esa-sceva/satcom-chunk-collection huggingface dataset:
# New Advances and Challenges of Fall Detection Systems: A Survey
## 3 Bird's Eye View of Fall Detection Research from 2014
### Performances of Fall Detection Systems  
To evaluate the performances of fall detection systems, the accuracy of fall detection algorithm is a significant indicator. Figure 3 shows the accuracies of most cited papers since 2014. Some algorithms are tested on several different datasets (e.g., [17; 18; 22]). We compute the accuracy of this algorithm in this system by mean of all accuracy values working on all datasets. In addition, some papers did not indicate the accuracy of fall detection system (e.g., [10; 13; 25]), or just provided sensitivity and specificity (e.g., [26]).  
The accuracy of fall detections from selected most cited works are relatively high. As observed in Figure 3, it is found that most fall detection algorithm accuracies are above 90%. Accuracy of the work by [11; 31] even reach 100%, which means that all falls were accurately detected in their testing scenarios. The lowest one is in [4], which reaches 79.57%. Note that most of these experiments are evaluated on a self-captured or simulated dataset.  
However, we did not find a successful fall detection application based on any kind of sensors in the market. We assumed that there are two reasons for difficulties of applying fall detection to the real world.  
First, most existing fall detection datasets are simulated datasets. Only a few work added limited real fall actions to dataset (e.g., [10], which included nine naturally occurring resident falls, from four residents). Fall is an occasional dangerous human action. Real fall is a kind of unpredictable action, so it is hard to build a dataset associating with real data. Researchers attempt to solve this problem. For example, some studies divided fall into several sub-categories: forward, backward and sideward falls, as well as mimicking them by stunt men or volunteers, respectively. Nonetheless, simulated data are subtlety different from real data. Another issue is that datasets include limited human actions,  
Figure 3: The accuracy of fall detection algorithm obtained from the twenty most cited papers since 2014.  
such as sitting, walking and lying. Researchers can only classify the fall from the other few kinds of action. However, real life is not like that: there are hundreds or thousands of different kinds of actions. Therefore, these fall detection algorithms with high accuracy in lab performs are unsatisfactory in practice.  
Second, the current features of fall cannot profoundly describe the fall action. Fall detection can be considered as a classification problem, which distinguishes the fall from other actions. The core point is to define the fall based on different features. For example, the threshold-based method used the feature to detect a fall: the speed of a fall differs from other actions. Nevertheless, this method is not robust for classifying the actions being similar speeds with the fall, such as jumping. This problem will be stated in details in the next section.
## 2 Network Model  
In this paper, we considered a WSN consisting of a sink node (or a gateway) and sensor nodes with a half-duplex radio interface. A WSN is denoted by a directed graph \(G=(V,E)\) where V is a set of sensor nodes including the sink, and E is a set of the links between nodes in the network, respectively. Wireless links between two nodes i and j consist of two directed edges: edge from node i to j denoted by <ij> and edge from j to i, <ji>. Link quality estimation in WSNs is a fundamental building block in several mechanisms such as routing and topology control, but is a challenging problem due to the lossy and dynamic behavior of the wireless links. So far, a large number of link quality estimators have been proposed [13]. We used the PRR (Packet Reception Rate) estimator in this paper, which is a receiver side estimator that is simple to measure and has been widely used in WSNs. The PRR of link <ij> is denoted by \(R_{i}\) in this paper. In WSNs, each sensor node generates a packet periodically and needs to transmit through a path from the node to the sink. The packet transmitted by source node k is denoted by \(p_{k}\), and the hop count from the node k to the sink by \(h_{k}\). We assumed that the routing paths are given by a directed convergecast tree constructed by using routing protocols such as RPL [14] or other reliable routing protocols [7]. A single path tree is assumed in this paper, thus, each node k has a parent node \(P_{k}\) to transmit its data toward the sink.  
