esa-sceva/satcom-chunk-collection huggingface dataset:
# Calibrating Mini-Mental State Examination Scores to Predict Misdiagnosed Dementia Patients
## 3 Problem & Approach
### Problem Statement  
The Mini-Mental State Examination (MMSE) is used in clinical research studies to assess the degree of cognitive impairment [8] that has resulted at a given point in time from a dementia-causing condition. It is expressed as a score, with a maximum of 30, based on a patient's ability to answer questions that make demands on different categories of cognitive ability, such as orientation in time and place, use of words, attention, calculation, and both short term and long term memory [30]. The score achieved on the MMSE has been conventionally used to place a patient in one of four categories of cognitive ability, namely: cognitively normal, mild dementia, moderate dementia and severe dementia [4]. In a patient with dementia due to the [PERSON]'s disease, the score becomes progressively lower as the disease progresses [31]. However, the accuracy with which a patient's MMSE measurement is either measured, recorded or documented, is compromised. Such inaccuracies may occur for multiple reasons, including the patient being without hearing aids or glasses, a transient motor problem, low motivation, or even an inadequate rapport with the administering clinician [32]. Miscalculations become evident when a patient's MMSE is recorded serially, and an unexpected increase or decrease in the recorded MMSE score.  
By searching for instances in which one of the a priori criteria for a miscalculation is met, MMSE scores may be classified as being either inaccurate (\"misdiagnosed = YES\") or accurate (\"misdiagnosed = NO\"). This classification indicates whether the patients have been correctly diagnosed, in the sense of whether the MMSE score has been miscalculated or erroneously recorded in any of their documented assessments. In our case, the problem we aim to address is twofold: (a) MMSE Prediction: use those features to predict the MMSE scores for each patient episode, in order to correct misdiagnosed cases (by replacing miscalculated scores with more reasonable predicted values). (b) Classification problem: predict the class (\"misdiagnosed = YES\"/\"misdiagnosed = NO\") of patients using patient clinical record features including MMSE features from the OPTIMA dataset [2,12,13]. Machine learning predictive models address these problems.
## 4 Experiments
### Implementation Details  
For each input utterance, the PPGs are obtained using a DNN-based SI-ASR model with four hidden layers. This model is implemented using the Kaldi speech recognition toolkit [42] and trained on TIMIT corpus [48]. For DNN-ASR training, we use 40-dimensional filterbank acoustic features, extracted every 10 ms frameshift, with 25 ms window size as input. The output of this model is the sequence of PPG feature vectors. For each waveform input, PPG feature vectors of dimension 512 (\(D_{p}=512\)) are extracted. The conversion model takes as input a sequence of 512-D PPG features and speaker embeddings, which are used as additional features to better capture different aspects of the target speaker's characteristics. The speaker embeddings are derived from a DNN-based speaker encoder trained for multi-speaker classification as applied in the [40] study. To compute speaker embeddings for the target speaker, we choose a reference recording with a long duration from the training data. We also extract 39 th-order mel-cepstral coefficients (MCCs) from spectral envelopes, which are utilized as spectral features for objective evaluation.  
To train the conversion model, we choose a batch size of 32, an Adam optimizer with \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), and an initial learning rate of 0.001. We used Mean Square Error (MSE) as the loss function. Firstly, we used a Pre-Net, which consisted of a dense layer with a dropout rate of 0.5, and an output dimension of 512. Secondly, we incorporated a CBHG module with a 1-D convolutional bank comprising 16 sets of 1-D convolutional filters, each with 128 output channels and ReLU activation. These convolutional outputs were then fed into a highway network (consisting of four fully connected layers) followed by a Bi-GRU (with 128 units per direction) to extract 256-dimensional context-feature representations (where \(D_{h}=256\)) as encoder outputs. In the third step, the 256-dimensional encoder outputs were combined with previously predicted acoustic features through an attention RNN layer.  
The fused representations were then passed through an LSTM layer of 512 cells. Finally, the outputs of the LSTM layer were fed into a decoder Pre-Net with a dropout rate of 0.5 to predict acoustic features (where \(D_{o}=32\)) to be used by LPCNet for speech synthesis.  
