esa-sceva/satcom-chunk-collection huggingface dataset:
# Instantaneous Networking Service Availability for Disaster Recovery
## 3 System Architecture
### LACS: Locally Accessible Cloud System  
LACS (Locally Accessible Cloud System) is a portable LI-Cloud system that we have developed with an aim of providing conventional Internet services even when the Internet connectivity is not available. LACS attempts to deliver cloud service anytime anywhere and in various network environments, even in the devastated disaster situation [10].  
As shown in Figure 2, an LACS server is comprised of a compact server, Wi-Fi access point, battery, and peripheral devices. As shown in the figure, functions offered by an LACS include important notices, a bulletin board, and messaging. These functions support information broadcasting, information sharing, and bidirectional, voice, and video communication among users in the area surrounding the LACS. A user is able to access the service using his/her network device, such as a smartphone, over Wi-Fi functionality. An LACS enables us to handle large size contents in the form of text, voice, moving picture, or still image. LACS collaborates with other systems on the Internet, such as a system for a disaster countermeasure.  
Figure 1: System overview.  
An LI-Cloud system involves the software and hardware development that support the networking services as a standalone server for the local user scope [26; 27]. LACS offers a tool for communication in the disaster-affected area that provides various services such as:  
* Operation without power feed
* Delivery information by local government
* Collection and delivery of disaster information
* Offer person-to-person bidirectional communication tool  
In addition to providing prompt services in emergencies, the LACS is also available for use as a local information system in a normal situation, such as information sharing in schools and shopping malls. The major energy consumption at the server of a LI-Cloud system involves the energy consumption at the Wi-Fi Access Point regrading communication activities, and the energy consumption at the CPU and data processing. The LI-Cloud adopts a battery power support and a backup battery. The coverage of the LI-Cloud is associated with a Wi-Fi-based communication system. There are several ways to extend the coverage of LI-Cloud such as employing multiple LI-Clouds and enabling interconnection of multiple LI-Clouds [14]. Furthermore, in emergencies, deploying the LI-Clouds to shelters facilitates the effective coverage of users when they are gathered at the shelters.
## 2 Structure and Faults Analysis of the Direct-Drive EMA  
### Structure of the Direct-Drive EMA  
The actuator is essentially a position servo control system, which drives the load by controlling the operation of the motor to achieve the target position control. The difference between EMA and other actuators is that there is only one type of energy transmission in the electromechanical actuation system, and mechanical transmission is used instead of hydraulic transmission. The structure of the direct-drive EMA is shown in Figure 1. It is mainly composed of a controller, power conversion circuit, motor, ball screw, and load and feedback components (current sensor, resolver, and LVDT linear displacement sensor).  
Among them, the motor adopts a switched reluctance motor (SRM) with a certain fault tolerance and is matched with a direct-drive structure that cancels the gearbox, which greatly improves the reliability of EMA. When the EMA works, the controller controls the power converter by processing the flight commands as well as the feedback sensor signals, and the switching signals generated by the power converter control the motor rotation. The ball screw then converts the rotational motion of the rotor into a straight-line motion, which drives the swing of the aircraft control surface. In the whole process, the real-time measured position, velocity, and current information are fed back to the controller by the current sensor, i.e., the rotary transformer and the linear variable differential transformer (LVDT) so as to realize the closed-loop control.  
This study built an dSPACE-based EMA system experimental platform, as shown in the Figure 2. The experimental platform is composed of PC, dSPACE, auxiliary power supply, IGBT drive circuit, rotary decoding circuit, power supply, EMA, load rudder,  
Figure 1: The structural diagram of the EMA.  
sensors, and oscilloscope. The dSPACE hardware platform is mainly used to provide a real-time control platform for semi-real objects. In this experiment, DS1103 PPC was used to output 8 PWM motor control signals through the digital I/O port of the main processor to control the opening and closing of IGBT. The SRM current analog signal output by the Hall sensor and the LVDT position signal output by the decoder board are collected by the ADC module. In addition, through the incremental encoder interface, the rotary signal of the motor can be directly input to the DS1103 PPC through the rotary decoding board so as to be used for the measurement of the motor position and speed.
