esa-sceva/satcom-chunk-collection huggingface dataset:
# A Decision Framework for Choosing Telecommunication Technologies in Limited-Resource Settings
## 2 Related Work
### Studies on Comparison of Different Telecom Technologies  
In a study by [PERSON] et al. [PERSON] et al. (2018), the authors focus on the different aspects of providing last-mile rural telecommunication access considering interfering factors, technology options, and deployment trends. Their choice of contributing factors includes geographic location, economic conditions, motivation and adaptability, and sustainable business framework. Technology options considered are wired and fixed wireless. Development trends are long-reach Wi-Fi, cellular, WiMax, Long-term Evolution (LTE), delay-tolerant networks, cognitive usage of TV white spaces, power line communications, MIMO wireless networks, and other methods such as Zigbee, and combinations of the above technical approaches. They consider only purely technical aspects and provide no decision framework.  
In a discussion of an alternative form of network coverage for rural villages by [PERSON] et al. [PERSON] et al. (2016), the authors point out that coverage is often lacking in areas of low population density in impoverished rural areas of the developing world. The telecommunication giants seldom deploy expensive infrastructure in such areas. Their proposed solution is called VillageCell. VillageCell is a low-cost alternative to high-end cell phone networks. They show that call quality of VillageCell is good, and they discuss energy requirements, licensing issues, and integration into the existing rural-area wireless network.  
A business approach was adopted by [PERSON] et al. [PERSON] et al. (2018) who gave specific cost estimates and demonstrated the business case for a Wi-Fi/CDMA450 network with nearly fully subsidized cellular handsets as end-user devices. The authors used the Akshaya Network located in Kerala, India as a specific case study to show that a wireless network using Wi-Fi for the backhaul, CDMA450 for the access network, and shared personal computers (PCs) for end-user devices have the lowest deployment cost. However, the authors did not compare their proposed solution to other solutions.  
A spectrum measurement study [PERSON] et al. (2018) in the Dallas-Fort Worth metropolitan and surrounding areas proposes a measurement-driven band selection framework. The authors measure the white space and Wi-Fi bands with in-field spectrum utility measurements, revealing the number of access points required for an area with channels in multiple bands. They conclude that as more channels are available in the white space bands, fewer access points are required for a given area, and as such white space solution is superior.  
In addition, there is extensive literature [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018) regarding a design of networks in rural locations, mostly for health applications using solar energy and heterogeneous wireless networks. These solutions utilize network optimization in order to provide broadband in low-resource settings.
## 3 Results
### Drinking Activity Identification Using Multimodal Signal  
The sample-based evaluation results mark the highest and lowest standard deviation in each column in bold, as shown in Table 5. All classifiers in the table demonstrate better performance in classifying multimodal signals than in classifying single-modal signals. Among the results of single-modal signals classification, the performance of single-modal motion signals is better than that of single-modal acoustic signals. Among the results of identifying drinking activities on multimodal signals with different classifiers, support vector machine (SVM) and extreme gradient boosting (XGBoost) show better performance and the average \(F1\)-score of the two reaching 83.7% and 83.9%, respectively, with 3.1% and 2.7% higher than that of single motion signals, and significantly higher than single acoustic signals by 47.5% and 56.2%. The standard deviations of SVM and XGBoost are only 3.8%  
Figure 11: Multimodal information diagram of taking a small \(\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{ \{ \mathrm{ }}}}}}}}}}}}}}}}}\) with the right hand while sitting (D05). The blue background area presents the intervals labeled as drinking in the trial, while the remaining white areas are marked as non-drinking. Table 6 shows the results of the event-based method, which better indicates whether the classifier correctly identifies drinking activities and allows for further evaluation of the result of misclassified events. Similar to the results of the sample-based approach, the event-based evaluation results also show better performance in classifying multimodal signals. Among them, the results of the support vector machine are superior. Accuracy, recall, and \(F1\)-score are 95%, 98.1%, and 96.5%, respectively. The lowest \(F1\)-score of other classifiers is 94.7%. From the above, it can be inferred that using multimodal signals for drinking activity recognition performs better than using single-motion or acoustic signals alone. To further investigate the necessity of sensors at each position in multimodal signals, motion signals are divided into both wrists (W) and cups (C) based on the placement of inertial measurement units. Tables 7 and 8 present the results of drinking activity recognition using a support vector machine with different combinations of input signals from the inertial sensors on both wrists and the cup, as well as signals received from the microphone. Among them, the combination of signals from both wrists and the cup (WC) corresponds to the performance of the motion signals (M), as shown in Tables 5 and 6, whereas the combination of signals from both wrists, the cup, and the acoustic (WCA) presents the
# Frequency-Dependent Schroeder Allpass Filters
## 2 Schroeder Allpass Filters
### [PERSON]'s Absorbent Allpass  
Schroeder allpass filters have been in use in larger feedback structures for artificial reverberation [4; 10]. [PERSON] and [PERSON] [11] realized that Schroeder allpass should have a similar decay characteristic as the overall reverberator to blend in well. To achieve this, they introduced a lowpass filter in series to the delay, which follows the overall reverberation time specification (see Figure 3).  
