esa-sceva/satcom-chunk-collection huggingface dataset:
# Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems
### Related Works
To flatten the fading channel over a wide band and maximize the system capacity, a signal-to-interference ratio constrained capacity maximization algorithm to design the precoder and the combiner was proposed in [24]. A closed-form solution for fully connected orthogonal frequency division multiplexing (OFDM) hybrid analog/digital precoding for frequency selective mmWave single user systems was developed in [25]. This approach was then extended to the partially connected case, and a novel technique that dynamically constructs the hybrid subarrays knowing the long-term channel characteristics was proposed. Recently, solutions for wideband mmWave multi-user downlink massive MIMO-OFDM systems were proposed in [26], and for uplink in [27; 28; 29; 30; 31]. Hybrid precoders for downlink OFDM wideband mMIMO systems, aimed at minimizing the total transmit power of the base station, subject to both the coverage constraint of signaling and data rate requirements of users were proposed in [26]. The authors of [27] designed a joint spatio-radio resource and three hybrid precoding algorithms for systems with limited feedback: (1) a hybrid precoder with user-beam selection to maximize the sum proportional rate; (2) a low complexity suboptimal solution using limited statistical channel state information (CSI) feedback; (3) a k-mean algorithm based on an unsupervised machine learning scheme. In [28], a hybrid linear equalizer for sub-connected hybrid architectures that minimizes the average BER over all the subcarriers was designed. Also for sub-connected architecture, but using a dynamic subarray antennas, it was designed in [29], a two-step hybrid equalizer, where in the first step, the antennas are dynamically mapped to the RF chains and then, in the second step, an iterative digital equalizer is designed. In [30; 31], it was also applied the two-step approach, both for full-connected architectures, using in [30] the constant envelope OFDM (CE-OFDM) modulation technique, and in [31], SC-FDMA.
## 3 Results
### Assessment of Diazotrophy Parameters  
The lowest values of the parameters characterising the diazotrophy level in the white lupine plants were noted in the control variant, where the seeds had not been inoculated (Table 3).  
The intensity of biological nitrogen fixation (BNF), expressed as the nitrogenase activity level, was the highest after the coinoculation of plants with bacteria of the _Bradyrhizobium_ sp. genus isolated from the Nitrofora preparation in combination with _Bacillus subtilis_ (variant nine). Moreover, the coinoculation of white lupine seeds with rhizobia isolated from the Nitragina preparation in combination with _Pseudomonas fluorescens_ (variant six), as well as with _Bacillus subtilis_ (variant seven) also resulted in a statistically significant increase in biological nitrogen fixation.  
Another parameter characterising the diazotrophy level was the number of nodules. The largest number of nodules was noted after the coinoculation of white lupine seeds with _Bradyrhizobium_ bacteria isolated from the Nitrofora preparation in combination with _Bacillus subtilis_ (variant nine) (Table 3). The number of nodules was also high in the other variants (six-eight), where coinoculation was applied, regardless of the consortium of rhizobia and endophytic bacteria.  
The statistical analysis of the effect of different variants of inoculation of lupine seeds on the dry weight of the nodules also showed that the strongest nodulation, expressed as the dry weight of the lupine root nodules, was observed in the variants in which the seeds had been inoculated with bacteria of the _Bradyrhizobium_ genus isolated from the Nitrofora preparation in combination with _Bacillus subtilis_ (variant nine). The dry weight of the nodules was 0.027 g greater than in the control variant (Table 3).  
Figure 3: The count of endophytic bacteria colonising the white lupine roots—measurement with the FISH method (fluorescence in situ hybridisation) (\(10^{7}\times 0.5\) g – fresh weight of the root). Explanation: 1. Uninoculated plant (control variant); 2. Plant inoculated with _Bradyrhizobium_ sp. bacteria (isolated from the Nitragina preparation); 3. Plant inoculated with _Bradyrhizobium_ sp. bacteria (isolated from the Nitrofora preparation); 4. Plant inoculated with _Bacillus subtilis_ bacteria; 5. Plant inoculated with _Pseudomonas fluorescens_ bacteria; 6. Plant coincoculated with _Bradyrhizobium_ sp. (isolated from the Nitragina preparation) and _Bacillus subtilis_ bacteria; 7. Plant coincoculated with _Bradyrhizobium_ sp. (isolated from the Nitragina preparation) and _Pseudomonas fluorescens_ bacteria; 8. Plant coincoculated with _Bradyrhizobium_ sp. (isolated from the Nitrofora preparation) and _Bacillus subtilis_ bacteria; 9. Plant coincoculated with _Bradyrhizobium_ sp. (isolated from the Nitrofora preparation) and _Pseudomonas fluorescens_ bacteria.
