esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Review of Studies Related to Integration in the Context of Multi-Modal Mobility
### Potential Future Research Directions
In [120], a two-stage Stackelberg game under different pricing schemes was proposed for the vehicle dispatching service pricing and demand problem. Analysis of a highly competitive game between mobility service providers and passengers in the MaaS market is one challenging issue. To ensure trust, consideration of blockchain for secure and transparent trading is another interesting issue. A multi-leader multi-follower game model has been proposed in [121] to model the interactions between multiple leaders and multiple followers, where leaders represent MaaS platforms and followers represents travelers. An algorithm has been developed to solve the proposed multi-leader multi-follower game. However, network configurations were not considered in the model. The influence of network configurations on design of MaaS bundle, allocation of resource, and the efficiency is an important issue. Another research direction is to study the effects of various incentives on traveler behavior, platform competition, and systemic sustainability. Integration of the data-driven MaaS system with the power system or smart grid technologies to optimize the charging infrastructure for different travel modes and enhance management of energy and sustainability is also an interesting research direction. Although [122] have established a stability condition under which operators and travelers of a MaaS system are willing to participate, the condition is based on several assumption. There are several future research directions related to [122]:  
1. Incorporating the operators' decisions on service capacities and MaaS fares into the model. 2. Relaxing the fixed total demand and analyzing the robustness of a MaaS system with respect to the future growth of travel demand. 3. Considering a realistic setting with sophisticated operational strategies and heterogeneous access time, instead of uniform distribution of vehicles and bikes. 4. Exploring the impact of topological properties of the multi-modal transportation network on the social cost saving and profitability of MaaS. 5. Considering multi-objective optimization to ensure all stakeholders can be better off. 6. Considering wholesale capacity constraint from the perspective of operators and investigating the influence of wholesale capacity constraint on properties and solution algorithms of the model. 7. Studying the differences between matching stability and supply-chain equilibrium and investigating whether matching stability and supply-chain equilibrium fits better with real practice. The idea of \"Pay-As-You-Move\" insurance product for multi-modal mobility in the study [123] sparks two future research directions: (1) considering contextual information related to policyholders' trips, e.g., time of day, weather, and light conditions, in assessingthe risk of different mobility types to enhance the underwriting information; (2) developing the pricing structures for the novel insurance by modifying traditional insurance pricing structures to meet the need of a multi-modal insurance policy.
# The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines
## 3 ML for Wind Energy Prediction  
This section delineates the procedures for data collecting, data preprocessing, and the implementation of ML algorithms.  
Wind energy prediction analysis jobs involve the processing and interpretation of large quantities of weather-related data to generate accurate estimates of future energy generation. These tasks employ a diverse range of analytical methodologies and tactics to enhance the precision of forecasts. The meteorological data, consisting of wind speed, wind direction, temperature, humidity, wind gusts, and dewpoint, is now accessible and can be utilized for power generation predictions, as Figure 8 shows [20]. Significant statistical connections can be identified between different meteorological factors and energy production. These correlations can be used to construct models that accurately represent the influence of certain weather conditions on the efficiency of wind turbines. The selection of features for predicting models is determined by this examination. Selecting suitable  
Figure 7: The objectives of upcoming SCADA systems for WECS.  
machine learning techniques, like regression models, for predictive modeling depends on the unique demands of the prediction issue and the features of the data.
# Application of Machine Learning Techniques to
## 4 Results and Discussion
### Climate Zones  
The Principal Component Analysis (PCA) performed as the first step in climate zoning provided the details of the climate change pattern in the entire study area. A statistically significant climate pattern heterogeneity was observed in the region when PCA was performed on a seasonal basis.
