esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries
### Security Goals  
In light of these challenges, we determined four security objectives that necessitate careful consideration.  
_Secure search._ Only authorized workers (designated as \(dr\)) have access to the environmental data of \(dg_{i}\) and can perform \(k\) searches. The opponent cannot deduce any other private information from the trapdoor or distinguish between the \(k\)s supplied in the trapdoor and the \(k\) ciphertexts.  
_Dual verification._ Possible search result manipulation could arise from \(dg_{i}\) and the FS working together. In order to detect any malicious behavior from the FS or \(dg_{i}\), the suggested approach needs to validate the search results. Initially, the \(dr\) might be permitted to confirm the correctness of ciphertexts that contain environmental information retrieved from the FS. The \(dr\) can access the decrypted environmental data and carry out additional checks to verify its accuracy following a successful verification.  
_Accountability and access control._ Only one \(k\) from the specified \(k\) collection shows up in the \(dr\) search trapdoor, thanks to accountability. Once the \(dg_{i}\) has found a valid \(k\) from the \(k\) collection, it can call the \(SC\) to create a trapdoor for the matching \(dr\). Regarding access control, a \(k\) search cannot be carried out by an attacker or an unauthorized \(dr\) without \(dg_{i}\)'s permission. Stated differently, they are not allowed to look for the \(k\) in files that have nothing to do with the known trapdoor. Additionally, attempting to construct a new trapdoor for a different collection of files than the one that is currently in place is strictly prohibited.  
_Requester privacy._ By utilizing the request tokens from the \(dr\) or employing search trapdoors, both the FS and the \(dg_{i}\) can access the term. The two \(k\) ciphertexts remain indistinguishable to any potential adversary. This ensures that the privacy of the \(dr\) is consistently safeguarded.
# Ext-LOUDS: A Space Efficient Extended LOUDS Index for Superset Query
## 2 Problem Definition and Necessary Preliminaries  
### Set Superset Query  
A database D consists of a collection of sets, where each set \(S\in D\) comprises a set of elements drawn from a finite universe U. Each set \(S\) has a unique set identifier (SID). We assume that S is not multiset, that is to say, there is no duplicate element in R. We use \(|\)D\(|\) to represent the number of sets in D, \(|\)U\(|\) to represent the number of distinct elements in D and \(|\)S\(|\) to represent the number of elements in \(S\).  
Now we formally define Set Superset Query as shown in Definition 1.  
**Definition 1**.: _(Set Superset Query). Given a query set \(Q\) and a database \(D\), find all sets \(S\in D\) satisfying \(Q\supseteq S\)._
## 4 The Proposed ECPSO-MTP
### Communication Rules
#### 4.3.2 Backward Communication Rules  
Under the hierarchical structure of proposed ECPSO-MTP, backward communication rules based on the backward direction of information are applied from \(\sigma_{o}\) (\(1\leq o\leq m\)) to \(\sigma_{m+2}\). The backward communication rules from \(\sigma_{o}\) to \(\sigma_{m+2}\) are given in the form, \(R^{{}^{\prime}}_{o,m+2}:\ \big{[}\lambda\big{|}_{q}\big{]}_{o}\Big{[}u_{m+2}^{ \mathit{gbest}}(t)\Big{]}_{m+2}\rightarrow\Big{[}u_{o}^{\mathit{gbest}}(t) \Big{]}_{o}\big{[}\lambda\big{]}_{m+2}\), for \(1\leq o\leq m\). It only can be applied on a configuration if both \(q\) and \(u_{m+2}^{\mathit{gbest}}(t)\) are respectively present in \(\sigma_{o}\) and \(\sigma_{m+2}\) at \(t\), noted that \(q\
eq p\). When this communication rule associated with \(\sigma_{o}\) and \(\sigma_{m+2}\) is applied, under the presence of \(q\) in \(\sigma_{o}\), \(u_{m+2}^{\mathit{gbest}}(t)\) in \(\sigma_{m+2}\) is simultaneously sent to \(\sigma_{o}\) and evolves into \(u_{o}^{\mathit{gbest}}(t)\).
