esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Consumption Prediction for Fused Deposition Modelling 3D Printing Using Machine Learning
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**AM Process Type** & **Brief Description** & **Material Used** & **Technologies** \\ \hline \multirow{6}{*}{Vat photopolymerization} & photopolymers are exposed to repeated forms of radiation & \multirow{6}{*}{Photopolymers} & Stereolithography, digital light processing (DLP) \\  & corresponding to cross sections of the part under construction. & & \\ \hline \multirow{6}{*}{Powder bed fusion} & Thermal energy & \multirow{6}{*}{Metals, polymers} & Electron beam melting (EBM), selective laser sintering (SLS),selective heat sintering (SHS), direct metal laser sintering(DMLS) \\  & & & \\ \cline{1-1}  & The melted material is selected through a nozzle. & Polymers & Fused deposition modelling (FDM) \\ \hline \multirow{6}{*}{Material jetting} & Droplets of build material are placed selectively. & Polymers, waves & Multi-jet modelling (MJM) \\ \hline \multirow{6}{*}{Binder jetting} & A binder is printed onto a powder bed to form the part’s cross section. & Polymers, foundry sand, metals & Powder bed and inkjet head (PBIH), plaster-based 3D printing (PP) \\ \hline \multirow{6}{*}{Sheet lamination} & Sheets of materials are cut, stacked, and bonded to form an object. & Paper, metals & Laminated object manufacturing (LOM), ultrasonic consolidation (UC) \\ \cline{1-1}  & Thermal energy is used to merge materials by melting as the material is being deposited. & \multirow{2}{*}{Metals} & Laser metal deposition (LMD), electron beam metal deposition, wire arc additive manufacturing (WAAM) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Brief description of different additive manufacturing processes [7, 8]. In this paper, the Section 2 presents a literature overview on energy consumption estimation in 3D printing. The Section 3 is devoted to the presentation of experimental data extracted from [11]. The Section 4 is dedicated to the methodologies of the ML algorithms used to model the energy consumption use of 3D printing isovolumetric mechanical components, while the prediction results and discussion are presented in the Sections 5 and 6. The Section 7 presents conclusions.
## 6 Algorithms and Frameworks in Federated Learning
### Adaptive Learning Rates in Federated Contexts
#### 6.4.5 Motivation for Asynchronous Updates in FL  
The complexities of real-world distributed systems present unique challenges that asynchronous methods can address.  
Network Disparities: In environments where devices span a wide geographical area, the network speeds and stability vary drastically. A smartphone in an urban setting with 5G connectivity contrasts starkly with an IoT sensor in a remote location that is reliant on low-bandwidth connections. Asynchronous updates respect these disparities, allowing each device to communicate based on its optimal conditions [1].  
Computational Constraints: The devices participating in FL range from powerful servers to resource-constrained sensors. Expecting them to process and deliver updates concurrently becomes unreasonable. Asynchronous methods provide an avenue for each device to contribute based on its computational pace.  
Intermittent Availability: Devices might have sporadic connectivity or might be set to participate only at specific intervals (e.g., during low-usage times). Asynchronous updates accommodate such varied participation patterns, ensuring that every device, regardless of its schedule, contributes to the global model.
## 3 Results and Discussion
The outflow temperature of geothermal water at the wellhead is 100 \({}^{\circ}\)C, which allows it to progressively be used for greenhouse heating (100-80 \({}^{\circ}\)C), domestic hot water and food industry (80-70 \({}^{\circ}\)C), animal breeding (60 \({}^{\circ}\)C), biomass and agricultural culture (50 \({}^{\circ}\)C). On the contrary, the thermal load accumulated in correspondence with Tempa Rossa 1D and Gela 38 wells turns out to be sufficient neither to justify the costs of plant retrofitting nor to plan a cascading exploitation of the geothermal fluid produced. Figure 8: (**a**). Temperature profile associated with the coaxial WBHE configuration considering the site-specific stratigraphy of Tempa Rossa 1D well. (**b**) Wellhead temperature behaviour as the flow rate changes. Figure 9: (**a**) Temperature profile associated with the coaxial WBHE configuration considering the Scheme 38 well. (**b**) Wellhead temperature behaviour as the flow rate changes. As can be seen in Figures 7b, 8b and 9b, the inlet flow rate strongly influenced the temperature of the wellhead thermal fluid. In each case, it is possible to identify an inlet flow rate value (kg/s) in order to obtain a higher fluid temperature at the outlet and optimize the quantity of extracted thermal power. When considering inlet flow rate values between 0.5 and 0.8 kg/s, the output fluid temperature increased up to about 122 \({}^{\circ}\)C (0.8 kg/s, Villafortuna 1), 65.5 \({}^{\circ}\)C (0.7 kg/s, Gela 38) and 72 \({}^{\circ}\)C (0.5 kg/s Tempa Rossa 1D). For higher values, the trend was inverted in all three cases. However, for coaxial WBHE systems like the one analysed, such low inlet flow rates may not be technically appropriate. The heat-exchange modalities are in fact not the only aspect that must be considered to carry out the correct analysis of the heat transfer mechanisms associated with coaxial WBHE. Pressure loss phenomena need to be analysed as they affect pumping costs and are not negligible in the management of a closed-loop geothermal system. In coaxial WBHE, the inlet flow rate strongly influences the temperature of the wellhead thermal fluid. As pressure losses are proportional to the velocity, an increase in the inlet flow rate values will cause an increase in the required pumping power.
