esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Solution
### Optimization Problem  
Let the measured RSS at the relay node from the client node be \(RSS_{c}=RSS_{c}^{fs}(\|p_{r}-p_{c}\|)+\Phi_{c}^{sh}(p_{r},p_{c})+\Omega_{c}^{mp }(p_{r},p_{c},t)\) and from the server node be \(RSS_{s}=RSS_{s}^{fs}(\|p_{r}-p_{s}\|)+\Phi_{s}^{sh}(p_{r},p_{s})+\Omega_{s}^{mp }(p_{r},p_{s},t)\). A way to solve Problem 1 is to find a function that maximizes both the \(RSS_{c}\) and \(RSS_{s}\) at the same time, so that the total goodput is at a maximum. Figure 2(b) shows the RSS readings as the relay node travels between the server and client nodes. The reason for non-symmetrical RSS values between the two nodes is that the environment has NLOS conditions and, hence, has different propagation constants. It can be observed from this figure that the goodput is maximal when \(RSS_{s}\) and \(RSS_{c}\) become close to each other, and this is the basis for the objective of this work, _i.e._, to maximize the network capacity by maintaining a balance between radio signal strengths received from the server and the client nodes while attempting to maximize the RSS from both sides at the same time. It is also worth noting that this figure shows a one-dimensional RSS measurement, and if extrapolated to a 2D scenario, there can be multiple possible positions where \(RSS_{c}\) and \(RSS_{s}\) are equal, as will be shown later in Section 5. Hence, it is important to consider a function that maximizes the values of \(RSS_{c}\) and \(RSS_{s}\) together while maintaining them as close as possible. The objective of the optimization problem can be formulated as follows:  
\[p_{r}^{*}=\operatorname*{arg\,max}_{p_{r}}f(p_{r});\,s.t.\,G(p_{r}^{*})=\max_{p_{ r}}G(p_{r})\,\,and\,\,RSS_{c}(p_{r}^{*})\doteq RSS_{s}(p_{r}^{*});\,\,\,\forall\,\,p_{r} \in[p_{s},p_{c}] \tag{9}\]  
where \(G(p_{r})\) is the goodput value when the relay node is at position \(p_{r}\). The position of the relay node \(p_{r}=(x_{r}\in\mathbb{R},y_{r}\in\mathbb{R})\) (whose value ranges between the positions of server and client nodes) is the control variable (argument), and the function \(f(p_{r}):\mathbb{R}^{n}\rightarrow\mathbb{R}\) is the objective function to be maximized in the optimization problem. \(p_{r}^{*}\) is the position at which the objective function \(f(p_{r})\) is maximum and also the following two constraints are achieved: maximum goodput; \(RSS_{c}\) and \(RSS_{s}\) values match each other.
# Stratosphere: A Fast Virtual Circuit Switch Layer for Data Center Network
## 5 Performance Analysis
### Optimization of the Parameters
#### 5.2.2 The Size of Cells  
Increasing the size of cells could improve the utilization of the link due to the higher ratio of the header to the payload, while utilization of the link will decrease when it exceeds the threshold because of raising the unused data in one cell. Meanwhile, when the size of cells is increased, the delay of cells is also augmented. Several sizes of cells are tested under the minimal value of the preamble, and the result is in Figure 12. We can conclude that the size of 128 bytes is the best size for cells.  
All the parameters are tested and optimized for the 40 G link and 10 G link. For other links, the parameter should be re-tested and re-selected in future work.
## 3 Results and Discussion
### Topic 1: Health Status and Needs
#### 3.2.3 Impact of FIFO Work Arrangements on Mental Health  
Mental health concerns have also been documented in FIFO workers, with over 30% of FIFO workers in Australia experiencing psychological distress, compared to just 10% of the general (non-FIFO) population [12]. Poor mental health in FIFO workers may be linked to conflicting home/work demands [2; 12], loneliness or isolation [12], and bullying or poorworkplace cultures [2]. In contrast, self-reported mental and physical health were better where workers felt they had high levels of social and managerial support [2].