# Theory of AdaDelSPGD Algorithm in Fiber Laser-Phased Array Multiplex Communication Systems
## 3 Experiment
### Experiments on Different Communication Modulation Frequencies  
When a continuous laser and a laser with different frequencies of communication signals were used as the light source, the experimental results of the three-channel fiber laser-phased array multiplex communication system were as presented in Figure 5. The interference fringes of the continuous laser are shown in Figure 5a; when the communication modulation frequency of each channel was 200 MHz and 500 MHz, respectively, the interference fringes were as shown in Figure 5b,c.  
The streak contrast and main lobe energy concentration were selected as the evaluation criteria for interference fringe quality. (Streak contrast \(V=(I_{\text{max}}-I_{\text{min}})/(I_{\text{max}}+I_{\text{min}})\), in which \(I_{\text{max}}\) is the maximum value of far-field beam intensity, and \(I_{\text{min}}\) is the minimum value near the spot of \(I_{\text{max}}\).) The concentration of the main lobe energy was obtained by dividing  
Figure 5: Images of interference fringes. (**a**) Continuous laser; (**b**) each way with 200 MHz communication; (**c**) each way with 500 MHz communication.  
the sum of the energy of the main lobe by the sum of the energy of all the interference fringes after taking 10,000 points spaced equally by the interference pattern.  
The streak contrast and main lobe energy concentration of interference fringes modulated by different communication modulation frequencies are shown in Table 2. It could be concluded that the beam quality decreased with the addition of the laser to the communication modulation, and the higher the frequency of communication modulation, the worse the beam quality.
## 3 Methods and Materials
### Biomass and Operating Data
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Biomass**} & \multicolumn{4}{c}{**Ultimate Analysis (\% wt)**} & \multicolumn{4}{c}{**Proximate Analysis (\% wt)**} & \multirow{2}{*}{**HHV**} & \multirow{2}{*}{**Case**} \\ \cline{2-2} \cline{5-12}  & **C** & **H** & **O** & **N** & **S** & **VM** & **FC** & **Ash** & **MS** & **(MJ/kg)** & **Number** & **Reference** \\ \hline Municipal solid waste & 50.40 & 5.70 & 41.70 & 2.20 & 0.00 & 75.80 & 17.00 & 1.30 & 5.90 & 19.939 & 5 & [38] \\ Municipal solid waste & 58.10 & 6.50 & 31.40 & 2.60 & 0.60 & 62.80 & 24.80 & 12.40 & 11.7 & 24.5 & 2 & [39] \\ Municipal solid waste & 47.26 & 6.70 & 45.54 & 0.49 & 0.01 & 65.78 & 20.19 & 4.21 & 9.82 & 19.589 & 2 & [40] \\ Palm kernel oil & 49.80 & 6.50 & 38.20 & 0.80 & 0.12 & N/A & N/A & 8.40 & 11.00 & 20.917 & 3 & [41] \\ Palm kernel oil & 42.40 & 5.80 & 48.20 & 3.60 & 0.00 & 76.35 & 11.50 & 3.40 & 8.75 & 16.526 & 4 & [42] \\ Palm kernel oil & 44.58 & 4.53 & 48.80 & 0.71 & 0.07 & 83.50 & 15.20 & 1.30 & 16.00 & 15.824 & 1 & [43] \\ Palm kernel oil & 42.08 & 7.00 & 49.90 & 0.99 & 0.00 & 83.00 & 9.00 & 3.00 & 5.00 & 17.700 & 1 & [44] \\ Rice husk & 47.18 & 3.60 & 48.20 & 1.01 & 0.12 & 73.40 & 20.60 & 6.00 & 13.63 & 15.599 & 2 & [45] \\ Rice husk & 38.92 & 5.10 & 53.89 & 2.17 & 0.12 & 63.80 & 16.87 & 19.33 & 8.1 & 13.596 & 1 & [46] \\ Sawdust & 49.15 & 5.74 & 44.31 & 0.81 & 0.00 & 68.01 & 13.02 & 0.89 & 18 & 19.309 & 2 & [47] \\ Sawdust & 51.73 & 6.00 & 41.48 & 0.62 & 0.00 & 70.30 & 15.10 & 3.30 & 11.20 & 20.761 & 2 & [48] \\ Sawdust & 48.91 & 5.80 & 45.11 & 0.18 & 0.00 & 80.63 & 17.27 & 2.10 & 0.00 & 19.197 & 1 & [49] \\ Sawdust & 50.40 & 6.60 & 39.67 & 0.90 & 0.48 & 84.34 & 13.74 & 1.92 & 9.82 & 21.264 & 3 & [37] \\ Woodchips & 45.60 & 5.90 & 48.40 & 1.00 & 0.00 & 77.50 & 12.30 & 1.50 & 8.80 & 17.820 & 2 & [50] \\ Woodchips & 49.20 & 5.50 & 45.20 & 0.10 & 0.00 & 78.10 & 14.70 & 0.40 & 6.80 & 18.973 & 4 & [51] \\ Woodchips & 49.22 & 6.06 & 43.21 & 0.13 & 0.08 & 78.10 & 14.70 & 1.69 & 8 & 19.826 & 3 & [52] \\ Wood pellets & 50.00 & 6.00 & 42.60 & 3.19 & 0.00 & 77.80 & 14.00 & 0.30 & 7.9 & 20.065 & 1 & [53] \\ Wood pellets & 52.60 & 5.80 & 40.60 & 0.10 & 0.00 & 76.80 & 22.30 & 0.90 & 4.2 & 20.978 & 1 & [54] \\ Wood pellets & 49.80 & 5.80 & 42.20 & 2.00 & 0.06 & 81.00 & 18.40 & 0.70 & 6.30 & 19.817 & 3 & [55] \\ Wood pellets & 50.70 & 6.90 & 42.40 & 0.30 & 0.18 & N/A & N/A & 0.39 & 7.50 & 21.451 & 4 & [20] \\ \hline Total & 20 & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The ultimate and proximate analyses of the biomasses used to develop stoichiometric models with correction factors and the new empirical correlation for the reaction equilibrium constant.
# Brake Torque Sensor Calibration Device
## 5 Calculation of the Calibration Device Error  
In order to verify the possible error that arose due to the inaccurate production of the calibration wheel and inaccurate production of calibration weights, the theoretical error of the calibration device was calculated. For this reason, it was necessary to find out the manufacturing tolerance of the calibration weights given by the manufacturer and to find out the actual diameter of the calibration wheel. The calculation was based on known tolerance values for calibration weights given by the manufacturer, which are shown in Table 3, and the known value of the local gravitational acceleration, \(g=9.80853\) m/s\({}^{2}\), calculated by the weight manufacturer based on geographical coordinates. An important piece of information was the actual diameter of the calibration wheel, which was found using a 3D scanner, the value of which was \(d_{s}=400.1334\) mm. Since the value of the weight was given in newtons, it was necessary to calculate the value of the mass of the weight using relation (10), as follows:  
\[m_{i}=\frac{F_{i}}{g}\quad i=\{2.5;\ 5;\ 10;\ 50\} \tag{10}\]  
where  
\(m_{i}\) is the mass of the given weight [kg]. \(F_{i}\) is the value of the given calibration weight [N]. \(g\) is the local gravitational acceleration [m/s\({}^{2}\)]. \[m_{2.5}=\frac{F_{i}}{g}=\frac{2.5}{9.80853}=0.25488\ k\]  
Subsequently, the weight tolerance value is added or subtracted from the weight value according to relation (11), as follows:  
\[m_{i\pm\mu}=m_{i}\pm\mu_{i}\quad i=\{2.5;\ 5;\ 10;\ 50\} \tag{11}\]  
where  
\(m_{i\pm\mu}\) is the mass of the weight increased or reduced by the tolerance value [kg]. \(m_{i}\) is the value of the mass of the given calibration weight [kg]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Variant of Calibration Device** & **Advantages** & **Disadvantages** \\ \hline
2. Angular gearbox & Easier and faster setting of the torque value, which could be set in both directions of rotation. & Problematic axial adjustment of the torque sensor to the standard and the introduction of errors due to the effect of rolling bearings. Higher mass and size due to the gearbox. \\ \hline
