esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Image Processing  
After algorithm training, the system was put to work in real-time; for this, the NIR camera was used to set communication with the computer through USB protocol and show live video in the software interface implemented in the previously designed PC. First, the system was tested with the first configuration; the way to do this was after the NIR camera and the infrared lamp were collocated in their respective positions. The software interface has the option to capture video, and when this is performed automatically in an interface implemented, it appears on the length and weight of the fish. Figure 6 shows how the algorithm performs the image processing.  
As shown in Figure 6, the main algorithm comprises two main blocks: the pre-processing and the estimation. It starts when the NIR camera plays the captured video, and the algorithm instantly detects if the objects that appear in the camera are fish or not, and it is possible to perform by using Haar classifiers in the algorithm using only a region of interest for the investigation. The fish detector generates a box containing the fish detected correctly; that is, when the fish is entirely in a lateral position, this is shown in Figure 7. This performed through a process that is transparent for the user. Once the region of interest is delimited, the image is extracted and processed to extract its contour, obtaining the width and length measurements of the fish in pixel dimensions.  
Figure 6: Flow chart of the image processing.  
Figure 7: Fish detection by the algorithm.  
This kind of processing is considered a machine learning approach because later it could automatically identify the objects of interest by marking them with a box chart, as shown in Figure 7; the MATLAB software was used to train the algorithm. The way that the algorithm identifies the fish apart from the positive and negative images requires a way to identify the object rapidly; in this regard, the Haar classifiers are used. The image is detected, then the algorithm applies the Haar classifiers, and instantly the parts are discarded from the image that do have not the object of interest; this is made with statistics, and the Haar classifiers implemented in the algorithm use rectangles (Figure 8) to identify the objects of interest.  
The below algorithm performs using the integral image at locations \(x,y\) that contains the sum of pixels above and left \(x,y\), adding.  
\[ii(x,y)=\sum_{x^{\prime}\leq x,y^{\prime}\leq y}i(x^{\prime},y^{\prime}), \tag{7}\]  
where \(ii(x,y)\) is the integral image and \(i(x,y)\) is the original image, and the following occurrences are used:  
\[s(x,y)=s(x,y-1)+i(x,y) \tag{8}\]  
Figure 8: The process to identify a fish.  
\[ii(x,y)=ii(x-1,\;y)+s(x,y) \tag{9}\]  
where \((x,y)\) is the cumulative row sum, \(s(x,y)=0\), and \(ii(-1,\;y)=0\) the integral image can be compounded in one pass over the original image [4]. All the basis to this processing was initiated by [25]
## 1 Introduction and Review of Existing Research
### SEMS Related to Fault Detection and Diagnosis in HVAC Systems  
Fault Detection and Diagnosis (FDD) is used to describe an automated process that identifies and locates faults, developed primarily for industrial systems, by analysing real-time data. It uses modelling algorithms, signal analysis, and machine learning to detect anomalies that indicate potential faults. According to FDD methods applied in HVAC systems, [PERSON] et al. [4] classifies them into data-driven-based methods (DDBM), knowledge-driven-based methods (KDBM), and model-based methods (MBM).  
On the one hand, DDBM extract information from historical process data [5] and detect faults through finding changes in patterns in the measurements of selected variables. On the other hand, the methods in the KDBM subcategory rely heavily on domain knowledge. Every fault detection rule or model is developed based on prior knowledge, which generally has clear physical meanings [6]. Lastly, MBM or physical models are based on physical models built on analytical redundancy in the form of mathematical equations of real-word physical processes [5]. Most of the literature has been found to concur that a hybrid model would be optimal for the task of FDD in building HVAC systems, although not everyone agrees on what such a model should consist of. That is why in this work it is suggested a hybrid approach between DDBM and MBM.