## 2 Materials and Methods
### Inventory Analysis
#### 2.3.2 Usage Process
The power consumption of the combination of desktops and laptops was calculated using the ratio of domestic shipments of desktops and laptops in 2020 [43]. In contrast to PCs, very little information is available on the power consumption by smartphones. Therefore, the data published by Smil [44] was adopted. CO2 emission per 1 MB of communication volume was calculated by multiplying CO2 emissions with the annual average usage time and communication volume per hour. The annual usage time and amount of communication volume related to listening and/or watching were calculated based on the report of ICT Research and Consulting Inc. [32] and streaming services, such as Apple Music and LINE Music. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Item** & & **Unit** & **Source** \\ \hline Power consumption of PCs (median) & 6.87 \(\times\) 10\({}^{-2}\) & kWh & Adopted based on \\ Power consumption of PCs (25\% percentile) & 4.79 \(\times\) 10\({}^{-2}\) & kWh & information of PCs \\ Power consumption of PCs (75\% percentile) & 1.28 \(\times\) 10\({}^{-1}\) & kWh & manufactures and suppliers \\ \hline Power consumption of smartphones & 4.57 \(\times\) 10\({}^{-4}\) & kWh & [44] \\ \hline CO2 emission intensity of electricity in 2019 & 4.70 \(\times\) 10\({}^{-1}\) & kg-CO2/kWh & [45] \\ \hline Annual average usage time: music streaming/subscription & 730 & h \\ \hline Annual average usage time: music streaming/free & 100 & h \\ \hline Annual average usage time: videos streaming/subscription & 124 & h \\ \hline Annual average usage time: videos streaming/free & 100 & h \\ \hline Communication volume: music streaming/free & 72 & MB/h \\ \hline Communication volume: videos streaming/subscription & 1900 & MB/h \\ \hline Communication volume: videos streaming free & 1000 & MB/h \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Utilized data for usage process: listening and/or watching streaming by users. The load on PCs and smartphones and power consumption changes depended on the task being performed, such as listening to music, watching videos, and/or multitasking. However, in this study, such changes in power consumption were not considered, because measuring power consumption for these is difficult.
# Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation
## 3 Problem Formulation  
The prognosis in predictive maintenance is related to the forecasting of HI values for future time instances or estimation of a RUL time [43; 44; 45; 46; 47; 48]. In any case, to deal with prediction, one needs a model of the degradation of the machine. It is the most frequent case that advanced mathematical models or machine learning (ML) approaches are used for prognosis. ML approach requires historical data to train the prediction system or establish a data-driven model for the prognosis. From the reliability theory, one may notice that there are several \"general\" degradation models. As mentioned, in this paper, we follow the idea that machine lifetime consists of three phases (see Figure 1):  
1. Good condition, where HI is nearly constant (no degradation);
2. Slow degradation (HI is increasing slowly and approximately linearly);
3. Fast degradation (HI is rapidly growing like exponential function).  
In such a case, the model of the degradation, in fact, consists of three sub-models (three regimes). It means that before the identification of the model, the data need to be segmented. Segments corresponding to given sub-models should be extracted, and the data in each segment should be modeled separately. Thus, for long-term data from the condition monitoring system, one needs to apply the segmentation procedure before modeling. In other words, one needs to find so called regime change points, where the data are changing their nature in the sense of statistical properties. In this paper, the general idea is to analyze locally statistical parameters by the moving window approach. For a priori selected window length and overlapping, simple descriptive statistics are calculated, and then these features for each segment are subjected to clustering. There are some challenging issues to solve. They are related to the segment size, overlapping, statistical parameters selection (features), and the clustering method. According to some earlier work and our experience, it may be necessary to use the further processing of features. In the next section, an original methodology to segment historical data from condition monitoring system is presented in detail.  
In the case of non-Gaussian noise, many theoretical assumptions cannot be fulfilled, so a number of techniques cannot be applied. This happens because the high content of outliers in the data negatively affects the calculation of many statistics, especially the ones related to variance.
# A Portable Non-Motorized Smart IoT Weather Station Platform for Urban Thermal Comfort Studies
## 2 Proposed Weather Station Platform
### Sensor Selection  
Range, precision, accuracy, and response time of the sensors are the four factors that need to be taken care of while selecting a sensor for UHI/microlimate studies [26; 46]. The international standard ISO 7726:1998 provides a framework for outdoor thermal comfort studies [26]. Based on this framework, we selected the sensors to develop the prototype of the edge device. Table 1 presents the selected sensors' range, accuracy, precision, and cost, along with the ISO 7726:1998 requirements.  
