers monitor citrus fruits in an all-around way, improve orchard management efficiency, and assist in yield estimation. The UAV equipment used in this paper is Mavic air 2, it is produced by Dajiang and released on 28 April 2020, which can take high-pixel photos and fly at low altitudes for one hour. The main task of the UAV is to collect image data about 1 m above the citrus fruit tree and upload the image to the edge computer device. The image is further processed by using the target detection algorithm to achieve the real-time detection effect of citrus fruit.  
UAV capture image has high security and flexibility. The change of environment has little impact on its image acquisition, and the image acquisition cycle is also short, which improves work efficiency and reduces the labor cost. Figure 2 depicts the process of capturing citrus images using UAV. First, we used the camera carried on the UAV to capture the citrus image, transmit it to the edge computing device through the wireless network. Then, we used the deployed target detection algorithm to detect the citrus and visualize it through the display device.  
Figure 1: UAV device for taking citrus images. The citrus tree is about 2 m high. We used UAV to collect images in an all-round way at about 1 m around the fruit tree.  
We used the self-made citrus data set to train the model provided in this paper and collected data in the citrus orchard in Yizhang County, Hunan Province, China. A total of 1800 citrus fruit images taken by mobile camera and UAV low altitude aerial photography, about 1 m away from citrus fruit trees, and annotated manually with Labellmg. The dataset system is used as a format for coco datasets to standardize training data. We selected 400 aerial images as the test set, 1300 citrus images as the training set, and 100 as the verification set. We note that the problem of leaf occlusion and citrus fruit occlusion significantly impacts the recognition effect. The test set is manually divided into two parts: A and B. A test set contains 250 slightly occluded citrus fruit images, and the B test set includes 150 severely occluded citrus fruit images.
## 4 Mathematical Modeling
### System Dynamics with Lead Time  
In order to incorporate the lead time for each echelon of the four nodes of the SC, the set of coupled ordinary differential equations are:  
\[\frac{dI_{1}}{dt}=\varphi_{i}-\frac{\varphi_{1}}{C_{1}}I_{1}\big{(}t-\hat{ \theta}_{1}\big{)} \tag{18}\]  
\[\frac{dI_{2}}{dt}=\varphi_{1}\frac{I_{1}}{C_{1}}-\frac{\varphi_{2}}{C_{2}}I_{2} \big{(}t-\hat{\theta}_{2}\big{)} \tag{19}\]  
\[\frac{dI_{3}}{dt}=\varphi_{2}\frac{I_{2}}{C_{2}}-\frac{\varphi_{3}}{C_{3}}I_{3} \big{(}t-\hat{\theta}_{3}\big{)} \tag{20}\]  
\[\frac{dI_{4}}{dt}=\varphi_{3}\frac{I_{3}}{C_{3}}-\frac{\varphi_{4}}{C_{4}}I_{4 }\big{(}t-\hat{\theta}_{4}\big{)} \tag{21}\]  
Consider the fractional lead time, \(\hat{\theta}_{i}\), which is a ratio between the lead time and the total lead time at each stage of the supply chain, as:  
\[\hat{\theta}_{1}=\frac{\theta_{j}}{\sum_{i}^{n}\theta_{j}} \tag{22}\]  
Expanding the delayed-state inventory level, \(I_{j}\big{(}t-\hat{\theta}_{j}\big{)}\), in terms of a Taylor series, we have:  
\[I_{j}\big{(}t-\hat{\theta}_{j}\big{)}=I_{j}-\hat{\theta}_{j}\dot{I}_{j} \tag{23}\]  
Applying Equation (23) to Equations (18)-(21):  
\[\frac{dI_{1}}{dt}=\bigg{(}1-\frac{\varphi_{1}}{C_{1}}\hat{\theta}_{1}\bigg{)} ^{-1}\bigg{\{}\varphi_{i}-\varphi_{1}\frac{I_{1}}{C_{1}}\bigg{\}} \tag{24}\]  
