esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated System for Official Vehicles with Online Reservation and Moving Path Monitoring
## 2 System Architecture
### Key Cabinet  
The key cabinet is composed of multiple sub-cabinets. Each sub-cabinet stores the key and STB of a vehicle (Figure 8). It has an RFID device, electromagnetic lock, charging device, and Wi-Fi communication module, which are powered by an AC/DC conversion power supply (AC110 V to DC 5.0 V). Each cabinet has an indicator light that is turned on when the key and the STB are taken away. The sub-cabinet is then in an idle mode and can be used for other keys and STBs.  
The internal layout of a sub-cabinet is shown in Figure 9. The control circuit manipulates components such as power supply, 5 V voltage regulator, 110 V main socket, RFID sensor, MCU control circuit, WiFi module, and electromagnetic valve lock. The material of the key cabinet is acryl which does not affect the RFID data and WiFi communications.  
Figure 10 shows the architecture of the sub-cabinet. The placement of the STB from its charging stand activates the RFID to read the information from the STB. When the STB and key are put in or moved from the sub-cabinet, the RFID sensor transmits the license plate information to the MCU that processes the information coding in JSON format. The coded information is uploaded to the cloud server. The return code in JSON format is sent back to the MCU to confirm the placement or displacement of the key and STB. The RFID card has a 1 KB EEPROM memory. To manage and achieve the multi-purpose function properly, the memory space is divided into 16 sectors, each of which has 4 blocks. Block 0 of segment 0 contains the card's unique identification code (user identifier, UID). In this study, the license plate information is stored in block 0 of Section 1 (Figure 11). The WiFi communication format uploads data through the POST method in the HTTP, which is also encoded in JSON format. The uploaded information includes data on the location, license plate, sub-cabinet, and the open/close of cabinet doors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Response Code** & **Status** & **GPS STB Action** \\ \hline
0 & Data upload error & Specific data retransmission \\
1 & Data reconstruction fail & All data retransmission \\
2 & Success & Lighting-off/Sleep-mode \\
3 & Unknown error & The buzzer keeps beeping \\ \hline \hline \end{tabular}
\end{table}
Table 2: Background data system return code status table and actions.  
Figure 8: Three-dimensional design of key cabinet unit and independent key cabinet.  
Figure 11: RFID memory configuration diagram.  
Figure 10: Architecture diagram of the key cabinet communication system.  
Figure 9: Layout of the interior of the key cabinet.
### Carrier Peptides
Pep 4-17: Tetrapeptide-17* [47] Pepha-Timp: Human oligopeptide-20
* [48] Peptamide 6: Hexapeptide-11
* [49] Peptide AC29: Acetyl Tripeptide-30 citrulline
* [50] Peptide Q10: Pentapeptide-34 Trifluoroacetate
* [51] Peptide Vinci 01: Penta-decapeptide-1
* [52] Peptide Vinci 02: Hexapeptide-3
* [53] Peptiskin: Arginine/Lysine polypeptide
* [54] Preregen: Glycine soja (Soybean) Protein, Oxide Reductases
* [55] Preventhelia: Diaminopropionoyl Tripeptide-33
* [56] Prodjeine: Yeast Extract
* [57] Prolixir S20: Dimer Tripeptide-43
* [58] Quintescine: Dipeptide-4
* [59] Raffermine: Hydrolyzed Soy Flour
* [60] Relistase: Acetylarginyltripophyll Diphenylglycine
* [61] Renaissance: Hydrolyzed Wheat Protein, Palmitoyl Decapeptide-21, Decapeptide-22, Oligopeptide-78, Zinc Palmitoyl Nonapeptide-14
* [62] Regu-Age: Hydrolyzed Rice Bran Protein, Glycine Soja Protein
* [63] Ridulisse C: Hydrolyzed Soy Protein
* [64] Serilesine: Hexapeptide-10
* [65] Silusyne: Soybean (Glycine Soja) Oil, Lauryldimonium Hydroxypropyl Hydrolized Soy Protein, Acetyl Hexapeptide-39
* [66] SNAP-7: Acetyl Hepteptide-4
* [67] Survivxyl PS: Pentapeptide-31
* [68] Syn-Glycam: Tetradecyl Aminobutylroylvalyl-aminobutyric Urea Trifluoroacetate
* [69] Syn-Tacks: Palmitoyl Dipeptide-5 Diaminobutylrooyl Hydroxy threonine, Palmitoyl Dipeptide-6 Diaminohydroybutylrate
* [70] Syn-TC: Tetradecyl Aminobutylroylylylaminobutyric Urea Trifluoroacetate, Palmitoyl Tripeptide-5, Palmitoyl Dipeptide-5 Diaminobutylroyl Hydroxy threonine
* [71] Syniorage: Acetyl Tetrapeptide-11
* [72] TEGO Pep 4-Even: Tetrapeptide-30
* [73] Telangyn: Acetyl Tetrapeptide-33
* [74] Telosense: Hydrolized Soy Protein, Hydrolized Yeast Protein
* [75] Thermostressing: Acetyl Tetrapeptide-22
