esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Inoculation and Operation  
The anaerobic sludge and aerobic sludge, collected from secondary sedimentation tank and aeration tank, were respectively used as the anodic and cathodic inoculums. During the acclimation period, the external resistor was gradually reduced from 500 \(\Omega\) to 10 \(\Omega\) (500, 200, 100, 50, 10 \(\Omega\)), with the whole acclimation period lasting for nearly 500 h. The system was operated at a hydraulic retention time (HRT) of 48 h, with the influent continuously pumped into the anode chamber. The anode effluent was then flowed through a loop connection, sprinkled to the cathode chamber through a spray nozzle, and finally drained out through an outlet. The operation period of TB-MES was divided into two phases. The artificial medium was employed as feed in Phase I, while that of Phase II was raw domestic wastewater. The synthetic medium contained 0.71 g L\({}^{-1}\) sucrose, 0.16 g L\({}^{-1}\) NH\({}_{4}\)Cl, 100 mL L\({}^{-1}\) 500 mM PBS (comprised of 33.2 g L\({}^{-1}\) NaH\({}_{2}\)PO\({}_{4}\)2H\({}_{2}\)O, 1.3 g L\({}^{-1}\)KCl and 103.2 g L\({}^{-1}\) Na\({}_{2}\)HPO\({}_{4}\)12 H\({}_{2}\)O), 5 mL L\({}^{-1}\) vitamins, and 12.5 mL L\({}^{-1}\) trace minerals [27]. In Phase I, the effects of catholyte recirculation rates on power generation, reoxygenation ability of the biocathode, and nitrogen removal were investigated. By adjusting the peristaltic pump, the TB-MES was operated at three recirculation rate of 50, 200, and 400 mL min\({}^{-1}\), each of which was maintained for two weeks. In Phase II, domestic wastewater collected from wastewater pipeline in Harbin Institute of Technology, was employed as the influent. The characteristics of the domestic wastewater was as follows: COD, 285 \(\pm\) 36 mg L\({}^{-1}\); NH\({}_{4}^{+}\)-N, 36 \(\pm\) 5 mg L\({}^{-1}\); NO\({}_{3}^{-}\)-N, 2 \(\pm\) 0.8 mg L\({}^{-1}\); NO\({}_{2}^{-}\)-N, 0 mg L\({}^{-1}\); conductivity, 850 \(\pm\) 118 \(\mu\)s cm\({}^{-1}\); pH, 7.2 \(\pm\) 1.6. Real wastewater treatment, in terms of COD removal, ammonia removal, and total nitrogen removal, was evaluated in Phase II, and microbial community composition was analyzed to determine their contribution to power generation and pollutant removal.
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 3 Method  
### Overview  
The proposed anomaly detection framework is based on the noise-to-norm paradigm, as shown in Figure 1.  
Specifically, we introduce random Gaussian noise to corrupt the original image, and the process of adding noise \(\epsilon\) is defined as follows:  
\[x=(1-\lambda)x_{0}+\lambda\epsilon,\epsilon\sim N(0.5,0.5) \tag{1}\]  
where \(\lambda\in(0,1),x_{0}\) is the data obtained by normalizing each channel of the original image according to a Gaussian distribution \((\mu=0.5,\sigma=0.5)\). We add random noise generated from the same Gaussian distribution to the original image using weighted blending, thereby allowing us to control the degree to which the noise corrupts the original image. In contrast to the methods that simulate anomalies [20], our approach of adding noise is not intended to simulate anomalies. Instead, its purpose is to completely obscure the distinguishable appearance of anomalous regions, allowing the reconstruction network to transform the anomalous image into a normal image.  
Figure 1: Framework of the proposed method.  
After adding noise, the images were down-sampled to varied sizes to serve as multiple inputs. These inputs were then utilized by the reconstruction network to generate anomaly free images. During the training phase, only anomaly free samples were used to train the reconstruction network. The reconstructed images were compared to the original images using a loss function, and the reconstruction capability of the model was continuously improved. During the inference phase, anomaly localization was achieved by generating an anomaly map that captured pixel-level differences between the reconstructed and original images. The specific details of the reconstructed network are described below.
