esa-sceva/satcom-chunk-collection huggingface dataset:
# Victim Detection and Localization in Emergencies
## 3 Overview of Location Technologies
### Bluetooth Low-Energy  
Bluetooth Low-Energy (BLE) is also another omnipresent wireless technology used in Personal Area Networks (PANs) for data transmission. Several studies have researched within the scope of detecting, localizing, and tracking people in indoor scenarios [37; 38; 39]. Most of these studies work in indoor spaces, applying methods such mapping and fingerprinting, which require previous knowledge of the terrain. In this work, we assumed a potential collapse of the infrastructure due to flooding or an earthquake, which makes fingerprinting techniques unfeasible for localizing victims, due to the outdated maps collected prior to the collapse.
# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
## 4 Emerging Technologies in Human Mobility
### Integration of Emerging IoT Communication Technologies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **LoRa** & **SigFox** & **EC-GSM** & **LTE-M** & **NB-IoT** \\ \hline Range & \textless{}15 Km & \textless{}50 Km & \textless{}15 Km & \textless{}11 Km & \textless{}15 Km \\ Max peak & 50 kbps & 100 bps & 74 kbps & 1 Mbps & 250 kbps \\ data rate & & & & \\ Maximum & 157 dB & 153 dB & 164 dB & 160 dB & 164 dB \\ coupling loss & & & & \\ Bandwidth & \textless{}500 KHz & 100 KHz & 200 KHz/channel & 1.08 MHz & 180 KHz \\ Radio & Spread & Ultra Narrow & TDMA/FDMA & OFDM & OFDM \\ Technology & Spectrum & Band & TDMA/FDMA & OFDM \\ Autonomy & \textgreater{}10 years & \textgreater{}10 years & \textgreater{}10 years & \textgreater{}10 years & \textgreater{}10 years \\ \multirow{2}{*}{Standardization} &
\begin{tabular}{c} LoRa- \\ Alliance \\ \end{tabular} & Sigfox Company & 3 GPP & 3 GPP & 3 GPP \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of LPWAN technologies: LoRa, Sigfox, and 3 GPP. Figure 6: Qualitative perspective analysis between emerging technologies and traditional cellular. Although the Sigfox network business model is differentiated by its top-down approach. Some properties of this network are the cloud server, endpoints software, and the back-end data. Although, the company owns all of these technologies, manufacturers like STMicroelectronics, Atmel, and Texas Instruments have been taking advantage of the fact its an open market for the endpoints and implementation of cost-effective IoT and machine-to-machine (M2M), producing Sigfox radios. However, as the company has exclusive arrangements with network operators, if they work together, only one Sigfox can be deployed in an area [93]. Finally, 3 GPP is another emerging technology that defines standards for 5G. Currently, it is made up by different groups, i.e., EC-GSM, LTE-M and NB-IoT which each with a specific focus area. EC-GSM improved security, privacy, and data integrity compared to GSM/EDGE, as well as presents a low cost compared to GSM/GPRS. It can be deployed via software, thereby guaranteeing widespread coverage and accelerated upgrade, benefiting the mobile network features [94]. LTE-M is another component of the 3 GPP structure. It reuses existing LTE base stations and supports features such as multicast, higher data rates, positioning, and mobility for inter-frequency measurements [95]. In its turn, NB-IoT has a lower cost than LTE-M. Mobility support, reduced bandwidth and data rate, and further protocol optimizations are main simplifications. With the last upgrades, it enhanced positioning for inter-frequency, consumption and latency reduction, improving service [96]. All these groups give a positive contribution to objectives for many works, encouraging new service and application development in human mobility [97; 98; 99].
## 2 Background and Preliminaries
### Federated Learning Algorithm
#### 2.4.4 Federated SGD: Peer-to-Peer (FedSGDP2P)  
FedAvgP2P is an extension or variation of the FedAVG algorithm in federated learning. In FedAvg, a central server coordinates the model aggregation process, where local models from participating clients are averaged to update a global model.  
In the FedAvgP2P variant, the aggregation process involves peer-to-peer communication among participating clients, bypassing the need for a central server. Clients directly communicate with each other to exchange their local model updates and collectively compute the global model through decentralized means.