###### Abstract
The geometric deformation of the axial curvature center of the outer raceway of the bearing is defined by the sine wave function, and its influence on the static bearing capacity of a large double row slewing ball bearing has been studied. [PERSON] [20] proposed a numerical method for determining the three-dimensional contact stress of cageless bearings, and studied the influence of a variable diameter bearing raceway on the contact characteristics between the rolling element and the raceway of cageless bearings and the bearing wear. [PERSON] et al. [21] studied the influence of outer raceway ellipticity on bearing life, and put forward the requirement of raceway ellipticity control. [PERSON] [22] established a quasi-static model of an elliptical raceway of angular contact ball bearings under combined loads, studied the influence mechanism of the elliptical raceway on contact characteristics of the bearings, and estimated the fatigue life of bearings under elliptical raceway. [PERSON] [23] used the finite element method to analyze the deformation of the pitch bearing considering the installation structure. The results show that the inner ring of the wind turbine pitch bearing produces warping deformation in the axial plane and elliptical deformation in the radial plane. These deformations cause the load distribution curve of the steel ball to deviate from the sinusoidal distribution curve obtained under the traditional rigid ring assumption. The above scholars have studied the elliptical deformation in the radial plane and the warping deformation in the axial plane of the bearing raceway, and studied some effects of deformation on the mechanical properties of the bearing. However, their research mainly involves the small deformation of the raceway caused by the errors generated during the manufacturing and assembly of the bearing, and does not study the non-circular optimization of the initial raceway of the bearing and its influence on the load distribution of the raceway. This paper takes a single-row four-point contact wind turbine slewing ball bearing as an example, establishes the static vector balance equation of the bearing under any raceway based on the Hertzian contact theory, uses the Newton iteration method to solve the balance equation, and studies the bearing inside the traditional circular raceway. Basedon the research results, the rated life of the bearing ring is calculated using the L-P theory, and the maximum life is used as the objective function to optimize the optimal non-circular deformation of the raceway corresponding to the maximum life of the bearing. The research on non-circular raceways of wind turbine slewing bearings will provide a new idea and method for improving the carrying capacity and life of small-angle slewing bearings, which can not only be used in wind turbine bearings, but also in lifting, mining, and medical treatment.
# Virtualization Airborne Trusted General Computing Technology
## 3 Key Technologies  
### High-Performance Processing and High-Security Hardware Units  
The high-performance processing hardware unit and the high-security hardware unit constitute the hardware facility layer, of which the former offers basic hardware support for the operation of the entire virtual environment and the latter offers security support for the entire virtual environment. Figure 2 displays the relevant architecture.  
The high-performance processing hardware unit comprises an \(\times\)86 architecture-based embedded multi-core processing module and gigabit Ethernet interfaces. The embedded multi-core processing module provides a high-performance processing capacity, with its four-core eight-thread processor and 16 GB memory, providing a mass storage capacity with 4 TB SSD, etc. Integrated with a 1 channel 100 Mbps ARINC664P7 interface and six-channel gigabit Ethernet interface, it also provides the resident airborne system applications with network communication services. Compared with AGCSP's 950 g weight and 45 W power consumption, the VAGCSP weigh  
Figure 1: VAGCSP architecture.  
The high-security hardware unit is implemented using an independent hardware card, including the high-security main control module, PCIE communication module, interface protocol processing module, flash storage module, and algorithm engine module, which conforms to trusted computing standards. The major functions of the various parts are as follows:  
1. High-security main control module: implements the main control functions; calls the PCIE communication module to communicate with the embedded multi-core processing module; calls the interface protocol processing module to parse or encapsulate data packets, according to existing protocols; calls the flash storage module to store or read operation results; calls the algorithm engine module to request for high-security calculation and returns the corresponding results.
