esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
The 3D haptic model of the historic Wencelsas Hill in Olomouc (Czechia) was made with 3D printing technology. In addition, the interaction between the user and the object was achieved by applying layers of conductive material responding to touch on selected surfaces of the model. Activating connections by touch leads to the display of detailed information on the tablet located next to the 3D model. Creating replicas of museum objects, even with complex shapes, using 3D printing technology is possible thanks to the use of 3D scanning and 3D modelling. Technical and practical aspects concerning the use of various scanning techniques for historic museum exhibits were discussed in previous works [25; 26; 27; 28; 29; 30]. Three-dimensional scanning using structured light technology was used to scan the statue of [PERSON] from the Antalya Museum in Turkey and the Khmer head from the Rietberg Museum in Zurich [25]. This work also presents the issues of selecting 3D scanning techniques for historic museum exhibits in terms of costs, experience and training of staff. Article [26] concerns checking the suitability of stationary and mobile 3D laser scanners for transferring archaeological objects to the digital world. The research confirmed the inability to collect information about the texture, and in the case of a mobile scanner there was a need to stick markers, and in the case of a stationary scanner there were limitations regarding the size of the artefacts. The authors of [27], apart from the aforementioned aspects, also deal with the issues of digital reconstruction. Work [28] presents a low-cost small object 3D data acquisition system consisting of a set of Kinect v2 devices cooperating with the open source LiveScan3D software. The system is difficult to transport over long distances (for example, by air transport) due to its large number of components. In article [29], the authors describe a significant number of available technologies and devices that in various situations can be used to acquire 3D data of large and small objects of tangible cultural heritage. Monograph [30] presents both various theoretical aspects concerning the issues of 3D digitisation of architectural objects and museum artefacts, as well as many practical implementations. Another approach, alternative to 3D scanning technology, is described by the authors of [31]. The article discusses the creation of digital architectural objects through the use of classic 3D modelling. The replicas made with the 3D printing technology were used to better understand the kinaesthetic in the board game. The authors of the articles discussed used 3D models printed with EFF technology for kinaesthetic research, but in the conducted survey research in the form of pilot studies, they did not have a group of blind people. The analysis of the articles shows that the issue of making tangible cultural heritage objects available to blind people is a problem that has not yet been properly resolved, both in terms of organisation and technology.
## 3 System Model  
Figure 1 illustrates a wireless transmission system. We consider a point to point system with a single user which includes a transmitter and receiver. It transmits data from the finite buffer queue over a time-varying channel. At the same time, the transmission time is divided into equal time slots \(\Delta t\), and the discrete time interval is represented as [\(n\Delta t\), (\(n\)+1)\(\Delta t\)] (n is the time slot). We assume that the system state is time-varying in each time slot. According to the delay feedback, the receiver can obtain the throughput and channel state information, and the transmitter can adaptively adjust the transmission rate and transmission power [32].  
Figure 1: \(Cont.\)
## 6 Experiments and Results
### The Result of the ST-LRMA Based Data Recovery  
The proposed ST-LRMA based method are compared with the conventional interpolation methods (linear, cubic and nearest interpolation), the recently proposed LRMA based methods, B-LRMA [14] and T-LRMA [16], and the LRMA based method with only spatial constraint, denoted by S-LRMA. The results, shown in Figure 8a-d, indicate that the LRMA based methods have dramatic improvement in data recovery accuracy compared the conventional interpolation methods. Moreover, the proposed ST-LRMA method outperforms the other LRMA based methods. Especially in the low sampling rate, the proposed method shows a more impressive superiority. This means the proposed method needs only fairly few measurements to obtain high data recovery accuracy. It can be regarded as an energy efficient data collection method and this can be also proved by the following optimized sampling data collection experiments.  
Figure 7: The data recovery results based on different estimated rank and different sampling rate. (**a**) The results in the simulated network; (**b**) The results in the real network.  
Figure 8: Data recovery results in the simulated and real networks: (**a**) B-LRMA compared with the interpolation methods in the simulated network; (**b**) B-LRMA compared with the interpolation methods in the real network; (**c**) ST-LRMA compared with B-LRMA, S-LRMA and T-LRMA in the simulated network; (**d**) ST-LRMA compared with B-LRMA, S-LRMA and T-LRMA in the real network; (**e**) ST-LRMA compared with B-LRMA, S-LRMA and T-LRMA in the simulated network with the measurement \(\mathbf{X}\) missing rows and columns; (**f**) ST-LRMA compared with B-LRMA, S-LRMA and T-LRMA in the real network with the measurement \(\mathbf{X}\) missing rows and columns.  