# Measures and Methods for the Evaluation of ATO Algorithms
## 3 Approach for ATO Testing
### Layer Model
Dynamic objects in the railway sector are passengers (on a platform), other trains, cars driving on level crossings, or other vehicles and objects connected to railway operation like cranes for loading and unloading freight trains. They might not exist in such a great amount as in road traffic. Nevertheless, they can play a significant role within a scenario. Layer 5 copies the equivalent layer of the 6 LM. All environment conditions are included in this layer. In [10], proposing an ODD for ATO systems of Chinese highspeed trains the following environmental parameters are conducted: humidity, wind speed and temperature. Furthermore, the amount of precipitation, as well as the range of vision due to fog or rainfall, can also be added to the list of environmental parameters. On Layer 6 all digital information is incorporated, like in the original 6 LM. Besides the signal state, all information received from the ATO trackside component is subject to this  
Figure 2: Layer model for the railway sector derived from the 6 LM in [32]. layer. Examples of such information are journey profile updates with timetable changes, temporary speed restrictions, disruptions, rerouting, and more. Generally, all transferred information is modelled by this layer including connections of onboard systems. Within the railway sector, several simulation environments are available either including a proprietary scenario description language and format or making use of standardized formats in the sector. The latter can also be highly relevant when aiming for a universal scenario description. Potentially, the layer model can provide a basic structure for a new scenario description format.
# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 5 Evaluation
### Evaluation Methodology
#### 5.1.2 Performance Evaluation Metrics  
This section introduces the metrics for the performance evaluation, namely throughput, average delivery delay, message transmission overhead, and average energy consumption.  
**Throughput.** As shown in Equation (15), throughput is the ratio between \(d\), the total number of messages successfully delivered at their respective destinations, and \(g\), the total number of messages generated. This signifies the message delivery efficiency of a forwarding algorithm, within the assigned TTL.  
\[Throughput=d/g \tag{15}\]  
Figure 4: The Skudai simulation area (map data provided by OpenStreetMap, 2015).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Total simulation time (days) & 5 \\ Warm-up period (days) & 1 \\ Cool-down period (days) & 1 \\ Wireless communication interface & Bluetooth \\ Transmission range (m) & 10 \\ Transmission rate (MBps) & 2 \\ Buffer size (MB) & 10 \\ Message size (KB) & 10 to 15 \\ Message TTL (days) & 1 \\ Number of nodes & 1349 \\ Ave. message generation rate/node & 1 message/hour \\ Battery capacity (Joules) & 4800 \\ Receive/transmit energy (mW/s) & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation parameters.  
**Average delivery delay.** Delivery delay is the time elapsed between message generation and delivery. As shown in Equation (16), the average delivery delay is the mean delivery delay in the network, where \(G_{i}\) and \(D_{i}\) are the generation time and the delivery time of the \(i\)th message, respectively. This gives an insight on how long it takes to deliver a message.  
\[\textit{Average delivery delay}=\ \sum_{i=1}^{d}(D_{i}-G_{i})/d \tag{16}\]  
**Message transmission overhead.** As shown in Equation (17), message transmission overhead represents the average number of transmissions required to deliver a message copy; where \(T\). is the total number of message transmissions, and \(V\) is the number of times messages were delivered. This is equivalent to the cost of delivering messages with a forwarding algorithm, since every transmission consumes energy on user devices.  
\[\textit{Message transmission overhead}=(T-V)/V \tag{17}\]  
**Average energy consumption.** Energy consumption (in Joules) is the amount of initial energy expended at the end of the simulation. As shown in Equation (18), the average energy consumption is the mean energy consumed by nodes in the network, where \(n\) is the total number of nodes, and \(E_{i}\) and \(e_{i}\) are the initial energy and the remaining energy of the \(i\)th node, respectively. This gives an insight into how routing impacts resource consumption.  
