esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### PHASE 3--Application for the 3D Simulation of the Maintenance Management of the Building Facilities
#### 3.3.3 Step 3--Develop Pre-Maintenance Activities
This categorization enables users to efficiently manage and prioritize maintenance tasks, ensuring that appropriate resources and actions are allocated for each work order type. Figure 22: Detailed asset management within the CMMS for the Civil Engineering School. This view highlights the “view details” option, which provides users with comprehensive information on specific assets. Displays the technical data sheet of a cistern toilet (**a**) and Ceiling light fixture (**b**), located on floor 0. It includes information such as the serial number, installation date, warranty, supplier, and associated documents (technical data sheet and diagram). The interface allows for the management of information and files related to the asset. be notified via email, from its creation to its closure, including the documentation that is attached to the asset while the WO is open. Room Management (Spaces)  
The spaces are those areas that contain the systems and/or assets to be managed in the CMMS. These function as a filter or list (Figure 26). Once the systems and assets to be treated are identified, the management becomes direct on the asset. Figure 24: (**a**) Work order (WO) listing for the “_Las Llamas_” Campus. This listing provides an overview of all active and pending work orders associated with the site, facilitating effective tracking and management of maintenance tasks and projects within the campus. (**b**) Calendarization of work orders (WOs) within the CMMS for the “_Las Llamas_” Campus. This feature enables users to visualize and schedule maintenance tasks over time, ensuring efficient planning and timely execution of work orders to support campus operations. Figure 25: Work order (WO) assignment notifications include a prompt to link the WO to the user’s Outlook calendar, improving time management and task tracking. Systems Management  
Systems are sets of assets that are grouped together to provide a service to the building. An example of such a system is the boiler room of Civil Engineering School (Figure 27a). In the _YouBIM_ system tab, users can access the set of subsystems that compose the boiler room (Figure 27b), considering the assets that make up each of them (Figure 27c) as if they were common to each other. Consequently, most of the maintenance scheduled for an asset within a subsystem (Figure 27d) will affect all the assets that compose that subsystem. Figure 27: Boiler room system of the Civil Engineering School. (**a**) A 3D model of the boiler room within the CMMS _YouBIM_, providing a detailed view of the facility. (**b**) List of subsystems that constitute the boiler room system, highlighting the various components involved. (**c**) List of assets composing these subsystems, offering insights into individual items and their roles within the system. (**d**) Specifically, the subsystem labeled “Piping Systems_Hydronic Supply \(1^{\prime}\) is included, illustrating its significance within the overall boiler room infrastructure.
## 4 Experimental Results
### Comparison with Benchmark  
After having described the parametric inquiry, how the audited parameters affect the networks and having acquired a better understanding of the behavior of the models along with their remarkable scores on the test set, we proceed to examine and compare the efficiency of the proposed methodology in contrast to well-established machine learning algorithms in the same task and thereupon consider the best performing methodology. For this matter, the ML algorithms introduced and implemented are:  
1. Support Vector Machine Classifier (SVMC or SVC)
2. Random Forest Classifier (RFC)  
The implementation with the benchmark classifiers was undertaken by extending the inspected instances of the parametric analysis described in Figures 13 and 16, according to the instances shown in Table 7, subsequently developing the respective 1D-CNNs and matching them with two counterpart benchmark classifiers. The dataset utilized is the same as the one used for the implementation of the proposed CNN models, and the percentages of the dataset allocating the input samples to the each subset remain also intact at 70%-10%-20% for training, validation, and test set, accordingly providing uniformity to the comparisons being held.  
ROC curve plots and calculation of the respective AUC are employed to facilitate the comparisons among the different classifiers. First, Table 8 presents the AUC values corresponding to each classifier for every instance. Subsequently, Figure 20 and Table 9 present indicative ROC curves and the respective AUC values, respectively, from selected instances, illustrating the defectiveness of the ML classifiers in the task of the 1D signal classification, in contrast to the near-perfect classification of the proposed 1D-CNNs on every occasion, with AUC scores constantly in the vicinity of 0.99. Additionally, the metric scores of the three classifiers in each of the instances developed and inspected are recorded for verifying the preeminence of the proposed models, achieving near 100% to every metric across all nine instances. Furthermore, on numerous occasions, the accuracy is proximate to 15%, which means that the models cannot evaluate the state of the vessel and randomly predict one out of the seven possible states.