In industrial WSNs, there is a strict requirement in the end-to-end reliability of the sensor data transmitted by the nodes depending on the application. In order to ensure the reliability requirement, we assumed a TDMA-based MAC with channel hopping such as IEEE 802.15.4e TSCH [4] as a MAC protocol. In a TSCH network, there are multiple channels up to 16 and a slot frame with a fixed number of time slots. The number of available channels is denoted by \(N_{ch}\) and the number of time slots in a slot frame by \(N_{sf}\). Thus, the scheduling problem is to decide which node transmits its packet, on which channel offset, and which time slot in a slot frame [8]. In TSCH networks, a frame transmission and an acknowledgment (ACK) of indicating successful reception of the frame is finished within a time slot. Two different types of time slots are defined in TSCH: dedicated and shared time slots. We assumed that the time slots are dedicated to guarantee the reliability of frame transmission in a time slot. As in TASA [7], we defined a set of duplex-conflict nodes of node k, \(DC_{k}\), as a set of nodes that cannot transmit in the same slot due to conflict. The nodes in \(DC_{k}\) consists of the parent node \(P_{k}\), the children of node k, and the children of \(P_{k}\). If a node is not in \(DC_{k}\), it can transmit its frame in the same time slot with node k using different channels to each other. In the example graph in Figure 1,\(DC_{1}\) is [2; 4] and \(DC_{4}\) is [1, 2], thus, node 1 and node 3 (also node 3 and 4) can transmit in the same time slot using different channels.
# Portable Smart Spectrometer Integrated with Blockchain and Big Data Technology
## 1 Introduction  
Spectrometers and spectroscopy have been widely used in the field of material measurement and analysis, such as in food inspection, industrial production, agricultural production, safety testing, and environmental monitoring, due to their advantages of rapid and non-destructive detection [1; 2]. However, traditional spectrometers have the disadvantages of occupying a large volume, single function, a limited range of use, and the requirement of high operator skill. In contrast, although portable spectrometers have lower accuracy, they have the advantages of compactness, low power consumption, flexibility, good portability, etc. [3; 4]. In addition to the general use, portable spectrometers have a certain market in the fields of food inspection and environmental monitoring. However, in recent years, there has been no new development in the design of miniature and portable spectrometers, despite the disparity between traditional and portable spectrometers. Spectrometers for professional use have been developed over the years; however, non-specialist and smart spectrometers still need to be studied. Rather than the complex spectra, non-professional operators focus on the interpretation of spectra, the price of the spectrometer, whether the spectrometer can be operated easily, whether the spectral data can be reused, whether spectral data has other uses and the reliability of spectral data.  
Traditional portable spectrometers can provide rapid on-site spectra in real time; however, the spectra can only be analyzed and processed by those with the relevant knowledge. The application threshold is still relatively high, and such spectrometers cannot be popularized. When spectral data is applied to food inspection and environmental monitoring, its review costs are high because it may be tampered with. After being analyzed and processed, spectra are generally discarded; thus, the data value is low. Hence, this paper proposes a portable smart spectrometer PSS-1.0 and related systems based on the PSS-1.0. In this system, the PSS-1.0 can connect to smart devices and use technologiessuch as spectral analysis, big data, and blockchain to lower the use threshold for use and achieve functions, such as data rapid analysis, storage, reuse, and tamper-proofing. As shown in Figure 1, the PSS-1.0 can measure spectral data and perform on-site analysis using the downloaded spectral analysis models. Blockchain technology can be used to prevent data from being tampered [5]. Big data technology can be used for data analysis, data storage, and data calls [6]. This paper uses the techniques of deep learning, big data and blockchain to intelligentize the spectrometer, which is one of the main directions for the development of spectroscopic detection technology. We hope to design an easy-to-use smart spectrometer and enhance the quality of spectral data for civil use, production, monitoring, and safety testing.
## 5 Results
### Adversarial Example Attacks
#### 5.2.7 Adversarial Examples and Knowledge Requirement  
The majority of the research focus is on white-box attacks, as shown in Table 5, perhaps because such attacks are known to be efficient and effective. Less research focuses on black-box attacks and few studies recognise gray-box attacks that need only partial model knowledge. Gray-box attacks will likely have advantages over black-box attacks. Adversaries will undoubtedly use any and all information available to them.  