According to the low rank completion theory [13], to recover a complete matrix, there should be at least one valid entry at each row and column. However, the data loss often occurs in WSNs. So this condition for the LRMA model may not be guaranteed at some data measurements. To further test the robust of the proposed method, we implement the data recovery experiments with element lost in entire rows and columns of the measurement matrix. Here we randomly remove 10% of rows and columns from the measurement matrices \(\mathbf{X}\) both in the simulated and real networks. Then the above data recovery experiments are repeated using the new inputs. The experimental results, shown in Figure 8e,f, show the proposed ST-LRMA has stable recovery results, while B-LRMA, S-LRMA and T-LRMA generate large error and their curves behave un-convergently. It is considered that the improved stability of the proposed method is due to the newly introduced consistency constraints over measurements.
## 3 Result and Discussion
### Comparison of the Analysis Methods
The data of \(\dot{E}_{\text{D,HE4}}^{\text{AV,EN}}\) is negative, which clears that \(\dot{E}_{\text{D,HE4}}\) can be lessened by raising the magnitude of its pinch temperature which results in a decline of the liquid carrying capacity at the outlet of T2. Table 6 lists the promotion priority of all the components in the presented CCHP system based on TC-CCES determined by different analysis methods. It can be observed that high diverse happens between conventional and advanced priorities because of different criteria. For an instance, the HE3 is considered the third priority in conventional exergy analysis. However, it is the eighth component for optimization in the advanced method. In short, the advanced exergy method offers much more trustworthy results since both technological limitations of each component and the interconnections among components are considered. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Component** & \(\dot{E}_{F,k}\) **(kW)** & \(\dot{E}_{P,k}\) **(kW)** & \(\dot{E}_{\text{D,k}}\) **(kW)** & \(\dot{E}_{\text{L}}\) **(kW)** & \(\epsilon_{k}\) & \(y_{k}\) & \(y^{*}_{k}\) \\ \hline C1 & 2178.20 & 1939.77 & 238.43 & - & 89.05 & 5.58 & 12.93 \\ C2 & 2091.40 & 1883.59 & 207.81 & - & 90.06 & 4.87 & 11.27 \\ T1 & 1282.52 & 1101.13 & 181.39 & - & 85.86 & 4.25 & 9.84 \\ T2 & 1169.12 & 978.46 & 190.66 & - & 83.69 & 4.47 & 10.34 \\ HE1 & 389.516 & 340.01 & 49.51 & - & 87.29 & 1.16 & 2.69 \\ HE2 & 1183.48 & 1014.93 & 168.55 & - & 85.76 & 3.95 & 9.14 \\ HE3 & 994.18 & 758.42 & 235.76 & - & 76.29 & 5.52 & 12.79 \\ HE4 & 77.99 & 58.98 & 19.01 & - & 75.63 & 0.45 & 1.03 \\ CS & 2154.99 & 1727.23 & 427.76 & - & 80.15 & 10.02 & 23.20 \\ TV1 & 456.97 & 432.31 & 24.66 & - & 94.60 & 0.58 & 1.34 \\ TV2 & 4589.13 & 4488.95 & 100.18 & - & 97.82 & 2.35 & 5.43 \\ Overall system & 4269.6 & 2401.65 & 1843.72 & 24.23 & 56.25 & 43.20 & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Conventional exergy analysis results.
## 3 Proposed Fault Detection and Classification Algorithm
### Flowchart of the Proposed Technique  
The developed approach for detecting and classifying the faults is illustrated with the aid of the flowchart given in Figure 2. The events of faults are detected by the maximum values of the FI compared with the threshold value. Various fault conditions are classified depending on the quantity of phases having a faulty nature. The LL and LL-G faults are differentiated by utilizing the GFI. The LL fault is detected if the amount of the GFI is higher than the ground fault threshold (GFTH), while the LL-G is sensed when the GFI is lower than the threshold.
