esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Stability Control Strategy of the 4 WS Vehicle
### Weighting Function Selection  
As mentioned above, the weighting functions are used to make the 4 WS control system have better system performance and robustness. For the mixed sensitivity problem, in general, the selection criteria of the weighting functions are described below.  
1. \(W_{1}\) is on behalf of the tracking and disturbance resistance of the control system, and it needs to possess low pass characteristics. Therefore, \(W_{1}\) is the low pass transfer function. \(W_{2}\) reflects the requirements of the sensitivity function \(KS\) and can reduce the control energy output. \(W_{2}\) is defined as a constant. \(W_{3}\) is responsible for the requirements of the complementary sensitivity function \(T\) and needs to have high pass characteristics. Hence, \(W_{3}\) is represented by the high pass transfer function.
2. In order to reduce the order of the designed robust controller and make it easier to be applied in the 4 WS vehicle, the weighting functions \(W_{1}\) and \(W_{2}\) are selected as the first-order transfer functions in this paper.  
Based on the selection principles mentioned above, the weighting functions can be denoted as follows.  
\[\begin{array}{l}W_{1}=\frac{s/M+w_{0}}{s+w_{0}A}\\ W_{2}=const\\ W_{3}=\frac{s+w_{0}/M}{s+w_{0}}\end{array} \tag{34}\]  
Figure 11: Mixed sensitivity problem with weighting functions (\(W_{1},W_{2},W_{3}\) are the weighting functions).  
where \(A\) represents the maximum steady-state error, \(A<1\); \(w_{0}\) is the expected bandwidth; \(M\) denotes the peak sensitivity; \(const\) denotes a constant.  
Significant efforts and extensive experiments are needed to find the best weighting functions, which are defined as follows.  
\[\begin{array}{c}W_{1}=\frac{0.2\varepsilon+1}{s+0.001}\\ W_{2}=0.01\\ W_{3}=\frac{s+0.2}{s+1}.\end{array} \tag{35}\]  
The bode diagrams of the weighting functions \(W_{1}\) and \(W_{3}\) are shown in Figure 12. Obviously, \(W_{1}\) and \(W_{3}\) are the low pass and high pass transfer functions, respectively.
## 2 Materials and Methods
### Construction Stage  
In November 2008, December 2008, and January 2009, between Chainage 4 + 369.8 and 4 + 595, H\({}_{2}\)S, SO\({}_{2}\), and NO concentrations were monitored for 30 days, while CO, CO\({}_{2}\), and O\({}_{2}\) concentrations were monitored for 32 days. The IR multi-gas monitor-900614 was used to measure CO, CO\({}_{2}\), and O\({}_{2}\) gases. The gases pass through an active filter  
Figure 1: (**a**) Tunnel length, geological units and overburden (modified from [PERSON], et al. [40]. (**b**) Lowari Tunnel’s profile. (**c**) Real-time image.  
that absorbs infrared light. A reference filter allows infrared light to pass that does not interact with the target gas molecules. Two infrared beams of different wavelengths are transmitted into the measurement chamber, which houses the measuring and reference detectors. The lower intensity of a measurement beam correlates to the gas concentration. The RAE Systems V-Rae PGM-7840 multi-gas detector with an electrochemical sensor and monitors was utilized to measure H\({}_{2}\)S, SO\({}_{2}\), and NO gases. Both portable multi-gas monitors were calibrated by the manufacturer and operated by RAE System Inc., San Jose, CA, USA (Document 008-4028, 2001). The six monitored gases were selected on the basis of the likelihood of their existence inside the tunnel throughout construction, enabling concentrations to be compared before and after blasting. Following tunnel blasting, the data were obtained within 250 m of the working face. For each day, monitoring of the gases was conducted with a monitoring duration of 1 h and a sampling frequency of 60 s, and the average values are reported. Through use of the Pro RAE-Suite Software, the collected readings were then compiled and analyzed. The monitoring data for gases were processed and studied to identify the concentration trends and compared with the Threshold Limits of Safety and Administration (2003).