\[\textit{Average energy consumption}=\sum_{i=1}^{n}(E_{i}-e_{i})/n \tag{18}\]
## 6 Papain Use as an Exfoliant Agent in Cosmetic Applications
### Chemical Properties and Structure of Papain  
#### 6.3.1 Molecular Structure  
Papain's molecular weight is found to be approximately between 23 and 25 kDa [94; 96], and it is a globular cysteine protein composed of a single polypeptide chain containing 212 amino acid residues [100; 113]. Its chain is stabilized by three disulfide bridges, contributing to its structural stability [91]. Papain also features a sulfhydryl group, which is essential for its proteolytic character [91; 98], while the enzyme's three-dimensional structure is characterized by two distinct structural domains separated by a cleft, within which the active site is located. This active site contains a catalytic dyad consisting of cysteine-25 (Cys-25) and histidine-159 (His-159), which are both critical for its catalytic activity [91; 98; 107; 113]. The papain molecule has both \(\alpha\)-helices and anti-parallel \(\beta\)-sheet domains, contributing to its complex structure. The polypeptide chain is folded along the three disulfide bridges, leading to strong interactions between side chains, enhancing the enzyme's stability [74; 91; 98].
## Materials and Methods
### The Architecture of Our Designed Network
 \tag{4}\]  
where \(\sigma_{1}\) is the standard deviation of distances; \(\sigma_{2}\) is the standard deviation of similarities; \(w_{1}+w_{2}=1\); and \(\kappa\) is the threshold. **Detailed Three Time Series Segments:** Suppose that the sampling frequency is p times a day. The current time is \(t_{0}\), and the length of the sequence to be predicted is \(T_{p}\). As described in Figure 5, we intercept three time series fragments of length \(T_{h}\), \(T_{d}\), and \(T_{w}\) along the time axis as the inputs of three spatial-temporal components, respectively. Here, \(T_{h}\), \(T_{d}\), and \(T_{w}\) are all integer multiples of \(T_{p}\). The details of the three time series fragments are as follows:  
* **The recent segment:**\(\mathcal{X}_{h}=(X_{t_{0}-T_{h}+1},X_{t_{0}-T_{h}+2},...,X_{t_{0}})\in\mathbb{R} ^{N\times F\times T_{h}}\). As shown by the red part in Figure 5, this segment is directly adjacent to the time series to be predicted. Since the traffic condition of the sensors gradually spreads to the vicinity over time, the adjacent historical time series have a great impact on it. \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Description** \\ \hline lanes & the number of lanes \\ speedLimit & speed limit of the road segment (km/h) \\ isBridge & whether the road leads to a bridge \\ roadLength & length of the road segment (km) \\ type & the type of road segment \\ oneway & whether roads allow driving in only one direction \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Road-related features. * **The daily-periodic segment:**\(\mathcal{X}_{d}=(X_{t_{0}-(T_{d}/T_{p})*q+1},X_{t_{0}-(T_{d}/T_{p})*q+2},\cdots,X _{t_{0}-(T_{d}/T_{p})*q+T_{p}},\\ X_{t_{0}-(T_{d}/T_{p}-1)*q+1},X_{t_{0}-(T_{d}/T_{p}-1)*q+2},\cdots,X_{t_{0}-(T_{d}/T_{p }-1)*q+T_{p}},\cdots,X_{t_{0}-q+1},X_{t_{0}-q+2},\cdots,X_{t_{0}-q+T_{p}})\in \mathbb{R}^{N\times F\times T_{d}}\). It includes several time segments in the past few days that are the same as the predicted time period, as shown by the green part in Figure 5. As people's daily routines are almost fixed on workdays, such as morning peaks and evening peaks, traffic data may show repeating patterns. The purpose of this component is to model the daily periodicity of traffic data. * **The weekly-periodic segment:**\(\mathcal{X}_{w}=(X_{t_{0}-(T_{w}/T_{p})*7*q+1},X_{t_{0}-(T_{w}/T_{p})*7*q+2}, \cdots,X_{t_{0}-(T_{w}/T_{p})*7*q+T_{p}},\\ X_{t_{0}-(T_{w}/T_{p}-1)*7*q+1},X_{t_{0}-(T_{w}/T_{p}-1)*7*q+2},\cdots,X_{t_{0}-(T_{w}/T_{ p}-1)*7*q+T_{p}},\cdots,X_{t_{0}-q+1},X_{t_{0}-q+2},\cdots,X_{t_{0}-q+T_{p}})\in \mathbb{R}^{N\times F\times T_{w}}\). It is composed of the same periods of the past few weeks with the same week attribute as the time segment to be predicted, as shown by the blue part in Figure 5. Usually, the traffic pattern on a weekday is similar to those on the historical weekday, but the weekend would be different. The weekly-periodic component is designed to capture the weekly periodic patterns of traffic data.
