esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Surgical site infection (SSI) after tibial plateau definitive fixation is a serious complication that may negatively affect long-term outcomes related to this pathology [1, 2]. The literature reports a higher SSI incidence related to tibial plateau fractures (2.5-12.3%) when compared to fractures in other locations (2.3% in pilon fractures or 3.6% in proximal femoral fractures) [3, 4, 5, 6]. Different conditions have been related to a higher incidence of postoperative infection, for instance, prolonged surgical time, worse Schatzker classification fracture pattern, higher American Society of Anesthesiologists classification (ASA), open fractures, smoking, obesity, and the presence of diabetes mellitus. Based on this knowledge, the prevention of these infectious complications has met different approaches that have proven, in combination, to decrease the incidence of SSI, from reduction in surgical time to smoking cessation programs [7].  
The administration of perioperative antibiotic prophylaxis is the cornerstone of the SSI prevention strategy regarding tibial plateau fracture surgery, in concordance with most prophylaxis guidelines [8]. The use of certain antimicrobial families, like cephalosporores and monobactams in the case of prophylaxis for open fractures, has been previously assessed in the literature [9], but to the best knowledge of the authors, there are no studiesthat have assessed the effect of different perioperative antibiotic prophylaxises in surgical site infection incidence after tibial plateau fracture fixation.  
The hypothesis of this study was that the use of different antibiotic prophylaxises may result in different effects regarding the incidence of postoperative SSI. The objective was to compare the impact of three different types of antibiotic prophylaxis on the postoperative incidence of SSI.
Many such wells are in existence worldwide either drilled as testing boreholes or utilized in thermal applications [34]. Organic ranking cycle (ORC) engines are currently the most mature commercially available technology for small to medium rated power systems from a few kW\({}_{\text{e}}\) to a few MW\({}_{\text{e}}\)[35]. The decreasing cost of RE coupled with strategies based on renewables for powering populations without access to electricity [36] and policies for complete decarbonization of the economy such as the European Green Deal [37] make the combination of renewables and desalination a really interesting approach. This paper aims to investigate combinations of small-scale RO desalination systems (for the purposes of this paper small-scale desalination systems are defined as being able to produce a few thousands m\({}^{3}\) of fresh water per day) with PV and wind energy systems, both in grid-connected scenarios, as well as in autonomous/off-grid scenarios. The main questions set to respond are:  
* What is currently the cost of water from desalination systems employing RE technologies? * How does grid-connected and autonomous systems compare? * Does it make sense to invest in renewables when grid connection is possible? * How do PV based, wind turbine based, and hybrid systems compare? * What is the importance of potable water tank size? * How does the capacity of the desalination plant affect the final price? * Does it make sense to install a higher production capacity desalination plant in an autonomous system operating for less hours a day than installing a plant operating practically all day long? * How does the cost of renewables affect different water production capacity desalination plants? In order to respond to these questions, multiple case studies set in a Greek Island in the Aegean Sea are considered and investigated in this paper and the net present cost of water for over a 20-year period is going to be utilized as the main comparison indicator.
# Deep Learning for Detecting Verticillium Fungus in Olive Trees: Using YOLO in UAV Imagery
## 2 Background
### You Only Look Once (YOLO) Algorithm  
The YOLO algorithm proposed in 2015 adopts an approach of framing object detection as a regression problem to spatially separated bounding boxes and associated class probabilities [24]. Essentially, classification and bounding box location calculation happen simultaneously.  
The algorithm begins by partitioning the image in an N \(\times\) N grid, and then, for each cell of the grid, it predicts bounding box locations, confidence of detection of an object, and class probability for every class, thus having the entire object detection and classification process happen in a single pass over the image.  
YOLO as a project is being actively developed, with newer and more enhanced versions coming out in the form of versions steadily throughout the last few years, with improvements made in the algorithm's speed and performance [25] through the change in the algorithm's architecture or the addition of new capabilities. Each new release receives a new version number; we are currently in the eighth official release of YOLO, YOLOv8; however, in the case presented in this paper, YOLOv5 [26] is used. The reasoning behind the choice is that while newer versions of the YOLO algorithm have been developed, YOLOv5 has been the most recent version preferred by researchers in related works in the literature, as also shown in the next chapter.  