3. Weight carriers & Simpler and lighter construction. & There is an imperfect alignment of the system caused by friction between the arm and the weight carriers. There remains a problem with the axial adjustment of the torque sensor to the standard and with errors caused by the rolling of bearings. \\ \hline
4. Calibration wheel & Eliminated errors due to friction from rolling bearings, problematic adjustment of the measuring assembly, and relatively massive construction. Easy handling and good positioning of the calibration device on the measuring device. The universality of using the calibration device for various measuring devices of the laboratory. The system can stabilize itself into an equilibrium position. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._\(\mu_{i}\) is the tolerance value for the given weight [kg].
## 3 Results and Discussion
### Effect of GO Reduction on Adsorption  
Compared with the adsorption capacity of PE, ME, IPE, BPA and PPE on GO and RGO, it turned out that the \(q_{\rm e}\) values of each phenol on RGO was higher than that on GO (Figure 4). To reveal the adsorption mechanisms of five adsorbates on GO and RGO, we selected naphthalene as a probe to estimate the strength of the \(\pi\)-\(\pi\) interaction of GO and RGO. It was unambiguous that the \(\pi\)-\(\pi\) interaction of GO was weaker than that of RGO (Figure 3). The reason was that surface oxygenic groups might draw and delocalize \(\pi\)-electrons from the basal plane of GO, inhibiting the \(\pi\)-\(\pi\) interaction between the phenols' aromatic rings and the \(\pi\) orbital on GO [38]. The surface oxygenic groups sharp decreased by the reduction of GO, resulting in the enhancement of the \(\pi\)-\(\pi\) interaction. Therefore, it was observed that the reduction-induced stronger \(\pi\)-\(\pi\) interaction was more favorable to the adsorption of phenols.
## 3 Results and Discussion
### Gas Sensing Mechanisms and Data Analysis
The results from porosity (Figure 6) indicate that there is high surface area in sensors based on PBM nanoinks, peaking around a grinding speed of 400 rpm and grinding time of 30 min, which ultimately creates more active sites and gas diffusion channels and hence improves sensing signal magnitude. To probe the gas sensor performance further, we examined the effect of operating temperature on sensor response; thermally activated processes can impact reaction kinetics, carrier concentration, and mobility on/near the sensing surface, all of which affect gas sensor detection response and dynamic behavior [112]. Figure 7a shows temperature-dependent sensor response data indicating optimal operation near 100 \({}^{\circ}\)C, followed by a decline at higher temperatures, consistent with prior work using metal oxides [113]. Similarly, a shortening of response and recovery times (Figure 7b) at elevated temperatures is due to reduction in activation energy required for surface reactions, i.e., faster absorption and desorption occurs on the surface of the ZnO, leading to shorter response/recovery time [114]. Table 1 summarizes data for ZnO gas sensors fabricated using various synthesis techniques, including ball milling, and their response to different gas species.