# Review of Physical Layer Security in 5G Wireless Networks
## 5 Future Research
### Quantum-Safe Security  
The vulnerability of typical cryptography algorithms is a concern with the development of quantum computers. The development of encryption and key distribution techniques that are specifically designed for 5G networks should be the main emphasis of future research. For example, lattice-based cryptography has demonstrated remarkable potential in fending off attacks from quantum computers and can be investigated for 5 Gcommunication security, designing and assessing secure networks for use in 5G systems. Examine how physical layer security techniques can be incorporated into the entire network design while taking into account elements such as network slicing, virtualization, and software-defined networking. Create scalable, effective security frameworks that can accommodate the dynamic, heterogeneous character of 5G networks while offering strong defense against hacking and other security risks.
## 4 Model of Preferences and Judgments of a DM with a Non-Compensatory Aggregation of Preferences
### The Interval Outranking Model
Given \(\gamma\in\Omega\) we say that \(a^{\prime}\) outranks a for the \(\gamma\)-possible concordance coalition with credibility index \(\sigma_{\gamma}\) and majority threshold \(\Lambda>0.5\) with \(\lambda^{-}>0.5\) iff \(\sigma_{\gamma}=\) min {\(\gamma,\,P(\sigma(a^{\prime},\,a,\,\gamma)\geq\Lambda),\,(1-\max\limits_{fj \in D(a^{\prime}S_{\gamma}a)}d_{j}(a^{\prime},a))\)}._For each particular value of \(\gamma\), this index can be interpreted as a credibility degree of the conjunction of two predicates: (i) \"the \(\gamma\)-concordance coalition is strong enough\" and (ii) \"the \(\gamma\)-discordance coalition does not exert veto\". The above marginal (single coalition) credibility indices are merged by using the \"_max_\" operator in the following definition:  
**Definition 9** (interval outranking credibility index). : _From the point of view of the internal outranking approach and under the above notation, \(a^{\prime}\) outranks a (\"\(a^{\prime}\) is at least as good as \(a\)\") with credibility index \(\sigma(a^{\prime},a,\lambda)=\text{max}\{\sigma_{\gamma}\}\) (\(\gamma\in\Omega\)) and majority threshold \(\lambda>0.5\) with \(\lambda^{-}>0.5\). If \(\Omega\) is empty, then \(\sigma(a^{\prime},a,\lambda)\) is zero._  
Let \(\beta\) an interval number (\(0.5<\beta<1\) with \(\beta^{-}>0.5\)) considered as a credibility threshold to establish crisp preference relations. In the following we will use \(a^{\prime}S(\beta,\lambda)a,a^{\prime}I(\beta,\lambda)a\), and \(a^{\prime}P_{r}(\beta,\lambda)a\) similarly to Definition 1, but using the interval number \(\beta\) instead of the real number \(\beta\). Such a change gives more flexibility to the DM in setting the credibility threshold. **Remark 2**. :
1. _As was proved in_ _[_20_]__, if_ _f_(_a_) _are real numbers for_ \(j=1\)_,...,_ \(N\)_, then_ _a_\(S(1,1)a\)_._
2. \(a^{\prime}s(\beta,\lambda)a\Rightarrow a^{\prime}S(\zeta,\lambda)a\) _or_ \(\zeta<\beta\)_._  
**Proposition 1**. : _Some properties of the interval dominance combined with the interval outranking and preference relations:_  
1. _For all (_x_,\(y\),_z_) \(\in O\times O\times O\)_:_ 1. \(yD(a)x\) _and_ \(xS(\beta,\lambda)z=yS(\epsilon,\lambda)z\) _for some_ \(\epsilon\geq\text{min}(\alpha,\beta)\)__ 2. \(zP_{r}(\beta,\lambda)y\) _and_ \(yD(a)x\Rightarrow zP_{r}(\epsilon,\lambda)x\) _for some_ \(\epsilon\geq\text{min}(\alpha,\beta)\)__ 3. \(yD(a)x\) _and_ \(xP_{r}(\beta,\lambda)z\Rightarrow yP_{r}(\epsilon,\lambda)z\) _for some_ \(\epsilon\geq\text{min}(\alpha,\beta)\)__ 4. _If_ \(\alpha>0.5\)_, then_ \(xD(a)y\Rightarrow xP_{r}(\beta,\lambda)y\) _for some_ \(\beta\geq\alpha\) _and for all_ \(\lambda\leq\) _[_1,1_]_ 5. \(xD(\alpha)y\) _and_ \(yD(\eta)z\Rightarrow xD(\epsilon)z\) _with_ \(\epsilon=\text{min}\) _(_\(\alpha,\eta\)_)_  
The proofs are similar to the ones in [20]. The single difference consists of replacing the real value of \(\beta\) with the middle points of the interval number \(\beta\).
