esa-sceva/satcom-chunk-collection huggingface dataset:
# Challenges in Wireless System Integration as Enablers for Indoor Context Aware Environments
## 3 Wireless Sensor Network Radioelectric Analysis
#### Total Surface of Test Scenario: 216 \(m^{2}\)
As it can be seen, delay spread distributions are strongly dependent on the transmitting node location and exhibit large variability (\(\Delta_{\text{delay-spread}}\approx\) 50-150 ns), a direct consequence on the influence of the specific characteristics of the surrounding environment in wireless channel propagation, particularly in the case of multipath components. The analysis of delay spread results as a function of the active transmitter under consideration has been performed for the case of several nodes, which are presented in Figure 3:3 Node locations have been selected in order to provide different cases of observable object density and hence, node-scenario interaction. Average values are  
Figure 3:4 Estimation of received power levels, non-uniform distribution, for a frequency of 5.8 GHz, at bidimensional planes at a height of 0.5 m and 2 m, respectively. Figure 3:5 Linear received power level distribution for a frequency of operation of 2.4 GHz vs. 5.8 GHz. similar in all cases (in the order of 115 ns, corresponding to approximately 35 m), with variations in standard deviation values (from 24 ns to 39 ns, corresponding to 7.5 m to 12 m). The average values are determined by the dimensions of the indoor scenario and the conditions for the extinction of reflected rays, whereas the variations in standard deviation are more site-specific, related with the density and location of objects. The use of deterministic analysis can aid in the estimation of both average values and standard deviation, providing a qualitative assessment in terms of node location and density, with site-specific consideration for the particular scenario under analysis. In the case of low bit rate transmissions, impact will be low (variations within coherence bandwidth). However, care should be taken in the case of increasing bit rate (vicinity of Gbps capacities in certain future applications linked to ultra-high definition video streaming, virtual or augmented reality, etc.). Delay spread estimations can be then employed in the effective design of dispersion mitigation techniques, such as channel equalization.
## 1 Introduction
From the set of considerations above, it is easy to see that the impact of COVID-19 on logistics and SC processes has been widely debated and various (even interrelated) facets have been explored. As we are now in the post-COVID-19 era, time has come to summarize the evidence about the impact of COVID-19 on logistics and SC processes. This is the intended aim of this study, which can be formally delineated through the following research questions (RQs):  
* RQ1 (scientific): How have researchers studied the impact of COVID-19 on logistics and supply chain processes? Which industrial sectors were mostly studied and why? Which additional topics can be related to COVID-19 and logistics/supply chain? * RQ2 (practical): What effects of COVID-19 on logistics and supply chain processes were experienced by companies? For answering these questions, this paper follows a two-step methodology. The first step is a comprehensive literature review, which allows for gathering scientific evidence on the subject and thus answering RQ1. Related outcomes were elaborated through statistical analyses and cross-analyses for a better representation of the key findings. The second step is a case study, intended to explore the impact of the COVID-19 pandemic on a logistics operator and capture the practical facets of the topic, thus allowing to answer RQ2. The case study also complements the findings from the literature and provides insights to companies. This study moves from previous research by [30], who carried out a preliminary analysis and classification of the literature about COVID-19 and supply chains, published up to 2022. The previous research is enriched by including an enlarged timespan of the papers reviewed, deepening the analysis and classification of the literature, and, most importantly, adding a further step of research, in the form of a case study. The paper is structured as follows: Section 2 outlines the research methodology; Section 3 presents the results from the literature review; in Section 4, the case study is presented and, finally, in Section 5, some conclusions are summarized, together with implications, limitations and suggestions for future research.
## 2 System Model and Channel Modeling Methods  
In this section, the proposed V2I-VLC scenario, system model, and channel model are introduced and discussed in detail. As shown in Figure 1, a typical V2I-VLC communication scenario on a mountain road is considered, where the infrastructure _Infra_ is located on the roadside at the turn of the road. The resulting V2I-VLC scenarios will include both LOS and NLOS situations. The V2I-VLC system models based on LOS and NLOS will be discussed later, respectively. As shown in Figure 1, the communication vehicle \(V_{1}\) is located on the road \(RS\) and is traveling in the direction of _Infra_. The two headlights of \(V_{1}\) are used as Txs, namely Tx1 and Tx2, and the PD-based Rx is deployed on the _Infra_ at the same height as Tx1 and Tx2 (relative to \(RS\)). Mountain \(M\) is located on the left side of \(V_{1}\).  