# Low-Cost Area-Efficient FPGA-Based Multi-Functional ECDSA/EdDSA
## 5 Conclusions  
In this study, a low-cost, area-efficient ECC processor is developed for multiple ECC algorithms in a single design. Based on the proposed architecture, four well-known algorithms, which are Ed25519, ECDSA with NIST P-256, NIST P-384, and NIST P-521, are deployed. The novel hardware architecture satisfies multiple requirements that were trade-offs from the previous designs. It consumes significantly few resources and does not use any DSP. Therefore, it can be easily deployed on multiple hardware platforms without re-design. Especially, this proposal provides an approach to deploy multiple ECC algorithms in a single architecture without re-synthesis.  
In the experiments, we suggest a system that can improve the flexibility of the design. By combining with a separate SHA2 processor, the system not only performs the EdDSA/ECDSA, but also the SHA2 hash functions. The experimental results show that the core consumes 10,259 slices, which is lower than other single-functional ECC designs and can run up to 112.2 MHz on a Virtex-7 device. Another implementation that can perform two lightweight ECC algorithms, which are Ed25519 and ECDSA with NIST-P256, is also deployed. It consumes only 4276 slices and can run up to 122.05 MHz on the Virtex-7 device. Based on the features of the proposed EdDSA/ECDSA core and the experimental results, it can be concluded that our design can serve a wide range of applications and be able to deploy on multiple hardware platforms, especially for the resource-constrained devices that need a high level of security.  
For future works, we are working on higher performance for the processing elements. Two targets can be achieved if the processing elements are able to operate with a lower delay but keep the same resource usage. For the first target, our core can provide higher overall performance while ensuring efficiency in the area. For the second target, the resource consumption can be further reduced by removing some processing elements while maintaining the current working performance.  
**Author Contributions:** Supervision, C.-K.P. and T.-T.-H.; methodology, B.K.-D.-N. and T.-T.-H.; investigation, B.K.-D.-N., C.P.-Q. and N.-T.T.; writing--original draft preparation, B.K.-D.-N.; writing--review and editing, C.-K.P. and T.-T.H. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Experimental system.  
**Funding:** This research was funded by New Energy and Industrial Technology Development Organization (NEDO) project JPNP16007.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Acknowledgments:** This paper is based on results obtained from project JPNP16007, commissioned by the New Energy and Industrial Technology Development Organization (NEDO).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Discussion
Using the coordinates of the ground station in Tsinghua University and the precise orbit data of the Zhongxing-10 satellite over a 24 h period from September 1 to September 2, 2023 as an example, we calculated that if the satellite orbit determination accuracy is at the 10 m level, the resulting uncertainty introduced into the relativistic correction is approximately 0.04 ps, which is negligible. Therefore, based on the above analysis, we conclude that when extrapolated to satellite-to-ground links, the performance of the proposed system architecture is primarily limited by the accuracy of ionospheric correction. In the Ku-band, it is expected to reach an accuracy level on the order of several tens of picoseconds, and further improvement is possible when extended to the Ka-band or using a GNSS dual-frequency receiver. A small nonlinearity exists between the actual output of the phase shifter and the input, which is the reason for the regular spikes in the residual phase difference in the closed loop shown in the enlarged portion of Figure 11d. Moreover, the temperature sensitivity of the device should be further investigated. Using thermostats is expected to enhance the clock recovery performance. The long-term stability of the system is not superior to that of current state-of-the-art active hydrogen masers. Nevertheless, considering the structural design, cost, and realizable precision, the methodology and system outlined in this study retain considerable practical utility. Notably, in our prior work [24], after applying preliminary relativistic effect corrections and ionospheric compensation, we achieved a simultaneous clock frequency comparison precision of 18.06 ps 1\(\sigma\) and a Allan deviation of 7.67 \(\times\) 10\({}^{-15}\)/1000 s at a 10 km baseline. This demonstrated the viability of our correction methods at this level of comparative precision. However, these results were obtained using conventional commercial communication satellites and a classic transparent repeater architecture. Therefore, we believe that this architecture and method have the potential to enhance the current frequency comparison precision. Furthermore, this method can also serve other time and frequency transfer applications and provide a complementary solution to spaceborne time and frequency standards.
