esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In response to these difficulties, a more precise formulation of the satellite frequency signal transfer (SFST) approach, based on the tri-frequency combination technique, has been developed [20; 21]. This approach aims to determine the GP difference between a satellite and a ground station or between two satellites with an accuracy level of several centimeters when establishing high-precision frequency signal links. For the practical application of the SFST method in geodesy, the relative stability of clocks should reach about \(10^{-18}\) over several hours, corresponding to approximately 1 cm in height. In the present study, we propose an approach to determine the GP of a LEO satellite along its trajectory, employing the SFST technique. The GP of a LEO satellite can be utilized to construct an Earth Gravitational Model. We refer to the LEO satellite as the Target Satellite (TS) for ease of reference. In Section 2, we provide a concise overview of the SFST technique and the gravitational frequency shift approach, demonstrating how these methods can ascertain the GP difference between a TS and a GEO satellite. Subsequently, we propose an approach to determine a GP distribution over the TS-sphere by establishing frequency links between a TS and three GEO satellites. In Section 3, we conduct simulation experiments under various conditions to evaluate the approach proposed in this study. We summarize the experimental results and discuss potential issues in Sections 4 and 5.
# Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
## 3 Ellipsoidal State Estimation Procedure
### Ellipsoidal State Prediction Step
**Step P5:**: Compute an ellipsoidal enclosure of the Minkowski sum of the two intermediate results \(\mathscr{E}_{\boldsymbol{\Phi},k+1}\) and \(\mathscr{E}_{\boldsymbol{\Psi},k+1}\) according to  
\[\mathscr{E}_{\boldsymbol{\Phi},k+1}\oplus\mathscr{E}_{\boldsymbol{\Psi},k+1} \subseteq\mathscr{E}_{k+1}(\boldsymbol{\mu}_{k+1},\boldsymbol{\Gamma}_{k+1}) \tag{43}\]  
with the new midpoint  
\[\boldsymbol{\mu}_{k+1}=\boldsymbol{\mu}_{\boldsymbol{\Phi},k+1}+\boldsymbol{ \mu}_{\boldsymbol{\Psi},k+1} \tag{44}\]  
and the updated shape matrix  
\[\boldsymbol{\Gamma}_{k+1}=\mathbf{Q}_{k+1}^{\frac{1}{2}}\enspace, \tag{45}\]  
resulting from the closed-form expression  
\[\mathbf{Q}_{k+1}=\left(1+\frac{1}{\beta}\right)\cdot\left(\boldsymbol{\Gamma} _{\boldsymbol{\Phi},k+1}\cdot\boldsymbol{\Gamma}_{\boldsymbol{\Phi},k+1}^{T} \right)+\left(1+\beta\right)\cdot\left(\boldsymbol{\Gamma}_{\boldsymbol{\Psi},k+1}\cdot\boldsymbol{\Gamma}_{\boldsymbol{\Psi},k+1}^{T}\right) \tag{46}\]  
with  
\[\beta=\sqrt{\frac{\text{trace}\Bigl{\{}\boldsymbol{\Gamma}_{\boldsymbol{\Phi},k+1}\cdot\boldsymbol{\Gamma}_{\boldsymbol{\Phi},k+1}^{T}\Bigr{\}}}{\text{ trace}\Bigl{\{}\boldsymbol{\Gamma}_{\boldsymbol{\Psi},k+1}\cdot \boldsymbol{\Gamma}_{\boldsymbol{\Psi},k+1}^{T}\Bigr{\}}}}\enspace. \tag{47}\]  
For a derivation of this expression, the reader is referred to [7; 25; 26].
## 4 Role of URLLC in Operating IoT
### IoT Energy Efficiency (EE)  
In IoT and machine-to-machine (M2M) communications, EE will play an important role, especially with sensor-type equipments with limited resources, for example, limited battery and computing power [41]. A number of URLLC applications require a lot of computation, which is not handled by some IoT devices [42]. From a PHY perspective, it is a challenge for URLLC to achieve low latency and high reliability in mission-critical IoT devices. The use of short packet in order to achieve low latency can degrade channel-coding gain, and it causes reliability issues in wireless channels. To mitigate reliability issue, re-transmission is required, but it involves additional resources and increases latency [43].
