esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Integrity Preserving Scheme for Secure Data Exchange in Cloud-Based IoT Systems
## 4 Proposed Scheme
### Proposed Scheme Details
#### 4.2.1 Basic Scheme  
This section discusses a scenario where a single file is audited.  
1. Setup: An owner key \(Wk\) is derived by the smart device user: \[Wk=H(UID\mid 1\,TPAID\mid 1\,CSID)\] (1) where: \[\bullet\] _UID_ identifies the smart device user and is usually a biometric value. It is also the secret key for the smart device users.
2. TPAID identifies the semi-trust server. Using a secure channel, (\(Wk\), \(F\)) is sent to the semi-trust server by the smart device user.  
* _CSID_ identifies the IoT-cloud system.
2. Upload: The smart device user sends (Wk, F) to the semi-trust server, which starts calculating the metadata of file F. The challenge value of file c, which has a random value, is chosen. Thereafter, Wk and c generate the challenge key Ck, as highlighted: \[Ck=H(Wk\mid c)\] (2) The following step is for the semi-trust server to calculate the challenge hash value \(Ch\), which is derived from \(F\) and \(Ck\) as follows: \[Ch=H(F\mid Ck)\] (3) The verification key \(Vk\) is then generated by the semi-trust server, which selects the random verification value \(v\) and then deploys this value along with \(Wk\): \[Vk=H(Wk\mid v)\] (4) The verification key \(Vk\) is used to compute the metadata \(\sigma\) using XOR and the challenge hash value \(Ch\) as follows: \[\sigma=Ch\ XOR\ Vk\] (5) After \(\sigma\) is calculated, the IoT-cloud system receives the file and metadata \(\{\sigma,F\}\) from the semi-trust server. Alternatively, the smart device user receives \(\sigma\) as a receipt from the semi-trust server, which thereafter retains the keys, and removes the file \(F\) from its local storage position.
3. Auditing: Using a secure channel, the IoT-cloud system receives a request that includes a challenge key \(Ck\) from the semi-trust server. This process validates the integrity of the data that has been outsourced. When it receives the key the IoT-cloud system it initially calculates \(Cf\) from \(F\) and \(Ck\) by employing the cryptographic hash function, as per the following: \[Cf=H(F\mid Ck)\] (6) Lastly, the audit key \(Ak\) is generated by the IoT-cloud system, from the metadata \(\sigma\) and \(Cf\) as follows: \[Ak=\sigma\ XOR\ Cf\] (7) The semi-trust server then receives the audit key \(Ak\) from the IoT-cloud system, and upon receipt, the semi-trust server compares it to the verification key \(Vk\). When these two keys match each other, it is certain that the file has not been modified and has been stored correctly. The verification equation's validity can thus be proven. \[V\ k=\ ^{7}\ Ak\] (8) The validity of the preceding verification equation can be proven as follows:  
* \[Ak = \sigma\ XOR\ Cf\] \[= \sigma\ XOR\ \ H(F\mid Ck)\] \[= \sigma\ XOR\ Ch\] \[= \ Ch\ XOR\ Vk\ XOR\ Ch\] \[= 1\ XOR\ Vk\] \[Ak = Vk\]
# First Evidences of Ionospheric Plasma Depletions Observations Using GNSS-R Data from CYGNSS
## 2 Materials and Methods  
The current study uses open data from NASA CYGNSS (Cyclone Global Navigation Satellite System) mission [12; 13], which continuously delivers GNSS-R data since March 2017. NASA CYGNSS is a microsatellite constellation led by the University of Michigan and the Southwest Research Institute, launched and operated by NASA in December 2016. The mission main goal is to provide a better forecast of hurricanes by studying the interaction between the sea and the atmosphere.
# A Proposal for a Federated Learning Protocol for Mobile and Management Systems
## 4 The Proposed Communication Protocol  
The communication protocol proposed in this design is designed to be compatible with both Apache Kafka and the Firebase Realtime Database. In this protocol, all information, including details about model weights, is transmitted in the JavaScript Object Notation (JSON) format. This approach ensures that the data exchanged among the various components of the system can be easily parsed, interpreted, and processed by both Apache Kafka and the real-time database, enabling seamless communication and data synchronization across the system. The learning model within this system encompasses several distinct states, including:  
* Recovering from a Checkpoint: Involves restoring the model to a previously saved state.
* Training: Entails the iterative process of optimizing the model's parameters using training data.
* Resaving the Model to a Checkpoint File: Preserves the updated state for future reference.
* Exporting the Weights of the Trained Model to JSON Format: Facilitates their transfer and storage in a human-readable format.