NLOS identification and ranging error mitigation methods: a critical aspect of improving ranging and positioning accuracy involves the detection, identification, and correction of NLOS errors in UWB and Wi-Fi RTT ranging measurements. 3. Advanced positioning methodologies: A variety of advanced positioning methodologies are reviewed, including methods for enhancing positioning accuracy and continuity, and practical considerations for efficient field deployment. Multi-sensor fusion is highlighted as a fundamental approach in this context. 4. Advantages and disadvantages: We thoroughly discuss the strengths and limitations of UWB and Wi-Fi RTT high-precision indoor positioning technologies, providing a balanced perspective on their applicability, potential challenges, and future development. By addressing these key areas, this survey aims to contribute significantly to the understanding, development, and optimization of UWB- and Wi-Fi RTT-based indoor positioning, serving as a guiding reference for young researchers, engineers, and system designers exploring novel research directions and solutions within these domains. The structural framework is illustrated in Figure 2. \begin{table}
\begin{tabular}{c c c} \hline
**Reference** & **Year** & **Technology** \\ \hline
[16] & 2015 & Overview of the Wi-Fi Fine time measurement-based positioning principle \\ \hline
[7] & 2016 & Overview of UWB positioning principle, algorithms and challenges \\ \hline
[6] & 2017 & Overview of fundamental UWB positioning principle and algorithms \\ \hline
[17] & 2018 & Overview of indoor positioning technology \\ \hline
[18] & 2021 & Overview of UWB self-calibration and collaborative localization \\ \hline
[8] & 2022 & Overview of the main positioning algorithms in general \\ \hline
[19] & 2022 & Overview of UWB-based smart logistics \\ \hline
[20] & 2022 & Overview of indoor high-precision positioning technology \\ \hline
[21] & 2023 & Overview of indoor high-precision positioning technology \\ \hline
[22] & 2023 & Overview of indoor positioning technology \\ \hline
[23] & 2023 & Overview of the classification of NLOS error and its impact on UWB positioning accuracy \\ \hline
[24] & 2023 & Overview of fundamental UWB positioning principle and algorithms and their recent development. \\ \hline
[25] & 2023 & Overview of NLOS identification and error mitigation for UWB indoor positioning \\ \hline
[26] & 2023 & Overview of Wi-Fi assisted indoor positioning on different principles \\ \hline
[27] & 2024 & Overview of deep learning-based Wi-Fi indoor positioning \\ \hline \end{tabular}
\end{table} 
Table 1: Previous survey about UWB/Wi-Fi RTT positioning.
## 5 Security Verification
### Formal Analysis Using ProVerif  
We used the ProVerif tool to formalize the proposed protocol in two parts. ProVerif is an automated protocol verification tool that emulates protocols and validates secure protocols against known active and passive attacks. It can handle various encryption primitives, such as key exchange schemes, hash functions, asymmetric encryption, and symmetric encryption. Since the proposed scheme is based on bilinear mapping, we used equations in ProVerif to add specific rules to analyze the protocol more accurately.  
The ProVerif code of the proposed scheme consists of two parts: unlinkable authentication verification and batch authentication verification. The roaming authentication phase is included in each part. All verification results are shown in Figures 6 and 7. Specially, _sk_LEO_ and _sk_LEO2_ indicate secret keys for different LEOs; and _psk_, _K0_, and _k1_ indicate the keys used in the roaming authentication phase described in Section 4.3. For unlinkable authentication verification, (_phi_, _A_) and (_epsilon1_, _epsilon2_) indicate the UE's group secret keys and NCC's group master secret keys in Section 4.1. For batch authentication verification, \(s\) and _sk_NCC_ indicate the secret keys of the NCC, and (_BMK_, _BK_, _RK_) indicate the keys used in batch authentication in Section 4.1. There are injective correspondences between the participants in each step of authentication, pre-negotiation, and roaming authentication. The figures show that the two parts of the proposed scheme are reliable, and the individual keys and the negotiated session keys are secure.
