esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling the VLC Networks: Architecture, System Model and Scenarios
### Multi-Carrier Modulations
#### 2.1.3 Backhaul Link  
When a multi-cell scenario aims to provide coverage in a large area, there must be a connection (link) among all or part of the APs, which is denoted usually as a backhaul link. A backhaul link is essential in cellular networks [82] and it must be considered and studied also in a realistic way to evaluate the performance of the whole communication system. The backhaul can be either wired or wireless. Different wired technologies for backhaul in VLC have been demonstrated in the literature, such as power line communication (PLC) [83; 84; 85], Ethernet [86] and optical fiber [87]. However, when the network becomes large, the use of wired backhaul links imposes an extra cost and cabling infrastructure that sometimes means a high complexity [88]. Furthermore, wired backhauls present a low flexibility when the topology, i.e., location of APs, must be changed. Thus, alternative wireless backhaul links were presented in the literature.  
Practical wireless backhaul links have been studied using RF systems [89] or free-space optical (FSO) communications [90; 91]. The implementation of VLC or LiFi systems may be complicated when we must connect multiple light fixtures, provided with communication capabilities, by using a wired backhaul. To this end, the first work that studies the feasibility of a wireless backhaul in LiFi systems corresponds to [92], where each AP needs 6 extra LEDs in addition to several PDs for receiving data from neighbouring APs. Later on, new studies such as [45] propose a practical yet sophisticated wireless backhaul link based on VLC reflections. In any case, the backhaul topology must be taken into careful consideration to avoid the system performance decreasing [87].
## 3 Results and Discussion
### Effects of HWWC Dosage and PS Concentration on OTC Degradation  
The catalyst dosage and PS concentration are important factors in pollutant degradation. Therefore, degradation experiments with various HWWC dosages (0.05, 0.20, and 0.40 g/L) and PS concentrations (0.5, 1.0, and 2.0 mM) were investigated. As shown in Figure 6, the degradation efficiency of OTC increased in proportion to the HWWC dosage and PS concentration. The \(k\) value increased from 0.02 \(\pm\) 0.00 min\({}^{-1}\) to 0.14 \(\pm\) 0.02 min\({}^{-1}\) and increased from 0.09 \(\pm\) 0.01 min\({}^{-1}\) to 0.12 \(\pm\) 0.01 min\({}^{-1}\) when the HWWC dosage  
Figure 4: Control experiment for oxytetracycline (OTC) degradation ([PS]\({}_{0}\) = 1 mM, [OTC]\({}_{0}\) = 20 uM, [HWWC]\({}_{0}\) = 0.2 g/L, pH = 6). PS: persulfate; HWWC: hand-warmer waste catalyst.  
and PS concentration increased, respectively. HWWC and PS were the activator and source of reactive radical species, respectively. Thus, their increase could promote OTC degradation [39].
## 3 Results and Discussion  
### kNDVI Analysis  
By comparing the _kNDVI_ of the study area in 2019 and 2024, it was found that the _kNDVI_ gradually increased after the implementation of the restoration measures, indicating that the vegetation growth was improved. The average value of _kNDVI_ in the study area in 2019 was 0.4086, and the _kNDVI_ vegetation index in 2024 reached 0.4927. The _kNDVI_ value in 2024 was significantly higher than that in 2019, indicating that the ecological restoration measures were gradually effective. From the spatial distribution of _kNDVI_ changes in the study area of Xiwuzhumuqin sandy land restoration from 2019 to 2024 (Figure 5), _kNDVI_ increased significantly, and the increased areas were mainly distributed in the northwest, south, and central regions of the study area. The eastern region has little change, and there is a sporadic increase in distribution. Among them, the most obvious increase was in the northwest and south of the study area, while the _kNDVI_ in the southwest of the study area showed a patchy decreasing trend.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters/Configuration** & _kNDVI_-PT-Former \\ \hline Framework & PyTorch 2.0 + CUDA 12.1 \\ Hardware & NVIDIA A100 40 GB GPU \(\times\) 2 \\ Optimizer & AdamW (learning rate = 5 \(\times\) 10\({}^{-5}\)) weight decay = 0.01) \\ Batch size & 32 \\ Training rounds & 200 epochs (early stop method patience = 10) \\ Loss function & binary cross-entropy + Dice loss \\ Evaluation index & _CPA_, _IoU_, \(F\)1-score \\ \hline \hline \end{tabular}
\end{table}
Table 3: Technical parameters and configuration.