## 2 Material and Methods  
Large-Scale Screening of Strains with High Salt, High Acid, and High-Temperature Tolerance and High Esterase Activity  
For large-scale screening, 708 strains isolated from Chinese fermented meat products (sour meat) and fermented fish products (_Suanzhayu_ and _Chouguiyu_) stored in our lab were used. Strains were first cultured for 16 h in 96-well plates in their original isolated culture media, such as MRS, PCA, or LB [14]. The strains were collected by centrifugation (5000\(\times\)\(g\) rpm, 5 min, 4 \({}^{\circ}\)C) and suspended in 0.9% (\(w/v\)) NaCl, and diluted to a final \(OD_{\rm 600 nm}\) of 0.5. The diluted culture was then inoculated at 5% (\(v/v\)) into a 1.0 mL LB medium. The original culture conditions are 1% NaCl, pH 7.0, and 37 \({}^{\circ}\)C cultivation. For the test of high salt, high acid, or high-temperature tolerance, the corresponding condition was changed into 8% NaCl, pH value at 4.0, or cultivated at 42 \({}^{\circ}\)C, respectively, with the other conditions staying the same. After 24 hours of cultivation, the absorbance at \(OD_{\rm 600 nm}\) was recorded. For the determination of esterase activity, 100 \(\mu\)L diluted broth was mixed with 860 \(\mu\)L of 0.1 M McIlvane buffer (0.1 M citric acid, 0.2 M K\({}_{2}\)HPO\({}_{4}\), pH = 5.0) and 40 \(\mu\)L of \(p\)-nitrophenyl acetate (Aladdin, Shanghai, China) stock solution (25 mM). After that, this reaction was held at 37 \({}^{\circ}\)C for 2 h, and the absorbance at \(OD_{\rm 350 nm}\) was recorded [23]. All the above centrifugations, dilutions, inoculations, and assays were performed on Biomek i7 (Beckman, CA, USA).  
Venn diagrams were used to analyze the top 80 strains with high salt, high acid, and high-temperature tolerance and high esterase activity. For the next stage of the study, 15 strains with high esterase activity and at least two levels of stress tolerance were chosen (Table 1).  
Growth Characteristics of Strains under Different Conditions and Re-Measurement of Esterase Activity  
In 200 \(\mu\)L of LB medium, 15 strains were inoculated with a 2% (\(v/v\)) inoculum. The medium was set at different NaCl concentrations (8%, 6%, 4%) (\(w/v\)), pH values (7.0, 4.0, 3.0), and cultivation temperatures (20 \({}^{\circ}\)C, 37 \({}^{\circ}\)C, 42 \({}^{\circ}\)C) to determine the strains' growth in response to different stresses. The absorbance at \(OD_{\rm 600 nm}\) was measured using BioScreen C MBR (Oy Growth Curves Ab Ltd., Turku, Finland) in the experiments described above. For a total of 48 h, all strains were incubated to the plateau stage. The esterase activity was measured again in triplicate, as described above. The dry cell weight was determined using a 20 mL culture that had been dried to a constant weight. One unit of enzymatic activity was defined as \(p\)-nitropenol (\(\mu\)L) released per minute per gram of dry cell weight.  
Three strains of _L. plantarum_, namely M22, MB1, and 3-14-LJ, exhibited better resistance to stress. Among them, _L. plantarum_ M22 and 3-14-LJ showed significantly higher esterase activity than MB1. These three strains were used for further studies.
## 3 Analysis and Clustering Methodology
### Expert-Based Analysis and Representation  
An expert-based analysis and representation step is performed as the last stage of the analysis methodology to corroborate the appropriateness of the \(Q^{*}\) clustering solution and enrich the interpretation of the results.  
To that effect, a similarity matrix, together with a mapping function, are computed to allow experts to analyze the relationships between the obtained STP clusters. The similarity matrix \(\hat{\mathbf{S}}=(s_{i,j})\in\mathbb{R}^{N\times N}\) is computed, being the element \(s_{i,j}\), the Euclidean distance between STPs of sensed site \(I(i)\) and \(I(j)\), i.e., \(S_{i,j}=||\mathbf{STP}_{I(i)}-\mathbf{STP}_{I(j)}||_{2}\), being \(I(j)\) a bijective mapping function \(I:\mathbb{N}\rightarrow\mathbb{N}\) that covers the range of sensor numbers \(N\).  