## 2 Accelerator
### Injector
#### 2.2.1 Gun Laser  
Compact and industrial-grade laser systems with high power stability and ultra-low timing jitter have become a key component in free electron lasers. At SwissFEL the drive laser for the electron gun consists of solid state Yb:CaF\({}_{2}\) chirped pulse amplifier. For electron production the stability of the drive laser plays a crucial role. While Ti:sapphire lasers are standard technology used in many FELs around the world, we considered an Ytterbium-doped gain medium. This laser system offers exceptional long-term amplitude stability, low intrinsic timing jitter, a compact design and very high up-time. The Yb-doped laser is pumped with a telecom-standard semiconductor diode emitting at 976 nm. Such pump diodes are favourable in view of long-term performance and low maintenance costs. The oscillator delivers sub-200 fs, transform-limited soliton pulses centered at 1041.3 nm with an amplitude stability of 0.19% rms over 18 hours. The measured free running jitter of 6.3 fs rms (integrated over 1 kHz to 1 MHz) is ultralow. The system can be actively synchronized with an RF reference signal with a locked timing jitter of 18 fs rms (10 Hz-1 MHz).  
The oscillator seeds a commercial Yb:CaF\({}_{2}\) regenerative chirped pulse amplifier system. The amplifier is pumped with a single CW diode module delivering high power at 980 nm. This provides high reliability and a long lifetime of up to 20,000 h. After amplification to 2.4 mJ the stretched pulse is compressed to 700 fs FWHM by means of a transmission grating pulse compressor. Temporal drifts of the chirped-pulse amplification (CPA) system are compensated by employing a feedback loop which stabilizes the drift to \(<\)32 fs rms over 250 min. For UV generation two home-made nonlinear frequency conversion stages based on BBO crystals are employed which provide pulses of up to 600 uJ at 260 nm. To lower the electron beam emittance the Gaussian-like temporal pulse shape is transformed into a flattop-like pulse by a set of four birefringent \(\alpha\)-BBO crystals. Three different sets of crystals provide the three pulse durations of 3.6, 6.7 and 10 ps for electron bunch production. For transverse beam shaping a variable circular aperture is used to produce a truncated Gaussian beam profile which is imaged over a 20 m distance onto the Cs\({}_{2}\)Te cathode. At the cathode a pulse energy of approximately 100 nJ is used to produce the 200 pC charge.  
A small part of the chirped amplified infrared laser beam (150 uJ) is split, compressed (40-50 ps) and directed to the laser heater. Overlapped with the electron bunch in time and space the scheme allows the energy spread of the electrons to be increased to avoid unwanted coherent radiation downstream of the linear accelerator.
# Study on Experimental and Constitutive Model of Granite Gneiss under Hydro-Mechanical Properties
## 2 Materials and Methods
### Experiment Plan and Method  
The fully automatic triaxial servo system for rocks could control axial pressure, confining pressure, and seepage pressure, as shown in Figure 1. Different seepage pressures are set at the rock sample at both ends so as to form seepage pressure differences to achieve the application of seepage pressure. The control system of seepage water pressure can accurately dominate the inlet seepage pressure, achieve the constant-flow pump method, and convert the rock sample permeability by monitoring the seepage water flow rate. The range of applied load for hydraulic pumps controlling the confining pressure is 0-60 MPa, and applied bias pressure range for hydraulic pumps controlling the axial pressure is 0-500 MPa. A linear displacement sensor (LVDT) and a circumferential strain measurement ring composed the strain measurement system. The experimental data can be recorded in real-time and transmitted to the computer to achieve full digitalization and mapping during the experimental process, facilitating real-time dynamic monitoring of the experiment.  
Before starting the experiment, a rock saturation test was conducted to simulate the rock sample's natural state, as the granite genesis had been in a saturated state when it was taken from an underground, underwater cavern. In order to decline the temperature changes during the experiment, maintain the indoor temperature at 20 \(\pm\) 0.5 \({}^{\circ}\)C; this temperature is the basic requirement for room temperature testing [24]. The experiment adopted a loading method of stress control followed by strain control. At the initial loading stage, maintain the confining pressure and pore pressure values. Using 0.75 MPa/min loading rate, paying attention to observing the changes in the circumferential strain curve. When nonlinear characteristics are exhibited on the circumferential strain curve, the rock sample undergoes plastic deformation; that is, near the yield stress point, the loading method changed from stress control to strain control and controlled 0.01 mm/min loading rate till the rock sample failed. The two types of rock samples used for seepage pressure were 1 MPa and 3 MPa. The test plan is shown in Table 1.  
