esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Chromatographic Analysis of Gut Microbiota-Derived Metabolites
#### 2.4.2 Biogenic Amines by UPLC-FLD  
The preparation of fermentation samples and derivatization were adapted from a previous work [44]. To 500 \(\mu\)L of the samples were added 25 \(\mu\)L of the internal standard solution (1,7-diaminoheptane (100 ng/\(\mu\)L) prepared in trichloroacetic acid 5%). Then, 475 \(\mu\)L of 0.4 M of perchloric acid were added, vortexed for 20 s, and centrifuged (17,746 g, 5 min, at room temperature). This extraction was performed twice, and both supernatants were combined. One milliliter was then transferred into a 15-mL Falcon tube, and 200 \(\mu\)L NaOH (2 N) and 300 \(\mu\)L of saturated NaHCO\({}_{3}\) were added, vortexing the tube after each addition. The dansylation was realized by adding 2 mL dansyl chloride (10 mg/mL in acetone), followed by incubation for 15 min at 70 \({}^{\circ}\)C. A volume of 100 \(\mu\)L of glycine (150 mg/mL in water) was then added to bind to the dansyl chloride in excess, followed by vortex and a second incubation of 15 min at 70 \({}^{\circ}\)C. Finally, samples were centrifuged (5 min, 3700 g at room temperature) and maintained at 5 \({}^{\circ}\)C until 5 \(\mu\)L of the final extract were injected into the system for analysis.  
UPLC-Fluorescence Analysis. Biogenic amines were analyzed in an Acquity system ultra-performance liquid chromatography with fluorescence detection (UPLC-FLD), using the Acquity UPLC BEH C18 (2.1 \(\times\) 100 mm, 1.7 \(\mu\)m) column, with a UPLC BEH C18 VanGuard pre-column (2.1 \(\times\) 5 mm, 1.7 \(\mu\)m), all from Waters Corporation (Milford, MA, USA), using previously validated conditions [44]. Thus, the concentrations of nine amines (i.e., tryptamine, tyramine, cadaverine, spermine, spermidine, 2-phenylethylamine, putrescine, histamine, methylamine) in the fermentation samples were determined in a single run. The LOQ ranged between 8.4 mM and 314.5 mM for histamine and between 0.002 and 1.986 mM for the rest of the biogenic amines.
## 4 Numerical Simulation Analysis of Surface Subsidence
### Determination of Boundary Conditions  
According to practical experience, the boundary conditions of the numerical model were determined as follows [43; 44]:  
1. Horizontal constraints were imposed on the front, back, and left and right boundaries of the model, and the initial horizontal displacement of the boundary was set to 0.
2. The gravitational acceleration was set to 9.8 N/cm\({}^{2}\), and equilibrium was initially reached when the maximum unbalanced force reached 10-5.
3. The horizontal and vertical initial displacements of the bottom boundary of the model were all set to 0. The top of the model was set to a free boundary.
## 3 Results and Discussions  
### Scanning Electron Microscopy  
Figure 2a-c shows scanning electron microscopy (SEM) pictures of activated carbon prepared from olive tree, which illustrate that due to H\({}_{3}\)PO\({}_{4}\) chemical activation, macropores are formed, which are responsible for the higher surface area. Figure 2d-f shows the scanning electron microscopy (SEM) pictures of activated carbon prepared from _Lantana camera_, which illustrate that on the external surface, crevices are formed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Impact Category** & **Label** & **Unit** \\ \hline Photo-chemical oxidation & PO & kg C\({}_{2}\)H\({}_{4}\) eq \\ Human toxicity & HT & kg 1,4-DB eq \\ Global warming potential & GWP & kg CO\({}_{2}\) eq \\ Terrestrial ecotoxicity & TE & kg 1,4-DB eq \\ Terrestrial acidification & TA & kg SO\({}_{2}\) eq \\ Abiotic depletion & AD & kg Sp eq \\ Ozone layer depletion potential & ODP & kg CFC-11 eq \\ Freshwater aquatic ecotoxicity & FWAE & kg 1,4-DB eq \\ Eutrophication & EU & kg PO\({}_{4}\) eq \\ Marine aquatic ecotoxicity & MAE & kg 1,4-DB eq \\ \hline \hline \end{tabular}
\end{table}
Table 2: CML 2 baseline 2000 impact categories.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Raw** & **Input** & **Grinding/Sieving** & **Chemical Activation** & **Washing/Drying** & **Output** \\
**Material** & **Amount** & & & & & \\
**Collection** & **Amount** & & & & & \\ \hline AC-L-H-24 & & & & & & \\ (_Lantana_ & 6.00 g & Electricity & 0.333 kWh & H\({}_{3}\)PO\({}_{4}\) & 3.03 mL & Distilled water & 1923 mL \\ (_Lantana_ & 6.00 g & Water & 900 mL & Electricity & 12 kWh & Filter paper & 2 units \\ (_cmna_)) & & & & Distilled water & 5.15 mL & Electricity & 6 kWh \\ AC-O-H-24 & & & & & & \\ (_Olive tree_) & 6.00 g & Electricity & 0.333 kWh & H\({}_{3}\)PO\({}_{4}\) & 3.03 mL & Distilled water & 2027 mL \\ Water & 900 mL & Electricity & 12 kWh & Filter paper & 2 units & 1 g of AC \\  & & & & Distilled water & 5.15 mL & Electricity & 6 kWh \\ \hline \hline \end{tabular}
\end{table}
Table 3: Life cycle inventory of _Lantana camera_ and olive tree conversion to AC.
# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
## 3 XBeats System Architecture
### Hardware Specifications  
The building blocks of the proposed platform are shown in Figure 1. The operation modes that govern the delivered functionalities and behavior of the proposed ECG patch are:  
1. The continuous mode provides an unbounded real-time high-resolution data stream of the 12-lead ECG data transmitted directly to a backend system. Physicians sometimes require this mode of operation if abnormal heart conditions are detected or the patient's case requires 24/7 monitoring. However, this mode has a significant power consumption profile that affects the device's battery lifetime due to the continuous transmission of the collected ECG data wirelessly to the backend system via a communication gateway;The offline mode records the 12-lead ECG data on the Multimedia Card (MMC) storage when no paired BLE device is nearby to connect to the ECG patch. This mode is enabled for the entire data acquisition period until a paired BLE device connects to the patch and synchronizes the data transfer to the backend system; and
3. The triggered mode is optimized for power saving. The device sends keep-alive signals in normal heart conditions and only transmits ECG signals when a potential heart abnormality is detected. The ECG patch chooses from three data acquisition settings where the number of leads is configurable. The default setting for this operation mode is three ECG leads (e.g., Lead I, II, and V1), which can be changed dynamically in real-time. The patient and healthcare provider can reconfigure the number of enabled ECG leads through a paired BLE-enabled device or the backend system.  
The design of the ECG patch implements a modular architecture that focuses on the specifications and functionalities of the hardware components needed for the device operation. Therefore, our ECG patch is not limited to a specific hardware component or vendor, where the proposed hardware components can replace other components with similar capabilities.
# Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots
## 1 Introduction
### Contribution of the Paper  
Charging scheduling for electric bus fleets is a relatively new and poorly researched topic. While there are some publications focusing on smaller and mixed bus fleets, there are only a few researchers analyzing large-scale electric bus fleets. They address the centralized charging on big bus depots but mostly from the perspective of optimizing the operational costs. They do not analyze pure load peak minimization. This paper addresses this issue and proposes two algorithms for charging scheduling on large-scale bus depots with the goal of load peak minimization. The first algorithm follows a simple greedy logic. It attempts to schedule all charging tasks while respecting the limit for a maximum number of buses charging simultaneously. The limit is iteratively decreased until the algorithm fails in its scheduling attempt. The last limit, for which the algorithm successfully scheduled all charging tasks, is the minimum peak demand. The second algorithm is a heuristic that creates a charging schedule with a goal to minimize the peak demand.  
Real data from the electrical bus depot Alsterdorf were used to generate the charging schedules with the both proposed algorithms. The schedules were tested and validated with the Bus Depot Simulator.
## 1 Introduction  
Porous nanoparticle based multilayer photonic crystals are produced by very simple and versatile fabrication methods, as for example spin coating [1; 2] and sputtering [3]. Their photonic band gap [4; 5; 6] can be modified by exploiting the properties of the constituent nanoparticles as well as the multilayer porosity, of interest for sensing, lasing, and switching [7; 8]. The refractive index contrast between the two layers accounts for the photonic stop band and can be adjusted to deliver a targeted optical response. This becomes particularly interesting when implementing refractive index tunable materials, such as transparent conducting oxides (TCOs) [9; 10; 11]. A very interesting property of such materials is the near infrared (NIR) plasmonic response due to the free carrier density in the range of \(10^{26}\)-\(10^{27}\) m\({}^{-3}\). This latter can be manipulated by the applying an electric [12] or an electrochemical potential [13]. In this way, carriers are injected in a capacitive manner adding to the predefined carrier density, resulting in enhanced NIR absorption [9]. In a porous multilayer photonic crystal, this modification of the dielectric response automatically affects the refractive index contrast and therefore provides a means to actively manipulate the photonic stop band [14; 15]. Gaining the independent control over the photonic bandgap and the plasmonic absorption (in the NIR) would be of major interest for the application in electrochromic smart windows that can expel separately either heat or sunlight by applying a potential.  
