esa-sceva/satcom-chunk-collection huggingface dataset:
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 4 The Proposed Scheme
### Wi-Fi Threshold Adjustment  
To make the coexistence of LTE and Wi-Fi more flexible, we dynamically adjust the \(P_{th}\) and \(Throughput_{th}^{WiFi}\) as shown in Figure 6. When the LTE eNB is heavily loaded, we would like to improve LTE users' opportunity to access the unlicensed band. Therefore, we examine if we can relax the restrictions on \(P_{th}\) and \(Throughput_{th}^{WiFi}\). Conversely, if the loading of the LTE eNB is not heavy, there is no need to lower the thresholds. However, the premise of the above adjustment is that the Wi-Fi AP is not heavily loaded, such that the QoS requirements of the Wi-Fi system can be ensured.  
The load of LTE eNB is denoted as \(\rho_{n}\), which is defined as the ratio of the number of resource blocks (RBs) requested by users to the total number of RBs available from eNB \(n\), as shown in (4).  
\[\rho_{n}=\frac{1}{N_{PRB}}\sum_{x=1}^{N_{UE}}\frac{D_{x}}{R(SINR_{x})} \tag{4}\]  
where \(N_{PRB}\) means the total number of RBs in eNB \(n\); \(N_{UE}\) is the number of LET users; \(D_{x}\) represents the number of RBs requested by user \(x\); \(R(SINR_{x})\) denotes the transmission rate per RB for user \(x\).  
When the load of LTE eNB is not high, the threshold of \(P_{th}\), and \(Throughput_{th}^{WiFi}\) are set to normal, i.e., \(P_{th}=P_{th,normal}\) and \(Throughput_{th}^{WiFi}=Throughput_{th,normal}\). \(P_{th,normal}\) and \(Throughput_{th,normal}^{WiFi}\) are the normal threshold of successful transmission probability and the normal throughput threshold of Wi-Fi AP when the load of Wi-Fi AP is not highly. If the load of LTE exceeds the threshold, we would examine the load of Wi-Fi. If the Wi-Fi is not heavily loaded, the Wi-Fi threshold can be dynamically adjusted according to the overloading of LTE, as in (5) and (6).  
\[P_{th}=P_{th\_normal}\times\left(1-\left(Load_{LTE}-Load_{LTE_{ normal}}\right)\right) \tag{5}\]  
\[Throughput_{th}^{WiFi}=Throughput_{th,normal}^{WiFi}\times\left(1-\left(Load_ {LTE}-Load_{LTE_{normal}}\right)\right) \tag{6}\]  
Figure 6: Evaluation of the threshold of time-division.  
In the dynamic adaptation of the \(P_{th}\) and \(Throughput_{th}^{WiFi}\), the minimum allowed values are \(P_{th\_low}\) and \(Throughput_{th\_low}^{WiFi}\), which represent the lowest threshold of successful transmission probability and the lowest throughput threshold of WiFi, respectively.
## 4 Proposed Framework
### Smart Contract Implementation for Access Control and Model Updates
```
1:Set of edge devices \(\mathcal{ED}=\{ed_{1},ed_{2},\ldots,ed_{N}\}\), local models \(\mathcal{W}=\{w_{1},w_{2},\ldots,w_{N}\}\), quality scores \(\mathcal{Q}=\{q_{1},q_{2},\ldots,q_{N}\}\), reliability scores \(\mathcal{R}=\{r_{1},r_{2},\ldots,r_{N}\}\)
2:Verified global model update \(w_{g}\)
3:Initialize DeviceInfo, ModelUpdate structures
4:for each registered device \(ed_{i}\in\mathcal{ED}\)do
5:\(\textit{DeviceInfo}[ed_{i}]\leftarrow\{\textit{active}:\textit{true},\textit{ reliability}:r_{i},\textit{timestamp}:t\}\)
6:\(\textit{ModelUpdate}[ed_{i}]\leftarrow\{\textit{model}:w_{i},\textit{ quality}:q_{i},\textit{verified}:\textit{false}\}\)
7:endfor
8:for each update round \(t\)do
9:for each active device \(ed_{i}\in\mathcal{ED}\)do
10:if\(\textit{ValidateDevice}(ed_{i})\)then
11:\(\textit{commitment}\leftarrow\textit{GenerateCommitment}(w_{i})\)
12:\(\textit{Commitments}[ed_{i}]\leftarrow\textit{commitment}\)
13:\(\textit{shares}[ed_{i}]\leftarrow\textit{SecureShare}(w_{i})\)
14:endif
15:endfor
16:\(\textit{maskedUpdates}\leftarrow\mathcal{O}\)
17:for each committed device \(ed_{i}\)do
18:if\(\textit{ValidateCommitment}(ed_{i})\)then
19:\(\alpha_{i}\gets q_{i}\cdot r_{i}\cdot\exp(-\beta\|w_{i}-\hat{w}\|^{2})\)\(\triangleright\) From Equation (36)
20:\(\textit{maskedUpdates}\leftarrow\textit{maskedUpdates}\cup\{\textit{ DecryptShare}(\textit{shares}[ed_{i}])\times\alpha_{i}\}\)