Measuring the granite genesis permeability in test accurately by the rock seepage water flow changes and its relationship with the confining pressure and pore pressure difference [25]. As shown in Formula (1):  
\[K=\frac{\mu LV}{A\Delta P\Delta t}, \tag{1}\]  
where \(K\) is the permeability (m\({}^{2}\)); \(\Delta P\) is the pore pressure difference between the specimen two ends (Pa); \(\mu\) is the water dynamic viscosity (Pa-s), \(\mu\) = 1 \(\times\) 10\({}^{-3}\) Pa-s at 20 \({}^{\circ}\)C; \(A\) is the rock specimen cross-sectional area (m\({}^{2}\)); \(L\) is the rock specimen length (m); \(V\) is the instantaneous flow volume (m\({}^{3}\)); and \(\Delta t\) is the time interval (s).
## 3 Results and Discussions  
### Solar PV  
The total power drawn by the compressor and its accessories is 443 kW, which provides a cooling capacity of 1.2 MW with a COP of 2.8. To run this device merely with solar system, nominal array output of 2.5 MW is needed for a place with 6-h of sun peak. Since most of the remote mines are located in high solar intensity zones, this solar irradiation intensity is considered as a standard number and is used in analysis hereafter. Additionally, 3.4 acres of land is needed to install the proposed solar photovoltaic system. Figure 2 illustrates the design change in the solar PV system, and the corresponding change in the Levelized Cost of Energy (LCOE) and the area of the solar field required for different sun peak hours. As expected, there is a strong correlation between solar intensity, LCOE, and the area of the solar field required associated with sun peak hours. An increase in sun peak hours leads to an obvious decrease in installation capacity, LCOE, and area of installation field required.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time Parameters** & \multicolumn{3}{c}{**Solar Cost Share (USD per W)**} & \multicolumn{2}{c}{**Battery Cost Share (USD per kWh)**} \\ \hline Analysis year & 2020 & Solar Panel & 0.47 & Battery & 209 \\ Year construction starts & 2020 & Inverter & 0.08 & Structural & 13 \\ Construction period (year) & 1 & Structural and electrical & 0.25 & Electrical & 36 \\ Project life (years) & 20 & Installations and labor & 0.13 & Installations and labor & 23 \\ Inflation/discount rate (\%) & 2 & Shipping and handling & 0.16 & Shipping and handling & 12 \\ \hline
**Financing Parameters** & \multicolumn{3}{c}{PII} & 0.09 & PII & 8 \\ \hline Equity (\%) & 50 & Sales tax & 0.05 & Sales tax & 22 \\ Loan (\%) & 50 & Developer overhead & 0.36 & Developer overhead & 10 \\ Financing rate (\%) & 8 & Contingency & 0.04 & Contingency & 10 \\ Loan term years & 10 & Developer net profit & 0.11 & Developer net profit & 18 \\ Salvage factor (\%) & 5 & Total & 1.74 & Total & 361 \\ \hline \hline \end{tabular} Note: PII = Construction permitting, interconnection, testing, and commissioning.  
\end{table}
Table 1: Economic evaluation parameters of proposed systems.
## 3 Lightweight Cryptography for Smart Objects
### Public-Key (Asymmetric) LWC Algorithms
#### 3.2.1 RSA Algorithm  
The Rivest, Shamir, and Adleman (RSA) algorithm is the most known and widely used public-key scheme. It is based on exponentiation in a finite field over integers modulo \(N\). Consider a modulus \(N\) and a pair of public and private keys \((e,d)\). The encryption of a message \(m\) is given by \(c=m^{e}\bmod N\), while the decryption is \(m=c^{d}\bmod N\). The key generation phase of RSA, aiming to generate the public-private key pair, consists of the following steps:  
1. select two large prime numbers denoted as \(p\) and \(q\) such that \(p\
eq q\);
2. compute \(n=p\cdot q\);  
\begin{table}
\begin{tabular}{|l|l|r|r|r|r|r|} \cline{2-7} \multicolumn{1}{c|}{} & **Cipher** & **Key size** & **Block size** & **Rounds** & **GE (hardware impl.)** & **Code size (software impl.)** \\ \hline \multirow{3}{*}{Software ciphers} & AES & 128 & 128 & 10 & 3400 [32; 33] & 2606 \\ \cline{2-7}  & TEA & 128 & 64 & 32 & 3490 [34] & 1140 \\ \cline{2-7}  & \multirow{3}{*}{\(\text{SEA}_{96,8}\)} & \multirow{3}{*}{96} & \multirow{3}{*}{8} & \multirow{3}{*}{\(\geq 3n/4\)} & 3758 1 [35] & \multirow{3}{*}{2132} \\  & & & & & 3925 2 [35] & \\ \cline{1-1} \cline{4-7}  & PRESENT & 80 & 64 & 32 & 1570 [28] & 936 \\ \cline{1-1} \cline{4-7}  & HIGHT & 128 & 64 & 32 & 3048 [30] & 5672 \\ \hline \end{tabular}  
* Round-based implementation with datapath of size \(n\); 2 Serialized implementation with datapath of size \(b\).  
