esa-sceva/satcom-chunk-collection huggingface dataset:
# Deducing Energy Consumer Behavior from Smart Meter Data +
## 5 Experimental Validation
### Data Recovery
#### 5.2.2 SmartHG Dataset Reconstruction  
The sample by sample comparison is created by finding the mean square error of the samples compared to their true values. This can be explained by the stochastic part of a signal. When the _knowledge_ is small it is more likely that the information around the gap is relevant. The more _knowledge_ grows the more likely it is that the stochastic event will bring false information to the model. This can be illustrated as in Figure 7.  
Figure 7 shows an original signal with a gap, indicated by the red area. The true value of the gap is illustrated by the full red line in the original signal. The two subsequent graphs is the reconstruction, at different _knowledge_. The blue area is the known area and the dotted red line is the predicted values. When the known area is small there is not enough frequency information to make any grant changes, so the prediction will look more or less like a linear interpolation. This is compared to the true value not a bad guess. When there are more samples known it is possible to make more complex estimates of the signal. In the figure is shown that a _knowledge_ of 15 will include the stochastic event in the model. Taking this into the model, changes the prediction to a less correct value, due to the frequencies added by the stochastic event.  
For many smart meters it is possible to get both the instantaneous current power and accumulated power over time, as the samples. By looking at the accumulated power before and after a gap it is possible to gesture about the changes in power during the gap. This information can be used to further improve the gap-filling.  
Frequency Comparison  
Another metric to compare is how well the frequency response is preserved in the reconstruction, since this is often more important that the true value itself for smaller devices with many states, like computers. Such devices is often recognized by their usage pattern and not the true power draw. By looking at the Fourier transform of the original signal and the reconstructed power was the frequency components compared.  
As shown in Figure 8 both the Wiener and the Papoulis-Gerchberg (P-G) algorithm seems to perform well. Like for the sample comparison the most successful reconstruction seems to be at low _knowledge_.  
Figure 8: Frequency comparison of the reconstruction methods.  
Figure 7: Stochastic effect on prediction.  
Jitter Comparison  
The jitter is a different metric as it tells how well we have modeled the noise of the signal. In a jitter analysis the signal is thought of as a slowly changing signal with a faster noise signal embedded in it.  
The slow signal is assumed to be found by taking a low pass filter over the signal to filter out the noise. The jitter analysis measures the power in the jitter from this signal as illustrated in Figure 9.  
It the estimated jitter power have been compared with the real jitter power for all the reconstruction methods.
## 4 Backgrounds and Methods  
In this section, we first give an overview of the HO procedure and the parameters involved. Moreover, we look at RLFs, HPI and related work on self-optimization HO parameters. Then, we present our proposed HO parameter optimization and dual MRNs network model. Two main concepts are presented: (i) we present our optimization scheme for the HH and CIO parameters; the proposed scheme selects a suitable HH and changes the CIO of each cell to facilitate the HO decision step of the HO process when the train is moving at a high speed; and (ii) we outline our proposed dual MRNs network model and its HO procedure. The proposed model provides seamless mobility and uses the co-operating dual MRNs to reduce connection interruptions.