21:endif
22:endfor
23:\(w^{t+1}\leftarrow\sum(\textit{maskedUpdates})/\sum(\alpha_{i})\)\(\triangleright\) Adaptive aggregation
24:if\(H(T(w^{t+1}\parallel n))\)\(\triangleright\)Target then
25:\(\textit{RecordUpdate}(w^{t+1})\)
26:for each \(ed_{i}\in\mathcal{ED}\)do
27:\(\textit{ModelUpdate}[ed_{i}].\textit{model}\gets w^{t+1}\)
28:\(\textit{ModelUpdate}[ed_{i}].\textit{verified}\leftarrow\textit{true}\)
29:endfor
30:if convergence criteria are met then
31:break
32:endif
33:endfor
34:return\(w_{g}\gets w^{t+1}\)
```  
**Algorithm 1** EdgeGuard smart contract protocol.
## 4 Results  
### Analysis of the Phase  
We found that 8.73% of the studies were in phase I, 12.70% were between phases I- II, 52.38% were in phase II, 2.38% were between phases II-III, and 23.81% were in phase III. Significant differences were found (\(p=0.0485\)). Phase II trials were the most frequent in both groups. In GA (neoadjuvant) [20; 21; 22], 15.09% of the trials were in phase I and 58.49% in phase II. In GB (adjuvant) [73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134; 135; 136; 137; 138; 139; 140; 141; 142; 143], 47.95% of the trials were in phase II.  
Figure 1 and Supplementary Figures S1 and S2 show the findings in the random-effects meta-analysis with odds ratios between either neoadjuvant or adjuvant treatments and other treatments. In phase I (Figure 1), the distribution of the studies was homogeneous. In the neoadjuvant group, the total effect was OR = 1.03 with confidence intervals 0.90-1.18, \(p=1.000\). In the adjuvant group, the total effect was OR = 1.04 with confidence intervals 0.89-1.21, \(p=1.000\). There was the same distribution in both groups. In phase II (Supplementary Figures S1 and S2), the distribution of the studies was homogeneous; for the neoadjuvant group, the total effect was OR = 1.06 with confidence intervals 0.97-1.17 \(p=1.000\); in the adjuvant group, the total effect was OR = 1.04 with confidence intervals 0.96-1.13 \(p=1.000\). In phase III, the distribution of the studies was homogeneous, and the total effect was OR = 1.03 with confidence intervals 0.97-1.09 \(p=0.76\). Hormone therapy as primary adjuvant treatment in phase III was a protective factor and favoured the treatment of prostate cancer OR: 0.96 (95% CI 0.84-1.10). Both groups had the same distribution (Supplementary Figures S1 and S2).
# Photogrammetric Measurement and Analysis of the Shape Profile of Pneumatic Artificial Muscles
## 3 Procedure  
The methods of image acquisition and analysis described in the previous section are employed by this research, as detailed in this section. Photogrammetric data is acquired and the image is then reduced down to a traced outline of the PAM braid for further analysis of the shape profile.  
Photogrammetric data of the shape profile is acquired while simultaneously characterizing the isobaric force-contraction response of the PAM. Testing of the force response is performed using the same methods as done in past PAM research [5; 24]. The PAM is mounted in a MTS servo-hydraulic test machine, and is quasi-statically stroked between its minimum and maximum contraction length states while maintaining a constant internal working fluid pressure. The MTS machine directs a controlled stroking motion of the PAM while recording external inputs of pressure (Omega PC209-200 GSV), and force (Honeywell Model 31 Mid 1000 lbf load cell), which provides a complete data set to characterize the isobaric force-contraction response of the PAM. The PAM is mounted in the MTS machine  
Figure 5: Example depiction of Moore-Neighbor boundary tracing. (**a**) Moore-Neighborhood of pixel C, and prior boundary pixel P. (**b**) Tracing of a pixel cluster by stepping through pixels 1–8.  
at each end through clamped fixtures (Figure 6). The top fixture serves as both a mounting point for the pressure sensor and as a conduit for air to travel into and out of the PAM. The bottom fixture connects to the load cell that is mounted inline with the PAM.