* Importing the Weights from the JSON Format Delivered by the Server: Allows the model to incorporate the updated weights received from the server into its current state.  
These defined states enable the model to undergo checkpoint recovery, training, and the exchange of model weights in a structured and coherent manner throughout the learning process.  
Furthermore, an additional feature currently in development involves two approaches for calculating the average of the weights. Firstly, the average of the weights is calculated directly on the clients. Secondly, the model itself utilizes TensorFlow functions to calculate the average of the weights.  
Figure 2 provides an illustration of the design and implementation of the communication protocol, which is followed by the client and server-side logic, as depicted in Figures 3 and 4. The protocol commences when the client initializes and retrieves the configuration information from the Firebase Realtime Database. This configuration includes details like the current model version. Subsequently, the client queries the database to select the server intended for training by sending a server selection message. All queries, including this one, are sent to the Firebase Realtime Database and/or Kafka.  
Furthermore, the communication protocol incorporates a \"learning exchange message\" designed to convey critical information related to model training. This message comprises several essential components, including a designated client number, priority level, sequence number, and a type entry specifying the message's nature. The type entry distinguishes
## 3 Results  
### Effect of BHRF1 on Cell Physiology  
#### 3.1.1 Noncontrolled Culture Conditions: Shake Flask Cultures  
After engineering KB26.5 hybridoma cell lines with the BHRF1 gene, the transformed cell population was kept under selective pressure for several passages. Then, differences in cell physiology between the cell line KB26.5 and the engineered KB26.5-BHRF1 were observed after comparing both cell lines in shake flask cultures. The time profiles for cell growth, cell viability, glucose and lactate concentrations for both cell lines (A1 for KB26.5 and B1 for KB26.5-BHRF1) are displayed in Figure 1. A Napierian logarithm of viable cells versus time is plotted to better understand the variations in cell growth, as will be discussed later (A2 for KB26.5 and B2 for KB26.5-BHRF1). Considering that the experiments were carried out in duplicate (same time points), the mean values are plotted together with the standard deviation.  
The parental KB26.5 cell line reached a cell density of 1.79 \(\pm\) 0.3 \(\times\) 10\({}^{6}\) cells/mL after 72 h of culture, at which point cell viability decreased sharply. In contrast, KB26.5-BHRF1 reached 3.92 \(\pm\) 0.03 \(\times\) 10\({}^{6}\) cell/mL in about the same time. Regarding glucose and lactate concentration, high consumption and production rates respectively could be deduced by the sharp decay/increase of the concentration profiles, obtaining more lactate generation in the parental KB26.5 cell line. The slopes of the Napierian logarithm plots of viable cells showed a higher cell growth rate for the KB26.5-BHRF1 compared with the parental line. Parallel to the plateau of the cell growth profile, glucose consumption and lactate production also indicated a metabolic arrest at 72 h of culture, when their concentration values became almost constant. No morphology changes were observed between KB26.5 and KB26.5-BHRF1 on different days from manual counting using a microscope (data not shown).
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 3 Wi-Fi 6 Overview
### Targeted Use Cases
#### 3.3.1 Dense Environments  
In dense environments, multiple access points with overlapping BSSs cohabit within a small area. Wi-Fi 6 supports dense indoor or outdoor environments that require massive access, e.g., apartments, stadiums, university campuses, airports, industrial plants, etc.  
Such settings profit from SR as this improves parallel transmissions in neighboring BSSs on the same channel. OFDMA reduces collisions that occur due to a large number of users. Finally, MU MIMO increases the overall capacity needed for massive access.
## 7 Experimental Analysis and Results
As shown in Figure 7, the following tables have been devised: (i) table Path, shown in Figure 7a, is a repository of paths containing all the data modeling a path (_i.e._, _Row key_, _Column family_, _etc._); (_ii_) for each sink, table Sink-Path, shown in Figure 7b, contains the list of the path IDs (related to the first table) that ends with that node; (_iii_) for each node, table Node-Label, shown in Figure 7c, contains the labels of each node. On top of this HBase data storage, we ran simple operations like Get, Batch and Scan, of course being the framework running our proposed algorithm. This allowed us to retrieve one million (1M) of rows within 5.5 ms through _cold-cache_ experiments and within 1.3 ms through _warm-cache_ experiments. Table 3 reports these results. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Reading-Like Operations** & **Writing-Like Operations** \\ \hline
**Throughput (Op/s)** & 1300 & 3500 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Throughput performance. Figure 7: HBase configuration for the running experiment: (a) table Path; (b) table Sink-PathID; (c) table Node-Label. Finally, by analyzing our experimental results, we conclude that our framework for computing BSF-based traversals of RDF graphs clearly exposes both effectiveness and efficiency. As future extension, in order to further improve performance, the proposed framework could be implemented on top of the well-known _Apache Spark analytical engine_[79], as Spark exposes an optimized in-memory computation scheme for large-scale data processing. This would allow us to improve response times.
