esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Existing Routing Methods for the DTN
An upper limit is set to the number of hops within a destination area in order to avoid traffic concentrating on any specific receiving node when a receiving node receives a bundle from a traveling node. A service of local content delivery assumed for the DTN is shown in Figure 2. In this service, a bundle is destined to unspecified terminals within a specific destination area. The destination area is located not very far from the sending terminal. Delivery of content is considered to be useful even if there is a transmission delay in the order of several hours to several days. It is also assumed that it is not a serious problem, even if some intended receiving terminals cannot receive a bundle. This paper focuses on two issues related to the traveling route information-based routing. The first is how to define a multipoint address based on location information when terminals within a specific destination area are receiving terminals. The second is how to enable the sending node to learn in advance whether the bundle it is going to send can reach its destinations. The aim is to reduce the network load that would be generated by futile attempts to store and relay bundles that cannot reach their destinations. For the former issue, we introduce the concept of \"destination area\", which will be described in detail in Section 3.1. For the latter, we introduce the concept of \"covered area\". Whether a bundle can reach its destinations is determined based on whether a covered area overlaps with the destination area, as will be described in Section 3.2 to Section 3.3. Figure 2: Traveling route information-based routing and local content delivery.
###### Abstract  
_Objectives_: Tinnitus, defined as the perception of sound without an external stimulus, is a complex condition with unclear etiology. Emerging evidence suggests a link between vascular dysfunction, particularly arterial stiffness, and tinnitus. This study aimed to evaluate vertebral artery stiffness in patients with idiopathic subjective tinnitus and assess the utility of Doppler ultrasonography as a non-invasive diagnostic tool. _Materials and Methods_: In this prospective study, 31 patients with idiopathic tinnitus (11 males, 20 females; mean age: \(50.42\pm 9.64\) years) and 24 healthy controls (12 males, 12 females; mean age: \(39.67\pm 10.63\) years) underwent comprehensive clinical evaluations, including pure tone audiometry, blood tests, and vertebral Doppler ultrasonography. Vertebral artery stiffness index (VAS), resistive index (RI), and pulsatility index (PI) were measured bilaterally. _Results_: A total of 31 patients with idiopathic subjective tinnitus and 24 healthy controls were evaluated. The mean age was significantly higher in the tinnitus group compared to controls (50.42 \(\pm\) 9.64 vs. 39.67 \(\pm\) 10.63 years, \(p\) < 0.001). Lipid profile analysis revealed significantly higher levels of total cholesterol (193.6 \(\pm\) 47.28 vs. 167.5 \(\pm\) 28.99 mg/dL, \(p\) = 0.021), LDL (149.4 \(\pm\) 37.9 vs. 106.1 \(\pm\) 10.7 mg/dL, \(p\) < 0.005), and triglycerides (202.2 \(\pm\) 83.5 vs. 148.6 \(\pm\) 26.4 mg/dL, \(p\) < 0.005) in tinnitus patients. Doppler ultrasonography demonstrated significantly higher vertebral artery stiffness values in the tinnitus group (left: 2.87 \(\pm\) 0.72 vs. 2.12 \(\pm\) 0.22; right: 2.99 \(\pm\) 0.77 vs. 2.14 \(\pm\) 0.5; both \(p\) < 0.005). Similarly, pulsatility index (PI) was significantly elevated in patients compared to controls (left: 2.45 \(\pm\) 1.2 vs. 1.2 \(\pm\) 0.43; right: 2.49 \(\pm\) 1.02 vs. 1.19 \(\pm\) 0.42; both \(p\) < 0.005). No significant differences were observed in resistive index (RI) or vertebral artery diameters between groups. Among tinnitus patients, PI and VAS were significantly higher on the side corresponding to reported tinnitus symptoms (\(p\) < 0.05), suggesting a lateralized vascular contribution. _Conclusions_: The findings suggested a potential relationship between idiopathic subjective tinnitus and vertebral artery stiffness. We demonstrated the utility of Doppler ultrasonography, a cost-effective and non-invasive imaging modality, for evaluating vascular parameters in tinnitus patients, paving the way for broader clinical applications. By uncovering a significant association between vertebral artery stiffness and tinnitus, our findings suggest that vascular health assessments could enhance diagnostic and therapeutic strategies for tinnitus management.  