The YOLO algorithm is especially powerful in the task of detecting olive tree infections by the Verticillium wilt from UAV images, since, as shown in the literature [25], it has both high inference speeds that allow for the classification of images in near real time and can achieve high classification performance metrics. In fact, it is not an overstatement to say that it is currently the only algorithm that offers such a balanced solution in terms of speed and performance for the simultaneous object detection and classification task.  
YOLOv5's network design is comprised of a Backbone, a Neck and a Head, as described by its authors. All of these are essentially sub-networks that, when assembled together in the right order mentioned above, create the YOLOv5 model. The architecture of YOLOv5 that is used in the current paper employs the New CSP-Darknet53 as the backbone, the SPPF and the New CSP-PAN as the neck and the YOLO head layer as the Head, as shown in Figure 1.  
The differences between the medium, small and nano architectures of YOLOv5 concern the size of the network according to depth and width multipliers, the values of which are shown in Table 1.  
The model can be used as is, without initialised weights, or with weights that have been created through prior training on a given dataset.
# State Observer Based on an Accelerometer for an Elastic Joint with Nonlinear Friction
## 2 Problem Formulation and Solution
Inspired by [12], a solution to the problem proposed in the above is given in terms of a state observer for a Lipschitz nonlinear system, which will be described below. First of all, it follows from (4) with (2) and (3) that  
\[\dot{x} =Ax+Y+\Omega(x)+Bu \tag{6}\] \[y_{1} =Cx\]  
where \(u:=\tau\) is the input torque and \(y:= \begin{bmatrix}y_{1}&y_{2}\end{bmatrix} ^{T}:= \begin{bmatrix}\theta_{m}& \dot{\omega}_{l}\end{bmatrix} ^{T}\) are the values measurable by Assumption 1, and  
\[A = \begin{bmatrix}0&1&0&0\\ 0&0&0&0\\ 0&0&0&1\\ J_{m}^{-1}k_{1}&J_{m}^{-1}D&-J_{m}^{-1}k_{1}&-J_{m}^{-1}(D+f_{\rm{\tiny{F}}}) \end{bmatrix} ,\,Y= \begin{bmatrix}0\\ y_{2}\\ 0\\ 0\\ -J_{m}^{-1}(\phi(x_{1},x_{3})+\psi(x_{4}))\end{bmatrix} ,\,B= \begin{bmatrix}0 \\ 0\\ 0\\ J_{m}^{-1}\end{bmatrix} ,\,C= \begin{bmatrix}0\\ 0\\ 1\\ 0\end{bmatrix} ^{T},\] \[\phi(x_{1},x_{3}) =\left\{\begin{array}{ll}\phi_{1}(x_{1},x_{3})=3k_{2}\theta_{ B}^{2}(x_{3}-x_{1})+2k_{2}\theta_{B}^{3},&\text{if }x_{3}-x_{1}<-\theta_{B},\\ \phi_{2}(x_{1},x_{3})=k_{2}(x_{3}-x_{1})^{3},  
Next, we present a state observer for the system (6) as follows:  
\[ \begin{split}\dot{\hat{x}}&=A\hat{x}+Y+\Omega(\hat{x})+ Bu+L(y_{1}-\hat{y}_{1})\\ \hat{y}_{1}&=C\hat{x}\end{split}  \tag{7}\]  
where the observer gain \(L\) is  
\[L=\frac{P^{-1}C^{\top}}{2\epsilon}. \tag{8}\]  
Here, the matrix \(P>0\) is chosen such that for some small \(\epsilon>0\), the following LMI (linear matrix inequality) [16] holds:  
\[ \begin{bmatrix}A^{\top}P+PA+\gamma^{2}I-\frac{1}{\epsilon}C^{\top}C&P\\ P&-I\end{bmatrix} <0. \tag{9}\]  
Now, we state the main result of this note, the proof of which is given in the following section, along with a detailed explanation about the proposed observer. Theorem 1: _Under Assumption 1 and 2, the state observer (7) guarantees that the estimation error \(e(t):=x(t)-\hat{x}(t)\) converges to zero as time goes to infinity._
# Analysis of New Orthogonal Transforms for Digital Watermarking
## 3 Watermark Embedding
### Method 1: Direct LSB Modification of Image Spectrum (M1)  
In this approach, the hidden message is evenly distributed in the image spectrum. Channel grouping is not performed; thus, the orange blocks from Figure 1 are skipped. The information bits are inserted into least significant bits of the selected spectral coefficients, similarly as in the JSteg algorithm [17]. This method is very simple, but its main drawback is that it does not analyze the spectrum of an image, and the embedding process for the given message length is always performed in the same way.