# 5G/B5G mmWave Cellular Networks with MEC Prefetching Based on User Context Information +
## 5 Conclusions  
In this paper, we proposed a novel 5G/B5G cellular network architecture enabled by mmWave Edge Cloud incorporated with the data prefetching based on users' context information. In order to fully leverage the potential of mmWave broadband communication, prefetching is essential under the constraint of the backhaul capacity. In our proposal, user demand is predicted by utilizing context information, such as the destination, commu  
Figure 12: The energy efficiency (1 Gbps backhaul capacity, 200 UE).  
nication history, and mobility. Furthermore, the prefetching data amount is determined according to the weighted PF algorithm. Simulation evaluation confirmed the effectiveness supposing mmWave small cells operated at the 28 or 60 GHz bands.  
Our results clarified that the proposed prefetching with WPF can maintain about 95% of the maximum system rate and can reduce about 67% of the average download time, even if the backhaul capacity is constrained to 1 Gbps. Therefore, the proposed scheme, including the overall system concept, could greatly contribute to the true realization of B5G. Our future work includes introducing a more practical model for mobility prediction with context information as well as joint optimization of access/backhaul resource assignment. Furthermore, a full-scale proof-of-concept will be conducted using an outdoor private 5G testbed.  
Conceptualization, K.S.; methodology, K.S., G.K.T. and H.N.; validation, software, formal analysis and investigation, H.N. and J.N.; writing--original draft preparation, H.N.; writing--review and editing, K.M., J.N. and G.K.T; visualization, H.N. and K.M.; supervision, project administration, and funding acquisition, K.S. All authors have read and agreed to the published version of the manuscript.  
This work was largely supported by grants from the following research programs: European Commission H2020 programme under grant agreement #723171 (5G-MiEdge project), The Ministry of Internal Affairs and Communications, Japan under grant agreement #0159-0048 (5G-MiEdge project), Ministry of Internal Affairs and Communications, Japan under grant agreement #0155-0062 (MiEdge+ project), and NICT Beyond 5G R&D Promotion Programs (#00101).  
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.
## 2 Description of the THID Tag and of the Measurement Setup
where \(\widetilde{S}(\omega)\) and \(\widetilde{R}(\omega)\) are the complex Fourier transform of the THz waveforms of \(S(t)\) and \(R(t)\), respectively. For example, Figure 3 exhibits the frequency-dependent transmission coefficient of the tag previously described, in which a structural defect has been created by modifying the thickness of the central layer made of pure LDPE: \(e_{d}=1180\)\(\upmu\)m, instead of 240 \(\upmu\)m. As previously pointed out, some peaks (two in the present case) of transmitted energy, occur in the first PBG. Then, we propose to use the presence of such transmitted peaks at some given frequencies to encode binary information, as described in the next section. We can also notice that the modulus of the THz transmission of the tag can be perfectly predicted by the theory (see black continuous line) calculating by using a transfer matrix method [28]. The theoretical expression of \(\widetilde{T}(\omega)\) is then given by:  
\[T(\omega)=\frac{1}{|P_{11}|} \tag{2}\]  
where \(P_{11}\) is the upper left element of the transfer matrix \(P\) that links the incident (\(E_{i}\)), and reflected (\(E_{r}\)) THz electric fields, with the transmitted one (\(E_{t}\)):  
\[\left( \begin{array}{c}E_{i}\\ E_{r}\end{array} \right)=\left( \begin{array}{cc}P_{11}&P_{12}\\ P_{21}&P_{22}\end{array} \right)\left( \begin{array}{c}E_{t}\\ 0\end{array} \right) \tag{3}\]  
considering the above-described tag, and assuming an incoming THz beam under normal incidence, the transfer matrix writes:  
\[P=\underbrace{A.S^{4}.\Delta}_{M}(M^{*})^{-1} \tag{4}\]  
\(M^{*}\) is the complex conjugate of \(M\), this latter being calculated from:  
\[A=\frac{1}{1+r_{1}}\left( \begin{array}{cc}e^{-i\frac{\omega}{2 }n_{i}e_{H}}&r_{1}e^{i\frac{\omega}{2}n_{i}e_{H}}\\ r_{1}e^{-i\frac{\omega}{2}n_{i}e_{H}}&e^{i\frac{\omega}{2}n_{i}e_{H}}\end{array} \right) \tag{5}\]  
