esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principles  
### Principle of NM-CAP Modulation  
NM-CAP modulation is a high spectral efficiency modulation technique suitable for improving the transmission capacity of systems in bandwidth-limited scenarios. The fundamental principle involves mapping data onto multiple frequency bands while applying carrierless amplitude phase (CAP) modulation to each band, thereby achieving efficient data transmission. Compared to conventional orthogonal multi-carrier modulation techniques, such as OFDM, NM-CAP enhances spectral efficiency by reducing the frequency gap between sub-bands while avoiding the high complexity of frequency-domain processing.  
The original bit sequence at the transmitter is first mapped to quadrature amplitude modulation (QAM) symbols. QAM encodes information by adjusting the amplitude and phase of the signal. Each QAM symbol consists of two components: the in-phase (I) component and the quadrature (Q) component, denoted as \(\mathrm{s}^{\mathrm{I}}(\mathrm{t})\) and \(\mathrm{s}^{\mathrm{Q}}(\mathrm{t})\), respectively. Next, the signal is upsampled according to the minimum number of samples required for each symbol to prevent spectral aliasing. After upsampling, the I and Q signals are passed through pulse-shaping filters (PSFs) to form the transmitted signal.  
In NM-CAP, the entire spectrum is divided into multiple sub-bands, with CAP modulation applied to each sub-band. The filter for each sub-band can be expressed as follows:  
\[\mathrm{f}_{\mathrm{n}}^{\mathrm{I}}(\mathrm{t})=\mathrm{g}(\mathrm{t})\mathrm{ cos}(2\pi\mathrm{v}_{\mathrm{n}}\mathrm{t}),\ \mathrm{f}_{\mathrm{n}}^{\mathrm{Q}}(\mathrm{t})=\mathrm{g}(\mathrm{t})\mathrm{ sin}(2\pi\mathrm{v}_{\mathrm{n}}\mathrm{t}), \tag{1}\]where g(t) is the square-root-raised cosine (SRRC) filter used to control the bandwidth of each sub-band [27]. \(\rm v_{n}\) is the center frequency of the n-th sub-band, which is defined as follows:  
\[\rm v_{n}=\left[\frac{1+\alpha}{2}+(n-1)(1+\alpha\beta)\right]R_{s}, \tag{2}\]  
where \(\alpha\) is the roll-off factor of the SRRC filter, \(\beta\) is the sub-band spacing factor, and \(R_{s}\) is the symbol rate of the sub-band. The I and Q signals of all sub-bands are processed through their respective filters and then summed to generate the final transmitted signal:  
\[\rm s(t)=\sum_{n=1}^{N}\left[s_{n}^{I}(t)\otimes f_{n}^{I}(t)+s_{n}^{Q}(t) \otimes f_{n}^{Q}(t)\right], \tag{3}\]  
where N is the number of sub-bands, and \(\otimes\) denotes the convolution operation. The bandwidth of the transmitted signal can be expressed as follows:  
\[\rm BW=[1+\alpha+(N-1)(1+\alpha\beta)]R_{s}, \tag{4}\]  
The core of NM-CAP lies in the fact that the sub-bands are not strictly orthogonal to each other, which allows for a tighter packing of the spectrum, thereby improving spectral efficiency. However, this also introduces the issues of ISI and IBI, which need to be mitigated by designing appropriate filters and signal processing algorithms to suppress these interferences [28].
## 2 Payload Characteristics and Thermal Control Requirements
### Thermal Characteristics of Payloads and Control Requirements  
The payload racks in CSS are divided into several types according to their functions and working characteristics, which are suitable for different scientific and technical fields. As shown in Table 1, different payload racks present different characteristics and thermal control requirements. According to the layout of payload rack, some of the loads are installed in the form of standard drawer; the standard size of a single drawer is 450 mm \(\times\) 560 mm \(\times\) 173.5 mm. The rack can install up to eight standard drawers; individual load takes up maximum space of four standard drawers. In addition, some loads in individual fields are irregular non-standard structures that are installed inside the rack.  
