esa-sceva/satcom-chunk-collection huggingface dataset:
# Lower-Limb Flexibility Profile in 142 Competitive Female Football Players: A Cross-Sectional Study
## 4 Discussion
The ballistic stretching training the stretch reflex, which causes contraction of the stretched muscle by the facilitatory influences of muscle spindles type Ia and II receptors upon homonymous alpha motor neuron excitability [44]. The dynamic stretching has a positive effect on ROM and dynamic flexibility, and can increase the spindle reflex afferent excitation of the motor neurons and improve athletic performance (running, jumping, sprinting, and agility) [45]. Active statics stretching improves intermuscular agonist-antagonist coordination [46] and also avoids compensatory movements (maintaining neutral joint stability) during muscle stretching at maximum ROM. Proprioceptive neuromuscular facilitation and Eccentric stretching are techniques that are recommended during the during the main part of a training session or competition. Proprioceptive neuromuscular facilitation, which involves passive static stretches and isometric contractions in a cyclical pattern, is a very effective stretching technique to achieve passive and active ROM and optimize motor performance [47]. Eccentric stretching improves tolerance to eccentric actions and can help prevent muscle-tendon injuries through a full range of motion by training the muscle in a more functional type of activity [48]. Finally, the passive static stretching technique is recommended after of a training session or competition to reduce residual muscle tension (or muscle tone) and muscle stiffness and also to recover muscle tightness caused by exercise [49]. The programs for preventing football injuries [50; 51] and improving athletic performance [52; 53; 54] recommend a multicomponent intervention that includes a combination of different stretching techniques. It is recommended to apply dynamic stretching and active static stretching before, and passive static stretching after football training or competition; it is also recommended to perform a pure stretching program at home [55]. In general, at least four weeks [56], at least three days per week and six stretching exercises (e.g., iliopsoas, quadriceps, hamstrings, liliotibial band, internal and external rotators of the hip, gastrocnemius/soleus and low back) are recommended [51]. Each exercise is performed in at least 2-3 sets of 15-30 s (10-15 repetitions for dynamic stretching) with a 10 s rest between sets and moderate intensity or discomfort sensation (between four and six RPE points) [53; 57; 58].
## 3 Results and Discussion
### Mathematical Modelling of Drug Release Kinetics
Interestingly, compared to the systems that are governed by pH stimulated release, drug release that has been stimulated by NIR or ultrasound shows the highest degree of correlation towards the Higuchi model with \(R^{2}\) values as high as 0.9990, suggesting that these systems are governed by a diffusion-based drug release [91]. It should be noted that two conditions displayed anomalous results and were excluded from further evaluation; these are highlighted in red in Table 2.  
The \(p\) values were recorded to evaluate the strength of the results; a smaller \(p\) value suggests a lower probability that the data could randomly produce a linear regression like that observed. Most data show a \(p\) value below 0.05, suggesting significant findings. The only non-significant results were recorded for NGO-PEG-DA at pH 7.4 [65]; this experimental condition also showed low \(R^{2}\) values, highlighting its anonymity from the rest of the data. The standard error of estimate, \(Sy.x\) is another statistical measure that validates the goodness-of-fit of the data to a specific model. For better fit results, the value of \(Sy.x\) will be small. The \(Sy.x\) is consistently the smallest for the first-order model in most publications closely followed by the Weibull model. The \(Sy.x\) values for the zero-order and Higuchi models were significantly higher, suggesting a large deviation of the residuals. This is unsurprising as the zero-order model has the least correlation to any of the nanohybrids and rarely describes release from complex nanocarriers. The release constant for the zero-order (\(k_{0}\)), first-order (\(k_{1}\)), and Higuchi model (\(k_{H}\)) as well as the \(\beta\) value for the Weibull Model were recorded by extracting the linear regression gradients. These are significant as they describe the rate of DOX release from the system. The values for \(k_{0}\), \(k_{1}\), and \(k_{H}\) are greatest, at pH 5/5.5, compared to pH 7/7.4 in all of the pH stimulated systems. The release rate also tends to be slightly higher at a more basic pH compared to a neutral pH. These results are expected due to the partial dissociation of hydrogen bonds at both acidic and basic conditions.