\end{table}
Table 1: Comparison of different symmetric-key cryptographic algorithms. Key size and Block size are expressed in number of bits. Code size is expressed in number of bytes.  
3. compute the [PERSON]'s totient function \(\Phi(n)=(p-1)\cdot(q-1)\);
4. choose an integer \(e\) such that \(1<e<\Phi(n)\) and that the \(GCD\)(\(e\),\(\Phi(n)\)) = 1;
5. compute \(d=e^{-1}\) mod \(\Phi(n)\);
6. the pair \((n,e)\) is the public key, while \(d\) is the private key.  
The security of the RSA algorithm depends on the hard problem of factorizing large integers. In order to achieve an acceptable level of security, \(n\) should be at least 1024 bits long, so that \(p\) and \(q\) and consequently \(\Phi(n)\) cannot be obtained, thus protecting the (\(e,d\)) pair.  
RSA is unsuitable for adoption in constrained devices due to the need to operate on large numbers and the fact that long keys are required to achieve sufficient security. Moreover, both key generation and encryption/decryption are demanding procedures that result in higher energy consumption.
# Lossless Compression of Large Aperture Static Imaging Spectrometer Data +
## 5 Conclusions  
The LASIS instrument encounters unprecedented problems in the hyperspectral image compression approach because of its unique data characteristics. This paper briefly outlined the imaging principle and the characteristics of data acquired by the LASIS. A new lossless compression technique for the LASIS has been proposed, which combines a novel data rearrange method with the CCSDS-123. The interference information superimposed on one frame image of the acquired data can be separated with the proposed rearrange approach, and then, the CCSDS-123 standard is employed to process the rearranged data cube. Several experiments were conducted on 30 hyperspectral images for different scenes acquired by the LASIS instrument to examine the performance of the proposed compression scheme. The experimental results indicate that the average compression ratio of the proposed compression method is approximately 32.9% higher than that obtained by combining the traditional rearrange approach with the standard. In addition, an adequate optimal combination of the compression parameters has been presented, and it yields a 19.6% improvement over the default settings. These results can provide instructive information for the lossless compression of other hyperspectral interferometers based on a LASIS-like principle.  
Conceptualization, L.Y. and H.L.; methodology, L.Y.; investigation, L.Y.; writing--original draft preparation, L.Y.; writing--review and editing, W.L. and J.L.; funding acquisition, L.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific Research Program of Shaanxi Provincial Education Department (Grant No. 21)K0787), the Natural Science Foundation of Shaanxi Province (Grant No. 2020)Q-651), and the Ph.D. Stand-Up Fund of Xi'an University of Technology (Grant No. 108-451122002).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Name** & **Symbol** & **Setting** \\ \hline \multirow{8}{*}{Predictor parameters} & Prediction mode & & Reduced mode \\  & Local sum type & & Neighbor-oriented \\  & Number of prediction bands & \(P\) & 5 \\  & Weight update scaling exponent initial parameter & \(v_{min}\) & \(-6\) \\  & Weight update scaling exponent final parameter & \(v_{max}\) & 6 \\  & Weight update scaling exponent change interval & \(t_{inc}\) & \(2^{7}\) \\  & Weight component resolution & \(\Omega\) & 13 \\  & Register size & \(R\) & 32 \\ \hline \multirow{4}{*}{Entropy coder parameters (sample-adaptive)} & Initial count exponent & \(\gamma_{0}\) & 1 \\  & Rescaling counter size & \(\gamma^{*}\) & 4 \\ \cline{1-1}  & Accumulator initialization constant & \(K\) & 4 \\ \cline{1-1}  & Unary length limit & \(U_{max}\) & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Adequate compression settings of the proposed compression scheme.