## 2 Literature Review
### Time Series Dependency  
The electrical load shows good correlation with previous load data. Observing the change in load and other variables can be linked to the electric load in reference to time. However, identifying accurate time series models is a challenging task, as it requires training several models to select the best among them. In [8], a linear regression model was used to perform short-term load forecasting. It classified the load on an hourly basis to model the load for the entire following day. A simple hour-by-hour relation was established using MLR such that the model was developed with a simple least square for general order correlation with a lag load. The lag load tends to have relatively high importance in comparison to other factors affecting the demand. It was not just the regression but the ensemble models that emphasized time lags as the most important feature in comparison with the environmental variables [17]. In the case of France, the model developed showed that the previous 10 days of load data held the highest importance, followed by the temperature and other deterministic variables. When studying the day-to-day load variation, the weekdays and weekends had a visible impact on electricity demand. Studies showed that the demand for weekdays was stable in comparison with the weekends [27]. To better track the effect of special days, the authors of [9] classified the data into three categories: a Sunday and holidays group, a Monday to Friday group, and a Saturday group. With such classification of the load data to different time sets, the forecast obtained an appealing MAPE of 1.17% for the Sunday and holidays group, 0.62% for the working days, and 0.83% for the weekends. The short-term load forecasting methods have a limitation on their use when it comes to special days, weekends, or holidays. One study [10] used a hybrid approach of ANN-based methods and fuzzy logic to forecast the hourly load of special days, as a load on a special day tends to spike up or stay high, load addressing for these days becomes important. Such separation, as well as interactive coupling with other environmental factors, can reduce the prediction error to 2% [10; 29].
# A Universal Low-Complexity Demapping Algorithm for Non-Uniform Constellations
## 6 Conclusions  
This paper proposes a low-complexity NUC demapping algorithm, i.e., the SCSR algorithm. SCSR algorithm creates the subsets based on the quadrant of the 2D-NUC received symbol or the sign of the I/Q component after the 1D-NUC received symbol is decomposed. The number of constellation points contained in the subsets is further reduced through the condensation characteristic. When demapping the received symbols, EDs calculation and comparison only need to be performed on the constellation points contained in the subsets. Compared with the Max-Log-MAP algorithm, when demapping 2D-256 NUC, the number of EDs that need to be calculated can be reduced by 67.58% to 96.48%, and the number of EDs that need to be compared can be reduced by 74.07% to 96.88%. When demapping 1D-1024 NUC, the number of EDs that need to be calculated can be reduced by 46.88% to 90.63%, and the number of EDs that need to be compared can be reduced by 49.38% to 96.88%. The BER simulation results show that, compared with the Max-Log-MAP algorithm, the performance loss of the SCSR algorithm is less than 0.01 dB. This paper also compares the complexity and performance of the SCSR algorithm and QCSR algorithm. The results show that the SCSR algorithm can always achieve the same or better BER performance as the QCSR algorithm, and the complexity of the SCSR algorithm is almost always lower than that of the QCSR algorithm.  
Conceptualization, H.W. and M.L.; methodology, H.W.; software, H.W.; validation, H.W., M.L., and C.W.; formal analysis, H.W.; investigation, H.W.; resources, M.L.; data curation, H.W.; writing--original draft preparation, H.W.; writing--review and editing, H.W., M.L., and C.W.; visualization, H.W.; supervision, M.L. and C.W.; project administration, M.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China (grant number 2018 YFB1802200) and Shanghai Technical Standard Project (grant number 18 DZ2203900).  
The authors would like to thank the Shanghai Advanced Research Institute, Chinese Academy of Sciences for supporting in this investigative project.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Heat Map  
Figure 12 illustrates a heat map showing the key parameters used to evaluate the performance of configurations with respect to heat pump size and cut-off temperature. The heat map provides a visual comparison and detailed analysis of multiple parameters simultaneously. A green color gradient is used to indicate the best choices, where darker green represents better or more desirable values and lighter green represents less optimal values. The optimum condition for HHP systems occurs when \(\eta_{\text{S}}\), \(\text{SCOP}_{\text{net}}\), and APF (annual performance of HHP) are at high values and OC and EWI are at low values. Overall, the heat map reinforces that serial operation generally performs better across most metrics, especially at higher power levels and cut-off temperatures. Moreover, configuration 2 often outperforms configuration 1 in serial operation, and in parallel operation, configuration 4 generally shows better performance than configuration 3.