# The Hydroological Balance in Micro-Watersheds Is Affected by Climate Change and Land Use Changes
## 1 Introduction
However, besides results with acceptable effectiveness, evidence is scant on the importance of relating the natural processes that are generated from the effects of climate change, changes in forest cover, and the provision of hydrological ecosystem services. WEAP software was developed by SEI [32] and is a tool used for the modeling, planning, and distribution of water resources. It can be applied at a variety of scales, from a small watershed to a large watershed [33]. WEAP includes routines designed to analyze the distribution of water among different types of users from a human and ecosystem perspective; these characteristics make WEAP an ideal model for climate change studies, in which it is important to estimate changes in the water supply. (e.g., changes in projected precipitation) and in water demand (e.g., changes in demand due to changes in land use), which will produce a different hydrological balance at the basin level [32]. It solves water allocation problems at any time step using a standard linear programming model [34; 35]. The WEAP tool operates under the water balance model, based on the variation of the volume of water stored in the soil, and has the ability to simulate processes such as: surface runoff, base flow, and infiltration, among others [33; 36; 37]. Scenarios provide a basis for decision-making, the development of adaptation strategies, and regional policies for the conservation of ecosystems and water [15; 38]. The WEAP model can be adapted according to the existing data; in regions with limited data, the model can build a full hydrological representation [39]. The present research aimed to analyze the impact of land-use change and climate change on the hydrological balance of micro-watersheds in temperate forests of central Mexico. In this study, we use the classification of \"micro-watershed\" as the land area of less than 100 km\({}^{2}\) that drains all streams. To this end, scenarios were projected with estimates of land use change (positive and negative) and climate scenarios called shared socioeconomic pathways (SSPs 5-8.5) recently proposed in the sixth evaluation report of the Intergovernmental Panel on Climate Change [40], of three global models, MPI-ESM1-2-LR, CNRM-CM6-1, and HadGEM3-GC31-LL (CMIP6), using the WEAP hydrological model. The importance that the changes represent to the hydrological balance in the 2081-2100 time horizon was determined. This horizon was selected because it best visualizes the changes in the watersheds, given the scenarios applied.
## 4 Results
### Geochemical Modelling of Experiments with Cumulative Gabbro
#### 4.3.2 Minerals' Dissolution and Precipitation  
Simulated primary and secondary minerals saturation indices (SI) are shown in Figure 9a,b. All primary minerals (except albite) are seen to dissolve (negative saturation index and rate, Table 3). SI of albite remains positive over time, implying that the modest decrease in Na+ (from 0.371 to 3.69 mol.kgw\({}^{-1}\)) is a consequence of albite volume increase. The dissolution rates (Table 8) indicate that olivine, plagioclase and diopsidic minerals are the most reactive phases.  
Figure 8: Variation in the solution composition with time compared to the experimental data. (**a**) Ca2+; (**b**) Mg2+; (**c**) SiO\({}_{2}\)(aq); (**d**) Fe2+.  
Two mineral assemblages resulting from the CG alteration are predicted by the geochemical model. A Ca and Fe-Mg carbonate assemblage and another composed of Ca-Mg-Fe silicates (and aluminum) such as calcium-rich zeolites, clay minerals and amorphous silica. Ca-zeolites (mesotile and mordenite) and kaolinite are the first secondary minerals to form simultaneously with albite volume increase, followed by the amorphous silica (Figure 9c). The Si-Al-rich assemblage formation results in a sudden decrease in aluminum and silicon from the outlet solution. The carbonates' mineral phases correspond to calcite, dolomite, magnesite, siderite and ankerite. Calcite and dolomite appear in the simulations after 16 days, followed by siderite and magnesite (after 40 days). Ankerite was the last carbonate phase to precipitate, concomitant with Fe-Mg-smectite.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Primary Minerals** & **Reaction Rate (mol-L\({}^{-1}\).s\({}^{-1}\))** & **Secondary Minerals** & **Reaction Rate (mol-L\({}^{-1}\).s\({}^{-1}\))** \\ \hline Albite & \(2.77\times 10^{-11}\) & Siderite & \(4.13\times 10^{-9}\) \\ Anorthite & \(-1.08\times 10^{-9}\) & Calcite & \(5.01\times 10^{-10}\) \\ Fayalite & \(-1.24\times 10^{-10}\) & Magnesite & \(4.01\times 10^{-10}\) \\ Forsterite & \(-1.64\times 10^{-10}\) & Ca\_zeolites & \(2.41\times 10^{-10}\) \\ Enstatite & \(-1.73\times 10^{-10}\) & Ankerite & \(8.58\times 10^{-11}\) \\ Diopside & \(-6.48\times 10^{-11}\) & Dolomite & \(4.84\times 10^{-11}\) \\ Ferrosilite & \(-2.57\times 10^{-14}\) & Smetite & \(1.31\times 10^{-12}\) \\  & & Kaolinite & \(5.37\times 10^{-15}\) \\  & & SiO\({}_{2}\) & \(2.14\times 10^{-16}\) \\ \hline \end{tabular}
\end{table}
Table 8: Simulated dissolution and precipitation rate at \(t=200\) days (simulation with specific surface areas (SSA) = 120 cm\({}^{2}\).g\({}^{-1}\)).  