## 3 Research Methodology
### Data Preprocessing  
For ML models, we first remove any null values and drop duplicates. We retain only the Email Text and Email Type columns, renaming them for clarity. For label encoding, we converted Email Type labels into a numerical form: Safe Email as 0 and Phishing Email as 1. Next, we eliminated hyperlinks, punctuation, and extra spaces from the text. Finally, we converted the text into vectors using TF-IDF Vectorizer, chosen for its superior performance as noted in [12]. To train the DL models, we opted not to preprocess the text, aligning with the approach recommended by [13]. Their study demonstrates that DL models, particularly LSTMs, achieve superior performance--higher accuracy, recall, precision, and F1 score--when trained on raw email text without preprocessing.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 3 Carbon Satellite Remote Sensing Inversion Techniques
### Optimal Estimation Algorithm
#### 3.2.4 Empirical Algorithms
A one-dimensional line-by-line forward radiation model is developed to simulate the GOSAT observational spectra, generating training data for the machine learning model. In the first step of the model, spectral deconvolution is performed to derive the atmospheric spectral optical thickness. This result is then used in the second step to extract XCO\({}_{2}\) based on the optical thickness. The results indicate that this method offers a promising alternative for efficient XCO\({}_{2}\) retrieval and that further improvements could enhance the model's accuracy [109]. Figure 15 illustrates the schematic of the two-step machine learning model. In the first step, the input layer neurons represent the measured spectral intensities of 502 data  
Figure 14: Graphical representation of the neural network. points, denoted as \(I_{C,\eta}\). Each spectral value is assigned to a neuron, producing the output \(L_{C,\eta}\). In the second step, the model uses the lower resolution spectral optical thickness \(\tau_{C,\eta}\), obtained from the first step, as input to predict XCO\({}_{2}\) directly after network training. [PERSON] et al. [110] developed a machine-learning-based remote sensing inversion method for short-wave infrared channel CO\({}_{2}\) satellites. They created a training dataset incorporating satellite observation irradiance, aerosol optical thickness, and temperature. A feed-forward neural network, combined with a quantized conjugate gradient algorithm, was used to develop an inversion model for CO\({}_{2}\) concentrations based on GOSAT satellite spectral data. The results confirmed the effectiveness of this inversion method. [PERSON] et al. [111] integrated HITRAN database simulations with a BP neural network deep learning approach to propose a CO\({}_{2}\) concentration inversion algorithm. This algorithm, which can be implemented on SIM32F407, showed minimal detection errors, providing a theoretical foundation for embedded concentration inversion systems. Neural network-based models and inversion algorithms can offer accurate and rapid processing for real-time satellite atmospheric parameter applications, with accuracy controlled within 2% [96]. However, these methods demand high spectral resolution and extensive statistical data for training and may struggle with unknown scenarios. At present, neural network algorithms are less mature and reliable compared to physical methods.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Other Projects for Energy Management through ICT Tools
#### 4.4.5 Smart City Kalundborg  
The construction of a platform (hub) has been the subject of the \"Smart City Kalundborg\" project, which entailed participants of two countries. The platform entailed user friendly energy services to people and businesses. The goal was to have selected demonstration sites connected to this hub with the appropriate technical equipment. Specific software has been used to observe data on energy production and consumption. Among others, solar panels and electric vehicles have been tested and renewable energy production and integration has been monitored [96].