# Dynamic Multi-Compartment Vehicle Routing Problem for Smart Waste Collection +
## 4 Mathematical Modelling
### Decision Variables  
**We define the variables of our model as follows:**  
\[x_{ij}^{v}=\left\{\begin{aligned} & 1,\text{ if the vehicle $v$ moves from $i$ to $j$,}\\ & 0,\text{ otherwise,}\end{aligned}\right.\]  
\[g_{i}\,=\left\{\begin{aligned} & 1,\text{ if station $i$ is visited,}\\ & 0,\text{ otherwise,}\end{aligned}\right.\]\[y^{v}_{wj}=\left\{\begin{array}{c}1,\;if\;the\;vehicle\;v\;loads\;the\;type\\ w\;in\;the\;station\;j,\\ \\ 0,\;otherwise,\end{array}\right.\]  
\[z^{v}_{wm}\;=\left\{\begin{array}{c}1,\;if\;the\;conpartment\;m\;is\;selected\;for\\ \;the\;waste\;type\;w\;in\;the\;vehicle\;v,\\ \\ 0,\;otherwise,\end{array}\right.\]  
\(u_{wj}\) : Level of a bin's overflow  
\(F\;^{v}_{wj}\) : The amount of waste type \(w\in W\) flowing on arc \((i,\;j)\), by vehicle \(v\) in kg  
The static M-CVRP in each continuity period for each time slice can be formulated as the following integer program:
# A Study on the Optimization of the Coil Defect Detection Model Based on Deep Learning
###### Abstract  
With increasing interest in smart factories, considerable attention has been paid to the development of deep-learning-based quality inspection systems. Deep-learning-based quality inspection helps productivity improvements by solving the limitations of existing quality inspection methods (e.g., an inspector's human errors, various defects, and so on). In this study, we propose an optimized YOLO (You Only Look Once) v5-based model for inspecting small coils. Performance improvement techniques (model structure modification, model scaling, pruning) are applied for model optimization. Furthermore, the model is prepared by adding data applied with histogram equalization to improve model performance. Compared with the base model, the proposed YOLOv5 model takes nearly half the time for coil inspection and improves the accuracy of inspection by up to approximately 1.6%, thereby enhancing the reliability and productivity of the final products.  
quality inspection system; deep learning; model optimization Article
## 4 Discussion  
In this study the sEMG signal provided by a low-cost device--the Myo armband--has been treated by using two different complexity measures in order to assess whether they could be useful as distinctive features for hand gesture recognition in healthy subjects. The FEn was proposed by [PERSON] et al. [16] and its use as the only feature extracted from sEMG was adequate to obtain proper recognition of four different arm gestures, outperforming more classical entropy measures such as the approximate and sample entropy [16; 27]. However, [PERSON] and colleagues considered only four gestures, i.e., hand grasping, hand opening, forearm supination and forearm pronation [16] and sEMG signals were acquired by means of a gold-standard device, with a proper sampling rate (1 kHz). Results of the present study suggest the suitability of FEn to describe a wide range of arm gestures, since the FEn was tested on three different sets of hand gestures: movements miming finger numbering (set 1), wrist movements (set 2) and more complex gestures, involving both fingers and wrist (set 3), for a total of 14 gestures. However, the parameter selection for FEn computation seems to be crucial: the choice of a gradient of the exponential function (4) as \(n=1\) did not allow a correct recognition of the actual number of gestures for any of the considered sets, independently from the \(r\) value (width of the exponential function). This agrees with [16], where a value of \(n\geq 2\) was suggested in order to avoid an entropy computation over-affected by noise. However, despite the fact that \(n=2\) and \(n=5\) allowed for correct cluster identification, the best performance in terms of GS index resulted from \(n=5\) (see Section 3). Therefore, a wider gradient of \(\psi(\cdot)\) led to a better characterization of sEMG signal in terms of the recognition of different gestures. The latter aspect could appear to not exactly match the suggestions reported in [16], where \(n=2\) was used in sEMG-based hand motion recognition. However, it is noteworthy that the latter indications were based on the low variations of the FEn standard deviation for \(n>2\) and not on the numerical quantification of the quality of gesture clustering. Therefore, the gradient \(n\) being a weight of vector similarity, the results of this study suggest that for a more reliable gesture classification closer vectors have to be weighted more than the dissimilar ones, as happens for \(n>1\). However, the higher \(n\) is, the greater the loss of detailed information, considering that as \(n\rightarrow\infty\), the \(\psi(\cdot)\) function becomes the Heaviside step function, where the contributions of samples are equalized depending on their values with respect to the hard boundaries [16]. Thus, despite gradient values \(>5\) not being considered in the present study, their use in sEMG signal analysis seems to be not recommendable.