# An Efficient and Reliable Algorithm for Wireless Sensor Network
## 2 Related Work
In addition, due to the inter-cluster approach when the end receiver of each cluster came in the range of two cluster head then the movement of nodes within the clusters increases due to shifting of nodes from large cluster to small cluster and hence increases the node exchange. This paper [24] also uses the mesh approach for data forwarding. In this approach, as soon as the data packet is received from the sender to the group member then it immediately floods the packet within the group to reach to the desire destination. As a result, the flooding increases the overhead and decreases the lifetime of the network. However, in the proposed approach, the flooding is avoided to decrease the overhead and increases the life time of the network. All the protocols use the internet protocol (IP) service model for allowing the sender in the network to send its data to the multicast group [16]. In the Modified ERASCAprotocol, a bootstrapping is used where a node makes a multicast group and starts a periodic broadcast message in a network, called SD or hello message. Through the SD message, a group is periodically flooded at a regular interval by the core node and forms the network's connectivity list. The connectivity list forms the best possible route in a network as well as in the mesh. After reaching the data inside the mesh, it will ultimately reach the desired receiver in the group. In the aforementioned approaches, flooding is used immediately when any mesh member of the group receives the data. After flooding, the data reaches the desired receiver. This flooding creates overhead and congestion, which stage the network's resources and lifetime [25; 26]. In the proposed EERASCA, multicasting is used for a distant destination, and unicasting is used for the nearest node within the mesh for data transferring to the desired receiver, which increases the efficiency and reliability in terms of overhead, network lifetime and battery consumption as compared to benchmark protocols such as PUMA and ERASCA.
## 4 Conclusions  
For ease in practical applications, microporous ACFs for CO\({}_{2}\) adsorption have been investigated. The KOH activation of ACFs generated increased pore volume, not only in the micropore but also in the mesopore ranges. A microporovosity of 68% and ultra-micro-porosity of 28% were obtained in KOH-activated ACFs. Due to the total or partial blinding of micropores by N-functionalities, the TPEA-modified ACFs had a lower surface area and pore volume. Surface modification of ACFs gave rise to a change of the predominant surface functional groups. An increase in the amount of chemisorbed water on ACFs treated with TPEA should be the outcome of the introduction of polar nitrogen functionalities. The existence of basic nitrogen-containing functionalities was thought to lead to higher CO\({}_{2}\) uptakes; however, there was no explicit relationship between them in our findings. Instead, the O-functionalities showed a highly associated linear relationship between the functional groups and the CO\({}_{2}\) uptake. Despite this, the values of isosteric heat of adsorption still confirmed that in this study, the influence of micro-porosity on CO\({}_{2}\) adsorption was prevailing. Moreover, the ACF  
Figure 8: Successive cyclic adsorption/desorption curves and the variation of adsorption capacities at 25 \({}^{\circ}\)C: (**a**, **b**) as-received ACF; (**c**, **d**) KOH-activated ACF (aACF); (**e**, **f**) TPEA-modified ACF (nACF).  
samples exhibited excellent CO\({}_{2}\) capture cyclability. Further studies should aim to find the optimal conditions for KOH activation of ACFs in order to improve their CO\({}_{2}\) adsorption performance.  
[PERSON] conceived and designed the experiments, prepared, reviewed and edited this manuscript; [PERSON] and [PERSON] performed the experiments and analyzed the data. All authors have read and approved the final version of the manuscript to be submitted.  
This research was funded by the Ministry of Science and Technology of Taiwan, grant number MOST 104-2221-E-0155-002.  
The authors appreciated National Taiwan University and National Tsing Hau University to provide the technical support.  
The authors declare no conflict of interest.
# Energy-Efficient On-Off Power Control of Femto-Cell Base Stations for Cooperative Cellular Networks
## 2 Limitation of Conventional On-Off Power Control Schemes  
In this section, we illustrate the limitation of conventional on-off power control schemes in which the set of FBS to be turned off is determined based on incurred traffic load, when cooperative transmission is used, by using the toy example shown in Figure 1. In the considered toy example, we assume that two types of cooperative transmission are used: (1) three FBSs including FBS 1 cooperatively transmit data to MS 1 (i.e., Cluster 1); and (2) two FBSs, which are FBS 1 and 2, cooperatively transmit data to MS 2 (i.e., Cluster 2) [27]. Moreover, we assume that only one MS can be served at the same time, i.e., only one cluster can be activated simultaneously, in order to avoid excessive interference among cooperative transmissions, e.g., CSMA [28]. Furthermore, we assume that FBSs which are within the same cooperative cluster transmit the same data, i.e., CoMP, such that the amount of traffic load of the FBSs which participate in cooperative transmission is the same.  