## 3 Results and Discussion
### Effect of Different Ratios of CH\({}_{4}\) as An Assistant Agent on Final Products  
The FTIR spectra for the reducing agent with different ratios of NH\({}_{3}\) and CH\({}_{4}\) under 2% O\({}_{2}\) are shown in Figure 7. They show that with the increase in the CH\({}_{4}\) proportion, N\({}_{2}\)O decreased rapidly from 13.97 ppm to 0.80 ppm and there were few N\({}_{2}\)O (0.80 ppm) products when the ratio of NH\({}_{3}\) and CH\({}_{4}\) was 1:1. This result indicates that the production of new NOx could be controlled in DBD-NH\({}_{3}\)-CH\({}_{4}\)-SCR (NH\({}_{3}\):CH\({}_{4}\) = 1:1) with the favorable product selectivity.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Feed Gas Concentration**} & \multicolumn{3}{c}{**Product Concentration of Outlet (ppm)**} & \multicolumn{3}{c}{**N\({}_{2}\) Selectivity**} \\  & \multicolumn{3}{c}{**of Inlet (ppm)**} & \multicolumn{3}{c}{**(\%)**} \\ \cline{2-10}  & **NO** & **CH\({}_{4}\)** & **NH\({}_{3}\)** & **NO** & **NO\({}_{2}\)** & **N\({}_{2}\)O** & **NO\({}_{3}\)** & **NH\({}_{3}\)** & \\ \hline
**DBD-NH\({}_{3}\)-SCR** & 400 & 0 & 90 & 220 & 32.5 & 19.8 & 37.2 & 7.7 & 31.2 \\ \hline
**DBD-NH\({}_{3}\)-CH\({}_{4}\)-SCR** & 400 & 45 & 45 & 80 & 15.4 & 4.71 & 25.5 & 3.8 & 69.9 \\ \hline \hline \end{tabular} Experiment condition: 12% O\({}_{2}\), SIE 512 J/L, the NO\({}_{3}\) was collected from outlet.  
\end{table}
Table 1: The N\({}_{2}\) selectivity and concentration of feed gas as well as products at outlet.  
Figure 7: FTIR spectra and products detected in the DBD-NH\({}_{3}\)-CH\({}_{4}\)-SCR with different ratios of NH\({}_{3}\) and CH\({}_{4}\). Experimental conditions: 2% O\({}_{2}\), SIE 512 J/L, NH\({}_{3}\) 1:1 CH\({}_{4}\): 45 ppm NH\({}_{3}\) and 45 ppm CH\({}_{4}\), NH\({}_{3}\) 2:1 CH\({}_{4}\): 60 ppm NH\({}_{3}\) and 30 ppm CH\({}_{4}\), NH\({}_{3}\) 3:1 CH\({}_{4}\): 67.5 ppm NH\({}_{3}\) and 22.5 ppm CH\({}_{4}\).