# Training and Inference of Optical Neural Networks with Noise and Low-Bits Control
## 1 Introduction  
With the explosion of information, more data need to be processed. The neural network is considered to be a promising candidate for bulk information processing [1]. Thus far, we have many optimization methods and non-iterative linear supervised learning predictors that can improve computing power, such as multilayer perceptron, support vector machines, and neural-like structures of the successive geometric transformations model (SGTM) [2]. In recent years, optical neural networks (ONNs) have gained a large amount of attention due to their high-speed, low power consumption, and low delay [3, 4, 5, 6]. It has been shown that matrix multiplication and parameterization can be obtained on an optical neural network made by Mach-Zehnder interferometer (MZI) arrays [7, 8, 9].  
With the development of ONNs, some important issues have occurred in the non-ideal case, which may reduce the accuracy of the optical neural network. Therefore, these issues need to be understood deeply. In practice, the following conditions may increase the error of the optical device. One is the phase shift generated by optical devices, which cannot achieve arbitrary precision in physics [3, 4, 10, 11]. The other is quantum-limit noise on optical devices [10, 11, 12]. Up to now, the goal of large-scale, rapidly reprogrammable photonic neural networks has not been realized. There are still plenty of opportunities for improving ONNs [13, 14]. Processing large amounts of data remain challenging for ONNs for computer vision in real life.  
Most research on ONN has focused on different types of devices and novel architectures, while limited work has been undertaken on the impact of noise problems onaccuracy in photonic chips. Several groups have begun to study these issues. In 2017, [PERSON]'s chip implemented a neural network that can recognize four basic vowels [3]. In 2019, [PERSON] presented a new type of photon accelerator based on coherence detection capabilities [4]. They also simulated noise in this device. Other papers propose a noise perception quantization scheme to help design a robust ONN model [10]. The above work illustrates that ONN architecture requires special hardware implementation and, ideally, low-bit control.  
In this paper, based on the new chip structure we designed previously [15], a noise quantization model is proposed to analyze the influence of quantization on the accuracy of ONNs, so as to make it closer to reality. We also optimize the algorithm so that the chips that are run in real conditions can achieve high precision. A method that solves ONNs' low-bit control and simulation on devices is proposed for the first time.
## 5 Outlier Detection  
### Isolation Forest (iForest)  
Isolation Forest (iForest) is an unsupervised outlier detection method that was proposed by [PERSON] et al. (2014). iForest creates a forest of decision trees and assigns anomaly scores to every data point in the forest according to the length from the root node. The basic idea is to assign high anomaly scores to the data points with the least length and thus identify outliers.  
iForest consists of two phases, the training and evaluating phases. Considering a dataset X with n samples and m features, training starts by selecting a subset of data and building the first Isolation Tree. The procedure is repeated until a certain number of Isolation Trees are built. Every tree is built by splitting the selected samples. Splitting is performed by randomly selecting a feature and a value between the maximum and minimum value of the selected feature. The splitting procedure is repeated until a certain depth in the tree is achieved. In the second phase, the evaluating phase, each data point receives a score based on its position in the created forest. The score of every data point is calculated as:  
\[s(x,n)=2^{-\frac{E(h(x))}{c(n)}} \tag{10}\]  
The path of every sample from the root node is denoted as \(h(x)\) and the average value of the paths from the forest as \(E(h(x))\). By using BST ([PERSON] et al., 2014), the non-successful search length is computed, which is denoted as \(c(n)\). An illustration of the Isolation Forest is shown in Figure 2. Outliers are defined based on shorter paths from the root node of the created Isolation Trees.