**Keywords:** tinnitus; vertebral artery; arterial stiffness; pulsatility index; doppler ultrasonography
## 3 ME-SSA Transmitter Design and Waveform Definition  
This section describes the characteristics of the ME-SSA PHY layer used for burst generation and introduces the main features of the MASSIVE waveforms.  
Figure 2 illustrates a high-level block diagram of the burst generator. Each burst is composed by the sequence of the following fields: preamble symbols, transmit format indicator (TFI) symbols, data symbols and periodically inserted pilot symbols. The data symbols are constructed as follows.  
A cyclic redundancy check (CRC) bit sequence was appended to the incoming information bits from the upper layer. The resulting data packet was encoded by a forward error correction (FEC) turbo coding scheme and mapped into QPSK modulated symbols.  
Preamble and pilot symbols were inserted as shown in Figure 2 to ease the burst acquisition and the estimation of the burst symbols timing offset, carrier frequency and phase of the received burst at the gateway.  
The TFI field was used as an identifier of the transmitted waveform containing parameters such as coding rate, modulation, spreading factor and payload data size. The TFI field  
Figure 1: MASSIVE system scenario overview.  
Figure 2: MASSIVE burst generator.  
allows the receiver to tune the proper demodulation parameters according to the detected burst.  
In the following subsections, the impact of the two new features with respect to the conventional E-SSA burst format is discussed, i.e., the addition of the QPSK modulation (Section 3.1) and the adoption of CE spreading (Section 3.2). Finally, in Section 3.3 the MASSIVE waveforms are defined.
## 4 Methodology
### Social Robot Movement
#### 4.9.2 Executing Specific Movements  
In addition to tracking, [PERSON] is capable of performing precise movements based on verbal commands given by the user. This process involves several key steps that transform spoken instructions into executable actions for [PERSON]:  
1. Transcription: The user's spoken command is first captured by [PERSON]'s audio system and transcribed into text using built-in speech recognition technologies.
2. Command Parsing: The transcribed text is then analyzed and broken down into its components. The system identifies the action word (e.g., \"move\", \"turn\") and additional parameters such as direction (e.g., \"left\", \"forward\") and magnitude (e.g., distance in meters or angle in degrees).
3. Action Execution: After parsing the command, it is translated into executable instructions that are sent to [PERSON]'s motion control system. These instructions are processed in real time, allowing [PERSON] to perform the desired movement with precision.  
For example, if the user issues the command \"[PERSON], move forward two meters\", the system will recognize the action (\"move\"), the direction (\"forward\"), and the distance (\"two meters\"). The parsed command is then converted into low-level control signals that guide [PERSON] to move forward by exactly two meters.
## 3 Results and Discussion
### Future Changes in Streamflow at Bernam Basin Outlet  
The validated HEC-HMS Model was used to assess the future changes in streamflow at the basin outlet. Climate change effects were assessed by combining all simulations of similar carbon emission scenarios for the three future periods using the ensemble of the 10 GCMs. The changes are the differences relative to the baseline period (1976-2005). The changes in future streamflow at Bernam river basin is more pronounced during the dry season period (Figure 7). This might be associated with the warmer temperature during the dry season usually, which consequently increases the rate of evapotranspiration more compared to wet season period, thus affects the future streamflow. The average changes under RCP 4.5, RCP 6.0, and RCP 8.5 are \(-\)0.40%, \(-\)1.68%, and \(-\)5.71%, respectively, during the dry season. In general, the future periods indicate a decreasing streamflow trend in this season, with a higher percentage (\(-\)5.94%) predicted in the 2080s under RCP 8.5 scenario. Similarly, in the wet season, streamflow decreases in all future periods except for RCP 4.5 where is expected to increase (0.36%). The average changes under RCP 6.0 and 8.5 are \(-\)0.67% and \(-\)3.83%, respectively. The changes in future streamflow at the Bernam River Basin would generally be more apparent during the dry season period. This may be connected with the warmer temperature during this time, thus affecting the future flow. The available water for supply to the scheme is largely reliant on the streamflow at the Basin outlet. A significant change in the future streamflow translates to the available irrigation supply. Therefore, the scheme is predicted to experience water shortage, particularly during the dry season period. This would then affect the water resources in the scheme, which could lead to adverse impacts on the overall crop production in the area. A Similar decrease in annual streamflow was observed by [5]. A decrease in annual streamflow in the wettest scenarios and decline in the driest scenarios was also reported by [52].