2. PCIE communication module: connects high-performance processing hardware unit via the PCIE interface hardware; completes data communication with the embedded multi-core processing module; realizes data interaction by calling the interface and high-security main control module.
3. Interface protocol processing module: implements established protocols; completes the encapsulation and parsing of data packets.
4. Flash storage module: implements the flash storage access interface; realizes the storage of private data, such as certificates, digital signatures, and hashes.
5. Algorithm engine module: achieves high-performance computing of cryptographic algorithms, such as hash, grouping, and public key algorithms; supports the international data encryption algorithm.
# Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud
## 3 Network-on-Chip Architecture
### Router Micro-Architecture
Aggressive speculation techniques can be implemented to maximise the network throughput. Such techniques allow VA and SA operations to be overlapped. The exploitation of speculative routing allows restricting the entire packet transfer to a single cycle. To this end, the following design choices have also been made: (i) use of store-and-forward mode; (ii) architectural optimization for single-flit packets with a short length; and (iii) use of speculative allocation techniques. Router reconfiguration (see Section 4) is enabled by augmenting the routing control logic with specific hardware structures. Specifically, combinatorial logic is added to detect and parse configuration and control packets, as well as dedicated hardware tables and counters are used to keep information  
Figure 3: Internal micro-architecture of the 2D-mesh router connecting up to four ringlets. A router with all of the ringlets connected will form a physical tile. Red arrows are the power-gating signals. on virtual tiling and collect traffic statistics. This additional logic is also able to eventually turn-off (via PG hardware blocks, see red arrows on Figure 3) the internal router's structures whenever applications do not use them, thus contributing to saving power. Performance counters are coupled to the output channels so that they are incremented by one every time a new flit is transmitted through the link.
###### Abstract  
A personal audio system has a wide application prospect in people's lives, which can be implemented by sound field control technology. However, the current sound field control technology is mainly based on sound pressure or its improvement, ignoring another physical property of sound: particle velocity, which is not conducive to the stability of the entire reconstruction system. To address the problem, a sound field method is constructed in this paper, which minimizes the reconstruction error in the bright zone, minimizes the loudspeaker array effort in the reconstruction system, and at the same time controls the particle velocity and sound pressure of the dark zone. Five unevenly placed loudspeakers were used as the initial setup for the computer simulation experiment. Simulation results suggest that the proposed method is better than the PM (pressure matching) and EDPM (eigen decomposition pseudoinverse method) methods in the bright zone in an acoustic contrast index, the ACC (acoustic contrast control) method in a reconstruction error index, and the ACC, PM, and EDPM methods in the bright zone in a loudspeaker array effort index. The average array effort of the proposed method is the smallest, which is about 9.4790, 8.0712, and 4.8176 dB less than that of the ACC method, the PM method in the bright zone, and the EDPM method in the bright zone, respectively, so the proposed method can produce the most stable reconstruction system when the loudspeaker system is not evenly placed. The results of computer experiments demonstrate the performance of the proposed method, and suggest that compared with traditional methods, the proposed method can achieve more balanced results in the three indexes of acoustic contrast, reconstruction error, and loudspeaker array effort on the whole.  