* [76] Thymulen 4: Acetyl Tetrapeptide-2
* [77] TIMP Peptide: Acetylhexapeptide-20
* [78] Triactigen: Yeast Extract
* [79] Trylagen: Hydrolyzed Wheat Protein, Hydrolyzed Soy Protein, Tripeptide-10 citrulline, Tripeptide-1
## 2 Methodology
### Multimodal Feature Fusion  
Multimodal feature fusion [50] enables the acquisition and interpretation of information from different dimensions, providing a more comprehensive and accurate understanding. The two most commonly used modalities in speech emotion recognition are audio and text data. Audio data contain information such as speech rate, intonation, and volume, which can represent the speaker's emotions but may struggle to convey contextual semantic information. Text data can capture rich semantic information but may suffer from ambiguity and are significantly influenced by the text recognition accuracy. This paper proposes an algorithm that integrates the strengths and mitigates the weaknesses of both audio and text data, achieving complementary multimodal feature information.  
The focus of multimodal feature fusion lies in the fusion stage and the fusion method, which will be discussed separately below.
## 4 Physiological Verifications  
The NeXus-10 MKII biofeedback system was employed to assess the HRV of homeowners as a method of physiological validation following the completion of projects. Participants were instructed to wear head-mounted displays for approximately 5 min to observe the photos and films, which are the recorded images of their homes before executing the projects. This process was then repeated to observe the photos and videos taken from their homes after finishing the projects. The NeXus-10 MKII device collected data at a sampling rate of 32 samples per second to guarantee precise acquisition of HRV-related information (Figure 14). The waveform data obtained during the monitoring procedure was displayed on the screen, as illustrated in Figure 15.  
Figure 12: The area around the living room of Project 3 after the execution of the interior design project.  
Figure 13: The floor plan for Project 3 illustrates the placement of the designed indoor elements with the top five DWx values.  
Figure 14: Photos (a-c) show the homeowners of projects 1, 2, and 3 as they watched images displayed in head-mounted displays and simultaneously measured HRV through NeXus-10 MKII.  
Figure 15: A captured image of the waveform graph during an HRV assessment of the homeowner of Project 1. While the display indicates that heartbeat-related data can be derived from the detection of the subject’s heartbeat, this study exclusively utilizes the data pertaining to HRV amplitude for further analysis.  
Upon analyzing the data collected by NeXus-10 MKII, it is evident that the average HRV recorded during the homeowner's viewing of finished indoor photos and videos through head-mounted displays surpasses the average HRV recorded during the observation of indoor images taken before execution of the projects (Table 7). This finding aligns with the literature discussed in Chapter 1, suggesting that the new environment positively influences the homeowner's physical and mental well-being. Furthermore, it validates the accuracy of interior designs guided by design-weighting calculations.
## 4 The Performance Parameters and Application Scenarios of 6G Networks
### Application Scenarios
#### 4.2.2 XR (Extended Reality) Based on Holographic Communication  
Augmented reality/virtual reality (AR/VR) is a crucial component of 5G technology that is distinguished by its mobility and independence from geographical restrictions, which are major factors driving the development of AR/VR technology and its applications. By 2030, information interaction will change from AR/VR to XR, including holographic communication systems, and wireless holographic communication will be a reality due to the rapid advancement of technology. Users will be able to take advantage of the improvements brought about by holographic communication and presentation at any time and from any location thanks to this advancement. By engaging their senses of sight, hearing, touch, smell, taste, and even emotion, XR will empower people and free them from the confines of space and time. This would allow users to fully immerse themselves in a variety of totally immersive holographic experiences, including games, concerts, sports, art, and educational activities.  
Figure 7: Categories of potential 6G application scenarios. Adopted from [42].