The LPCNet operates at a 16 kHz sampling rate and a frame rate network that processes 10 ms frames. We use 32-D acoustic features including 30-D Bark-scale frequency cepstral coefficients, 1-D pitch period, and 1-D pitch correlation. The LPCNet is trained for 200 epochs, the batch size is 32, and the learning rate is set to \(1\times 10^{3}\).
# State of the Art: Bipedal Robots for Lower Limb Rehabilitation
## 4 Challenges and Outlooks
These drives are faster and more capable of load, making them good prospects for the field of rehabilitation robots. * Energy Resources  
Most rehabilitation robots are mainly powered by batteries, and the range of movement is limited by the capacity and efficiency of the battery. The ideal energy source should have a very high energy density, high-temperature resistance, corrosion resistance, be renewable, and inexpensive. Therefore, improving the drive source to make it small, lightweight, and have a large capacity is a problem that must be solved in the development of bipedal robots. Researchers in the future can seek new energy technologies, including solar energy and bioenergy, to address the energy development bottleneck. With the development of nanotechnology, high-density batteries are expected to solve the energy problem, such as with graphene batteries. Wireless charging is also a means of solving the energy problem with rehabilitation robots. However, the current wireless charging technology is not mature enough and cannot achieve long-distance transmission. * Sensing Technology  
To accurately obtain the user's motion information, many sensors, such as force sensors, torque sensors, gyroscopes, vision sensors, and acoustic sensors, are installed in bipedal robots. The control of the robot is sensor-based. However, the sensor accuracy can be improved and the cost can be decreased. Additionally, the traditional rigid sensors tend to be relatively large and limit the movement of the human body. Many new types of sensors based on soft material have been presented, which are well-suited to human body detection. A soft structure not only fits the surface of the human body, but is also more comfortable for the user. A micro-sensor is a new type of sensor based on semiconductor technology. Micro-sensors are increasingly popular because of their miniaturization, intelligence, low power, and high integration. Although these new sensors are still facing problems in manufacturing, sensitivity, and stability, they have potential for robot sensing, especially for application in rehabilitation. * Human-Robot Co-Fusion  
In the traditional training process, rehabilitation is the interaction between a patient and a physical therapist. When using robots, the ideal robot should have the same or similar ability as physical therapists in terms of being able to detect the rehabilitation condition of the patient, design a personalized training plan, and dynamically optimize the training process. However, current robotic systems are still in a very early stage. The systems can perform some basic tasks through programming, but the results are far from intelligent. A new concept called \"human-in-the-loop\" has been proposed by the research team at CMU, where the human and robot are integrated in a closed loop and the robot is able to adjust the working conditions according to the human's walking condition. This type of human-robot co-fusion could be a trend for the development of rehabilitation robots.
## 3 New Pulse Duration Limits of Modelocked Thin-Disk Lasers
### State-of-the Art and Recent Experimental Results
The achieved SNR should be large enough for initial locking tests of the CEO beat, in particular given the high stability of the observed beats. We believe better performance, in particular a higher SNR, is achievable by improving opto-mechanics, boxing, and pump operation point of the oscillator. Furthermore, optimizing the CEO-beat detection scheme (fiber length, input peak power level, fiber design, shorter pulse duration [107], temperature stabilization of the highly nonlinear fiber [108], _etc._) should also result in an improved SNR. It is worth emphasizing that CEO detection was possible in spite of the strongly multimode pumping scheme of TDLs, usually associated with a high noise level. This seems to indicate that state-of-the-art high-power systems such as the one presented in [26] or the one presented in [17] would already be suitable to achieve 100-W-level stabilized frequency combs. Taking into account the pulse duration of such state-of-the-art systems, an external temporal pulse compression stage will be required to reach the necessary short pulse duration for CEO detection. However, we believe 100 W level sub-100 fs pulses will soon be obtained directly from a TDL, which will significantly simplify CEO detection. Meanwhile, the performance of our Yb:LuO laser with short pulse duration was improved to 25 W and 185 fs [64]. In this experiment, power scaling was straightforward by using larger spot sizes on the disk and on the SESAM, and by using a SESAM with a high-damage threshold, obtained following the guidelines presented in 2.3.6. Figure 17: **(a)** Emission spectrum of Yb:LuO and Yb:YAG. **(b)** Spectrum of the modelocked laser delivering 142 fs, showing a 8.5 nm-wide (FWHM) modelocked spectrum, together with the 12.5 nm wide emission spectrum of Yb:LuO. The observed red-shift of the modelocked spectrum is due to the low inversion level at which the laser is operated. Strictly speaking, the modelocked spectrum should be compared not to the emission spectrum, but to the exact gain cross section at the inversion level \(\beta\) at which the laser operates (see Figure 16). However, different sources of spectrally dependent loss (such as the reflectivity of the different dielectric mirrors in the cavity, or the SESAM) make the precise determination of \(\beta\) a challenging task. Therefore, a comparison to the emission spectrum is here more adequate. **(c)** Autocorrelation trace of the 142 fs pulses obtained at 7 W of average output power.