Figure 9: Evolution of the simulated saturation index: for the primary (**a**) and secondary minerals (**b**) and the variation of minerals’ volume fraction (in m\({}^{3}\) mineral/m\({}^{3}\) rock) vs. time (**c**).
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 6 PULSAR Applications in Forest and Wildfire Management
### Payload Categorization and Integration
#### 6.4.3 RGB and Thermal Sensor [FLIR Duo PRO R and DragonEye2]  
The FLIR Duo Pro R (Teledyne FLIR, Wilsonville, OR, USA) is a versatile and compact IR and RGB camera designed for a wide range of high-performance commercial, industrial, and public safety drone applications. Equipped with a built-in IMU (inertial measurement unit), altitude sensors, and GPS receivers, it ensures precise positioning information is embedded directly into the image metadata. This dual-sensor camera combines a high-resolution radiometric thermal imager and a 4 K RGB camera into a single package, enabling professional operators to capture both thermal and visible data in a single flight [47].  
In Table 4, the technical specifications are provided, while in Figure 17a, the FLIR Duo Pro R thermal camera is depicted alongside the PIXY F gimbal--a lightweight and compact design tailored specifically for this sensor. The PIXY F gimbal provides a three-axis stabilization system, guaranteeing stable footage under various conditions. With features like a hyper-quick release system and integrated HDMI with multiple I/O interfaces, it facilitates rapid assembly and direct camera signal monitoring, thereby enhancing operational efficiency.  
The NextVision DragonEye2 (NextVision, Ra'anana, Israel) is a dual EO/IR stabilized camera that offers unparalleled long-range observation capabilities, making it ideal for monitoring applications. The camera is mounted on an embedded gimbal capable of rotating about the pitch and yaw axes, allowing targeting directly downwards perpendicular to the Earth's surface. The camera's low maximum power consumption is an additional advantage that reinforces its desirability. It is paired with the TRIP5 computer module, which accommodates all necessary ports, including HDMI.  
The technical specifications of DragonEye2 are detailed in Table 5, and in Figure 18 the designed payload case along with the associated configuration.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Overall dimensions (L \(\times\) W \(\times\) H) & 87 \(\times\) 82 \(\times\) 69 mm \\ Payload weight & 0.375 kg \\ Resolution IR & 640 \(\times\) 512 \\ Resolution RGB & 4 K/12 MP \\ Power consumption & 12 W \\ \hline \hline \end{tabular}
\end{table}
Table 4: FLIR Duo PRO R—Technical Specifications.  
Figure 17: FLIR Duo PRO R: (**a**) dedicated custom payload case; (**b**) co-axial quadcopter configuration with the payload attached.
# Solution Algorithms for the Capacitated Location Tree Problem with Interconnections
## 2 Literature Review
The CRTP and the RTFLP consider a limit for the number of rings used to connect all customers and capacity constraints in the number of clients connected to each ring. The RTFLP considers facility opening costs (in the vertices that are part of the rings) and capacities in the outer level of the network, while the CRTP does not. The RTFLP can be considered a generalization of the CRTP. Among the most important differences  
Figure 1: [PERSON] et al. proposed LRPs over tree-shaped graphs [11]. (**a**) Tree and edges. (**b**) Allocation to two facilities. (**c**) Allocation to one facility. between the CLTPI and RTFLP are that facilities can be opened for any customer (therefore becoming part of a ring) and that customers are connected to a central depot. Finally, the Tree of Hubs Location Problem (THLP) is another problem related to the CLTPI. In this problem, a set of hubs must be located to transport people or commodities between the origin and destination points in the network. The main difference between the THLP and the classical Hub Location Problem (HLP), is that solutions in THLP must be tree-shaped. The THLP does not consider capacity over the hubs or the links between the hubs, differing from the CLTPI [18]. Table 1 presents a summary of problems related to the CLTPI. It presents the most relevant characteristics of each problem and some essential related works and methods used to solve them. The intention is to show some relevant related works, not an extensive literature review (for example, for the CFLP, the literature is quite comprehensive). The table shows that exact methods are predominant in the CFLNDP, the THLP, the RTFLP, and the LRP over tree-shaped networks. On the other hand, exact and heuristic methods are used for the CMST and the CFLP.