## 5 AI on Smart City Technologies
### AI Algorithms in Smart Cities
#### 5.1.2 Deep Learning (DL)  
DL refers to a category of ML that employs artificial neural networks to address intricate problems by acquiring knowledge from data. DL models have multiple layers of interconnected nodes that process specific aspects of the input data and pass it on to the next layer [116; 117]. This enables them to acquire a hierarchical understanding of the data, detecting intricate patterns and characteristics. Widely used DL structures comprise convolutional neural networks (CNNs), deep belief networks (DBNs), and recurrent neural networks (RNNs). DL can be applied in smart city scenarios to optimize traffic flow by predicting traffic patterns and tracking vehicles and pedestrians using real-time analysis of traffic camera feeds. It can also predict energy demand, identify energy-efficient building settings, optimize waste collection, and reduce landfill waste using data from sensors and cameras [118; 119]. DL can also detect and respond to safety threats using data from surveillance cameras and improve the ability of autonomous vehicles to detect and respond to objects and obstacles [120]. While DL models demand significant data and computational resources, their implementation can enhance the efficiency, sustainability, and safety of smart cities for the inhabitants.
## 3 Results
### Electromyography Recording  
An example of the activation pattern in flexors and extensors muscles measured from the Myo Gesture armband placed on the forearm of a healthy subject and an SCI patient during the execution of the serious games is shown in Figure 8. These data were analyzed taking into account the potential values obtained from the Myo Gesture armband. The EMG data provided by the Myo Gesture armband was unitless, 8 bit value. Hence, it provided a potential range between 0 and 255. To represent the muscle contraction, the value of the data collected from the sensor placed in the muscles of interest had been calculated. In all trials, SCI patients showed a stronger activation than healthy people. However, for all users, the muscle contraction was low, obtaining an average of 38.13 in \"Following the path\" game, 39.64 in \"Picking bananas\" games, and, finally, 53.35 in \"Collecting stars\".  
Figure 7: Example of a Healthy (blue) and SCI (red) participant range of motion (ROM) measured from the end-effector of Novint Falcon device in the Collecting stars game.
# Time Slotted Channel Hopping and ContikiMAC for IPv6 Multicast-Enabled Wireless Sensor Networks
## 3 Background
### Bmrf  
BMRF [13] is specially designed for duty cycled wireless sensor networks. It is an IPv6-based multicast protocol that supports both the LL unicast and LL broadcast downward forwarding of IPv6 multicast packets. Regardless of the LL mode, unicast or broadcast, upward forwarding is always unicast. The unicast mode of BMRF is developed according to the RPL multicast guidelines described in [12], whereas the broadcast mode enhances SMRF [14] at the expense of a small increase in memory consumption. The main features of BMRF are:  
Figure 2: Message exchange during the construction of the DODAG for the different steps (denoted between brackets). DAO: destination advertisement object; DIO: DODAG Information Object; DIS: DODAG Information Solicitation.  
Three modes of operation: unicast, broadcast, and mixed; thus, in unicast mode, all nodes use LL unicast to forward packets, and in broadcast mode, they use LL broadcast. In the mixed mode, each node decides whether to forward the IPv6 multicast packet using either LL unicast or LL broadcast according to a threshold based on its number of interested children (children that are group members or must forward the IPv6 multicast packets).
* Bidirectionality: multicast packets can be forwarded both upwards and downwards, allowing for the source of multicast traffic to be located in whichever node of the network.
* Duplicate avoidance.
* In order delivery of IPv6 multicast packets.
* Multi-sourcing: more than one node can, at some point in time, send multicast packets to the same multicast destination address [13].
* Dynamic group registration: BMRF follows the subscription process described in RPL and implements an adequate un-subscription process. Group registration is possible via RPL DAO messages. A child can unsubscribe from the multicast group by sending a special DAO message called the No-Path DAO. In this case, the parent removes the route to the child.  
BMRF manages to tackle some of the shortcomings of SMRF and RPL multicast, allowing for sources of multicast traffic to be located inside the networks and supporting dynamic group registration. This new protocol is configurable and, with optimal settings, can lead to fewer radio transmissions, less energy consumption, and a higher packet delivery ratio compared to SMRF [13].