# A Femto-Satellite Localization Method Based on TDOA and AOA Using Two CubeSats
## 3 Results
We called \(\alpha\) the angle formed by the femto-satellite with the two CubeSats. When this angle is near \(180^{\circ}\), it means that the femto-satellite is between the CubeSats, and when it is near \(0^{\circ}\), it means that the mother-CubeSat is between the femto-satellite and the second CubeSat. In the simulations, we calculated the mean of \(\alpha\), the case with the maximum \(\alpha\), and the case with the minimum \(\alpha\). There are cases with angles near \(180^{\circ}\) or near \(0^{\circ}\) because the deployment simulation considers the accuracy of the ADCS. Sometimes the femto-satellite is launched towards the positive \(y\) axis deviated forward, and sometimes, it is deviated backward. If we also consider the oscillation of the distance between the femto-satellite and the mother-CubeSat, it is clear why the angle \(\alpha\) reaches these values. Figure 8 shows that these alignments coincide with the peaks of the root CRB where the  
Figure 7: Source localization RMSE and bias as \(\sigma_{\text{GNSS}}\) increases from 10 m to 120 m, at three days after the deployment. estimation error increases. This increase occurs because these localization geometries are detrimental for the AOA method [27; 28]. We obtained the best results when the angle \(\alpha\) is between \(80^{\circ}\) and \(100^{\circ}\).
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 4 Heuristic Algorithms for Joint Content Recommendation and Delivery  
In this section, we propose several heuristic algorithms that divide the task of joint content recommendation and delivery into two steps. The first step is resource allocation, which aims to optimize multi-user radio resource allocation based on certain heuristics, for example to maximize the sum capacity or user fairness. After this step, each user will have a pre-allocated transmission capacity. The second step is content recommendation, which aims to maximize user interests based on the pre-allocated user capacity. We note that the above heuristic algorithms still try to jointly optimize content recommendation and delivery. The difference with the optimal algorithm is that in the optimal algorithm, content recommendation and radio resource allocation are jointly optimized, so that the radio resource allocated to a user is directly related to the user's interest profile. In the proposed heuristic algorithms, radio resource allocation is based on other chosen criteria and is hence not directly related to the users' interest profiles. In what follows, we will introduce the heuristic algorithms in detail and analyze their performance.
# Bluetooth Communication Leveraging Ultra-Low Power Radio Design
## 3 BLE Radio Design Trends and Considerations
### Transmitter Design Trends
#### 3.2.3 Reduction of On-Chip/Off-Chip Components  
In order to reduce the overall cost of the BLE module, a lot of research has been done to reduce the on-chip and off-chip components. In the past decade, a lot of the research focused on reducing the number of passive devices in the matching networks. Ref. [10] proposed a BLE transceiver with embedded RX image-rejection filter and TX harmonic-suppression filter by reusing an on-chip matching network that reduced the number of passive devices. Ref. [53] proposed a BLE transmitter utilizing a co-designed loop antenna and combined enhanced power oscillator which embedded the matching network into the power oscillator while improving both the VCO phase noise and transmitter efficiency. These designs reduced the number of on chip inductors which typically occupy a large portion of the die area. As mentioned above, the all-digital BLE transmitter design proposed in [50, 54] replaced the LC-VCO with an ultra-low power ring oscillator and successfully removed all the inductors from the transmitter, resulting in a 40\(\times\) core area saving compared to the state-of-the-art.  