## 2 Materials and Methods
### Applications of the Automated SHS Quantification Algorithm  
To demonstrate the utility of the SHS quantification algorithm, we analyzed retrospective VLE data from BE patients to investigate the characteristics of SHSs in BE. For each VLE pullback, the maximum extent of contiguous BE (most proximal location, denoted by M) was identified manually by consensus of two OCT image experts and used as a reference for SHS quantification. Three different analysis and patient groups were employed for different purposes, detailed in Sections 2.4.1-2.4.3. Subgroup analysis stratified by the highest pathology records (low-grade dysplasia (LGD) vs high-grade dysplasia (HGD) and intramucosal carcinoma (IMC)) was also performed (Table 1, see also Tables S1-S6).
# LaRaWAN Metering Infrastructure Planning in Smart Cities
## 4 New Proposals and Results  
In the initial stage of this work, it was assumed that the specifics of the designed network consisting of metering nodes installed at AMI meters allowed for a static configuration of the radio parameters of these nodes. The nodes are pre-configured in such a way as to minimize potential collisions that may occur in the common transmission channel.
## 1 Introduction  
In 2019, it was estimated that 113 million people were living with cardiovascular diseases (CVD) in the 57 European Society of Cardiology member countries [1]. To reduce the risk of cardiovascular acute events and improve outcomes, the management of risk factors such as hypertension, smoking habits, lipid metabolism, etc. is vital.  
Hypersion is one of the most prevalent risk factors for most of the cardiovascular diseases [1,2]. Blood pressure measurement devices are commonly used daily by patients at home, however, one cannot be certain of the way the devices are used. Indeed, the European Society of Hypertension advised to avoid any physical activity during the previous 30 min and to stay still for 3 to 5 min prior to the measurement to avoid biases [3]. Therefore, monitoring physical activity in the context of blood pressure measurement might help to assess the recorded values' validity.  
Regular moderate to intense physical activity also plays an important role in the prevention of cardiovascular diseases. For stroke, it has a significant impact on the age of occurrence of the first stroke event but also on its long-term outcomes [4]. Indeed, [PERSON] et al. emphasized in their review that post-stroke physical activity is also of high importance for survivors, indicating that \"The recommendation now clearly states that increasing PA [Physical Activity] and exercises to improve CRF [Cardio-Respiratory Fitness] should be included in stroke rehabilitation programs\" [5]. Furthermore, regular physical activity has beneficial effects on other risk factors or concomitant chronic diseases such as diabetes mellitus [6].  
Finally, the introduction of a digital tool as a support for care and rehabilitation has been shown to significantly reduce the risk of acute events and to improve patient adherence, as described by [PERSON] et al. in the context of post-stroke prevention [7].  
Therefore, all these points testify for the relevance to monitor physical activity in a digital care support system which aims to reduce the risk of cardiovascular events.  
The presented method, primarily based on the standard version of the Euclidean norm minus one algorithm (ENMO), is adjusted to account for off-the-shelf accelerometers with imprecise performances. Furthermore, the approach is individualized by estimating subject-dependent thresholds for activity intensity. The method is then validated on a referenced data set collected on healthy subjects.  
After recapitulating the state of the art, the paper will describe in detail the experimental framework and the proposed algorithm in Section 2. Results are summarized in Section 3 and finally discussed in Section 4, while a conclusion is given in Section 5.
and intermittent links. While this may work on the small scale for small DTN networks, this is not a practical method for large-scale networks that span multiple network operators over several intermittent links. A solution must be found in order to enable the eventual deployment of the Solar System Internet, a cross space agency collaboration that aims to deploy a DTN network that spans all planets in our solar system [7].  
To tackle this issue of management of large DTN networks, we propose a Software-Defined Delay Tolerant Networking (SDDTN) architecture that adapts Software-Defined Networking (SDN) techniques, an approach to managing large traditional networks in a centralized manner, to the DTN environment. This SDDTN architecture tackles the issue of reacting to changes in the network in a challenged networking environment through a hybrid approach where networking decisions can be determined, distributed, and executed at both a local-neighborhood scale and at the individual node level.  