# Lossless Compression of Large Aperture Static Imaging Spectrometer Data +
## 4 Experimental Results
### Entropy Coder Settings  
#### 4.3.1 Sample-Adaptive Entropy Coders  
* Initialization and adaptation parameter  
Figure 10a shows the compression ratio as a function of \(\gamma^{*}\) for several combinations of the initial count exponent, \(\gamma_{0}\), and the accumulator initialization table, \(\{k^{\prime}_{z}\}_{z=0}^{N_{z}-1}\), which is simplified as an accumulator initialization constant, \(K\); i.e., \(k^{\prime}_{z}=K\) for all bands. The interval at which the accumulator and counter of this encoding approach are rescaled is controlled by the rescaling counter size parameter, \(\gamma^{*}\). A smaller value of \(\gamma^{*}\) is shown to yield a faster adaptation and better compression performance, and it generally makes the compression effectiveness less sensitive to the values of \(\gamma_{0}\) and \(K\). When \(\gamma_{0}\) is a small value, the compression performance is less sensitive to the value of \(K\). The value of \(\gamma_{0}\) has almost no impact on the compression performance by avoiding the minimum allowed value of \(K\).  
Figure 10b shows the compression ratio as a function of \(K\) obtained via the combination of \(\gamma^{*}=4\) and \(\gamma_{0}=1\). The curve is remarkably flat, which means that the value of \(K\) has almost no effect on the compression performance.  
* Unary length limit  
The compression ratio as a function of the unary length limit parameter, \(U_{max}\), is presented in Figure 10c, which shows that increasing the value of \(U_{max}\) yields a dramatic  
Figure 10: Compression ratio for different sample-adaptive entropy coder parameters: (**a**) rescaling counter size parameter, \(\gamma^{*}\); initial count exponent, \(\gamma_{0}\); (**b**) accumulator initialization constant, \(K\); (**c**) unary length limit parameter, \(U_{max}\).  
improvement in the compression ratio when \(U_{max}\) is close to the minimum allowed value. When the value of \(U_{max}\), which limits the length of the unary code part, is set too small, it results in notably poor coding efficiency for the mapped prediction residual, with relatively smaller magnitudes. However, the cost of hardware implementation will remove this benefit to the compression effectiveness when the value of \(U_{max}\) is sufficiently large. Based on the results displayed in Figure 10, \(U_{max}\approx 18\) appears to offer a reasonable compromise between the hardware implementation complexity and compression performance.
# Improving Data Communication of Enhanced Loran Systems Using 128-_ary_ Polar Codes
## 3 Results
### Doppler Shift Error Performance Sensitivity  
To evaluate the impact of relative motion on error performance, this subsection examines the Doppler shifts that can arise in the eLoran system and analyzes their effect on the eLoran communication schemes.  
Since the eLoran system typically involves ground-based transmission and reception, we first consider a mobile communication scenario with a maximum relative velocity of 100 km/h, according to the Doppler shift formula \(\Delta f=\frac{v}{c}\cdot f_{c}\), where \(\Delta f\) is the Doppler shift, \(v\) is the maximum relative velocity between the source and the receiver, \(c=3\times 10^{8}\) m/s is the light velocity, and \(f_{c}=100\) kHz is the eLoran signal frequence [6]. The resulting frequency shift is approximately 0.0093 Hz, which is negligible. Furthermore, we consider the case of low earth orbit (LEO) satellite communication. Assuming a satellite-to-ground station distance of 550 km (corresponding to the Starlink satellites), the maximum relative velocity between the satellite and the ground station can reach approximately 7.5 km/s, resulting in a Doppler shift of about 2.5 Hz. We conducted error performance simulations under a 2.5 Hz Doppler shift, and the results (presented in Figure 7) are nearly identical to those obtained without Doppler shift, regardless of the coding and decoding methods employed. The above results indicate that the insensitivity to Doppler shift is a characteristic of eLoran modulation and is independent of the encoding and decoding methods used. This is because the principle of eLoran is primarily based on the pulse's time of arrival, rather than carrier phase or frequency, making the impact of Doppler shift on the eLoran system negligible. Therefore, the Doppler shift error performance sensitivity will not be further discussed in the following sections.  
Figure 6: Error performance comparisons between the 128-_ary_ polar code with SH demodulation and the RS code with SH and HH demodulation.