# Person Re-Identification by Low-Dimensional Features and Metric Learning
## 4 Experiments and Discussion
### Datasets and Evaluation Protocol  
#### 4.1.1 DukeMTMC-reID  
DukeMTMC-reID is a subset of the DukeMTMC dataset for image-based Re-ID proposed by Duke University. It consists of 36,411 images of 1812 identities collected by eight different cameras. A total of 16,522 images of 702 persons are divided into a training set. A total of 19,889 images of the remaining identities are divided into a testing set, with 2228 in the query set and 17,661 in the gallery set.
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 4 Connectivity Properties of Ship MANET  
For a ship's ad hoc network, it is crucial to analyze its connectivity. To the best of our knowledge, there is no formal method to complete the modeling and connectivity analysis of ad hoc networks by considering marine ship motion scenarios. In this part, we can regard each ship as a node, and establish the ship MANET as a connectivity model with four attributes by studying the position of each node and the connection relationship between nodes.  
Firstly, we analyzed the boundary conditions of node connectivity through the critical signal strength at the receiving end, then we studied the stability of the nodes and analyzed the relative motion situation of the nodes in combination with the ship encounter situation. On this basis, we study the possibility of node connectivity and the calculation method of link retention time.
## 2 Study Area and Data
### Data
#### 2.2.2. Other Model Input Data  
The BTOP model, which is used as a hydrological model in this study, requires a variety of input data such as precipitation, Digital Elevation Model (DEM), land cover, soil type, Normalized Difference Vegetation Index (NDVI), and meteorological data.  
DEM was obtained from NASA's Shuttle Radar Topography Mission (SRTM) with an original resolution of 3 arc-seconds [41]. We employed the International Geosphere-Biosphere Program (IGBP) map as land cover data with an original resolution of 500 m [42]. Soil properties of this study area are obtained by using the soil map of the Food and Agriculture Organization (FAO) at the scale of 1:5 million [43]. NDVI came from the National Oceanic and Atmospheric Administration (NOAA) of the United States with an original resolution of 0.05 degree [44]. The Climate Research Unit (CRU) provides meteorological data [45], such as temperature, radiation, humidity, wind speed, and vapor pressure. All the above datasets could be accessed freely on their official website and were resampled to 30 arc-seconds (approximately 1 km) for BTOP model simulation. Moreover, the discharge data at the Kitamatsuo station were downloaded from MLIT as the same source with gauge rainfall.
# Systematic Review on Individualized Versus Standardized Parental Nutrition in Preterm Infants
## 4 Discussion
### Conclusion 1: The LoE of the Available Studies Comparing SPN vs. IPN Is Very Low (LoE1- and LoE 2-)  
Overall, the evidence provided by the studies available in the present systematic review is very low (one study LoE 1-, eight studies LoE 2-). All new identified trials and all but one of the trials of the 2018 review were nonrandomized observational trials using historical controls. There is only one underpowered randomized controlled trial, conducted more than 40 years ago (1981) when the \"standard of care\" was very different from current clinical practice and standard bags were not yet commercially available. Based on poor allocation concealment (alternate assignment to treatments) in the previous guideline the trial was downgraded to LoE 2+. A total of 28 preterm infants were assigned by alternation to IPN or SPN. IPN including pharmaceutical individualization increased amino acid (AA) intake (2.2 \(\pm\) 0.2 vs. 1.9 \(\pm\) 0.3 g/kg/d, \(p\) \(<\) 0.01), non protein energy intake (63.0 \(\pm\) 7.0 vs. 53.0 \(\pm\) 6.0 kcal/kg/d, _p_\(<\) 0.001) and finally weight gain (11.8 \(\pm\) 5.2 vs. 4.9 \(\pm\) 8.0 g/kg/day, \(p\) \(<\) 0.02) [20]. Of note, glucose manipulations in the SPN group required diluting the standardized TPN formulation with a glucose solution and consequently reducing the intake of other nutrients including amino acids. Of note, the study by [PERSON] et al., 2019 is not an RCT comparing SPN vs. IPN. It compared two recent previous RCTs (one IPN and one SPN) each compared standard (control) and high (intervention) parenteral protein and energy dosage regimens [23].  