Recent works addressed this target by implementing NbO\({}_{\mathrm{x}}\) glasses as matrix [16]. This involves an additional chemical step in the coloration, and thus, the devices become more prone to degradation. Implementing a physical effect such as the photonic bandgap in the coloration therefore is a highly attractive approach. In this work, we study the light transmission properties of a multilayer photoniccrystal made by alternating silica nanoparticle layers and of electrochemically tunable indium tin oxide (ITO) nanoparticle/titania (TiO\({}_{2}\)) nanoparticle layers. The implementation of the three material mixture (ITO:TiO\({}_{2}\):air) delivers an additional ingredient to fine tune the optical response of the entire structure due to the option to further modify the refractive index of this layer. We predict that the tuning of the ITO nanoparticle dielectric function in the three-phase mixture, and thus the overall refractive index of this layer, will modify both the plasmon resonance of ITO in the NIR and the photonic band gap of the overall structure. Remarkably, for the ITO:TiO\({}_{2}\):air 60:10:30 ratio, the increase of the number of carriers of ITO induces a simultaneous increase of the plasmon resonance intensity and decrease of the photonic band gap intensity, thus taking a step towards the independent control over the optical signatures of our multilayer structure.
# Smart City Pilot Projects Using LoRa and IEEE802.15.4 Technologies
## 3 Technologies  
### IEEE 802.15.4 Technology  
IEEE 802.15.4 [4] is a short-range wireless technology supporting applications with relaxed throughput and latency requirements in wireless personal area networks (PANs). The key features of 802.15.4 are low complexity, low cost, low power consumption, and low data rate transmissions. Each PAN works on a different channel and is managed by a coordinator, gathering data from devices in the PAN.  
As far as the physical layer is concerned, in this paper we consider the 2.4 GHz band physical layer, characterized by a bit rate of 250 kbit/s and by a minimum shift keying modulation on top of which direct sequence spread spectrum is applied.  
As for the medium access control (MAC) protocol, we consider the non beacon-enabled mode, where nodes may access to the channel in a distributed way (without the need of being synchronized to the coordinator), using an unslotted carrier sense multiple access with collision avoidance (CSMA/CA) protocol [4].  
One important operational aspect of the IEEE 802.15.4 technology is its ability to define different types of devices: Reduced Function Devices, which have limited capabilities and cannot forward data, and Full Function Devices, that can act as dynamic routers in a mesh topology. Different solutions may be adopted at the network layer to create mesh topologies (e.g., Zigbee, 6 LoWPAN, etc.); in this work we used a proprietary routing algorithm inspired by the Many-to-One routing strategy defined in Zigbee.  
This is the technology we adopted to implement the smart lighting testbed described in Section 4, which is based, in fact, on a mesh network among smart street lamps.
# Context-Aware Enhanced Application-Specific Handover in 5G V2X Networks
## 2 Related Work
The computational complexity of this approach introduces additional delays that consequently slow down the handover decision, which is critical for high-mobility users. To optimize the HO decision, a hybrid MCDM could be a more effective way to set the parameter weight values for the cell selection method. Hybrid MCDM is a combination of subjective and objective weighting approaches. A novel opportune context-aware network selection algorithm (OCAN) is proposed in [18] to maximize user satisfaction for the selection of the best network. The handover strategy is developed based on the user satisfaction indicator (USI) and the stability of the best network. The USI evaluates the potential networks by considering the diversity of context parameters, including user preference, energy state, user mobility, and conventional application-specific parameters. This approach is employed using the Analytic Hierarchy Process (AHP) and utility function theory. It effectively combines user and network factors. The relevant parameters are traffic organized in hierarchal multi-layer structures, where the USI is at the top layer, serving as a global indicator that is connected to a number of attributes. The author in [19] proposed a hybrid MCDM method by combining the Fuzzy Analytical Hierarchy Process (FAHP) to acquire the weight of the handover metrics and the Fuzzy Technique for Order Performance by Similarity to the Ideal Solution (FTOPSIS) method to rank cells as determinants for the best