The corresponding transfer function is  
\[H_{\text{Dahl}}(z)=\frac{X(z)}{Y(z)}=\frac{g^{*}+z^{-m}c(z)}{1+gz^{-m}c(z)} \tag{17}\]  
We illustrate the filter with the following example in Figure 4. The main delay is \(m=50\), the feedforward/-back gain \(g=0.7\) and the low-shelving filter is  
\[g(z)=\frac{0.4644-1.2175z^{-1}+0.9000z^{-2}}{1.0000-1.3799z^{-1}+0.5310z^{-2}}. \tag{18}\]  
As also realized by the original authors, the resulting filter is no longer allpass.  
The proposed application of the absorbent allpass filter is as additional diffusion in a feedback delay network (FDN) [11]. However, the pole decay rate of the FDN depends on the magnitude response of the recursion filter [21]. Figure 4b demonstrates that the FDN pole decay rate fluctuates highly which results in an uneven reverberation tail. Nonetheless, the set goal to construct a Schroeder allpass filter with a given decay characteristic serves further as an inspiration.  
Figure 3: [PERSON]’s allpass filter with an additional lowpass filter \(c(z)\)[11].
## 3 Modeling for Typical Task Uncertainty Evaluation
### Uncertainty Model for Location and Orientation Errors Measurement Tasks
When measuring the angularity and perpendicularity, CMM will determine the fitting element (direction) according to the reference element and theoretical angle and calculate the difference of the limit distance \(t=L_{1}-L_{2}\) between the measured elements and the fitting elements. Therein, \(L_{1}\) and \(L_{2}\) indicate the distances from the measured elements to the fitting elements. Relative to the dimensional measurement, \(L_{1}\) and \(L_{2}\) both are miniature dimensions, not considering the influence of linearity but only the constant term of the measuring instrument deviation. The uncertainty \(u_{\text{EW}_{2}}\) of angularity and perpendicularity measurement tasks caused by the indication errors is as follows according to uncertainty type B evaluation method:  
\[u_{\text{EW}2}=\frac{2\cdot A}{\sqrt{6}} \tag{15}\]  
Where, \(A\) is the constant term of \(E_{\text{L, MPE}}\)=A+B-L. The position degrees can be divided into point, line and plane position degrees. The position degree is essentially two times the maximum distance between extracted elements (point, line and plane) and fitting elements (determined by reference elements and theoretical size), that is, the micro-level dimensional measurement, which can neglect the influence of dimension sizes and only consider the constant term of the deviation. Similarly, coaxiality represents the maximum distance from the point of the measured element to the reference element (point or line) and is essentially a micro-level dimensional measurement. The indication error also only considers the constant term of the deviation. The uncertainty component \(u_{\text{EW}_{3}}\) of the CMM position degree and coaxiality measurement tasks caused by the indication errors is as follows according to the uncertainty type B evaluation method:  
\[u_{\text{EW}_{3}}=\frac{A}{\sqrt{3}} \tag{16}\]  
Symmetry is essentially two times the maximum difference between the two extracted elements to be measured and the reference vertical distance, namely, \(t=2(L_{1}-L_{2})\). Essentially, it is the difference of the micro-level dimensions and the indication error only considers the constant term of the deviation. Then, the uncertainty component \(u_{\text{EW}_{4}}\) caused by the indication error is as follows for CMM symmetry measurement task:  
\[u_{\text{EW}_{4}}=\frac{2\cdot A}{\sqrt{6}} \tag{17}\]  
Similarly, the uncertainty components caused by repeatability and reproducibility of position error measurements are Formula (6) and Formula (7). Substitute Formula (14)-Formula (17) into Formula (3) and a universal model can be obtained for evaluating the uncertainty of the CMM position measurement tasks.