## 4 Electrochemical bAA Sensors and Biosensors  
Electrochemical techniques rest on the fabrication of sensors or biosensors and are considered suitable for in situ determination of substances because they are extremely sensitive, simple, reproducible, cheep, relatively fast, and direct, without the use of extraction or preconcentration steps [49; 50]. They can also be easily used in miniaturization [51]. Electrochemical detection is usually based on monitoring the signal of oxidation or reduction of the electroactive groups accumulated on the electrode surface [51] and measure features such as electrode potential, current intensity, the amount of electricity passing through the cell, resistance and the time [52].  
Cyclic voltammetry (CV) is commonly used on electrochemical procedures, due to its qualitative and quantitative information. By applying CV, a wide range of sensors and biosensors could be developed, determining the analyte. Differential pulse voltammetry (DPV) and square-wave voltammetry (SWV) are pulsed methods [53; 54]. The excitation potential in SWV inheres of a symmetrical square-wave pulse of a settled amplitude superimposed on a staircase waveform of step height. In this method the forward pulse of the square wave concurs with the staircase step [55]. DPV is a voltammetric method, comparable to SWV, where the potential excitation inheres of small pulses, which are superimposed upon a staircase waveform [56]. The major profit of the particular methods is the insignificant capacitive current which emerges on the enrichment of the sensitivity of the pulse voltammetric strategies. However, DPV is mostly administered on irreversible processes or on systems posing slow-reaction kinetics, whereas SWV is usually administered on the examination of reversible processes (rapid reaction kinetics systems) [56; 57]. Electrochemical sensors have been hardly practiced in the detection of b AAs such as Leu, Ile, and Val, associated with rare diseases clinical diagnostics. This is expounded by the evidence that these substances are electrochemically inert on bare electrodes [33]. Thus, in the subsequence chapters, electrochemical (bio)sensors for the determination of b AA are summarized.
## 2 Materials and Methods
### Model Interpretability (SHAP Analysis)  
Model transparency was assessed using SHAP (SHapley Additive exPlanations), which quantified the contribution of each input feature to the classification decision. Global and class-specific SHAP values were visualized to confirm alignment between expert-defined logic and model behavior.
# Interface Design for Responsible Remote Driving: A Study on Technological Mediation
## 4 Interface Design Analysis
### Physical Feedback
#### 4.3.1 Steering Wheels and Pedals  
Steering wheels and pedals (e.g., Figure 5) endowed with force feedback capabilities are commonly used in RD stations to replicate the tactile sensations experienced by drivers in traditional vehicles. While in regular driving this feedback is part and parcel of the vehicle's operation, remote steering wheels and pedals reduce the connection with the operated vehicle, so that the link must be restored by design. These HMIs, while allowing control, also provide realistic resistance, torque, and feedback, enabling ROs to feel the forces as if they were in the cockpit holding the steering wheel and pressing the pedals. For example, steering force feedback allows drivers to gather information on road surfaces, tire-road force, and vehicle motion. The absence of steering force feedback leads to perceptions of increased difficulty and decreased safety during RD compared to real-life driving experiences.  
Since physical resistance must be reproduced by design, it is crucial to select the appropriate levels of force. Several solutions are currently adopted to provide force feedback to the steering wheel, each displaying its own advantages and drawbacks [2; 42; 43]. Pedals designed to replicate the resistance of a car's brake and accelerator are widespread. This replication is essential to ensure accurate responsiveness and driving fluidity. For what concerns steering wheels, conventional steer-by-wire systems and RD steering systems share similarities, indicating that classical steering force feedback models used in steer-by-wire may be applicable to RD scenarios. Other possibilities include gear-driven steering wheels employing gears to deliver feedback, an affordable solution, which, however, may be perceived as less realistic; belt-driven wheels, which implement a belt mechanism for force feedback transmission and strike a more even balance between realism and cost;  
Figure 5: Example of steering wheel and pedals for RD prototype.  
and direct drive wheels, high-end systems where algorithms simulate driving forces, a dedicated motor reproduces them, and a shaft physically relays them to the steering wheel, thus offering both accuracy, power, and realism.  
