esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Review of Previous Studies
### Previous Studies Related to the GSCPI
[13] employed the time-varying parameter-stochastic volatility-vector auto-regressive (TVP-SV-VAR) methodology to identify the dynamic relationship between The Global Energy-Related Uncertainty Index (GEUI), the GSCPI, and the Artificial Intelligence Index (AII). Quantitative discussions indicated that AII has a negative impact on GSCPI and GEUI, highlighting that advancements in AI could promote energy sustainability and global supply chain stability. [PERSON] and [PERSON] [51] used a local projection approach to examine how global supply chain pressures affect inflation in the Philippines and a few other East Asian countries and discovered that headline inflation in the Philippines, South Korea, Thailand, and Singapore rises significantly and statistically significantly after supply chain disruptions. [PERSON] et al. [43] explored how stress in global supply chains affects monetary policy and inflation in Mexico, a typical emerging-market nation. The findings indicated that an increase in the monetary policy interest rate over a one-year period has a significantly smaller impact on inflation in a high-stress regime. [PERSON] et al. [44] analyzed how pressures from the global supply chain affect inflation in Europe and what that means for monetary policy. According to their results from a Bayesian structural VARM, in 2022, shocks to global supply chain pressures were the main cause of inflation in Europe. These shocks have a hump-shaped and extremely persistent effect on inflation. [PERSON] et al. [63] investigated how a supply chain-oriented variable affects South Korea's crude oil import price forecasting. The study found that models that include the GSCPI perform better than those that do not, highlighting the significance of supply chain-related variable-monitoring for policymakers to stabilize domestic prices. Furthermore, a new hybrid factor-based strategy that combines machine learning and time series models was put forth to improve oil price prediction accuracy. [PERSON] [64] offered a collection of current bibliometric, network, and thematic analyses to pinpoint the key players, primary research areas, and disruption control tactics associated with supply chain performance in COVID-19 environments. The results of the analyses show that the two main topics of the supply chain are sustainability and resilience.
## 2 Materials and Methods
### Methods
#### 2.3.2 Model Improvements  
In order to obtain productivity results that are more consistent with arid regional ecosystems, we performed a partial improvement of the model. Firstly, there is an improvement to the FPAR simulation. We used MODIS-derived NDVI and EVI data, which provides a higher spatial resolution over the NOAA/AVHRR data applied for the original CASA model. It may cause the algorithm for the FPAR of the original model to no longer be applicable. Therefore, the value of FPAR could not be accurately simulated [36]. Considering that FPAR is well correlated with both NDVI and EVI and that the FPAR calculated from them is higher and lower, respectively, we took their average value and used it for the FPAR input to the model [48; 49].  
\[FPAR=\alpha FPAR_{NDVI}\times(1-\alpha)FPAR_{EVI} \tag{4}\]  
\[FPAR_{NDVI}=\frac{NDVI_{(x,t)}-NDVI_{(i,min)}}{NDVI_{(i,max)}-NDVI_{(i,min)}} \times(FPAR_{max}-FPAR_{min})+FPAR_{min} \tag{5}\]  
\[FPAR_{EVI}=\frac{EVI_{(x,t)}-EVI_{(i,min)}}{EVI_{(i,max)}-EVI_{(i,min)}}\times( FPAR_{max}-FPAR_{min})+FPAR_{min} \tag{6}\]  
where \(FPAR_{NDVI}\) denotes \(FPAR\) calculated with \(NDVI\); \(FPAR_{EVI}\) denotes \(FPAR\) calculated with \(EVI\); the value of \(\alpha\) is taken as 0.5; \(VI_{(x,t)}\) denotes the vegetation index of pixel \(x\) in month \(t\); \(VI_{(i,max)}\) and \(VI_{(i,min)}\) denote the maximum and minimum values of the vegetation index for the \(i\)-th vegetation type in month \(t\), respectively; and \(FPAR_{min}\) and \(FPAR_{max}\) take the values of 0.001 and 0.95, respectively.  
Furthermore, it is clear that \(\epsilon_{\max}\) has a significant impact on the simulation results of NPP. The original CASA model used a constant value of 0.389 g C/M) for \(\epsilon_{\max}\), but it varies for different vegetation types [50]. Therefore, we classified the land cover types in the ARNWC into five categories: cropland, forestland, grassland, urban areas, and bare areas, and determined the \(\epsilon_{\max}\) for different land cover types with reference to the findings of previous studies (Table 2) [51; 52]. In these studies, the \(\epsilon_{\max}\) of different land cover types in the arid region was inverted by substituting the NPP-measured data into the formula of the CASA model and finally determined according to the principle of minimum error.