## 2 Literature Review
\begin{table}
\begin{tabular}{c l r r r r r} \hline \hline
**EDITION 2022** & & **2018** & **2019** & **2020** & **2021** & **2022** \\ \hline \multirow{3}{*}{Blockchain in logistics} & TOTAL & 43 & 171 & 321 & 639 & 911 \\  & Review articles & 10 & 16 & 56 & 89 & 152 \\  & Research articles & 33 & 155 & 265 & 550 & 759 \\ \hline \multirow{3}{*}{Intelligent transport systems (ITS)} & TOTAL & 2971 & 3526 & 4336 & 5641 & 7195 \\  & Review articles & 409 & 438 & 636 & 1049 & 1464 \\  & Research articles & 2562 & 3088 & 3700 & 4592 & 5731 \\ \hline \multirow{3}{*}{Robotic processes automation} & TOTAL & 2378 & 2779 & 3449 & 3986 & 4759 \\  & Review articles & 177 & 260 & 315 & 458 & 620 \\  & Research articles & 2201 & 2519 & 3134 & 3528 & 4139 \\ \hline \multirow{3}{*}{IoT in logistics} & TOTAL & 319 & 536 & 672 & 946 & 1273 \\  & Review articles & 36 & 48 & 95 & 136 & 201 \\  & Research articles & 283 & 488 & 577 & 810 & 1072 \\ \hline \multirow{3}{*}{Big data analytics} & TOTAL & 13,877 & 15,336 & 17,629 & 20,301 & 22,029 \\  & Review articles & 1041 & 1196 & 1484 & 1964 & 2346 \\  & Research articles & 12,836 & 14,140 & 16,145 & 18,337 & 19,683 \\ \hline \multirow{3}{*}{Cloud and APIs} & TOTAL & 1163 & 1325 & 1545 & 1880 & 2226 \\  & Review articles & 98 & 110 & 150 & 172 & 275 \\ \cline{1-1}  & Research articles & 1065 & 1215 & 1395 & 1708 & 1951 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Analysis of the degree of research interest in innovative solutions (ScienceDirect database, December 2022).
## 2 Materials and Methods
### Liquid Chromatography-Tandem Mass Spectrometry (LC-MS) Analysis
#### 2.10.2 LC-MS Analysis  
LC-MS analysis was conducted as reported previously [15]. Briefly, a nanoflow high-performance liquid chromatograph (HPLC) system (Easy nLC1200 system, Thermo Fisher Scientific, USA) coupled to an Orbitrap Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific, USA) with a nanoelectrospray ion source (Thermo Fisher, USA) was used. The sample dissolved in buffer A (0.1% formic acid [FA]) was injected into a 2-cm trap column (75-um inner diameter, Acclaim PepMap 100C18, 3 um, Thermo Scientific) and separated on a 75-um-inner-diameter column with a length of 25 cm (Acclaim PepMap 100C18, 2 um; Thermo Scientific) over a 120-min gradient (buffer A, 0.1% FA in water; buffer B, 0.1% FA in 80% Acetonitrile [ACN]) at a flow rate of 300 nL/min (0-3 min, 3-8% B; 3-99 min, 8-28% B; 99-113 min, 28-33% B; 113-116 min, 33-98% B; and 116-120 min, 98% B). The Q Exactive HF mass spectrometer was operated in positive ion mode at ion transfer tube temperature 320 \({}^{\circ}\)C, and the positive ion spray voltage was 3.7 kV. MS scans were acquired at resolution of 60,000, and the maximum injection time was 50 ms. High-energy collisional dissociation fragmentation was carried out at 28% normalized collision energy. MS2 AGC target was set to 1 \(\times\) 10\({}^{5}\), the maximum injection time was set to 30 ms, and the dynamic exclusion was set to 60 s.  
The MS data were searched against the Sus scrofa (pig) UniProt database (taxon identifier 9823, version 2018061348865) using Proteome Discoverer (software version 2.3, Thermo Scientific). The Precursor_Quan_LFQ Sequest(tm) HT search engine was used to search the data. The search parameters were set as follows: trypsin was selected as proteolytic enzyme, and two missed cleavage sites were allowed; cysteine carbamylation was used as immobilization modification; oxidation of M and acetylation of N-terminal were used as modification variables; search quality tolerance was set as 10 ppm; false discovery rates of peptide-spectrum matching (PSMs) and protein were set as less than 1%.