## 1 Introduction
### Related Works  
[PERSON] et al. [22] proposed the first MPSI protocol based on the additive homomorphic encryption technique. This protocol represents the set elements as the roots of polynomials and implements two-party PSI under the semi-honest adversary model. Additionally, they introduced the idea of constructing a Multiparty PSI protocol for the case of malicious adversaries. In 2005, [PERSON] et al. [23] used additive homomorphic encryption and private key secret-sharing techniques to design an MPSI protocol, whose computational and communication complexity was twice the size of the set and the number of participants. The computational complexity of such algorithms does not meet the requirements of practical applications. After that, reducing the computational and communication overhead of MPSI protocols based on homomorphic cryptography has become an important research goal.  
In 2017, [PERSON] et al. [26] gave an MPSI protocol based on Bloom filters and homomorphic encryption. This protocol maps the set into a Bloom filter, encrypts it, and performs a homomorphic multiplication operation on the encrypted Bloom filter. The intersection set is then obtained by comparing the results based on the corresponding decryption operation. However, the protocol of [PERSON] et al. [26] has a rather obvious drawback that the Bloom filter to be encrypted by each participant is equally large, even if the original set is small.  
In the same year, [PERSON] et al. [27] also proposed a PSI protocol based on Bloom filters and Paillier homomorphic encryption, which overcomes the drawback of [PERSON] et al. [26]. In the protocol [27], the operations of mapping the aggregate data and encrypting the Bloom filter are basically the same as in [26]. The difference is that the participant who wants to obtain the intersection maps his/her aggregate data into the encrypted Bloom filter using the same hash functions as those used in the mapping. The intersection is obtained by performing the interactive decryption operation and utilizing the homomorphic properties of the Paillier encryption scheme.  
In 2022, [PERSON] et al. [28] extended the protocol [27] to multiple participants by performing threshold Paillier additive homomorphic encryptions on the Bloom filters of each of them. They conducted interactive decryption operations, utilized homomorphic properties, and obtained the intersection as long as the number of decryptors exceeded the threshold. Although their protocol has a longer runtime, it is an open-source Multiparty PSI protocol, and it has a greater advantage in scenarios where multiple participants have small datasets.
## 2 Materials and Methods
### Instrumentation  
Cyclic voltammetric measurements were carried out using an Autolab PGSTAT 30 potentiostat from Eco-Chemie (Utrecht, The Netherlands), controlled by the GPES 4.9 (General Purpose Electrochemical Experiments) software package and a \(\upmu\)Stat-100 potentiostat from DropSens (Metrohm-DropSens, Oviedo, Spain), controlled by the PSLite 1.6 (PalmSens, Houten, The Netherlands) software. A SPE (0.126 cm\({}^{2}\) geometric area) from Metrohm-Dropsens was employed as a miniaturized equivalent of a traditional electrochemical cell. The SPE comprised three electrodes printed on a ceramic substrate: a working electrode (carbon, carbon modified with Multiwalled Carbon Nanotubes, MWCNTs, or gold), a silver pseudoreference electrode and an auxiliary electrode (carbon or gold). In this experimental work, carbon (SPCE), carbon modified with Multiwalled Carbon Nanotubes (MWCNT-SPCE) and gold with high temperature (AuAT-SPE) and low temperature (AuBT-SPE) cured inks SPEs were used. The SPE was connected to the potentiostat via a SPE connector (Metrohm-Dropsens), which served as an interface, and it was placed inside a Faraday cage.  
Surface-enhanced Raman spectroscopy (SERS) measurements were performed using an inVia Reflex Raman spectrometer coupled to a confocal microscope (Renishaw, Barcelona, Spain) and a 2D-CCD camera. Raman spectra were recorded in the range of 200 to 1800 cm\({}^{-1}\), employing a 785 nm excitation laser, a 20\(\times\) objective lens and an excitation time of 10 s.