The mapping function \(I(i)\) is initially defined as \(\Psi\). Then, this function is adjusted through an iterative manual process the objective of which is to obtain an \(\mathbf{S}\) matrix that tends to have lower distances for those positions close to the diagonal, being \(S_{ij}=0\) for \(i=j\), while having higher values for positions far from it. At each iteration, the mapping function \(I(i)\) is adjusted following two criteria: (i) the cluster indices positionscan be interchanged, e.g., \(\{I_{2},I_{1},\ldots,I_{Q^{*}}\}\) or \(\{I_{Q^{*}},I_{Q^{*}-1},\ldots,I_{1}\}\), etc.; (ii) the indices \(I_{k}\) (for \(k\in\{1,..,N\}\)) can be reordered within each cluster \(k\), while they are kept together.  
Therefore, the expert-based analysis considers the \(Q^{*}\) clustering and obtains a final similitude matrix \(\mathbf{S}\) driven by this partition but including a fine-grained analysis at a lower level (i.e., adjusting the ordering between clusters but also within each cluster), taking into account the Euclidean distances between STPs to improve the analysis of the results.
## 6 Verification of the Proposed Protocols
### Improved Version of DDSec Protocol  
To address the attacks discovered in the previous version of DDSec protocol, changes were made in the protocol modeling. To improve the protocol, \(UE_{A}\) sends a nonce rand_na with the hash of its public key \(hash(PK_{UE_{A}})\) to gNB in Message 1 and \(UE_{B}\) sends rand_nb with the hash of its public key \(hash(PK_{UE_{B}})\) to gNB in Message 2. gNB returns rand_na in Message 3 and rand_nb to \(UE_{B}\) in Message 4. That prevents the attacker from starting a session with \(UE_{B}\) as it will have to get nonce rand_nb, ID UB and the right hash for the public key to be trusted by \(UE_{B}\). The nonces together with the hashes of the public key provide the replay attack and integrity protection for Messages 1-4. The hashing of the keys adds another cryptographic operation to the protocol running as public keys are only known to the owner and gNB reducing the risk of being altered or leaked. The nonces sent in Messages 3 and 4 by gNB give assurance to UEA that UEB is trusted and vice versa. After the changes were made, the improved version of the protocol was run again and formally verified, as shown in Figure 8. ProVerif found no attack this time, hence the protocol is secure. ProVerif results indicate that the secrecy of rand_a, rand_b, d1, Acap, UA, UB, skuea, skueb holds and mutual authentication between \(UE_{A}\) and \(UE_{B}\) also holds as desired on injective and non-injective agreements.  
Figure 7: DDSec protocol attack trace.
## 3 The Proposed Method and Simulation Verification  
### Dataset Process  
The cantilever beam is used as the structural model to simulate the slender aerospace structure [29], and the ambient excitation of the structure is simulated by base excitation. The beam is shown in Figure 1, which is divided into 10 elements. Generally, the damping ratio of the actual aluminum beam is 0.01-0.03, so the damping ratio of the beam is 0.01 in the simulation. In practical engineering, the system is not constant, such as a rocket launch. With the consumption of fuel, the mass of the system decreases continuously [47].  
Therefore, 11 beams with different masses are constructed for the simulation. Take the first beam as a reference; the mass change of the other 10 beams are (0.001; 0.002; 0.003; 0.004; 0.005; 0.006; 0.007; 0.008; 0.009; and 0.01) kg. Therefore, the mass change of the first beam and the 11 th beam is 0.01 kg. The mass change is located on the 10 th node of the 10 th unit of the beam, and the others are the same. The length is 0.8 m, the section height is 0.012 m, and the width is 0.06 m. The elastic modulus is 7.1 \(\times\) 10\({}^{10}\) Pa, and the mass density is 2770 kg/m\({}^{3}\). In addition, the spectral lines is 1600 and the analysis frequency band is 1000 Hz.  
The construction process of the dataset is as follows. Taking the response directly as the input data of the network not only simplifies the data processing process, but also retains all the characteristics of the data. The noise with different signal-to-noise ratios (SNRs) [48] is added to the acceleration response. Additionally, the acceleration response signal is then processed according to the _O (f)_ (Section 2.1) to obtain the output data of the network (Figure 2). More specifically, each beam has 500 samples for model training. Therefore, the dataset is composed of 500 \(\times\) 11 samples, and each sample represents a two-dimensional matrix.