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
## 4 Receiver Design  
This section presents the proposed receiver scheme. First, the receiver model is introduced, and then an expression to compute the bit error rate (BER) is derived. After that, the hybrid analog-digital equalizer is designed. A decoupled transmitter-receiver design is assumed for formulating the optimization problem and computing the equalizer matrices. For the analog part, the equalizer elements are selected from a dictionary based on the channel array response vectors. The selection procedure considers as a metric the weighted error between the hybrid and the full digital equalizer matrices. Because of hardware constraints, we assume that the analog equalizer is constant over the subcarriers, as in [23; 27]. Then, the digital part is computed on a per subcarrier basis, over the effective channel, to remove the interference ignored on the analog part.
# Toxins of Prokaryotic Toxin-Antitoxin Systems with Sequence-Specific Endoribonuclease Activity
## 4 Network of TA Systems
### Transcriptional Cross-Activation  
Activation of one TA module has been shown to induce the transcription of specific sets of TA modules in a Lon-independent manner, as well. Using a _lon_ deletion strain, [PERSON] and colleagues demonstrated that RelE expression leads to the induction of the transcription of _mqsRA_, _mazEF_, _dinJ-fayQ_, _hicAB_, _yefM-yoeB_ and _prlF-yhaV_ within 1 h [156]. In return, the expression of toxins MazF, MqsR, HicA and HipA leads to the induction of _relBE_ operon. In contrast, induction of YafQ, a ribosome-dependent ribonuclease, did not induce _relBE_ overexpression, showing a specificity in the pattern of cross-activation. These activations do not require Lon or other proteases (e.g., Clp). An analysis of transcriptomes showed that the overproduced toxins indeed digested the target TA transcripts [156]. mRNA fragments were further digested at different rates within the toxin and antitoxin-encoding regions. mRNA fragments containing antitoxin genes were preferentially digested, which lead to the accumulations of fragments containing toxin gene sequences from which toxin proteins were successfully translated [156].  
These cross-links do not seem to be an artifact of ectopic expression, but have physiological relevance. Amino acid starvation activates _relBE_, which in turn activates _mazEF_[156]. In the _relBE_ deletion strain, _mazEF_ induction does not occur during amino acid starvation. While _mazEF_ induction during the stringent response is dependent on _relBE_, the induction of _mqsR_ under the same conditions does not depend on _relBE_ despite _relBE_ being able to induce _mqsR_ when _RelBE_ is over produced. Similarly, the overexpression of _doc_ toxin transactivates _relBE_ expression [160]. MqsR activates the expression of _ghoT_ by preferentially cleaving the antitoxin-encoding portion of the _ghoS-ghoT_ mRNA [126].
## 5 Case Study: Experimental Results
### User Clustering
#### 5.2.1 Visual Analytics with User Replying Network
For example in \(r\)/politics, users interested in the special counsel investigation are at the top of the graph. Users who like to discuss people close to the president are located close to them (below and right), and users who frequently reacted to the White House Press Briefing are located below and left. As another example, users who are interested in [PERSON] are located at the bottom-left corner of the graph. Close to this group, we find several groups of users who have actively participated in articles with content on racism, gun control, and social inequality, issues closely related to this politician. Figure 4: Visualizing the user replying network for Reddit datasets. Figure 5: Visualizing the user replying network for Slashdot and PGR21 datasets. In r/AskReddit, there are also groups of users who are active at specific times. At the top left and center of the graph in Figure 4b, we find two user groups who usually participated in articles posted around 3-4 p.m. and 5-7 p.m. EDT time, respectively. We also find separate groups of users who leave comments in the articles with their own topics, although the group clusters are not as clear as those in r/politics. Users with related topics tend to be located nearby. For example, users interested in political questions are located close to those who answer country-specific questions such as \"What is better in Europe than America?\" There are also questions about serious social problems, such as asking opinions on suicide rates in America. Users who discuss such topics are also located close to the country-specific topics. We believe this is because these questions naturally involve comparisons among different countries. Compared to the Reddit datasets, we observe different distributions in vertex degrees in the two datasets in Figure 5, indicated by the vertex sizes. We observe that there are a small number of users who are excessively active, and interact with a large number of other users, especially for controversial topics. We also observe that these users are clustered according to their interests. For example, in Slashdot, users interested in energy issues such as nuclear power are close to users who are concerned about climate change, as we find them close together at the bottom-right corner of the graph. In PGR21, users interested in political issues are located close to one another, and a number of small groups have been formed. Among these small groups, users sensitive to gender issues are in the middle of the other groups, which means they are likely to simultaneously participate in the articles with political topics around them. Users with these political topics tend to have large degrees, meaning they interact with a large number of users across many articles.