We classify the attacks on the knowledge required by the adversary. White-box attacks are likely the most effective and efficient method of attack, because the adversary has _complete knowledge_ of the model architecture, and information on how the model was trained. However, access to this knowledge is harder to attain, although it might also be gained through insider threats [120] or model extraction attacks [121]. Extracted models might be a feasible proxy on which to develop and test adversarial examples.  
Notwithstanding the efficiency of white-box attacks, effective black-box attacks are possible. Black-box (or oracle) attacks require no knowledge of the model. Adversaries only need the ability to query the model and receive its output. Adversaries generate inputs and receive the output of the model. Typical black-box attacks include GA [95], and GANs [89, 97].  
Gray-box attacks require only limited model knowledge, perhaps including knowledge of the features used by the model. This is a realistic prospect, as adversaries will likely have or gain at least partial knowledge of the model.
## 3 Designed Structure of GMM-FBG Current Sensor  
According to GMM magnetostrictive and ferrite magnetization characteristics, the designed GMM-FBG current sensor consists of a ferrite magnetizer and GMM magnetic-strain sensor (rod) on which the FBG strain-wavelength optical modulator is firmly attached in the rod axial direction, while the rare earth giant magnetostrictive material (Tb-Dy-Fe alloy) and soft magnetic ferrite are adopted for the GMM rod and substantial magnetizer, respectively, as shown by the 3D model given in Figure 1. A notched ferrite ring (diameter = 300 mm) with a circular section (diameter = 30 mm) is used as the magnetic conduction loop (magnetizer), in which the two notch ends can be regarded as two magnetic poles and a sunken cylindrical hole with a depth being identical to diameter (4 mm) is designed on the surface of one pole to be inserted and fixed on the bottom by the longitudinal cylindrical GMM rod with a diameter of 4 mm and an effective length (of ferrite) of 15 mm. This sag hole is designed to fix the GMM rod and avoid its axis deviating from the magnetic field direction, which can increase the contact area between ferrite and GMM and thus increase the magnetic flux injected from the ferrite magnetizer into the GMM rod. An air gap of 0.1 mm width is left between the end of the GMM rod and the other opening pole of the magnetic-conducting ferrite as a free space for GMM magnetostrictively stretching. In order to effectively introduce magnetic flux from the ferrite magnetizer into the GMM rod, the counterpart surfaces of the GMM tip and ferrite magnetic pole, respectively, at the two sides of the air gap are designed into convex and concave concentric hemispheres with a radius difference just being identical to the gap width. These design schemes can fix the GMM rod, avoiding its axis deviating from the magnetic field direction and enlarging the contact area between ferrite and GMM, which favors the magnetic flux injection from the ferrite magnetizer into the GMM rod and thus increases the sensing efficiency under electrical current excitation.
###### Abstract  
Direct-drive permanent magnet synchronous machines (DDPMSMs) have recently become an ideal candidate for applications such as military, robotics, electric vehicles, etc. These machines eliminate the need for a transmission mechanism and excitation coil circuits, which enhances the system's overall efficiency and decreases the likelihood of failures. However, it may incur demagnetization faults. Due to the characteristic of having a large number of pole pairs, this type of machine exhibits numerous demagnetization fault modes, which poses a challenge in locating demagnetization faults. This paper proposed a probabilistic neural network (PNN)-based diagnostic system to detect and locate demagnetization faults in DDPMSMs, using information obtained through three toroidal-yoke-type search coils arranged at the bottom of the stator slot. A rotor partition method is proposed to solve the problem of demagnetization fault location difficulty caused by various fault modes. Demagnetization fault location is achieved by sequentially diagnosing the condition of each partition of permanent magnets. Three demagnetization fault identified signals (DFISs) are constructed by the voltage of the three toroidal-yoke coils, which are used as inputs of PNNs. Three PNNs have been designed to map the extracted features and their corresponding types of demagnetization faults. The database for training and testing the PNNs is generated from a DDPMSM with different demagnetization conditions and different operating conditions, which are established through an experimentally validated mathematical model, an FEM model, and experiments. The simulation and experimental test results showed that the accuracy in diagnosing the location of the demagnetization fault is 99.2% when the demagnetization severity is 10%, which demonstrates the effectiveness of the proposed three PNN-based diagnostic approach.  