According to the function of the lab module, the total power consumption is in the range of 300-2300 W. In addition, the temperature of the conventional electronic equipment inside the module should be controlled in the range of \(-\)10-45 \({}^{\circ}\)C, the stability is controlled at \(\pm\)5 \({}^{\circ}\)C/h, some low-temperature loads are required to work at (0-10)\({}^{\circ}\)C, optical loads are required to work at \(\pm\)(0.5-1)\({}^{\circ}\)C/h and basic physics classes are required to work at mK/h level; the temperature difference cannot exceed 10 mK. In addition, some samples of space organisms and life-like organisms require a cryogenic storage environment, with temperature requirements ranging from \(-\)80 \({}^{\circ}\)C to 4 \({}^{\circ}\)C.  
It can be seen that the payload rack is loaded with various kinds of scientific devices; different fields and devices have various requirements on temperature, and the load structure layout and on-orbit working mode reflect great differences. It is the key point of this paper to plan all kinds of load demands and put forward a hierarchical and classified thermal control system scheme and regulation method according to load characteristics.  
Figure 2: Thermal environment and heat transfer path of payload rack.
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 5 Security Analysis and Comparison
### Informal Security Analysis
#### 5.3.4 SM Impersonation Attack Resistance  
Adversary \(A\) needs to obtain the value of \(E_{i}\) to forge message \(M_{4}\) in order to impersonate a legitimate smart meter. \(E_{i}\) can be calculated by two means: (1) the legitimate smart meter can compute it; and (2) the data concentrator knows \(\{r_{i},s\}\) and can calculate it. It is computationally impossible for \(A\) to compute these parameters, so \(A\) does not have the ability to impersonate smart meters within the scope of \(DC\). So, the scheme can effectively resist smart meter impersonation attack.
## 3 Proposed Solution  
### Proposed Feature-Selection Method Based on Improved Fitness Function  
In the proposed feature-selection algorithm based on an improved fitness function, an optimized partial feature set is selected to improve anomaly detection and error rates, thus improving detection performance and solving data imbalance. The problem of feature selection is an optimization problem: selecting optimal partial feature sets from all feature sets, which is an nondeterministic polynomial (NP)-complete problem [35]. We used GA as an optimization algorithm to verify the performance of the proposed fitness function. Therefore, the core elements of the proposed feature-selection algorithm in this study are chromosomes and fitness functions that allow the GA to solve given optimization problems and, in doing so, select the optimal feature set to improve anomaly-detection performance. The proposed fitness function can find the optimal solution, enabling detection performance improvement and a solution for data imbalance, which confirms that a feature subset improves anomaly detection performance. Each process in the proposed algorithm is described below.
# Anti-Screenshot Watermarking Algorithm for Archival Image Based on Deep Learning Model
## 4 Experimental Results
### Robustness Comparison of Screenshot at Different Distances  
We evaluate the performance of the proposed ScreenNet model compared with HiDDeN and Stegastamp against screenshot attacks using different distances. Table 4 shows the anti-screenshot experiment.  
The experimental results show that the HiDDeN model has poor robustness to screenshot attacks, and the accuracy of the watermark information extraction is in the range of 66-80%. This is because the noise layer of HiDDeN model only considers the common attacks such as cropping, JPEG compression, Gaussian blur, etc. However, it does not consider the screenshot attack. The Stegastamp model maintains a good watermark extraction accuracy under four different distances, and the accuracy rate is above 92%. This is mainly due to the application of various differentiable perturbations in the noise layer of Stegastamp to approach the screenshot attack. Compared with Stegastamp, the proposed ScreenNet model is more robust against screenshots. The proposed ScreenNet model also uses archival image dataset of moire to add real disturbances. Therefore, the proposed ScreenNet has stronger antiscreenshot robustness. The average accuracy of watermark extraction with ScreenNet is higher than that of Stegastamp and HiDDeN models. Figure 11a shows the accuracy of screenshot attacks against different distances and angles. Furthermore, Figure 11a intuitively shows that the proposed ScreenNet model has a stronger robustness against screenshot under different distances.