## 1 Introduction
Therefore, in this study, we propose OCC as the WBAN communication method in the IoMT scenario. In our context, aligned with the aforementioned fact, security cameras such as CCTV are commonly installed in many buildings. Hence, OCC-based IoT can be  
Figure 1: IoT architecture layers. Figure 2: Illustration of WBAN. implemented locally for those buildings. Using multiple cameras, every time a patient equipped with sensing devices connected to the LED transmitter passes through a CCTV camera or similar device, the LED matrix transmitter can be detected by those cameras and the data can be transmitted from the LED matrix to the camera. In this work, the use of the YOLO [14] object detection task is also proposed to detect the LED matrix for multiple user (patient) scenarios. Therefore, real-time and continuous monitoring of the patient using OCC is possible since installed cameras are available on every corner of the building and even in the building hallway. Therefore, the overall contributions of this study are as follows:  
* An IoT system scheme using OCC is provided, taking advantage of the CCTV camera system that is common in every building in present days, especially in health centers or hospitals, to perform continuous and real-time patient health monitoring;
* Implementation of deep learning-based OCC on multiple patients. In the proposed scheme, object detection is used to receive data from multiple patients represented by the use of several LED matrices;
* Indoor real-time patient relative location based on camera locations. Using several cameras installed in the building for OCC, patient location can be determined. The subsequent sections of this study are organized as follows: Section 2 highlights recent work on OCC in IoT systems, Section 3 explains the design of an OCC-based IoMT system, Section 4 details the experimental conduction and results analysis, and Section 5 concludes.
# A Decision Framework for Choosing Telecommunication Technologies in Limited-Resource Settings
## 4 Evaluating Existing Networks
### Hpwren  
The High Performance Wireless Research and Education Network (HPWREN) is an original Wi-Fi Long Distance network that is economically viable while functioning autonomously as a part of the University of California San Diego (UCSD). This network was developed as an opportunity based on community needs. The development started in early 2000, and the need at that time was for the transmission of the rapidly increasing data output from Mount Palomar, an observatory with a large telescope collecting data, to computers where the data could be processed. UCSD arrays were already by that point generating much more data than could be stored, yet there was no high-speed internet in the mountains of Southern California at that time. There were some existing links from the defunct National Science Foundation Net program (NSFNET), and commercial networks through the Southern California suburbs, but there were also numerous rugged mountain areas and sparsely populated desert areas with no cell coverage.  
In 1985, NSF funded a significant build-out of internet networking equipment in a 10-year program that is now known as NSFNET. A conclusion made by researchers in the program was that the government cannot compete with the private industry when it comes to problems that require a high initial capital expenditure (CAPEX). In 1995, NSF pulled out of NSFNET in favor of the private industry that was driving network expansion at that time. Owners of the equipment were not so quick to abandon what seemed like a very significant asset. Researchers at UCSD instead decided to apply the infrastructure to a problem that was both scientifically and commercially interesting, that of remote sensing over the internet.  
HPWREN became a highly heterogeneous public-private partnership (PPP) that responded directly to the existing needs. Soon after the first implementation of ISM band Wi-Fi, researchers and developers realized that coverage of 4 pi steradians for a limited range could be converted to long range (up to 40 km) for a diffraction-limited 2.4 GHz beam carrying a broadband information stream. The 40-km range of original WiLD was enough to move relatively large bandwidths from mountains to suburbs, but the suburban transmission was not as easy as transmission through open space. The network then used WiLD links where possible and leased from commercial networks where necessary. The network linked villages on Indian reservations with data-intensive links to astronomical facilities, together with commercial traffic, to generate an economically viable network that simultaneously served as a testbed. The network ranges from Southern California, from the southern edge of Orange County to the Mexican border and from the Salton Sea on the east to its hub at University of California at San Diego, with some links to the barrier islands to the west.  
From the perspective of the proposed decision framework, HPWREN has used local carriers whenever they could and built a private network to close the gap in connectivity.
## 4 Discussion  
### Overview  
The objective of this study was to compare the effects of autonomous vehicle occupant safety-relevant parameters--such as AEB activation, seatback angle, and seat track position--on occupant injury risk. This effort also aimed to help prioritize these parameters in autonomous vehicle crash safety evaluations. To enable broader conclusions, the study incorporated variations in vehicle crash conditions, levels of muscle activation during AEB events, seating locations, and seat belt types, which significantly increased the number of re  
Figure 15: Effect of belt-in-seat excluding the concept rear seat track position: (**a**) both upright and inclined seatback conditions, (**b**) upright seatback condition, and (**c**) reclined seatback condition. * Indicates a statistically significant difference between the two groups.  
quired simulations. To manage the resulting computational burden, a validated multi-body (MB) human body model was employed.  