## 5 Simulation
### Results and Analysis  
As shown in Figure 5, network lifetime decreases with the increment of network size. The reason is that, in a large network, the data amount forwarded by relay nodes is much larger than a small network that consumes more energy on data forwarding. We can also observe that centralized algorithms achieve better performance than distributed algorithms. This is because centralized algorithms can leverage all sensors' information to make a sub-optimal solution.  
Due to the channel capacity, more \(S_{A}\) sensors are needed in a larger network. Thus, as shown in Figure 6, AUV travels longer in a larger network than a small one.  
Figures 7 and 8 illustrate how the network lifetime changes with different trade-off factors in different network sizes. As we can see, in centralized HAS\({}^{4}\), the network lifetime grows with the growth of trade-off factor. This is because the bigger the trade-off factor is, the less \(S_{U}\) nodes exist, which reduces the amount of acoustic data communication. In distributed HAS\({}^{4}\), since the trade-off factor is the longest source to end path. Thus, with a small trade-off factor, there are more \(S_{A}\) nodes, which reduce the acoustic communications.  
Figure 5: Network lifetime vs. network size with different sink selection algorithms.  
Figure 6: Tour vs. network size with different sink sensor selection algorithms.  
As we designed in centralized HAS\({}^{4}\), the trade-off factor is the maximum percentage of \(S_{A}\) nodes, the larger the factor is, the more \(S_{A}\) nodes will be. Thus, the reason that the AUV tour increases with the increment of trade-off factor in Figure 9 is with the trade-off factor grows, \(S_{A}\) nodes correspondingly increase, which leads to the increment of the average AUV tour. However, the threshold of distributed HAS\({}^{4}\) is designed as the maximum acoustic path from any \(S_{U}\) sensors to \(S_{A}\) sensors, which results in that as shown in Figure 10, and the number of \(S_{A}\) nodes will decrease with the increase of threshold.  
Figure 8: Effect of parameter \(l\) in distributed HAS\({}^{4}\) on network lifetime.  
Figure 7: Effect of parameter \(l\) in centralized HAS\({}^{4}\) on network lifetime.
## 2 Materials and Methods
### Spatial Analysis Approach Aspect of the Study  
#### 2.6.1 Land Use and Land Cover  
To achieve the research objectives, three land cover maps for 2010, 2015 and 2020 were created, comparing the growth or decline of criteria such as vegetation, urban planning, agriculture, and other factors to determine the impact of urbanization on Kimbanseke. The land cover maps for 2010, 2015 and 2020 were created using Landscapes 2010, 2015 and 2020. Using ArcGIS 10.5 software, the research region in Kimbanseke was retrieved from a Landsat picture and projected in the UTM 33 South zone for further examination and classification of the data.
# Nonlinear Vibrations of Carbon Nanotubes with Thermal-Electro-Mechanical Coupling
## 4 Discussion  
### Primary Resonance  
We can obtain the visible pattern of the relationship between the vibration amplitude and the parameters from Equation (21), as shown in Figures 3-9. These Figures reveal the influence of the temperature field and the electric field on vibrations for the primary resonance. For the first mode (Figure 3), the initial value of the numerical solutions for the low branch is \(\left(\eta_{1},\dot{\eta}_{1}\right)=(0.6,0)\), and, at the high branch, it is \(\left(\eta_{1},\dot{\eta}_{1}\right)=(0.6,0)\). For the second mode (Figure 4), the initial value of the numerical solutions at the low branch is \(\left(\eta_{1},\dot{\eta}_{1}\right)=(0.012,0)\), and, at the high branch it is \(\left(\eta_{1},\dot{\eta}_{1}\right)=(0.36,0)\). From Figures 3 and 4, it can be found that the multi-scale method is accurate. Figure 5 shows that the second mode is hardly excited when the first mode is excited. For example, when the load amplitude of the second mode is 20 times that of the first mode, the maximal vibrational amplitude of the second is only one-fifth of the first, as shown in Figure 5. It is found from Figure 6 that the maximum amplitudes with detuning parameters will increase when the temperature field and electric field increase, and the region being in multi values will also become larger. This rule agrees also with the second mode; however, the temperature field and electric field have a smaller effect than the first, as shown in Figure 7.  