Additionally, in this paper we examine one possible path towards implementing a SDDTN architecture via data plane programming. In traditional SDN networks, networking switches are deployed that communicate with a centralized controller to send telemetry and receive decisions. However, there is currently no hardware that is able to understand the protocols behind DTN, as all implementations of the DTN protocols have been in software. We attempted to use the Programming Protocol-Independent Packet Processors (P4) (P4 is a registered trademark of Open Networking Foundation (ONF) and is a programming language used under Open Source license) language to create a program for a networking switch that would enable the switch to (1) understand the requisite DTN protocols and (2) communicate with an SDN controller. The specific program we aimed to create is a network middlebox that would be able to translate between the two primary versions of the Bundle Protocol (BP) in use by current and future DTN networks (BP version 6 and version 7), for reasons discussed in Section 4.2. A proof-of-concept BP translator was developed that enabled DTN nodes to communicate despite utilizing different BP versions.  
This paper is laid out as follows: Section 2 provides necessary background details on DTN, Bundle Protocol, and routing within DTN networks; Section 3 explains SDN and our proposed SDDTN architecture; Section 4 describes and provides context for our approach towards implementing a SDDTN; Section 5 presents our proof of concept, details the challenges we faced, and evaluates its capabilities; and Section 6 contains our concluding thoughts and recommended next steps.
## 2 Materials and Methods
### Climatic Dryness Indicators and Canopy Photosynthesis Proxies for Comparing the MTVDI Seasonality
#### 2.4.6 Solar-Induced Chlorophyll Fluorescence  
The Solar-Induced Chlorophyll Fluorescence (SIF) is radiation generated between 650 nm and 800 nm after vegetation absorbs energy [66]. It is not saturated in dense forests, and is sensitive to the rate of photosynthetic electron transmission and the ratio of absorbed radiation. It can directly measure the functional status of vegetation and can eliminate the constraints imposed by the sunlight or sensor geometry on the vegetation index [67; 68]. Therefore, the SIF can characterize the growth status and photosynthesis ability of vegetation. The satellite SIF data has been widely used to capture the photosynthetic activity of terrestrial forests [34; 68; 69; 70]. In the previous study, [PERSON] et al. [61] identified a large-scale, dry-season green-up area located in the northern, wetter part of Amazonia based on SIF and MODIS EVI. In this study, the satellite SIF data covering the Amazon tropical forests was provided from the monthly GOME-2 product-level 3 version 26 (V26) [34], which was retrieved from observation from multiple wavelengths from 2007 to 2015.
## 2 Related Research  
Consequently, over the past decade, there have been large endeavors in handling security concerns related to intrusion/cyber-attacks detection in the IoT system. Most of these anomaly-based IDS systems were developed by employing the techniques of machine learning (ML) and deep learning (DL) techniques to provide intelligent cybersecurity decision-making. Since ML/DL techniques operate using datasets of records and features that are used to train and test the predictive IDS models, it should be noted that not all of the features/records in a dataset are relevant or significant while training/testing classification/detection models.  
Therefore, data engineering and feature preprocessing have formulated a core phase of every ML/DL-based IDS model that played a major role in making the raw data collected from the IoT ecosystem usable for further analysis and predictions. In anomaly detection challenges, for example, feature/data engineering is more significant in the IoT ecosystem since the features may include null or zero features. Relevant features, in some cases, are more difficult to extract by only ML/DL algorithms without using feature/data engineering approaches. Techniques of relevant features to identify attacks have been made to classify the data by industrial companies and researchers.  
Several auspicious state-of-the-art models for anomaly intrusion detection models have been implemented for IoT cybersecurity using machine and deep learning approaches [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. Table 1 summarizes the reviewed research models for anomaly-based IDS using machine/deep learning approaches to solve cybersecurity concerns of cyber-attacks on IoT systems.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Learning Models** & **Datasets** & **Number of Features/Number of Records** & **Cyber-Attacks** \\ \hline \multirow{4}{*}{[17]} & Auto-Encoder, random forest & & & Distributed DoS (DDoS), Heartbleed, structured query language (SQL) \\  & Linear/Quadratic & & 2,830,540 records & Injection, Botnet. \\ \cline{1-1}  & Discrimator & & & \\ \hline \multirow{2}{*}{[18]} & Particle Swarm (PSO), & \multirow{2}{*}{IoTID20} & 83 Features/ & \multirow{2}{*}{Mirai, DoS, Scan, MITM} \\  & XG Boost, RF & & & 450,00 records \\ \hline \multirow{2}{*}{[19]} & \multirow{2}{*}{Auto-Encoders (AEs)} & NSI-KDD/IoTID20/ & 43 Features/140,000 & Norm, DoS, Probe, R2L, U2R \\  & & N-BaloT & 83 Features/450,000 & /Mirai, DoS, Scan, MITM \\ \cline{1-1}  & & & 114 Features/612,000 & /Normal, Bashlite, Mirai \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of surveyed related research articles of supervised ML-based anomaly IDS.