personal audio system; sound field control; acoustic contrast; reconstruction error; array effort
# Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images
## 4 Discussion
### Spectral Response Difference between Leaf Blade Lesions and Leaf Sheath Lesions
These results are similar to those reported in previous studies [17; 18; 19]. Figure 6: Spectral curves of leaf sheath lesions and the variable SDr/SDg: (**a**) Original spectrum, (**b**) first derivative transformation spectrum, (**c**) second derivative transformation spectrum, and (**d**) the ability of SDr/SDg to distinguish the leaf sheath lesions from the leaf blade lesions. High reflectance in the near-infrared band of 783-890 nm results from the multiple scattering in the inner tissue of leaves [40; 41]. Compared with broad bands, narrow bands can provide additional information for better quantifying of the biophysical characteristics of agricultural crops. Hence, narrow-band vegetation indices can highlight the specific information of plants under stress to a large extent. Because rice ShB is caused by a fungal pathogen, some of the secretions from the fungus can also affect the normal metabolism of the host plant, causing a series of abnormal physiological and biochemical changes in the host plant. These include the changes in cell membrane permeability and electrolyte leakage, followed by changes in respiration and photosynthesis; in metabolism of nucleic acids, proteins, phenols and related enzymes; and in water physiology and others [3]. The anthocyanin reflectance index (ARI) reflects the change in anthocyanin content in leaves, and the red-edge vegetation stress index RVSI illustrates the variation of pigments in leaves. Rice ShB can also influence the photosynthetic efficiency of leaves [3]. The photochemical/physiological reflectance index (PRI) is related to photosynthetic efficiency. Besides, it has been reported that there are effects of different nitrogen, phosphorus, and potassium levels on the incidence of rice ShB [42]. The NRI vegetation index is also selected and has higher relevance to rice ShB at the leaf scale. However, further investigations are needed to verify these results by hyperspectral images obtained in the field due to many factors affecting extraction of rice leaves under the natural environments.
# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
## 3 Signal Acquisition and Feature Extraction  
### Signal Acquisition  
A 1.5 MW wind turbine is taken as the research object in this paper. The three-dimensional model of the 1.5 MW wind turbine drive system is established by Solidworks, and then it is imported into ADAMS for dynamic simulation. In the dynamic simulation model, parallel misalignment, angular misalignment and comprehensive misalignment are simulated. The acceleration signal of the high-speed output of the gearbox is used as the vibration signal (details in literature [40]). In MATLAB, the wind turbine and its control system are established to obtain the stator current for misalignment faults (details in literature [41]). The simulation model of the wind turbine is in the Maximum Power Points Tracking (MPPT) stage, in which the input speed is 81.3\({}^{\circ}\)/s, and the vibration signal and stator current signal are obtained under parallel misalignment fault for researching.
# Adaptive Vector Quantization for Lossy Compression of Image Sequences +
## 4 Experimental Results
### Dataset 1  
The first dataset we consider (Dataset 1) is composed of three image sequences. In Table 1, we report a short description. All the sequences are publicly available at [11]. In the following, for brevity, we refer to an image sequence of Dataset 1, by using a short name (reported in the second column of Table 1).  
The parameters we used for our experiments are the following:  
* _Wave Heuristic_ as Growing Heuristic;
* _OneRow+OneColumn_ as Dictionary Update Heuristic;
* _Freeze_ as Deletion Heuristic;
* _MSE-based_ as Match Heuristic (threshold value equal to 10).  
The dictionary is initialized with all of pixels for continuous grayscale (IDH), and we set the size of the dictionary (AVQ) as well as the shared dictionary (AVQIS) of 16384 (\(2^{14}\)).  
In Figure 9, we report a graphical comparison of the achieved experimental results, in terms of _Compression Ratio_ (_C.R._), reported on the \(y\)-axis, achieved by the AVQ algorithm (blue line) and the AVQIS algorithm (red line) for all of the frames (on the \(x\)-axis) of the three image sequences.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Short Name** & **Resolution** & **\# of Frames** \\ \hline _[PERSON] moving head_ & _Seq. 1_ & 256 \(\times\) 256 & 16 \\ _Chemical plant flyover (close view)_ & _Seq. 2_ & 256 \(\times\) 256 & 32 \\ _Chemical plant flyover (far view)_ & _Seq. 3_ & 256 \(\times\) 256 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset 1 description.
# An Alternative Series Solution for Free Vibration Analysis of Asymmetric L-Shaped Membranes
## 4 Discussion
### Convergence Behavior  
Our findings indicate that while both strategies tend to stabilize around \(N=12\), the path to stability and the reliability of results differ significantly between the two strategies. The 3-SDD strategy demonstrates rapid convergence, achieving near-stable results even at low \(N\) values (\(N=4\) to 6). As \(N\) increases, the relative error for 3-SDD quickly diminishes, showing a steep decline in the early stages (\(N=2\) to 6) and maintaining a consistently low error level. This is evident across all four eigenvalues, with the error reduction being particularly pronounced in the third and fourth eigenvalues shown in Figure 2c,d.  