From Equation (21), we also obtain the curves of vibration amplitudes and loads for the first and second modes, as shown in Figures 8 and 9. The two figures show that the temperature and electric fields impact outstandingly the vibration amplitudes of the first mode, while the influence is weak on the second mode. Furthermore, there are two bifurcation points in the frequency-response curves and the load-amplitude curves. They will induce the jump phenomenon of vibrational amplitude. These details of the jump have been discussed in reference [28].
## 4 Performance Evaluation
### (Simulation Results) SINR and Throughput of the Cellular Links  
In order to evaluate the performance, SINR and throughput of cellular links and D2D links are compared in FFR-3 and semi-static scheme. Figures 12 and 13 illustrate the CDF of the SINR of the cellular link for semi-static and FFR-3. In the legend of Figures 12 and 13, the numbers in parentheses indicate the number of sensors per zone. Figure 12 compares SINR distributions of semi-static and FFR-3 when the transmission powers of cellular and D2D links are 30 dB and the number of sensors per zone is four. As shown in the results, the SINR performance of the cellular link in FFR-3 and the semi-static scheme is almost the same when the transmission power of the D2D and cellular link is the same. However, as shown in Figure 13, when the transmission power of the D2D link is less than that of the cellular link, FFR-3 has better SINR of the cellular link than the semi-static scheme.  
Figures 14 and 15 show the average throughput of the cellular links according to the transmission (Tx) power of the D2D links when the cellular link Tx power is 30 dBm and 40 dBm, respectively. In the legend used in Figures 14 and 15, the numbers in parentheses indicate the number of sensors per cell. In the semi-static scheme, the cellular link is affected by other cellular links in neighboring cells. Thus, the average throughput of the cellular links is constant even if the Tx power of the D2D links changes. On the other hand, the throughput of the cellular links in FFR-3 is affected by the Tx power of D2D links in neighboring cell. As shown in Figures 14 and 15, when the transmission power of the D2D link is less than the transmission power of the cellular link, the throughput of the proposed algorithm is superior to that of the semi-static scheme. In the sensor deployed system, the D2D link is designed for short-range communication between sensors, and the cellular link is designed for communication between the sensor and the base station. Therefore, the transmission power of the D2D link is generally much less than the transmission power of the cellular link. This means that a spot in a real field would be on the left side of the graphs in Figures 14 and 15. Thus, FFR-3 would be much better than semi-static in terms of the average throughput of the cellular links.  
Figure 12: CDF of sensor SINR in cellular link (D2D Tx Power = 30 dBm).  
Figure 14: Cellular link throughput versus D2D transmit power (cellular Tx power = 30 dBm).  
Figure 13: CDF of sensor SINR in cellular link (D2D Tx power = 25 dBm).  
Figure 15: Cellular link throughput versus D2D transmit power (cellular Tx power = 40 dBm).
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 2 Related Works
### Theoretical and Methodological Contributions  
* A comparison of WirelessHART and ZigBee is provided in [19]. The authors explain why ZigBee is not the most appropriate protocol for use in many industrial use cases. They claim that the protocol was missing some characteristic features, which gave ground to the development of other protocols and standards such as WirelessHART.  
The authors also demonstrate the design directives that render WirelessHART more appropriate for industrial use cases, constraints and requirements.
* A comparison of WirelessHART and ISA100 Wireless, both from a technical and a systematical point of view, is provided in [20]. The authors presnet the inherent strengths and weaknesses of the two protocols and how these influence their suitability for different industrial use cases. The authors conclude that both protocols are capable of robust and reliable communication in harsh industrial environments. As a result, predicting which protocol will emerge as the de facto standard for industrial use cases in the shop-floor is very difficult.