## 2 Background  
### MIMO Detection Model  
The MIMO system under consideration consists of \(N\) transmit antennas and \(N\) receive antennas. The complex-valued matrix \(\mathbf{H}\) of size \(N\times N\) represents the MIMO channel; the complex-valued \(N\times 1\) vector \(\mathbf{s}=\left[s_{1},s_{2},\cdots,s_{N}\right]^{T}\) and the complex-valued \(N\times 1\) vector \(\mathbf{y}=\left[y_{1},y_{2},\cdots,y_{N}\right]^{T}\) denote the transmit signal and the receive signal, respectively; the \(N\times 1\) vector \(\mathbf{n}=\left[n_{1},n_{2},\cdots,n_{N}\right]^{T}\) describesthe zero-mean i.i.d. complex additive Gaussian noise, where \(n_{i}\sim\mathcal{N}\!\left(0,\delta^{2}\right)\) and \(i=1,2,\cdots,N\). The complex-valued MIMO system model is given by  
\[\mathbf{y}=\mathbf{H}\mathbf{s}+\mathbf{n}. \tag{1}\]  
The purpose of MIMO detection is to recover the transmit signal \(\mathbf{s}\) from the receive signal \(\mathbf{y}\). According to reference [10; 11], this can be realized by the maximum likelihood (ML) principle. Therefore, the optimum solution of MIMO detection can be obtained by  
\[\mathbf{s}_{\text{ML}}=\underset{\mathbf{s}\in\Omega}{\text{argmin}}\| \mathbf{y}-\mathbf{H}\mathbf{s}\|^{2}, \tag{2}\]  
where \(\Omega\) is the solution space. The derivation process of Equation (2) is given in Appendix A. To solve the least square problem like Equation (2), QR decomposition [12] and Cholesky factorization [13] can be performed on the matrix \(\mathbf{H}\) to simplify the solution procedure. However, the detection procedure based on Cholesky factorization consists of successive forward substitution and backward substitution [14], which is incompatible with column iterative sorting. Whereas, when performed with QR decomposition, the detection procedure can be executed with only backward substitution, which is suitable for column iterative sorting. On the other hand, performing QR decomposition on \(\mathbf{H}\) can obtain a unitary matrix \(\mathbf{Q}\) and an upper triangular matrix \(\mathbf{R}\), i.e., \(\mathbf{H}=\mathbf{Q}\mathbf{R}\) and pre-multiplying \(\mathbf{y}-\mathbf{H}\mathbf{s}\) by a unitary matrix \(\mathbf{Q}^{H}(\mathbf{Q}^{H}=\mathbf{Q}^{T})\) does not change its norm and thereby has no impact on the MIMO detection. Thus, Equation (2) can be reformulated as  
\[\mathbf{s}_{\text{ML}}^{\text{\tiny{$\Lambda$}}}=\underset{\mathbf{s}\in \Omega}{\text{argmin}}\|\mathbf{Q}^{H}\mathbf{y}-\mathbf{R}\mathbf{s}\|^{2}. \tag{3}\]  
Therefore, QR decomposition is preferred in MIMO detection. With SQRD performed on the channel matrix, the columns of \(\mathbf{R}\) are rearranged by iterative sorting to try to ensure that the detection of the signals in different layers of \(\mathbf{s}\) is conducted in the order of signal-to-noise ratio (SNR) from large to small. In this way, the error propagation in the detection process is alleviated, which could improve the detection performance.
# The Influence of Processing on the Bioactive Compounds of Small Berries
## 4 Processing of Small Berries and Their Effects on Bioactive Compounds
### Concentration  
The concentration of berry liquids is applied in the food industry from both technological and economic perspectives. Concentrates have a higher microbial stability than juices, as the soluble solid content reaches a maximum of 75%. In addition, the costs associated with packaging, transportation, distribution, and storage are minimized [110]. Concentration can be performed by using conventional heating methods and alternative methods. Conventional methods, such as vacuum concentration, require high temperatures and high energy consumption, and might exert negative effects on concentrate quality. Thus, alternative technologies based on ohmic heating, freezing, or membrane technology can be selected to produce concentrates of high quality in terms of sensorial, nutritional, and health-related properties. Ohmic heating showed excellent results in producing high-quality berry concentrates while reducing the energy consumption with respect to vacuum concentration. For example, [PERSON] et al. [110] compared conventional heating with ohmic heating for producing mulberry juice concentrates. The phytochemical content of concentrates obtained by ohmic heating was on average 4 times higher than the conventional concentrates. The increased content of phytochemicals was attributed to the reduced processing time in ohmic heating performed at 20-30 V/cm. Moreover, ohmic heating showed an increased energy performance up to 46% higher than conventional heating. Freeze concentration better preserved the quality of concentrated strawberry juice, with regard to total anthocyanins and total polyphenols. The principle is based on the separation of the liquid phase at a low temperature, followed by freezing and separation of frozen water from the juice by defrosting [111]. However, the high processing costs and complex operations limit the use of freeze concentration for industrial applications [58]. Reverse osmosis is a membrane-based technology that proved to be effective in improving the flavonoid, chlorogenic acid, and anthocyanin concentration of blueberry and cranberry juice [112]. On the other hand, the high osmotic pressure limits the concentration to up to 40 degBx, which is very low compared to vacuum evaporation [58].