## 3 Results and Discussion  
### Image Dataset Establishment  
#### 3.1.1 Virtual Degraded Image Generation  
Utilizing the derived 3 ch PSF in conjunction with the process outlined in Figure 7, we generated virtual degraded images from various types of images. To create these images, every pixel in each channel of an original color image was convolved with its respective PSF kernel, and the results were integrated to form the degraded image. This procedure was repeated to compile a large dataset of virtual degraded images. The original image datasets comprised self-taken photographs from daily life and DIV2K images provided by 'DIVerse 2K resolution high-quality images for the challenges of CVPR'. To quantify image degradation, we used two metrics: the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR). SSIM is widely recognized for its ability to assess perceptual similarity between two images [50; 51; 52; 53; 54], while PSNR quantifies the ratio between the maximum possible signal power (the original image) and the noise power that distorts its representation (the reconstructed or processed image) [55; 56]. For the virtual image set, the average SSIM was 0.8168, and the average PSNR was 27.981 dB. An SSIM value of 1 indicates an original, pristine image, and an average SSIM value of 0.8168 reflects approximately 18.3% image degradation caused by the UDC panel. PSNR provides an additional perspective on image quality, with higher values indicating greater fidelity. Generally, PSNR values between 30 and 50 dB are considered acceptable for most practical applications [57; 58]. A PSNR value below 30 dB suggests noticeable quality degradation, while values above 40 dB imply that the processed image is nearly indistinguishable from the original. The virtual data generated in this manner were used alongside actual degraded images as training data, serving to compensate for the limitations of real data.
## 3 Power-Stage Design  
As seen in Table 3, the DC-bus voltage values vary between 250 and 400 V in the designed power system. The voltage, current, and system's ambient temperature values are among the critical selection criteria of IGBTs. These values are given in Tables 4 and 5, respectively.  
Considering the motor voltage and current values in Table 4, IGBT modules with V\({}_{\text{ces}}\) = 1200 V, T\({}_{\text{J}}\) = 175 \({}^{\circ}\)C were selected to prevent damage to the IGBTs in the design due to voltage and current exceedances of 2 or 3 times the nominal value that may occur on the IGBT. Three different sheathed modules were used in the designs. The preferred IGBT modules are shown in Figure 6. Here, switching loss values, high switching frequency, and cost are the reasons for the preference.  
In Table 6, the IGBT modules of the Semikron company that are suitable for the determined values are given collectively. These modules were selected by considering Semikron's recommendations for choosing the module values according to the DC-Bus voltage and nominal current.  
In addition, designs were made along with SKM300 GB126D and SKiM459 GD12E4V2, composed of six combined modules, to compare performance using different cases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Value** & **Unit** & **Definition** \\ \hline V\_Buss & 375.0 & Volt & Busbar voltage \\ \hline Lq & 0.6 & mH & 2-phase inductances \\ \hline Rq & 0.1634 & Ohm & 2-phase resistance \\ \hline Kp & 1.55 & Nm/A & Moment constant \\ \hline Ki & 1.55 & Volt\(\cdot\)s/rad & Back EMF constant \\ \hline Jm & 0.341 & kg\(\cdot\)m\({}^{2}\) & Motor moment of inertia \\ \hline In & 200 & A & Nominal current \\ \hline Pn & 75 & kW & Motor power \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the BLDC motor and power system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Minimum DC-Bus Voltage** & **Nominal DC-Bus Voltage** & **Maximum DC-Bus Voltage** \\ \hline
250 V & 375 V & 400 V \\ \hline \hline \end{tabular}
\end{table}
Table 3: Busbar voltage values.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**P\_nom-motor** & **P\_max** & **V\_nommotor** & **V\_max** & **I\_nom-motor** & **I\_max** \\ \hline
75 kW & 100 kW & 375 V & 400 V & 200 A & 300 A \\ \hline \hline \end{tabular}
\end{table}
Table 4: Nominal and maximum values of electric motor.  