demagnetization fault; fault detection; fault localization; permanent magnet synchronous machine (DDPMSM); probabilistic neural network (PNN); toroidal-yoke-search coil +
Footnote †: journal: Applied sciences  
Article  
A Novel Method for Automatically and Accurately Diagnosing Demagnetization Fault in Direct-Drive PMSMs Using Three PNNs
# Transparent Redirections in Content Delivery Networks
## 2 State of the Art
### CDN Redirections
#### 2.2.2 Application Layer Request Routing  
Application layer request routing redirects the requests based on application layer parameters, e.g., in HTTP, additional headers such as User-Agent or Uniform Resource Locator (URL) might further enhance the accuracy of redirection. For HTTP, the request router might act as an HTTP server which redirects the requests to surrogate servers using 301/302 redirection messages. This procedure is depicted in Figure 2. Such redirection is slower compared to a DNS based redirection but might be more efficient as multiple parameters might be taken into consideration during redirection decision. Another widely used application layer redirection for HTTP is URL rewriting when the rendered website on the origin server replaces the URLs of the distributed content to the domain name or IP  
Figure 1: DNS-based redirection.  
address of a specific surrogate server. This approach eliminates the need to process redirect messages for the clients.
# Molecular Techniques and Target Selection for the Identification of _Candida_ spp. in Oral Samples
## 3 Molecular Identification of _Candida_ spp.
### Multilocus Sequence Typing (MLST)  
MLST is a sequencing-based method that analyses nucleotide polymorphisms in fragments from essential genes, the \"housekeeping genes\" [96; 97]. MLST generates a molecular characterization with high discriminatory power and reproducibility. MLST can be used in the epidemiological differentiation of several clinical isolates from _Candida_ species and polymorphism search [98; 99; 100]. MLST has been used to obtain information about allele diversity in _C. tropicalis_[101] and to access the evolution of virulence-associated mechanisms of the emergent pathogen _C. Krusei_[102].
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 2 Motivation
### Financial Gain  
The first motivation is the desire to steal money. Financial institutions have increased their efforts to combat fraud by using voiceprints as the primary method for user identification. However, the security of using voiceprints as a check for uniqueness is becoming increasingly vulnerable due to the rapid advances in deep voice-faking technology. A real-world example occurred when a BBC reporter established an HSBC voice-ID authenticated account, and his non-identical twin managed to deceive the system, gaining unauthorised access to his brother's funds [29]. Another method for implementing attacks based on fake voices is by impersonating CEO voices over the phone. Such an instance occurred when an energy company based in the UK fell victim to a CEO fraud that used AI-generated deepfake audio, resulting in a loss of $243,000 [23]. In another similar case, criminals utilised AI voice cloning to deceive a bank in the United Arab Emirates and managed to illegally acquire a staggering sum of $35 million [24].
# A Sigfox Energy Consumption Model
## 3 Sigfox Overview
### Network Architecture  
The Sigfox network architecture comprises devices, base stations and a core network (Figure 1). Devices (e.g., sensors or actuators) are provided with wireless connectivity via neighboring base stations. A device is not bound to a particular base station. Therefore, association signaling is not needed. The base stations are connected through the public Internet with a single cloud-based core network. This approach avoids handover procedures to support device mobility. The core network is composed of the Service Center and the Registration Authority. The Service Center controls and manages the base stations and the devices. The Registration Authority is responsible for authorizing the network access of devices. Applications may interact with the data collected by devices, and with devices themselves, via a web interface and a number of Application Program Interfaces (APIs).
# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 2 Background
### BCIs  
BCIs are some of the most promising neuroimaging technologies in the neuromarketing domain. This technology helps facilitate effective communication between the users and computer systems. BCIs do not require any nerves, muscles, or movement interferences to issue a command [5] and employ the voluntarily-generated user brain activity to control a system through signals to communicate or interact with the nearby environment. Such environments can include wheelchairs, artificial arms/hands, and entertainment applications that involve skillful visualization, digital painting, and game playing [6].  
BCI systems have contributed to numerous fields, including manufacturing, education, marketing, smart transportation, biomedical engineering, clinical neurology, and neuroscience [5; 12]. A BCI system includes an input (i.e., the user's mental activity), output (i.e., states or commands), a decoder component between the input and output, and a protocol that regulates the beginning, offset, and timing of the action [6]. The BCI research is expected to lead to an approach in which the brain signals are operated to aid people in interaction actions [6].  
In a BCI, the brain signals require processing in non-clinical situations, which corresponds to a new challenge in computational neuroscience research. Currently, most of the application-oriented BCI research is focused on endowing users--not only disabled people--the ability to control systems or sensors with various environments [6].  
Different neuroimaging techniques can be recorded with non-invasive BCI, such as EEG, fNIRS, fMRI, PET, MEG, SST, and TMS. EEG has better temporal resolution than fNIRS, which is a relatively new neuroimaging technique in neuromarketing research. However, recent fNIRS research is still in the substantial validation phase [13; 14]. EEG is most commonly used in neuromarketing research due to its advantages that are detailed in the next subsection.
## 4 Conclusions  
In the current paper, an ORC system with a dish collector heat source was considered. Three types of tubular cavity receivers including cubical, cylindrical and hemispherical were examined in the current study. Also, four types of nanofluids including Al\({}_{2}\)O\({}_{3}\)/oil, CuO/oil, and SiO\({}_{2}\)/oil were considered as the solar heat transfer fluid. Experimental results of the hemispherical cavity receiver operating with thermal oil were used to validate a numerical model. The solar ORC analysis was under superheated conditions, with a constant evaporator pressure of 2.5 MPa, and a condenser temperature of 38 \({}^{\circ}\)C. Methanol was considered as the ORC working fluid. A fixed solar heat transfer fluid mass flow rate of 60 mL/s and dish diameter of 1.9 m was investigated. Results showed that the working fluid outlet temperature, and thermal efficiency is the highest for the cubical cavity receiver. Also, the total irreversibility rate of the ORC, the ORC mass flow rate, and the ORC overall efficiency are the highest for the cubical cavity receiver. Furthermore, it was shown that the total irreversibility rate of the three cavity receivers investigated is increased by increasing the TIT of the ORC system. Results showed that the pressure drop through the cavity receivers was increased by the application of nanofluids. For all three cavity receivers, different thermal parameters were insignificantly increased with the application of nanofluids. Further improvements are recommended based on the optimization of variables, such as the mass flow rate, fixed in this work. The application of the SiO\({}_{2}\)/oil nanofluid had the lowest effect on improving the ORC performance. The cubical cavity receiver, using oil/Al\({}_{2}\)O\({}_{3}\), was found to be the most efficient choice for application as the investigated solar ORC's heat source. Finally, the thermal efficiency improved with about 2%-3% using the application of Al\({}_{2}\)O\({}_{3}\)/oil or CuO/oil nanofluid with higher nanofluid concentration as the solar ORC system's working fluid.  
Methodology and investigation: R.L. and W.G.L.R.; supervision: E.A.A.-A. and B.G.; project administration: G.N. and T.Y. Authors are grateful to the University of Mohaghegh Ardabili (UMA), and the Tarbiat Modares University ([[http://www.modares.ac.ir](http://www.modares.ac.ir)]([http://www.modares.ac.ir](http://www.modares.ac.ir))) for financial supports given under IG/39705 grant for renewable Energies of Modares research group.  
The authors declare no conflict of interest.  
Figure 14: Variation of overall efficiency improvement of the solar ORC system with variation of nanofluid concentration at \(T_{inlet,oil}=40\)\({}^{\circ}\)C, TIT = 229 \({}^{\circ}\)C, and turbine inlet pressure (TIP) = 2.5 MPa.