# Design of Indoor Visible Light Communication PAM4 System
## 3 Key Component Design  
### PAM4 Encoding and Decoding  
In order to generate the PAM4 signal, the coding scheme of the PAM4 signal designed in this paper is shown in Figure 3. The scheme uses two non-return-to-zero (NRZ) codes to generate the PAM4 signal. There are generally two kinds of binary code stream information for sending data, namely logic \"0\" and logic \"1\". For logic \"0\", choose a lower amplitude level to act as the least significant bit (LSB); for logic \"1\", choose a higher amplitude level to act as the most significant bit (MSB) and superimpose the two channels of NRZ signals to obtain the PAM4 signal. Note that before superimposing, the two NRZ codes must be clock synchronized; otherwise, the signal waveform will be mutated due to different delays.  
The decoding scheme is shown in Figure 4. Three deciders are designed according to the amplitude of the PAM4 signal at the receiving end. By comparing the set threshold values, the corresponding symbol information is determined. The rules of the decider are: when the amplitude of PAM4 is greater than the threshold value of the decider, the output  
Figure 3: The coding scheme of the PAM4 signal.  
Figure 2: The format of the PAM4 data frame [19].  
logic \"1\"; when the amplitude of PAM4 is smaller than the threshold value of the decider, the output logic \"0\". The output of the decider2 is taken as the output1, the decider1 and the decider2 perform an \"exclusive OR\" operation, and the output obtained is then \"OR\" with the output of the decider3 to obtain the output2, and then complete the decoding and recovery of the two NRZ signals. In Figure 4, the threshold value of the decider1 is the smallest, and the threshold value of the decider3 is the largest.  
In order to verify the accuracy of the decoding scheme, we assume that the four amplitudes of the PAM4 signal are 0, 1, 2, and 3, then the three decision threshold values are set to 0.5, 1.5, and 2.5, respectively, NRZ1 is \"0 1 0 1 0 1 0 1\", and NRZ2 is \"0 1 0 0 1 0 1 0\". The output PAM4 signal is \"00 11 01 10 00 11 0\". This PAM4 signal is brought into Figure 4 as input, and the symbol output values of each part are shown in Table 2. It can be seen from the table that the output of the decider2 is exactly NRZ1, and the result of the output2 is \"0 1 1 0 1 1 0\", which is NRZ2, so two NRZ codes can be recovered.
## 1 Introduction
### Pre-Design Evaluation (PDE), Models, and Performance Simulations
In construction surveys, it has already been used to improve the flow of information [44], to evaluate the building's adaptability [45], to sustainably prolong the building's useful life [46], and so on. The DSM allows us to determine (and describe), through a mathematical matrix, whether the dependencies between the activities in a given complex process have serial relationships, parallel relationships, or if they have iteration relationships between their realization [47]. It is used to optimize collaborative work dependencies in order to reduce iteration and rework and thus improve the collaborative efficiency and optimize resources [46]. Another use of this strategy in architecture, engineering, construction, and maintenance is in improving the architecture of buildings. The DSM is an effective method for illustrating the dependency relationships between components and environments in building architecture [46]. In order to reduce the complexity of the built environment, it is possible to discretize the geometry of the spaces of a given layout in a topological model, which is a graph with spaces as vertex attributes [27; 34; 46; 48]. With the graph matrix of the spatial arrangement, it is possible to relate the dependencies with other matrices through a multi-domain matrix (MDM) [45; 49]. This relationship, which recurs in other research areas, such as structural design [46], product design [50], and systems development [51], is the basis of the functionality of the program that is presented here.