# Satellite Image Compression Guided by Regions of Interest
Thus, the ROI can be compressed with higher compression rates than the other regions, preserving the image quality for the useful content while ensuring data savings from the others. This paper targets significant data savings by proposing an on-the-fly analysis to guide the compression of images on satellites before transmission. Thus, additional bandwidth reduction can be achieved. The proposed method performs the detection of content having available measurements of the red band, green band, blue band (i.e., the RGB bands), and the near-infrared band (NIR band). The challenge is to achieve a sufficient probability of detection (PD) while keeping false alarm ratio (FAR) as low as possible, but within the expected platform's constraints with respect to processing time and energy expenditure. Preliminary results illustrating achievable gains in terms of information saved are described in [12]. In [12], information is discarded that is considered not useful content: (1) images covered by clouds and (2) water images without vessels. Thus, the methodology described in [12] is composed of a cloud detection algorithm and a vessel detection algorithm, and was evaluated using four satellite images. In our study, the proposed method is based on three detection algorithms: a cloud detection algorithm, a change detection algorithm, and a vessel detection algorithm. In turn, better gains in dry-land regions can be achieved, ensuring the image quality when something interesting has significantly changed in an area or when a new type of object appears. Experiments using 25 satellite images were conducted, representing typical scenarios of summer and winter days in Stockholm, Sweden. Precisely, the behavior of the system was evaluated in scenarios involving clouds, cloud shadows, fog, snow/ice, and vessels; and the strengths and weaknesses of the proposed method are discussed. The results show that a large amount of data can be saved, paving the way for large reductions in transmission costs to the ground station. For the images analyzed, 77.7% of the information can be discarded without compromising the predefined useful information. The merits of the proposed method are also shown through an individual analysis of some key images belonging to the dataset. Moreover, processing time analyses were performed using Unibap's iX10-100 space cloud platform, and we point out future directions. To the best of our knowledge, is there no other related work combining detection algorithms with one image compression algorithm aiming at remote sensing satellite data saving. This paper is organized as follows. Section 2 describes the proposed method and the experimental framework used to evaluate its performance. We present our results in Section 3, and in Section 4 we discuss them in the larger context. Finally, Section 5 presents our conclusions.
## 2 Materials and Methods
### Testing Procedures  
After warming up, participants performed FW squats on a custom-made FW device (Figure 1). Based on the results of a previous study [17] two sets of 10 consecutive squats were performed at four inertias (0.025, 0.075, 0.225, and 0.25 kg\(\cdot\)m\({}^{2}\)). To avoid any systematic inter-load effect of fatigue, inertias were administered in a random order. The first two repetitions were used to achieve correct squat execution (tempo and amplitude) and the last two to safely decelerate the spinning FW. For repetitions three through eight, participants were instructed to perform the concentric phase as quickly as possible and to delay deceleration during the first third of the eccentric phase and to make the transition from the eccentric to the concentric phase as short as possible. The execution of the squat was determined from the bottom position (approximately 90\({}^{\circ}\) knee angle) to full extension of the knees (approximately 0\({}^{\circ}\) knee angle). The arms were crossed with hands on opposite shoulders, and lifting the heels off the ground was not allowed. Participants were verbally encouraged loudly during all test sessions. To standardize the depth of the squat, vertical displacement was monitored, and real-time feedback was displayed on the screen in front of the subject. Rest periods between different inertias were at least 2 min long [37], allowing participants to maintain maximal power under the different exercise intensities.  
The linear position encoder, force platform, and shaft-integrated rotary encoder were used simultaneously. A low-range draw-wire sensor up to 1250 mm with a linearity of \(\pm\) 0.02% (Way-Con SX-50, Taufkirchen, Germany) was mounted perpendicular to the FW device below the standing platform and a draw-wire was attached to the sling (between the legs). The sensor assembly provided position-time data for the concentric phase of the squat. A bilateral force plate system (type 9260A, Kistler Instruments AG, Winterthur, Switzerland) with Kistler MARS software was used to collect \(GRF\) data (Figure 1).  
The latter two sensory systems were synchronised in time and acquired using a USB Data Acquisition System (type 5695B, Kistler Instrumenting AG, Winterthur, Switzerland). Rotary encoder data were collected using the shaft rotation sensor (slot type Optocoupler Module Speed Measuring Sensor for Arduino/51/AVR/PICCG, Jingliang, China), which records FW angular frequency data based on angular displacement (Figure 2). The sensor detects the holes in a sprocket-wheel mounted on the FW axis at a rate of 1 pulse per 7.5\({}^{\circ}\). This method of data acquisition allows for greater data sampling at high speeds.