# Improving Sustainability of Steel Roofs: Life Cycle Assessment of a Case Study Roof
## 1 Introduction
[55] used the ReCiPe LCIA methodology for a full building LCA and noted significant variations in all impact category emissions, except climate change, when using the SimaPro software with the ecoinvent database compared with the Gabi software and database. [PERSON] and [PERSON] [66] compared the impact potentials for a single unit process and found that the Gabi software and SimaPro reported significant variations in at least some impact categories for various LCIA methods. Therefore, selecting a database and software for the LCA study is crucial because more specific data may result in better outcomes and accuracy. Generic data for steel coil used in roofing can be obtained from Worldsteel, a global steel association, which has published a database based on investigations from various global mills, and it is available in most popular LCA software. Alternatively, life cycle effects can be directly extracted from EPDs completed by selected companies. Local companies such as Colorsteel and Colorcote have published EPDs for their finished coil products [67; 68; 69]. Although imported steel coil products only form a small part of NZ's roofing market, any notable differences in environmental impacts from production and how to implement this in a simple whole-building life cycle assessment (WBLCA) are not well understood. The impact data from both EPDs and generic data from Worldsteel does not include the final production process to convert steel coil into the finished roofing profile, including transport, storage, and roll forming of the coil. Only a limited number of steel roofing products, such as Tata's RoofDeck D35 profile available in Europe, have EPDs for the finished profiled roofing product. However, this data is not available for NZ products. Roofing has the largest environmental impact on roof components; other ancillary items such as gutters, flashings, downpipes, underlay, fixings, purlins, and other items could also have a notable effect on overall emissions. Therefore, the calculation of environmental impacts of steel roofs and the ancillary items needs to be quantified throughout their life cycles, and it would be valuable to understand the gaps in environmental impact from the steel roofing products in New Zealand. The aim of the study was to better understand the nature of gaps for steel roof LCA. The study analyzed the long run roofing profile with five ribs produced by a local manufacturer and used the GaBi Ts software [61] in calculating the quantity of environmental impacts from the steel roofing product. The study included ancillary items for the roof installation, such as gutters, flashings, underlay, and other roofing elements. The intention of this investigation was to be able to provide recommendations to improve the accuracy of measurements of the environmental impacts of steel roofs while providing insights for reducing the environmental impacts of roofs in New Zealand.
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 5 Evaluation
### Under Manipulation Attacks  
We proceed to compare the performance of FTSP, FTSP with threshold detection, and RSTS under manipulation attacks. Assume that nodes 3 and 6 in the chain topology are manipulation attackers who broadcast invalid timestamps every \(3T\) periods and attack the network after 100 iterations. The attack power \(\omega\) is randomly selected in [0, 0.01] s. With the existence of manipulation attackers, the time source's time information cannot be effectively transmitted to the nodes after node 2. The synchronization performance of safe nodes is shown in Figure 6. We can see that safe nodes 1 and 2 can still synchronize with the time source, but the subsequent nodes lose synchronization in Figure 6a. For threshold detection, invalid timestamps could not pass the detection process. Hence, malicious nodes are marked and excluded from the network. RSTS updates the clock based on the difference in receiving timestamps and removes attackers' unfavorable effects. Comparing Figure 5a with Figure 6, we can see that both RSTS and threshold detection can defend against manipulation attacks. Moreover, RSTS exhibits a higher synchronization accuracy.  
Figure 5: Performance of FTSP and RSTS under no attacks. The representation of the lines is the same in (a,b). (a) FTSP; (b) RSTS.
## 3 Experiments and Results
### Experimental Results and Analysis
#### 3.4.4 Comparative Experiments on Different Loss Functions  
To address class imbalance, this study introduces the EMASlideLoss function and benchmarks it against mainstream IoU variants. As shown in Table 9, compared with other loss functions, EMASlideLoss achieves the best performance. It significantly improves the detection accuracy of hard samples without increasing computational load.
