esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fully Integrated Low-Power Multi-Mode RF Receiver for BDS-3/GPS
###### Abstract  
A fully integrated low-power area-efficient receiver using a low-intermediate frequency topology for BDS-3 and GPS L1 bands is presented in this paper. Accurate localization can be achieved without requiring off-chip low-noise amplifiers. The receiver bandwidths for GPS and BDS-3 are 2 MHz and 4 MHz, respectively. Digitally assisted calibration schemes, such as RC calibration, automatic gain control, and DC offset correction are integrated to resist the effects of the process, voltage, and temperature (PVT) variations. The receiver--fabricated in a standard 55 nm CMOS technology--provides a maximum gain of 113.2 dB, a gain control range of 61 dB, and a minimum noise figure of 1.74 dB under a 1.2 V supply. The receiver, with and without the frequency synthesizer that provides the local oscillator frequency, consumes 8.7 mA and 4.8 mA, with areas of 0.73 mm\({}^{2}\) and 0.345 mm\({}^{2}\), respectively.  
RF receiver; BeiDou navigation satellite system-3 (BDS-3); global positioning system (GPS); multi-mode receiver +
Footnote †: journal: Accepted: 1 September 2023
## 4 IoMT Authentication Taxonomy
### Authentication Schemes
#### 4.6.2 Authentication and Key Agreement  
IoMT authentication can be achieved by creating a key shared between the communicating entities to ensure secure communication. Authentication protocols can adopt a simple key agreement where two entities negotiate upon a key to secure their communication [98,98,99]. [PERSON] et al. [105] proposed a key agreement between two entities with a blind signing mechanism based on blockchain technology. On the other hand, authentication protocols can adopt a group key agreement [106,107]. Group-key agreement protocols require more than two entities to generate a group-key such that anyone of these entities can use it for communication [105]. Figure 15a shows the key agreement classification.  
Moreover, keys can be classified according to the type of shared key, either symmetric or asymmetric (see Figure 15b). In symmetric key-based authentication schemes, entities employ the same key for encrypting and decrypting data. [PERSON] et al. [108] developed a symmetric key agreement scheme where entities use the same key for encrypting their messages, which is more lightweight for the IoMT environment. [PERSON] et al. [80] designed a streaming data authentication technique for the IoMT system using symmetric key agreement. In asymmetric key agreement, different keys are used for encrypting and decrypting authentication messages between entities. The researchers of [109,110] supported cross-domain authentication through asymmetric key a greenent.
## 5 Results and Discussion
### Statistical Description of the Oscillatory Behavior  
After providing a quantification of the main properties defining the oscillatory behavior in the studied device, the evaluated channel-wise dominant frequencies were statistically reduced for a complete description of fluid oscillations. To this aim, the arithmetic mean (\(f_{d,av}\)) and the standard deviation (\(f_{d,std}\)) of \(f_{d}\) were computed by considering every FPPHP channel. In Figure 16, \(f_{d,av}\) and \(f_{d,std}\) are shown for the only power inputs to the evaporator in which \(f_{d}\) could be evaluated in every channel of the device, i.e., during its full activation. Here, the average dominant oscillation frequency (Figure 16a) exhibits a general increasing trend, in accordance with other studies also concerning tubular layouts [20; 27; 48]. For the vertical BHM orientation, \(f_{d,av}\) ranges from about 0.78 Hz to 1 Hz, while the horizontal orientation highlights values of \(f_{d,av}\) that increase from a minimum of about 0.84 Hz to a maximum of 1.25 Hz, confirming the typical fluid oscillation frequencies found in the literature [2]. It has to be observed that while \(f_{d,av}\) denotes a linear increment with the power input to the evaporator in the horizontal orientation, it seems to approach an asymptotic value with the increase in \(Q\) in the vertical BHM. This suggests that the semi-annular or annular flow, promoted by the gravity effects on the device at high heat loads, results in  
Figure 14: Mean continuity time for average and high heat loads and the two considered orientations.  
Figure 15: Average activity time of channels as percentage of the observation window length.  
different oscillatory behavior with respect to that in the horizontal orientation (where a slug-plug flow is more likely to occur), probably due to significantly different phase-change phenomena. In fact, at high heat loads, significant fluid motion towards the condenser may occur, while liquid backflow by gravity may be, to some extent, less predominant during operation, affecting the overall oscillatory behavior, which is in agreement with [27]. Additionally, the standard deviation of \(f_{d}\) (Figure 16b) highlights a different behavior in terms of oscillations from channel to channel in the vertical BHM orientation, which persists even during the full activation of the device. For the horizontal orientation, \(f_{d,std}\) seems to decrease instead with the increasing of the power input, suggesting that during the full activation, every channel approaches the same oscillatory behavior. This is probably due to the fact that when buoyancy forces are negligible, as in the horizontal orientation, flow reactivations may occur in a waveform over the entire device, leading to similar fluid oscillation frequencies [19; 49; 42].