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
###### Abstract  
Since the conception of the Internet of things (IoT), a large number of promising applications and technologies have been developed, which will change different aspects in our daily life. This paper explores the key characteristics of the forthcoming IEEE 802.11 ah specification. This future IEEE 802.11 standard aims to amend the IEEE 802.11 legacy specification to support IoT requirements. We present a thorough evaluation of the foregoing amendment in comparison to the most notable IEEE 802.11 standards. In addition, we expose the capabilities of future IEEE 802.11 ah in supporting different IoT applications. Also, we provide a brief overview of the technology contenders that are competing to cover the IoT communications framework. Numerical results are presented showing how the future IEEE 802.11 ah specification offers the features required by IoT communications, thus putting forward IEEE 802.11 ah as a technology to cater the needs of the Internet of Things paradigm.  
802.11 ah; IoT; applications Article
## 4 Survey of Existing Works
### Genetic Algorithm
#### 4.3.4 GA in WSN Data Aggregation  
As we discussed previously, the fundamental challenge in the design of WSNs is to maximize their lifetimes especially when they have a limited energy supply, so a good data aggregation scheme can change the scenario immensely. In [123] the authors present a genetic algorithm-based approach to generate balanced and energy efficient data aggregation spanning trees for WSNs. In the algorithm, the gene index determines a node and the gene's value identifies the parent node. In the data gathering round, a single best tree consumes the lowest energy among all nodes but assigns more load to selected sensors. The chromosome fitness is determined by four factors: residual energy, transmission, received load, and the distribution of load. Results showed that proposed GA outperforms a few other data aggregation tree-based approaches in terms of extending network lifetime.  
In [124], [PERSON] _et al._ presented Grid-based Routing and Aggregator Selection Scheme (GRASS). GRASS is said to provide good solutions for the data gathering and routing problem with in-network aggregation in WSNs with a focus on the joint problem of optimal data routing with data aggregation. They claim that the method can achieve low energy dissipation and low latency without sacrificing quality. GRASS embodies optimal approaches as well as heuristic approaches like Clustering-Based Aggregation Heuristic (CBAH). These algorithms are used to find the minimum number of aggregation points while routing data to the BS. When compared to other schemes, GRASS improves system lifetime with acceptable levels of latency in data aggregation without sacrificing data quality. With 100 nodes CBAH provides almost two times better performance then PEGASIS and almost 1.5 times better performance then LEACH when aggregation is used. Without aggregation the performance of CBAH slightly decreases. Results also demonstrate that the CBAH can increase the system lifetime of large WSNs.  
Commonly, the data aggregation tree concept is used to find an energy efficient solution and is largely accepted by the researchers in this area, but fair load sharing is missing in most of these works. To address this issue [PERSON] _et al._[125] presented a method that utilizes genetic algorithm to find routes which balance energy and data load in a network. In this study, nodes monitor the area to aggregate data and then remove the redundant nodes in order to aggregate them according to the data aggregation spanning tree. GA is used here to create an efficient data aggregation tree in which any node has a value property. Like some other methods, the fitness function is determined on the basis of residual energy, number of transmission, and received data packets from individual nodes. The technique is suitable for a homogeneous WSN environment monitoring. Simulation results indicated that this method practically increases the network lifetime compared to other works [112].
# Solution Merging in Matheuristics for Resource Constrained Job Scheduling
## 7 Conclusions  
This study investigates the efficacy of two population-based matheuristics--Merge search (MS) and construct, merge, solve & adapt (CMSA)--for solving resource constrained job scheduling (RCJS). Both methods are shown to be more effective when hybridized with a learning mechanism--i.e., ant colony optimisation (ACO). Furthermore, the whole framework is parallelized in a multi-core shared memory architecture, leading to large gains in run-time. We find that both hybrids are overall more effective than both the individual methods on their own, and better than previous attempts to combine ACO with integer programming, which used column generation and Lagrangian relaxation in combination with ACO. Furthermore, MS and CMSA are competitive with the state-of-the-art hybrid of column generation and differential evolution, especially outperforming this method on small-medium and large problem instances. Comparing MS and CMSA, we see that both methods easily solve small problems (up to five machines), while MS is more effective for medium-sized problem instances (up to eight machines) and CMSA for large problem instances (starting from 11 machines).  
We investigate in detail several aspects of the algorithms, including their parallel components, the search spaces considered at each iteration, and algorithm specific components (e.g., random splitting in MS). We find that parallel ACO is very important for identifying good areas of the search space, within which MS and CMSA can very efficiently find improving solutions. The search spaces considered by CMSA at each iteration are typically larger than those of MS, which is advantageous for large problem instances but generally disadvantageous for problem instances of medium size.