## 2 Experimental Facility and Procedure
### Arrangement of the Monitoring Points  
To acquire the frequency distribution characteristics of radiation noise in different direction and corresponding amplitudes, 16 monitoring points were arranged on the measurement surface around the pump. As shown in Figure 2, the monitoring points were 1000 mm away from the center of the impeller and arranged evenly in a circumferential direction [26]. During the measurement process, the SPL of every monitoring point was measured sequentially by a microphone. Here, SPL is defined as,  
\[SPL=20 log\frac{P_{e}}{P_{ref}} \tag{2}\]  
\[P_{e}=\sqrt{\frac{1}{T}}\int_{0}^{T}p^{\prime 2}dt \tag{3}\]  
where \(P_{ref}\), \(P_{e}\) and \(p^{\prime}\) represent the reference sound pressure (\(2\times 10^{-5}\) Pa in air), effective sound pressure and instantaneous sound pressure, respectively. Additionally, acoustic energy was introduced to compare the proportion of radiation noise in different frequency ranges because it can be superimposed by arithmetic, moreover, the propagation of sound is essentially the propagation of energy. Briefly,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Instruments** & **Type** & **Application** & **Measuring Range** & \begin{tabular}{c} **Accuracy or** \\ **Sensitivity** \\ \end{tabular} \\ \hline Flow meter & SLDG-800 & Measuring flow rate & 0–100 m\({}^{3}\)/h & 0.2\% (accuracy) \\ \hline Pressure transducer & MIK-300 & Measuring inlet pressure & \(-\)100- kPa (inlet pipe) & 0.5\% (accuracy) \\ \hline Pressure recorder & RX-200D & Recording pressure & / & / \\ \hline Microphone & AWA14423L & Measuring radiation noise & 10–20,000 Hz & \
\begin{tabular}{c} 50 mV/Pa \\ (sensitivity) \\ \end{tabular} \\ \hline Two channel & AWA6290M+ &
\begin{tabular}{c} Analyzing radiation \\ noise signal \\ \end{tabular} & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measurement characteristics of instruments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Rotational Speed, rpm** & **Flow Rate, m\({}^{3}\)/h** \\ \hline
1700 & 56.9 \\
1900 & 64.8 \\
2100 & 69.6 \\
2300 & 74 \\
2500 & 78.2 \\
2700 & 82.1 \\
2900 & 86 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rotational speeds and corresponding flow rates.  
the application of acoustic energy can reveal the proportion of radiation noise in different frequency ranges to overall noise intuitively. Therefore, the average acoustic energy density [27] is analyzed and it is defined as,  
\[\varepsilon=\frac{P_{\varepsilon}^{2}}{\rho c^{2}} \tag{4}\]  
where \(\varepsilon\), \(\rho\) and \(c\) represent the acoustic energy density, medium density (1.29 kg/m\({}^{3}\) in air) and the sound speed in medium (343 m/s in air), respectively.  
To figure out the radiation noise overall intensity of the 16 monitoring points at certain frequencies, total sound pressure level (TSPL) is introduced and expressed as,  
\[TPSL=10 lg\sum_{i=1}^{16}10^{SPL_{i}/10} \tag{5}\]
## 3 Proposed SPMIPv6 Protocol
### Message Flow in SPMIPv6  
In the SPMIPv6 architecture authentication features are integrated in SLMA instead of performing these two functions separately. Proxy binding update message and SAAA query message are combined together and named as binding update and authentication query. As well as SAAA reply and proxy binding acknowledge message are merged together and named as binding acknowledge and authentication reply. For this reason the number of communications is greatly reduced in SPMIPv6 with comparison to conventional PMIPv6. The reduced communication scenario has been drawn and compared with the conventional PMIPv6 sequence diagram. The steps of the sequence diagram in both PMIPv6 and SPMIPv6 are discussed below.  