The possibility of customizing force feedback through software design makes the last option particularly suitable for RD, especially in cases where operators control multiple vehicles during their shift. Indeed, different vehicles might present different levels of resistance, torque, and feedback even if they have similar dimension. Transitioning from one vehicle to another would pose a challenge for ROs if differences in physical feedback were overlooked. In this sense, the technological mediation of physical forces might run the risk of reducing vehicle-specific differences to a single, generalized, but inaccurate driving experience. Adjusting force feedback to the specific characteristics of each vehicle seems more promising a strategy to support responsible RD
## 2 Materials and Methods  
### The Kalman Filter Model  
The KF is based on the following mathematical theory. Consider \(\mathrm{B_{m}}\) = 1, 2, 3,... data observed from \(\mathrm{p}\)\(\times\) 1 vectors. \(\mathrm{B_{m}}\) is a sequence that is presumed to have the following distribution:  
\[\mathrm{B_{m}}=\mathrm{D_{m}}+\varphi_{\mathrm{m}}=\mathrm{Z_{m}A_{m}}+\varphi _{\mathrm{m}},\]  
where  
* \(\varphi_{\mathrm{m}}\)--white noise
* \(\mathrm{D_{m}}\)--carrier signal.  
In this regard, the dynamic representation is as follows:  
\[\mathrm{D_{m}}=\mathrm{Z_{m}A_{m}},\,\mathrm{m}=1,2,3\,\ldots\,.\]  
whose factors are defined below.  
Next, we continue with an observed data model:  
\[\mathrm{B_{m}}=\mathrm{Z_{m}A_{m}}+\varphi_{\mathrm{m}}. \tag{1}\]  
Equation (1) is related to the \(\mathrm{p}\)\(\times\) 1 vector of \(\mathrm{B_{m}}\) data points, with an \(\mathrm{r}\)\(\times\) 1 \(\mathrm{A_{m}}\) vector. In this case, \(\mathrm{Z_{m}}\) is a \(\mathrm{p}\)\(\times\) r matrix, while \(\varphi_{\mathrm{m}}\) is a \(\mathrm{p}\)\(\times\) 1 vector with \(\mathrm{E[\varphi_{\mathrm{m}}]}\) = 0 and \(\mathrm{Var[\varphi_{\mathrm{m}}]}\) = \(\mathrm{M_{m}}\).  
The vector \(\mathrm{A_{m}}\) is not straightforwardly observable, but we could assume that it follows a Markov chain process of order 1, i.e.,  
\[\mathrm{A_{m}}=\mathrm{X_{m}A_{m-1}}+\theta_{\mathrm{m}},\,\mathrm{m}=1,2,3,\ldots \tag{2}\]  
The states' behavior is described by Equation (2). In this case, \(\mathrm{X_{m}}\) is an \(\mathrm{r}\)\(\times\) r matrix and \(\theta_{\mathrm{m}}\) is an \(\mathrm{r}\)\(\times\) 1 error vector, which is not correlated with \(\theta_{\mathrm{m}}\) since \(\mathrm{E[\theta_{\mathrm{m}}]}\) = 0 and \(\mathrm{Var[\theta_{\mathrm{m}}]}\) = \(\mathrm{P_{m}}\).  
\(\mathrm{A_{0}}\), the initial vector, was assumed to have:  
\[\mathrm{E[A_{0}]}=\mathrm{x_{0}}\,\,\mathrm{and}\,\,\mathrm{Var[A_{0}]}= \mathrm{Q_{0}}. \tag{3}\]Furthermore, we assumed that the error terms were not two-by-two correlated, i.e.,  
\[\mathrm{E}[\theta_{\mathrm{n}}\varphi^{\prime}{}_{\mathrm{d}}]=0\text{ for all }n,\,\mathrm{d}=1,\,2,\,3,\ldots \tag{4}\]  
and  
\[\mathrm{E}[\theta_{\mathrm{n}}\mathrm{A}_{0^{\prime}}]=0\text{ and }\mathrm{E}[ \varphi_{\mathrm{n}}\mathrm{A}_{0^{\prime}}]=0\text{ for all }n=1,\,2,\,3,\ldots \tag{5}\]  
We also assumed that the matrices \(\mathrm{X}_{\mathrm{m}}\), \(\mathrm{M}_{\mathrm{m}}\), \(\mathrm{P}_{\mathrm{m}}\), and \(\mathrm{Z}_{\mathrm{m}}\), as defined before, are not random matrices. The above two statements (1) and (2) reveal that \(\mathrm{A}_{\mathrm{m}}\) could be constructed as a linear combination of \(\theta_{\mathrm{d}}\) and \(\varphi_{\mathrm{d}}\), for \(\mathrm{d}\leq\mathrm{m}-1\) and \(\mathrm{A}_{0}\) for any \(\mathrm{m}\).