## 6 Comparison
### Scalability  
Scalability is one of the primary concerns for any system that is, or in the future will be, expected to serve the growing demands of users or organizations. Centralised systems like OAuth and LDAP are still widely used and have proven to show significant strength in this regard. Even with complex directory structures, these systems allow for vertical and horizontal scaling by adding more resources to the central server as demand increases. SAML's federated model allows it to scale across multiple domains, making it a favourable approach where SSO is needed. OAuth and other centralised models support integration with web applications or third-party services and are effective in handling large volumes of token-based access. Over time, these systems have become optimised with existing hardware and server configurations; however, reliance on a single point of control can sometimes introduce bottlenecks as the system grows. For decentralised systems, scalability challenges are rooted in the distributed architecture and consensus mechanisms. For example, Ethereum, used by uPort, has a current transaction throughput of 15-30 TPS, with layer-two solutions like Optimistic Rollups and zkRollups potentially increasing it to 1000-4000 TPS [2; 17]. Blockstack leverages Bitcoin's Proof of Work (PoW), inherently limited to approximately 7 TPS due to the computational requirements of mining. By comparison, Hyperledger Indy uses Redundant Byzantine Fault Tolerance (RBFT), which achieves scalability with up to 300 TPS in practical implementations while maintaining resilience to up to 33% node failures [18]. Recent developments such as Indy Besu propose leveraging smart contract modules on permissioned and public blockchains to further scale identity operations while ensuring consistency across systems. Centralised systems, like LDAP, can scale horizontally by adding servers, achieving re
## 1 Introduction  
The dairy industry heavily relies on fermentation processes driven in high proportion by _Lactococcus lactis_. The fermentation process can be perturbed or even stopped by bacteriophage activity leading to huge economic losses [1; 2; 3]. Among bacteriophages infecting _L. lactis_[4], those belonging to species c2, 936, or P335 are most commonly encountered in dairy plants [5; 6]. Most in-house dairy plants' laboratories use traditional, indirect methods for phage detection in their routine analyses. These methods are based on activity tests (assessment of a decrease in acid production or a reduction of an indicator compound). Additionally, standard microbiological methods, i.e., plaque assays or spot tests, are used for direct phage detection [1; 7]. Molecular methods (mostly PCR-based) give the advantage of short time from sampling to results, high sensitivity, and independence of sensitive strain [8]. Apart from detection methods, analysis of diversity and dynamics of _lactococcal_ bacteriophages is crucial for preventing the spread of infections in dairy environments. Knowledge of phage strains presence in milk or dairy environment may allow to plan starter culture rotation.  
Analysis of microbial community composition and dynamics especially in the case of unculturable bacteria or viruses always posed a challenge. Especially, lack of knowledge on growth requirements or undetermined permissive host is a limiting factor. Today in the era of next-generation sequencing (NGS), the problem can be solved both in the determination of community composition [9] and its functional profile [10]. However, NGS is still an expensive method. Its ability to produce a massive output of data in a relatively short time can sometimes be a disadvantage. The relatively low price per sample can be achieved only by filling the whole throughput (lane, chamber, or chip of the sequencer) with samples. In case of low complexity samples or targeted approaches to use the whole throughput, extensive barcoding is necessary. It is often necessary to collect a large number of samples to cover the whole throughput of a chip to run the sequencing. The NGS technique was already successfully used to study microbial communities in dairy technologies [11; 12]. In this respect, we looked for a technique that may substitute NGS in the case of low-complexity communities. Here, we describe a novel approach that was used to compare the diversity of _Lactococcus_ phages from dairy industrial and spontaneous fermentation processes. The new method is based on well-established and affordable techniques accompanied by simple bioinformatics analysis to study the diversity of the microbial community.
## 5 Numerical Model Validation
### Rebar Stress  
Through the finite element numerical simulation, the stress distribution of the anchored part of the steel bar could be obtained, and it was compared with the measured stress of the steel bar. When the free end of the steel bar reached the yield strength, the distribution of the stress along the relative depth of the steel bar was drawn. The actual rebar stress in the test was obtained by the strain value, measured by the strain gauge through the following formula:  
\[\sigma_{i}=E\epsilon_{i} \tag{13}\]  
where \(E\) is the elastic modulus of the steel bar and \(\epsilon_{i}\) is the strain at the measuring point.  
It can be seen from Figure 12 that the simulated value of the stress of the anchored part of the steel bar had a higher stress level at the orifice, which is close to the actual yield strength of the steel bar and was the same as the actual changes trend. In the comparison of the numerical deviation between the simulated value and the measured value, the overall simulation curve is consistent with the actual situation, and the stress simulated value of all specimens is close to the measured value.  