\[\frac{dI_{2}}{dt}=\bigg{(}1-\frac{\varphi_{2}}{C_{2}}\hat{\theta}_{2}\bigg{)} ^{-1}\bigg{\{}\varphi_{1}\frac{I_{1}}{C_{1}}-\varphi_{2}\frac{I_{2}}{C_{2}} \bigg{\}} \tag{25}\]  
\[\frac{dI_{3}}{dt}=\bigg{(}1-\frac{\varphi_{3}}{C_{3}}\hat{\theta}_{3}\bigg{)} ^{-1}\bigg{\{}\varphi_{2}\frac{I_{2}}{C_{2}}-\varphi_{3}\frac{I_{3}}{C_{3}} \bigg{\}} \tag{26}\]  
\[\frac{dI_{4}}{dt}=\bigg{(}1-\frac{\varphi_{4}}{C_{4}}\hat{\theta}_{4}\bigg{)} ^{-1}\bigg{\{}\varphi_{3}\frac{I_{3}}{C_{3}}-\varphi_{4}\frac{I_{4}}{C_{4}} \bigg{\}} \tag{27}\]
# Magnetic Solid-Phase Extraction of Organic Compounds Based on Graphene Oxide Nanocomposites
## 2 Preparation and Applications of GO for the MSPE of Organic Compounds  
### Nanocomposites of GO with Fe\({}_{3}\)O\({}_{4}\) Nanoparticles  
Due to its high surface area and its superparamagnetic properties GO/Fe\({}_{3}\)O\({}_{4}\) has been employed for the extraction of a wide variety of organic compounds from various samples. The surface of magnetic graphene oxide is rich in hydroxyl and carboxyl groups, which assist the interaction between the sorbent and the target analytes through strong \(\pi\)-\(\pi\) stacking, hydrophobic interaction as well as hydrogen bonding [26; 27; 28; 29; 30; 31]. Figure 1 shows the structure of graphite, graphene oxide, reduced graphene oxide, and magnetic graphene oxide. The one-step co-precipitation approach is the most common synthetic route for the preparation of magnetic GO. In this approach, graphene oxide is dispersed in water. Subsequently, salts of Fe\({}^{2+}\) (e.g., ferrous chloride) and of Fe\({}^{3+}\) (e.g., ferric chloride) are added, the mixture is heated and ammonium hydroxide is slowly added and the magnetite nanoparticles are formed [26]. Another common synthetic procedure is the solvothermal approach, in which GO is added in a dispersion of a Fe\({}^{3+}\) salt (e.g., ferric chloride hexahydrate) and sodium acetate, under vigorously stirring for 30 min at room temperature [33; 40]. The dispersion is transferred in an autoclave and heated under reflux for a specific time span. The ferric salt is added as the iron source, while the sodium acetate assists both the electrostatic stabilization of the magnetic nanoparticles onto GO in order to prevent agglomeration of the particles and the reduction of Fe\({}^{3+}\) to Fe\({}_{3}\)O\({}_{4}\). Magnetic GO can be prepared by mixing GO and Fe\({}_{3}\)O\({}_{4}\) under ultrasonic irradiation, stirring or mechanical shaking. In this case, Fe\({}_{3}\)O\({}_{4}\) nanoparticles are dispersed in nitric acid solution in an ultrasonic bath for 30 min in order to generate a positively charged surface. Graphene oxide is dispersed in deionized water in order to generate a negatively charged surface. Subsequently, the dispersions of Fe\({}_{3}\)O\({}_{4}\) and GO are mixed, the pH of the mixture is adjusted to a desired value and the mixture is subjected to vigorous magnetic stirring, mechanical shaking or ultrasonic radiation for a certain time span (e.g., 5 h) [41]. Finally, the GO/Fe\({}_{3}\)O\({}_{4}\) material can be prepared after hydroxylation of Fe\({}_{3}\)O\({}_{4}\) nanoparticles by mixing with GO and dimethyl sulfoxide. After ultrasonic treatment of the mixture for a certain time at room temperature, the desired material is obtained [42]. Organic molecules of various chemical groups have been extracted with GO/Fe\({}_{3}\)O\({}_{4}\). Table 1 summarizes the applications of GO/Fe\({}_{3}\)O\({}_{4}\) for the MSPE of organic compounds from biological, environmental and food samples. Figure 1: Structure of graphite, graphene oxide, reduced graphene oxide, and magnetic graphene oxide.