## 5 Results, Analysis, and Discussion
### Experimental Results  
Experiments on the unstiffened cylindrical shell are used as a reference signal for different \(f_{\rm c}\). Several minutes are maintained between each measurement to allow the experimental results to not be interfered by previous signals. It is noteworthy that the sensor attached at the reflection measuring point should be removed to avoid the influence of the sensor on the transmission signal when collecting the transmission waveform. The reflection waveform and the transmission waveform are received by following a similar procedure on the stiffened cylindrical shell. The experimental results are depicted in Figures 16 and 17.  
Figure 15: Distribution of reflection and transmission coefficients in different regions with \(f_{\rm c}\) = 150 kHz.  
Figure 16 shows a comparison between the reflection waveforms collected from specimen 1 and specimen 2, while Figure 17 focuses on a comparison of the transmitted waves. The results show that the incident L(0,1) mode, the reflected L(0,1) mode, and the transmitted L(0,1) mode can be successfully distinguished and recognized based on the sensor arrangement, which confirms that arranging sensors based on the proposed model are not able to distinguish the transmitted waves.  
Figure 16: Reflected waveforms collected experimentally on specimen 1 (blue curves) and specimen 2 (red curves) under different \(f_{c}\).  
Figure 17: Transmitted waveforms collected experimentally on specimen 1 (blue curves) and specimen 2 (red curves) under different \(f_{c}\).  
in Section 2.3 is feasible. Figure 18 compares the experimentally calculated reflection and transmission coefficients related to the incident L(0,1) mode with the simulated reflection and transmission coefficients as well as the errors between them.  
It can be seen from Figure 18 that the experimental points are always close to the predicted points, which demonstrates that scattering coefficients curves are satisfactorily verified within the acceptable margin of errors. These results are encouraging in that the simulation characterizing the propagation of the AE signal in a cylindrical shell with a stiffener has been demonstrated.
# Modified Conditional Restricted Boltzmann Machines for Query Recommendation in Digital Archives
## 5 Dataset
### ARC-UPD Dataset
#### 5.1.1 Preprocessing  
In the preprocessing phase, we performed the following operations on the raw access log data to obtain the query-item pairs, which were used to find configurations for training the M-CRBMs:  
1. We removed all the web robots' access by recognizing the agent names that appeared in the access logs. If the agent name of the record was listed in that web robot list, then the record was removed. The suspicious web robot list was provided by the Apache HTTP Server, which is the front-end web server of the ARC-UPD.
2. We removed records from users who frequently accessed (accessed more than 50 times within a time interval of less than 10 s) the ARC-UPD, because these users were perhaps scraping the web page or checking the system. In this work, a unique user is defined by the same IP address and browser (user agent) information.
3. We only kept the records that indicated a user used the formulated query (or queries) to access a certain item. In the search engine of the ARC-UPD, a user can input search queries in multiple search fields, such as keyword, artist, publication year, and genre. Although all of the queries to different search fields include extensive expert knowledge, in this research, we only used the queries input in the keyword search field. The reason for this is due to the difficulties of extracting the needed training data from other fields. In the search system's design, if the users click some metadata shown on the web page and then access a certain item from the access log, it will be recorded as the same as users making a search query in the corresponding metadata field and accessing a certain item. Therefore, only choosing the queries input in the keyword search field would ensure that most of the queries were really formulated by real users.
4. We constructed a dictionary to store the query-item pairs from the previous steps with the format of \(\{itemID:\{query:frequency,\dots\},itemID:\{query:frequency,\dots\}\dots\}\) We call this a query dictionary. Here, \"frequency\" is the frequency of the query that has been used to search for the item.
# A G-Code Generator for Volumetric Models
###### Abstract  
In layered manufacturing (LM), slicers are employed to convert input geometric models into G-codes. Conventional slicers accept only surface models as input data. Thus, volumetric models have to be converted into polygonal representations to fit the data format of the slicers. This results in extra computational costs and geometric errors. In this article, we present an efficient slicer aiming to generate G-codes for volumetric models. At first, our slicer computes the printing direction by exploring the inertia tensor of the input model to enhance the stability of the printed part and to decrease the build time. Then, it detects and classifies overhangs in the input model and generates necessary support structures by using a pattern-based method. Thirdly, the proposed slicer divides the input model into the skin and internal regions and cuts the model into 2D images. Subsequently, these images are transformed into toolpaths by utilizing texture mapping and graph traversal methods. Finally, the resultant toolpaths are smoothed to reduce staircases and encoded into G-codes. Test results verify that the proposed slicer produces decent G-codes for volumetric models. Scanned objects hidden in volume data can be directly manufactured without generating intermediate polygonal representations. LM processes become more efficient.  