Figure 4(a) depicts the sequence diagram of the message flow with respect to PMIPv6. For the simplicity in Figure 4(a), we combine more than one communication within single step.  
Step 1: When a sensor node first attaches to a SMAG domain, the access authentication procedure is performed using the sensor node address.  
Step 2: After successful access authentication, the SMAG obtains the sensor node's profile from the AAA service policy store. This profile contains the sensor node's address, SLMA address, supported address configuration mode, and other associated information.  
Step 3: The SMAG sends a proxy binding update (PBU) message, including the MN address, to the sensor node's SLMA on behalf of the sensor node.  
Step 4: Once the SLMA receives the PBU message, it checks the policy store to ensure that the sender is authorized to send the PBU message. If the sender is a trusted SMAG, the SLMA accepts the PBU message.  
Step 5: The SLMA sends a proxy binding acknowledgment (PBA) message, including the MN's home network prefix option, and establishes a route for the sensor node's home network prefix over the tunnel to the SMAG.  
Figure 4(b) shows the sequence diagram of the overall message flow with respect to SPMIPv6. The number of communications is noticeably reduced in SPMIPv6 with comparison to conventional PMIPv6 shown in Figure 4(a).  
Step 1: When the location of an IP sensor node is changed, it sends solicitation message for the nearest SMAG discovery mechanism.  
Step 2: SMAG sends binding update and authentication query to SLMA integrated with SAAA.  
Step 3: In response to the binding update and authentication query message, SLMA integrated with SAAA send back binding acknowledge and authentication reply message.  
Step 4: Finally SMAG send the advertisement message to respective IP sensor node. And thus the IP sensor node is connected to the nearest SMAG.  
Step 5: Now the sensor node is able to communicate with the corresponding node based on SPMIPv6. Thus data can be transmitted from IP sensor node to correspondence node and _vice versa_.  
Figure 4: **(a)** Sequence diagram in PMIPv6. **(b)** Sequence diagram in SPMIPv6.
# A System Architecture of a Fusion System for Multiple LiDARs Image Processing
## 3 Proposed System Architecture for Multiple LiDARs Image Processing
### Server Structure  
The server is a CPU-bound process, i.e., it implements algorithms having a large number of calculations. The receive buffer is set based on the size of one frame of each LiDAR sensor and the number of connected LiDAR sensors. In the server, the threshold value of the unit frame processing time of each LiDAR sensor is set based on the input period of the LiDAR sensors. The server has three functions: data aggregation, compression, and transmission. The server process operates continuously by monopolizing one core and consists of three threads. Multithreading involves two or more threads performing a task simultaneously within one process. Multithreading has the advantage of reducing the overload of system resources by sharing the memory of the process. There are operations such as object recognition by collecting the data of multiple LiDAR sensors, but we do not consider them in this study. We consider only the operation of collecting and compressing data and transmitting them to the external system. Scheduling is necessary to implement efficient multithreading. Multithreading occupies less memory space and context switching is fast because of the absence of cache updates. This system applies a multi-threading method with concurrency instead of parallelism. Scheduling is implemented in a way that considers the priority. Among the three functions, the thread collecting data has the highest priority, followed by data compression and transmission to the external system. The priority can be controlled using code assigned to the thread object. Thread scheduling is divided into two types: local scheduling, in which thread scheduling is determined by the process to which the thread belongs, and global scheduling, in which the kernel's CPU scheduler determines scheduling. In this study, there is no system call for synchronization, and multithreading is implemented based on local scheduling without context switching to maximize performance. The server operates the collection thread until data reception from all connected clients is completed, and the collection thread occupies the CPU whenever the socket receive buffer is full. Figure 5 shows the structure of multiple threads within the server process. A thread library provides the programmer an API for creating and managing threads. It provides a library entirely in user space with no kernel support and makes it possible that invoking a function in the library results in a local function call in the user space.  
Figure 5: Multithreading in the server process.