The findings of this study indicate that the seatback angle is one of the most influential parameters affecting the risk to head and neck regions, particularly for conventional seat track positions. Furthermore, when the seat track was moved to the concept rear position, a substantial increase in injury risk in the brain and l-spine regions was observed. In this study, the reclined seatback angle and the concept of rear seat track position were the most influential factors, as they deviated the most from the baseline seating posture and location. A detailed discussion of these findings is presented in the following sections.
## 8 Discussion
### Extension and Future Work  
In this work, we assumed that the area of interest is obstacle-free. Therefore, we did not consider the ability of model to work when obstacles exist nor how the MA can avoid them. As a future work, we aim to study the effects of the obstacles existed in the area on localization accuracy, localization ratio, precision and the path length. Moreover, we will work on proposing a technique for the MA to sense and avoid the obstacles and continues its journey. We also plan to extend our experiments to the three-dimensional scenarios and test its ability to work in such environments. Another task is to assess the current model when multiple MAs are used. Recently, a new research area suggested using the Particle Swarm Optimization (PSO) to optimize the membership functions of the Fuzzy Logic Controller (FLC) in WSNs [48]. PSO has shown promising results in many research including [49; 50]. Such proposal can be useful to improve the performance of our model.
# Binanual Movements and Chronic Stroke Rehabilitation: Looking Back and Looking Forward
## 4 Looking Forward
Further, standardizing tDCS protocols for chronic stroke intervention must include when and duration of the anodal and cathodal stimulation combinations [106]. Questions on the optimal stimulation time are still being debated. Should chronic stroke individuals receive 20-30 min of tDCS before performing bimanual movement training or should 20-30 min of stimulation occur simultaneously with bimanual movements? Moreover, developing isometric rehabilitation programs may be a viable option for facilitating functional recovery of the paretic arm. Given that isometric contraction requires no dynamic movements, patients with stroke can safely participate in the isometric training regardless of their muscle weakness and spasticity in the paretic arms as prior findings suggested [107; 108; 109]. Moreover, [PERSON] and colleagues raised a possibility that bimanual actions transiently increased motor functions in the paretic arms by demonstrating greater maximal and submaximal mean forces and less force variability and regularity produced by the paretic arm during bimanual force control tasks than those during unimanual force control tasks [110; 111]. These findings indicated that applying bimanual isometric training protocols can be an additional effective approach to improvements in acquiring coordinative motor skills post-stroke. To facilitate motor recovery progress post-stroke, pharmacological therapies can be viable alternatives [13; 112]. For example, a meta-analysis study reported that the serotonin reuptake inhibitor (SSRI) fluoxetine improved motor recovery in acute and subacute patients (less than 3 months since stroke) [113]. Potentially, the SSRI fluoxetine may be beneficial for motor improvements via the facilitation of neurogenesis and anti-inflammatory neuroprotection and enhancing cerebral blood flow according to the findings from animal models [114]. Importantly, the appropriate timing of these pharmacological treatments would be within first three months since the stroke because this period presumably increases a possibility of interactive effects between pharmacological treatments and spontaneous recovery maximizing motor rehabilitation. Despite controversial treatment effects on stroke patients with increased time since the stroke (e.g., >6 months), pharmacological interventions would be an additional option for improving bimanual motor functions in chronic patients.
# Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review
## 6 Design of the Flexible Wearable Heart Rate Signals Transmission and Output Module
### Low-Power Algorithm Design for Flexible Wearable Heart Rate Signal Transmission and Output Module
The algorithm optimizes the communication process by leveraging the time-varying correlation of wireless channels: the transmitter predicts the duration of channel states based on current conditions and triggers feedback only during state transitions, reducing overall feedback energy consumption by minimizing high-power feedback operations. Additionally, the DF-AM algorithm dynamically selects modulation indices according to real-time channel states, ensuring link adaptability while fully utilizing channel conditions to achieve low-power operation by design. When encountering harsh channel conditions, such as deep fading, the transmitter enters a power-saving mode to avoid energy waste from transmissions in unfavorable environments. The total power consumption of the system is approximately 60 mW. Compared with traditional Amplitude Modulation (AM) schemes, this system achieves over 22% energy savings and more than 13% throughput improvement. This subsection focuses on the low-power algorithm design of flexible wearable heart rate signal transmission and output, adopting a \"data streamlining + transmission optimization\" strategy. It eliminates signal redundancy through compression and reduces ineffective transmission via adaptive modulation, thereby breaking through traditional power consumption limitations through their synergistic effect. For instance, the improved DCT-IV-based data compression algorithm optimizes dynamic range suppression, non-uniform quantization, and Huffman lossless coding. While maintaining accuracy, it achieves a compression ratio exceeding 25:1, significantly reducing the data volume for transmission. The DF-AM algorithm innovatively leverages channel time-domain correlation. By dynamically adjusting modulation indices and employing an intelligent feedback mechanism, it achieves a 22% energy efficiency improvement in complex wireless scenarios.