# Evaluation of IEEE802.15.4g for Environmental Observations
## 2 Materials and Methods
### Scenarios  
The range test experiments are carried out in the city of Paris, France, in 4 scenarios, each being a likely IoT application environment. These are:  
* **Line of Sight (LoS)**: nodes are deployed in the _Bois de Vincennes_, on a pedestrian 12 m wide asphalted route (_Rue Dauphine_). The area is characterized by dense vegetation with tall trees at both sides of the route. No important obstruction is between the TX and RX nodes during the length of the experiment with people occasionally crossing this path. Numerous IoT applications are foreseen in this scenario: monitoring natural resources on a prairie-like environment, smart metering in the country side, smart grid in rural areas, livestock monitoring, mining and more. Figure 3 depicts the location where the nodes are deployed, and the distances between them.
* **Smart Agriculture**: nodes are deployed in the Parc de Vincennes, next to the Lac Daumesnil. There are trees between the TX and RX nodes, obstructing the direct path between the nodes. This scenario mimics IoT application environments such as: Smart agriculture, monitoring natural resources on a forest-like environment, livestock monitoring on a vegetation-abundant terrain and more. Figure 4 shows the deployment setup and the distances between the nodes.  
Figure 3: LoS scenario. Distances show where the RX nodes are placed away from the TX node.  
* **Urban canyon**: we chose the Avenue Daumesnil, a 35 m wide urban canyon. This is a busy environment, with people walking and automobiles transiting across and along the path between the nodes. There are buildings on both sides of the street (up to 10 stories), and with trees along the avenue. This scenario is representative of some Smart City applications, including parking, metering, lighting, traffic control, pollution monitoring, etc. Figure 5 depicts the nodes' locations and the distance between them.  
Figure 4: Smart Agriculture scenario.  
Figure 5: Urban Canyon scenario.  
* **Advanced Metering Infrastructure (AMI)**: nodes are located along the street Jorge Senprum, next to the Inria buildings in Paris. The TX node is located at one extreme of a small neighborhood; the RX nodes are positioned between buildings, with no LoS to the TX node. Urban Advanced Metering applications are deployed in this type of scenario. Figure 6 details the position of the nodes and the distance between them.
## 3 Comprehensive Evaluation Properties and Implementation Procedures
### Practicality  
Practicality, as defined in previous research [24], is related to the sensitivity of the correlation between IMs and DMs. This sensitivity reflects how the amplitude modulation of ground motion affects the structural dynamic response. The linear regression coefficient \(b\) in Equation (3) is generally used for this assessment. The more significant the value of \(\rm b\), the more substantial the practicality of IM and DM, which indicate the more significant influence of ground motion amplitude modulation on the structural dynamic response, that is, with continuous increases in ground motion intensity, the whole process of the structural dynamic response from elastic, elastic-plastic, to collapse can be better captured.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 3 Methodology
### Data Collection  
Monthly data were collected from the Federal Reserve Economic Data (FRED) database, covering the period from January 2000 to December 2023. The dependent variable is the 10-year U.S. Treasury bond yield (FRED code: DGS10). The independent variables include the following:  
Effective Federal Funds Rate (DFF): A short-term interest rate target set by the Federal Reserve, used as a proxy for U.S. monetary policy [3].  
Core Consumer Price Index (CPILFESL): A version of the Consumer Price Index (CPI) that excludes food and energy prices to measure underlying inflation trends [15].  
Real Gross Domestic Product Growth Rate (GDP_growth): The inflation-adjusted rate of change in the value of all goods and services produced in the U.S. economy, indicating overall economic output trends [2].  