## 1 Introduction
The factors that directly affect path loss are the distance between the source and the user, operating frequency, fading effect, environmental surface, and meteorological conditions [35; 36]. Hence, researchers have been employing several probabilistic techniques in modelling various path losses for various noise and interference-limited scenarios, due to the nature of randomness in wireless channels. Experiments and extensive results have attained diverse datasets, and keeping those studies in mind, researchers have designed many path loss models to predict signal attenuation of the propagation channels. Researches are currently exploring radio channels, such as 28, 32, 36, 38, 60, and 73 GHz [37; 38; 39]. Some of the latest developments that predict propagation channels are explained in the next section. In this paper, the propagation channel was estimated by extracting the path loss exponents (PLEs) value at the 38 GHz frequency band. The simulation results were calculated to investigate the potential of mmWave propagation path loss models, such as FI and CI [37; 40]. The measurement readings were collected by using a directional horn antenna in an outdoor urban microcell environment for both LOS and NLOS scenarios with co (vertical-vertical) and cross (vertical-horizontal) antenna polarization settings. On the basis of real measurement of path loss parameters, the various network performance parameters, such as average user throughput, fairness index, cell-edge user throughput, and spectral efficiency, as well as average cell throughput, have been examined. The outcomes were also analyzed at various MIMO antenna configurations to enhance the overall achieved network performance. The rest of the paper is organized as follows: Section 2 highlights the most recent and relevant works publishedwithin the same area; the measurement setup, hardware description, and experimental procedure are explained in Section 3; both FI and CI path loss models are discussed in Section 4; the experimental findings of PLEs values are provided in Section 5; simulation setup description is expressed in Section 6; Section 7 depicts the detailed results to evaluate various network performances for a varied number users and MIMO conditions; lastly, the conclusion, along with future work, is discussed in Section 8.
## 2 Structure and Design
### ESIW Integration  
The rod is placed in the middle width of the ESIW line (\(a/2\)) because this is where the electric field is maximum and allows for the symmetrisation of the radiation pattern. The ESIW ends in a short circuit, generating a standing wave. To achieve the maximum electric field, the rod must be placed at a distance of \(\lambda_{g}/4\) from the short circuit, converting it into a virtual open circuit, and thus obtaining the maximum electric field. Initially, _lshort_ equals \(\lambda_{g}/4\), proving to be a good starting point for optimisation for the patch (see Figure 4).  
The rod must go through the patch and the ESIW line, as shown in Figure 3. To achieve this, a hole slightly wider than _drod_ has to be made in the patch, allowing the rod to pass through without removing the metallisation of the hole (see Figure 3). Then, another hole in the top cover with a diameter _Dhrod_ is needed. This hole must be large enough to prevent shortcuts between the rod and the cover, but not so large as to allow the field to escape. In the bottom cover, another hole similar to the one used in the patch has been included; it must be slightly wider than _drod_. To achieve electrical continuity, the post is welded at the junction with the bottom cover.  
Simulations have shown that the diameters of the rod (_drod_) and hole (_Dhrod_) have minimal impact as long as they remain small compared to the length of the patch. Therefore, both parameters are arbitrary and have been set to a diameter available in the laboratory for the rod and an easy-to-manufacture value for the hole diameter.
## 3 System Model
### Network Model  
WSN clustering aims to minimize energy usage by dividing sensors into clusters. Common nodes frequently monitor the environment and transmit sensory data to the cluster head. The CH node is always chosen among the common nodes. The significant role of the CH is to accumulate data from each cluster node and send it to the BS. The process of grouping aids in avoiding direct communication between receivers and sensors. The WSN system model is depicted in Figure 1.  
Figure 1: General architecture of a wireless sensor network.
# Analyzing Key Features of Open Source Software Survivability with Random Forest
###### Abstract  
Open source software (OSS) projects rely on voluntary contributions, but their long-term survivability depends on sustained community engagement and effective problem-solving. Survivability, critical for maintaining project quality and trustworthiness, is closely linked to issue activity, as unresolved issues reflect a decline in maintenance capacity and problem-solving ability. Thus, analyzing issue retention rates provides valuable insights into a project's health. This study evaluates OSS survivability by identifying the features that influence issue activity and analyzing their relationships with survivability. Kaplan-Meier survival analysis is employed to quantify issue activity and visualize trends in unresolved issue rates, providing a measure of project maintenance dynamics. A random forest model is used to examine the relationships between project features--such as popularity metrics, community engagement, code complexity, and project age--and issue retention rates. The results show that stars significantly reduce issue retention rates, with rates dropping from 0.62 to 0.52 as stars increase to 4000, while larger codebases, higher cyclomatic complexity, and older project age are associated with unresolved issue rates, rising by up to 15%. Forks also have a nonlinear impact, initially stabilizing retention rates but increasing unresolved issues as contributions became unmanageable. By identifying these critical factors and quantifying their impacts, this research offers actionable insights for OSS project managers to enhance project survivability and address key maintenance challenges, ensuring sustainable long-term success.  