HO target. The priority level for different traffic classes is considered with the aim of reducing the number of unnecessary HOs using closer coefficient values for the current and best networks. However, this approach is implemented for conventional dense networks and does not consider high-mobility users (V2X). Despite the fact that studies have employed various methods to assign weight to criteria, the assigned weight stays constant across different networks. Table 1 provides a comparative summary of MCDM-based handover decisions. In [8], a technique is presented that integrates adaptive context-aware algorithms, efficient scanning, load balancing, and hybrid channel allocation to improve vertical handover decisions in heterogeneous networks. The fundamental feature of the proposed mechanism
## 4 Performance Evaluation and Validation
### Damage Detection and Classification Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Damage Type**} & \multicolumn{3}{c}{**Cross-Entropy Loss**} & \multicolumn{3}{c}{**Focal Loss**} & \multicolumn{3}{c}{**IoU Loss—Proposed**} \\ \cline{2-13}  & **AC** & **PR** & **RE** & **F1** & **AC** & **PR** & **RE** & **F1** & **AC** & **PR** & **RE** & **F1** \\ \hline Crack & 0.801 & 0.727 & 0.831 & 0.776 & 0.851 & 0.812 & 0.834 & 0.823 & 0.892 & 0.873 & 0.866 & 0.869 \\ Concrete Scaling/Spalling & 0.865 & 0.620 & 0.355 & 0.451 & 0.953 & 0.883 & 0.807 & 0.843 & 0.974 & 0.894 & 0.950 & 0.921 \\ Rebar Exposed & 0.740 & 1.000 & 0.740 & 0.850 & 0.760 & 1.000 & 0.760 & 0.863 & 0.960 & 1.000 & 0.960 & 0.979 \\ Leak & 0.878 & 0.650 & 0.262 & 0.374 & 0.965 & 0.894 & 0.846 & 0.869 & 0.980 & 0.937 & 0.922 & 0.929 \\ Paint Peeling & 0.952 & 0.951 & 0.858 & 0.902 & 0.955 & 0.964 & 0.857 & 0.908 & 0.983 & 0.970 & 0.963 & 0.967 \\ \hline
**Average** & **0.874** & **0.774** & **0.681** & **0.725** & **0.930** & **0.873** & **0.836** & **0.854** & **0.957** & **0.912** & **0.914** & **0.913** \\ \hline \hline \end{tabular} Note: AC = Accuracy, PR = Precision, RE = Recall, F1 = F1 Score.  
\end{table} 
Table 8: Results for multi-class damage with each loss function. improvement. For leakage, the Recall rose from 0.262 to 0.922, yielding a 251.5% increase, while the F1-Score nearly doubled from 0.374 to 0.929. These results strongly validate the IoU Loss function's ability to enhance the detection of minor or partially visible damage areas that conventional methods often fail to capture. While Focal Loss also led to notable gains in these categories (e.g., Recall of 0.807 for concrete spalling and 0.846 for leakage), limitations remained in detecting rare classes such as exposed rebar, which only improved marginally in Recall (from 0.740 to 0.760) under Focal Loss. To address these residual shortcomings, the IoU Loss function, by directly optimizing the spatial overlap between predictions and the ground truth, proved effective, achieved the highest average recall (0.914) and F1 Score (0.913) across all classes. Consequently, IoU Loss is proposed as a more robust optimization strategy for multi-class surface damage segmentation in real-world railway bridge inspection scenarios.
## 2 Materials and Methods
### MODIS Land Surface Reflectance Data and Calculation of Vegetation Indices during 2000-2018  
The MODIS instruments onboard both the Terra and Aqua satellites acquire images with different spatial resolutions and 36 spectral brands. Seven spectral bands were primarily used for vegetation and land surface measurements, including blue (459-479 nm), green (545-565 nm), red (620-670 nm), near infrared (841-875 nm, 1230-1250 nm), and shortwave infrared (1628-1652 nm, 2105-2155 nm).  
The MOD09A1 Collection 6 product provides 8-day estimates of surface reflectance of seven spectral bands at 500 m spatial resolution. A detailed description of MOD09A1 is given at [[https://lpdaac.usgs.gov/products/mod09a1v006/](https://lpdaac.usgs.gov/products/mod09a1v006/)]([https://lpdaac.usgs.gov/products/mod09a1v006/](https://lpdaac.usgs.gov/products/mod09a1v006/)). The MOD09A1 data are available in the Google Earth Engine (GEE), a powerful satellite image editing platform [52; 53]. After the data quality control of the MOD09A1 files, we used land surface reflectance data to calculate the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) (Equations (1)-(3)). The algorithm documented in the previous publication [54] was used to gap fill the bad quality NDVI and EVI observations. We used times series MODIS data for these two sugarcane sites during 2000-2018, based on the geographical sites (latitude and longitude) of the sugarcane sites. The seasonal and interannual variation of three vegetation indexes during 2000-2018 for these two sugarcane sites is shown in Figure S4.  