The edge device prototype used two MCP980 temperature sensors for AT and GT measurements. This sensor communicates its measurements over the Inter-Integrated Circuit (I2C) protocol. Although the DHT22 and similar sensors facilitate both AT and RH measurements, the MCP980 was selected for AT measurements due to its higher range, accuracy, and precision, meeting or exceeding the ISO 7726:1998 requirement. A second MCP980 was used to develop a globe thermometer for GT, following well-established methods in [26].  
One DHT22 sensor, with an RH range of 0% to 99.9%, was used to measure RH using the I2C protocol.  
One U-block NEO-7M GPS sensor was used to gather location information. This sensor provides latitude, longitude, altitude, and speed information, along with the measurement date and time.  
One SEN0483 cup anemometer was used to measure wind speed. This sensor uses the Modbus protocol over the RS485 serial communication; therefore, a Universal Serial Bus (USB) to RS485 module was used to convert the RS485 to Universal Asynchronous Receiver-Transmitter (UART), to make it compatible with a broader range of microcontrollers. Apart  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & **Selected** & \multirow{2}{*}{**Cost**} & \multicolumn{2}{c}{**Measuring Range**} & \multicolumn{2}{c}{**Accuracy and Precision**} \\ \cline{3-6}  & & & **Selected Sensor** & **Required (ISO 7726:1998)** & **Selected Sensor** & **Required (ISO 7726:1998)** \\ \hline AT, GT & \begin{tabular}{c} MCP9808 \\ (I2C) \\ \end{tabular} & \(\varepsilon\)3.96 & \(-\)40 \({}^{\circ}\)C to +125 \({}^{\circ}\)C & \(-\)40 \({}^{\circ}\)C to +120 \({}^{\circ}\)C & \(\pm\)0.25 \({}^{\circ}\)C \\ \hline RH & DHT22 & \(\varepsilon\)4.72 & AT: \(-\)40 \({}^{\circ}\)C to +80 \({}^{\circ}\)C & 0.5 kPa to 6.0 kPa & AT: \(\pm\)0.5 \({}^{\circ}\)C & \(\pm\)0.15 kPa \\  & & & RH: 0-99.9\% & (25 \({}^{\circ}\)C) & RH: \(\pm\)4.73\% (25 \({}^{\circ}\)C) \\ \hline WS & \
\begin{tabular}{c} RS485 (SKU: \\ SEN0483) \\ \end{tabular} & \(\varepsilon\)35.57 &
\begin{tabular}{c} 0 to 32.4 m/s \\ -Starting 0.2 to \\ 0.4 m/s \\ \end{tabular} & 0.05 m/s to 20 m/s & \(\pm\)0.3 m/s & \(\pm\)(0.05 + 0.05 Va) m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Range, accuracy, precision, and cost of the selected sensors to measure AT, GT, RH, and WS.  
from offering the full ISO 7726:1998 required range, this sensor is lightweight, affordable, and consumes less power than ultrasound and hot wire sensors.