# Central Kurdish Text-to-Speech Synthesis with Novel End-to-End Transformer Training
## 3 Related Work for the Kurdish Language
### Model Architecture  
#### 4.6.1 Text Encoder  
To handle Central Kurdish text, our initial step involves converting text sequences into International Phonetic Alphabet (IPA) sequences through the utilization of open-source software [34]. Additionally, we incorporate several custom-defined phonemes to accommodate the distinct characters present in Central Kurdish, as outlined in Appendix A.2. Then, the text encoder converts the phoneme embedding sequence into the hidden phoneme representation \(h_{text}\). We follow the encoder structure of the transformer [12], as shown in Figure 2, with some slight modifications. We remove the positional encoding and add learnable positional encoding. We build the text encoder with eight blocks of transformer encoders, each with eight multi-head self-attention modules. The dimension of phonemes embeddings and the hidden size of the self-attention (hidden representations) are set to 256 following the recommendation by FASTSPEECH [10].  
The positional encoder depicted in Figure 3 employs a grouped 1D convolution comprising 64 filters with a kernel size of 3 to generate a relative positional vector from the latent features. This vector is subsequently combined with the embedding of phonemes (tokens) to encode their positions relative to each other. We append a linear projection layer on top of the transformer encoder to predict the statistics of the prior distribution, \(\mu_{\theta}\) and \(\sigma_{\theta}\), from the hidden representation \(h_{text}\).  
Figure 3: The positional encoder uses a 64-filter grouped 1D convolution to generate relative positional vectors.  
Figure 2: The text encoder featuring a modified transformer encoder with learnable positional encoding.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 4 Security Hardening of Kubernetes Components
### Identity and Access Management
#### 4.4.3 Using an Authentication Proxy  
An authentication proxy functions by intercepting API requests and validating the identity of the client or user initiating the request. Diverse proxy solutions, such as OAuth2_proxy or nginx, can be employed for this objective. Upon successful authentication, the proxy transmits the request to the Kubernetes API server. Should the authentication be unsuccessful, the proxy generates an error message. The API server must be configured to recognize users based on request header values, such as X-Remote-User. It is intended for use alongside an authenticating proxy. Figure 9 illustrates a configuration example for the kube-api server in this context:  
The options used in the example are as follows:  
* --requestheader-client-ca-file=/etc/kubernetes/pki/ca.crt: designates the Certificate Authority certificate file utilized to authenticate client certificates submitted by the proxy, thereby ensuring that the proxy interacting with the API server is both trusted and authorized.
* --requestheader-username-headers=X-Remote-User: designates the HTTP header (X-Remote-User) utilized by the proxy to transmit the authenticated user's identity. The initial non-empty header is regarded as the username. The proxy establishes this header, which the API server utilizes to recognize the authenticated user.
* --requestheader-group-headers=X-Remote-Group: establishes the header (X-Remote-Group) that enumerates the groups associated with the authenticated user.
* --proxy-client-cert-file=/etc/kubernetes/pki/apiserver.crt: supplies the client certificate for the API server to authenticate with the proxy server, which is utilized during mutual TLS authentication when the API server interacts with the proxy.
* --proxy-client-key-file=/etc/kubernetes/pki/apiserver.key: designates the private key associated with the certificate mentioned above (apiserver.crt), utilized to establish a secure connection between the API server and the proxy.  
To establish an authentication proxy, nginx may be utilized by implementing ConfigMap and Deployment configuration files. A service to expose the nginx proxy should be deployed. The authentication proxy secures access to the Kubernetes API server by functioning as an intermediary between clients and the API server, enforcing authentication and authorization protocols before requests reach the API server.  
Figure 9: Kube-api auth proxy configuration.  
Upon successful request authentication, nginx inserts headers including X-Remote-User and X-Remote-Group, which the API server utilizes for user identification.
## 3 Condition Monitoring of the Impedance Bonds
### Condition Monitoring Device and Sensors Used for Monitoring the Impedance Bonds
#### 3.2.3 Temperature Sensors  
The temperature sensors used to measure the temperature values on the impedance bond terminals, respectively, on its housing, are of the Negative Temperature Coefficient (NTC) thermistor type. Generally, they are made of metal oxides (manganese, nickel, cobalt, copper, iron, etc.) and have a temperature measurement range of -75 \({}^{\circ}\)C to +300 \({}^{\circ}\)C, with accuracy \(\pm\)1%. They are based, as a working principle, on changing the electrical resistance with temperature changes. Among the advantages of this method of measurement, it can be noted that they have a high resolution, slow temperature inertia, high sensitivity. As a disadvantage, it mentions that they are not linear.  