## 3 Dynamics Model of the Multistage Transmission System
### Calculation of Mesh-Load Factor
D\({}_{\text{2 sp1}}\), D\({}_{\text{2 sp2}}\), and D\({}_{\text{2 sp3}}\) are the dynamic load-sharing coefficient of each external planet gear pair of the second-stage planetary gear train, respectively. D\({}_{\text{2 rp1}}\), D\({}_{\text{2 rp2}}\) and D\({}_{\text{2 rp3}}\) are the dynamic load-sharing coefficient of each internal planet gear pair respectively. Figure 8: Load-sharing coefficient fluctuation degree curve of the external meshing of the first stage and second stage planetary gear train. (**a**) Comparative analysis of load-sharing coefficient: D\({}_{\text{1 sp1}}\) and D\({}_{\text{2 sp1}}\). (**b**) Comparative analysis of load-sharing coefficient: D\({}_{\text{1 sp2}}\) and D\({}_{\text{2 sp2}}\). (**c**) Comparative analysis of load-sharing coefficient: D\({}_{\text{1 sp3}}\) and D\({}_{\text{2 sp3}}\). Here, the variance (\(s^{2}\)) is used to reflect the load-sharing coefficient fluctuation degree of each planet gear pair in the same meshing period of the transmission system under random input speed. Figure 9 indicates that the fluctuation degree of the load-sharing coefficient of each planet gear pair in the first stage planetary gear train is: \(s^{2}{}_{(\text{D1 sp1})}=2.2\times 10^{-3}\), \(s^{2}{}_{(\text{D1 sp2})}=2.2\times 10^{-3}\), \(s^{2}{}_{(\text{D1 sp3})}=2.8\times 10^{-3}\), \(s^{2}{}_{(\text{D1 rp1})}=2.2\times 10^{-3}\), \(s^{2}{}_{(\text{D1 rp2})}=2.2\times 10^{-3}\), \(s^{2}{}_{(\text{D1 rp3})}=2.8\times 10^{-3}\); the fluctuation degree of the load-sharing coefficient of each planet gear pair in the second stage planetary gear train is: \(s^{2}{}_{(\text{D2 sp1})}\) = \(5.72\times 10^{-4}\), \(s^{2}{}_{(\text{D2 sp2})}=4.11\times 10^{-4}\), \(s^{2}{}_{(\text{D2 sp3})}=6.36\times 10^{-4}\), \(s^{2}{}_{(\text{D2 rp1})}=5.43\times 10^{-4}\), \(s^{2}{}_{(\text{D2 rp2})}=4.1\times 10^{-4}\), \(s^{2}{}_{(\text{D2 rp3})}=6.82\times 10^{-4}\). By comparing and analyzing, the fluctuation degree of the load-sharing coefficient of each planet gear pair in the first-stage planetary gear train is greater than that of each planet gear pair in the second-stage planetary gear train. Figures 6-8 show that the fluctuation degree of the load-sharing coefficient of each planetary gear pair in the first-stage planetary gear train is more obvious.
## 3 System Structure
### System Assumptions  
The present study adopts three main assumptions, described as follows.  
* The status (i.e., normal or poisoned) of the dataset input to the system is unknown. However, the labels of the dataset are well-confirmed, and hence, the attacker cannot launch label flipping attacks or randomly destroy the data for this dataset. Furthermore, the method used by the attacker to poison the dataset is ruled to be out of the scope of the present research.
* Model training is performed using frozen transfer learning with all of the layers frozen except for the last fully connected layer. It can greatly reduce the model training time.
* Four poisoning attacks are considered, namely, TensorFlowCog, Feature Collisions, Convex Polytope, and a newly developed Category Diverse attack proposed in [33]. Note that the TensorCog and Category Diverse attacks are paralysis attacks, while the Feature Collisions and Convex Polytope attacks are target attacks.
## 2 Materials and Methods
### Data Preprocessing  
This study focused on analyzing the kinematic and kinetic characteristics of the hip, knee, and ankle joints in the sagittal plane during running, as well as the performance of the hip and ankle joints in the coronal plane [26]. The existing literature has revealed significant variability in the sagittal and frontal plane movements of the lower extremities during running activities [26]. To accurately capture these variations, marker trajectory data were filtered using a zero-phase fourth-order Butterworth low-pass filter with a cutoff frequency of 12 Hz. Subsequently, Matlab R2018b software was utilized to convert the marker trajectory data from C3D file format to a format compatible with OpenSim 3.2016 software (.mot and.tr formats), and then import them into OpenSim for further data processing. The muscle-skeletal model provided by OpenSim (gait 2392) was employed during this process.  