In contrast, the 2-SDD strategy exhibits slower convergence, with significant variations in results at lower \(N\) values. The relative error for 2-SDD starts much higher, especially for the third and fourth eigenvalues, and decreases more gradually as \(N\) increases. This is particularly noticeable in Figure 2c,d, where the 2-SDD strategy shows a more substantial initial error and a slower error reduction rate than 3-SDD.  
The \"cutoff\" point of \(N=12\) affects the reliability of results differently for each method. For 3-SDD, this point serves more as a confirmation of stability than a critical threshold for result reliability. The relative error is minimal well before \(N=12\), indicating high reliability even at lower \(N\) values. For 2-SDD, however, the cutoff is more crucial. The relative error decreases significantly up to and beyond \(N=12\), especially for higher-order eigenvalues, suggesting that more terms are needed to achieve comparable accuracy to 3-SDD.  
Figure 2: Comparison of 3-SDD and 2-SDD strategies for the first four eigenvalue squares of asymmetric L-shaped membranes: (**a**) \(k_{1}^{2}\); (**b**) \(k_{2}^{2}\); (**c**) \(k_{3}^{2}\); (**d**) \(k_{4}^{2}\)[38].
## Self-Adaptive Strategy Based on Fuzzy Control Systems  
for Improving Performance in Wireless Sensors Networks  
**[PERSON] \({}^{1,\dagger,\ast}\), [PERSON] \({}^{1,\dagger}\), [PERSON] \({}^{1,\dagger}\) and [PERSON] \({}^{2,\dagger}\)**  
\({}^{1}\) Centro de Investigacion en Tecnologias Software y Sistemas Multimedia para la Sostenibilidad (CITSEM), Universidad Politecnica de Madrid, [PERSON] 3, 28031 Madrid, Spain;  
E-Mails: [EMAIL_ADDRESS] (J.-F.M.); [EMAIL_ADDRESS] (N.L.M.)  
\({}^{2}\) Centro de Automatica y Robotica, Universidad Politecnica de Madrid, Carretera Campo Real Km. 0.2, 28500 Arganda del Rey, Spain; E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) These authors contributed equally to this work.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-91-452-4900 (ext. 20788); Fax: +34-91-336-7817.  
Academic Editor: [PERSON]  
_Received: 30 June 2015 / Accepted: 14 September 2015 / Published: 18 September 2015_  
**Abstract:** The solutions to cope with new challenges that societies have to face nowadays involve providing smarter daily systems. To achieve this, technology has to evolve and leverage physical systems automatic interactions, with less human intervention. Technological paradigms like Internet of Things (IoT) and Cyber-Physical Systems (CPS) are providing reference models, architectures, approaches and tools that are to support cross-domain solutions. Thus, CPS based solutions will be applied in different application domains like e-Health, Smart Grid, Smart Transportation and so on, to assure the expected response from a complex system that relies on the smooth interaction and cooperation of diverse networked physical systems. The Wireless Sensors Networks (WSN) are a well-known wireless technology that are part of large CPS. The WSN aims at monitoring a physical system, object, (e.g., the environmental condition of a cargo container), and relaying data to the targeted processing element. The WSN communication reliability, as well as a restrained energy consumption, are expected features in a WSN. This paper shows the results obtained in a real WSN deployment, based on SunSPOT nodes, which carries out a fuzzy based control strategy to improve energy consumption while keeping communication reliability and computational resources usage among boundaries.  
keywords:wireless sensors networks; fuzzy logic; self-adaptive; cyber-physical systems +
Footnote †: journal: Journal of Applied Physics