In conventional on-off power control scheme, it is more likely to turn off FBS 2 than switching off FBS 1 because FBS 2 participates in only one cooperative transmission, i.e., Cluster 2, while FBS 1 participates in both cooperative transmissions, i.e., Clusters 1 and 2. Accordingly, the traffic load of FBS 1 will always be larger than that of FBS 2 such that the operation of FBS 2 is more redundant in view of traffic load. However, if we take into account the throughput of cooperative transmission, it can be better to turn off FBS 1 because FBS 1 is far away from both MSs, i.e., MS 1 and MS 2, such that its effect on both cooperative transmission could be minor.  
The advantage of shutdown of FBS 1 can be easily confirmed through the calculation of throughput. In the calculation, we consider the IMT-A M.2135 indoor hotspot path-loss model and assume that the transmit power of FBSs is 1 mW [33]. In this case, if FBS 2 is turned off, the throughput of cooperative transmission in Cluster 2 is reduced by 60% compared to that without the shutdown of FBS. Note that the throughput of Cluster 1 is unaffected by the shutdown of FBS 2. On the other hand, if FBS 1 is switched off, the degradation of throughput of both cooperative transmission is less than 0.1%. It should be noted that the reduction of energy consumption is the same for both cases because the operation of one FBS is halted. From this toy example, the limitation of conventional on-off power control scheme can be confirmed when cooperative transmission is taken into account.
# Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review
## 3 Concept Drift Adaptation Methods under Deep Learning
### Concept Drift Adaptation Methods Based on Discriminant Learning
ECNN overcame the \"over-fitting\" and \"under-fitting\" problems. ECNN is the first online deep learning technique to be introduced into marine data prediction research, although it is relatively computationally expensive. Online CNN-based model selection using performance gradient-based saliency maps (OS-PGSM) [52] is mainly applied to time-series prediction and uses [PERSON]'s bound detection algorithm to actively detect the occurrence of concept drift. When concept drift occurs, the region of competence (ROC) of the model will be recalculated to update the weights. It has a low computational cost, using significance plots to provide an explanation for model selection,but hyperparameter settings need to be optimized. Deep incremental hashing (DIH) [53] focuses on semantic image retrieval using a CNN model. The parameters of the CNN are updated using a point-by-point loss function guided by the similarity of the current data block keeping the target code. DIH mainly adapts to gradual, incremental drift. It also has certain limitations, such as not considering the semantic relationships between labels. Table 1 summarizes the discriminant-learning-based concept drift adaptation methods. From this table, it can be seen that the MLP-based concept drift adaptation method focuses on the processing of streaming data samples to ensure the balance between old and new data and imbalanced data, thus improving the accuracy of prediction and reducing errors. However, it has certain limitations in dealing with high dimensionality, which is more suitable for dealing with gradual and abrupt concept drift. The RNN-based algorithm and its variants have good timeliness and can handle long-term serial data. However, it is necessary to overcome the problem of catastrophic forgetting, which is more sensitive to incremental and gradual concept drift. It is worth noting that the types of concept drift adaptation are rarely clearly specified in related studies based on LSTM and CNN. Further, most concept drift adaptation methods face the problem of slow convergence speed. In addition to the above types of mainstream algorithms, there are some other methods of discriminant learning. For example, the OeSNN-DRT algorithm based on a spike network [54] introduces two methods: active and passive adaptation methods. It uses the data reduction technique (DRT), a selective and generative data reduction technique, to  
\end{table}
Table 1: Concept drift adaptation methods based on discriminant learning. optimize the contents of the neuronal repository and update its structure. However, it does not take into account a priori information such as the speed and severity of the drift. Currently, there are few studies related to other types of concept drift adaptation methods compared to mainstream deep learning models, so they are not listed. However, it is a worthy direction for research.
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 7 Evaluation and Analysis
### Exfiltration
#### 7.1.1 Communication Rate  
In the case of exfiltration, the primary factor that restricts the communication rate is the direct correlation between the response time \(\Delta T\) from the CPU operation and fluctuations in power consumption. Experimental results obtained from a Gigabyte AERO 15 laptop demonstrated that the minimum \(\Delta T\) duration was 33 milliseconds. This limitation may arise due to voltage stabilization effects caused by capacitors present within both the computer itself and the power adapter. It is well-known that power adapters employ high-capacity capacitors to filter out power fluctuations and jitter, thereby stabilizing voltage through peak shaving and valley filling functions [47]. However, since capacitors have the ability to filter out high-frequency waveforms, they impose restrictions on maximum communication frequency and directly impact communication speed. Consequently, it becomes evident that the shortest time interval for symbol signals to avoid overlap determines both the highest effective signal frequency \(f=1/\Delta T\) as well as the maximum communication rate for exfiltration.  