# Closing Connectivity Gap: An Overview of Mobile Coverage Solutions for Not-Spots in Rural Zones
## 3 Potential Solutions  
There are several challenges to face in rural areas in terms of reliable and enhanced mobile networks. This need triggers the state-of-art study of diverse network models for the countryside in order to introduce ubiquitous solutions in which connectivity is available at anytime and at any location with respect to the population's demands in a fully connected society.  
By the first attempts to overcome likely hardships, such as insufficient population for deploying infrastructure, adaptive solutions struggle with the current unfolding of Mobile Network Operators (MNO). The new platforms or devices--that enhance coverage and other rural Key Performance Indicators (KPIs)--leverage practical alternatives for outdoor environments.  
There have been studies that cater to rural coverage by using the TVWS-spectrum sharing approach that utilizes free UHF band channels from analog switch-offs at a specific  
Figure 5: Innovations for rural connectivity.  
time and space location [15]. Indeed, the primary user (PU) exclusively uses frequency resources on bands 470 MHz and 710 MHz.  
On the other hand, [PERSON] et al. [44] aimed to recover GSM whitespace--or the non-actively used and licensed GSM spectrum--to support dynamic spectrum sharing, hence achieving a suitable QoS would not be attached to low throughput and high latency. Regardless, other kinds of solutions have arisen so far that aim for a fully connected countryside.  
In the following subsections, several trustworthy approaches will be set forth for diverse rural outdoor solutions, such as unmanned aerial vehicles (Section 3.1), low altitude platforms, and high altitude platforms, and satellites (Section 3.2). Then, Figure 3 graphically summarizes the solutions as mentioned above to cope with rural not-spots.
## 4 Statistical Multi-Node MIMO Switching in Mobile WMSNs  
In this section, we describe the proposed statistical, multi-node, MIMO mode switching algorithm. We use the term statistical because the mode decision is based on the transmit correlation information and the path gains contained in the transmit correlation matrix. Thus, the link level capacity derived in the previous section can be used as a performance measure, assuming coding and interleaving over a large number of channel realizations. Statistical-based mode switching is effective at the boundary region among different sensor nodes.
## 4 Application Programming Interface  
The core of SOMM is a virtual machine which abstracts the complexities of underlying operating system API and facilitates the programming using a simple script language. The instructions which SOMM provides to programmer can be divided in four categories:  
* **General purpose instructions:** This category includes instructions for mathematical operations, reading sensors, toggling LEDs, accessing heap and sending data. Also because SOMM is a stack-based virtual machine, it provides necessary instruction for manipulating stack. Some of these instructions are _add_, _halt_, _putled_, _rand_, _or_, _sense_, _eq_, _pop_, _pushc_, _pushacc_, _send_.
* **Migration instructions:** These instructions provide facilities for agents to migrate across nodes or copy themselves in another node. SOMM supports weak and strong migration. In weak migration, only the code of agent is transmitted to destination node and the migrated agent restarts its execution at destination node. But in a strong migration, in addition to code of agent, also its executing context is transmitted to destination node and as a result, migrated agent can continue its execution at destination node. The instructions in this category are _scopy_, _wcopy_, _smove_ and _wmove_ which stand for strong copy, weak copy, strong move and weak move respectively.
* **Service management instructions:** This group includes six instructions: _qsreg_, _qcrep_, _dlserv_, _regeserv_, _disserv_, and _enserv_. The _qsreg_ instruction allows the agent to query the service registry and search for a specified service. An agent can connect to code repository and gets the information about different implementations of a service using instruction _qcrep_. Instruction _dlserv_ is for downloading a specified service from code repository. The _regeserv_ instruction is for registering a service in service registry and instructions _disserv_ and _enserv_ enables and disables a service in service registry, respectively.