Keywords:Kaplan-Meier survival function; open source software; random forest; software maintenance; survivability +
Footnote †: journal: Applied sciences
# Design and Test of Real-Time Detection System for Cleaning Loss of Rapeseed Harvester
## 4 Discussion  
At present, the main methods of loss detection for rapeseed crops are to collect signals through sensitive elements and design filter circuits to distinguish the difference between signals in frequency domains. However, in traditional filter circuit processing, the calculation of the cut-off frequency and the circuit design of the filter circuit are more complicated. When the signal cut-off frequency to be processed changes, the circuit often needs to be redesigned and its parameters need to be adjusted, which is time-consuming and laborious.  
In practice, there is a certain overlap between the frequency domain of the kernel and the frequency domain of the impurity, and the threshold range is difficult to determine, and there are obvious differences in the amplitude characteristics of the signal, and compared with the way to distinguish signals by their frequency, amplitude distinction has obvious advantages in terms of the cost and feasibility.  
In view of the limitations of the existing frequency domain method to distinguish rapeseed kernels from impurities, this paper proposes an innovative energy distinction method. In this research, the significant differences in the amplitude characteristics of the impact signals collected from rapeseed kernels and impurities were discussed. An innovative hardware integration method was proposed to transform the impact signals generated by rapeseed kernels and impurities into energy values, and the energy distinction threshold of impact signals generated by rapeseed kernels and impurities was found through experiments. In the hardware integral circuit, when the energy of impact signals generated by rapeseed kernels and impurities is changed due to a change in the crop state and field working conditions, the energy distinction threshold can be adjusted by a potentiometer, without the need to redesign the signal processing circuit.  
At the same time, in order to improve the accuracy of the detection system, based on the impact of the fan speed for the cleaning system on the speed of the collision between the kernel and the impurity and the inductive plate (affecting the amplitude of the impact signal), the energy distinction threshold under different fan speeds for the cleaning system was calibrated through experiments, and a fitting model relating the fan speed to the distinction threshold was constructed. The distinction threshold was realized for different fan speeds for a real-time adjustment system, so that the system has high detection accuracy under different operating conditions. The system has high detection accuracy, good overall performance, and a certain universality under complex and varied working conditions in the field, and can provide feedback information for the control of harvesters.
## 1 Introduction  
Computer-aided design/computer-aided manufacturing (CAD/CAM) use in dentistry started in the early nineties with subtractive manufacturing [1; 2]. Recently, additive manufacturing technology, three dimensional (3D) printing, has been progressing rapidly and is applied in many fields of dentistry [3] such as: fabrication of surgical guides for placement of dental implants [4; 5; 6; 7], guides for endodontic access and apical surgeries [8; 9], fabrication of surgical guides and tooth replicas for tooth autotransplantation (TAT) [10; 11; 12], construction of physical models for orthodontics [13], manufacturing of dental implants [14], and study models, splints, and guides in orthognathic surgeries [15; 16; 17].  
The process of 3D printing in dentistry goes through three stages, starting with data acquisition using low dose cone beam computed tomography (CBCT) [18; 19] and/or intraoral scanner (IOS) [20; 21], followed by processing and designing using dedicated software tools, and, finally, printing [22; 23; 24; 12].  
Different 3D printing technologies are being used in dentistry, which can be technically classified into: fused deposition modelling (FDM), stereolithography (SLA), PolyJet printing, MultiJet printing, ColorJet printing, selective laser sintering, and digital light processing (DLP) [3; 24].  
As aforementioned, CAD/CAM and 3D printing technology are already being applied in tooth autotransplantation (TAT) procedures [10; 12]. TAT offers a viable biological approach to tooth replacement in children and adolescents after traumatic dental injuries (TDIs), agenesis, developmental anomalies, or specific orthodontic problems [25; 26; 27; 28; 29]. The treatment options available, for example implant placement, are limited by the ongoing dentoalveolar development [30], while orthodontic tooth alignment is challenging unless skeletal anchorage is applied [31; 32]. TAT allows for periodontal healing and enables preservation of the alveolar ridge, maintaining the possibility of function and growth [25; 33; 34; 35]. Restoring or reshaping the transplanted tooth to the anterior maxilla should be done as soon as possible for psychological and aesthetic reasons [36]. However, to avoid interfering with the natural healing process [33; 37], reshaping of transplanted teeth is usually delayed three to four months after transplantation [38]. This delay creates a need for a simple indirect temporary aesthetic restoration for TAT, which so far has not yet been reported.  