## 4 HPS-Based Catalysts for the Synthesis and Transformation of Platform Chemicals
### Hydrogenation of Furfural
The catalyst was synthesized by impregnating HPS with a solution containing both palladium and copper acetates, followed by treatment with Na\({}_{2}\)CO\({}_{3}\) to precipitate Pd-Cu mixed oxide nanoparticles in the pores of HPS. These as synthesized samples are denoted \"as\". The reduction of Pd and Cu species was performed prior to the catalytic reaction in the hydrogen flow at 275 \({}^{\circ}\)C. The reduced samples are denoted \"r\". SAXS and transmission electron microscopy (TEM) methods were used to estimate the size of  
Figure 12: Pd 3d XPS spectra for both Pd/HPS samples and their deconvolution on metal and oxide form of Pd (black line—raw spectrum; red line—synthetic spectrum; blue line—chemical state of palladium Pd\({}^{0}\); purple line—chemical state of palladium Pd\({}^{2+}\)). Reprinted from ref. [177], Copyright 2019, with permission from Elsevier (Amsterdam, The Netherlands). nanoparticles (Figure 13). The average nanoparticle size determined by using both methods was about 6-7 nm. XRD data for Pd/HPS-r shows typical Pd\({}^{0}\) reflections for a monometallic sample and a crystallite size of 6 nm, indicating that the Pd nanoparticles are most likely single crystals. For bimetallic Pd-Cu nanoparticles in the Pd-Cu/HPS-r sample, the X-ray diffraction pattern is almost identical, but its reflections are shifted towards large angles, and the peak positions are between the positions characteristic of the Pd and Cu phases. According to data from the literature, this peak location indicates the formation of the structure of a Pd-Cu bimetallic alloy. The absence of reflections from Cu metal, as well as its oxides or hydroxides, once again confirms the formation of the alloy. The size of Pd-Cu nanoparticles remains \(\sim\)6 nm. XPS showed the enrichment of the surface of nanoparticles with Cu atoms, as well as the presence of both zero-valent and cationic forms of Pd and Cu, i.e., heterogeneity of nanoparticles. This structure of Pd-Cu alloy nanoparticles immobilized in HPS provides almost 100% conversion and excellent selectivity towards FA (95.2%). These exceptional performances were attributed to the prevention of furan ring adsorption on Pd due to neighboring Cu species and facilitated desorption of FA, resulting in higher selectivity. Controlled adsorption of hydrogen and FF due to the mixed valence states of Pd and Cu species leads to higher conversion. These factors, as well as the remarkable  
Figure 13: Volume NP size distribution from the SAXS data (**a,b**), and a comparison of the NP sizes from TEM and SAXS data (**c,d**) for Pd–Cu/HPS-r (**a,c**) and Pd/HPS-r (**b,d**). Reprinted from ref. [175], Copyright 2020, with permission from John Wiley and Sons (Hoboken, New Jersey, USA). ability to reuse the catalyst in ten successive reactions, make this catalyst promising for industrial applications.
###### Abstract  
While the main contributor for drop jump (DJ) performance is the calf muscle-tendon unit (MTU), for countermovement jump (CMJ) performance, it is the quadriceps MTU. However, to date, it is not clear if the muscle and/or tendon stiffness of the respective MTUs can be related to DJ or CMJ performance. Therefore, the purpose of this study was to investigate the relationships between DJ and CMJ performance parameters and tissue stiffness (i.e., muscle stiffness, tendon stiffness) of the calf MTU and quadriceps MTU, respectively. Consequently, with 16 healthy volunteers, the tissue stiffness of the gastrocnemius medialis (GM), gastrocnemius lateralis (GL) Achilles tendon (AT), vastus medialis (VM), vastus lateralis (VL), rectus femoris (RF), and patellar tendon (PT) were recorded with a Myoton device. Moreover, DJ and CMJ performances were assessed with a force plate. The alpha level was set to 0.05. Pearson correlation coefficients revealed no significant association between DJ performance and GM, GL, or AT stiffness (\(-0.07\) to \(0.24\); \(p>0.05\)). Similarly, no association was found between CMJ performance parameters and VM, VL, RF, or PT stiffness (\(-0.13\)-\(0.36\); \(p>0.05\)). According to our results, other variables, such as jump technique, body weight, or strength, were likely play a more important role in DJ and CMJ performance.  
jumping; stiffness; Myoton; muscle; tendon +
Footnote †: journal: Applied Sciences
## 2 Prototype
### Two-Stage Detection
#### 2.3.4 Central Database  
The central database was implemented in MySQL [66] on a machine running the Ubuntu 20.04 LTS operating system [67]. It adopts a relational architecture and contains five tables: _KML_Files, Alerts, Equipment, Users_data_ and _User_. The purpose of the _KML_Files_ table is to store the path of Keyhole Markup Language (KML) files [68]. These files are used to display geographical data on a map [69], such as Google Earth or Google Maps. These files are used to draw cycling and hiking trails on the web application map described in Section 2.3.5. This table has the following fields as attributes: _id_, which uniquely identifies a file, and the _full_path_ attribute, which represents the path where the file is stored. These files are stored in a public folder on Drive, because a KML file must be accessed via a publicly accessible server on the Internet [70; 71; 72].  