## 3 Results  
Table 3 presents a comparative analysis, prior to fairness adjustments, of several binary classification deep learning models based on their performance metrics across two demographic stratifications of the dataset: White and non-White groups. We observe a consistent trend of higher binary accuracy, precision, and recall for the White group across all models. The Ensemble model achieves relatively high precision and recall for the White group but exhibits a significant drop in performance for the non-White group, especially in terms of accuracy and F1-score. These findings highlight the disparities in model performance for under-represented demographic groups and emphasize the need for more balanced and fair machine learning algorithms. Figure 3 illustrates this performance disparity in FPRs and TPRs among the various CNN models between groups. Figure 3: FPR and TPR for several binary deep learning models, distinguishing between White and non-White group performance. Table 4 presents the results of independent \(t\)-tests conducted to compare the FPR and TPR between groups across various models before applying post-processing adjustment. The majority of the models show a statistically significant difference in FPR, highlighting concerns regarding biases in model performances across demographic groups. Although we did not consistently observe statistical significance at the 0.05 \(p\)-value cutoff for TPR, we note that the trend was always towards better performance for White groups, and some models still showed statistically significant differences in TPR. There were no models where the trend was reversed: that is, no models where the performance was better for the non-White groups. Table 5 and Figure 4 offer a comprehensive view of model performance before and after fairness adjustments in the binary classification setting. Notably, we do not observe consistent improvements in either FPR or TPR post-adjustment. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **FPR** & \multicolumn{2}{c}{**TPR**} \\ \hline  & t-statistic & \(p\)-value & t-statistic & \(p\)-value \\ \hline ResNet152 & 1.93 & 0.0606 & 1.93 & 0.0607 \\ \hline Wide ResNet101 & 0.06 & 0.9507 & 0.32 & 0.7474 \\ \hline VGG & 1.87 & 0.0693 & 3.04 & 0.0043 \\ \hline ResNet50 & 3.93 & 0.0003 & 1.46 & 0.1514 \\ \hline ResNet18 & 3.24 & 0.0024 & 1.33 & 0.1923 \\ \hline EfficientNet & \(-0.35\) & 0.7272 & 0.84 & 0.4034 \\ \hline RegNet & 1.83 & 0.0746 & 1.87 & 0.0696 \\ \hline ConvNeXt & 1.76 & 0.0860 & 0.47 & 0.6399 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of independent \(t\)-tests comparing FPR and TPR between White (n = 32 in the test set) and non-White (n = 9 in the test set) groups across different models before applying post-processing adjustment.
# Geomicrobiology: Latest Advances and Prospects
###### Abstract  
Geomicrobiology is an interdisciplinary field that involved the study of the role of microbes in the geosphere. This discipline integrates geology, microbiology, ecology, biochemistry, molecular biology, and bioinformatics, in the broadest sense. Some authors have stated that geomicrobiology is a subdiscipline of geobiology, which focuses on the interactions between life and the physical Earth. In contrast, geomicrobiology is more focused on the interactions of microbial life with the environment. [PERSON] and [PERSON] [1], in the _Encyclopedia of Geology_, fully incorporated geomicrobiology into geobiology, mentioning the term only in the title and once in the first line of the chapter, considering it a subfield. However, many authors prefer to use the term geomicrobiology in a broader sense rather than geobiology.  
The earliest geomicrobiological studies date back to the 19 th century. [PERSON] described the biomineralization of iron oxyhydroxydes by _Gallionella ferruginea_[2], followed by [PERSON]'s research on _Beggiato_ and _Leptothrix ochracea_[3,4]. However, the foundations of geomicrobiology were laid by [PERSON] and his book on _Geobiology_, published in 1934, where he discussed the Earth, organisms, the environment, water, and symbiosis, among other topics [5]. [PERSON] defined geobiology as the study of the relationship between organisms and the Earth and proposed the following principle: \"Everything is everywhere, but the environment selects\".  
An important contribution to the development of geomicrobiology came from [PERSON], recognized as a pioneer in the field. His research, which began in 1958, extended over several decades and included numerous publications, culminating in his book, published in 1981 and its subsequent editions [6,7].  
Since the edition of the three volumes on _Environmental Biogeochemistry and Geomicrobiology_ by [PERSON] [8, 9, 10], and _Geomicrobiology_ by [PERSON] [7], a large number of books and papers have explored various aspects of geomicrobiology over the past half-century. Over the years, geomicrobiology has evolved into a broad field encompassing a wide range of environmental and geochemical significant processes, such as those occurring in deep-sea hydrothermal vents, marine and lacustrine sediments, ore deposits, petroleum reservoirs, deep terrestrial subsurfaces, caves, mines, and extreme acidic environments [11, 12, 13, 14, 15, 16].  
The awareness of a deep microbial biosphere has advanced over the past two decades due to the rapid advances in \"omics\" techniques and the discovery of an incredible diversity of genotypes and species in habitats above or below the Earth's surface [17, 18]. Other related disciplines such as Geobiotechnology have developed bioremediation solutions over the last decade [19, 20].