## 3 Research Questions and Communication Efficient Methods
### RQ2-How Can Communication Be More Efficient in an FL Environment?  
In order to train the global ML model with decentralized data, the global model needs to be downloaded on the participating devices on that federated network. This allows the data generated by those remote devices to remain preserved on the device while subsequently improving the ML model. The steps that make this possible can be summarized into three communication steps: (1) the global model needs to be shared across devices within the federated network, which can sometimes be millions of IoT devices of mobiles phone; (2) the model is then downloaded by the devices and trained locally on-device on the private dataset that is generated by those devices; (3) the ML model is uploaded back to the central server, where it is pooled with numerous other models that have been uploaded to aggregate them all together and find a federated average to generate a new and updated global model. Figure 6 depicts the whole process of uploading and downloading, along with the aggregation of local models. Considering the steps of communication that take part in obtaining an improvement in the global ML model, it is important to seek out the most communication-efficient methods that could make the transfer of data from (1) to (2) and on to (3).  
In this subsection, the recent findings and conducted works on improving the communication efficiency in ML models over the federated network are presented: findings that are tackling constraints where devices can drop out due to a poor or limited network bandwidth, along with other constraints where data are sampled differently and how these could affect communication. Then, some of the main research methods towards enhancing the communication efficiency are discussed, such as local updating, client selection, reduction in model updates, decentralized training and peer-to-peer learning, and various compression schemes.
## 2 Materials and Method  
### Design  
In this research, we designated illuminance, CCT, and reading modalities as the independent variables, while the participants' subjective evaluations of the lighting environment and their attention served as the dependent variables. Existing research indicates that reading on paper requires significantly higher illumination levels compared to reading on electronic devices [33]. Therefore, we chose four illuminance levels (50 lx, 100 lx, 150 lx, and 200 lx) and three color temperatures (3000 K, 4000 K, and 5000 K) to investigate their effects on reading behavior in a vehicle setting. These selections cover a range of common lighting conditions found in both automotive and residential environments.  
The illuminance levels range from dim (50 lx) to bright (200 lx), simulating various lighting scenarios one might encounter while reading in a vehicle. The color temperatures were selected to represent warm (3000 K), neutral (4000 K), and cool (5000 K) light, mirroring the spectrum of light individuals are exposed to throughout the day and in different settings.  
This range of illuminance and color temperatures allows us to explore how various lighting conditions affect reading comfort, preference, and performance, providing insights into optimal lighting configurations for vehicle interiors.  
For the subjective evaluation, we employed a five-point Likert scale, crafting sub-questions to assess preferences, positive and negative emotions, and fatigue levels. As shown in Table 1, for preferences, the criteria included: (a) Overall satisfaction (1 = very dissatisfied to 5 = very satisfied), (b) Reading experience (1 = very dissatisfied to 5 = very satisfied) and (c) Perceived brightness (1 = very dissatisfied to 5 = very satisfied). To enhance the experimental results' comparability and practical relevance, we utilized the widely adopted 0-5 Likert scale for evaluating emotional responses and fatigue. For positive emotions, the items were: (a) Alertness (0 = very slightly or not at all to 4 = extremely), (b) Energy level (0 = very slightly or not at all to 4 = extremely), and (c) Vigor (0 = very slightly or not at all to 4 = extremely). The negative emotion items included: (a) Distress (0 = very slightly or not at all to 4 = extremely), (b) Nervousness (0 = very slightly or not at all to 4 = extremely), and (c) Jitteriness (0 = very slightly or not at all to 4 = extremely). For fatigue assessment, we queried: (a) Sleepiness (0 = very slightly or not at all to 4 = extremely), (b) Fatigue(0 = very slightly or not at all to 4 = extremely), (c) Eye ache (0 = very slightly or not at all to 4 = extremely), and (d) Eye burning (0 = very slightly or not at all to 4 = extremely). To evaluate participants' attention, we utilized the D2 Test of Attention [34].