The data to be sent are modulated on both Tx1 and Tx2 by the on-off keying (OOK) method. Two headlights emit light with modulation information simultaneously, forming LOS and NLOS links. The LOS link light is directed to the PD-based Rx surface, while the NLOS link light is reflected by the \(RS\) or \(M\) and then irradiated on the Rx surface. As a result, the Rx can receive LOS and NLOS rays, as well as noise.
# Hyperspectral Super-Resolution Technique Using Histogram Matching and Endmember Optimization
## 4 Experiment
### Baseline Study for Spatial Information Mismatch
The datasets were tested using the root-mean-square error (RMSE) of the estimated high-resolution HSI \(\mathbf{Z}\), with respect to the ground truth image \(\hat{\mathbf{Z}}\):  
\[\text{RMSE}=\sqrt{\frac{1}{BN_{m}}\sum\sum\bigl{(}\hat{z}_{ij}-z_{ij}\bigr{)}^ {2}}=\sqrt{\frac{\left\|\hat{\mathbf{Z}}-\mathbf{Z}\right\|_{F}^{2}}{BN_{m}}} \tag{9}\]  
A spectral angle mapper (SAM) [35] was also adopted; it is defined as the angle in \(\mathbb{R}^{B}\) between the estimated pixel \(\hat{z}_{ij}\) and the ground truth pixel \(z_{ij}\), averaged over the entire image and given in degrees:  
\[\text{SAM}=\frac{1}{N_{m}}\sum\arccos\frac{\hat{z}_{ij}^{\intercal}z_{ij}}{ \left\|\hat{z}_{ij}\right\|_{2}\left\|z_{ij}\right\|_{2}} \tag{10}\]  
where \(\left\|\cdot\right\|_{2}\) is the \(l_{2}\) vector norm. The test results were tabulated in Table 2 for comparison with the results of [28]. The algorithm of [28] showed accurate results compared to other methods, such as those in [24; 26; 36; 37], and [38], but the experimental results indicate that a small pixel mismatch and color difference would lead to a higher error rate. This phenomenon is theoretically inevitable for algorithms that optimize the high-resolution HSI for both the RGB image and low-resolution HSI. For instance, the low-resolution step of [28], minimizing the objective function of endmembers in the image, is highly governed by the abundance of the high-resolution HSI. Therefore, inputting two images with pixel mismatch, as displayed in Figure 2a,b, means optimization toward two different points, and inevitably results in poor accuracy. This phenomenon appears in Figure 4a, which shows an error rate of \(>10\%\) where the spatial mismatch is significant. Color difference is another factor that should be considered in super-resolution problems. The high-resolution step requires an accurate spectral sensitivity function to recover an RGB image close to the input RGB image. However, the sensitivity of the hyperspectral camera usually differs from that of the RGB camera, as displayed in Figure 2a,c, complicating the initialization of the color space between the RGB image and HSI. As displayed in Figure 4b, the color distribution difference caused significant errors in parts of the HSI. The method is also inefficient in finding the spectral function of the RGB camera every time the input RGB image is changed. Therefore, poor results in both the pixel mismatch and color difference dataset indicate the need for the algorithm to account for these issues in the super-resolution problem.
# A Hierarchical Orthographic Similarity Measure for Interconnected Texts Represented by Graphs
## 3 Preliminaries
### Graphs and Graph Databases  
A graph \(G\) is a mathematical structure that represents a pair \((N,E)\), where \(N\) represents a set of nodes (or vertices), and \(E\subseteq N\times N\) denotes a set of edges (or relationships) connecting these nodes. Graph databases are structured like such graphs, unlike relational databases where data are modelled and stored by means of relations. Additionally, graph databases do not have a fixed database schema, allowing them to manage entities with complex, variable data structures [35]. These graph-based database systems have shown great performance in handling highly interconnected data, rendering them particularly useful for storing the numerous similarities and interconnections between texts. In fact, their native graph-like structure makes them the optimal tool for performing network analysis on the links between the stored information or text documents [36].  
Various extensions of the basic graph structure have been proposed for graph database systems, including the (labelled) property graph. In a labelled property graph, each node and relationship can be associated with one or more labels and can contain one or more named properties that contain at least one value [35,37]. A formal notation of the labelled property graph model is provided in Definition 1. This graph model has been adopted by some of the most popular graph database systems like Neo4j [38], Amazon Neptune [39] and Titan [40].  