To ensure the accuracy of the model, static calibration was performed based on the positions of markers on the subjects and their respective body weights to adjust the model scale. The static weight of each marker was manually adjusted based on the root mean square (RMS) error between the experimental marker points and virtual marker points in the model, ensuring that the error was less than 0.02. Segment lengths and masses were scaled based on the subject's anthropometric measurements. These included lengths of the femur, tibia, and foot segments, as well as their respective mass properties. The joint centers were defined using the positions of anatomical landmarks placed on the subjects. These landmarks were used to establish coordinate systems for each body segment and to define the location of joint centers. The adjusted scaled model was subsequently applied to appropriate analyses to calculate the data. Joint angles were computed using the inverse kinematics (IK) tool in OpenSim, while joint moments were estimated using the inverse dynamics (ID) tool in OpenSim. This methodology aims to provide more precise data support for understanding the kinematics and kinetics of lower limb joints during running.
# Smart Rings vs. Smartwatches: Utilizing Motion Sensors for Gesture Recognition
## 4 Evaluation and Results
### Evaluation
#### 4.3.2 Activation Mechanism-Dependent  
The data set of the watch session achieves an accuracy of 96.6%, while the ring session results show a maximum accuracy of 93.2%. Generally, all models of the ring session are slightly weaker compared to the watch session results. This suggests that the activation mechanism introduces additional variance to the data set.  
Since the participants were allowed to choose an ending point for the directional gestures, some gestures of the same type might contain a different amount of information. The ring session results clearly show confusion between the directional gestures _Flick Left_ and _Flick Right_ as well as for the gesture _Flick Up_. These gestures implicitly contain gesturesin the opposite direction when the ending point of the gesture is equal to the starting point. This is the case because the hand is moved back to the origin after the gesture is performed. Depending on the chosen ending point, this might introduce ambiguity between the directional gestures.  
However, the most significant difference between the sessions is the confusion between the _Clap Once_ and _Clap Twice_ clap gestures. The reason for this confusion can be found in the user feedback. Most participants reported that the mechanism is not very well suited for these two gestures, because every clap could move the thumb slightly, which in turn could lead to deactivation of the sensors by releasing the activation mechanism unwillingly. An early deactivation for the _Clap Twice_ gesture results in a gesture that is very similar to the _Clap Once_ gesture. The deactivation cannot be observed visually due to the location of the sensor and therefore, cannot be corrected. Thus, a _Clap Once_ gesture might be labeled unintentionally as a _Clap Twice_ gesture, which explains the high false positive rate of 31%.