## 2 Materials and Methods
### Data
#### 2.2.3 MODIS Products  
Version-6 MODIS eight-day surface reflectance composite data (MOD09A1, 500 m) [83], LST product (MOD11A2, 1000 m) [84], and GPP products (MOD17A2H, 500 m) [70] were used in this study. The pixel values in which the flux towers were located were acquired from MODIS Land Product Subsets at ORNL DAAC ([[https://modis.ornl.gov/cgi-bin/MODIS/global/subset.pl](https://modis.ornl.gov/cgi-bin/MODIS/global/subset.pl)]([https://modis.ornl.gov/cgi-bin/MODIS/global/subset.pl](https://modis.ornl.gov/cgi-bin/MODIS/global/subset.pl))). The surface reflectance composite data were utilized to calculate LSWI as Equation (1g) and EVI as follows:  
\[EVI\ =\ 2.5\times\frac{\rho_{NIR}-\rho_{Rad}}{\rho_{NIR}+6\times\rho_{Red}-7.5 \times\rho_{Blue}+1} \tag{9}\]  
where \(\rho_{\rm Blue}\) and \(\rho_{\rm Red}\) are the surface reflectance values of blue (459-479 nm) and red (620-670 nm), respectively. The daytime (10:30 am) and nighttime (10:30 pm) LST digital number (DN) values were converted to temperature (\({}^{\circ}\)C unit) using the equation below:  
\[LST\ =\ DN\times 0.02-273.15 \tag{10}\]  
The GPP products were generated by the Numerical Terradynamic Simulation Group (NTSG), University of Montana (UMT) [70]. The algorithms are presented in Section 2.1.3. The missing or unreliable values with the \"F\" flag due to failed quality control (QC) criteria were temporally filled in by the linear interpolation method [18; 85; 86].
# A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
### Contribution  
We suggest improved Markov path prediction and cache management algorithms on the basis of some of the aforementioned difficult issues. When designing the algorithm, we make an effort to deliver messages as quickly as feasible. Considering that device memory is typically small, then we suggest a proven cache management technique. The following are the major contributions of this paper.  
* In this study, we distinguish between intra-group forwarding and extra-group forwarding when it comes to messaging. When a message needs to be sent between groups, we use a novel Markov model to determine the probability that the sender and the recipient will be in the same place. We then send the message to the nodes with a higher probability of doing so. The message only needs to be delivered within the group when the recipient and the source node are both members of the same group. This not only gets the message to its target quickly but also saves a significant amount of cache space by sending the message to those nodes that have high centrality within the group.
* The utility value of a message is defined in terms of both the message's degree of diffusion and the present node's energy usage. According to our theory, if a message has a high degree of diffusion, there are likely already some copies of it in the network. As a result, priority should be given to receiving messages with a lower degree of diffusion. Moreover, if a message requires a lot of energy from the current node that node might not be the best choice to serve as its relay.
* The node also keeps track of both the message list and the delivered message list, prompting the node to remove any messages that have already been delivered.
* Our suggested strategy enhances network performance in terms of packet delivery rate, average delivery delay, average cost and overhead when compared to current methods.  
The remaining portions of the paper are structured as follows: related work is discussed in Section 2. Section 3 improves [PERSON]'s method and mentions the way messages are forwarded in the network. Section 4 defines message utility values and proposes cachemanagement methods. Section 5 evaluates the experimental results. Section 6 concludes the paper.
## 3 Methods
### Strong-Weak Contrastive Consistency
#### 3.2.1 Dual-Stream Perturbation and Contrastive Learning  
UniMatch [25] employs the DusPerb method to expand the perturbation space from two distinct perspectives, thereby fully capturing the diversity of original perturbations. Inspired by this approach, we adopted a similar perturbation strategy in this work. Specifically, for each unlabeled sample \(x_{u}\), two strongly augmented feature representations, denoted as \(q_{u,s1}^{\text{strong}}\) and \(q_{u,s2}^{\text{strong}}\), are generated. These perturbations aim to enhance the discriminative representation of cell regions, thereby improving the ability of the model to distinguish cellular structures from complex backgrounds. In addition, we introduced the CutMix [38] data augmentation technique, which utilizes randomly sampled templates to synthesize images with diverse staining styles. This strategy not only increases data diversity but also overcomes the limitations of using fixed staining templates.  