**Definition 1** (labelled property graph [37,41]).: _Let \(P\) be a set of property names, \(V\) a set of atomic values and \(L\) a set of labels. A labelled property graph \(G\) is a tuple \((N,E,\rho,\lambda_{N},\lambda_{E},\sigma)\), where_  
* \(N\) _is a finite set of nodes;_
* \(E\) _is a finite set of edges;_
* \(\rho:E\to N\times N\) _is a function used to associate each edge in_ \(E\) _with a pair of nodes and indicate its direction;_
* \(\lambda_{N}:N\rightarrow\mathcal{P}(L)\)_, where_ \(\mathcal{P}\) _is the powerset operator, is a function that assigns a subset of labels to specific nodes;_
* \(\lambda_{E}:E\to L\) _is a function that assigns a label to specific edges;_
* \(\sigma:(N\cup E)\times P\rightarrow\mathcal{P}(V)\) _is a function that assigns a set of properties to vertices and edges._  
In what follows, the formal notation for labelled property graphs (i.e., Definition 1) is used to describe graphs as well as graph databases. The notations  
\[N_{l\in L}=\{n|n\in N,l\in\lambda_{N}(n)\} \tag{10}\]  
and  
\[E_{l\in L}=\{e|e\in E,\lambda_{E}(e)=l\} \tag{11}\]  
are shorthand notations to indicate a set of nodes or edges with a specific label \(l\).
## 3 Results and Discussion  
### Irrigation Stress, Water Used and Production  
Figure 2 shows the daily crop reference evapotranspiration (ET\({}_{0}\)) and daily rainfall for the 2018 and 2019 seasons. In the 2018 season, last spring rains occurred at the end of May (day of year, DOY, 146) and autumn rains started on DOY 284 (11 October). In the 2019 season, the dry period was longer, from 24 April to 19 October (DOY 116 and 292, respectively). These climatic conditions are typical of Mediterranean areas, with dry and hot periods from late spring until early autumn. Similar reference evapotranspiration (ET\({}_{0}\)) behaviors were observed in both seasons, with the highest ET\({}_{0}\) values reached in July and August.  
Figure 2: Daily crop reference evapotranspiration (ET\({}_{0}\), dots) and daily rainfall (vertical bars) for the 2018 and 2019 seasons. Data obtained from Villanueva de Rio y Minas (Seville) meteorological station.  
Crop water status (minimum stem water potential, min \(\Psi_{stem}\) and water stress integral, \(SI\)), applied water (\(AW\)) and olive production are summarized in Table 1. The water used in 2019 to reach the targeted water conditions was higher (484 L m\({}^{-2}\) or mm) compared to that needed (304 L m\({}^{-2}\)) in 2018. In both seasons under study, the highest volume of irrigation water was that used in the control treatment, T\({}_{0}\) (mean of the two seasons 753 L m\({}^{-2}\)) while the lowest volume was applied in T\({}_{2}\) and T\({}_{3}\) treatments, with 221 L m\({}^{-2}\) and 212 L m\({}^{-2}\), respectively. Minimum \(\Psi_{stem}\) values were negatively correlated with the applied water volume (R\({}^{2}\) = 0.6689); this way, the lowest values of \(\Psi_{stem}\) were observed in T\({}_{2}\) and T\({}_{3}\) olive trees.  
Regarding the applied water, the T\({}_{2}\) and T\({}_{3}\) treatments (Guadalquivir hydrographic confederation RDI and confederation SDI) resulted in the highest reductions regarding the volume of applied water, reaching values as high as 69.9 and 70.8%, respectively (mean of seasons 2018 and 2019), of the total water applied in the control trees.  
The seasonal stress integral (SI total) was not affected by the factor season, but it was affected by the irrigation treatment, as expected. The highest SI value was achieved in T\({}_{2}\) trees (179 MPa day\({}^{-1}\)) followed by those included in T\({}_{3}\). Finally, the SI values also depended on the phenological stage, with the highest values being found in the T\({}_{2}\) trees, especially at phase II (pit hardening). Moreover, the minimum \(\Psi_{stem}\) and the SI were negatively correlated, with SI total increasing as the \(\Psi_{stem}\) decreased (R\({}^{2}\) = 0.9337).