The _Alerts_ table stores the alerts generated by the system. These alerts are triggered by a lack of data synchronization with the central database for more than 10 days. Its attributes are _description_, which represents a brief description of the alert, _time_alert_, which stores the date and time the alert was generated, and finally _device_id_, which stores the _id_ of the sensor node to which the alert is associated.  
The _Equipment_ table is very similar to the table with the same name described in Section 2.3.3, except for two additional attributes: _user_id_, which keeps track of which user the sensor node belongs to, and _location_, which indicates in full the nearest location where that same node is located. The _Users_data_ table is also similar to the table described in Section 2.3.3, but instead of having the _isSynced_ control attribute, it uses the _sync_timstamp_ attribute, which records the date and time of synchronization with the central database. Finally, the _Users_ table stores the session data of the users who access the web application. It has four attributes: the _id_ which uniquely identifies a user, the _username_ which stores the username, the _password_hash_ which stores the user's encrypted password, and the _verified_ which serves as access control. Figure 28 shows the entity-relationship model of this database.  
To interact with the central database, a Rest API was created with the Python Flask RESTful library [73]. Only two methods were created: a GET/_equipment_ method to obtain the information from the sensor nodes registered in the central database, shown in Figure 29, and a POST/_userdata_ method to send the records from the bridge node to the central database, to which the API returns the number of records received, as shown in Figure 30.  
Figure 28: Central database.
## 4 Decentralized Data Valuation
### KL Divergence in Distributed Data  
The Kullback-Leibler (KL) divergence of probability distribution \(p\) diverging from the referenced probability distribution \(q\) is denoted as \(D_{KL}(p\|q)\)[38]. In a general interpretation, KL divergence quantifies the expected information loss because of using distribution \(q\) to approximate the actual data distribution \(p\).  
In DML, training a deep network relies on the assumption that a stochastic gradient on distributed data is an unbiased estimate of the full gradient on the entire dataset (i.e., distributed health data are assumed to be IID) [5]. However, as introduced in Section 1, real datasets are non-IID. Therefore, a decrease in DML performance is inevitable. We use KL divergence as a metric to evaluate the information loss when applying SGD, which was originally designed for IID training data, in non-IID cases.  
For smartphone \(n\) in round \(r\), the KL divergence between the real data distribution \(p_{n,r}\) and IID data distribution \(q\) is  
\[D_{KL}(p_{n,r}\|q)=\sum_{l\in\mathcal{L}}p_{n,r,l}\ln(\frac{p_{n,r,l}}{q_{l}}), \tag{11}\]  
where \(q_{l}\) is unknown. Note KL divergence is asymmetric (i.e., \(D_{KL}(p_{n,r}\|q)\
eq D_{KL}(q\|p_{n,r})\)) in most cases [38]. According to (11), we can calculate the information loss of applying SGD on \(p_{n,r}\) instead of the unknown \(q\).  
Let \(Q_{n,r}\) be the data quality of \(\mathbf{d}_{n,r}\) for \(N\) smartphones in round \(r\). Suppose the data quantity \(d_{n,r}\) is the same across \(N\) smartphones; \(Q_{n,r}\) is calculated by  
\[Q_{n,r}=-D_{KL}(p_{n,r}\|q). \tag{12}\]  
\(Q_{n,r}\) can be used to indicate the true quality of distributed data in DML [39]. However, it is very challenging to compute \(Q_{n,r}\) due to the uncertainty of \(q\) and data quantity skews. In this paper, we investigate an alternative method to evaluate the valuation of any \(\mathbf{d}_{n,r}\).