## 2 Problem Formulation
### Mesh Generation and Boundary Condition  
Simplified computational domains for co-swirling and count-swirling four-point LDI combustors were modeled respectively. Using the commercial gambit software, the unstructured tetrahedralvolume grids were generated and are shown in Figure 3. Using a multi-block approach, the meshes of the flow domain for the inlet passages, seven vanes passages of swirlers, the venturi and the dump were refined to ensure uniform grid quality control. The hexahedral cells are used for each vane passage and jet holes of the injector and the mesh size was identical. To reduce computing time and memory spaces of workstation, the appropriate minimum grid quantity was pursued. Therefore, the grid-sensitivities for single and four-point LDI combustors with non-reaction flow were investigated, as shown in Table 1. Compared with the effective areas of simulation and experiment results, the error of all the cases are the range of \(\pm 3.6\%\), which indicates that the computation grids for single and four point LDI combustors are feasible. For the single LDI, the computation grids with 2.2 million cells were an appropriate minimum grid quantity. Based on the computation grids of single LDI, the computation grids with 6.266 million cells and 6.265 million cells were acceptable meshes for co-swirling and count-swirling four-point LDI combustors, respectively.  
The inflow boundary condition of the four-point LDI combustors includes an airflow mass-flow rate (\(m_{in}\)), fuel mass-flow rate (\(m_{f}\)) and inlet temperature (\(T_{t3}\)). An outflow boundary was set for the exit. Table 2 lists the boundary conditions for the four-point LDI combustors computation, in which  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ModelsParameter** & **Number of Mesh (M)** & \(M_{in}\) **(g/s)** & \(\Delta\)**P (\%)** & \(A_{cd}\) (mm\({}^{2}\)) & \(\rho_{\text{air}}\) (kg/m\({}^{3}\))** & \(A_{cd}\) (Expt) (mm\({}^{2}\)) & **Error (\%)** \\ \hline Single-LDI-CW & 2.25 & 8.495 & & 97.644 & & & \(-0.15\) \\  & 4.0 & 8.636 & & 99.25 & & 97.5 & \(-1.8\) \\ \hline Single-LDI-ACW & 2.197 & 8.232 & & 94.621 & 1.245 & & \(1.95\) \\  & 4.0 & 8.572 & & 98.529 & & \(-2.1\) \\ \hline Four-point LDI-Co & 6.266 & 33.22 & & 381.84 & & 395.6 & 3.48 \\ \hline Four-point LDI-Count & 6.265 & 33.42 & & 384.14 & & 398.4 & 3.58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grid-sensitivities for single and four-point LDI combustors with non-reaction flow.  
Figure 3: Mesh for a four-point LDI combustor.  
and \(m_{in\text{-}2}\) represent the airflow mass-flow rate of the single-element LDI combustor and four-point LDI combustor, respectively. The inlet temperature and operating pressure (\(P_{3}\)) are constant for valid comparison. The airflow mass-flow rates of the four-point LDI combustors are four times that of the single-element LDI combustor. The fuel mass-flow rates vary with different equivalence ratio.
## 4 Results and Analysis
### Temperature Difference  
As another direct evaluation criterion, \(\Delta\)T reflects the temperature uniformity of a BTMS. Figure 8 shows the \(\Delta\)T of seven designs at five flow rates. At 10.56 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s, only Design 2 (4.40 K) and Design 3 (4.75 K) met the evaluation criterion of \(\Delta\)T (\(\leq\)5 K). The GVSG designs (Design 5-7) showed an average \(\Delta\)T of 8.61 K, revealing the poor temperature uniformity of the gradient vertical spacing designs at low flow rates. Figure 9 shows the air flow velocity streamlines and temperature contour of Design 6 at 10.56 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s. The locations with red (higher) velocity streamlines (especially at the vertical clearances of the six-cell columns close to the inlet) are usually the places where sufficient heat transfer between air flow and battery cells is conducted, leading to lower cell temperatures. On the contrary, the air flow velocity streamlines along the wider vertical cell clearances in the four-cell columns close to the outlet are much lower (the green velocity streamline represents nearly half of the velocity of the red one), leading to less heat transfer and the higher temperature distributions of the surrounding cells.  