* **Tuple space instructions:** This category contains the standard tuple space operations which an agent can manipulate local tuple space using them. These instructions are _out_, _in_, _inp_, _rd_, _rdp_. The instructions _in_ and _rd_ are blocking ones, this means that if they do not find any matching tuple, the calling agent blocks until a matching tuple is added to the tuple space. _rdp_ and _inp_ are non-blocking instructions and in the case that they do not find any matching tuple, the calling agent does not block.  
All of instructions pop their needed parameters from the top of the stack. It is the task of programmer to push the appropriate parameters into the stack in a proper order. For example, Figure 6 shows the sample code for using _regeserv_ instruction. In this piece of code, to register a video service with QoS range of 3-7, first the needed parameters are pushed to stack using instructions _pushc_ and _pushs_ and then the _regeserv_ is called for registering the service.  
Figure 6: A sample code for registering a service in service registry using _regeserv_ instruction.
# A Mobility-Assisted Localization Algorithm for Three-Dimensional Large-Scale UWSNs
## 5 Simulation
However, the longer transmission range requires anchor nodes to have a higher power, thereby increasing energy consumption and localization costs. As shown in Figure 12, after 200 m of transmission range, the localization ratio tends to be flat. Therefore, in the following simulation, the transmission range is set to 200 m. Under the same transmission distance, the localization ratio of other schemes is lower than that of this scheme. According to the change of the time interval of the anchor node propagating the localization message, the localization ratio of the corresponding sensor nodes is given, as shown in Figure 13. Under the condition that the transmission range is 200 m, its localization ratio decreases with the increase of the propagation time interval of anchor node. When the time interval is 30 s, the localization ratio reaches about 95%. When the time interval is 100 s, the localization ratio is more than 85%. Overall, the localization scheme has a high positioning rate. In Reference [20], the authors did not give the corresponding monitoring environment parameter settings, so the localization scheme in this paper could not be compared with it. In Reference [21], the authors set the transmission range of anchor nodes as 250 m in their localization scheme, but the overall localization ratio was less than that of the localization scheme. Figure 11: Distribution of ordinary sensor nodes before and after drifting on the projection plane. Figure 12: The localization ratio versus transmission range. When the transmission range of anchor nodes is 200 m and the time interval is the 30 s, the localization effect of sensor nodes in the proposed localization scheme is shown in Figure 14. The figure shows the real and estimated locations of the ordinary sensor nodes, respectively. It can be seen intuitively from Figure 14 that most of the ordinary sensor nodes are located. Some ordinary sensor nodes at the edge of the monitoring area are not located. This can be caused by not receiving enough localization messages, which is inevitable. Therefore, in future work, we will consider the reasonable positioning of the marginalized ordinary sensor nodes.
# Evaluation of Emotional Satisfaction Using Questionnaires in Voice-Based Human-AI Interaction
## 3 Results
### Classification of Emotional Satisfaction According to the Design Parameters  
The ANOVA results proved that among the four hypotheses, H2 and H4 caused a difference in emotional satisfaction. The results of the classification of user emotional satisfaction for the final VIS obtained by integrating the proven \"number of trials to get the right answer for a question\" (three levels) and \"sentence structure of the answer\" (three levels) are shown in Figure 5.  
Figure 4: Emotional factor scores obtained using ANOVA for different voice-based intelligent system design parameters.  
In the graph, \"number of trials to get the right answer for a question\" can be distinguished by shape, and \"sentence structure of the answer\" can be identified by color. The influence of these two parameters on each other was confirmed using ANOVA, and it was confirmed that the two parameters independently influenced emotional satisfaction with pleasurability (\(p\) = 0.749) and reliability (\(p\) = 0.808) axes. Furthermore, emotional satisfaction was classified using Tukey analysis, and the pleasurability and reliability axes were classified into three and four levels, respectively, as shown in Table 4.  