## 3 Approach
### Anomaly Classification  
#### 3.4.1 Anomaly Classification Model  
The proposed abnormal classification model for system calls with parameters is illustrated in Figure 5. Initially, the input system call sequences are embedded using Syscall2 vec, resulting in input vector sequences for the model. These sequences are then fed into a model based on multi-variable time series reconstruction. After obtaining the reconstructed output, the loss is calculated using the mean square error. The model is divided into offline training and online detection phases.  
In the offline training phase, the model takes the full system call sequences for each application as input, assuming that these sequences do not contain any abnormal behavior. In addition to the Syscall2 vec embedding preprocessing, random masking of system call types is performed during training to enhance the model's robustness. Following training, system call type sequence patterns with loss values below the 60% percentile are fed back to the rule mining module as the primary filtering rules for the rule filtering module.  
In the online detection phase, the model takes the filtered application system call sequence window as input. After obtaining the reconstructed output and the corresponding loss values, the current sequence's abnormality threshold is determined using the POT (Peak Over Threshold) algorithm. If the current loss exceeds this threshold, the sequence is classified as abnormal.
## 5 Conclusions  
Despite the growing interest on modelling radiowave propagation at 60 GHz for future 5G systems, there are only a few models that incorporate this frequency band. The ones that do are limited to urban propagation environments and take the fitting parameters from a small number of measurement campaigns. In this paper, we present the measured APD for four different environments. These include the urban environment and, for the first time, three other environments: open, suburban, and wooded.  
We analyzed the measurement results and found that they varied significantly from one environment to the other. We have calculated the APD parameters that would allow incorporating these three environments to 5G millimeter-wave models. We also compared the urban environment results with previous results in the literature and found they agreed.  
We also analyzed the angular combining improvement and showed again that it depends on the environment multipath characteristics. Higher combing improvements are found precisely in the environment where higher propagation losses could be expected, which makes these techniques interesting to improve millimeter-wave 5G link performance. We also demonstrated that the combining improvement depends on the number of combined SLs and analytically calculated the maximum combining improvement, both for coherent and non-coherent combining. Both our results and previous results in the literature fulfill this limit.  
Finally, we conducted a study to determine how the combining improvement would vary with the Tx-Rx distance and concluded that it would increase till reaching the limit we calculated. However, more experimental results are needed to confirm this point.  
Channel sounder set-up E.L.C.; Measurement campaign, all; Data processing, graphs M.G.S.; Results discussion, all; writing--review and editing, M.G.S.  
Figure 10: Combining improvement as a function of the distance.  
**Funding:** This research was partially funded by the Ministerio de Ciencia, Innovacion y Universidades (Spain), under grant number TEC2017-85529-C3-3-R.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experiments
### Evaluation Metrics and Parameter Settings  
Use four evaluation metrics to measure the performance of clustering results: Accuracy (ACC), Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and F-score. These metrics collectively provide a comprehensive evaluation framework to assess the quality of clustering results from various angles, with better-performing clustering algorithms expected to score higher across these metrics.  
Baseline Settings:  
For the baseline algorithms, parameters were carefully chosen, and implementation details were strictly followed as recommended in their respective original papers, ensuring each algorithm could be compared in its best state.  
Parameter Settings:  
For our proposed method, the encoder was designed with a hidden layer of 256 neurons and an embedding layer of 16 neurons.  
The experiments began with preprocessing described in Section 4.1. The data were then fed into various clustering algorithms, yielding clustering results analyzed across the specified metrics.  
Through these rigorous evaluation processes and multi-faceted performance metrics, our goal was to accurately identify clustering methods that are capable of revealing the relationship between EEG background waves and age changes.
# Agriculture 4.0: Is Sub-Saharan Africa Ready?
## 2 Prospects and Challenges for Agriculture 4.0 Adoption in Sub-Saharan Africa
### Institutions, Governance, and Ethics  
Agriculture 4.0 will almost certainly change the way of work for farmers and the culture around traditional ways of farming [15], whether in higher or lower-income countries. Some scholars are already asking whether agriculture 4.0 is the way forward for society in general, expressing concern that the social and ethical implications of adopting such technologies have not received adequate consideration in the design and implementation processes [6; 13].  
These concerns are likely to be just as relevant for African smallholder farmers as for those in higher-income countries, and indeed concerns over institutions, governance and the ethical implications of new technologies are nothing new. There have long been concerns over the ethics of hybrid seeds, and plant breeders' rights, e.g., [66]. Efforts to increase mechanisation in African farming have in the past been hampered by rent-seeking through elite capture and the lack of access to spare parts [67]. And sustainable intensification of agricultural systems across African countries has been hampered by weak institutions, whether due to poorly functioning markets or property rights over land [68].