Federal Government Debt Growth Rate (GFDEBTN_growth): The percentage change in the total outstanding debt issued by the U.S. federal government, reflecting fiscal sustainability and long-term borrowing concerns [4].  
The independent variables include the following:  
Effective Federal Funds Rate (DFF): A short-term interest rate determined by the Federal Reserve, representing U.S. monetary policy stance.  
Core Consumer Price Index (CPILFESL): A measure of core inflation that excludes food and energy prices to reduce short-term volatility.  
Real Gross Domestic Product Growth Rate (GDP_growth): Calculated as the year-over-year percentage change in real GDP using the formula:  
\[\text{GDP\_growth}_{\text{t}}=(\text{GDP}_{\text{t}}-\text{GDP}_{\text{t-12} })\times 100/\text{GDP}_{\text{t-12}}\]  
where GDP\({}_{\text{t}}\) is the real GDP in month t.  
Federal Government Debt Growth Rate (GFDEBTN_growth): Calculated as the year-over-year percentage change in the total federal debt outstanding:  
\[\text{GDPEBTN\_growth}_{\text{t}}=(\text{GDPEBTN}_{\text{t}}-\text{GDPEBT}_{ \text{t-12}})\times 100/\text{GDPEBTN}_{\text{t-12}}\]  
where GFDEBTN\({}_{\text{t}}\) is the federal debt level at time t.  
These transformations ensure consistency in scale and comparability across variables and account for seasonality and long-term trends, both of which are critical for meaningful macroeconomic analysis.
## 1 Introduction: Problem Setting
### Randomized Quasi-Monte Carlo (RQMC)
Then, nested scrambling proposed by [PERSON] [6; 24] can be defined as follows: \(z_{i,1j}=\pi_{\bullet}(x_{i,i})\) and \(z_{i,j}=\pi_{\bullet x_{i1},x_{i2},\ldots,x_{i-1,j}}(x_{i,i})\), with independent permutations \(\pi_{\bullet x_{i1},x_{i2},\ldots,x_{i-1,j}}\) for \(l\geq 2\), where \(\pi_{\bullet}\) means the permutation used for \(x_{i1,j}\). The permutation used for the next element depends on the value of \(x_{i1,i}\), and it is denoted by \(\pi_{\bullet x_{i1,j}}\). The permutation applied for \(x_{i1,j}\) for \(l\geq 2\) depends on all the previous values, and it is denoted by \(\pi_{\bullet x_{i1},x_{i2},\ldots,x_{i-1,j}}\). Of course, \((t,m,s)\)-net remains \((t,m,s)\)-net under nested scrambling. However, nested scrambling requires \(b^{l-1}\) permutations to scramble the \(l\)-th digit. The rate for scrambled-net Monte Carlo is \(n^{-3/2}(log\ n)^{(s-1)/2}\) in probability, while the rate for unscrambled nets is \(n^{-1}(log\ n)^{s-1}\) or \(n^{-1}(log\ n)^{s}\) along \((t,s)\) sequences [28]. The first rate is an average case result for a fixed function \(f\) taken over random permutations. Other findings pertain to the worst-case performance over functions given a fixed set of integration points. Since scrambled nets retain their net structure, these worst-case bounds are also applicable to them [28]. Some scrambling methods do not alter the asymptotic discrepancy of quasi-random sequences [6]. Despite improving the quality of quasi-random sequences, this enhancement is not immediately evident in the computation of the \(L_{2}\) discrepancy. Moreover, it is presently impossible to theoretically demonstrate that one scrambled quasi-random sequence outperforms another. While scrambling does not impact the theoretical bounds on the discrepancy of these sequences, it does enhance the measures of two-dimensional projections and the evaluation of high-dimensional integrals. At its core, [PERSON]'s nested scrambling [6] relies on the randomization of a single digit during each iteration, and it is a powerful technique applicable to all \((t,s)\)-sequences. However, from an implementation perspective, nested scrambling--or what is often referred to as path-dependent permutations--requires significant bookkeeping and can lead to more complex implementations. On the other hand, it has been established that its convergence rate is \(n^{-3/2}(log\ n)^{(s-1)/2}\). While this rate is commendable, it is still not optimal, even when dealing with integrands in \(\mathbf{W}^{1}(L;U^{s})\). Removing the logarithmic term from the estimate would make the rate optimal. Nevertheless, it remains an open question whether this estimate is precise, i.e., whether the logarithm can indeed be eliminated. Importantly, the demonstrated convergence rate for [PERSON]'s nested scrambling algorithm surpasses that of unscrambled nets, which stands at \(n^{-1}(log\ n)^{s-1}\). Consequently, it becomes essential to conduct numerical comparisons between our algorithms and [PERSON]'s nested scrambling.