Clusters that share texts have similar relationships. Both pleasure and confidence are detailed concerning the answer and obtain high emotional satisfaction when the user understands it at once. In terms of pleasure, \"2 Trials/Answer Only\" has a lower satisfaction than \"3 Trials/Q + A + Ref\". Therefore, the \"sentence structure of the answer\" seems to have a greater influence on the satisfaction of the pleasurability axis as compared to that of the \"number of trials\". In addition, in terms of reliability, \"number of trials\" is the key to increasing the user's voice recognition rate of the system to obtain reliability because attempting three questions is the least satisfactory, regardless of the structure of the answer.
## 4 Proposed Solution
### Pre-Processing of the RSS Data
#### 4.4.1 Spatial Smoothing  
It has been shown in [43] that the (spatial) multipath fading effects at each sensor can be mitigated by linear spatial averaging of the RSS measurements with several measurements per wavelength (\(\lambda\)) of the radio signal used. Moreover, it is mentioned in [44] that there should be a minimum spacing of \(0.38\lambda\) between two RSS spacial samples to obtain independent uncorrelated measurements. Therefore, we propose to use an average filter for RSS spatial smoothing when the relay node is moving from \((x^{i},y^{i})\) to \((x^{i+1},y^{i+1})\). Assuming that we use the 2.4 GHz (\(\lambda=12.5\,\mathrm{cm}\)) radio frequency band, the spatial sampling (note: the spatial sampling here refers to the sampling (in space) of the RSS by each sensor on the relay node, whereas the purpose of the spacing of multiple sensors within the relay node is to estimate the RSS gradient around the relay node) frequency (\(f_{s}\)) has been set to a value greater than or equal to 5 cm, meeting both the minimum spacing for uncorrelated measurements, as well as the resolution needs of the linear spatial averaging (around 2.5 measurements per \(\lambda\)). The spatial averaging filter is modeled as follows:  
\[RSS=\sum_{k=1}^{N}RSS(x^{k},y^{k}),\ \ \text{where}\ N=\frac{\|((x^{i+1},y^{i+1})-(x^{i},y^{i}) \|}{f_{s}} \tag{15}\]  
At each receiver, the RSS sampling time is \(t_{s}=f_{s}\
u\), \(\
u\) being the velocity of the relay node. This means that the outcome of the smoothed RSS will be the RSS sample at \((x^{i+\frac{1}{2}},y^{i+\frac{1}{2}})\) instead of \((x^{i+1},y^{i+1})\). As we are concerned only about the RSS gradient measurements, this spatial averaging has a positive impact [19].
## 2 Materials and Methods
### Wireless Transmission Protocol  
The system includes an xBee module to offer wireless support [30]. This device is able to route messages using the IEEE 802.15.4 standards. This protocol offers to these modules the next features: low tax of noise interference, low tax of interferences, low power consumption (depends on the actuation range), high taxes of transmission power and reception sensibility, extra encryption services (application and network keys implement extra 128b AES encryption), association and authentication (only valid nodes can join to the network), AODV routing protocol [30]. This allows the data-logger to send information about its sensors to a Master device in order to store data. Furthermore, the system can include as many wireless data-loggers as is necessary to accomplish the number of sensors and distance requirements.
# On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space
## 5 Discussion
However, the RST-FLARE configuration partially takes into account and mitigates the atmospheric effects impact by using the <BT20\({}_{j,bg}\)> spatial mean as reference value for BTEs computation as well as the ALICE index. For its construction, \(\mu_{20}\)(\(x\),\(y\)) is representative of average conditions in terms of the above-cited noise sources, therefore, if in one image their contribution is quite relevant, producing a significant attenuation in BT20, the corresponding BTE will result lower. The ALICE, using the temporal mean, enables to further reduce the impact of signal variability in the image at hand due to possible noise sources, such as the geometry of observation and atmospheric contributions. Ref. [12] provided a similar indication, defining their bi-monthly composite aggregation scheme a practical and effective atmospheric correction method. Figure 13: Two examples of false positives by: **(a)** RST-FLARE; **(b)** VNF. In background are GoogleEarth® images of **(a)** 19/12/2013 and **(b)** 23/12/2015. The visual inspection has been carried out for the whole temporal period exploiting the GoogleEarth timeline tool (clock button).