\[NDVI=\frac{NIR-Red}{NIR+Red}, \tag{1}\]  
\[EVI=2.5\times\frac{NIR-Red}{NIR+6\times Red-7.5\times Blue+1}, \tag{2}\]  
\[LSWI=\frac{NIR-SWIR}{NIR+SWIR}. \tag{3}\]
# Dynamic Behavior Analysis and Stability Control of Tethered Satellite Formation Deployment
## 3 Dynamic Analysis of Simplified Models of Single-DOF and Two-DOFs
### Two-DOFs (\(\theta,\phi\))  
The tether out-of-plane vibration angle, \(\phi\), is considered based on a simplified single-DOF model. In this case, Equations (15)-(17) can be rewritten as follows.  
\[\theta^{\prime\prime}=2(\theta^{\prime}+1)[\frac{e\sin{\
u}}{\kappa}+\phi^{ \prime}\tan{\phi}]-\frac{3}{2\kappa}\sin{2\theta} \tag{24}\]  
\[\phi^{\prime\prime}=\frac{2e\sin{\
u}}{\kappa}\phi^{\prime}-[(\theta^{\prime} +1)^{2}+\frac{3}{\kappa}\cos^{2}{\theta}]\frac{1}{2}\sin{2\phi} \tag{25}\]  
Figure 3 shows tether in-plane and out-of-plane vibration angles, \(\theta\), \(\phi\), versus \(\
u\).  
In Equations (24) and (25), orbital eccentricity is 0.1, and initial values of the single-DOF motion solution \(\theta_{0}\), \(\theta^{\prime}{}_{0}\) are adopted by Equation (24). As shown in Figure 3, numerical simulations show that the out-of-plane vibration angle is relatively small, and the maximum of \(\phi\) is 0.0247\({}^{\circ}\), far less than the in-plane angle, which has a slight effect on TSS dynamic response. Hence, the effect of the out-of-plane angle is negligible. However, coupling errors caused by out-of-plane vibrations to in-plane vibrations still require further numerical verification.  
Figure 3: Tether vibration angles, \(e=0.1\): (**a**) tether in-plane angles versus \(\
u\); (**b**) tether out-plane vibration angles versus \(\
u\).  
Figure 2: (**a**) Tether in-plane angle, \(\theta\), versus \(\
u\); (**b**) angular velocity of the in-plane angle, \(\theta^{\prime}\), versus \(\theta\).  
In order to further verify the accuracy of the single-DOF simplified model, different orbital eccentricity values are substituted into Equations (24) and (25). Both curves almost coincide in Figure 4a, and the maximum error of the in-plane vibration angle is \(0.0945^{\circ}\) with \(e=0.1\) in Figure 4b, which illustrates that coupling effects of the out-of-plane vibration angle are negligible. In particular, the solution of the single-DOF with the first five orders demonstrates sufficient accuracy in Equation (23), which proves that the error of the perturbation method is negligible. However, as shown in Figure 5, it can be easily observed that the error of the single-DOF simplified model increases as orbital eccentricity increases. In Figure 5, the error of the single-DOF simplified model was significantly smaller when value \(e\) decreased from \(0.42\) to \(0.1\), which means that a single DOF-simplified model can be applied to orbits with low orbital eccentricity so that accuracy can be guaranteed.  
**Remark 1**.: _The accuracy of the simplified model of TSS is strongly influenced by orbital eccentricity. For low-eccentricity orbits, a simplified model of TSS can significantly reduce calculation time._
# Benchmarking Message Queues
## 2 Background
### Redis [3]  
Redis is a widely used in-memory data store that can act as a cache or a database. However, it also has the capability to serve as a message broker, which is a crucial component in message-driven architectures. A message broker serves as a central hub for receiving, storing, and forwarding messages between producers and consumers. By implementing the publish-subscribe pattern through its Pub/Sub feature, Redis can act as a message broker.  
In Redis Pub/Sub, a publisher sends a message to a specific channel, and all the subscribers who have subscribed to that channel will receive the message. Clients can subscribe to one or more channels and receive messages whenever they are published to those channels. This enables messages to be broadcasted to a large number of subscribers in real-time, allowing for efficient and scalable communication between producers and consumers.  
To implement Redis as a message broker, one can simply use the PUB/SUB commands to send and receive messages. Publishers can send messages to specific channels using the PUBLISH command, while subscribers can listen for messages by subscribing to specific channels using the SUBSCRIBE command. Redis clients can also be configured to listen for messages using the message callback function that is triggered when a message is received.