## 2 Network Model and Operation
### Collision Constraints  
Let us consider that node \(j\) has to transmit in time slot \(t\). If the transmission range is one hop (distance \(d\) in Figure 1), there are three constraints to avoid interference: (i) node \(j+1\) cannot transmit in the previous slot \(t-1\); (ii) the destination node \(j-1\) cannot transmit in slot \(t+1\), when it expects to receive the data from node \(j\); and (iii) nodes \(j\) and \(j+2\) cannot transmit simultaneously. The reasons for these constraints are discussed in Figure 3 with the help of a graphic sketch. Besides that, since the network has a finite size, there are two obvious rules for nodes at both ends: the Gateway (node 1) never transmits to another node in the network, and node \(N\) never receives data from another node. These conditions will be considered in Section 2.2.2 to find valid schedules.
## 3 Test Results and Analysis
### Analysis of Influencing Factors  
#### 3.3.1 Impact of RPC Strength Levels  
The ultimate loads of the specimens are 11,036 kN, 12,125 kN, and 12,996 kN, and Figure 8 shows that the RPC strength grade has a significant effect on the load capacity of the specimens. The ultimate loads of the specimens were 11,036 kN, 12,125 kN, and 12,996 kN, respectively. The higher the RPC strength grade, the greater the slope of the elastic phase of the load-displacement curve, the slower the rate of increase in displacement and the steeper the decreasing section of the load capacity. This indicates that the higher the RPC strength grade is, the higher the stiffness of the specimen and the higher the ultimate load-carrying capacity. From the damage characteristics, it can be seen that increasing the strength of RPC can enhance the bonding force of concrete, and the better the bonding performance of RPC and steel sections, the more it inhibits the development of cracks, and the fewer the cracks that will be produced in the specimen. After the ultimate load, the ductility of the high-strength RPC specimens is poor due to the increased brittleness of the high-strength RPC, and the specimens show brittle damage.
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 3 Results
### On the Remarkably Large and Stable Characteristics of the Copolar Correlation Coefficient
#### 3.5.2 Small Dispersion of the Copolar Correlation Coefficient  
As it can be seen in Table 8, the dispersion around the mean of the \(\rho^{HV}\) values is small: The standard deviation (multiplied by 100) of \(\rho^{HV}\) for the five clear sky days ranges from between 0.09 and 0.23.
# RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell+
###### Abstract  
Low Power Wide Area Networks (LPWAN) have the ability to localize a mobile transmitter using signals of opportunity, as a low power and low cost alternative to satellite-based solutions. In this paper, we evaluate the accuracy of three localization approaches based on the Received Signal Strength (RSS). More specifically, the performance of a proximity, range-based and optimized fingerprint-based algorithm is evaluated in a large-scale urban environment using a public Narrowband Internet of Things (NB-IoT) network. The results show a mean location estimation error of 340, 320 and 204 m, respectively. During the measurement campaign, we discovered a mobility issue in NB-IoT. In contrast to other LPWAN and cellular technologies which use multiple gateways or cells to locate a device, only a single cell antenna can be used for RSS-based localization in NB-IoT. Therefore, we address this limitation in the current NB-IoT hardware and software by studying the mobility of the cellular-based 3 GPP standard in a localization context. Experimental results show that the lack of handover support leads to increased cell reselection time and poor cell sector reliability, which in turn results in reduced localization performance.  
Keywords:NB-IoT; LPWAN; localization; RSS; mobility +
Footnote †: This paper is an extended version of our paper published in [PERSON]; [PERSON]; [PERSON] [PERSON] A Primer on Real-world RSS-based Outdoor NB-IoT Localization. In proceedings of the 2020 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland, 2-4 June 2020; pp. 1-6.