Conclusion 2: IPN with Pharmaceutical Individualization, May Increase Protein Intake, and the Use of Ready to Use SPN Bags May Facilitate Early Achievement of Protein Needs
###### Abstract  
This paper presents a novel multibeam transmitting dual-reflectarray antenna able to generate a complete multiple spot coverage from a geostationary satellite in Ka-band (20 GHz). The bifocal design technique has been exploited for the first time to reduce by 50% the beam deviation factor with respect to the equivalent monofocal antenna, allowing to produce adjacent beams separated by only 0.56\({}^{\circ}\) in the antenna offset plane. In order to guarantee an acceptable spillover, the main reflectarray has been oversized in the same plane where the beams are compressed, resulting in an elliptical reflectarray of 3.5 m \(\times\) 1.8 m. The interleaved beams required to provide the complete multi-spot coverage are produced in the orthogonal polarization, using the same aperture and feeds. The proposed antenna requires a smaller main aperture (about half of the area) and a lower number of feeds than other configurations that use a single oversized reflector to generate a complete multi-spot coverage, showing promising results for communication satellite applications in the Ka-band.  
Keywords:bifocal antennas; dual-reflectarray antennas; Ka-band; multibeam antennas; multispot coverage; satellite communications +
Footnote †: journal: Electronics
# The Analysis of Service Convergence in an Optical Access Network +
## 1 Introduction  
The rapid advancement of communication technologies has significantly transformed digital interactions, increasing the demand for faster and more efficient networks. Optical access networks play a crucial role in providing reliable, high-speed connectivity. However, as the demand grows to integrate services like LTE and 5G, the challenge of converging these services into a unified infrastructure that maximizes resource efficiency becomes evident. This convergence, while beneficial, requires overcoming technical complexities due to the different bandwidth, latency, and jitter demands that each service imposes. It is essential, therefore, that passive optical networks (PONs) are kept up-to-date to effectively support the demands of emerging technologies, particularly within the framework of the fifth generation of mobile networks [1; 2].  
Converging services in an optical network requires not only a robust infrastructure, but also a thorough understanding of the underlying technologies, such as LTE, 5G, and filtered multi-carrier modulation waveforms, such as the UFMC and GFDM. These waveforms, which offer superior spectral efficiency, are key to the success of the 5G network, as they enable more efficient and flexible data transmission. In addition, RoF architecture and PON networks provide the necessary technological foundations for the transmission and distribution of signals over optical access networks, facilitating the integration of multiple services on a single channel. This approach is crucial to address growing connectivity demands, enabling optical networks not only to meet current needs, but also to anticipate and adapt to future technological challenges [3].  
This paper focuses on the analysis of service convergence within an optical access network, using both the simulation and practical implementation of an optical link capable of transmitting and receiving LTE and UFMC signals. Using advanced tools such as Matlab2024a and OptSim, simulation scenarios were developed that replicate real transmission conditions in PONs. This study not only seeks to optimize bandwidth usage and improve quality of service, but also to contribute to the evolution of optical access networks toward more adaptive and efficient infrastructures. The results obtained, based on key metrics such as the BER, signal-to-noise ratio (SNR), and EVM, provide a comprehensive view of network performance, enabling a better experience for end users in an increasingly digitized environment.
## 4 Results and Discussion
### Inactivation Efficiency and Kill Rate  
Figure 6a represents the inactivation efficiency of the plasma treatment for _V. cholerae_, and Figure 6b represents the kill rate of the plasma treatment time. The inactivation efficiency demonstrates the percentage of colonies inactivated by the plasma treatment and  
Figure 4: (Color online). Visual analysis of _V. cholerae_ CFUs. (**a**) Pure _V. cholerae_ strain 10\({}^{7}\) CFUs; (**b**) CFUs after 15 s plasma treatment without H\({}_{2}\)O\({}_{2}\) vapors; (**c**) CFUs after 30 s plasma treatment without H\({}_{2}\)O\({}_{2}\) vapors; (**d**) CFUs after 45 s plasma treatment without H\({}_{2}\)O\({}_{2}\) vapors; (**e**) CFUs after 15 s plasma treatment at 0.1 mbar air pressure and 5.9 sccm flow of vaporized H\({}_{2}\)O\({}_{2}\); (**f**) CFUs after 30 s plasma treatment at 0.1 mbar air pressure and 5.9 sccm flow of vaporized H\({}_{2}\)O\({}_{2}\); and (**g**) CFUs after 45 s treatment at 0.1 mbar air pressure and 5.9 sccm flow of vaporized H\({}_{2}\)O\({}_{2}\).  