The maximum bit rates of exfiltration in the experimental system of this article using two different modulations are recorded in Table 4. Signal transmission in the experimental system is conducted through five mainstream brands of laptops, while reception is achieved using our designed plug. The signal is modulated with 2 ASK, and the measured exfiltration rate ranges from 27 to 33 bits/s. Compared to common wired or wireless networks, this communication rate is relatively low, indicating that this channel can only accommodate a limited amount of content. The current signal is received by the smart plug, as shown in Figure 25.
# LPWAN-Based Vehicular Monitoring Platform with a Generic IP Network Interface
###### Abstract  
Remote vehicle monitoring is a field that has recently attracted the attention of both academia and industry. With the dawn of the Internet of Things (IoT) paradigm, the possibilities for performing this task have multiplied, due to the emergence of low-cost and multi-purpose monitoring devices and the evolution of wireless transmission technologies. Low Power-Wide Area Network (LPWAN) encompasses a set of IoT communication technologies that are gaining momentum, due to their highly valued features regarding transmission distance and end-device energy consumption. For that reason, in this work we present a vehicular monitoring platform enabled by LPWAN-based technology, namely Long Range Wide Area Network (LoRaWAN). Concretely, we explore the end-to-end architecture considering vehicle data retrieving by using an On-Board Diagnostics II (OBD-II) interface, their compression with a novel IETF compression scheme in order to transmit them over the constrained LoRaWAN link, and information visualization through a data server hosted in the cloud, by means of a web-based dashboard. A key advance of the proposal is the design and development of a UNIX-based network interface for LPWAN communications. The whole system has been tested in a university campus environment, showing its capabilities to remotely track vehicle status in real-time. The conducted performance evaluation also shows high levels of reliability in the transmission link, with packet delivery ratios over 95%. The platform boosts the process of monitoring vehicles, enabling a variety of services such as mechanical failure prediction and detection, fleet management, and traffic monitoring, and is extensible to light vehicles with severe power constraints.  
**Keywords:** ITS; vehicular communications; LoRa; LPWAN; IoT; monitoring platform +
Footnote †: journal:
## 4 Discussion  
The conversion of a Bayer pattern into a virtual linear array push-broom sensor is the first step in calibration. Moreover, accurately tracking the position relationship of each Bayer pattern before and after the Bayer pattern reconstruction is key to building a calibration sample for each detector. A method using linecounters as the correlation was designed to resolve this issue; however, the solution of the calibration parameters relies on statistical calibration theory, and thus the method has the limitations of statistical calibration methods. The accumulation of a large number of samples, the time-consuming nature of querying statistical samples, the large in-orbit calibration effort, and the low timeliness of the method [19] are among its major limitations. The calibration accuracy is also affected by the sample distribution and the large fluctuations in different brightness ranges [22]. An excessively large single calibration sample results in difficulty in calibrating the complex non-linear radiometric response, an inability to calibrate the full dynamic range radiometric response of the load, or even a failure in calibration. Therefore, on-orbit calibration samples should be selected to cover as many typical features as possible (such as oceans, cities, clouds, and snow), and the number of samples for the various types of features should be as equal as possible. Additionally, the samples should attempt to cover the dynamic range of the sensor to avoid or reduce the influence of the sample distribution on the calibration results.  
For the OVS satellite, the calibration process identified an occasional anomalous radiometric response issue with some of its CMOS detector edge detectors (Figure 17) with approximately 380 detectors at the CMOS5 edge with an anomalous radiometric response from October to November 2019. The effect of such short periods of large differential radiometric response on on-orbit radiometric calibrations can be fatal, directly causing calibration failure. Anomalous data must be removed from the calibration sample during the orbital radiometric calibration to exclude the partial failure of the calibration coefficients due to sample anomalies; however, this problem needs to be reported to the satellite manufacturer to investigate the fundamental cause of the anomalous radiometric response of the OVS sensor and reduce the difficulty of radiometric calibration. Preliminary analyses of possible causes include the following: the abnormal radiometric response of the camera due to a single particle event, which is automatically recovered after a camera restart; \"subtle\" differences in the bonding of the filter and the CMOS itself, which causes changes in the light transmission rate; and a camera designed to use the fixed pattern noise  
Figure 16: Distribution of image radiometric correction accuracy under various DNs.  
(FPN) parameter by default, which may cause abnormal radiometric response if there is a problem with the FPN setting. These shortcomings will be circumvented in subsequent satellite designs.