The chosen thermistors have nominal values of 10 k\(\Omega\) at 25 \({}^{\circ}\)C and are interfaced to the microcontroller via a voltage divider network, which is designed so that the power dissipated on thermistor never exceeds 1 mW (in reality, this power is even lower). Because of the non-linear response of the thermistors, a look-up table is used to determine the temperature in software. Taking into account the limited ADC accuracy, the estimated error when using the linear interpolation does not exceed 0.7 \({}^{\circ}\)C for the temperature range from \(-\)40 \({}^{\circ}\)C to 140 \({}^{\circ}\)C and approaches 1.6 \({}^{\circ}\)C at the temperature of 200 \({}^{\circ}\)C. These calculated errors are considered satisfactory for the purpose of the application, but they can further be improved by selecting a finer temperature step in the look-up table, i.e., by storing more ADC results.
## 2 Materials and Methods
A 40% relative humidity value eases the minimum R-value requirement (a significant issue for fenestration, less so for insulated walls and ceilings). On the other hand, a 60% relative humidity value reduces the dehumidification load (smaller equipment) [22]. Figure 3 shows the essential components of an indoor pool cover [21]. To find the amount of water, several factors must be considered:  
* Water activities. * Indoor air temperature. * Speed of air movement on the surface of the pool water. * Relative indoor air humidity. * Water heating temperature in the pool. The question of the type of activity is related to the intensity of water carried over the pool (drag force), the temperature, air velocity and relative humidity with the level of water evaporation [22]. The standard AHRI 911-2014 recommends a simulation with pool dehumidification equipment with an internal temperature of 28 \({}^{\circ}\)C and a wet bulb of 22 \({}^{\circ}\)C, that is, relative humidity of 60% (at sea level) [23], water temperature of 27 \({}^{\circ}\)C, and setting the condenser air temperature to 28 or 35 \({}^{\circ}\)C. ANSI/ASHRAE Standard 190-2013 does not include parameters for  
Figure 3: Schematic section of the indoor pool (Adapted from [15]). Figure 2: Cumulative dry-bulb temperature per year (Curitiba, Brazil) (Adapted from [21]). desicant wheels [24], while AHRI 1061 defines parameters for enthalpy wheels and enthalpy cubes but also lacks a specific AHRI for desiccant wheels [25]. For a water temperature of 27 \({}^{\circ}\)C and an indoor air temperature of 28 \({}^{\circ}\)C, with an air velocity of 0.125 m/s and assuming the swimming pool closed for public use, the usage factor is equal to 1. For these conditions, evaporation is 0.2142 kgwater/m\({}^{2}\) for a closed swimming pool of 40 m\({}^{2}\) with a water surface of 5 m\({}^{2}\)[26; 27]. The dehumidification index is given by Equation (2). \[ \begin{split}\text{Dehumidification Index}=\text{ Area }\times\text{ Evaporation factor}\\ \text{Dehumidification Index}=(40\times 0.2142)+(5\times 0.2142)=9.6 39\text{ kg/h}\end{split}  \tag{2}\]  
Thus, for this simulation, 231.33 kg of humidity is removed from the environment per day. With this value, the dehumidification systems, with either refrigeration or desiccant wheel, are compared, and the more efficient of the two will be determined through the results.
# VIS-HAPT: A Methodology Proposal to Develop Visuo-Haptic Environments in Education 4.0
## 4 The VIS-HAPT Methodology
### Design of Learning Experiences
#### 4.1.1 Selection of Educational Situation  
Visuo-haptic applications have been successfully applied in various simulators in both physics instruction [6; 22; 30] and medical training [14; 15]. In our case, the educational scenario selection is being crafted by first highlighting the main physics concepts in which forces are relevant. It is important to choose ideas that are usually hard to grasp and make them user friendly when using the visuo-haptic environment. Secondly, the careful simulator design of physical phenomena must be contextualized in a problem. Then, the main variables must be identified, and realistic ranges must be defined. Once all this is set, the student can freely choose the set of input numerical values for the corresponding variables in a particular exercise. Dynamic visualizations of the applied forces lead to a dynamic output that must be analyzed. The following drawbacks were identified in the previous process: (a) lengthy physics scenarios; (b) complex scenarios leading to hard programming codes; (c) input and output variables that are not properly visualized; and (d) an inadequate deployment of the graphic design.  
In order to construct a high-quality educational scenario, we suggested the following: (a) add the sense of touch to enrich the learning experience; (b) sketch a draft of the physical phenomena with the corresponding equations (see Figure 2); (c) carefully choose the input and output variables; (d) properly time the length of the simulation; and (e) ensure that the variables are clearly visualized so that the phenomena can be easily and better understood.