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 4 Experimental Results
### Optimal Band-to-Band Length  
To begin our investigation, we performed a series of simulated experiments on the conventional WPE method to identify the optimal filter length \(L_{\text{bb}}\) within the specific room configuration under consideration. In this set of experiments, we systematically varied the value of \(L_{\text{bb}}\) in the range of 5 to 25 while keeping the crossband filter length \(L_{\text{cb}}\) fixed at 0.  
The results of these experiments are presented in Figure 1, which showcases the scores obtained for each measure across the range of \(L_{\text{bb}}\). Upon close examination, it becomes evident that the optimal filter length varies for different performance measures. Specifically, the FWSegSNR measure attains its peak performance with \(L_{\text{bb}}=15\), while the CD measure achieves its optimal result at \(L_{\text{bb}}=14\). On the other hand, the PESQ measure demonstrates its best performance when \(L_{\text{bb}}\) is set to 20.  
Building upon these findings, we conducted further experiments, focusing exclusively on the optimal values of \(L_{\text{bb}}\). Consequently, we set \(L_{\text{bb}}\) to take on 14, 15, and 20 values, thereby allowing us to thoroughly compare the proposed and conventional WPE methods' performance under these specific settings.
## 2 Background and Paper Contribution  
### IoT for Precision Agriculture  
An IoT platform is an abstract and formal system to manage certain types of IoT object, often including their properties, relationships, and the operations that can be performed on them [20]. IoT platforms typically include (i) the designation of the type of physical object, that is, what it is and what it does, for example, a humidity sensor; (ii) the data formats and how they are represented and interpreted, for example, values in the form of a string, number, integer, or float; (iii) interactions and access methods, i.e., how to access the object to obtain its status and data or to activate intrinsic functions and outputs; (iv) metadata describing the attributes of objects and the context in which the data are acquired; and finally, (v) links to other objects, used to represent structural connections and the composition of objects.  
In precision agriculture, critical data are collected in real time during the agricultural production process and transmitted through wireless networks through the M2M (Machine-to-Machine) support platform [21]. This allows real-time environmental data to be obtained using a network-based protocol, both IP or non-IP based, with an cellular-based SMS fallback, helping farmers make timely decisions, improve production, and respond more quickly to changes and unpredictable events [22; 23]. In essence, the adoption of IoT technologies has the potential to improve agriculture in many ways. Some of the possibilities that these technologies offer are as follows:  
* _Data collected from sensors_, such as weather conditions, soil quality, crop growth trends, and livestock health, can be used to monitor the general state of operations, as well as staff performance and equipment efficiency.
* _Improved control over internal processes_, and consequently reduced production risks. The ability to predict production output allows for better product distribution planning [24].
* _Cost Optimization and waste reduction_ due to increased control over production [25]. By detecting anomalies in crop growth or livestock health, it is possible to mitigate the risks associated with crop loss.
* _Greater business efficiency_ through process automation. By using smart sensors, multiple processes during the production cycle can be automated, e.g., irrigation, fertilization, or pest control.
* _Improvement in product quality and volume_. Thanks to the use of new technologies, it is possible to achieve better control over the process and maintain a higher quality of the crops and growth capacity through automation [26].
## 1 Introduction
The communication model developed using high-precision satellite remote sensing images was employed to compare the average relative errors of BER (Bit Error Rate) and SNR (Signal-to-Noise Ratio) between simulated and measured results, thereby analyzing the system's communication performance. Based on the evaluation results, the EMC performance of vehicular wireless communication systems in urban environments are analyzed. Leveraging high-resolution satellite image data, this analysis provides critical references for optimizing vehicular communication system design. The remainder of this paper is structured as follows. Section 2 briefly introduces related evaluation methods. Section 3 presents an EMC evaluation method for vehicular wireless communication systems based on urban high-resolution satellite remote sensing images, encompassing establishment of indicators, data preprocessing, and the key techniques and process of evaluation. To validate the proposed method, Section 4 utilizes an example of a vehicular communication system, and the performance of the proposed method is discussed. Finally, Section 5 concludes the paper by summarizing the key findings and implications.