## 3 Survey of Techniques for Updating
### Dissemination
#### 3.1.4 Multicast Deluge (MDeluge)  
MDeluge [31] disseminates both binary code and Mate capsules (see 3.3.1) over a spanning tree to a designated subset of a WSN. Unlike Deluge, every node does not have to hold every update. Initially gateways (_micro servers_) act as source nodes for a new version. Source nodes broadcast a code version message for groups of sensor nodes throughout the network, each relay node records the upstream link. Designated sink nodes reply over their upstream link with a code request message, which is sent via upstream links to the source; relay nodes record the route to the sinks. This sets up a bi-directional tree between the source and all the sinks. The source node then propagates the code image in segments to the closest sinks; they in turn become source nodes for further dissemination. Nodes not involved in the update only keep minimal routing information to relay the messages. Sink nodes only relay code request messages if they do not already have the code--otherwise they become a source and return the code data. Segments are sent in multiple packets, and loss detection is done at the sink nodes using NACKs. Nodes maintain two routing tables: one to the root node, and one to the sinks. Image IDs and image version numbers are used to identify each update.  
The support for updating groups of nodes and for multiple update gateways are key features of MDeluge, which are likely to be of direct applicability to autonomous updates. It is likely that a more sophisticated ID field would be required.
## 3 Results
### Characterization Methods
#### 3.3.3 Raman Analysis  
Raman spectra of RPNS and thermally activated sorbents (200-500 \({}^{\circ}\)C) are presented in Figure 5. The spectrum of RPNS and all the thermally activated samples show a clear D- band at 1351 cm  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Code** & **N (\%)** & **C (\%)** & **H (\%)** \\ \hline RPNS & 0.559 & 45.720 & 6.059 \\ PNS250 & 0.595 & 56.313 & 5.369 \\ PNS300 & 0.201 & 66.041 & 4.801 \\ PNS350 & 0.251 & 73.501 & 4.339 \\ PNS400 & 0.204 & 68.184 & 3.948 \\ PNS450 & 0.288 & 91.874 & 3.620 \\ PNS500 & 0.447 & 85.494 & 3.155 \\ \hline \hline \end{tabular}
\end{table}
Table 6: CHN analysis of raw pistachio nutshells (RPNS) and thermally treated PNS (250–500 \({}^{\circ}\)C).  
Figure 4: Thermal gravimetric analyzer (TGA) analysis of the raw pistachio nutshells sample at a temperature range 50–800 \({}^{\circ}\)C.  
and a clear G- band at 1585 cm\({}^{-1}\). These two peaks are distinctive in the spectra of all carbonaceous materials and their arrangement is the same as graphene oxide. The presence of these two peaks in the RPNS could be attributed to and as previously mentioned to a prior roasting process at a high temperature which results in the formation of carbonaceous material.  
As previously reported, the characteristics of the carbon lattice including defects and sizes are presented by the D-band, however, it does not represent the chemical structure of the carbon materials. The wavelength of the laser beam changes the position of the D-band but the G-band identifies the stretching of C-C for the \(sp^{2}\) system. Furthermore, the intensity ratio \(I_{D}\): \(I_{G}\) has increased from RPNS (0.61) to PNS500 (0.65). This behavior occurred due to the thermal treatment process which increases the defect states in the \(sp^{2}\) plane of carbon. The obtained data confirm the formation of activated carbon with higher defects after thermal treatment compared to the roasted one.
# Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
## 3 Methods
### Software Setup  
The model development incorporates the workflow pipeline and checkpoint design patterns to ensure model reproducibility and flexibility. The workflow pipeline design pattern ensures model reproducibility, while the checkpoint design pattern reduces overfitting by saving the model instance with the lowest validation loss. The preprocessing layer removes low-correlation features. For short-term memory-based prediction models, the StatsModel API is used for exponential smoothing to maintain stability in the training data. In models built on quantum architecture, the dense angle encoding proposed by [PERSON] and [PERSON] is applied with Cirq [36].  
Python's TensorFlow and TensorFlow Quantum are used to build classical and quantum models. The checkpoint design pattern is used to prioritize fault tolerance and flexibility for all models. After each training epoch, the validation loss is measured. If the validation loss is lower than the previous epoch, a snapshot of the model state is saved for future training recovery. The patience time, which is the time to interrupt the training process if the validation loss does not improve, is set to 100 epochs. This ensures that the algorithm has enough time to avoid potential local minima during optimization. The data is continuously validated to maintain the consistent dimensionality of the dataset and the separation between training and testing data.  
Finally, the validation submodule initializes with the saved model and obtains the key metrics described in the methodology section. These metrics, along with the generated predictions, are saved in a subfolder.
## 4 Discussion
### Influence of Particle Size Ratio  
Particle size ratio: the ratio of the maximum particle size to the minimum particle size in the model. Rocks in nature have different structures, even if the rocks of the same lithology have different particle sizes, so it is necessary to study the effect of particle size on the thermodynamic properties of the model. The average particle size in different models remains constant, while the particle size ratio is changed to be 1.5, 2.5, 3.5, 4.5, and 5, respectively. As shown in Figure 13b, the maximum peak stress is about 150 MPa and the minimum peak stress is about 120 MPa; the peak strength shows a trend of decreasing first and then increasing. The particle size ratio has a significant effect on the peak strength. Similarly, the average temperature change also decreases first and then increases (see Figure 13a). It can also be concluded that the mechanical properties of the model affect the thermal properties.  
Figure 11: Effect of different friction coefficients on average temperature change (**a**); peak stress and the maximum value of average temperature change (**b**).  
Figure 12: Effect of different coefficients of thermal expansion on average temperature change.  
In summary, different parameters have different influences on the thermal and mechanical properties of the model. More consistent and comprehensive research should be carried out in the future to consider more influencing factors, and a better understanding will be obtained by comparing more laboratory experiments on different rock types.
## 3 Results
### Spatial and Temporal Distribution of NPP/NEP for Different Vegetation Types  
The mean NPP for Central Asia in 1979-2011 was 281.79 gC m\({}^{-2}\) yr\({}^{-1}\). NPP differed notably among the four vegetation types. The cropland had the highest NPP compared with other types, with a value of 646.25 gC m\({}^{-2}\) yr\({}^{-1}\). The forest had the second highest NPP, which was significantly higher than those of the shrubland and grassland. The dominant land cover types, that is, shrubland and grassland, presented the lowest NPP at approximately 112.50 gC m\({}^{-2}\) yr\({}^{-1}\) and 90.52 gC m\({}^{-2}\) yr\({}^{-1}\), respectively (Figure 5a).  
The results of NEP for the four vegetation types differed from NPP changes (Figure 5b). Grassland released carbon by by \(-\)0.21 gC m\({}^{-2}\) yr\({}^{-1}\), whereas the other three types were carbon sinks. Forest and shrub land had the highest carbon sequestration potential, and their NEP were 1.86 gC m\({}^{-2}\) yr\({}^{-1}\) and 1.91 gC m\({}^{-2}\) yr\({}^{-1}\), respectively. NEP for cropland was 0.86 gC m\({}^{-2}\) yr\({}^{-1}\), which presented as a weak carbon sink.  
Figure 4: Spatial variation of annual mean temperature (**a**) and precipitation (**c**) in Central Asia; trends of annual mean temperature (**b**) and precipitation (**d**) change from 1979 to 2011.