Figure 11: Comparison of load–slip curves between simulation and measured.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Name of Specimen**} & **Measured Value** & **Simulation Value** & \multirow{2}{*}{**Ratio**} \\  & **(kN)** & **(kN)** & \\ \hline B1 measured & 23.61 & 24.70 & 0.956 \\ B2 measured & 22.74 & 23.20 & 0.980 \\ B3 measured & 19.70 & 20.60 & 0.956 \\ B4 measured & 15.00 & 17.40 & 0.862 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ultimate loads in elastic bond stage.
# A Study on the Conductivity and Infrared Spectroscopy of Elastic Polymer Composite Materials
## 3 Results
### Graphite's Characteristic Equation  
Figure 12 shows the relationship between the graphite doping concentration (Cd) and the resistivity (\(\rho\)) of the specimens. When the graphite doping concentration reaches 75 M, the electric conductivity increases sharply; when the graphite doping concentration reaches 79 M, the electric conductivity enters into a steady state; when the graphite doping concentration increases to 90 M, graphite reaches adsorptive saturation and the polymer's elasticity starts to deteriorate, and as a result, the specimen's edge has small cracks.  
Figure 11: The relationship between activated carbon doping concentration (Cd) and resistivity (\(\rho\)).  
Figure 12: The relationship between graphite doping concentration and resistivity.  
After many experiments, the equation for the resistance characteristic of the graphite doping concentration is derived as follows:  
\[\rho=\rho_{\rm s}+\rho_{0}\cdot e^{-Q_{\rm m}\frac{C_{\rm d}-C_{\rm e}}{C_{\rm c }-C_{\rm o}}} \tag{3}\]  
In Formula (3), \(C_{0}\) is the minimum doping concentration required for the elastic polymer composite materials to become resistance materials; \(\rho_{0}\) is the resistivity of \(C_{0}\); \(C_{\rm c}\) is the critical doping concentration at which the conductor is in a steady state; \(C_{\rm s}\) is the adsorption-saturated doping concentration; \(\rho_{\rm s}\) is the resistivity of the adsorption-saturated doping concentration; and \(Q_{\rm m}\) is the quality factor of the material which is dimensionless, where the \(Q_{\rm m}\) value of graphite is 4.  
At a temperature of 300K, for the elastic polymer composite materials doped with graphite, \(C_{0}\) = 75 M, \(C_{\rm c}\) = 80 M, \(C_{\rm S}\) = 90 M, \(\rho_{0}\) = 2.08 \(\times\) 10\({}^{6}\)\(\Omega\)-cm, and \(\rho_{\rm s}\) = 3.04 \(\times\) 10\({}^{2}\)\(\Omega\)-cm. When \(C_{\rm d}\) is smaller than \(C_{\rm c}\), Equation (3) could be simplified as follows:  
\[\rho=\rho_{0}\cdot e^{-Q\frac{C_{\rm d}-C_{\rm o}}{C_{\rm c}-C_{\rm o}}} \tag{4}\]
# Near-Optimal Heuristics for Just-In-Time Jobs Maximization in Flow Shop Scheduling
## 5 Computational Experiments and Results
### Experiment 2: Quality of the Heuristic Solutions in Relation to the Optimal Solution
It is important to highlight the excellent performance of the best heuristic (H6) that had a deviation from the optimal solution of just 0.6% which meant that it provided a near-optimal solution. Of the 6000 instances optimally solved, H6 reached the optimal solution in 5830 cases (97.2% of instances) and, in the other 170 instances, the difference between its result and the optimal solution was only one just-in-time job in 168 cases and two just-in-time jobs in two other cases. It is also relevant to emphasize that the average running times of H6 were 0.1 ms and 0.39 s for the instances in Groups 1 and 2, respectively. According to the previous comparative analysis, the results for H5 and H6 were very close and H2 and H1 also achieved significant performances. This confirmed that the EDD was better than the MST rule for the instances considered and the procedure based on [PERSON]'s approach (used in H6 and H5) outperformed the insertion method of NEH (applied in H2 and H1). Similarly, in all cases of each pair of heuristics, those which applied a neighborhood search produced improved results. Another interesting observation is that the enumeration method, as explained in Section 5.3, did not guarantee the optimal solution in the case of a flow shop with just-in-time jobs. Of the instances optimally solved, the deviations from the enumeration method were on average 0.4%, as expected, with an average running time of 1.48 s. The average running time of CPLEX was 199.88 s (3.33 min). Given the difficulties to prove optimality, for Group 2, only one instance per class was solved by CPLEX with the CPU time was limited to 3600 s. Thus, 96 medium and large instances were executed. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **H6** & **H5** & **H2** & **H1** & **H4** & **H3** & **H9** & **H8** & **H10** & **H7** \\ \hline RPD & 0.6 & 0.8 & 1.4 & 1.6 & 4.0 & 7.4 & 10.0 & 16.6 & 21.4 & 41.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Overall performance rankings (average RPD) of heuristics in relation to optimal solution for instances in Group 1. Figure 3: Comparisons of performances of heuristics in relation to those of model (95% confidence intervals of average RPD). For 22 instances, no integer solution was found or the model provided a solution with zero value. In most cases, the heuristic solutions were much better than the lower bound given by CPLEX, leading to very negative RPD, as can be observed in Table 11.  