\[\Delta=\frac{1}{1+r_{2}}\left( \begin{array}{cc}e^{-i\frac{\omega}{2}n_{i} \frac{\omega}{2}}&r_{2}e^{i\frac{\omega}{2}n_{i}\frac{\omega}{2}}\\ r_{2}e^{-i\frac{\omega}{2}n_{i}\frac{\omega}{2}}&e^{i\frac{\omega}{2}n_{i}\frac {\omega}{2}}\end{array} \right) \tag{6}\]  
Figure 3: (**a**) Experimental (blue circles) and calculated (black continuous line) transmission spec-tra of the tag presented in Figure 1b. Focused on the first PBG whose limits are defined by blue vertical lines. Vertical strips represent the frequency coding channels. © 2021 IEEE: Figure (**b**) is plotted from [24]. \[S=\ \frac{1}{1-r_{2}^{2}}\left( \begin{array}{cc}e^{-i\frac{\omega}{\epsilon}n_{l }e_{l}}&r_{2}e^{i\frac{\omega}{\epsilon}n_{l}e_{l}}\\ r_{2}e^{-i\frac{\omega}{\epsilon}n_{l}e_{l}}&e^{i\frac{\omega}{\epsilon}n_{l} e_{l}}\end{array} \right)\cdot\left( \begin{array}{cc}e^{-i\frac{\omega}{\epsilon}n_{l }e_{l}}&-r_{2}e^{i\frac{\omega}{\epsilon}n_{l}e_{l}}\\ -r_{2}e^{-i\frac{\omega}{\epsilon}n_{l}e_{l}}&e^{i\frac{\omega}{\epsilon}n_{l} e_{l}}\end{array} \right) \tag{7}\]  
where \(r_{1}=\frac{1-n_{H}}{1+n_{H}}\) and \(r_{2}=\frac{n_{H}-n_{L}}{n_{H}+n_{L}}\) are the reflection coefficients at interfaces air/\(H\) and \(H/L\), respectively.
# GNSS Signal Distortion Estimation: A Comparative Analysis of L5 Signal from GPS II and GPS III
## 4 Discussion  
The first GPS III satellite was launched in December 2018, and five satellites have been launched so far. As the third important stage of GPS modernization, the GPS III signal has significantly improved. According to the performance of each component of the navigation payload transmission channel, unlike previous estimations of the channel, digital distortion was considered in signal distortion modeling in this paper. The accuracy of the signal distortion model and estimation method was verified on the basis of the measurement data of a large-aperture antenna, and the GPS II and GPS III L5 transmission channels and the resulting ranging biases were compared for the first time. However, the following aspects still need to be further explored in follow-up research: the consistency of I/Q branch modulation methods of the GPS L5 signal; signals of different modulation methods, such as the GPS L1 signal.
# Farmed Insect Frass as a Future Organic Fertilizer
## 3 Results and Discussion
### Changes in the Bacteriobime Community and Relationships with the Analyzed Parameters  
An analysis of diversity indices revealed higher counts of bacterial species numbers (except in the 200 mg N dm\({}^{-3}\) treatment) and lower species diversity in acid peat with frass than urea (Table 7). After frass application, the counts of bacterial species increased by 10% on average from the baseline but decreased by 10.3% compared with the control. Urea (especially U50 and U400) significantly decreased the number of taxa (by approximately 37%), and a lower decrease was induced by U100 (17%) and U200 (3.5%). The Shannon diversity index was lower in urea and higher in frass treatments, except for a notable increase at 200 mg N dm\({}^{-3}\). Frass-fertilized peat exhibited higher Shannon evenness, peaking at F200, while Simpson's dominance was highest in U400, U50, and U100, and lowest in F200. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Fettilizer**} & \multicolumn{6}{c}{**Nitrogen Rates (mg dm\({}^{-3}\))**} & \multirow{2}{*}{**Mean**} \\ \cline{2-2} \cline{4-6}  & **0** & **50** & **100** & **200** & **400** \\ \hline \multicolumn{6}{c}{_amoeba_ (log\({}_{10}\))} \\ \hline Frass & _7.25 d_ & _7.58 c_ & _7.91 b_ & _8.12 a_ & _7.92 b_ & _7.76_ \\ Urea & 7.25 d & 7.47 c & 8.09 b & 8.07 b & 8.82 a & 7.94 \\ Significant & ns & * & *** & * & *** & *** \\ Mean & 7.25 E & 7.53 D & 8.00 C & 8.09 B & 8.37 A & \\ \hline \multicolumn{6}{c}{_ureC_ (log\({}_{10}\))} \\ \hline Frass & _8.58 c_ & _8.83 b_ & _8.91 a_ & _8.61 c_ & _8.35 d_ & 8.66 \\ Urea & 8.58 a & 8.39 b & 8.40 b & 8.34 c & 8.20 d & 8.38 \\ Significant & ns & *** & *** & *** & *** \\ Mean & 8.58 C & 8.61 B & 8.65 A & 8.48 D & 8.28 E & \\ \hline \end{tabular}  
Significant differences between nitrogen rates in frass and urea groups: * significant at \(p\) < 0.05, ** significant at \(p\) < 0.01; *** significant at \(p\) < 0.001; *** significant at \(p\) < 0.0001; ns— not significant. Capital letters A, B,..., E indicate significant differences between nitrogen rates. Lowercase letters a, b,.., d indicate significant differences between nitrogen rates only within the frass group (fIALs) and only within the urea group (without italics).  