IGBT modules also have temperature resistance up to 175 \({}^{\circ}\)C and operating temperatures up to 150 \({}^{\circ}\)C. The switching losses vary between 19 and 25 mJ depending on temperature. This value is low compared to the module values of many companies, which provides the system with a reduced energy loss and a high efficiency [15]. An IGBT driver was designed. DC-bus and snubber capacities were calculated and specified.
# Electrochemical Detection of Penicillin G Using Molecularly Imprinted Conductive Co-Polymer Sensor
## 3 Results and Discussion
### Sensor's Performances
#### 3.2.2 Reproducibility and Selectivity
The easiest conclusion to draw from this graph is that the modulus response when the sensor is subjected to a concentration of 15 ppb of PenG is always much higher (at least four times higher) than those observed for the same amount of the different interfererents tested. We can therefore deduce from all this that the sensor seems more sensitive to PenG than to the other interfererents tested, despite their similarity.
# Lattice Structure for Improving Cooling Uniformity in HPDC Mould Corners
###### Abstract  
Conformal cooling channels have demonstrated significant advantages for cast parts and 3D-printed moulds in the high-pressure die casting (HPDC) process. However, the complex geometry of moulds, characterised by small intrusions, sharp corners, and fins, often results in nonuniform cooling in certain regions, leading to overcooling or overheating. This study proposes integrating lattice structures within specific regions of 3D-printed moulds or inserts as an additional control parameter to enhance cooling uniformity by increasing thermal resistance in targeted areas. A validated three-dimensional Computational Fluid Dynamics (CFD) model was employed to incorporate three types of lattice structures, aiming to limit local heat flux in overcooled areas. The model specifically addresses the cooling of an aluminium alloy profile with 90-degree-angled corners, using H13 steel mould properties. The results indicate that implementing a lattice structure as a sleeve around the cooling pipe at the corner two sides improved temperature uniformity by over 42%. However, this increased thermal resistance also led to a 16 \({}^{\circ}\)C rise in corner temperature. These findings suggest that implementing lattice structures in the mould can improve cooling uniformity. However, they should be positioned away from the thickest regions of the mould to avoid increasing the modelling time.  
lattice structure cooling; casting die-cooling; conformal cooling; die temperature uniformity; high-pressure die casting cooling Article
## 2 Materials and Methods
### Volatile Compounds Determination by GCMS  
Volatile compounds of summer savory (_Satureja hortensis_ L.) samples were extracted using solid-phase microextraction (SPME). The SPME was performed in headspace of the sample in 10 mL glass bottles closed with screw caps with silica/Teflon septa using Stableflex fiber coated with 50/30 \(\upmu\)m DVB/PDMS layer (Supelco, Bellefonte, PA, USA). 0.025 g of herbal raw material samples were weighed for the extraction. Headspace extraction of the essential oil was carried out at 30 \({}^{\circ}\)C temperature for 10 min. The thermal desorption was carried out at 280 \({}^{\circ}\)C for 1 min. Automated SPME system was coupled with the chromatographic module. For determination of repeatability of results, extractions were repeated three times (\(n=3\)).  
GC-2010 gas chromatograph and GCMS-QP2010 mass spectrometer (Shimadzu, Kyoto, Japan) were used for the determination of essential oil components. An automated injector AOC-5000 (Shimadzu, Kyoto, Japan), with SPME system was used for sample preparation and injection. The RTX-5 MS column (Restek, Bellefonte, PA, USA) 30 m length, 0.25 mm internal diameter and 0.25 \(\upmu\)m stationary phase thickness, was used for separation of volatile compounds. For mass spectrometry an electron ionization detector set at 70 eV was used. The conditions of GC-MS method were as described before [29; 30]. Initial column temperature 60 \({}^{\circ}\)C, injector temperature 280 \({}^{\circ}\)C, ion source temperature 260 \({}^{\circ}\)C, interface temperature 260 \({}^{\circ}\)C. The injection was performed at split ratio 1:10. The temperature was increased gradually from 60 \({}^{\circ}\)C to 150 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min and from 150 \({}^{\circ}\)C to 280 \({}^{\circ}\)C at 20 \({}^{\circ}\)C/min and kept for 3 min. Total analysis time was 30.5 min. Compounds were determined analyzing mass spectra and comparing to the electronic NIST v14.1 library of mass spectra.