The aim of this study was to develop and validate a digital solution for temporary restoration of autotransplanted teeth using 3D printing technology.
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 3 Separability Improvement  
### Separability of Time Series Components  
For the proper decomposition of a time series into a sum of components, these components must be separable. By separability we mean the following. Let the time series be a sum of two components in which we are interested separately. If the decomposition of the entire time series into elementary components can be divided into two parts, with one part referring to the first component and the other part referring to the second component, then by gathering these components into groups and summing them up, we will obtain exactly the components we are interested in, that is, to separate them. If the decomposition into elementary components is not unique (which is the case with singular value decomposition), then the notions of weak and strong separability arise. Weak separability is defined as the existence of a decomposition in which the components of the time series are separable. Strong separability means the separability of the components in any decomposition provided by the decomposition method in use. Therefore, the main goal in obtaining a decomposition of a time series is to achieve strong separability.  
Without loss of generality, let us formalize this notion for the sum of two time series, following (4) (Section 1.5 and Chapter 6). Let \(\mathbf{X}=\mathbf{X}_{1}+\mathbf{X}_{2}\) be a time series of length \(N\), the window length \(L\) be chosen and the time series \(\mathbf{X}_{1}\) and \(\mathbf{X}_{2}\) correspond to the trajectory matrices \(\mathbf{X}_{1}\) and \(\mathbf{X}_{2}\). The SVDs of the trajectory matrices are \(\mathbf{X}_{1}=\sum_{i=1}^{r_{1}}\sqrt{\lambda_{1i}}U_{1i}V_{1i}^{\mathrm{T}}\) and \(\mathbf{X}_{2}=\sum_{j=1}^{r_{2}}\sqrt{\lambda_{2j}}U_{2j}V_{2j}^{\mathrm{T}}\). Then  
* Time series \(\mathbf{X}_{1}\) and \(\mathbf{X}_{2}\) are _weakly separable_ using SSA, if \(\mathbf{X}_{1}\mathbf{X}_{2}^{\mathrm{T}}=\mathbf{0}_{L,L}\) and \(\mathbf{X}_{1}^{\mathrm{T}}\mathbf{X}_{2}=\mathbf{0}_{K,K}\).
* Weakly separable time series \(\mathbf{X}_{1}\) and \(\mathbf{X}_{2}\) are _strongly separable_, if \(\lambda_{1i}\
eq\lambda_{2j}\) for each \(i=1,\ldots,r_{1}\) and \(j=1,\ldots,r_{2}\).  
Thus, strong separability consists of weak separability, which is related to the orthogonality of the time series components (more precisely, orthogonality of their \(L\)- and \(K\)-lagged vectors), and of different contributions of these components (that is, different contributions of elementary components produced by them). We will call the latter _contribution separability_. The conclusion of this is that to improve weak separability one needs to adjust the notion of orthogonality of the time series components, and to improve contribution separability, one needs to change the contributions of the components. To obtain strong separability, both weak and contribution separabilities are necessary.
# Bi-Objective Power Optimization of Radio Stripe Uplink Communications
## 2 Radio Stripes Network Model  
A practical sequential implementation of the CF mMIMO network is obtained through the deployment of an RS which comprises \(L\) multi-antenna APs consisting on a linear array of \(N\) antennas. The APs are sequentially connected in the same stripe which is then connected to a CPU, allowing for the APs to share the same fronthaul link. It is assumed that there are \(K\) single-antenna UE randomly distributed within the area covered by the RS network [10; 12].  
The channel between UE \(k=1,\cdots,K\), and AP \(l=1,\cdots,L\) is \(\mathbf{h}_{kl}\sim\mathcal{N}_{\mathbb{C}}(0,\mathbf{R}_{kl})\), which follows a block flat-fading model, described by an independent correlated Rayleigh fading distribution, being constant and flat-fading in block intervals of \(\tau_{c}\) samples [2; 9]; where \(\mathbf{R}_{kl}\in\mathbb{C}^{N\times N}\) is the spatial correlation matrix that describes both the channel spatial correlation characteristics and the large-scale fading coefficients (path loss and shadow fading) between AP \(l\) and UE \(k\) by \(\beta_{kl}=\frac{\mathrm{tr}(\mathbf{R}_{kl})}{N}\)[2].  
This paper focuses on the UL and the frame format is based on the time-division-duplex protocol with two phases: a channel estimation phase leveraging on a pilot training sequence of \(\tau_{p}\) samples and a payload data sequence of \(\tau_{c}-\tau_{p}\) samples [12].