## 2 Materials and Methods
### Synthesis of Macroligand, Macrocomplex, and Reference Catalyst  
#### 2.2.1 Synthesis of Macroligand PLLA-Bipy  
In a Schlenk tube, \(\upiota\)-lactide (4.00 g, 28.0 mmol) was heated at 135 \({}^{\circ}\)C under a nitrogen atmosphere in the presence of Sn(Oct)\({}_{2}\) (56.3 mg, 0.139 mmol) and Bipy (0.400 mmol) for 3 h. Afterward, the reaction mixture was allowed to cool to room temperature and residual crystalline \(\upiota\)-lactide, which sublimated during the reaction, was removed mechanically from the Schlenk tube. The crude reaction product was then dissolved in CH\({}_{2}\)Cl\({}_{2}\) (20 mL) and precipitated upon the addition of n-hexane (30.0 mL), giving an off-white solid powder, which was separated from the solution by filtration, washed several times with n-hexane, and dried in vacuum at room temperature for 12 h.  
\({}^{1}\)H-NMR (CDCl\({}_{3}\), 400 MHz, ppm): \(\updelta\) = 8.67 (d, 1H, \(J\) = 1.7 Hz, H 6'), 8.53 (d, 1H, \(J\) = 1.7 Hz, H 6), 8.52 (s,1H, H 3), 8.23 (s,1H, H 3'), 7.21 (d, 1H, \(J\) = 2.1 Hz, H 5'), 7.19 (d, 1H, \(J\) = 2.6 Hz, H 5), 5.29 (s, 2H, CH\({}_{2}\) on C4 of Bipy), 5.24-4.96 (bs, 135 CH, in chain), 4.37 (q, 1H, \(J\) = 2.3 Hz, CH end chain), 2.47 (s, 3H, CH\({}_{3}\) of Bipy), 1.86-1.05 (bs, 406, CH\({}_{3}\) PLA).  
FT-IR (cm\({}^{-1}\)): 3007-2953 (w, vCH), 1748 (s, vCO), 1452 (m, vasHCH), 1382 (m, vCH), 1359 (m, vHCH), 1180 (s, vasCO), 1128-1043 (s, vCO), 995 (w, vCCH), 871 (m, vOCC).  
Molecular weight (g/mol): 11,590, Polydispersity index: 1.9, Yields (wt.%): 93, Tg: 55 \({}^{\circ}\)C, Tf: 161 \({}^{\circ}\)C,
# Machine Learning Detection of Cloud Services Abuse as C&C Infrastructure
###### Abstract  
The proliferation of cloud and public legitimate services (CLS) on a global scale has resulted in increasingly sophisticated malware attacks that abuse these services as command-and-control (C&C) communication channels. Conventional security solutions are inadequate for detecting malicious C&C traffic because it blends with legitimate traffic. This motivates the development of advanced detection techniques. We make the following contributions: First, we introduce a novel labeled dataset. This dataset serves as a valuable resource for training and evaluating detection techniques aimed at identifying malicious bots that abuse CLS as C&C channels. Second, we tailor our feature engineering to behaviors indicative of CLS abuse, such as connections to known CLS domains and potential C&C API calls. Third, to identify the most relevant features, we introduced a custom feature elimination (CFE) method designed to determine the exact number of features needed for filter selection approaches. Fourth, our approach focuses on both static and derivative features of Portable Executable (PE) files. After evaluating various machine learning (ML) classifiers, the random forest emerges as the most effective classifier, achieving a 98.26% detection rate. Fifth, we introduce the \"Replace Misclassified Parameter (RMCP)\" adversarial attack. This white-box strategy is designed to evaluate our system's detection robustness. The RMCP attack modifies feature values in malicious samples to make them appear as benign samples, thereby bypassing the ML model's classification while maintaining the malware's malicious capabilities. The results of the robustness evaluation demonstrate that our proposed method successfully maintains a high accuracy level of 84%. In sum, our comprehensive approach offers a robust solution to the growing threat of malware abusing CLS as C&C infrastructure.  
cloud-based and public legitimate services; malware; command and control; Portable Executable; dataset; malware detection; machine learning; feature selection; adversarial attack