* A theoretical study on the relationship between WirelessHART and IEEE 802.15.4e, as well as on what impact the latest IEEE 802.15.4e has on WirelessHART and how industrial operators could benefit from both standards, is provided in [21].  
To the best of our knowledge, there is no direct performance comparison of methods incorporating all three aforementioned elements (WirelessHART, RPL, proxies), and in most cases--the opposite of our approach--the comparisons are purely theoretical ([19; 20; 21]). Those observations lead us to the conclusion that there is a research gap in the litarature which refers to practical comparisons of WirelessHART, RPL and proxy-employing methods.
## 1 Introduction  
Terahertz (THz) is the electromagnetic wave of the frequency band between microwave and infrared light. It is a research hotspot in the application of penetrating imaging [1], high speed wireless communication [2], and medical sensing [3]. Modulation of wave amplitude is one of the basic operations in its applications [4; 5; 6]. Traditional modulation methods are based on conventional semiconductor materials or mechanical scanning [7; 8]. However, the modulation speed is limited with these methods due to low carrier mobility (e.g., \(\sim\)1400 cm\({}^{2}\)V\({}^{-1}\)s\({}^{-1}\) for Si [9]) or constraints on mechanical motion.  
Graphene is a two dimensional material with carrier mobility (\(\sim\)2 \(\times\) 10\({}^{5}\) cm\({}^{2}\)V\({}^{-1}\)s\({}^{-1}\)[10]) about one order of magnitude higher than that of semiconductors [11]. The Fermi level of graphene could be electrically tuned [12]. This property makes graphene hugely popular in making tunable devices, such as absorbers [13], detectors [14], sensors [15], and modulators [16]. However, the commonly used graphene is with the thickness of a single layer of carbon atoms, which leads to a weak interaction between the electromagnetic wave and the graphene. Metallic metamaterial has the unique property of field enhancement, which has been employed for ultra-sensitive detection [17], super-resolution imaging [18], and high-gain antennas [19]. A combination of graphene and metamaterial to enhance interaction with light has been reported recently [20; 21; 22]. It has been demonstrated that by introducing metamaterial into a graphene based THz modulator, the amplitude modulation depth could be enhanced by 50% at the resonant frequency of the metamaterial [23]. However, the resonant frequenciesare not tunable as the graphene was in direct contact with the metallic structures in most reported works, which limits the further increases of the modulation depth.  
In this work, a graphene based THz modulator with the hybrid structure of combined graphene and the metallic slit ring array separated by a dielectric layer is proposed. With resonant excitation in the metallic slit ring, the local THz field is enhanced by several orders of magnitude, and the interaction between graphene and THz field is strongly magnified. By inserting a thin layer of dielectric material between the metallic structure and graphene, the resonant frequency is tuned with the modulation voltage synchronously, which leads to a high modulation depth in the transmission wave. The enhancement of the modulation depth in the hybrid structure is discussed theoretically and demonstrated experimentally.
## 6 Discussion
### Limitations  
Studying has several limitations for the results to be generalizable. The first limitation stems from the simultaneous use of the two software applications. Sometimes, difficulties were encountered in recognizing emotions due to the students' body posture during their interaction with DARA. In these cases, Emolive did not correctly capture emotions due to the students' forward tilt of the face when interacting with the mobile AR device, which affected the quality of the facial expression analysis. In these cases, Emolive informed the teacher of this situation, and the teacher reminded the students to adopt a more appropriate posture.  
A second limitation relates to the constraints of both emotional measurement instruments. Although the AEQ is a validated psychometric tool, and the Emolive system has demonstrated an accuracy of 85%, both approaches present inherent limitations that may affect the results. Additionally, the assessment was conducted with a limited number of students (only 48 participants) and within a specific academic context--second-year undergraduate students in a computer science program--which limits the generalizability of the findings to other populations or educational settings.  
A third limitation lies in the use of historical data for the GEAQ group, which corresponds to a previous academic year. This introduces potential confounding variables, such as differences in group characteristics or educational conditions. Furthermore, the absence of a control group within the same academic cycle reduces the level of experimental control.