## 2 Materials and Methods
### Experimental Set up and Design  
The experimental set-up is portrayed in Figure 1. Patients were seated on a custom chair, allowing us to fix the knee angle at 120\({}^{\circ}\) and to guarantee their safety and comfort. The evaluation was performed on the non-injured leg. Firstly, the traction force was measured by a strain gauge positioned below the leg holder in order to compute the maximum voluntary contraction (MVC) during an isometric contraction of the quadriceps muscle. The MVC was calculated as the maximum of three consecutive trials so that the effective MVC was considered, similar to other studies [32; 33]. After a period of 60 s of baseline, patients were required to perform 20 isometric contractions at the 80% of MVC with a confidence interval of \(\pm\)10%. A visual real-time feedback of the exerted force and goal threshold was given to the patients. Each repetition consisted in 10 s of continuous contraction followed by 5 s of relaxation. A recovery period of 300 s was given at the end of the exercise. This protocol was conceived considering previous applications that selected similar evaluation sessions as a reasonable trade-off between fatigue elicitation and clinical practical applicability. Previous studies showed that quadriceps muscle underwent fatigue in protocols based on repeated contractions in timings similar to those adopted in this study [34; 35]. The MVC was acquired at two different time-points: PRE--at 6-8 days after the femur fracture and around 3 days after the surgery; POST--at 15 days after the PRE. Patients followed a daily physiotherapy rehabilitation program between PRE and POST time-points. The acquisition rate was 200 Hz. Torque and EMG signal were acquired during the whole exercise. The beginning and the end of each contraction were identified with an auditory stimulus (Presentation(r), Neurobehavioral Systems Inc.). All the acquisitions and stimuli were synchronized.  
During the trials, 5 EMG bipolar probes (EMG system: wireless Cometa 16-channels, Milan, Italy) were placed on the quadriceps muscle according to a multisite muscle recording protocol inspired by previous applications in the field [36; 37], through Ag-Cl electrodes on the following anatomical landmarks: vastus lateralis (proximal), vastus lateralis (distal), vastus medialis, rectus femoris (proximal), and rectus femoris (distal). Electrode placement is shown in Figure 2. This multi-channel mapping was chosen to account for multiple muscle heads contracting during isometric tests, since it is not a priori possible to address all muscle torque at knee level to a single muscle head.  
Figure 1: Set up for the experiment.
# Enhancing Survival Analysis Model Selection through XAI(t) in Healthcare
## 2 Materials and Methods
#### Data Pre-Processing  
The first operation involved dropping non-relevant features: patient I.D., the number of health records (N CC), admission date, discharge date, profession description, and follow-up date. Then, we converted the survival time from days to months without performing any aggregation operations to facilitate both the analysis and interpretation of data. Next, we checked for null values in EF, ODI, and anemia features. We dropped the EF and ODI features due to 90% null values and lack of information, while we imputed the anemia values based on hemoglobin levels, considering the patient's sex.  
Afterward, we discretized the variables wherever feasible: Age was categorized into two classes, 0 if Age \(\leq\) 65 years old, 1 otherwise. GFR and BMI were binned according to reference values present in the medical literature [29, 30]. The age cutoff value was chosen according to the relevant medical literature [31, 32, 33]. The adoption of unsupervised cutoffs that do not rely on data distribution (but are still relevant in medicine) allowed for better generalization of results. Finally, we included two other features: Continuous Positive Airway Pressure (CPAP) (a binary categorical feature indicating whether a subject followed CPAP treatment) and the corresponding treatment duration in years (Years_of_CPAP).  
Figure 1: Processing pipeline followed.  
Outlier removal was not performed, as in the medical domain, removing outliers from clinical data is not always feasible due to the possibility that they may indicate pathological conditions. Finally, since our study focuses on a respiratory disease, samples with last follow-up dates after 2020 (i.e., 198 samples) were excluded due to the COVID-19 pandemic to avoid the presence of bias in our results. As a result of pre-processing, we retained 1394 samples.
# Infrared Dim and Small Target Detection Based on the Improved Tensor Nuclear Norm
## 1 Introduction
[PERSON] [17] proposed a new non-convex low-rank constraint based on the IPT model by combining the tensor nuclear norm part with the L1 norm to effectively suppress the background images. [PERSON] [18] replaced the traditional nuclear norm with a weighted Schatten p-norm (WSN) based on a two-dimensional low-rank and sparse model to obtain each low-rank component more accurately. This model can better achieve the low-rank assumption of the original image. [PERSON] [19] introduced the weighted Schatten p-norm number (WSN) into the IPT model on this basis to achieve the deep mining of partial information in the time domain relative to the two-dimensional low-rank and sparse models. [PERSON] [20] combined multi-subspace learning with IPT to better preserve the low-rank components in the original image compared with the traditional IPT model. Although many scholars have improved the IPT model, the recovery results of sparse components are still unsatisfactory due to the large target contours and energy loss in the case of highly nonuniform cases or images with many edge contours in the background. To solve the problems associated with infrared dim and small target detection--by separating target and background images from highly nonuniform and contour-rich scenes and enhancing the target confidence--this paper proposes an infrared dim and small target detection method based on the improvement of the a priori tensor nuclear norm of local background features. The main contributions of this paper are as follows:  
First, to deeply explore the local prior information of the image, this paper improves the traditional local prior model and introduces the improved prior model as a weight matrix into the infrared dim and small target detection model to obtain better detection results. Second, to effectively suppress backgrounds with more edge contours, this paper establishes a new anisotropic background feature weight function to effectively characterize the background features of the prior model and avoid the influence of edge contours in the prior model on target detection. Third, to enhance the prior information of local features, this paper constructs an improved high-order cumulative model to build the local significance region of the target, which effectively enhances the significance target in the structure tensor.