## 4 Discussion
This presents the common set up for hip implant migration measurement [34; 35; 36]. The results of this study showed the reliability of out-of-plane migration measurements was inferior to in-plane migration measurements, which can be a common limitation of the uniplanar calibration set up [14]. However, the bi-planar set up overcomes this limitation but is rarely used for hip implant migration measurement due to its set up design. Knee and ankle implant migration measurements were much more common with the bi-planar set up [37; 38]. The RSA method is in the discussion and gets an increasing importance in the approval and pre-clinical testing process of new orthopaedic implants. Pre-clinical testing is essential to assess the safety and efficacy of new implant designs, coatings, materials, etc., not only for implants in the orthopedic area but also within dentistry [39]. However, sometimes pre-clinical testing results do not correlate with the clinical results and observations (meaning laboratory environment versus real life application). Because of its high accuracy and precision, only small patient cohorts are necessary to investigate the effect of changes in implant design, new bone cements, or additional implant coating on the implant fixation [12; 40]. Recently, the importance of RSA to become a tool for the pre-clinical testing and a stepwise introduction of new orthopaedic implant designs has been increasingly valued [40; 41; 42]. Therefore, it is necessary to validate the reliability of RSA methods. Classical marker-based RSA or mbRSA approach offers the opportunities: to measure in vivo implant fixation [43; 44], to investigate, e.g., new THA design or coatings, degenerative changes [45; 46; 47], joint kinematics [48] and pathomechanism [49], and bony fusion [50]. Additionally, it offers the opportunity to investigate the effect of joint material connection within total joint arthroplasty, likewise the head-taper junction [17]. The application of mbRSA eliminates the additional costs and corresponding risks caused by additional markers on the implant. The CAD/RE model can match the actual shape of implant precisely and has been proven to be as accurate as marker-based RSA [14; 20]. But there are certain difficulties in obtaining CAD models. The implant manufacturer is often reluctant to share their CAD models (representing sensitive construction data files) for some commercial reasons. RE model is more convenient to be obtained compared with CAD model. However, when applying RE models to a large number of implants with different sizes or variants (which is a common situation in clinical studies), it can result in a considerable expense. EGS model is a potential alternative when CAD/RE model is not available [51].
# An Electric Load Simulator for Engine
## 2 System Design
### Electromagnetic Actuator Design  
An electromagnetic actuator was designed to reproduce the gas force in view of the configurations and technical requirements of the electric load simulator. It was mainly made up of the mover, stator, displacement sensor, and the fixed part. The prototype and magnetic fluxes of the electromagnetic actuator are shown in Figure 2. The mover and the output shaft of the electromagnetic actuator were connected, which are moving parts. The stator and fixed part were connected. One side of the connection plate of the displacement sensor was pressed and fixed on the mover through the output shaft of the electromagnetic actuator. The other side was screwed with the inner core of the displacement sensor. The mover of the electromagnetic actuator followed the movement of the exhaust valve. Therefore, the speed and displacement signals of the mover could be measured and transmitted to the displacement sensor.  
Figure 2: Prototype and magnetic fluxes of the electromagnetic actuator.  
It can be seen from the magnetic fluxes of the electromagnetic actuator that the magnetic lines were densely, evenly, and basically symmetrically distributed after energization. It was less affected by the current. The magnetic induction intensity at the intermediate position of the permanent magnet was the smallest, and the numerical value was approximately zero. The magnetic induction intensity at the intermediate position of the two sets of permanent magnets was the largest, and the numerical value was about 2.24 T [1; 22]. The structural parameters of the electromagnetic actuator are shown in Table 2.  
The characteristic curve of electromagnetic force is shown in Figure 3. The range of electromagnetic force was from \(-\)300 N-300 N, and the magnitude of electromagnetic force was related to coil current and position.
## 3 Results and Discussion
### Digester Process Stability
#### 3.4.2 Total Ammonia Nitrogen (TAN) and Total Kjeldahl Nitrogen (TKN)  
It was found that in DE3, the concentration of TAN in the FL reached 5.55 \(\pm\) 1.55 g/kg. This is likely due to the feeding of CSM, which normally contains a higher concentration of ammonium (NH\({}_{3}\)-N) [23], while in CN1, where CSM was fed and DS circulated due to the difficulties of field application, a high TAN concentration resulted with high SD, both in FL and DS, at 6.40 \(\pm\) 3.48 and 7.70 \(\pm\) 4.25 g/kg, respectively. It is possible that the high concentration of TAN, reported between 5 and 14 g/kg, could inhibit the anaerobic digestion process [43,44]. However, even though CN2 and CN3 were fed with 100% livestock manure substrates (CSM and CM, PM, respectively), a low TAN concentration was found in both plants at 1.93 \(\pm\) 0.88 and 1.24 \(\pm\) 0.31 g/kg, respectively. This situation could have been caused by the (1) dilution of substrates by freshwater, which decreased the TAN concentration; (2) no digestate circulation to the digesters, leading to lower TAN accumulations; or (3) different substrate characteristics.