G-code generator; layered manufacturing; volumetric modeling; 3D image processing; volume visualization +
Footnote †: journal: Applied Sciences
## 3 Results  
### Regression Models and Statistical Testing  
All the experiments were carried out in a random order to reduce systematic errors. Table 2 shows the results of the experiments, and a quadratic model was applied to fit the results with all the coefficients determined by using multiple regression analysis.  
The relationship between adhesion strength and the factors were expressed through Equation (4) with significant terms in terms of coded values. The calculated values and experimental results are also shown in Table 2. The deviation between those calculated values and experimental results for adhesion strength were less than 3% (Figure 1).  
\[\mathrm{Y}=1119.5+36.5\mathrm{X}_{1}+143.68\mathrm{X}_{2}-53.29\mathrm{X}_{1 }\mathrm{X}_{2}-64.3\mathrm{X}_{1}^{2}-144\mathrm{X}_{2}^{2}-215\mathrm{X}_{3} ^{2} \tag{4}\]  
Table 3 shows the analysis of the variance of the quadratic model. The F-value of \"Model\" is 968.62, which implies that the model is significant. There is only a chance less than 0.01% that an F-value this large could occur due to noise. The values of \"Prob \(>\) F\" less than 0.05 indicate that the model terms are significant and the values greater than 0.1 indicate that the model terms are not significant. It can be seen from Table 3 that all the variables (curing time, treatment temperature, and curing agent dosage) were significant for the response Y as the values of x or x\({}^{2}\) were less than 0.05. The F-value of \"Lack of Fit\" is 1.48, which implies that the \"Lack of Fit\" is not significant relative to the pure error. There is a 36.24% chance that a \"Lack of Fit F-value\" this large could occur due to noise. The values of \(\mathrm{R_{adj}}^{2}\) for the model is 0.997, which means that the experimental data agree with the quadratic model well.  
Figure 1: The relationship of calculated values and experimental values.
## 1 Introduction
A circular microstrip patch antenna is used to excite the 3D printed lens in every evolution of the design. The transmission coefficient of a two-port ring resonator is used to check the dielectric constant of the lens parts, and it is consequently verified with a dielectric constant measurement. The design procedure and the working mechanism of the proposed lens structure are explained by numerical calculation results using ANSYS HFSS. The prototype is fabricated, and the computed expectations from the full-wave EM simulations are verified experimentally.
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 8 Challenges and Open Issues
These algorithms can improve the routing policy and build an optimal path between nodes in the network, but impose a lot of computational costs on UAVs in the network. When designing a RL-based routing method, researchers must determine whether these algorithms help routing protocols to improve network performance and reduce computational cost and energy consumption. In some cases, solving a challenge in FANETs may not need to use reinforcement learning techniques, and existing methods can successfully address this challenge. As a result, proper use of reinforcement learning techniques is a very important issue, which must be considered by researchers. * **Routing overhead:** In most RL-based routing methods in FANETs, the periodic exchange of control messages is essential to obtain the location and other information of neighboring nodes. However, this increases bandwidth consumption and routing overhead and greatly increases network congestion. Therefore, an essential need is to adaptively adjust the broadcast intervals of these messages based on network dynamics when designing RL-based routing methods. * **Convergence speed:** It is an important issue in reinforcement learning algorithms. When the size of the state and action spaces are large, the convergence speed of the RL algorithm is greatly reduced. Thus, obtaining an optimal response requires a long time. In this case, the Q-table size also increases sharply. This needs a large storage capacity to store this table. On the other hand, the update process of this table is also associated with high delay, computational costs, and communication overhead. Therefore, reducing the size of the state space by filtering some states based on specific criteria and utilizing clustering techniques in the routing process can be studied and evaluated in the future. Furthermore, the use of deep reinforcement learning techniques is a useful response to deal with this challenge. * **Trade-off between exploration and exploitation:** The dynamic adjustment of learning parameters, including learning rate and the discount factor, is very important in a RL-based routing algorithm to create a balance between exploration and exploitation. Researchers should consider this issue in the future.