\end{table} 
Table 6: Cont. In the hierarchical cluster analysis (Figure 2a), fertilization treatments formed two clades. Subgroups in the first clade included F100 and F50, and the second clade comprised U200, U100, U50, and the control treatment. In the second clade, U400, F400, and F200 formed a subgroup, with baseline data showing low similarity. PCA of the microbiome (Figure 2b) revealed a strong correlation between _Rhodanobacter_ and F400. _Dokdonella_ and _Bradyrhizobium_ correlated with F400, F200, and U400. Baseline values were weakly correlated with principal components. The presence of the genera _Candidatus_ Koribacter, Ca.
## 1 Introduction
In this context, an average of 300 laboratory model tests were carried out to investigate the effect of glass fiber, basalt fiber, macromesh fiber, and hybrid fiber-reinforced soils where these three fibers are used together in different combinations on the bearing capacity. In the experimental study, the scope of the study was expanded by investigating the effect of fiber length, fiber content, depth of reinforcement layer, and different relative density values, as well as fiber type. To investigate the effect of fiber length, two different fiber lengths of 24 mm and 48 mm were used. To see the effect of fiber content, fiber additives with two different contents of 1% and 2% were investigated. To see the effect of relative density, tests were carried out on soils with 30% and 50% relative density. In addition, soils were reinforced with fiber at depths of (B) 1, 2, and 3 times the foundation width, and the effect of three different reinforcement depths on soil stabilization was evaluated. Finally, to analyze the sand soil settlement behavior strengthened with different fiber types under varying environmental conditions within the scope of the study, the GeoPIV-RG program, an image analysis module running on MATLAB was used. For the image analysis of each test, a series of photographs taken at regular intervals during the test were used as data.
# A Cache Placement Strategy with Energy Consumption Optimization in Information-Centric Networking
## 5 Conclusions and Further Work  
In this paper, a cache placement strategy with energy consumption optimization has been proposed according to the built-in cache characteristics of content center network. Firstly, the network energy consumption model is constructed under the ICN architecture, and the problem of maximizing energy saving is transformed into the optimal stopping rule problem. Then, the optimal stopping problem is proved and solved, so the maximum expected value of energy saving is achieved. The energy saving of the current node is observed by the user group and compared with the maximum expected value to select the cache placement node so that the content distribution with low energy consumption and high cache efficiency can be realized. The simulation results show that the ESCPS strategy proposed in this paper obtains a larger caching benefit and higher delivery success rate, and has a better energy saving effect.  
Figure 8: The influence of node change energy consumption and node distribution range on delivery success rate.  
In future work, the energy consumption model is defined more accurately with the actual situation of the network, and other performance factors will be optimized on the basis of the optimization of energy consumption.  
For research articles with several authors, a short paragraph specifying their individual contributions must be provided. The following statements should be used \"conceptualization, X.Z. and G.W.; methodology, Q.Z.; writing--original draft preparation, G.W.; writing--review and editing, X.Z.; supervision, G.W.; project administration, X.X.; funding acquisition, G.W.\".  
This study was funded by the National Natural Science Foundation of China under Grant No. 61562006, in part by the Natural Science Foundation of Guangxi Province under Grant No. 2013 GXNSFGA019006, 2016 GXNSFBA380181, and in part by the Basic Ability Promotion Project for Young and Middle-aged Teachers in Universities of Guangxi under Grant No. 2018 KY0640.  
The authors declare no conflict of interest.