When the flow rates reached 21.12 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s, the \(\Delta\)T values of Design 1-3 (3.11 K, 4.24 K, and 3.83 K) were all lower than 5 K and lower than those of Design 5-7 (5.58 K, 4.90 K, and 4.15 K). At 31.68 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s, all designs except Design 4 delivered \(\Delta\)T lower than 5 K. The average \(\Delta\)T of CVSG (3.67 K) was 0.34 K lower than that of GVSG (4.01 K). The turning point appeared when the flow rate reached 42.24 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s, Design 7 started to deliver the lowest \(\Delta\)T (2.88 K) among all designs. Design 1 dropped to second place in terms of the temperature uniformity performance and its advantages over Design 2, 3, 5, and 6 gradually shrank with the increase of the flow rates. At 52.8 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s, the leading margin of \(\Delta\)T of Design 7 (2.6 K) increased to 0.44 K over Design 1 (3.04 K). At higher air flow rates, the temperature uniformity of the gradient vertical spacing designs with larger spacing values (44/33/26.4 mm) improves more than that of the constant vertical spacing designs.  
Figure 7: Minimum temperatures of 7 designs at 5 flow rates.
# A Multichannel, High-Bandwidth Wirelane Receiver for D2D Interconnects
## 2 Receiver Design  
To improve clock quality and reduce BER, forward clock (FCK) is often used in D2D interconnects. Figure 2 shows a FCK architecture [9; 10]. In the FCK, the frequency of the clock and the data are well matched, so the jitter introduced in the data transmission is small. The sampling clock can be adjusted by a phase interpolator (PI), so a length mismatch between the data line and the clock line are allowed in FCK transmission. The advantage of the mode is that the sending and the receiving ends are relatively independent, which is convenient for modular design and reduces the complexity of the receiver, so it is very suitable for D2D interconnects [6; 7; 11]. The additional clock path will occupy some interconnect channels, and will cause additional Si-area and power consumption. However, these are distributed among the individual data paths with the number of channels increasing.  
In D2D interconnects, each channel is often configured with its own clock recovery circuit (CDR), because the skew of the clock on each channel is different. In this design, CDA and PLL structures are used instead of CDR, and delay matching \(\delta\) is used to match the clocks on each channel. The advantage of this method is that it is not necessary to configure CDRs on each channel, so the chip area and power consumption can be greatly reduced in multi-channel transmission, thereby increasing the bandwidth density.  
Figure 1: Chips interconnection of different processes.  
Figure 2: Forward clock architecture.  
As shown in Figure 3, there is the Integral structure of the receiver. The output of PLL generates eight-phase equal-phase clocks, which contain PFD, filters, charge pumps (CP), VCO, divide-by-4 circuits (DIV4), and PI [12]. The MINI-PLL adjusts the phase of the PI, so that the clock generated by the VCO is sampled to the best position of the data. CDA is used to process the phase early/late information and send it to the PI [4; 13].
# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 2 Model Architecture
### Mixture Alignment Encoder  
Compared to the encoder in FastSpeech2, the mixture alignment encoder incorporates an additional Tibetan character encoder. The architecture of the mixture alignment encoder is depicted in Figure 3a. CP represents the pooling operation at the Tibetan character level, and LR represents the length regulator introduced in FastSpeech [11]. A sequence of Latin letters containing Tibetan character boundaries is passed as input to the encoder. The Latin letter encoder transforms the input sequence of Latin letters into a Latin letters hidden state, subsequently applying character pooling on the Latin letters hidden state to obtain input from the character encoder, and averaging the Latin letters hidden state within each character according to character boundary information. The character encoder processes the received Latin letters hidden states into character level hidden states, subsequently utilizing a length regulator to adjust the character level hidden states based on character-level durations in order to align with the length of the target Mel spectrogram. Lastly, the Latin letters hidden state and the character level hidden state are processed by character-level relative position encoding and then forwarded to the character-to-letter attention module.  
In the mixture alignment model, the extension of the Latin letters' hidden representations is achieved through the integration of the character level duration predictor and the character-to-letter attention mechanism within the mixture alignment encoder. The durations of Tibetan characters are determined by summing the durations of each Latin letter within a character to ensure precise alignment at the character level. Subsequently, the character-to-letter attention mechanism calculates the attentional weight of each Latin letter's hidden representation within a character to achieve soft alignment at the Latin letter level. The advantage of the mixture alignment mechanism lies in the fact that maintaining precise alignment at the character level results in reduced errors compared to alignment at the Latin letter level. Furthermore, implementing soft alignment at the Latin letter level facilitates the acquisition of more fine-grained information.  
Figure 2: Tibetan sentences and Wylie transcription. The Tibetan sentences in the figure translates to ‘Also includes a frying pan and other cooking utensils’.