Recent work focuses on removing the crystal oscillator (XO) in BLE transmitters [57, 58, 59]. The XO is one of the most expensive off-chip components in a BLE module. Several XOreplacements have been evaluated, such as a real time clock (RTC), FBAR oscillator and recovered RF signal. In [57], the high-frequency XO is replaced with a 32 kHz real-time clock (RTC) for frequency calibration and channel selection, while in [58], the XO is replaced with an FBAR resonator. The work in [46; 58] reported an XO-less BLE transmitter with clock recovery from GFSK-modulated BLE packets. These designs show that the BLE transmitter is capable of working without a typical MHz-range XO and it not only reduces the overall cost of the BLE module but also introduces some new design insights for BLE beacon transmitters.
## 6 Dependability for MCAs in 6G
### Dependability Analysis of 6G MCAs
#### 6.2.1 Reliability  
The reliability of an MCA is directly related to how well it complies with the stringent requirements in terms of latency and errors. From one point of view, these requirements are met with the help of capable underlying networks. However, reliability is also affected by the architecture chosen for a given MCA, since it will determine the overall behavior and qualities of such an application. From the 6G perspective, the importance given to software architecture is nowhere near that of the underlying network. Of course, a capable network greatly benefits an application's performance, but a proper architecture further improves these benefits. Focus should also be given to use case-specific hardware, as an MCA will depend on their quality and trustability, as many use cases revolve around their use--for example, telesurgery.
# Congestion Analysis of Transport Layer in a Multicell 5G DL Communication System
###### Abstract  
The fifth generation (5G) marks an important advance in mobile network capabilities. When it comes to high data rates, capacity, spectrum efficiency, and availability, 5G mobile broadband goes above and beyond what was previously possible with standard mobile broadband. The construction of 5G networks is still in the planning stages. These 5G networks will create intelligent networked communication environments by connecting people, things, data, applications, and transport networks. Mobile networks have made it possible for customers' mobile devices (such as smartphones, tablets, laptops, and so on) to connect to the internet. A variety of distinct protocols will be required to take into consideration the various aspects that 5G possess. One of these is the transport protocol, which is intended to deliver extremely high data transfer rates up to 400 Gbps. The transmission control protocol (TCP) is one of the numerous protocols that are necessary for supporting 5G's many capabilities. Our work focuses on the detection and analysis, on the downlink (DL) side, of the congestion of the transport layer in single- and multicell environments. For the purpose of the analysis, the following metrics were analyzed: physical resource blocks (PRBs), user throughput, cell throughput, cell edge user throughput, and delay. The work emphasizes the activation of the TCP slow-start algorithm using file transfer protocol (FTP) model two according to 3 GPP standards.  
5G communication systems; file transport protocol; transmission control protocol; slow-start algorithm +
Footnote †: journal: Sensors  
Citation: [PERSON]; [PERSON]; [PERSON]; [PERSON]; Congestion Analysis of Transport Layer in a Multicell 5G DL Communication System. _Sensors_**2023**, 23, 6111. [[https://doi.org/10.3390/s23136111](https://doi.org/10.3390/s23136111)]([https://doi.org/10.3390/s23136111](https://doi.org/10.3390/s23136111))
# Quantitative Assessment and Grading of Hardware Trojan Threat Based on Rough Set Theory
## 2 System Model and Assessment Formulation
### Weight Calculation and Quantitative Assessment  
In the framework of RST, the quantitative assessment of HT threat can be transformed into an attribute weight calculation of the information system. The existing attribute weight calculation needs a decision attribute as a reference, while the weight of the core attribute cannot be calculated in an information system lacking decision attributes. Thus, we use the information content and significance of each attribute here in order to obtain the relevant weights for both core and non-core attributes. As a linear superposition of all attribute values and their significance cannot directly assess HT threat, in order to solve this problem we propose a learning algorithm based on the weight of the attributes and the TOPSIS method to measure the HT threat.