## 4 Discussion
### The Response of GPP, R, and Ne to Nitrogen Addition
Net carbon exchange is an important indicator for evaluating carbon cycle and balance, which is jointly determined by the gross primary productivity and respiration. In our study, nitrogen addition has been observed to increase _Ne_ during the growing season because of the larger stimulation in _GPP_ than that in \(R\) (Figures 4-7). A similar observation has also been reported by [PERSON] et al. [57]. Owing to the small change of \(R\), the response of _Ne_ to different nitrogen addition levels was similar to _GPP_, with HN slightly higher than that in MN (\(p>\) 0.05), 55.68 and 52.31% higher compared with the control, respectively (Figure 7). Some other studies have found similar results, such as [PERSON] et al. [58] foundthe net photosynthetic rate of 1-year growing _Pinus palustris_ seedlings in high nitrogen treatment was significantly higher than that in low nitrogen treatment. However, the response of _Ne_ to nitrogen addition in other studies were somewhat different from our study; they found the _Ne_ under MN were highest. Probably, soil's available nitrogen content was small in our experiment, the addition nitrogen was absorbed as a nutrient. In addition, experiment duration was short, and the nitrogen threshold value, which can inhibit the net photosynthetic rate, was probably not been reached.
## Nomenclature  
\begin{tabular}{l l} VSC & voltage source converter \\ PLL & phase-locked loop \\ DC & Direct Current \\ \(V_{dc}\) & front-end DC voltage of VSC \\ \(v_{ox}\) (\(x=a\),\(b\),\(c\)) & VSC output voltage \\ \(i_{ox}\) (\(x=a\),\(b\),\(c\)) & VSC output current \\ PCC & point of common coupling \\ \(u_{pec}\) & PCC voltage \\ \(U_{gx}\) (\(x=a\),\(b\),\(c\)) & grid voltage \\ \(L_{f}\) & filter inductance \\ \end{tabular}
# Hybrid Locomotion Evaluation for a Novel Amphibious Spherical Robot
## 3 The Hybrid Locomotion
### Underwater Locomotion  
Every water-jet thruster of four legs can rotate in the vertical and horizontal planes. With the directions and propulsive forces of four vectored water-jet thrusters changing, the robot can move forward or backward, turn left or right, float or sink in the underwater environment. Figure 8 illustrates the underwater locomotion of the robot.  
The four legs are denoted by number 1, 2, 3 and 4, as shown in Figure 8d. Leg 1 and 2 are on the left of the robot, and leg 3 and 4 on the right. As depicted in Figure 8a,d, the robot can adjust the water-jet direction of thrusters in the same horizontal plane to realize forward or backward motion.  
Figure 8: Underwater locomotion. Side view: (**a**) forward motion; (**b**) floating motion; (**c**) sinking motion. Bottom view: (**d**) forward motion; (**e**) left turning motion; (**f**) right turning motion. The blue arrow indicates the water-jet direction of the thruster, and the red arrow represents the motion direction of the robot.  
Figure 7: The sequence of the gaits. (**a**) TPWG; (**b**) TTWG; (**c**) SSGTL; (**d**) the synchronous sliding gait with four legs. The gray and the black bars show that the leg is in the transfer and support phase, respectively. Over single cycles of the trotting and synchronous gaits, the robot simultaneously has two legs in the transfer phase and two legs in the support phase. When in the synchronous sliding gait, the four legs of the robot swing synchronously with the same stride, generating different types of locomotion (rotatory or forward, based on different leg swing directions). Over a single cycle of TPWG, only one leg is in the transfer phase and, under the same conditions, the cycle time is twice that of the other three synchronous gaits.  
In addition, by the difference of the left water-jet force and the right water-jet force, the robot can implement the left and right turning motion, as shown in Figure 8e,f. Besides, as shown in Figure 8b,c, with adjusting the direction and force of four water-jet thrusters in the vertical plane, the robot can float upward and sink down. With the flexible motion, our robot can be used for environment monitoring, object tracking and detection of pollution with proper sensors.