# Combined Channel Estimation with Interference Suppression in CPSS
## 4 Complexity Analysis and Performance Simulation
### Simulation Results
#### 4.2.2 Performance under Interference  
Table 4 describes the simulation scenes under different combinations of interference styles, jamming signal ratio (JSR) and interference suppression method.  
Figure 3: MSE of the channel estimation schemes.  
The system BER in different interference suppression schemes are shown in Figure 5. BER has a floor effect with nothing to do about the interference in the system. All of the transform domain channel estimation algorithms may have such a problem, including DFT-DA and DFT-WF. Linear interpolation on the interfered subcarriers outperforms zero force by 0.5 dB. Meanwhile, a BER performance loss of scene 4 which has JSR = 10 dB is within 1 dB compared to scene 1 that has no interference. It can be proved that DFT-KNN tends to be more effective in complex electromagnetic environments.  
The system BER in different interference styles is shown in Figure 6. Although the interference power of a single subcarrier in multitone jamming is lower than that of single tone jamming, the interfered subcarriers will lead to a series of inaccurate soft bits, which will degrade the performance of the channel decoder. Hence, BER in multitone jamming is lower than that of single tone jamming. Even with the JSR up to 15 dB, BER performance loss of scene 6 is within 2 dB compared to scene 1 that has no interference. DFT-KNN is proven robust in complex electromagnetic environments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Simulation Scene** & **Interference Style** & **JSR** & **Interference Suppression Method** \\ \hline Scene 1 & No interference & – & – \\ Scene 2 & Single tone & JSR = 10 dB & With nothing done \\ Scene 3 & Single tone & JSR = 10 dB & Zero force \\ Scene 4 & Single tone & JSR = 10 dB & Linear interpolation \\ Scene 5 & multitone with 10 subcarriers & JSR = 10 dB & Linear interpolation \\ Scene 6 & multitone with 10 subcarriers & JSR = 15 dB & Linear interpolation \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation scenes with different interference styles.  
Figure 4: BER based on different channel estimation schemes.
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 3 Materials and Methods
### Datasets Description
#### 3.1.3 Salinas Valley  
The Salinas scene, with a high spatial resolution of 3.7 m per pixel, is located in California. This scene is recorded by the AVIRIS sensor. It has a resolution of \(512\times 217\times 224\). Removed are the bands ranging from 108 to 112, 154 to 167, and 224. There are 204 bands on the updated picture. Figure 4 illustrates its 16 classes, which include stubble, Romaine lettuce, green broccoli weeds, and fallow.  
Figure 3: Pavia University image (**a**) Sample band image (**b**) Ground Truth.  
Figure 2: Indian Pines image (**a**) Sample band image (**b**) Ground Truth.
## 3 Mathematical Model
### Buffer Tank  
Considering the control volume \(V\) with a density \(\rho\), mass \(m\), pressure \(P\), and temperature \(T\), the time derivative of the chamber pressure can be obtained by using three important equations: the equation of state (ideal gas) law, the conservation of mass equation, and the energy equation.  
The unified form of the dynamic pressure equation in the control volume is Equation (13). The detailed derivation process can be seen in [21].  
\[\dot{p}=\frac{RT}{V}\Big{(}\alpha_{in}\dot{m}_{in}-\alpha_{out}\dot{m}_{out} \Big{)}-\alpha\frac{P}{V}\dot{V}, \tag{13}\]  
where \(\alpha\), \(\alpha_{in}\), and \(\alpha_{out}\) represent the coefficients of gas inflow and outflow, whose value depends on the heat transfer process in the actual process, and their values range are between 1 and k.  
The volume of the inlet and exhaust buffer tank is fixed, and the gas in the tank can fully exchange heat with the outside through the pipe wall, which can be approximately regarded as an isothermal process, namely \(\alpha_{in}=\alpha_{out}=1\). The pressure of the buffer tank can be expressed as:  
\[\dot{P}_{buff}=\frac{RT}{V_{buff}}\Big{(}\dot{m}_{in\_buff}-\dot{m}_{out\_buff }\Big{)}, \tag{14}\]  
where, \(\dot{P}_{buff}\) represents the change of pressure in the buffer tank, \(V_{buff}\) represents buffer tank volume, and \(\dot{m}_{in\_buff}\) and \(\dot{m}_{out\_buff}\) represent the gas mass flow into and out of the buffer tank, respectively.