## 3 Results
### Experimental Results  
Experimental setup of the data acquisition is shown in Figure 2a, demonstrating our two methods of illumination: (ii) full-field illumination and (iii) single-fiber overhead illumination. The fast DMAS reconstruction algorithm was used for image reconstruction with the near-field compensation curve applied prior (please see the exclamation in Section 2.2). We demonstrated in Figure 2b that full-field illumination (v,vi) has significantly improved the reconstruction compared to overhead illumination (Figure 2b(vii,viii). The simulated target object has also been reconstructed for comparison purposes as shown in Figure 2b(iii,iv). The main reason was that a more homogeneous fluence map was generated by full-field illumination compared to overhead illumination (see the explanation provided in Section 3.1). CNR of the reconstructed images using both methods on two imaging targets was quantified; they are given in Figure 2c. As expected, the full-field illumination yielded improved image quality due to the even deposition of optical energy around the imaging target, thus producing a uniform initial induced pressure. The absolute CNRs were calculated for experimental images. For the triangle-shaped phantom, CNR of the image generated by the full-field illumination is 3.2 times greater than that generated by the overhead illumination. For the pyramid-shaped phantom, the improvement is 1.6 times. In terms of spatial resolution, the developed system was able to reconstruct objects to be as close as 0.9 mm to each other.
## 3 Methodology  
### Rock Sample Characterization  
Rock samples were collected from 10 different outcrops in south Korea to perform weathering experiments. X-ray diffraction (XRD) was performed on each sample at the Institute for Basic Science, Korea, to characterize their mineral contents. Experimental results are shown in Figure 4 and the mineral composition of each sample is summarized in Figure 5, which presents the proportions of the three most abundant minerals in each case, among albite, biotite, calcite, chlorite, dolomite, hematite, hornblende, magnetite, microcline, muscovite, orthoclase, and/or quartz. Following the sample order shown in Figure 5, the most abundant mineral in each rock was albite (42.4%), albite (52.9%), albite (28.2%), albite (39.4%), quartz (68.6%), quartz (49.2%), quartz (43.4%), biotite (30.6%), calcite (97.4%), and chlorite (26%). Each rock was named according to the most abundant mineral constituent. Although 10 different sites were examined to reflect the variability of specimens throughout the study region, albite, quartz, biotite, calcite, and chlorite formed the main mineral constituents in most cases. Each sample was extracted in the shape of a cylinder with a diameter of 46 mm, and was subsequently abraded to a length of 30 mm in order to promote the weathering procedure and effectively measure elastic wave velocities.  
Figure 4: Results of X-ray diffraction for samples (**a**) albite 1, (**b**) albite 2, (**c**) albite 3, (**d**) albite 4, (**e**) quartz 1, (**f**) quartz 2, (**g**) quartz 3, (**h**) biotite, (**i**) calcite, and (**j**) chlorite.
## 3 Results and Discussion
### Electrochemical Characterization of IIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE Sensors
#### 3.2.4 Characterization of Sensor Surface Morphology  
Scanning electron microscopy (SEM) images characterize the surface roughness and micromorphology on the nanoscale in order to better study the elution and adsorption processes of uranyl ions. Figure 4A-E shows the surface topography of bare MCPE, MIIP/MCE, N-MIIP/Fe3O4@SiO2/MCPE, MIIP/Fe3O4@SiO2/MCPE before elution, and MIIP/Fe3O4@SiO2/MCPE after elution utilizing SEM. As can be seen from Figure 4A, the surface of the electrode was relatively flat and there is basically no polymer formation. While MIIP/MCE can be observed that lots of spherical particles were deposited on the surface of the imprint electrode (Figure 4B), the IIP tube diameter with Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) modification attached was larger compared to the Figure 4B modified electrode, which also indicates that the larger specific surface area was conducive to IIP attachment and better dispersion with smaller particle size (Figure 4D). However, the surface of the non-ionic imprinted polymer modified electrode was disordered because the surface of electrode N-MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE did not form imprinted cavities (Figure 4C). As presented in Figure 4E, the surface morphology of the sensor became rough and loose after the template ion elution, showing a porous morphology. Meanwhile, numerous tiny 'imprinted holes' can be observed. This is due to the fact that the template ion went away the polymer membrane, an imprinted pore matching the structure of the template ion would be left on the polymer membrane. Depending on the shape of the imprinted pore and the site of recognition, the imprinted polymer membrane can be recognized specifically and reabsorbed. The existence of these template ions 'imprinted holes' contributes a lot to improving the adsorption selectivity.