its dependence on the hydrogen peroxide injection rate and plasma treatment time. The inactivation efficiency \(\eta\) of _V. cholera_ is calculated as [22]:  
\[\eta=(\;1-N_{0}/N_{T})\times 100\% \tag{3}\]  
\(N_{0}=\) initial concentration of colonies without any treatment;  
\(N_{T}=\) concentration of colonies after plasma treatment.  
The log reduction (kill rate) of _V. cholera_ was calculated by the formula:  
\[ln\left(\frac{N_{t}}{N_{0}}\right) \tag{4}\]  
\(N=\) number of colonies after plasma treatment;  
\(N_{0}=\) number of colonies before plasma treatment.  
It describes the relative number of living and dead cells.  
Figure 5: Dependence of _V. cholera_ on treatment time and H\({}_{2}\)O\({}_{2}\).  
Figure 6: (a,b) Dependence of _V. cholera_ inactivation efficiency and kill rate on plasma treatment time with and without H\({}_{2}\)O\({}_{2}\) vapors.  
The plasma-generated ROS reacts with the protein and amino acid of the _V. cholerae_ DNA structure and inactivates it. These reactive species accumulate, leading to vigorous _V. cholera_ cell damage.
## 4 Results  
### Aquaculture Maps Obtained Using Multiple Features  
Six maps were obtained using different feature combinations; their accuracies were assessed with 1000 validation samples (Table 3), and four typical sites of the maps were presented in Figure 7. The performances using only spectral indices were poor (Figure 7A and Table 3), and many water pixels were wrongly classified as aquaculture pixels, for some aquaculture facilities are submerged in waters and their spectral signatures are not obliviously different from water. The combination of spectral indices and SAR image features improved the performance (Figure 7B), especially for the second typical site, for SAR images reflect the backscatter characteristics of aquaculture facilities, which is totally different from a pure water body. However, many misclassifications still existed near shorelines, and the overall accuracy and Kappa coefficient are not satisfactory. The use of spectral indices and index-derived texture features greatly improved the classification performance (Figure 7C and Table 3), and most aquaculture ponds and marculture areas were correctly detected, and an overall accuracy of 88.2% was obtained.  
The results of combination D (Figure 7D) show the effectiveness of using SAR images and their corresponding textures. Comparing the results on the second site with those from combination C, it can be easily found that marculture areas were better detected. However, there are many commission errors along coastlines, resulting in a lower OA (86.9%). The results in Figure 7 show that the combination of E and F obtained better performances than the combination of A, B, C, and D. However, the results of combination E had less holes, and the overall accuracy (89.5%) and Kappa coefficient (0.719) were both the highest compared with other combinations. Therefore, the feature in combination E was applied to obtain the final map.
# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
## 2 Background
### Wireless Sensor Networks  
In smart cities, WSNs are a major source of sensory information [12]. A general example of the infrastructure and technologies used in a smart city crowd monitoring system is shown in Figure 2[9]. The WSN is used along with crowd sensing devices, such as smartphones and smart wearables. Then, different communication technologies, such as ZigBee, Wi-Fi, VANET, LTE, and 5G, are used to transmit the sensed data to a data center/cloud server for analysis. In the computing layer, data from different sources are merged and analyzed to extract meaningful information to be used later for decision-making by smart city services.  
A single WSN generally consists of multiple sensor nodes and a sink node. Sensor nodes are deployed over a specified sensing area to sense information about the surroundings and communicate to the sink node through a multi-hop mechanism [20; 21]. The sink node is responsible for collecting the sensed information from the sensor nodes, aggregating the information, and sending the aggregated information to higher-level network devices for further processing and information extraction.  
Figure 1: Smart city architecture.  
It is worth noting that, for smart city sensing applications, wired sensor nodes can be incorporated into the WSN. These wired sensor nodes could have better communications, computation, and storage capabilities than normal wireless sensor nodes. Although the network may include some wired sensors, it is still called a WSN because most of the sensor nodes are wireless [9].