To maximize consistency between the strongly perturbed features, a contrastive learning loss is defined, as shown in Equation (7):  
\[L_{\text{contast}}^{\text{steng}}=-\frac{1}{N}\sum_{j\in\{s1,s2\}}\log\frac{ \exp(q_{u,s1}^{\text{strong}}\cdot q_{u,s2}^{\text{strong}})}{\sum_{i=0}^{\text {C}}\exp(q_{u,j}^{\text{strong}}k_{i})} \tag{7}\]  
Among them, \(N\) represents the sample quantity, \(q_{u,s1}^{\text{strong}}\) and \(q_{u,s2}^{\text{strong}}\) represent strong disturbance features, \(k_{i}\) is the classifier weight, and \(C\) represents the number of classifier weights. By maximizing the similarity between features, the model can learn robust feature representations in complex backgrounds.  
Figure 2: A comparison chart illustrating the SWC strategy versus other methods.
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 4 Proposed Architecture  
In order to share intelligence pertaining to the part of observed traffic in a network, a way to identify similar parts of traffic in other networks must be devised. There are two ways to identify similarities in network traffic: remote identity identification and behavioral correlation.  
* Remote identity identification: is a simple way to correlate traffic across networks by using hostname or IP address. It can be used in a straight-forward manner to assign the reputation scores to different endpoints or form a blacklist of malicious entities. It is a standard and functional way to enhance network security. Identity identification offers an added benefit of reliably binning captured HTTP(S) traffic together with the corresponding traffic where a MITM attack is used to inspect the contents. On the other hand, pervasive use of identity identification has led to change the originating hosts which is mostly used by attackers to make black-listing less effective.
* Behavioral correlation: is able to overcome such evasive tactics by using the timing, request sizes and request methods, as the resulting features are designed not to be tied to a single identity. Moreover, behavioral correlation techniques exploit a wide range of traffic characteristics and sharing.  
Because of its relative merits, identity identification is used as the basis in our intelligence sharing system. There are several ways of identifying the remote parties that are available for network flows such as URI, hostname, IP address and the second-level domain. Out of these, the hostnames can be used as the most specific identity information that is available for both encrypted and non-encrypted flows. IP addresses are potentially more specific because IP addresses are used to serve identical resources for the purpose of load balancing.
## 7 Conclusions  
In this paper, a novel, compact, and polarization-insensitive electromagnetic shield with dual-stopband characteristics is presented for SATCOM applications. The unit cell of the anticipated FSS design consists of MJCL and CMSL elements printed over a low-profile dielectric. The FSS offers closely spaced dual-band-reject operation, with a minimum band ratio of 1.52. This miniaturized FSS provides at least 35 dB and 40 dB measured suppression at the 7.9 GHz, X-band uplink, and the 11.9 GHz, Ku-band downlink, frequencies, respectively. More importantly, the FSS possesses a higher level of angular stability up to \(\pm 75^{\circ}\) and polarization insensitivity, owing to its four-fold structural symmetry. In addition, a lumped circuit model of the FSS is devised to further explain its resonance mechanism. A finite prototype of 25 \(\times\) 33 unit cells is fabricated, and its performance is tested at various oblique incident angles for both the TE and TM polarization states. Thus, the comparison of the simulated and measured results validates that the proposed FSS geometry is a potential candidate for EMI suppression applications, including, but not limited to, SATCOMs, radome designs, microwave filters, antenna reflectors, and many more.  
S.U. Writing-review and editing; M.I. Investigation; [PERSON] and [PERSON] supervision; [PERSON] and [PERSON] revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work is supported by National Natural Science Foundation of China under grant number 62171289.  
Studies not involving humans and animals.  
Not applicable.  
Data availability StatementData are contain within article.  
The authors declare no conflicts of interest.