## 6 Open Challenges, Future Prospects and Research Directions
### Optimizing Spatial Modulation with AI and ML
#### 6.4.2 Adaptive Transmission  
Future research on adaptive transmission schemes for FSO communication systems promises to bring substantial advancements to the field. In this context, adaptive transmission schemes in FSO communication systems are crucial for overcoming the inherent challenges posed by atmospheric conditions and ensuring robust, high-quality data transmission. So, as FSO technology advances, several promising directions for future research and development can significantly advance adaptive transmission techniques, enhancing their reliability, efficiency, and overall performance.
# Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager
## 2 Materials and Methods
### Data Sources  
#### 2.2.1 Sustainable Development Goal Satellite 1 Thermal Infrared Imager Product  
SDSGAT-1 was successfully launched as the world's first space science satellite in support of the Sustainable Development Goals (SDGs) on 5 November 2021 [28]. The satellite carries a 30 m resolution TIS, a 10 m resolution multi-spectral imager (MII), and a 10/40 m resolution microlight image (GIU) [34]. It has an imaging width of 300 km and a temporal resolution of 11 days. The TIS image has 3 thermal infrared bands, namely, Band 1 (8.0-10.5 \(\upmu\)m), Band 2 (10.60-11.19 \(\upmu\)m), and Band 3 (10.50-12.51 \(\upmu\)m). Three thermal bands provide a substantial advantage over two thermal bands in that the three-channel split-window technique allows for more precision in land surface temperature retrieval and environmental monitoring [34]. The TIS, combined with MII and GIU data from SDSGAT-1, can provide valuable information that can be utilized to depict human activities and urbanization. The data can be utilized to support both the \"Belt and Road Initiative\" and programs promoting sustainable development [35].  
SDSGAT-1 flies in a sun-synchronous orbit at a 505 km altitude and an inclination angle of 97.5\({}^{\circ}\). It can gather several types of datasets through its synergistic observations both during the day and at night by operating in the \"TIS + MII\", \"TIS + GI\", and single-payload observing modes in orbit. Therefore, TIS images contain data from the day and night. The daytime surface thermal radiation mainly contains the reflection of shortwave radiation from the sun and the longwave radiation emitted by the surface object itself, and it is influenced by the type of surface coverage. The night TIS data were adopted to depict the thermal radiation emitted from one object. Therefore, cloudless SDSGAT-1 TIS data on the evening of 15 June 2022 were utilized from [[http://124.16.184.48:6008/home](http://124.16.184.48:6008/home)]([http://124.16.184.48:6008/home](http://124.16.184.48:6008/home)) (accessed on 27 August 2022).
# Is It Possible to Restore a Heavily Polluted, Shallow, Urban Lake?
## 3 Results
### Density and Hydration of the Analyzed Bottom Sediment  
Spatially, the bottom sediments of Karczemne Lake were characterized by a heterogeneous structure and variations in thickness and degree of consolidation.  
The maximum thickness of modern sediments was 1.8 m. The thickness of the deposit is related to the long-term supply of sewage, which results in clear shallowing of the lake bed. Littoral deposits were characterized by the smallest thicknesses, with an average of 60 cm.  
The deposits with the largest thickness occurred in the region of the maximum depth of the lake and in the places of inflow of municipal and industrial sewage.  
The bottom sediments of Karczemne Lake had a loose structure that was susceptible to resuspension (agitation). The sediment mean hydration in the 30 cm thick layer a fluctuated from approximately 94-95% (Figure 6).  
More dense and compact structures were observed only in the lower layers of the cores, especially in the littoral zone (Figure 7). The boundaries of structures within the cores were not clearly marked. The floor layer, constituting the native soil, was gyttja, i.e., organic and mineral sediment comprised mainly of the remains of allochthonous organisms with an admixture of sands. The mean density of the deposits of Karczemne Lake ranged from 0.99 to 1.45 g cm\({}^{-3}\), and on average, it was 1.05 g cm\({}^{-3}\) (Figure 7).  
Figure 5: Mean content of total nitrogen in the interstitial water of Karczemne Lake (mg N L\({}^{-1}\)).