## 3 Results Analysis
### Pore Pressure in the Reclaimed Coral Sand Foundation  
The dynamic response of pore pressure directly affects the strength of reclaimed coral sand foundation below and behind the revetment breakwater. Excessive pore pressure would lead to the reduction in effective stress, resulting in the softening or liquefaction of sand foundation. In this section, the physical mechanism of the behavior of the reclaimed coral calcareous will be further revealed by analyzing the dynamic response of pore pressure. The time history of the dynamic response of pore pressure recorded by the 13 pore pressure sensors in Test 3 is shown in Figure 12.  
As can be seen in Figure 12, pore pressure in the reclaimed coral sand foundation overall gradually accumulates under the cyclic wave loading and reaches their maximum value in about 10 cycles of wave loading. The maximum increment of pore pressure is 1.5 kPa. Does the accumulation of pore pressure cause softening or liquefaction in the reclaimed coral sand foundation or not?  
Firstly, as shown in Figure 12, the accumulation of pore pressure only occurs in the first 10 cycles of wave impact. Secondly, the revetment breakwater finally reaches a stable state and there is no obvious failure in Test 3, according to the previous analysis. It can be concluded that no softening or liquefaction occurs in the reclaimed coral sand foundation. The physical mechanism of interaction of waves, breakwater and loose reclaimed coral sand foundation can be explained.  
Figure 12: Time history of the wave-induced pore pressure in the reclaimed coral sand foundation in Test 3 (in the first 2 min).  
Soil particles in the reclaimed loose coral sand foundation rearrange towards a dense state under the cyclic wave impacting. As a result, structure B settles under the influence of gravity, and excessive pore pressure is generated in the reclaimed coral sand foundation. Finally, rearrangement of the soil particles is completely finished, and the loose reclaimed coral sand foundation becomes dense. The system composed of the revetment breakwater and the reclaimed coral sand foundation reach a new equilibrium.  
The momentary and residual pore pressure recorded at P1, P4, P8 and P12 in Test 3 are shown in Figure 13. As illustrated in Figure 13, the residual pore pressure in the reclaimed sand foundation increases significantly at first, and then oscillates within a certain range. The oscillation period of the residual pore pressure is about 100 s, which is 33 times of the wave period. The oscillation of the residual pore pressure may be caused by the gradual accumulation of reflected wave energy in the wave flume.
## 5 Deployment and Evaluation
### Evaluation Criteria  
For this section, the parameters used to evaluate the performance of our solution are defined. As we mentioned in Section 1, two factors need to be considered that are important in safety tracking systems. First, the system should continuously monitor the entities, and the time interval between two measurements should not exceed more than a certain time. The second important parameter in our study is the error in estimating the location. Inaccurate pose and location estimates can lead to either negative false or positive false alarms in the system. Thus, we need to evaluate the estimation error of each pose estimation method as another evaluation parameter.  
The update ratio defines the ratio of time the system can calculate the location. As we explained in Section 5.2, there is a 0.2-s time interval between two consecutive pose updates. If the total tracking time is divided into timeslots of 0.2 s, the ratio of time the system needs to compute the calculated pose for the entity is the **update ratio**.  
Since all our operators for both worker and vehicle tracking were human, their location or pose may not completely align with the ground truth. We considered 1 m as the error threshold in our work. If the distance between the estimated location or pose and the ground truth is less than 1 m, we interpret that estimation as a correct pose/location estimate. Otherwise, it will mark it as an incorrect estimation. The _error ratio_ represents the ratio of incorrect estimation among all the pose/location estimates throughout the trajectory of the worker or the vehicle. The average error is also the average distance of incorrect measurements from the ground truth.