The more negative is the RPD of a heuristic, the better is its result in relation to the lower bound. It could be noted that even the worst heuristic (H7) provided results that were better than the lower bound given by CPLEX. These results confirm all previous inferences. The coincidence of the results for H5 and H6 is remarkable, suggesting that neighborhood searches do not improve the solution of medium and large instances.
# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 3 Experimental Results and Analysis
### Datasets
Notably, each video sequence features at least 3-5 of these challenging attributes, creating a demanding environment for evaluating and comparing various object tracking algorithms. This setup effectively tests the robustness and performance of these algorithms. 3. UAV123 [27]: Comprising 123 video sequences and over 110,000 frames, the UAV123 dataset is designed to evaluate the performance of object tracking algorithms on unmanned aerial vehicles (UAVs). It provides videos captured in diverse and complex environments, assisting researchers in developing and testing robust and reliable tracking algorithms. The videos feature a wide range of scenes, including cities, villages, and coastlines, as well as various object types such as pedestrians, vehicles, and bicycles, which closely simulate real-world scenarios that drones may encounter during object tracking. The dataset's video sequences are annotated with 12 challenging attributes: aspect ratio change (ARC), background clutter (BC), out-of-view (OV), camera motion (CM), fast motion (FM), similar objects (SOB), full occlusion (FOC), illumination variation (IV), viewpoint change (VC), low resolution (LR), partial occlusion (POC), and scale variation (SV).
# Density Self-Adaptive Hybrid Clustering Routing Protocol for Wireless Sensor Networks
## 4 Performance Evaluation
### Simulation Results
#### 4.2.3 The Throughput of the BS  
The ultimate mission of the node is to get the sensed data and transmit them to the BS. Thus, the throughput of the BS is an important indicator to measure the performance of a network. The greater throughput of the BS means that more data packets have been transmitted, and the performance of the network is better. Figures 9 and 10, respectively, are the throughput of the BS at different locations. Obviously, the throughput of the DAHC protocol is greater than the LEACH-C and O-LEACH protocols. From Figures 5 and 6, we know that the first node dies later than the others with our protocol. In wireless sensor networks, data packets are mostly collected in the stable period. Once the node begins to die, the network will become unstable, and the throughput of the BS will decrease. Thus, the longer the stable period is, the more data packets are received. In clustering protocols, data aggregation is used. The CHs will remove redundant information and then send the processed data to the BS. In addition, the number of CHs and the network topology of the three protocols are different each round. Finally, the successful transmission of data is affected by many aspects. Some nodes may fail to send the sensed data. As mentioned above, the throughput is different in the initial stage.  
Figure 8: The residual energy of nodes versus rounds when the BS is outside.  
Figure 9: The throughput of the BS when it is inside.  
In summary, to make the throughput larger, we must extend the network's stability as much as possible. The effective measure is to save energy consumption and balance the network load, so that the time of the first node dies can be deferred. The experimental results show that the DAHC protocol has better performance in energy efficiency, cluster load, and prolonging the network lifetime, compared with the LEACH-C and O-LEACH protocols.